hermes-webui/api/streaming.py

"""
Hermes Web UI -- SSE streaming engine and agent thread runner.
Includes Sprint 10 cancel support via CANCEL_FLAGS.
"""
import base64
import contextlib
import json
import logging
import mimetypes
import os
import queue
import re
import shlex
import sys
import subprocess
import threading
import time
import traceback
import copy
from pathlib import Path
from typing import Optional

logger = logging.getLogger(__name__)

from api.config import (
    get_config,
    STREAMS, STREAMS_LOCK, CANCEL_FLAGS, AGENT_INSTANCES, STREAM_PARTIAL_TEXT,
    STREAM_REASONING_TEXT, STREAM_LIVE_TOOL_CALLS,
    STREAM_GOAL_RELATED, PENDING_GOAL_CONTINUATION,
    STREAM_LAST_EVENT_ID,
    LOCK, SESSIONS, SESSIONS_MAX, SESSION_DIR,
    _get_session_agent_lock, _set_thread_env, _clear_thread_env,
    register_active_run, update_active_run, unregister_active_run,
    SESSION_AGENT_LOCKS, SESSION_AGENT_LOCKS_LOCK,
    resolve_model_provider,
    resolve_custom_provider_connection,
    model_with_provider_context,
    load_settings,
    parse_reasoning_effort,
    coerce_reasoning_effort_for_model,
)
from api.helpers import redact_session_data, _redact_text
from api.compression_anchor import is_context_compression_marker, visible_messages_for_anchor
from api.metering import meter
from api.run_journal import RunJournalWriter
from api.todo_state import emit_todo_state
from api.turn_journal import append_turn_journal_event_for_stream
from api.usage import prompt_cache_hit_percent
from api.models import (
    _is_empty_partial_activity_message,
    get_state_db_session_messages,
    reconciled_state_db_messages_for_session,
)
from api.session_ops import mark_session_title_generated, session_has_manual_title

# Global lock for os.environ writes. Per-session locks (_agent_lock) prevent
# concurrent runs of the SAME session, but two DIFFERENT sessions can still
# interleave their os.environ writes. This global lock serializes the env
# save/restore — held only briefly across the env-mutation critical section,
# NOT for the entire agent run. The agent runs outside the lock; the finally
# block re-acquires to atomically restore env vars. See narrow-lock pattern
# in _run_agent_streaming (line ~2719) and profile_env_for_background_worker
# (api/profiles.py:715).
_ENV_LOCK = threading.Lock()

_KEYLESS_CUSTOM_API_KEY = "dummy-key"

_PERSISTENT_MEMORY_FILES = (
    ("memory", ("memories", "MEMORY.md")),
    ("user", ("memories", "USER.md")),
    ("soul", ("SOUL.md",)),
)


def _file_signature(path: Path) -> tuple[int, int] | None:
    try:
        st = path.stat()
        return (int(st.st_mtime_ns), int(st.st_size))
    except OSError:
        return None


def _persistent_state_snapshot(profile_home: str | None) -> dict:
    """Capture lightweight memory/skill file signatures for save toasts."""
    if not profile_home:
        return {"memory": {}, "skills": {}}
    root = Path(profile_home)
    memory = {}
    for key, parts in _PERSISTENT_MEMORY_FILES:
        sig = _file_signature(root.joinpath(*parts))
        if sig is not None:
            memory[key] = sig
    skills = {}
    skills_dir = root / "skills"
    try:
        for skill_md in skills_dir.rglob("SKILL.md"):
            try:
                rel = str(skill_md.relative_to(skills_dir)).replace("\\", "/")
            except ValueError:
                rel = str(skill_md)
            sig = _file_signature(skill_md)
            if sig is not None:
                skills[rel] = sig
    except OSError:
        pass
    return {"memory": memory, "skills": skills}


def _persistent_state_changes(before: dict | None, after: dict | None) -> dict:
    before = before or {"memory": {}, "skills": {}}
    after = after or {"memory": {}, "skills": {}}
    memory_before = before.get("memory") or {}
    memory_after = after.get("memory") or {}
    skills_before = before.get("skills") or {}
    skills_after = after.get("skills") or {}
    memory_changed = any(memory_before.get(key) != sig for key, sig in memory_after.items())
    skills = []
    for rel, sig in skills_after.items():
        old_sig = skills_before.get(rel)
        if old_sig == sig:
            continue
        name = Path(rel).parent.name or Path(rel).stem
        skills.append({
            "name": name,
            "path": rel,
            "action": "created" if old_sig is None else "updated",
        })
    return {"memory_saved": memory_changed, "skills": skills[:10]}


def _apply_profile_provider_context_to_streaming_model(
    model: str | None,
    provider_context: str | None,
    profile_provider: str | None,
    profile_default_model: str | None,
) -> tuple[str | None, str | None, bool]:
    """Attach profile provider context and repair stale cross-provider models."""
    if provider_context or not profile_provider:
        return model, provider_context, False

    provider_context = profile_provider.lower()
    if not profile_default_model:
        return model, provider_context, False

    from api.routes import _normalize_provider_id

    profile_provider_normalized = _normalize_provider_id(profile_provider)
    model_lower = (model or "").lower()
    for prefix in ("gpt", "claude", "gemini"):
        if model_lower.startswith(prefix):
            if _normalize_provider_id(prefix) != profile_provider_normalized:
                return profile_default_model, provider_context, True
            return model, provider_context, False

    if "/" in model_lower:
        slash_prefix = model_lower.split("/", 1)[0]
        if provider_context == "openai-codex" and slash_prefix == "openai":
            return profile_default_model, provider_context, True

        slash_provider = _normalize_provider_id(slash_prefix)
        if (
            slash_provider
            and slash_provider != profile_provider_normalized
            and profile_provider_normalized not in {"openrouter", "custom", ""}
        ):
            return profile_default_model, provider_context, True

    return model, provider_context, False


def _apply_profile_home_context_to_streaming_model(
    model: str | None,
    provider_context: str | None,
    profile_home: str | None,
    has_profile: bool,
) -> tuple[str | None, str | None, bool]:
    """Apply profile provider/model context from a profile config if present."""
    if not (profile_home and has_profile and not provider_context):
        return model, provider_context, False

    try:
        import yaml as _yaml_pp

        _pp_cfg_path = Path(profile_home) / "config.yaml"
        if not _pp_cfg_path.is_file():
            return model, provider_context, False

        _pp_cfg = _yaml_pp.safe_load(_pp_cfg_path.read_text(encoding="utf-8")) or {}
        if not isinstance(_pp_cfg, dict):
            return model, provider_context, False

        _pp = (_pp_cfg.get("model", {}).get("provider") or "").strip()
        if not _pp:
            return model, provider_context, False

        _pp_default = (_pp_cfg.get("model", {}).get("default") or "").strip()
        return _apply_profile_provider_context_to_streaming_model(
            model,
            provider_context,
            _pp,
            _pp_default,
        )
    except Exception:
        logger.warning("profile provider read failed", exc_info=True)
        return model, provider_context, False


def _resolve_custom_provider_runtime_overrides(
    resolved_provider: str | None,
    resolved_api_key: str | None,
    resolved_base_url: str | None,
) -> tuple[str | None, str | None, str | None]:
    """Return provider/key/base_url overrides for ``custom:*`` endpoints.

    Hermes Agent treats named custom providers as routing hints around an
    OpenAI-compatible base URL.  Local OpenAI-compatible servers often run
    without authentication, so a missing key should not fail before the first
    request; pass a harmless placeholder to the SDK and let the endpoint accept
    it or return its own auth error.
    """
    if not (isinstance(resolved_provider, str) and resolved_provider.startswith("custom:")):
        return resolved_provider, resolved_api_key, resolved_base_url

    _cp_key, _cp_base = resolve_custom_provider_connection(resolved_provider)
    if not resolved_api_key and _cp_key:
        resolved_api_key = _cp_key
    if not resolved_base_url and _cp_base:
        resolved_base_url = _cp_base
    if resolved_base_url:
        # Route through the generic custom OpenAI-compatible client once the
        # named provider has supplied the concrete endpoint. Keeping the
        # provider as custom:<slug> would make Agent init synthesize invalid
        # env-var hints like CUSTOM:SOMETHING-8000_API_KEY on keyless setups.
        resolved_provider = "custom"
        if not resolved_api_key:
            resolved_api_key = _KEYLESS_CUSTOM_API_KEY
    return resolved_provider, resolved_api_key, resolved_base_url


def _is_fallback_lifecycle_message(kind: str, message: str) -> bool:
    """Return True if an agent lifecycle status should surface as a fallback warning."""
    k = str(kind or '').strip().lower()
    m = str(message or '').strip().lower()
    return (
        k == 'lifecycle'
        and (
            'rate limited' in m
            or 'switching to fallback' in m
            or 'falling back' in m
            or 'fallback activated' in m
            or 'trying fallback' in m
        )
    )


def _prewarm_skill_tool_modules():
    """Import tools.skills_tool and tools.skill_manager_tool outside any lock.

    First-time module imports can trigger heavy initialisation (disk I/O,
    transitive imports, plugin discovery).  Performing those imports while
    holding ``_ENV_LOCK`` serialises every concurrent session behind the
    slowest import.  Prewarming ensures the modules are already in
    ``sys.modules`` before the lock is acquired, so the lock body only
    does lightweight attribute patching.

    We cannot place these at module top-level because ``tools.*`` lives
    in the hermes-agent package which may not be on ``sys.path`` at
    import time (Docker volume-mount ordering).  A dedicated helper
    keeps the lazy-import try/except in one place and makes the intent
    explicit.
    """
    for _mod_name in ('tools.skills_tool', 'tools.skill_manager_tool'):
        try:
            __import__(_mod_name)
        except ImportError:
            pass


# Lazy import to avoid circular deps -- hermes-agent is on sys.path via api/config.py
try:
    from run_agent import AIAgent
except ImportError:
    AIAgent = None

def _get_ai_agent():
    """Return AIAgent class, retrying the import if the initial attempt failed.

    auto_install_agent_deps() in server.py may install missing packages after
    this module is first imported (common in Docker with a volume-mounted agent).
    Re-attempting the import here picks up the newly installed packages without
    requiring a server restart.
    """
    global AIAgent
    if AIAgent is None:
        try:
            from run_agent import AIAgent as _cls  # noqa: PLC0415
            AIAgent = _cls
        except ImportError:
            pass
    return AIAgent


def _is_quota_error_text(err_text: str) -> bool:
    """Return True when provider text looks like quota/usage exhaustion."""
    _err_lower = str(err_text or '').lower()
    return (
        'insufficient credit' in _err_lower
        or 'credit balance' in _err_lower
        or 'credits exhausted' in _err_lower
        or 'more credits' in _err_lower
        or 'can only afford' in _err_lower
        or 'fewer max_tokens' in _err_lower
        or 'quota_exceeded' in _err_lower
        or 'quota exceeded' in _err_lower
        or 'exceeded your current quota' in _err_lower
        # OpenAI Codex OAuth usage-exhaustion shapes (#1765).
        or 'plan limit reached' in _err_lower
        or 'usage_limit_exceeded' in _err_lower
        or 'usage limit exceeded' in _err_lower
        or 'reached the limit of messages' in _err_lower
        or 'used up your usage' in _err_lower
        or ('plan' in _err_lower and 'limit' in _err_lower and 'reached' in _err_lower)
    )


def _clarify_timeout_seconds(default: int = 120) -> int:
    """Resolve clarify timeout from config, with bounded fallback."""
    try:
        cfg = get_config()
        raw = cfg.get("clarify", {}).get("timeout", default)
        timeout_seconds = int(raw)
        if timeout_seconds <= 0:
            return default
        return timeout_seconds
    except Exception:
        return default


_CANCEL_MARKER_PATTERNS = ('task cancelled', 'task canceled', 'response interrupted')


_WEBUI_PROGRESS_PROMPT = """
WebUI progress guidance:
- Match the normal Hermes messaging style, but do not let long tool-running WebUI turns appear silent.
- For long multi-step work that uses tools, emit brief user-visible progress updates as normal assistant content, not only as hidden reasoning.
- Before the first tool batch in a long task, say what you are about to inspect.
- After each meaningful batch of tool calls, say what you just confirmed and what you will check next before continuing with more tools.
- Do not run many independent tool batches back-to-back without visible assistant text between them when the task is still ongoing.
- Do not keep progress only in reasoning, thinking, or tool-result channels; those are not a substitute for visible interim updates.
- Each update should say what you are about to check, what you just confirmed, or why the next tool call is needed.
- Keep updates concise, factual, and in the user's language. One or two short sentences are enough.
- Do not reveal hidden reasoning, chain-of-thought, private scratchpads, secrets, raw logs, or long tool output.
- Final visible assistant replies must be clear, user-facing, and in the user's language, not private planning notes.
- Do not include terse planning fragments or scratchpad shorthand in visible assistant text. Avoid fragments like "Need script", "Need check logs", "Need inspect email", or "maybe invite"; either omit them or rewrite them as clear user-facing progress.
- For direct answers or very short tasks, skip progress updates and answer normally.
""".strip()


def _webui_surface_context_prompt(surface_context: Optional[dict]) -> str:
    """Return safe WebUI session metadata for the agent's ephemeral context.

    Messaging gateways inject platform/channel context before each run. Browser
    sessions do not have a chat platform wrapper, so provide an explicit, small
    surface description here instead of relying on the model to infer where it
    is running from the transcript alone.
    """
    if not isinstance(surface_context, dict):
        return ""

    lines = [
        "WebUI session context:",
        "- This browser session is not the same live transcript as Telegram, Discord, Slack, or other messaging surfaces.",
        "- Use durable memory, saved sessions, and available tools for cross-surface recall instead of assuming those transcripts are in this browser chat.",
        "- Do not copy or dump this browser transcript into external notes or durable memory by default.",
        "- Write to external notes or durable memory only for explicit captures, durable user preferences, decisions, blockers/open issues, runbook-worthy workflows, or other clearly reusable signals; otherwise leave notes unchanged.",
        "- When you do write or update a durable note, briefly tell the user what note/section changed so the write is reviewable.",
    ]
    fields = (
        ("source", "Source"),
        ("session_id", "Session ID"),
        ("profile", "Profile"),
        ("workspace", "Workspace"),
    )
    for key, label in fields:
        raw = surface_context.get(key)
        value = str(raw).strip() if raw is not None else ""
        if value:
            lines.append(f"- {label}: {value}")
    return "\n".join(lines)


def _webui_ephemeral_system_prompt(
    personality_prompt: Optional[str],
    surface_context: Optional[dict] = None,
    config_data: Optional[dict] = None,
) -> str:
    """Build WebUI-only runtime instructions that are not persisted to history."""
    parts = []
    if personality_prompt:
        parts.append(str(personality_prompt).strip())
    surface_prompt = _webui_surface_context_prompt(surface_context)
    if surface_prompt:
        parts.append(surface_prompt)
    parts.append(_WEBUI_PROGRESS_PROMPT)
    delivery_prompt = _webui_delivery_context_prompt(config_data)
    if delivery_prompt:
        parts.append(delivery_prompt)
    return "\n\n".join(part for part in parts if part)


_SECRET_SHAPED_RE = re.compile(
    r"(?i)(api[_-]?key|token|password|secret)\s*[:=]\s*[^\s]+|"
    r"\b(?:sk-[A-Za-z0-9_-]{16,}|ghp_[A-Za-z0-9_]{20,}|github_pat_[A-Za-z0-9_]{20,})\b|"
    r"[A-Za-z0-9_-]{24,}\.[A-Za-z0-9_-]{16,}\.[A-Za-z0-9_-]{16,}"
)

def _redact_prefill_status_text(text: str) -> str:
    """Return a short, non-secret diagnostic string for prefill status."""
    clean = _SECRET_SHAPED_RE.sub("[REDACTED]", str(text or ""))
    return " ".join(clean.split())[:240]


def _valid_prefill_messages(value) -> list[dict]:
    """Normalize a prefill payload to role/content messages."""
    if not isinstance(value, list):
        return []
    messages: list[dict] = []
    for item in value:
        if not isinstance(item, dict):
            continue
        role = item.get("role")
        content = item.get("content")
        if role not in {"system", "user", "assistant"} or not isinstance(content, str) or not content.strip():
            continue
        messages.append({"role": role, "content": content})
    return messages


def _resolve_prefill_path(raw: str) -> Path:
    path = Path(str(raw)).expanduser()
    if not path.is_absolute():
        try:
            from api.config import _get_config_path
            path = _get_config_path().parent / path
        except Exception:
            path = Path.cwd() / path
    return path


_PREFILL_SCRIPT_OUTPUT_LIMIT = 262_144
_PREFILL_CONTEXT_DEFAULT_MAX_CHARS = 12_000


def _prefill_context_max_chars(config_data: dict) -> int:
    raw = os.getenv("HERMES_WEBUI_PREFILL_CONTEXT_MAX_CHARS", "") or str(
        config_data.get("webui_prefill_context_max_chars") or ""
    )
    try:
        value = int(raw or _PREFILL_CONTEXT_DEFAULT_MAX_CHARS)
    except Exception:
        value = _PREFILL_CONTEXT_DEFAULT_MAX_CHARS
    return max(0, min(value, _PREFILL_SCRIPT_OUTPUT_LIMIT))


def _prefill_context_char_count(messages: list[dict]) -> int:
    return sum(len(str(message.get("content") or "")) for message in messages if isinstance(message, dict))


def _budget_compacted_prefill_context(context: dict, *, max_chars: int, char_count: int) -> dict:
    label = str(context.get("label") or "prefill context")
    message = (
        "A configured WebUI startup prefill source was available, but it exceeded "
        f"the WebUI prefill context budget ({char_count} chars > {max_chars} chars), "
        "so the note/body payload was omitted from this new chat. If the user's "
        "request depends on prior decisions, durable notes, runbooks, current "
        "context, or open issues, use the available retrieval/search/note tools "
        "to fetch only the relevant details before answering."
    )
    return {
        "status": "loaded",
        "source": "budget_compacted",
        "label": label,
        "messages": [{"role": "user", "content": message}],
        "message_count": 1,
        "compacted": True,
        "original_source": context.get("source", ""),
        "original_message_count": int(context.get("message_count") or 0),
        "original_char_count": char_count,
        "max_chars": max_chars,
    }


def _apply_prefill_context_budget(context: dict, config_data: dict) -> dict:
    if context.get("status") != "loaded":
        return context
    max_chars = _prefill_context_max_chars(config_data)
    if max_chars <= 0:
        return context
    messages = context.get("messages") or []
    char_count = _prefill_context_char_count(messages if isinstance(messages, list) else [])
    if char_count <= max_chars:
        return context

    file_raw = os.getenv("HERMES_PREFILL_MESSAGES_FILE", "") or str(config_data.get("prefill_messages_file") or "")
    if context.get("source") == "script" and file_raw:
        fallback = _load_prefill_messages_file(file_raw, source="file_budget_fallback")
        fallback_messages = fallback.get("messages") if isinstance(fallback, dict) else []
        fallback_chars = _prefill_context_char_count(fallback_messages if isinstance(fallback_messages, list) else [])
        if fallback.get("status") == "loaded" and fallback_chars <= max_chars:
            fallback["compacted"] = True
            fallback["original_source"] = context.get("source", "")
            fallback["original_label"] = context.get("label", "")
            fallback["original_message_count"] = int(context.get("message_count") or 0)
            fallback["original_char_count"] = char_count
            fallback["max_chars"] = max_chars
            return fallback

    return _budget_compacted_prefill_context(context, max_chars=max_chars, char_count=char_count)


def _prefill_not_configured() -> dict:
    return {"status": "not_configured", "source": "none", "label": "", "messages": [], "message_count": 0}


def _load_prefill_messages_file(file_raw: str, *, source: str = "file", status: str = "loaded") -> dict:
    path = _resolve_prefill_path(file_raw)
    label = path.name or "prefill file"
    if not path.exists():
        return {"status": "error", "source": source, "label": label, "messages": [], "message_count": 0, "error": "prefill file not found"}
    try:
        messages = _valid_prefill_messages(json.loads(path.read_text(encoding="utf-8")))
        return {"status": status, "source": source, "label": label, "messages": messages, "message_count": len(messages)}
    except Exception as exc:
        return {"status": "error", "source": source, "label": label, "messages": [], "message_count": 0, "error": _redact_prefill_status_text(str(exc))}


def _prefill_script_timeout(config_data: dict) -> float:
    raw = os.getenv("HERMES_WEBUI_PREFILL_MESSAGES_SCRIPT_TIMEOUT", "") or str(config_data.get("webui_prefill_messages_script_timeout") or "")
    try:
        return max(0.1, min(float(raw or 5), 30.0))
    except Exception:
        return 5.0


def _prefill_script_command(raw) -> list[str]:
    if isinstance(raw, (list, tuple)):
        return [str(part) for part in raw if str(part)]
    parts = shlex.split(str(raw or ""))
    if not parts:
        return []
    # A single script path mirrors prefill_messages_file path resolution.  More
    # complex commands keep their argv untouched so admins can pass arguments.
    if len(parts) == 1:
        parts[0] = str(_resolve_prefill_path(parts[0]))
    return parts


def _messages_from_prefill_script_output(text: str) -> list[dict]:
    stripped = str(text or "").strip()
    if not stripped:
        return []
    try:
        payload = json.loads(stripped)
    except Exception:
        payload = None
    if isinstance(payload, dict):
        payload = payload.get("messages")
    messages = _valid_prefill_messages(payload)
    if messages:
        return messages
    return [{"role": "user", "content": stripped}]


def _load_prefill_messages_script(config_data: dict) -> dict:
    script_raw = os.getenv("HERMES_WEBUI_PREFILL_MESSAGES_SCRIPT", "") or config_data.get("webui_prefill_messages_script")
    if not script_raw:
        return _prefill_not_configured()
    command = _prefill_script_command(script_raw)
    label = Path(command[0]).name if command else "prefill script"
    if not command:
        return {"status": "error", "source": "script", "label": label, "messages": [], "message_count": 0, "error": "prefill script is empty"}
    try:
        proc = subprocess.run(
            command,
            text=True,
            stdout=subprocess.PIPE,
            stderr=subprocess.PIPE,
            timeout=_prefill_script_timeout(config_data),
            check=False,
        )
    except subprocess.TimeoutExpired:
        return {"status": "error", "source": "script", "label": label, "messages": [], "message_count": 0, "error": "prefill script timed out"}
    except Exception as exc:
        return {"status": "error", "source": "script", "label": label, "messages": [], "message_count": 0, "error": _redact_prefill_status_text(str(exc))}
    if proc.returncode != 0:
        err = _redact_prefill_status_text(proc.stderr or proc.stdout or f"prefill script exited {proc.returncode}")
        return {"status": "error", "source": "script", "label": label, "messages": [], "message_count": 0, "error": err}
    if len(proc.stdout.encode("utf-8")) > _PREFILL_SCRIPT_OUTPUT_LIMIT:
        return {
            "status": "error",
            "source": "script",
            "label": label,
            "messages": [],
            "message_count": 0,
            "error": f"prefill script output exceeded {_PREFILL_SCRIPT_OUTPUT_LIMIT} bytes",
        }
    messages = _messages_from_prefill_script_output(proc.stdout)
    return {"status": "loaded", "source": "script", "label": label, "messages": messages, "message_count": len(messages)}


def _load_webui_prefill_context(
    config_data: Optional[dict] = None,
) -> dict:
    """Load configured WebUI session prefill messages.

    Supports the same bounded JSON-file shape used by Hermes Agent.  WebUI also
    supports its own explicitly opt-in script hook so admins can bridge Joplin,
    Obsidian, Notion, llm-wiki, or another local notes source into ephemeral
    turn context without baking any one note provider into the WebUI.
    """
    cfg = config_data if isinstance(config_data, dict) else get_config()
    script_context = _load_prefill_messages_script(cfg)
    file_raw = os.getenv("HERMES_PREFILL_MESSAGES_FILE", "") or str(cfg.get("prefill_messages_file") or "")
    if script_context.get("status") == "not_configured":
        if file_raw:
            return _apply_prefill_context_budget(_load_prefill_messages_file(file_raw), cfg)
        return _prefill_not_configured()
    if script_context.get("status") == "error" and file_raw:
        file_context = _load_prefill_messages_file(file_raw, source="file_fallback")
        if file_context.get("status") == "loaded":
            file_context["script_error"] = script_context.get("error", "")
            return _apply_prefill_context_budget(file_context, cfg)
    return _apply_prefill_context_budget(script_context, cfg)


def _public_prefill_context_status(prefill_context: dict) -> dict:
    """Strip message bodies before sending context status to the browser."""
    return {
        "status": prefill_context.get("status", "not_configured"),
        "source": prefill_context.get("source", "none"),
        "label": prefill_context.get("label", ""),
        "message_count": int(prefill_context.get("message_count") or 0),
        **({"error": prefill_context.get("error", "")} if prefill_context.get("error") else {}),
        **({"compacted": True} if prefill_context.get("compacted") else {}),
        **({"original_source": prefill_context.get("original_source", "")} if prefill_context.get("original_source") else {}),
        **({"original_message_count": int(prefill_context.get("original_message_count") or 0)} if prefill_context.get("original_message_count") else {}),
        **({"original_char_count": int(prefill_context.get("original_char_count") or 0)} if prefill_context.get("original_char_count") else {}),
        **({"max_chars": int(prefill_context.get("max_chars") or 0)} if prefill_context.get("max_chars") else {}),
    }


def _webui_delivery_context_prompt(config_data: Optional[dict] = None) -> str:
    """Return platform/delivery context for the ephemeral system prompt.

    Connected platforms, home channels, and scheduled-task delivery hints
    are injected into the system prompt (safe for role alternation) rather
    than as a prefill ``user`` message, which strict chat templates (Mistral,
    Gemma) reject.

    NOTE: This function only covers platform/delivery info.  The session
    framing (\"Source: WebUI\", \"Session ID\", \"Profile\", \"Workspace\") is
    emitted by ``_webui_surface_context_prompt()``, which is called from
    ``_webui_ephemeral_system_prompt()`` before this helper.  If you
    refactor this area, keep that surface call in place — the two helpers
    together produce the full session context block.
    """
    cfg = config_data if isinstance(config_data, dict) else get_config()
    lines: list[str] = []

    display_hermes_home = None
    try:
        from hermes_constants import get_hermes_home, display_hermes_home as _dh
        display_hermes_home = _dh
    except Exception:
        get_hermes_home = None  # type: ignore[assignment]

    connected = ["local (files on this machine)"]
    try:
        if get_hermes_home is not None:
            state_path = get_hermes_home() / "gateway_state.json"
            if state_path.exists():
                raw_state = json.loads(state_path.read_text(encoding="utf-8"))
                platforms = raw_state.get("platforms") if isinstance(raw_state, dict) else {}
                if isinstance(platforms, dict):
                    for name in sorted(platforms):
                        pdata = platforms.get(name) or {}
                        if isinstance(pdata, dict) and pdata.get("state") == "connected" and name != "local":
                            connected.append(f"{name}: Connected ✓")
    except Exception:
        pass
    lines.append(f"**Connected Platforms:** {', '.join(connected)}")

    home_channels = {}
    try:
        platforms_cfg = cfg.get("platforms", {}) if isinstance(cfg, dict) else {}
        if isinstance(platforms_cfg, dict):
            for name, pdata in platforms_cfg.items():
                if not isinstance(pdata, dict):
                    continue
                if pdata.get("enabled") is False:
                    continue
                home = pdata.get("home_channel")
                if isinstance(home, dict):
                    home_channels[str(name)] = str(home.get("name") or name)
    except Exception:
        home_channels = {}

    if home_channels:
        lines.append("")
        lines.append("**Home Channels (default destinations):**")
        for platform, label in sorted(home_channels.items()):
            lines.append(f"  - {platform}: {label}")

    lines.append("")
    lines.append("**Delivery options for scheduled tasks:**")
    lines.append("- `\"origin\"` → Back to this WebUI/browser session when the WebUI runtime supports origin delivery; otherwise prefer an explicit platform target.")
    try:
        home_display = display_hermes_home() if display_hermes_home else "~/.hermes"
    except Exception:
        home_display = "~/.hermes"
    lines.append(f"- `\"local\"` → Save to local files only ({home_display}/cron/output/)")
    for platform, label in sorted(home_channels.items()):
        lines.append(f"- `\"{platform}\"` → Home channel ({label})")
    lines.append("")
    lines.append("*For explicit targeting, use `\"platform:chat_id\"` format if the user provides a specific chat ID. Do not invent private IDs.*")

    return "\n".join(lines)


def _prefill_messages_with_webui_context(prefill_context: dict, config_data: Optional[dict] = None) -> list[dict]:
    """Combine recall prefill with WebUI session context.

    The session context (connected platforms, delivery hints) is injected
    via ``_webui_ephemeral_system_prompt`` / ``ephemeral_system_prompt``
    instead of as a prefill ``user`` message.  Adding it as a user message
    creates two consecutive user turns (prefill + actual) which strict chat
    templates (Mistral, Gemma) reject with a Jinja 500.
    """
    return list(prefill_context.get("messages") or [])


def _normalize_prefill_messages_before_user_turn(prefill_messages: list[dict]) -> list[dict]:
    """Ensure WebUI prefill does not end with user role before an appended turn.

    Some upstream prefill sources can end with `role: user` (for example,
    session context or recall snippets). WebUI always appends the current user
    turn after prefill in the streaming path, so a terminal user role creates an
    adjacent user/user sequence that strict chat templates (Gemma, Mistral/Jinja)
    reject.

    To keep behavior scoped, only consecutive terminal user messages are removed
    just before that boundary; earlier roles remain untouched.
    """
    sanitized = list(prefill_messages or [])
    n_dropped = 0
    while sanitized:
        last_message = sanitized[-1]
        if not isinstance(last_message, dict):
            break
        if str(last_message.get("role") or "").strip().lower() != "user":
            break
        sanitized.pop()
        n_dropped += 1
    if n_dropped:
        logger.debug("Dropped %d trailing user message(s) from prefill", n_dropped)
    return sanitized


def _has_new_assistant_reply(all_messages: list, prev_count: int) -> bool:
    """Return True if *new* messages (beyond ``prev_count``) contain an
    assistant message with non-empty content.

    ``all_messages`` is ``result.get('messages')`` which includes the full
    conversation history.  ``prev_count`` is ``len(_previous_context_messages)``
    — the number of messages present before the current turn started.  Only
    messages at index >= prev_count are inspected so that historical assistant
    replies don't mask a silent failure on the current turn.

    If ``len(all_messages) < prev_count`` (an edge-case shrink), there is no
    reliable new-message slice to inspect. Treat that as "no new assistant
    reply" so stale historical assistant replies cannot mask a silent failure.
    When ``len == prev_count``, there are no new messages and we return False.
    """
    if len(all_messages) > prev_count:
        # Normal case: new messages appended beyond the pre-turn history.
        candidates = all_messages[prev_count:]
    elif len(all_messages) < prev_count:
        return False
    else:
        # Same length. In production this means no new messages were appended.
        # However, some test fixtures replace the entire message list rather
        # than appending, so check whether the tail changed.
        return False
    return any(
        m.get('role') == 'assistant' and str(m.get('content') or '').strip()
        for m in candidates
    )


def _preferred_agent_display_name() -> str:
    """Return the configured assistant display name for user-facing copy."""
    try:
        name = str((load_settings() or {}).get('bot_name') or '').strip()
    except Exception:
        logger.debug("Failed to load bot_name for cancellation copy", exc_info=True)
        name = ''
    return name or 'Hermes'


def _preferred_agent_display_name_for_session(session) -> str:
    profile = str(getattr(session, 'profile', '') or '').strip()
    if profile and profile != 'default':
        return profile[:1].upper() + profile[1:]
    return _preferred_agent_display_name()


def _cancelled_turn_hint(agent_name: str | None = None) -> str:
    name = str(agent_name or _preferred_agent_display_name()).strip() or 'Hermes'
    return f'The run was cancelled by the user before {name} finished. No provider failure occurred.'


def _classify_provider_error(err_str: str, exc=None, *, silent_failure: bool = False) -> dict:
    """Classify provider/agent failure text for WebUI apperror UX.

    Keep this string-based until hermes-agent exposes stable structured
    provider error classes for Codex OAuth plan limits.
    """
    err_str = str(err_str or '')
    _err_lower = err_str.lower()
    _exc_name = type(exc).__name__ if exc is not None else ''
    _is_cancelled = (
        'cancelled by user' in _err_lower
        or 'canceled by user' in _err_lower
        or 'user cancelled' in _err_lower
        or 'user canceled' in _err_lower
        or 'task cancelled' in _err_lower
        or 'task canceled' in _err_lower
        or 'cancellederror' in _err_lower
        or (exc is not None and _exc_name in ('CancelledError', 'CanceledError'))
    )
    _is_interrupted = (
        not _is_cancelled
        and (
            'interrupted by user' in _err_lower
            or 'response interrupted' in _err_lower
            or 'operation interrupted' in _err_lower
            or 'operation was interrupted' in _err_lower
            or 'operation aborted' in _err_lower
            or 'request was aborted' in _err_lower
            or 'aborterror' in _err_lower
            or (exc is not None and type(exc).__name__ in ('KeyboardInterrupt', 'AbortError'))
        )
    )
    if _is_cancelled:
        return {
            'label': 'Task cancelled',
            'type': 'cancelled',
            'hint': _cancelled_turn_hint(),
        }
    if _is_interrupted:
        return {
            'label': 'Response interrupted',
            'type': 'interrupted',
            'hint': 'The run stopped before a provider response completed. If you did not cancel it, try again.',
        }
    _is_quota = _is_quota_error_text(err_str)
    _is_auth = (
        not _is_quota and (
            '401' in err_str
            or (exc is not None and 'AuthenticationError' in _exc_name)
            or 'authentication' in _err_lower
            or 'unauthorized' in _err_lower
            or 'invalid api key' in _err_lower
            or 'invalid_api_key' in _err_lower
            or 'no cookie auth credentials' in _err_lower
        )
    )
    _is_not_found = (
        # model_not_found hints mention Settings / `hermes model` below.
        '404' in err_str
        or 'not found' in _err_lower
        or 'does not exist' in _err_lower
        or 'model not found' in _err_lower
        or 'model_not_found' in _err_lower  # hint below points to Settings / `hermes model`
        or 'invalid model' in _err_lower
        or 'does not match any known model' in _err_lower
        or 'unknown model' in _err_lower
    )
    _is_rate_limit = (not _is_quota) and (
        'rate limit' in _err_lower or '429' in err_str or (exc is not None and 'RateLimitError' in _exc_name)
    )
    _is_compression_exhausted = (
        'compression_exhausted' in _err_lower
        or 'compression exhausted' in _err_lower
        or ('context length exceeded' in _err_lower and 'cannot compress further' in _err_lower)
        or ('context compression' in _err_lower and 'max compression attempts' in _err_lower)
    )
    if _is_quota:
        return {
            'label': 'Out of credits',
            'type': 'quota_exhausted',
            'hint': 'Your provider account is out of credits or usage. Top up, wait for the plan window to reset, or switch providers via `hermes model`.',
        }
    if _is_rate_limit:
        return {
            'label': 'Rate limit reached',
            'type': 'rate_limit',
            'hint': 'Rate limit reached. The fallback model (if configured) was also exhausted. Try again in a moment.',
        }
    if _is_auth:
        return {
            'label': 'Authentication failed',
            'type': 'auth_mismatch',
            'hint': 'The selected model may not be supported by your configured provider or your API key is invalid. Run `hermes model` in your terminal to update credentials, then restart the WebUI.',
        }
    if _is_not_found:
        return {
            'label': 'Model not found',
            'type': 'model_not_found',
            'hint': 'The selected model was not found by the provider. Check the model ID in Settings or run `hermes model` to verify it exists for your provider.',
        }
    if _is_compression_exhausted:
        return {
            'label': 'Context compression exhausted',
            'type': 'compression_exhausted',
            'hint': 'The conversation context is too large to compress safely. Start a new conversation or retry with a narrower task.',
        }
    if silent_failure:
        return {
            'label': 'No response from provider',
            # Preserve the existing no_response event type (#373) while making
            # the catch-all silent-failure message more specific for #1765.
            'type': 'no_response',
            'hint': 'The provider returned no content and no error. This often means a usage/rate limit was hit silently. Check provider status, switch providers via `hermes model`, or try again in a moment.',
        }
    return {'label': 'Error', 'type': 'error', 'hint': ''}


def _provider_error_payload(message: str, err_type: str, hint: str = '') -> dict:
    """Build a bounded, redacted apperror payload with provider details."""
    _message = str(message or '')
    _safe_message = _redact_text(_message).strip() if _message else ''
    payload: dict = {'message': _safe_message or _message, 'type': err_type}
    if hint:
        payload['hint'] = hint
    if _safe_message:
        _details = _safe_message
        if len(_details) > 1200:
            _details = _details[:1197].rstrip() + '…'
        if _details:
            payload['details'] = _details
    return payload


def _session_has_cancel_marker(session) -> bool:
    """Return True if a visible cancel/interrupted marker is already persisted."""
    for msg in reversed(getattr(session, 'messages', None) or []):
        if not isinstance(msg, dict):
            continue
        if msg.get('role') == 'user':
            return False
        if msg.get('role') != 'assistant':
            continue
        content = msg.get('content')
        text = ''
        if isinstance(content, str):
            text = content
        elif isinstance(content, list):
            parts = []
            for part in content:
                if isinstance(part, dict):
                    parts.append(str(part.get('text') or part.get('content') or ''))
            text = '\n'.join(parts)
        normalized = text.strip().lower()
        if any(pattern in normalized for pattern in _CANCEL_MARKER_PATTERNS):
            return True
    return False


def _cancelled_turn_content(message: str = 'Task cancelled.', agent_name: str | None = None) -> str:
    """Return cancelled-turn copy matching the verbose provider-error layout."""
    _message = str(message or 'Task cancelled.').strip()
    if not _message.endswith('.'):
        _message += '.'
    return (
        f"**Task cancelled:** {_message}\n\n"
        f"*{_cancelled_turn_hint(agent_name)}*"
    )


def _persist_cancelled_turn(session, *, message: str = 'Task cancelled.') -> None:
    """Persist a user-cancelled terminal state without provider-error wording.

    cancel_stream() usually writes this marker first, but the streaming thread can
    later unwind through the silent-failure or exception path. Those paths must
    not append a misleading provider no-response error after an explicit cancel.
    """
    _materialize_pending_user_turn_before_error(session)
    session.active_stream_id = None
    session.pending_user_message = None
    session.pending_attachments = []
    session.pending_started_at = None
    if not _session_has_cancel_marker(session):
        agent_name = _preferred_agent_display_name_for_session(session)
        session.messages.append({
            'role': 'assistant',
            'content': _cancelled_turn_content(message, agent_name),
            '_error': True,
            'provider_details': str(message or 'Task cancelled.').strip(),
            'provider_details_label': 'Cancellation details',
            'timestamp': int(time.time()),
        })


def _cleanup_ephemeral_cancelled_turn(session) -> None:
    """Remove transient /btw session state after a cancel without saving it."""
    session.active_stream_id = None
    session.pending_user_message = None
    session.pending_attachments = []
    session.pending_started_at = None
    try:
        import pathlib
        pathlib.Path(session.path).unlink(missing_ok=True)
    except Exception:
        logger.debug("Failed to clean up ephemeral cancelled session", exc_info=True)


def _finalize_cancelled_turn(session, *, ephemeral: bool = False, message: str = 'Task cancelled.') -> None:
    """Finalize a cancelled turn for persistent or ephemeral sessions."""
    if ephemeral:
        _cleanup_ephemeral_cancelled_turn(session)
        return
    _persist_cancelled_turn(session, message=message)
    try:
        session.save()
    except Exception:
        logger.debug("Failed to persist cancelled turn", exc_info=True)


def _aiagent_import_error_detail() -> str:
    """Return a multi-line diagnostic string for the "AIAgent not available" path.

    The bare ImportError ("AIAgent not available -- check that hermes-agent is
    on sys.path") leaves users guessing at which python is running, where it's
    looking, and what to fix. We assemble the same evidence a maintainer would
    ask for first (issue #1695): the python that's running, the agent_dir env
    var if set, the sys.path entries that mention 'hermes', and the most-common
    fix (`pip install -e .` in the agent dir).

    Kept as a separate helper so it stays out of the hot path until we actually
    need to raise — building it on every successful import would be wasted work.
    """
    import os as _os
    import sys as _sys

    lines = ["AIAgent not available -- check that hermes-agent is on sys.path"]
    lines.append("")
    lines.append(f"  python:  {_sys.executable}")
    agent_dir = _os.environ.get("HERMES_WEBUI_AGENT_DIR")
    if agent_dir:
        lines.append(f"  HERMES_WEBUI_AGENT_DIR: {agent_dir}")
    else:
        lines.append("  HERMES_WEBUI_AGENT_DIR: (not set)")

    # Show only the sys.path entries that look relevant — full sys.path is noisy.
    relevant = [p for p in _sys.path if "hermes" in p.lower() or "agent" in p.lower()]
    if relevant:
        lines.append("  sys.path entries mentioning hermes/agent:")
        for entry in relevant[:6]:
            lines.append(f"    - {entry}")
        if len(relevant) > 6:
            lines.append(f"    ... and {len(relevant) - 6} more")
    else:
        lines.append("  sys.path: (no entries mention hermes or agent)")

    lines.append("")
    lines.append("  Most common fix: install the agent in editable mode so its modules")
    lines.append("  appear on sys.path:")
    lines.append("")
    lines.append("    cd /path/to/hermes-agent")
    lines.append("    pip install -e .")
    lines.append("")
    lines.append("  Then restart the WebUI.")
    lines.append("")
    lines.append('  Full troubleshooting: docs/troubleshooting.md ("AIAgent not available")')
    return "\n".join(lines)
from api.models import get_session, title_from
from api.workspace import set_last_workspace

# Fields that are safe to send to LLM provider APIs.
# Everything else (attachments, timestamp, _ts, etc.) is display-only
# metadata added by the webui and must be stripped before the API call.
_API_SAFE_MSG_KEYS = {'role', 'content', 'tool_calls', 'tool_call_id', 'name', 'refusal', 'reasoning_content'}

_NATIVE_IMAGE_MAX_BYTES = 20 * 1024 * 1024

_GATEWAY_ROUTING_TOP_LEVEL_KEYS = {
    'used_provider',
    'used_model',
    'requested_provider',
    'requested_model',
}
_GATEWAY_ROUTING_CONTAINER_KEYS = (
    'llm_gateway',
    'gateway',
    'metadata',
    'response_metadata',
    'routing_metadata',
    'usage',
)
_GATEWAY_ROUTING_ATTEMPT_KEYS = {
    'provider', 'model', 'status', 'reason', 'selection_reason', 'score',
    'latency_ms', 'error', 'timestamp', 'selected', 'attempt', 'attempt_index',
}


def _clean_gateway_routing_scalar(value):
    if value is None:
        return None
    if isinstance(value, (str, int, float, bool)):
        text = str(value).strip()
        if not text:
            return None
        return value if isinstance(value, (int, float, bool)) else text[:240]
    return None


def _find_gateway_metadata_payload(payload):
    if not isinstance(payload, dict):
        return None
    if any(k in payload for k in _GATEWAY_ROUTING_TOP_LEVEL_KEYS) or isinstance(payload.get('routing'), list):
        return payload
    for key in _GATEWAY_ROUTING_CONTAINER_KEYS:
        nested = payload.get(key)
        found = _find_gateway_metadata_payload(nested)
        if found:
            return found
    return None


def _normalize_gateway_routing_metadata(payload, requested_model=None, requested_provider=None):
    """Return safe LLM Gateway routing metadata, or None when absent.

    LLM Gateway response metadata can contain provider/model routing details,
    but WebUI must only persist display-safe scalars and a bounded routing list.
    Secrets or provider-specific request objects are deliberately ignored.
    """
    src = _find_gateway_metadata_payload(payload)
    if not src:
        return None

    normalized = {}
    for key in _GATEWAY_ROUTING_TOP_LEVEL_KEYS:
        value = _clean_gateway_routing_scalar(src.get(key))
        if value is not None:
            normalized[key] = value

    if 'requested_model' not in normalized:
        fallback_model = _clean_gateway_routing_scalar(requested_model)
        if fallback_model is not None:
            normalized['requested_model'] = fallback_model
    if 'requested_provider' not in normalized:
        fallback_provider = _clean_gateway_routing_scalar(requested_provider)
        if fallback_provider is not None:
            normalized['requested_provider'] = fallback_provider

    routing = []
    raw_routing = src.get('routing')
    if isinstance(raw_routing, list):
        for attempt in raw_routing[:12]:
            if not isinstance(attempt, dict):
                continue
            clean_attempt = {}
            for key in _GATEWAY_ROUTING_ATTEMPT_KEYS:
                value = _clean_gateway_routing_scalar(attempt.get(key))
                if value is not None:
                    clean_attempt[key] = value
            if clean_attempt:
                routing.append(clean_attempt)
    if routing:
        normalized['routing'] = routing

    used_provider = str(normalized.get('used_provider') or '').strip().lower()
    requested_provider_norm = str(normalized.get('requested_provider') or '').strip().lower()
    used_model = str(normalized.get('used_model') or '').strip().lower()
    requested_model_norm = str(normalized.get('requested_model') or '').strip().lower()
    provider_changed = bool(used_provider and requested_provider_norm and used_provider != requested_provider_norm)
    model_changed = bool(used_model and requested_model_norm and used_model != requested_model_norm)
    attempted_providers = [
        str(a.get('provider') or '').strip().lower()
        for a in routing
        if a.get('provider')
    ]
    distinct_attempted_providers = {p for p in attempted_providers if p}
    failed_before_selection = any(
        str(a.get('status') or '').strip().lower() in {'failed', 'error', 'timeout', 'rejected'}
        for a in routing
    )
    has_failover = bool(provider_changed or len(distinct_attempted_providers) > 1 or failed_before_selection)

    if not (
        normalized.get('used_provider') or normalized.get('used_model') or routing or provider_changed or model_changed
    ):
        return None
    normalized['provider_changed'] = provider_changed
    normalized['model_changed'] = model_changed
    normalized['has_failover'] = has_failover
    return normalized


def _extract_gateway_routing_metadata(agent, result, requested_model=None, requested_provider=None):
    candidates = []
    if isinstance(result, dict):
        candidates.extend([
            result.get('llm_gateway'),
            result.get('gateway'),
            result.get('metadata'),
            result.get('response_metadata'),
            result.get('routing_metadata'),
            result.get('usage'),
            result,
        ])
    for attr in (
        'llm_gateway_metadata',
        'gateway_metadata',
        'last_response_metadata',
        'response_metadata',
        'routing_metadata',
        'last_usage',
    ):
        if agent is not None:
            candidates.append(getattr(agent, attr, None))
    for candidate in candidates:
        normalized = _normalize_gateway_routing_metadata(
            candidate,
            requested_model=requested_model,
            requested_provider=requested_provider,
        )
        if normalized:
            return normalized
    return None


def _build_agent_thread_env(profile_runtime_env: dict | None, workspace: str, session_id: str, profile_home: str) -> dict:
    """Build thread-local agent env with per-run values overriding profile defaults.

    Profile runtime env may include TERMINAL_CWD from config.yaml. Passing it as
    **kwargs alongside an explicit TERMINAL_CWD raises TypeError before the
    agent starts, so merge into one dict first and let the active workspace win.
    """
    env = dict(profile_runtime_env or {})
    env.update({
        'TERMINAL_CWD': str(workspace),
        'HERMES_EXEC_ASK': '1',
        'HERMES_SESSION_KEY': session_id,
        'HERMES_SESSION_ID': session_id,
        'HERMES_SESSION_PLATFORM': 'webui',
        'HERMES_HOME': profile_home,
    })
    return env


def _format_process_notification(evt: dict) -> str:
    """Format a completed background process notification for agent input."""
    if not isinstance(evt, dict):
        return ''
    if evt.get('type') != 'completion':
        return ''
    _sid = evt.get('session_id', '')
    _cmd = evt.get('command', '')
    _exit = evt.get('exit_code', '')
    _out = evt.get('output') or ''
    if len(_out) > 4000:
        _out = _out[:4000] + '\n... (truncated)'
    return (
        f"[IMPORTANT: Background process {_sid} completed (exit code {_exit}).\n"
        f"Command: {_cmd}\n"
        f"Output:\n{_out}]"
    )


def _mark_process_completion_consumed(process_registry, process_id: str) -> None:
    """Best-effort bridge to the agent registry's private completion marker."""
    try:
        with process_registry._lock:
            process_registry._completion_consumed.add(process_id)
    except Exception:
        logger.debug("Failed to mark process completion consumed", exc_info=True)


def _drain_webui_process_notifications(session_id: str) -> list[str]:
    """Return completion notifications that belong to this WebUI session.

    The agent registry completion queue is process-wide and events do not carry
    the WebUI session key directly. Look up the live process session before
    delivery so completions from other tabs remain queued for their owners.
    """
    if not session_id:
        return []
    try:
        from tools.process_registry import process_registry
    except Exception:
        return []

    notifications: list[str] = []
    skipped_events: list[dict] = []
    completion_queue = getattr(process_registry, 'completion_queue', None)
    if completion_queue is None:
        return []

    while True:
        try:
            evt = completion_queue.get_nowait()
        except queue.Empty:
            break
        except Exception:
            logger.debug("Failed to drain process completion queue", exc_info=True)
            break

        evt_sid = str(evt.get('session_id') or '') if isinstance(evt, dict) else ''
        if not evt_sid:
            skipped_events.append(evt)
            continue
        try:
            if process_registry.is_completion_consumed(evt_sid):
                continue
            proc = process_registry.get(evt_sid)
        except Exception:
            proc = None
        if getattr(proc, 'session_key', None) != session_id:
            skipped_events.append(evt)
            continue

        notification = _format_process_notification(evt)
        if notification:
            notifications.append(notification)
            _mark_process_completion_consumed(process_registry, evt_sid)

    for evt in skipped_events:
        try:
            completion_queue.put(evt)
        except Exception:
            logger.debug("Failed to requeue process completion event", exc_info=True)
            break
    return notifications


def _attachment_name(att) -> str:
    if isinstance(att, dict):
        return str(att.get('name') or att.get('filename') or att.get('path') or '').strip()
    return str(att or '').strip()


_IMAGE_MAGIC: dict[bytes | None, frozenset[str]] = {
    b'\x89PNG\r\n\x1a\n': frozenset({'image/png'}),
    b'\xff\xd8\xff': frozenset({'image/jpeg'}),
    b'GIF87a': frozenset({'image/gif'}),
    b'GIF89a': frozenset({'image/gif'}),
    b'RIFF': frozenset({'image/webp'}),
    b'BM': frozenset({'image/bmp'}),
    None: frozenset({'image/svg+xml'}),
}


def _is_valid_image(path: Path, mime: str) -> bool:
    """Check that the file's first bytes match the expected image MIME type.

    Uses simple magic-number detection (no external dependency). SVG is
    allowed through because it is text-based and has no binary signature.
    """
    if not mime.startswith('image/'):
        return False
    mime_base = mime.split(';', 1)[0]
    if mime_base == 'image/svg+xml':
        return True
    try:
        with path.open('rb') as fh:
            head = fh.read(16)
    except OSError:
        return False
    for magic, mimes in _IMAGE_MAGIC.items():
        if magic is not None and head.startswith(magic) and mime_base in mimes:
            return True
    return False


def _resolve_image_input_mode(cfg: dict) -> str:
    """Return ``"native"`` or ``"text"`` based on config, mirroring
    ``agent/image_routing.py:decide_image_input_mode``.

    The agent has this logic, but the WebUI's ``_build_native_multimodal_message``
    was unconditionally embedding images as native ``image_url`` parts, completely
    bypassing ``image_input_mode``.  This caused silent failures when the main model
    does not support images and the fallback model is also text-only (#21160-related).
    """
    agent_cfg = cfg.get("agent") or {}
    mode = str(agent_cfg.get("image_input_mode", "auto") or "auto").strip().lower()
    if mode not in ("auto", "native", "text"):
        mode = "auto"

    if mode == "native":
        return "native"
    if mode == "text":
        return "text"

    # auto: if auxiliary.vision is explicitly configured → text mode
    # (user opted into a dedicated vision backend)
    aux = cfg.get("auxiliary") or {}
    vision = aux.get("vision") or {}
    provider = str(vision.get("provider") or "").strip().lower()
    model_name = str(vision.get("model") or "").strip()
    base_url = str(vision.get("base_url") or "").strip()
    if provider not in ("", "auto") or model_name or base_url:
        return "text"

    # No explicit vision config, no model-capability lookup available in WebUI.
    # Default to native — the agent's ``_strip_images_from_messages`` guard will
    # strip images on rejection and retry as text.
    return "native"


def _build_native_multimodal_message(workspace_ctx: str, msg_text: str, attachments, workspace: str, *, cfg: dict = None):
    """Build native multimodal content parts for current-turn image uploads.

    WebUI uploads files into the active workspace. For image files, pass the
    bytes to Hermes as OpenAI-style image_url data URLs so vision-capable main
    models can consume them in the same request. Non-image files intentionally
    stay as text path attachments so the agent can inspect them with file tools.

    When *cfg* is provided, respects ``agent.image_input_mode`` — if the resolved
    mode is ``"text"``, returns a plain string (attachments are not embedded) so
    the agent's text-mode pipeline (``vision_analyze``) handles images.
    """
    if not attachments:
        return workspace_ctx + msg_text

    # ── Check image_input_mode before embedding anything ──
    if cfg is not None and _resolve_image_input_mode(cfg) == "text":
        return workspace_ctx + msg_text

    parts = [{'type': 'text', 'text': workspace_ctx + msg_text}]
    workspace_root = Path(workspace).expanduser().resolve()
    # Stage-361 maintainer fix (Opus SHOULD-FIX): chat uploads from #2319 now
    # land in ~/.hermes/webui/attachments/<sid>/ (outside workspace_root by
    # design). The pre-existing `path.relative_to(workspace_root)` guard would
    # silently reject every image upload for vision-capable models. Allow the
    # configured attachment root in addition to workspace_root so native
    # multimodal embeds still build the base64 image_url part. The
    # _attachment_root() helper applies expanduser+resolve and is also reused
    # by _upload_destination — single source of truth for the inbox root.
    try:
        from api.upload import _attachment_root
        attachment_root = _attachment_root()
        _allowed_roots = (workspace_root, attachment_root)
    except Exception:
        _allowed_roots = (workspace_root,)
    image_count = 0

    for att in attachments or []:
        if not isinstance(att, dict):
            continue
        raw_path = str(att.get('path') or '').strip()
        if not raw_path:
            continue
        try:
            path = Path(raw_path).expanduser().resolve()
            # Uploads should live inside the selected workspace OR the
            # session attachment inbox (#2319). Do not read arbitrary paths
            # from client-provided attachment metadata.
            if not any(path.is_relative_to(r) for r in _allowed_roots):
                continue
            if not path.is_file():
                continue
            size = path.stat().st_size
            if size <= 0 or size > _NATIVE_IMAGE_MAX_BYTES:
                continue
            mime = str(att.get('mime') or '').strip() or (mimetypes.guess_type(path.name)[0] or '')
            if not mime.startswith('image/') or not _is_valid_image(path, mime):
                continue
            data = base64.b64encode(path.read_bytes()).decode('ascii')
        except Exception:
            continue
        parts.append({
            'type': 'image_url',
            'image_url': {'url': f'data:{mime};base64,{data}'},
        })
        image_count += 1

    return parts if image_count else workspace_ctx + msg_text


_INLINE_THINKING_TAG_PAIRS = (
    ('<think>', '</think>'),
    ('<|channel>thought\n', '<channel|>'),
    ('<|turn|>thinking\n', '<turn|>'),
)


def _inline_thinking_fence_marker_at(text, index):
    # A fenced code block opener may be indented up to 3 spaces in Markdown
    # (4+ spaces is an indented code block, handled separately). The marker is
    # only a fence when it sits at the start of a line (after optional 1-3
    # spaces of indentation).
    if index > 0 and text[index - 1] != '\n':
        # Allow up to 3 leading spaces: walk back over spaces to a line start.
        back = index - 1
        spaces = 0
        while back >= 0 and text[back] == ' ' and spaces < 3:
            back -= 1
            spaces += 1
        if not (back < 0 or text[back] == '\n'):
            return ''
    if text.startswith('```', index):
        return '```'
    if text.startswith('~~~', index):
        return '~~~'
    return ''


def _line_is_indented_code(text, line_start):
    """True when the line beginning at `line_start` is a markdown indented code
    block line (>=4 leading spaces or a leading tab, and not blank). `line_start`
    must be the index of the first character of the line. O(1)-ish: only inspects
    the line's leading characters, not the whole document (the per-character
    variant was O(n^2) on long no-newline content — #3633 Codex perf catch)."""
    if line_start >= len(text):
        return False
    if text[line_start] == '\t':
        # A leading tab is indented code only if the line isn't otherwise blank.
        nl = text.find('\n', line_start)
        seg = text[line_start:(nl if nl != -1 else len(text))]
        return bool(seg.strip())
    if text.startswith('    ', line_start):
        nl = text.find('\n', line_start)
        seg = text[line_start:(nl if nl != -1 else len(text))]
        return bool(seg.strip())
    return False


def _merge_inline_thinking_reasoning(existing_reasoning, extracted_parts):
    out = str(existing_reasoning or '').strip()
    for part in extracted_parts or ():
        item = str(part or '').strip()
        if not item:
            continue
        if not out:
            out = item
            continue
        if out == item or any(existing.strip() == item for existing in out.split('\n\n')):
            continue
        out = out + '\n\n' + item
    return out


def _extract_inline_thinking_from_content(raw_content, existing_reasoning='', *, streaming=False):
    """Split inline thinking blocks out of assistant content.

    Code-aware: thinking tags inside a triple-fence (``` / ~~~), an inline
    single-backtick code span, or an indented (>=4-space / tab) code block are
    LEFT VISIBLE — they are literal text a user typed/pasted, not a real thinking
    trace. (#3633 deep-review / Codex catch: the earlier full-scan version only
    protected triple fences, so a literal `<think>` in an inline code span got
    silently extracted.)

    ``streaming`` gates partial/unclosed-block handling: during live streaming an
    unmatched open tag means "still thinking" and its tail is shown as reasoning;
    on the persist/reload path (streaming=False) an unclosed tag is LEFT VISIBLE
    so prose after a literal ``<think>`` is never silently truncated on save.
    """
    text = '' if raw_content is None else str(raw_content)
    if not text:
        return text, str(existing_reasoning or '').strip()
    visible = []
    extracted = []
    cursor = 0
    index = 0
    fence = ''
    in_backtick = False
    length = len(text)
    # Incremental, O(1)-per-iteration line state (the previous per-character line
    # scan made the whole pass O(n^2) on long no-newline content — #3633 Codex
    # perf catch). `line_is_indented_code` is recomputed only at a line start.
    line_is_indented_code = _line_is_indented_code(text, 0)
    # Whether any non-whitespace char appeared in text[:index] — the cheap
    # equivalent of the old `text[:index].strip() != ''` leading check.
    seen_nonspace = False
    # Whether a LEADING thinking block/prefix was removed — only then do we
    # lstrip the final content (so a reply that legitimately starts with
    # indented code / whitespace and has NO leading thinking wrapper keeps its
    # leading whitespace — #3633 Codex catch).
    leading_removed = False
    while index < length:
        ch = text[index]
        if index > 0 and text[index - 1] == '\n':
            line_is_indented_code = _line_is_indented_code(text, index)
        marker = _inline_thinking_fence_marker_at(text, index)
        if marker:
            fence = '' if fence == marker else (fence or marker)
        # Inline single-backtick code span toggles on each lone backtick that is
        # not part of a triple fence. Only tracked outside a triple fence.
        if not fence and not marker and ch == '`':
            in_backtick = not in_backtick
        in_code = bool(fence) or in_backtick or line_is_indented_code
        if not in_code:
            pair = None
            for open_tag, close_tag in _INLINE_THINKING_TAG_PAIRS:
                if text.startswith(open_tag, index):
                    pair = (open_tag, close_tag)
                    break
            if pair:
                open_tag, close_tag = pair
                close_index = text.find(close_tag, index + len(open_tag))
                if close_index == -1:
                    # Unclosed open tag. A LEADING unclosed block (nothing
                    # visible before it) is a genuine thinking trace that got
                    # cut off / persisted mid-thought → reasoning (master #3455
                    # leading-only intent, and the live-stream "still thinking"
                    # case). An unclosed tag AFTER visible content on the persist
                    # path is almost always a literal typed tag — leave it (and
                    # the prose after it) visible so nothing is silently
                    # truncated (#3633 Codex catch). During live streaming any
                    # unmatched open tag is treated as in-progress thinking.
                    leading = not seen_nonspace
                    if not streaming and not leading:
                        break
                    if leading:
                        leading_removed = True
                    visible.append(text[cursor:index])
                    partial = text[index + len(open_tag):]
                    if partial:
                        extracted.append(partial)
                    cursor = length
                    index = length
                    break
                visible.append(text[cursor:index])
                extracted.append(text[index + len(open_tag):close_index])
                if not seen_nonspace:
                    leading_removed = True
                seen_nonspace = True  # the extracted tag span is non-whitespace
                index = close_index + len(close_tag)
                cursor = index
                continue
            if streaming:
                matched_partial = False
                for open_tag, _close_tag in _INLINE_THINKING_TAG_PAIRS:
                    rest = text[index:]
                    if len(rest) < len(open_tag) and open_tag.startswith(rest):
                        if not seen_nonspace:
                            leading_removed = True
                        visible.append(text[cursor:index])
                        cursor = length
                        index = length
                        matched_partial = True
                        break
                if matched_partial or index >= length:
                    break
        if not ch.isspace():
            seen_nonspace = True
        index += 1
    if cursor < length:
        visible.append(text[cursor:])
    content = ''.join(visible)
    if leading_removed:
        content = content.lstrip()
    reasoning = _merge_inline_thinking_reasoning(existing_reasoning, extracted)
    return content, reasoning


def _split_thinking_from_content(raw_content, existing_reasoning=''):
    """Split inline thinking blocks out of assistant content for persistence.

    Persistence path: streaming=False, so an unclosed tag stays visible content
    (a partial block only means "still thinking" during a live stream).
    """
    return _extract_inline_thinking_from_content(
        raw_content,
        existing_reasoning=existing_reasoning,
        streaming=False,
    )


def _strip_thinking_markup(text: str) -> str:
    """Remove common reasoning/thinking wrappers from model text."""
    if not text:
        return ''
    s = str(text)
    # Treat provider thinking wrappers as metadata only when they lead the
    # response. Literal discussion of these tags later in normal prose should
    # stay visible (#2152).
    s = re.sub(r'^\s*<think>.*?</think>\s*', ' ', s, flags=re.IGNORECASE | re.DOTALL)
    s = re.sub(r'^\s*<\|channel\|?>thought\n?.*?<channel\|>\s*', ' ', s, flags=re.IGNORECASE | re.DOTALL)
    s = re.sub(r'^\s*<\|turn\|>thinking\n.*?<turn\|>\s*', ' ', s, flags=re.IGNORECASE | re.DOTALL)  # Gemma 4
    s = re.sub(r'^\s*(the|ther)\s+user\s+is\s+asking[^\n]*(?:\n|$)', ' ', s, flags=re.IGNORECASE)
    # Strip plain-text thinking preambles from models that don't use <think> tags (e.g. Qwen3).
    # These appear as the very first sentence of the assistant response and are not useful as titles.
    s = re.sub(
        r"^\s*(?:here(?:'s| is) (?:a |my )?(?:thinking|thought) (?:process|trace|through)\b[^\n]*\n?"
        r"|let me (?:think|work|reason|analyze|walk) (?:through|about|this|step)\b[^\n]*\n?"
        r"|i(?:'ll| will) (?:think|work|reason|analyze|break this down)\b[^\n]*\n?"
        r"|(?:okay|alright|sure|of course),?\s+let me\b[^\n]*\n?)",
        ' ', s, flags=re.IGNORECASE
    )
    s = re.sub(r'\s+', ' ', s).strip()
    return s


def _strip_xml_tool_calls(text: str) -> str:
    """Strip XML-style function_calls blocks that DeepSeek and similar models
    emit in their raw response text.  These blocks are processed separately as
    tool calls; leaving them in the assistant content causes them to render
    visibly in the chat bubble.

    Handles both complete blocks (<function_calls>…</function_calls>) and
    partial/orphaned opening tags that may appear at the tail of a stream.
    Also handles variants like <｜DSML｜function_calls> from DeepSeek on Bedrock.
    """
    if not text:
        return text
    s = str(text)
    # Check if contains any function_calls/DSML marker (case-insensitive)
    _lo = s.lower()
    if 'function_calls' not in _lo and 'dsml' not in _lo:
        return text
    
    _dsml_prefix = r'(?:\s*｜\s*DSML\s*[｜|]\s*)?'
    open_tag = rf'<{_dsml_prefix}function_calls'
    close_tag = rf'</{_dsml_prefix}function_calls>'
    # Strip complete blocks for both <function_calls> and <｜DSML｜function_calls>.
    s = re.sub(
        rf'{open_tag}>.*?{close_tag}',
        '',
        s,
        flags=re.IGNORECASE | re.DOTALL
    )
    # Strip orphaned/truncated opening tags, including missing ">" at stream tail.
    s = re.sub(
        rf'{open_tag}(?:>|$).*$',
        '',
        s,
        flags=re.IGNORECASE | re.DOTALL
    )
    # Remove malformed DSML fragments like "<｜DSML |" that can leak in tokens.
    s = re.sub(r'<\s*｜\s*DSML\s*[｜|]\s*', '', s, flags=re.IGNORECASE)
    return s.strip()


def _sanitize_generated_title(text: str) -> str:
    """Sanitize LLM-generated title text before persisting to session."""
    s = _strip_thinking_markup(text or '')
    s = re.sub(
        r'^\s*(?:[*_`~]+\s*)?(?:session\s+title|title)\s*:\s*(?:[*_`~]+\s*)?',
        '',
        s,
        flags=re.IGNORECASE,
    )
    s = re.sub(r'^\s*title\s*:\s*', '', s, flags=re.IGNORECASE)
    s = s.strip(" \t\r\n\"'`*_~")
    s = re.sub(r'\s+', ' ', s).strip()
    # Guard against chain-of-thought leakage and meta-reasoning patterns.
    if _looks_invalid_generated_title(s):
        return ''
    return s[:80]


def _looks_invalid_generated_title(text: str) -> bool:
    s = str(text or '')
    if not s.strip():
        return True
    return bool(
        re.search(r'<think>|<\|channel\|>thought|<\|turn\|>thinking', s, flags=re.IGNORECASE)
        or re.search(r'^\s*(the|ther)\s+user\s+', s, flags=re.IGNORECASE)
        or re.search(r'^\s*user\s+\w+\s+', s, flags=re.IGNORECASE)
        or re.search(r'\b(they|user)\s+want(s)?\s+me\s+to\b', s, flags=re.IGNORECASE)
        or re.search(r'^\s*(i|we)\s+(should|need to|will|can)\b', s, flags=re.IGNORECASE)
        or re.search(r'^\s*let me\b', s, flags=re.IGNORECASE)
        or re.search(r"^\s*here(?:'s| is) (?:a |my )?(?:thinking|thought)", s, flags=re.IGNORECASE)
        or re.search(r'^\s*(ok|okay|done|all set|complete|completed|finished)\b[\s.!?]*$', s, flags=re.IGNORECASE)
    )


def _message_text(value) -> str:
    """Extract plain text from mixed message content payloads."""
    if isinstance(value, list):
        parts = []
        for p in value:
            if not isinstance(p, dict):
                continue
            ptype = str(p.get('type') or '').lower()
            if ptype in ('', 'text', 'input_text', 'output_text'):
                parts.append(str(p.get('text') or p.get('content') or ''))
        return _strip_thinking_markup('\n'.join(parts).strip())
    return _strip_thinking_markup(str(value or '').strip())


_WORKSPACE_PREFIX_RE = re.compile(r'^\s*\[Workspace::v1:\s*(?:\\.|[^\]\\])+\]\s*')
_LEGACY_WORKSPACE_PREFIX_RE = re.compile(r'^\s*\[Workspace:[^\]]+\]\s*')
_WORKSPACE_PREFIX_ANY_RE = re.compile(r'\[Workspace::v1:\s*(?:\\.|[^\]\\])+\]\s*')
_LEGACY_WORKSPACE_PREFIX_ANY_RE = re.compile(r'\[Workspace:[^\]]+\]\s*')


def _escape_workspace_prefix_path(path: str) -> str:
    return str(path or '').replace('\\', '\\\\').replace(']', '\\]')


def _workspace_context_prefix(path: str) -> str:
    return f"[Workspace::v1: {_escape_workspace_prefix_path(path)}]\n"


def _strip_workspace_prefix(text: str, *, include_legacy: bool = False) -> str:
    """Remove WebUI-injected workspace tags without eating user-typed text."""
    value = str(text or '')
    stripped = _WORKSPACE_PREFIX_RE.sub('', value, count=1)
    if include_legacy and stripped == value:
        stripped = _LEGACY_WORKSPACE_PREFIX_RE.sub('', value, count=1)
    return stripped.strip()


def _looks_like_current_user_turn(msg, msg_text) -> bool:
    """Match the current human turn even if an internal workspace tag leaked mid-text.

    Normal model-facing messages start with the workspace sentinel. A failed
    retry/merge path can also return an optimistic draft followed by the
    sentinel and the real prompt. Only treat that shape as the current turn
    when the text after the sentinel exactly matches the submitted prompt.
    """
    if not isinstance(msg, dict) or msg.get('role') != 'user':
        return False
    needle = " ".join(str(msg_text or '').split())
    if not needle:
        return False
    text = _message_text(msg.get('content', ''))
    candidates = [_strip_workspace_prefix(text, include_legacy=True)]
    for pattern in (_WORKSPACE_PREFIX_ANY_RE, _LEGACY_WORKSPACE_PREFIX_ANY_RE):
        for match in pattern.finditer(text):
            candidates.append(text[match.end():])
    return any(" ".join(str(candidate or '').split()) == needle for candidate in candidates)


def _first_exchange_snippets(messages):
    """Return (first_user_text, first_assistant_text) snippets for title generation.

    Prefer the first substantive assistant answer in the opening exchange,
    skipping empty placeholders and assistant tool-call preambles.
    """
    user_text = ''
    asst_text = ''
    for m in messages or []:
        if not isinstance(m, dict):
            continue
        role = m.get('role')
        if role == 'user':
            candidate = _message_text(m.get('content'))
            if not user_text and candidate:
                user_text = candidate
                continue
            if user_text and candidate:
                break
        elif role == 'assistant' and user_text:
            candidate = _message_text(m.get('content'))
            # Skip tool-call preambles *only* when content is empty or looks
            # like meta-reasoning ("Let me check my memory first.", "The user
            # is asking...", etc.). Assistant rows that carry tool_calls but
            # also contain a substantive answer text are kept — those are
            # agentic first-turn plans that are legitimate title candidates.
            if m.get('tool_calls') and (not candidate or _looks_invalid_generated_title(candidate)):
                continue
            if candidate:
                asst_text = candidate
        if user_text and asst_text:
            break
    return user_text[:500], asst_text[:500]


def _latest_exchange_snippets(messages):
    """Return (last_user_text, last_assistant_text) snippets for title refresh.

    Walks the message list backwards to find the last user+assistant pair,
    skipping empty or tool-call-only assistant messages.
    """
    user_text = ''
    asst_text = ''
    for m in reversed(messages or []):
        if not isinstance(m, dict):
            continue
        role = m.get('role')
        if role == 'assistant' and not asst_text:
            candidate = _message_text(m.get('content'))
            # Skip tool-call-only preambles
            if m.get('tool_calls') and (not candidate or _looks_invalid_generated_title(candidate)):
                continue
            if candidate:
                asst_text = candidate
        elif role == 'user' and not user_text:
            candidate = _message_text(m.get('content'))
            if candidate:
                user_text = candidate
        if user_text and asst_text:
            break
    return user_text[:500], asst_text[:500]


def _count_exchanges(messages):
    """Count the number of user messages (rough exchange count)."""
    count = 0
    for m in messages or []:
        if isinstance(m, dict) and m.get('role') == 'user':
            content = m.get('content', '')
            if isinstance(content, list):
                content = ' '.join(p.get('text', '') for p in content if isinstance(p, dict) and p.get('type') == 'text')
            if str(content).strip():
                count += 1
    return count


def _get_title_refresh_interval() -> int:
    """Read the auto_title_refresh_every setting (0 = disabled)."""
    try:
        from api.config import load_settings
        settings = load_settings()
        val = settings.get('auto_title_refresh_every', '0')
        return int(val) if str(val).strip().isdigit() and int(val) > 0 else 0
    except Exception:
        return 0


def _is_provisional_title(current_title: str, messages) -> bool:
    """Heuristic: title equals first-message substring placeholder."""
    derived = title_from(messages, '') or ''
    if not derived:
        return False
    current = re.sub(r'\s+', ' ', str(current_title or '')).strip()
    candidate = re.sub(r'\s+', ' ', str(derived[:64] or '')).strip()
    if not current or not candidate:
        return False
    return current == candidate


def _detect_title_language(text: str) -> str:
    """Best-effort language hint for title generation/validation."""
    s = re.sub(r'\s+', ' ', str(text or '')).strip().lower()
    if not s:
        return ''
    german_markers = {
        'warum', 'werden', 'wird', 'wurde', 'hier', 'nicht', 'mehr', 'alte', 'alten',
        'bilder', 'angezeigt', 'prüfe', 'ich', 'und', 'oder', 'mit', 'für', 'von',
        'zu', 'ist', 'sind', 'bitte', 'kannst',
    }
    tokens = re.findall(r'[A-Za-zÀ-ÖØ-öø-ÿ]+', s)
    german_hits = sum(1 for tok in tokens if tok in german_markers)
    if re.search(r'[äöüß]', s) or german_hits >= 3:
        return 'de'
    return ''


def _script_counts(text: str) -> dict:
    """Return per-script alphabetic character counts for *text*.

    Buckets: ``latin``, ``cjk`` (Han/Hiragana/Katakana/Hangul), ``cyrillic``,
    ``arabic``, ``hebrew``, ``greek``, ``devanagari``. Non-alphabetic and
    unclassified characters are ignored.
    """
    counts: dict[str, int] = {}
    for ch in str(text or ''):
        if not ch.isalpha():
            continue
        o = ord(ch)
        if (0x0041 <= o <= 0x024F) or (0x1E00 <= o <= 0x1EFF):
            bucket = 'latin'
        elif (
            (0x4E00 <= o <= 0x9FFF) or (0x3400 <= o <= 0x4DBF)   # Han
            or (0x3040 <= o <= 0x30FF)                            # Hiragana/Katakana
            or (0xAC00 <= o <= 0xD7A3) or (0x1100 <= o <= 0x11FF) # Hangul
        ):
            bucket = 'cjk'
        elif 0x0400 <= o <= 0x04FF:
            bucket = 'cyrillic'
        elif (0x0600 <= o <= 0x06FF) or (0x0750 <= o <= 0x077F):
            bucket = 'arabic'
        elif 0x0590 <= o <= 0x05FF:
            bucket = 'hebrew'
        elif 0x0370 <= o <= 0x03FF:
            bucket = 'greek'
        elif 0x0900 <= o <= 0x097F:
            bucket = 'devanagari'
        else:
            continue
        counts[bucket] = counts.get(bucket, 0) + 1
    return counts


def _dominant_script(text: str) -> str:
    """Return a coarse writing-script bucket for *text*, or '' when undecidable.

    Script-level (not language-level) classification is cheap and dependency-free.
    Returns the dominant script only when it holds a clear (≥60%) majority of the
    alphabetic characters, so mixed/borrowed text doesn't flip the bucket. Used
    to establish the conversation start's expected script for cross-script title
    drift detection (#3293).
    """
    counts = _script_counts(text)
    total = sum(counts.values())
    if total < 2:
        return ''
    top, top_n = max(counts.items(), key=lambda kv: kv[1])
    if top_n / total >= 0.6:
        return top
    return ''


def _title_prompt_language_rule(user_text: str) -> str:
    return "Match the language of the user question.\n"


def _title_language_mismatch(user_text: str, title: str) -> bool:
    """Reject titles whose language clearly diverges from the conversation start.

    Two independent signals:
    1. Cross-script drift (#3293): when the conversation start has a clear
       dominant writing script (e.g. latin/English) and the generated title
       introduces a *substantial* amount of a different script (e.g. CJK or
       Cyrillic), reject. This is language-agnostic and catches the common
       "English chat -> Chinese/Spanish/Russian title" drift. Because titles are
       short and frequently embed a borrowed Latin technical term (e.g. a CJK
       title containing the word "Python"), the title side uses a proportion
       threshold (>=35% of the title's alphabetic characters in a non-start
       script, min 2 chars) rather than a strict majority -- so a CJK title with
       one English word still trips, while an English title with a single
       foreign place-name does not.
    2. The legacy German-start → English-title heuristic, preserved verbatim so
       the original behavior keeps working for same-script (latin) drift that
       the script check can't see.
    """
    candidate = str(title or '').strip()
    if not candidate:
        return False

    # (1) Cross-script mismatch — language-agnostic.
    user_script = _dominant_script(user_text)
    if user_script:
        title_counts = _script_counts(candidate)
        title_total = sum(title_counts.values())
        if title_total >= 2:
            for script, n in title_counts.items():
                if script != user_script and n >= 2 and (n / title_total) >= 0.35:
                    return True

    # (2) Legacy same-script German→English heuristic.
    if _detect_title_language(user_text) != 'de':
        return False
    candidate_lower = candidate.lower()
    if _detect_title_language(candidate_lower) == 'de':
        return False
    english_markers = {
        'old', 'image', 'display', 'issue', 'problem', 'discussion', 'conversation',
        'session', 'title', 'fix', 'bug', 'attachment', 'attachments', 'context',
    }
    tokens = re.findall(r'[a-z]+', candidate_lower)
    english_hits = sum(1 for tok in tokens if tok in english_markers)
    return english_hits >= 2


def _title_prompts(user_text: str, assistant_text: str) -> tuple[str, list[str]]:
    qa = f"User question:\n{user_text[:500]}\n\nAssistant answer:\n{assistant_text[:500]}"
    language_rule = _title_prompt_language_rule(user_text)
    prompts = [
        (
            "Generate a short session title from this conversation start.\n"
            "Use BOTH the user's question and the assistant's visible answer.\n"
            f"{language_rule}"
            "Return only the title text, 3-8 words, as a topic label.\n"
            "Do not use markdown, bullets, labels, or prefixes like Session Title:.\n"
            "Do not output a full sentence.\n"
            "Do not output acknowledgements or completion phrases like OK, done, or all set.\n"
            "Do not describe internal reasoning.\n"
            "Bad: The user is asking..., OK, all set.\n"
            "Good: Title Generation Test, Clarify Dialog Layout, GitHub Issue Triage"
        ),
        (
            "Rewrite this conversation start as a concise noun-phrase title.\n"
            "Use the actual topic, not the task outcome.\n"
            f"{language_rule}"
            "Return title text only.\n"
            "Do not use markdown, bullets, labels, or prefixes like Session Title:.\n"
            "Never output acknowledgements, completion status, or meta commentary."
        ),
    ]
    return qa, prompts


def _is_minimax_route(provider: str = '', model: str = '', base_url: str = '') -> bool:
    text = ' '.join([
        str(provider or '').lower(),
        str(model or '').lower(),
        str(base_url or '').lower(),
    ])
    return 'minimax' in text or 'minimaxi.com' in text


def _get_aux_title_config() -> dict:
    """Return title_generation auxiliary config, or an empty dict on errors."""
    try:
        from agent.auxiliary_client import _get_auxiliary_task_config
        tg = _get_auxiliary_task_config('title_generation')
        return tg if isinstance(tg, dict) else {}
    except Exception:
        return {}


def _aux_title_configured() -> bool:
    """Return True when any auxiliary title_generation config field is meaningfully set."""
    tg = _get_aux_title_config()
    provider = tg.get('provider', '') or ''
    model = tg.get('model', '') or ''
    base_url = tg.get('base_url', '') or ''
    return bool(model or base_url or (provider and provider.lower() != 'auto'))

def _aux_title_timeout(default: float = 15.0) -> float:
    """Return the configured timeout (seconds) for auxiliary title generation.

    Only accepts positive numeric values.  Falls back to *default* when the
    value is ``None``, non-numeric, zero, or negative, and emits a debug log
    so mis-configurations are visible in server output.
    """
    try:
        tg = _get_aux_title_config()
        raw = tg.get('timeout')
        if raw is None:
            return default
        try:
            value = float(raw)
        except (ValueError, TypeError):
            logger.debug("aux title timeout: non-numeric value %r, falling back to %s", raw, default)
            return default
        if value > 0:
            return value
        logger.debug("aux title timeout: non-positive value %s, falling back to %s", value, default)
        return default
    except Exception:
        return default

def _title_completion_budget(provider: str = '', model: str = '', base_url: str = '') -> int:
    # Title generation is a small auxiliary task, but reasoning models may
    # spend a surprising amount of the completion budget before emitting final
    # content.  Keep the budget high enough for MiniMax/Kimi-style reasoning
    # responses without making title generation depend on provider-specific
    # one-off branches.
    return 512


def _title_retry_completion_budget(provider: str = '', model: str = '', base_url: str = '') -> int:
    return max(1024, _title_completion_budget(provider, model, base_url) * 2)


def _title_retry_status(status: str) -> bool:
    # Whether to grant a second budget attempt within the same prompt+model
    # combination.  ``llm_length`` indicates the model would have produced
    # content with more headroom, so doubling the budget can help.
    #
    # ``llm_empty_reasoning`` historically also triggered a retry, but for
    # reasoning models (Qwen3-thinking, DeepSeek-R1, Kimi-K2, etc.) that
    # status means the model burned its entire budget on hidden reasoning
    # tokens and emitted nothing visible.  Doubling the budget in that case
    # just doubles the GPU/credit cost without changing the outcome — the
    # next attempt produces the same shape.  We skip the retry for empty-
    # reasoning statuses and let the title path fall through to the local
    # fallback summary.  See issue #2083 for the LM Studio + Qwen3 repro.
    return status in {
        'llm_length',
        'llm_length_aux',
    }


def _title_should_skip_remaining_attempts(status: str) -> bool:
    """Statuses where re-issuing the next prompt against the same model
    produces the same failing shape (model burned its budget on hidden
    reasoning, hit a hard provider gate, etc.).

    Short-circuit the prompt-iteration loop so we don't issue a second
    full-budget LLM call (and twice the GPU/credit burn) only to land in
    the same fallback path. See issue #2083.

    Add a status here only when retrying the next prompt is provably
    wasted work (single-call signal already establishes that the next
    call will return the same shape). Length-truncation WITHOUT
    reasoning is NOT in the set — that's legitimately recoverable by
    a larger budget on a different prompt and stays in
    :func:`_title_retry_status`.
    """
    return status in {
        'llm_empty_reasoning',
        'llm_empty_reasoning_aux',
    }


def _safe_obj_value(obj, key: str):
    if obj is None:
        return None
    if isinstance(obj, dict):
        return obj.get(key)
    value = getattr(obj, key, None)
    # Missing MagicMock attrs stringify as mock reprs and look truthy.  Treat
    # them as absent so tests model real provider objects accurately.
    if value.__class__.__module__.startswith('unittest.mock'):
        return None
    return value


def _safe_text_value(value) -> str:
    if value is None:
        return ''
    if value.__class__.__module__.startswith('unittest.mock'):
        return ''
    return str(value or '').strip()


def _extract_title_response(resp, *, aux: bool = False) -> tuple[str, str]:
    """Return (content, empty_status) from an OpenAI-compatible response."""
    suffix = '_aux' if aux else ''
    try:
        choices = _safe_obj_value(resp, 'choices') or []
        choice = choices[0] if choices else None
        message = _safe_obj_value(choice, 'message')
        content = _safe_text_value(_safe_obj_value(message, 'content'))
        if content:
            return content, ''
        finish_reason = _safe_text_value(_safe_obj_value(choice, 'finish_reason')).lower()
        reasoning = (
            _safe_text_value(_safe_obj_value(message, 'reasoning'))
            or _safe_text_value(_safe_obj_value(message, 'reasoning_content'))
            or _safe_text_value(_safe_obj_value(message, 'thinking'))
        )
        # When the model emitted reasoning tokens but no visible content, it
        # burned its budget on hidden thinking — retrying with a larger budget
        # almost never recovers a useful title (see issue #2083: Qwen3-thinking
        # via LM Studio loops indefinitely on auto-title generation).  Report
        # this case distinctly so callers can short-circuit instead of double-
        # billing the GPU/credit on a near-certain repeat.
        if reasoning:
            return '', f'llm_empty_reasoning{suffix}'
        if finish_reason == 'length':
            return '', f'llm_length{suffix}'
        return '', f'llm_empty{suffix}'
    except Exception:
        return '', f'llm_empty{suffix}'


def generate_title_raw_via_aux(
    user_text: str,
    assistant_text: str,
    provider: str = '',
    model: str = '',
    base_url: str = '',
) -> tuple[Optional[str], str]:
    """Return (raw_text, status) via auxiliary LLM route."""
    if not user_text or not assistant_text:
        return None, 'missing_exchange'
    qa, prompts = _title_prompts(user_text, assistant_text)
    configured = _get_aux_title_config()
    caller_supplied_route = bool(provider or model or base_url)
    provider = provider or configured.get('provider', '') or ''
    if str(provider).strip().lower() == 'auto':
        provider = ''
    model = model or configured.get('model', '') or ''
    base_url = base_url or configured.get('base_url', '') or ''
    try:
        from api.profiles import _split_webui_provider_model_value

        normalized_model, normalized_provider = _split_webui_provider_model_value(
            model or None,
            provider or None,
        )
        model = normalized_model or ''
        provider = normalized_provider or ''
    except ValueError:
        pass
    api_key = ''
    if not caller_supplied_route:
        api_key = str(configured.get('api_key', '') or '').strip()
    base_max_tokens = _title_completion_budget(provider, model, base_url)
    reasoning_extra = {"reasoning": {"enabled": False}}
    if _is_minimax_route(provider, model, base_url):
        reasoning_extra["reasoning_split"] = True
    try:
        _timeout = _aux_title_timeout()
        from agent.auxiliary_client import call_llm
        last_status = 'llm_error_aux'
        for idx, prompt in enumerate(prompts):
            messages = [
                {"role": "system", "content": prompt},
                {"role": "user", "content": qa},
            ]
            budgets = [base_max_tokens]
            try:
                for budget_idx, max_tokens in enumerate(budgets):
                    resp = call_llm(
                        task='title_generation',
                        provider=provider or None,
                        model=model or None,
                        base_url=base_url or None,
                        api_key=api_key or None,
                        messages=messages,
                        max_tokens=max_tokens,
                        temperature=0.2,
                        timeout=_timeout,
                        extra_body=reasoning_extra,
                    )
                    raw, empty_status = _extract_title_response(resp, aux=True)
                    if raw:
                        return raw, ('llm_aux' if idx == 0 and budget_idx == 0 else 'llm_aux_retry')
                    last_status = empty_status or 'llm_empty_aux'
                    if budget_idx == 0 and _title_retry_status(last_status):
                        budgets.append(_title_retry_completion_budget(provider, model, base_url))
            except Exception as e:
                last_status = 'llm_error_aux'
                logger.debug("Aux title generation attempt %s failed: %s", idx + 1, e)
            # If the model just burned its budget on hidden reasoning, retrying
            # the next prompt against the same model produces the same shape.
            # Short-circuit to the local fallback path (#2083).
            if _title_should_skip_remaining_attempts(last_status):
                logger.debug(
                    "Aux title generation short-circuiting after %s (reasoning-only response).",
                    last_status,
                )
                break
        return None, last_status
    except Exception as e:
        logger.debug("Aux title generation failed: %s", e)
        return None, 'llm_error_aux'


def generate_title_raw_via_agent(agent, user_text: str, assistant_text: str) -> tuple[Optional[str], str]:
    """Return (raw_text, status) via active-agent route."""
    if not user_text or not assistant_text:
        return None, 'missing_exchange'
    if agent is None:
        return None, 'missing_agent'

    qa, prompts = _title_prompts(user_text, assistant_text)
    base_max_tokens = _title_completion_budget(
        getattr(agent, 'provider', ''),
        getattr(agent, 'model', ''),
        getattr(agent, 'base_url', ''),
    )
    disabled_reasoning = {"enabled": False}
    prev_reasoning = getattr(agent, 'reasoning_config', None)
    try:
        agent.reasoning_config = disabled_reasoning
        for idx, prompt in enumerate(prompts):
            api_messages = [
                {"role": "system", "content": prompt},
                {"role": "user", "content": qa},
            ]
            budgets = [base_max_tokens]
            try:
                last_status = 'llm_empty'
                for budget_idx, max_tokens in enumerate(budgets):
                    raw = ""
                    empty_status = ''
                    if getattr(agent, 'api_mode', '') == 'codex_responses':
                        codex_kwargs = agent._build_api_kwargs(api_messages)
                        codex_kwargs.pop('tools', None)
                        if 'max_output_tokens' in codex_kwargs:
                            codex_kwargs['max_output_tokens'] = max_tokens
                        resp = agent._run_codex_stream(codex_kwargs)
                        assistant_message, _ = agent._normalize_codex_response(resp)
                        raw = (assistant_message.content or '') if assistant_message else ''
                        if not raw:
                            empty_status = 'llm_empty'
                    elif getattr(agent, 'api_mode', '') == 'anthropic_messages':
                        from agent.anthropic_adapter import build_anthropic_kwargs, normalize_anthropic_response
                        ant_kwargs = build_anthropic_kwargs(
                            model=agent.model,
                            messages=api_messages,
                            tools=None,
                            max_tokens=max_tokens,
                            reasoning_config=disabled_reasoning,
                            is_oauth=getattr(agent, '_is_anthropic_oauth', False),
                            preserve_dots=agent._anthropic_preserve_dots(),
                            base_url=getattr(agent, '_anthropic_base_url', None),
                        )
                        resp = agent._anthropic_messages_create(ant_kwargs)
                        assistant_message, _ = normalize_anthropic_response(
                            resp, strip_tool_prefix=getattr(agent, '_is_anthropic_oauth', False)
                        )
                        raw = (assistant_message.content or '') if assistant_message else ''
                        if not raw:
                            empty_status = 'llm_empty'
                    else:
                        api_kwargs = agent._build_api_kwargs(api_messages)
                        api_kwargs.pop('tools', None)
                        api_kwargs['temperature'] = 0.1
                        api_kwargs['timeout'] = 15.0
                        if _is_minimax_route(getattr(agent, 'provider', ''), getattr(agent, 'model', ''), getattr(agent, 'base_url', '')):
                            extra_body = dict(api_kwargs.get('extra_body') or {})
                            extra_body['reasoning_split'] = True
                            api_kwargs['extra_body'] = extra_body
                        if 'max_completion_tokens' in api_kwargs:
                            api_kwargs['max_completion_tokens'] = max_tokens
                        else:
                            api_kwargs['max_tokens'] = max_tokens
                        resp = agent._ensure_primary_openai_client(reason='title_generation').chat.completions.create(
                            **api_kwargs,
                        )
                        raw, empty_status = _extract_title_response(resp)
                    raw = str(raw or '').strip()
                    if raw:
                        return raw, ('llm' if idx == 0 and budget_idx == 0 else 'llm_retry')
                    last_status = empty_status or 'llm_empty'
                    if budget_idx == 0 and _title_retry_status(last_status):
                        budgets.append(_title_retry_completion_budget(
                            getattr(agent, 'provider', ''),
                            getattr(agent, 'model', ''),
                            getattr(agent, 'base_url', ''),
                        ))
            except Exception as e:
                last_status = 'llm_error'
                logger.debug(
                    "Agent title generation attempt %s failed: provider=%s model=%s error=%s",
                    idx + 1,
                    getattr(agent, 'provider', None),
                    getattr(agent, 'model', None),
                    e,
                )
            # If the model just burned its budget on hidden reasoning, retrying
            # the next prompt against the same model produces the same shape.
            # Short-circuit to the local fallback path (#2083).
            if _title_should_skip_remaining_attempts(last_status):
                logger.debug(
                    "Agent title generation short-circuiting after %s (reasoning-only response).",
                    last_status,
                )
                break
        return None, last_status
    except Exception as e:
        logger.debug("Agent title generation failed: %s", e)
        return None, 'llm_error'
    finally:
        agent.reasoning_config = prev_reasoning


def _generate_llm_session_title_for_agent(agent, user_text: str, assistant_text: str) -> tuple[Optional[str], str, str]:
    """Generate a title via active-agent route, then sanitize/validate result."""
    raw, status = generate_title_raw_via_agent(agent, user_text, assistant_text)
    if not raw:
        return None, status, ''
    title = _sanitize_generated_title(raw)
    if title:
        if _title_language_mismatch(user_text, title):
            return None, 'llm_language_mismatch', str(raw)[:120]
        return title, status, ''
    return None, 'llm_invalid', str(raw)[:120]


def _generate_llm_session_title_via_aux(user_text: str, assistant_text: str, agent=None, *, use_agent_model: bool = False) -> tuple[Optional[str], str, str]:
    """Generate a title via dedicated auxiliary LLM route, then sanitize/validate result.

    When use_agent_model is False (default), the auxiliary client resolves
    provider/model/base_url from config.yaml auxiliary.title_generation, which
    prevents the session's chat model (e.g. a Chinese model) from overriding
    the dedicated title model.  When True, the agent's attrs are passed through
    (legacy fallback behaviour).
    """
    if use_agent_model and agent:
        provider = getattr(agent, 'provider', '')
        model = getattr(agent, 'model', '')
        base_url = getattr(agent, 'base_url', '')
    else:
        provider = ''
        model = ''
        base_url = ''
    raw, status = generate_title_raw_via_aux(
        user_text,
        assistant_text,
        provider=provider,
        model=model,
        base_url=base_url,
    )
    if not raw:
        return None, status, ''
    title = _sanitize_generated_title(raw)
    if title:
        if _title_language_mismatch(user_text, title):
            return None, 'llm_language_mismatch_aux', str(raw)[:120]
        return title, status, ''
    return None, 'llm_invalid_aux', str(raw)[:120]


def _put_title_status(put_event, session_id: str, status: str, reason: str = '', title: str = '', raw_preview: str = '') -> None:
    payload = {'session_id': session_id, 'status': status}
    if reason:
        payload['reason'] = reason
    if title:
        payload['title'] = title
    if raw_preview:
        payload['raw_preview'] = raw_preview
    put_event('title_status', payload)
    logger.info(
        "title_status session=%s status=%s reason=%s title=%r raw_preview=%r",
        session_id,
        status,
        reason or '-',
        title or '',
        (raw_preview or '')[:120],
    )


def _fallback_title_from_exchange(user_text: str, assistant_text: str) -> Optional[str]:
    """Generate a readable local fallback title when LLM title generation fails."""
    user_text = (user_text or '').strip()
    assistant_text = _strip_thinking_markup(assistant_text or '').strip()
    if not user_text:
        return None
    user_text = _strip_workspace_prefix(user_text)
    user_text = re.sub(r'\s+', ' ', user_text).strip()
    assistant_text = re.sub(r'\s+', ' ', assistant_text).strip()
    combined = f"{user_text} {assistant_text}".strip().lower()
    combined_raw = f"{user_text} {assistant_text}".strip()
    def _contains_latin(text: str) -> bool:
        return bool(re.search(r'[A-Za-z]', text or ''))

    def _extract_named_topic(text: str) -> str:
        m = re.search(r'"([^"\n]{2,24})"', text)
        if m:
            return (m.group(1) or '').strip()
        m = re.search(r'“([^”\n]{2,24})”', text)
        if m:
            return (m.group(1) or '').strip()
        return ''

    topic_name = _extract_named_topic(combined_raw)
    if topic_name:
        if not _contains_latin(topic_name):
            if any(k in combined for k in ('time', 'schedule', 'efficiency', 'manage', 'fitness', 'singing', 'calligraphy')):
                return 'Time management discussion'
            if any(k in combined for k in ('hermes', 'codex', 'ai')):
                return 'AI productivity discussion'
            return 'Conversation topic'
        if any(k in combined for k in ('time', 'schedule', 'efficiency', 'manage', 'fitness', 'singing', 'calligraphy')):
            return f'{topic_name} time management'
        if any(k in combined for k in ('hermes', 'codex', 'ai')):
            return f'{topic_name} AI productivity'
        return f'{topic_name} discussion'

    if any(k in combined for k in ('title', 'session title')) and any(k in combined for k in ('summary', 'summar', 'short title')):
        if any(k in combined for k in ('test', 'ok', 'reply ok')):
            return 'Session title auto-summary test'
        return 'Session title auto-summary'
    if any(k in combined for k in ('clarify', 'clarification')) and any(k in combined for k in ('dialog', 'card')):
        return 'Clarify dialog card'
    if any(k in combined for k in ('issue', 'github', 'pr')) and any(k in combined for k in ('triage', 'bug', 'review')):
        return 'GitHub Issue Triage'

    head = re.split(r'[.!?\n]', user_text)[0].strip()
    if not head:
        return None

    stop_en = {
        'the', 'this', 'that', 'with', 'from', 'into', 'just', 'reply', 'please',
        'need', 'needs', 'want', 'wants', 'user', 'assistant', 'could', 'would',
        'should', 'about', 'there', 'here', 'test', 'testing', 'title', 'summary',
    }
    # Unicode-aware Latin tokenization: keep the old "no leading underscore"
    # and non-Latin placeholder behavior while allowing letters such as ä/ö/ü/ß.
    # The previous ASCII-only pattern turned "führe" into "f" + "hre"; the short
    # "f" was filtered and the broken "hre" became part of the title.
    latin_word = r'A-Za-z0-9À-ÖØ-öø-ÿ'
    tokens = re.findall(rf'[{latin_word}][{latin_word}_./+-]*', head)
    if not tokens:
        return 'Conversation topic'

    picked = []
    for tok in tokens:
        lower_tok = tok.lower()
        if lower_tok in stop_en or len(lower_tok) < 3:
            continue
        if tok not in picked:
            picked.append(tok)
        if len(picked) >= 4:
            break

    if picked:
        return ' '.join(picked)[:60]
    return 'Conversation topic'


def _is_generic_fallback_title(title: str) -> bool:
    """Return True for low-information fallback labels that should not be persisted."""
    return str(title or '').strip().lower() in {'conversation topic'}


def _run_background_title_update(session_id: str, user_text: str, assistant_text: str, placeholder_title: str, put_event, agent=None):
    """Generate and publish a better title after `done`, then end the stream."""
    try:
        try:
            s = get_session(session_id)
        except KeyError:
            _put_title_status(put_event, session_id, 'skipped', 'missing_session')
            return
        # Allow self-heal when a previously generated title leaked thinking text.
        _invalid_existing = _looks_invalid_generated_title(s.title)
        if getattr(s, 'llm_title_generated', False) and not _invalid_existing:
            _put_title_status(put_event, session_id, 'skipped', 'already_generated', str(s.title or ''))
            return
        current = str(s.title or '').strip()
        if session_has_manual_title(s):
            _put_title_status(put_event, session_id, 'skipped', 'manual_title', current)
            return
        still_auto = (
            current == placeholder_title
            or current in ('Untitled', 'New Chat', '')
            or _is_provisional_title(current, s.messages)
            or _invalid_existing
        )
        if not still_auto:
            _put_title_status(put_event, session_id, 'skipped', 'manual_title', current)
            return
        from api import profiles as profiles_api

        with profiles_api.profile_env_for_background_worker(s, "background title", logger_override=logger):
            aux_title_configured = _aux_title_configured()
            if agent and not aux_title_configured:
                next_title, llm_status, raw_preview = _generate_llm_session_title_for_agent(agent, user_text, assistant_text)
                if not next_title and llm_status in ('llm_error', 'llm_invalid'):
                    next_title, llm_status, raw_preview = _generate_llm_session_title_via_aux(user_text, assistant_text, agent=agent, use_agent_model=True)
            else:
                next_title, llm_status, raw_preview = _generate_llm_session_title_via_aux(user_text, assistant_text)
                if not next_title and agent and llm_status in ('llm_error_aux', 'llm_invalid_aux'):
                    next_title, llm_status, raw_preview = _generate_llm_session_title_for_agent(agent, user_text, assistant_text)
            source = llm_status
            if not next_title:
                fallback_title = _fallback_title_from_exchange(user_text, assistant_text)
                if fallback_title and not _is_generic_fallback_title(fallback_title):
                    logger.debug("Using local fallback for session title generation")
                    next_title = fallback_title
                    source = 'fallback'
                elif fallback_title:
                    logger.debug("Skipping generic local fallback for session title generation: %r", fallback_title)
        fallback_reason = (
            f'local_summary:{llm_status}'
            if source == 'fallback' and llm_status
            else 'local_summary'
        )
        wrote_title = False
        effective_title = current
        if next_title:
            with _get_session_agent_lock(session_id):
                with LOCK:
                    cached_session = SESSIONS.get(session_id)
                    if cached_session is not None and getattr(cached_session, 'session_id', None) == session_id:
                        s = cached_session
                    effective_title = str(s.title or '').strip()
                    manual_title = session_has_manual_title(s)
                    invalid_existing_now = _looks_invalid_generated_title(s.title)
                    still_auto = (
                        effective_title == placeholder_title
                        or effective_title in ('Untitled', 'New Chat', '')
                        or _is_provisional_title(effective_title, s.messages)
                        or invalid_existing_now
                    )
                if manual_title or not still_auto:
                    _put_title_status(put_event, session_id, 'skipped', 'manual_title', effective_title)
                    return
                if next_title != effective_title:
                    s.title = next_title
                    mark_session_title_generated(s)
                    # Keep chronological ordering stable in the sidebar.
                    s.save(touch_updated_at=False)
                    effective_title = s.title
                    wrote_title = True

        if wrote_title:
            if source == 'fallback':
                _put_title_status(put_event, session_id, source, fallback_reason, effective_title, raw_preview)
            else:
                _put_title_status(put_event, session_id, source, llm_status, effective_title, raw_preview)
            put_event('title', {'session_id': session_id, 'title': effective_title})
        else:
            _put_title_status(put_event, session_id, 'skipped', source or 'unchanged', effective_title, raw_preview)
    finally:
        put_event('stream_end', {'session_id': session_id})


def _run_background_title_refresh(session_id: str, user_text: str, assistant_text: str, current_title: str, put_event, agent=None):
    """Refresh an existing LLM-generated title using the latest exchange text.

    Unlike _run_background_title_update, this does NOT guard on
    llm_title_generated — it assumes the title was already LLM-generated
    and the session has progressed enough to warrant a refresh.
    It does NOT emit stream_end (the caller already did).
    """
    try:
        try:
            s = get_session(session_id)
        except KeyError:
            return
        # Safety: skip if user manually renamed since the check
        effective = str(s.title or '').strip()
        if session_has_manual_title(s):
            _put_title_status(put_event, session_id, 'skipped', 'manual_title', effective)
            return
        if effective != current_title:
            _put_title_status(put_event, session_id, 'skipped', 'manual_title', effective)
            return
        if not effective or effective in ('Untitled', 'New Chat'):
            return
        from api import profiles as profiles_api

        with profiles_api.profile_env_for_background_worker(s, "background title", logger_override=logger):
            aux_title_configured = _aux_title_configured()
            if agent and not aux_title_configured:
                next_title, llm_status, raw_preview = _generate_llm_session_title_for_agent(agent, user_text, assistant_text)
                if not next_title and llm_status in ('llm_error', 'llm_invalid'):
                    next_title, llm_status, raw_preview = _generate_llm_session_title_via_aux(user_text, assistant_text, agent=agent, use_agent_model=True)
            else:
                next_title, llm_status, raw_preview = _generate_llm_session_title_via_aux(user_text, assistant_text)
                if not next_title and agent and llm_status in ('llm_error_aux', 'llm_invalid_aux'):
                    next_title, llm_status, raw_preview = _generate_llm_session_title_for_agent(agent, user_text, assistant_text)
        if not next_title:
            _put_title_status(put_event, session_id, 'refresh_skipped', llm_status or 'empty', effective, raw_preview)
            return
        # Skip if the new title is essentially the same (after normalization)
        normalized_current = re.sub(r'\s+', ' ', effective).strip().lower()
        normalized_new = re.sub(r'\s+', ' ', next_title).strip().lower()
        if normalized_current == normalized_new:
            _put_title_status(put_event, session_id, 'refresh_skipped', 'same_title', effective, raw_preview)
            return
        with _get_session_agent_lock(session_id):
            with LOCK:
                cached_session = SESSIONS.get(session_id)
                if cached_session is not None and getattr(cached_session, 'session_id', None) == session_id:
                    s = cached_session
                # Re-check: user may have renamed while we were generating
                if session_has_manual_title(s) or str(s.title or '').strip() != current_title:
                    _put_title_status(put_event, session_id, 'skipped', 'manual_title', str(s.title or '').strip())
                    return
                s.title = next_title
                mark_session_title_generated(s)
                effective_title = s.title
            # Session.save() calls _write_session_index(), which acquires LOCK.
            # Keep the per-session agent lock for mutation serialization, but
            # release the global session LOCK before persisting to avoid a
            # self-deadlock in the background title-refresh thread.
            s.save(touch_updated_at=False)
        _put_title_status(put_event, session_id, 'refreshed', llm_status, effective_title, raw_preview)
        put_event('title', {'session_id': session_id, 'title': effective_title})
        logger.info("Adaptive title refresh: session=%s new_title=%r", session_id, effective_title)
    except Exception:
        logger.debug("Background title refresh failed for session %s", session_id, exc_info=True)




def generate_session_title_for_session(session, *, prefer_latest: bool = False, agent=None) -> tuple[Optional[str], str, str]:
    """Generate a session title on demand from persisted conversation messages.

    This helper powers explicit UI title-regeneration controls. It intentionally
    does not inspect or mutate ``llm_title_generated``; callers decide whether
    replacing the current title is allowed, then persist the returned title.
    """
    messages = getattr(session, 'messages', None) or []
    if prefer_latest:
        user_text, assistant_text = _latest_exchange_snippets(messages)
    else:
        user_text, assistant_text = _first_exchange_snippets(messages)
    if not user_text:
        return None, 'empty_user_message', ''
    from api import profiles as profiles_api

    with profiles_api.profile_env_for_background_worker(session, "manual title regeneration", logger_override=logger):
        next_title, llm_status, raw_preview = _generate_llm_session_title_via_aux(user_text, assistant_text, agent=agent)
    if next_title:
        return next_title, llm_status, raw_preview
    fallback_title = _fallback_title_from_exchange(user_text, assistant_text)
    if fallback_title and not _is_generic_fallback_title(fallback_title):
        reason = f'local_summary:{llm_status}' if llm_status else 'local_summary'
        return fallback_title, reason, raw_preview
    return None, llm_status or 'empty_title', raw_preview


def _preserve_pre_compression_snapshot(s, old_sid: str) -> None:
    """Persist old_sid as a read-only pre-compression snapshot.

    Context compression rotates the active WebUI session id from old_sid to the
    agent's new continuation id. The old JSON must remain on disk for lineage
    traversal, but it should not continue to appear as an active sidebar row.
    """
    old_path = SESSION_DIR / f'{old_sid}.json'
    if not old_path.exists():
        return
    try:
        existing_text = old_path.read_text(encoding='utf-8')
        try:
            existing = json.loads(existing_text)
            existing_msgs = len(existing.get('messages') or [])
            existing_snapshot = bool(existing.get('pre_compression_snapshot'))
        except (json.JSONDecodeError, ValueError):
            # Treat corrupt/malformed old JSON as missing history and rewrite it
            # from the in-memory pre-compression messages below. That is safer
            # than leaving an unreadable recovery snapshot behind.
            existing_msgs = -1
            existing_snapshot = False
        if len(s.messages) > existing_msgs:
            # In-memory messages are newer than the file; save the full old
            # snapshot from the current session object while preserving its
            # pre-existing parent_session_id lineage.
            saved_sid = s.session_id
            saved_snapshot = bool(getattr(s, 'pre_compression_snapshot', False))
            saved_pinned = bool(getattr(s, 'pinned', False))
            s.session_id = old_sid
            s.pre_compression_snapshot = True
            s.pinned = False
            # Stage-359 / PR #2295: clear runtime stream-state fields on the
            # archived snapshot so the sidebar does not reopen the parent as
            # a permanently-running session while the child already holds the
            # completed answer. The continuation session's live state is
            # restored from saved_* locals in the finally block.
            saved_active_stream_id = getattr(s, 'active_stream_id', None)
            saved_pending_user_message = getattr(s, 'pending_user_message', None)
            saved_pending_attachments = list(getattr(s, 'pending_attachments', []) or [])
            saved_pending_started_at = getattr(s, 'pending_started_at', None)
            s.active_stream_id = None
            s.pending_user_message = None
            s.pending_attachments = []
            s.pending_started_at = None
            try:
                # skip_index=False so the snapshot appears in _index.json with
                # the pre_compression_snapshot marker. The sidebar projection
                # (#2285) reads that marker to hide the snapshot from active
                # rows while keeping the JSON discoverable for lineage traversal.
                s.save(touch_updated_at=False, skip_index=False)
                logger.info(
                    "Preserved pre-compression session %s (%d messages) to disk",
                    old_sid, len(s.messages),
                )
            finally:
                s.session_id = saved_sid
                s.pre_compression_snapshot = saved_snapshot
                s.pinned = saved_pinned
                s.active_stream_id = saved_active_stream_id
                s.pending_user_message = saved_pending_user_message
                s.pending_attachments = saved_pending_attachments
                s.pending_started_at = saved_pending_started_at
            return
        # Existing file is already at least as complete as memory; stamp only
        # the snapshot marker so index/sidebar projection can hide it without
        # rewriting a shorter messages array over a fuller transcript.
        from api.models import Session
        snapshot = Session.load(old_sid)
        if snapshot:
            snapshot.pre_compression_snapshot = True
            snapshot.pinned = False
            # Stage-359 Opus SHOULD-FIX: clear runtime fields on the loaded
            # snapshot too. If the disk snapshot was last persisted while the
            # parent was live, it could carry a stale active_stream_id /
            # pending_* over to disk. The sidebar projection filters snapshot
            # rows so this is latent today, but the contract should match the
            # primary branch above so future readers can trust snapshot files
            # to never contain live runtime state.
            snapshot.active_stream_id = None
            snapshot.pending_user_message = None
            snapshot.pending_attachments = []
            snapshot.pending_started_at = None
            snapshot.save(touch_updated_at=False, skip_index=False)
            logger.info(
                "Marked pre-compression session %s as sidebar-hidden snapshot",
                old_sid,
            )
    except OSError:
        logger.debug("Could not read old session file before preservation")
    except Exception:
        logger.debug("Failed to preserve pre-compression session file", exc_info=True)


def _maybe_schedule_title_refresh(session, put_event, agent):
    """Check if the session is due for an adaptive title refresh and schedule it."""
    refresh_interval = _get_title_refresh_interval()
    if refresh_interval <= 0:
        return
    current_title = str(session.title or '').strip()
    if not current_title or current_title in ('Untitled', 'New Chat'):
        return
    if session_has_manual_title(session):
        return
    if not getattr(session, 'llm_title_generated', False):
        return
    exchange_count = _count_exchanges(session.messages)
    if exchange_count <= 0 or exchange_count % refresh_interval != 0:
        return
    last_u, last_a = _latest_exchange_snippets(session.messages)
    if not last_u and not last_a:
        return
    threading.Thread(
        target=_run_background_title_refresh,
        args=(session.session_id, last_u, last_a, current_title, put_event, agent),
        daemon=True,
    ).start()


def _strip_native_image_parts_from_content(content):
    """Return provider-safe content with native image parts removed.

    Text-only provider endpoints (for example DeepSeek/OpenAI-compatible text
    models) reject historical OpenAI-style ``image_url`` parts before the agent
    can recover.  When WebUI is configured for text-mode image handling, preserve
    textual content from mixed content arrays and drop only the native image
    blocks from replayed history.
    """
    if not isinstance(content, list):
        return content
    clean_parts = []
    for part in content:
        if not isinstance(part, dict):
            continue
        if part.get('type') == 'image_url' or 'image_url' in part:
            continue
        clean_parts.append(copy.deepcopy(part))
    if not clean_parts:
        return ''
    if len(clean_parts) == 1 and clean_parts[0].get('type') == 'text':
        return str(clean_parts[0].get('text') or '')
    return clean_parts


def _content_has_reasoning_only_parts(content) -> bool:
    if not isinstance(content, list) or not content:
        return False
    saw_reasoning = False
    for part in content:
        if not isinstance(part, dict):
            continue
        part_type = part.get('type')
        if part_type in {'thinking', 'reasoning'}:
            text = part.get('thinking') or part.get('reasoning') or part.get('text') or ''
            if str(text).strip():
                saw_reasoning = True
            continue
        if part_type == 'text' and str(part.get('text') or part.get('content') or '').strip():
            return False
        if part_type not in {'text', 'thinking', 'reasoning'}:
            return False
    return saw_reasoning


def _is_reasoning_only_assistant_message(msg) -> bool:
    """Return True for display-only assistant Thinking entries.

    These entries keep partial Thinking cards visible after reload/cancel, but
    they are not API-safe history: providers only see a blank assistant turn.
    Visible assistant replies that also carry reasoning metadata are kept.
    """
    if not isinstance(msg, dict) or msg.get('role') != 'assistant':
        return False
    if msg.get('tool_calls'):
        return False
    content = msg.get('content', '')
    if _message_text(content).strip():
        return False
    if str(msg.get('reasoning') or msg.get('reasoning_content') or '').strip():
        return True
    return _content_has_reasoning_only_parts(content)


def _sanitize_messages_for_api(messages, *, cfg: dict = None):
    """Return a deep copy of messages with only API-safe fields.

    The webui stores extra metadata on messages (attachments, timestamp, _ts)
    for display purposes. Some providers (e.g. Z.AI/GLM) reject unknown fields
    instead of ignoring them, causing HTTP 400 errors on subsequent messages.

    Also strips orphaned tool-role messages whose tool_call_id cannot be linked
    to a preceding assistant message with tool_calls. Strictly-conformant providers
    (Mercury-2/Inception, newer OpenAI models) reject histories containing dangling
    tool results with a 400 error: "Message has tool role, but there was no previous
    assistant message with a tool call."

    If ``agent.image_input_mode`` resolves to ``text``, native historical
    ``image_url`` content parts are stripped too.  Current-turn uploads already
    respect text mode in ``_build_native_multimodal_message``; this closes the
    remaining replay gap where an older native image in the saved transcript kept
    causing 400s on every later text-only turn (#2297).
    """
    strip_native_images = cfg is not None and _resolve_image_input_mode(cfg) == "text"
    # First pass: collect all tool_call_ids declared by assistant messages.
    # Handles both OpenAI ('id') and Anthropic ('call_id') field names.
    valid_tool_call_ids: set = set()
    for msg in messages:
        if not isinstance(msg, dict):
            continue
        if msg.get('role') == 'assistant':
            for tc in msg.get('tool_calls') or []:
                if isinstance(tc, dict):
                    tid = tc.get('id') or tc.get('call_id') or ''
                    if tid:
                        valid_tool_call_ids.add(tid)

    # Second pass: build the sanitized list, dropping orphaned tool messages.
    clean = []
    for msg in messages:
        if not isinstance(msg, dict):
            continue
        # Skip display-only Thinking entries. They are visible transcript
        # metadata, not provider-facing assistant turns.
        if _is_reasoning_only_assistant_message(msg):
            continue
        # Skip persisted error markers — never send them to the LLM as prior context.
        if msg.get('_error'):
            continue
        # Skip _partial markers with no visible content. Partial messages that
        # carry actual text (e.g. "Python is a high-level…") are kept so the
        # model can continue from the cut-off point (#893). But empty partials
        # (reasoning-only or tool-only cancellations where thinking markup was
        # stripped) have nothing for the model to continue from and cause
        # API 400 errors on strict providers (empty assistant content).
        if msg.get('_partial') and not str(msg.get('content') or '').strip():
            continue
        role = msg.get('role')
        if role == 'tool':
            tid = msg.get('tool_call_id') or ''
            if not tid or tid not in valid_tool_call_ids:
                # Orphaned tool result — skip to avoid 400 from strict providers.
                continue
        sanitized = {k: v for k, v in msg.items() if k in _API_SAFE_MSG_KEYS}
        if strip_native_images and 'content' in sanitized:
            sanitized['content'] = _strip_native_image_parts_from_content(sanitized.get('content'))
        if sanitized.get('role'):
            clean.append(sanitized)

    # Third pass: strip orphaned tool_calls from assistant messages — calls whose id
    # has no matching tool-role response in the clean list.  Strict providers (DeepSeek,
    # newer OpenAI) reject with 400 when an assistant message references a tool call that
    # was never answered (e.g. session aborted before results flushed).
    answered_ids: set = set()
    for msg in clean:
        if msg.get('role') == 'tool':
            tid = msg.get('tool_call_id') or ''
            if tid:
                answered_ids.add(tid)

    filtered_clean = []
    for msg in clean:
        if msg.get('role') == 'assistant' and msg.get('tool_calls'):
            kept = [
                tc for tc in msg['tool_calls']
                if isinstance(tc, dict) and
                (tc.get('id') or tc.get('call_id') or '') in answered_ids
            ]
            if not kept:
                # All calls orphaned: drop tool_calls key; if no content, drop message.
                msg = {k: v for k, v in msg.items() if k != 'tool_calls'}
                if not str(msg.get('content') or '').strip():
                    continue
            else:
                msg = dict(msg, tool_calls=kept)
        filtered_clean.append(msg)
    return filtered_clean


def _api_safe_message_positions(messages):
    """Return [(original_index, sanitized_message)] for API-safe messages."""
    valid_tool_call_ids: set = set()
    for msg in messages:
        if not isinstance(msg, dict):
            continue
        if msg.get('role') == 'assistant':
            for tc in msg.get('tool_calls') or []:
                if isinstance(tc, dict):
                    tid = tc.get('id') or tc.get('call_id') or ''
                    if tid:
                        valid_tool_call_ids.add(tid)

    out = []
    for idx, msg in enumerate(messages):
        if not isinstance(msg, dict):
            continue
        if _is_reasoning_only_assistant_message(msg):
            continue
        if msg.get('_error'):
            continue
        if msg.get('_partial') and not str(msg.get('content') or '').strip():
            continue
        role = msg.get('role')
        if role == 'tool':
            tid = msg.get('tool_call_id') or ''
            if not tid or tid not in valid_tool_call_ids:
                continue
        sanitized = {k: v for k, v in msg.items() if k in _API_SAFE_MSG_KEYS}
        if sanitized.get('role'):
            out.append((idx, sanitized))

    # Third pass: strip orphaned tool_calls from assistant messages (mirrors
    # _sanitize_messages_for_api pass 3).
    answered_ids: set = set()
    for _idx, msg in out:
        if msg.get('role') == 'tool':
            tid = msg.get('tool_call_id') or ''
            if tid:
                answered_ids.add(tid)

    filtered_out = []
    for idx, msg in out:
        if msg.get('role') == 'assistant' and msg.get('tool_calls'):
            kept = [
                tc for tc in msg['tool_calls']
                if isinstance(tc, dict) and
                (tc.get('id') or tc.get('call_id') or '') in answered_ids
            ]
            if not kept:
                msg = {k: v for k, v in msg.items() if k != 'tool_calls'}
                if not str(msg.get('content') or '').strip():
                    continue
            else:
                msg = dict(msg, tool_calls=kept)
        filtered_out.append((idx, msg))
    return filtered_out


def _deduplicate_context_messages(messages):
    """Remove duplicate messages from context by identity, keeping first occurrence.

    Prevents the agent from seeing the same message twice in conversation_history
    when result_messages contain duplicates that weren't caught by display-merge.
    """
    if not messages:
        return messages
    seen = set()
    deduped = []
    for msg in messages:
        key = _message_identity(msg)
        if key is not None and key in seen:
            continue
        if key is not None:
            seen.add(key)
        deduped.append(msg)
    return deduped


def _prune_context_tool_results_after_compression(agent, context_messages):
    """Run the active compressor's cheap tool-result pruning on model context.

    Auto-compression can happen mid-turn and then the agent may run more tools
    before producing the final answer. Those completed tail tool results are
    model-facing context, but they were produced after the compression pass and
    therefore did not go through the compressor's tool-output pruning. Apply the
    same cheap pruning once more after a confirmed compression event. This keeps
    the visible transcript untouched while preventing the next turn from seeing
    raw post-compression tool dumps.
    """
    if not context_messages:
        return context_messages
    compressor = getattr(agent, 'context_compressor', None)
    prune = getattr(compressor, '_prune_old_tool_results', None)
    if not callable(prune):
        return context_messages
    try:
        pruned_messages, pruned_count = prune(
            copy.deepcopy(context_messages),
            protect_tail_count=getattr(compressor, 'protect_last_n', 20),
            protect_tail_tokens=getattr(compressor, 'tail_token_budget', None),
        )
    except Exception:
        logger.debug("post-compression context tool-result pruning failed", exc_info=True)
        return context_messages
    if not pruned_count:
        return context_messages
    return _deduplicate_context_messages(pruned_messages)


def _restore_reasoning_metadata(previous_messages, updated_messages):
    """Carry forward display-only metadata lost during API-safe history sanitization.

    The provider-facing history strips WebUI-only fields like `reasoning`. When the
    agent returns its new full message history, prior assistant messages come back
    without that metadata unless we merge it back in by API-history position.

    This also preserves existing timestamps for unchanged historical messages.
    Without that, older turns that come back from the agent without `_ts` /
    `timestamp` can be re-stamped with the current time on every new assistant
    response, making prior messages appear to "move" in time.
    """
    if not previous_messages or not updated_messages:
        return updated_messages
    updated_messages = list(updated_messages)
    prev_safe = _api_safe_message_positions(previous_messages)

    def _safe_projection(msg):
        if not isinstance(msg, dict):
            return None
        return {k: v for k, v in msg.items() if k in _API_SAFE_MSG_KEYS and msg.get('role')}

    safe_pos = 0
    while safe_pos < len(prev_safe):
        prev_idx, _ = prev_safe[safe_pos]
        prev_msg = previous_messages[prev_idx]
        cur_msg = updated_messages[safe_pos] if safe_pos < len(updated_messages) else None

        if isinstance(prev_msg, dict) and isinstance(cur_msg, dict) and _safe_projection(prev_msg) == _safe_projection(cur_msg):
            if prev_msg.get('role') == 'assistant' and prev_msg.get('reasoning') and not cur_msg.get('reasoning'):
                cur_msg['reasoning'] = prev_msg['reasoning']
            if prev_msg.get('timestamp') and not cur_msg.get('timestamp'):
                cur_msg['timestamp'] = prev_msg['timestamp']
            elif prev_msg.get('_ts') and not cur_msg.get('_ts') and not cur_msg.get('timestamp'):
                cur_msg['_ts'] = prev_msg['_ts']
            safe_pos += 1
            continue

        safe_pos += 1

    return updated_messages


def _restore_display_reasoning_metadata(previous_messages, updated_messages):
    """Restore display-only thinking rows for visible transcript persistence."""
    updated_messages = _restore_reasoning_metadata(previous_messages, updated_messages)
    if not previous_messages or not updated_messages:
        return updated_messages
    prev_safe = _api_safe_message_positions(previous_messages)
    safe_indices = {idx for idx, _ in prev_safe}
    inserted_reasoning_only = 0
    for prev_idx, prev_msg in enumerate(previous_messages):
        if _is_empty_partial_activity_message(prev_msg):
            continue
        if prev_idx in safe_indices or not _is_reasoning_only_assistant_message(prev_msg):
            continue
        safe_pos = sum(1 for idx, _ in prev_safe if idx < prev_idx) + inserted_reasoning_only
        existing = updated_messages[safe_pos] if safe_pos < len(updated_messages) else None
        if isinstance(existing, dict) and _is_reasoning_only_assistant_message(existing):
            continue
        updated_messages.insert(safe_pos, copy.deepcopy(prev_msg))
        inserted_reasoning_only += 1
    return updated_messages


def _session_context_messages(session):
    """Return model-facing history without assuming it matches the UI transcript."""
    context_messages = getattr(session, 'context_messages', None)
    if isinstance(context_messages, list) and context_messages:
        return context_messages
    return session.messages or []


def _message_identity(msg):
    if not isinstance(msg, dict):
        return None
    role = str(msg.get('role') or '')
    content = msg.get('content', '')
    text = _message_text(content)
    if role == 'user':
        # WebUI sends the model a workspace-prefixed user_message while the
        # visible optimistic bubble contains only the human text. Treat them as
        # the same turn for merge/dedup purposes; otherwise compaction results
        # render two adjacent user bubbles ("Ok" and "[Workspace...]\nOk").
        text = _strip_workspace_prefix(text, include_legacy=True)
    if not text and not msg.get('tool_call_id') and not msg.get('tool_calls'):
        # Empty assistant messages (e.g. _partial markers with no visible
        # content) previously returned None, making them invisible to the
        # merge dedup in _merge_display_messages_after_agent_result. This
        # caused exponential accumulation: each turn's merge copied ALL
        # prior _partial messages because they had no identity to track.
        # Now, _partial messages with empty text get a stable identity
        # keyed on their role + _partial flag + reasoning/tool metadata,
        # so the merge can dedup identical empty partials.
        if msg.get('_partial'):
            reasoning_key = " ".join(str(msg.get('reasoning') or '').split())[:200]
            return (
                role,
                '',  # empty text
                '',  # no tool_call_id
                '__partial__' + reasoning_key,
            )
        return None
    return (
        role,
        " ".join(str(text or '').split())[:500],
        str(msg.get('tool_call_id') or ''),
        json.dumps(msg.get('tool_calls') or [], sort_keys=True, ensure_ascii=False),
    )


def _messages_have_prefix(messages, prefix):
    if len(messages or []) < len(prefix or []):
        return False
    for idx, expected in enumerate(prefix or []):
        if _message_identity((messages or [])[idx]) != _message_identity(expected):
            return False
    return True


def _message_replay_key(msg):
    """Return a stable comparison key for replay/overlap de-duplication."""
    identity = _message_identity(msg)
    if identity is not None:
        return identity
    if not isinstance(msg, dict):
        return None
    return (
        str(msg.get('role') or ''),
        _message_text(msg.get('content', '')),
        str(msg.get('tool_call_id') or ''),
        json.dumps(msg.get('tool_calls') or [], sort_keys=True, ensure_ascii=False),
    )


def _strip_replayed_prefix(existing_messages, candidates):
    """Drop a candidate prefix that is already the suffix of existing_messages.

    Compression/continuation can replay the active tail from state.db after the
    previous WebUI context/display already contains it. Prefix-only merge logic
    then treats that replayed tail as a fresh delta and duplicates a whole turn.
    Strip the largest exact suffix/prefix overlap before appending.
    """
    existing_messages = list(existing_messages or [])
    candidates = list(candidates or [])
    max_overlap = min(len(existing_messages), len(candidates))
    for overlap in range(max_overlap, 0, -1):
        left = [_message_replay_key(m) for m in existing_messages[-overlap:]]
        right = [_message_replay_key(m) for m in candidates[:overlap]]
        if left == right:
            return candidates[overlap:]
    return candidates


def _looks_like_replayed_session_arc_summary(previous_msg, candidate_msg):
    """Return True for repeated LCM/session summaries with refreshed hints.

    LCM summary cards can be re-injected with the same long recovered context
    and a different tail such as an expand hint. Exact identity misses those,
    but appending both copies bloats every later model prompt.
    """
    if not isinstance(previous_msg, dict) or not isinstance(candidate_msg, dict):
        return False
    if previous_msg.get('role') != candidate_msg.get('role'):
        return False
    previous_text = " ".join(_message_text(previous_msg.get('content', '')).split())
    candidate_text = " ".join(_message_text(candidate_msg.get('content', '')).split())
    if len(previous_text) < 2000 or len(candidate_text) < 2000:
        return False
    marker = '[Session Arc Summary'
    if not previous_text.startswith(marker) or not candidate_text.startswith(marker):
        return False
    return previous_text[:1500] == candidate_text[:1500]


def _strip_replayed_context_items(existing_messages, candidates):
    """Drop replayed non-adjacent context blocks before persisting context."""
    existing_messages = list(existing_messages or [])
    candidates = list(candidates or [])
    if not existing_messages or not candidates:
        return candidates

    existing_keys = [_message_replay_key(m) for m in existing_messages]
    candidate_keys = [_message_replay_key(m) for m in candidates]
    existing_large = [m for m in existing_messages if isinstance(m, dict)]
    cleaned = []
    idx = 0
    min_block = 3
    while idx < len(candidates):
        msg = candidates[idx]
        if any(_looks_like_replayed_session_arc_summary(prev, msg) for prev in existing_large):
            idx += 1
            continue

        best = 0
        for start in range(len(existing_keys)):
            length = 0
            while (
                idx + length < len(candidate_keys)
                and start + length < len(existing_keys)
                and candidate_keys[idx + length] == existing_keys[start + length]
            ):
                length += 1
            if length > best:
                best = length
        if best >= min_block:
            idx += best
            continue

        cleaned.append(msg)
        idx += 1
    return cleaned


def _dedupe_replayed_context_messages(previous_context, result_messages):
    """Keep model context append-only without replayed blocks/summaries."""
    previous_context = list(previous_context or [])
    result_messages = list(result_messages or [])
    if not previous_context or not result_messages:
        return result_messages
    if not _messages_have_prefix(result_messages, previous_context):
        return result_messages
    candidates = result_messages[len(previous_context):]
    candidates = _strip_replayed_prefix(previous_context, candidates)
    if candidates:
        candidates = _strip_replayed_context_items(previous_context, candidates)
    return previous_context + candidates


def _dedupe_replayed_active_context(previous_context, result_messages):
    """Keep model context append-only without re-appending a replayed tail."""
    return _dedupe_replayed_context_messages(previous_context, result_messages)


def _is_context_compression_marker(msg):
    return is_context_compression_marker(msg)


def _compact_summary_text(raw_text: str | None) -> str | None:
    """Normalize a text blob used in compression summary cards."""
    if not isinstance(raw_text, str):
        return None
    txt = raw_text.strip()
    if not txt:
        return None
    return re.sub(r"\s+", " ", txt).strip()


def _compression_anchor_message_key(message):
    if not isinstance(message, dict):
        return None
    role = str(message.get('role') or '')
    if not role or role == 'tool':
        return None
    content = message.get('content', '')
    text = _message_text(content)
    if len(text) > 160:
        text = text[:160]
    ts = message.get('_ts') or message.get('timestamp')
    attachments = message.get('attachments')
    attach_count = len(attachments) if isinstance(attachments, list) else 0
    if not text and not attach_count and not ts:
        return None
    return {'role': role, 'ts': ts, 'text': text, 'attachments': attach_count}


def _compression_summary_from_messages(messages):
    for m in reversed(messages or []):
        if not isinstance(m, dict):
            continue
        if not _is_context_compression_marker(m):
            continue
        text = _message_text(m.get('content'))
        if text:
            return text
    return None


def _find_current_user_turn(messages, msg_text):
    needle = " ".join(str(msg_text or '').split())
    last_strong_match = None  # _looks_like_current_user_turn (high confidence)
    last_weak_match = None    # needle substring match (lower confidence)
    fallback = None
    for idx, msg in enumerate(messages or []):
        if not isinstance(msg, dict) or msg.get('role') != 'user':
            continue
        fallback = idx
        if _looks_like_current_user_turn(msg, msg_text):
            last_strong_match = idx
            continue
        text = " ".join(
            _strip_workspace_prefix(
                _message_text(msg.get('content', '')),
                include_legacy=True,
            ).split()
        )
        if needle and (needle in text or text in needle):
            last_weak_match = idx
    # Return the LAST matching user turn. After context compression the agent's
    # result_messages contain the full conversation history; if the user asked a
    # similar question in an earlier turn, first-match would return that old
    # index, causing the merge to replay the entire history from that point.
    # Last-match anchors on the current turn instead.
    #
    # Prefer the last STRONG match (an exact `_looks_like_current_user_turn`
    # hit) over the last WEAK substring match. The agent loop appends synthetic
    # `role:"user"` continuation prompts (e.g. "Continue", empty-recovery nudges
    # — see conversation_loop.py) AFTER the real user turn; those can weak-match
    # `msg_text` and, if weak matches were allowed to win, would anchor the merge
    # PAST the real turn and drop the assistant/tool output in between. The real
    # current turn is the last strong match, so it must take priority.
    if last_strong_match is not None:
        return last_strong_match
    if last_weak_match is not None:
        return last_weak_match
    return fallback


def _drop_checkpointed_current_user_from_context(messages, msg_text):
    """Return model history without an eager-checkpointed current user turn."""
    history = list(messages or [])
    if not history:
        return history
    current_user_key = _message_identity({'role': 'user', 'content': msg_text})
    if current_user_key and _message_identity(history[-1]) == current_user_key:
        return history[:-1]
    return history


def _save_streaming_checkpoint(session):
    """Persist a streaming checkpoint under the session's profile context."""
    from api import profiles as profiles_api

    with profiles_api.profile_env_for_background_worker(
        session,
        "streaming checkpoint",
        logger_override=logger,
    ):
        session.save(skip_index=True)


def _normalize_fresh_chat_text(text):
    text = _strip_workspace_prefix(str(text or ''), include_legacy=True)
    text = re.sub(r"\s+", " ", text).strip().lower()
    return text.strip(" \t\r\n.!?。！？,，~～")


def _is_casual_fresh_chat_message(msg_text):
    """Return True for short opener messages that should not resume old tasks."""
    text = _normalize_fresh_chat_text(msg_text)
    if not text or len(text) > 24:
        return False
    continuation_terms = (
        "continue",
        "resume",
        "carry on",
        "go on",
        # CJK continuation terms (zh-CN): jixu, jiezhe, wangxia, xiayibu.
        # Encoded as Python escape sequences (not literal CJK) so api/streaming.py
        # passes tests/test_title_sanitization.py::test_title_generation_source_has_no_cjk_literals,
        # which scans this file for any U+4E00-U+9FFF code points. Runtime
        # comparisons still use the real CJK strings — Python decodes the
        # escapes at compile time.
        "\u7ee7\u7eed",
        "\u63a5\u7740",
        "\u5f80\u4e0b",
        "\u4e0b\u4e00\u6b65",
    )
    if any(term in text for term in continuation_terms):
        return False
    return text in {
        "hi",
        "hello",
        "hey",
        "hello there",
        "hi there",
        # CJK greetings (zh-CN): nihao, ninhao, hai, haluo, zaima, zaime.
        # Same escape-sequence rationale as the continuation block above.
        "\u4f60\u597d",         # nihao
        "\u60a8\u597d",         # ninhao
        "\u55e8",               # hai (was \u5616 = "click of tongue", not a greeting)
        "\u54c8\u55bd",         # haluo (was \u54c8\u5582 = uncommon "ha-wei" variant)
        "\u5728\u5417",         # zaima
        "\u5728\u4e48",         # zaime
    }


def _has_task_resume_compaction_marker(messages):
    """Detect compacted model context that tells the agent to resume an old task."""
    for msg in messages or []:
        if not isinstance(msg, dict):
            continue
        text = _message_text(msg.get('content', '')).lower()
        if not text:
            continue
        if "context compaction" not in text and "context compression" not in text:
            continue
        if (
            "active task" in text
            or "resume exactly" in text
            or "current task" in text
            or "task list was preserved" in text
            or "in_progress" in text
        ):
            return True
    return False


def _new_turn_context_from_messages(messages, msg_text):
    """Return provider-facing history for a new user turn from a message list."""
    history = _drop_checkpointed_current_user_from_context(messages, msg_text)
    if _is_casual_fresh_chat_message(msg_text) and _has_task_resume_compaction_marker(history):
        return []
    return history


def _context_messages_for_new_turn(session, msg_text):
    """Return provider-facing history for a new user turn.

    Compacted agent sessions can carry a hidden "resume the active task" summary
    in context_messages. If the user starts a fresh casual greeting in that old
    session, do not feed that stale active-task summary back to the model.
    """
    return _new_turn_context_from_messages(_session_context_messages(session), msg_text)


def _stream_writeback_is_current(session, stream_id):
    """Return True only while a worker still owns the session writeback.

    cancel_stream() intentionally clears ``active_stream_id`` early so the UI can
    accept a follow-up turn while the old worker is unwinding. That old worker
    must not later persist its stale result over the newer transcript.
    """
    return bool(stream_id) and getattr(session, 'active_stream_id', None) == stream_id


def _stream_writeback_can_supersede_recovery_marker(session, msg_text):
    """Allow a finishing worker to replace its own stale-repair marker.

    The stale-pending repair path can occasionally run while the original worker
    is still alive but temporarily missing from the in-memory stream registry. It
    clears ``active_stream_id`` and appends a "Response interrupted" marker. If
    the original worker later finishes, treating ``active_stream_id is None`` as
    stale drops the real answer and leaves the misleading marker visible.

    This is intentionally narrow: only a session with no active/pending turn and
    whose last visible row is the recovery marker for this exact user prompt may
    be superseded. If a newer turn has appended anything after the marker, the
    normal stale-writeback guard still wins.
    """
    if getattr(session, 'active_stream_id', None):
        return False
    if getattr(session, 'pending_user_message', None):
        return False
    if getattr(session, 'pending_attachments', None):
        return False
    messages = list(getattr(session, 'messages', None) or [])
    if len(messages) < 2:
        return False
    last = messages[-1]
    if not isinstance(last, dict) or not last.get('_error'):
        return False
    if last.get('type') != 'interrupted':
        return False
    content = str(last.get('content') or '')
    if 'Response interrupted' not in content or 'before this turn finished' not in content:
        return False

    expected = ' '.join(str(msg_text or '').split())
    if not expected:
        return False
    for msg in reversed(messages[:-1]):
        if not isinstance(msg, dict):
            continue
        if msg.get('_error'):
            continue
        if msg.get('role') != 'user':
            continue
        actual = ' '.join(str(msg.get('content') or '').split())
        return actual == expected
    return False


def _merge_display_messages_after_agent_result(previous_display, previous_context, result_messages, msg_text):
    """Keep UI transcript durable while allowing model context to compact.

    If Hermes Agent returns a normal append-only history, append that delta to
    the UI transcript. If the model/context history was compacted and no longer
    has the prior context as a prefix, keep the previous UI transcript and append
    only compaction marker messages plus the current user turn onward.
    """
    previous_display = list(previous_display or [])
    # Deduplicate stale _partial messages that accumulated in previous_display.
    # A bug in cancel_stream() could insert multiple identical _partial messages
    # when _stripped was empty but _has_reasoning/_has_tools was True. The
    # merge's _message_identity previously returned None for empty _partial
    # messages, so the seen-set couldn't catch them — they doubled each turn.
    # Scan backwards and keep only the LAST occurrence of each unique _partial
    # identity, then reverse back to original order.
    _partial_seen = set()
    _deduped_rev = []
    for m in reversed(previous_display):
        if isinstance(m, dict) and m.get('_partial'):
            key = _message_identity(m)
            if key is not None:
                if key in _partial_seen:
                    continue
                _partial_seen.add(key)
        _deduped_rev.append(m)
    _deduped = list(reversed(_deduped_rev))
    if len(_deduped) < len(previous_display):
        logger.debug(
            "Deduplicated %d stale _partial messages from previous_display (was %d, now %d)",
            len(previous_display) - len(_deduped), len(previous_display), len(_deduped),
        )
    previous_display = _deduped
    previous_context = list(previous_context or [])
    result_messages = list(result_messages or [])
    if not result_messages:
        return previous_display

    # ── Backfill normal turns from previous_context that are missing from
    # previous_display.  After context compression recovery, previous_context
    # can contain user/assistant turns that were never rendered in the visible
    # transcript (they were behind a compression marker). On the next
    # append-only merge those turns sit inside the shared prefix and get
    # stripped, leaving them permanently invisible.  Reinsert them now.
    #
    # Use display as the backbone to preserve visible order. Walk display in
    # order and for each display message search for its identity in context
    # at/after a cursor. Any context messages between the cursor and that
    # match are context-only gaps that get spliced in before the display msg.
    if previous_display and previous_context:
        _display_id_set = {_message_identity(m) for m in previous_display}
        _context_id_set = {
            _message_identity(m)
            for m in previous_context
            if not _is_context_compression_marker(m)
        }
        _has_context_only_turns = bool(_context_id_set - _display_id_set)
        if _has_context_only_turns:
            context_keys = [_message_identity(m) for m in previous_context]
            _backfilled = []
            # #3300 fix: track ONLY context rows we splice in, so the
            # visible-display backbone is never suppressed. Sharing one set
            # between context inserts and display rows (and _message_identity
            # ignoring timestamps) dropped a legitimate second identical visible
            # user turn. Display rows are always appended in order; a context
            # row is backfilled only if it isn't already a display row and
            # hasn't already been inserted.
            _context_inserted = set()
            _cursor = 0
            for _display_idx, _dmsg in enumerate(previous_display):
                _dkey = _message_identity(_dmsg)
                if _dkey is not None:
                    _j = _cursor
                    while _j < len(context_keys) and context_keys[_j] != _dkey:
                        _j += 1
                    if _j < len(context_keys):
                        for _k in range(_cursor, _j):
                            _ckey = context_keys[_k]
                            _cmsg = previous_context[_k]
                            if _ckey is not None and _ckey not in _context_inserted and _ckey not in _display_id_set and not _is_context_compression_marker(_cmsg):
                                _backfilled.append(copy.deepcopy(_cmsg))
                                _context_inserted.add(_ckey)
                        _cursor = _j + 1
                    elif not any(
                        _message_identity(_future_dmsg) in context_keys[_cursor:]
                        for _future_dmsg in previous_display[_display_idx + 1:]
                    ):
                        for _k in range(_cursor, len(context_keys)):
                            _ckey = context_keys[_k]
                            _cmsg = previous_context[_k]
                            if _ckey is not None and _ckey not in _context_inserted and _ckey not in _display_id_set and not _is_context_compression_marker(_cmsg):
                                _backfilled.append(copy.deepcopy(_cmsg))
                                _context_inserted.add(_ckey)
                        _cursor = len(context_keys)
                # The display row is the visible backbone — always preserve it,
                # in order, even when an earlier (identical-content) turn or a
                # backfilled context row shares its timestamp-less identity.
                _backfilled.append(_dmsg)
            while _cursor < len(context_keys):
                _ckey = context_keys[_cursor]
                _cmsg = previous_context[_cursor]
                _cursor += 1
                if _ckey is not None and _ckey not in _context_inserted and _ckey not in _display_id_set and not _is_context_compression_marker(_cmsg):
                    _backfilled.append(copy.deepcopy(_cmsg))
                    _context_inserted.add(_ckey)
            if len(_backfilled) > len(previous_display):
                logger.debug(
                    "Backfilled %d context-only turns into previous_display (was %d, now %d)",
                    len(_backfilled) - len(previous_display),
                    len(previous_display),
                    len(_backfilled),
                )
                previous_display = _backfilled

    if _messages_have_prefix(result_messages, previous_context):
        candidates = result_messages[len(previous_context):]
        candidates = _strip_replayed_prefix(previous_display, candidates)
        candidates = _strip_replayed_prefix(previous_context, candidates)
    else:
        current_user_idx = _find_current_user_turn(result_messages, msg_text)
        marker_candidates = [
            m for m in result_messages[:current_user_idx if current_user_idx is not None else len(result_messages)]
            if _is_context_compression_marker(m)
        ]
        turn_candidates = result_messages[current_user_idx:] if current_user_idx is not None else []
        candidates = marker_candidates + turn_candidates

    merged = previous_display[:]
    seen = {_message_identity(m) for m in merged}
    current_user_key = _message_identity({'role': 'user', 'content': msg_text})
    current_user_in_candidates = any(
        _message_identity(m) == current_user_key or _looks_like_current_user_turn(m, msg_text)
        for m in candidates
    )
    current_user_already_checkpointed = bool(
        merged
        and (
            _message_identity(merged[-1]) == current_user_key
            or _looks_like_current_user_turn(merged[-1], msg_text)
        )
    )
    if (
        current_user_key is not None
        and not current_user_in_candidates
        and not current_user_already_checkpointed
        and any(
            isinstance(m, dict) and m.get('role') in ('assistant', 'tool')
            for m in candidates
        )
    ):
        # Some provider retry/fallback paths can return an assistant/tool delta
        # without echoing the current user turn. In deferred session-save mode
        # the prompt exists only in pending_user_message, so appending that delta
        # directly would make the assistant bubble appear attached to the prior
        # exchange and then clear the pending prompt. Materialize the current
        # turn at the transcript boundary before the assistant/tool response.
        current_user_msg = {'role': 'user', 'content': msg_text}
        insert_at = 0
        while insert_at < len(candidates) and _is_context_compression_marker(candidates[insert_at]):
            insert_at += 1
        candidates = candidates[:insert_at] + [current_user_msg] + candidates[insert_at:]

    for msg in candidates:
        key = _message_identity(msg)
        is_current_user_turn = _looks_like_current_user_turn(msg, msg_text)
        if (
            ((key is not None and key == current_user_key) or is_current_user_turn)
            and merged
            and (
                _message_identity(merged[-1]) == current_user_key
                or _looks_like_current_user_turn(merged[-1], msg_text)
            )
        ):
            # Eager session-save mode can checkpoint the current user turn
            # before the agent runs. When the agent returns that same user turn
            # in result_messages, keep the durable checkpoint and append only
            # the assistant/tool delta.
            continue
        if (
            key is not None
            and isinstance(msg, dict)
            and msg.get('role') == 'assistant'
            and merged
            and _message_identity(merged[-1]) == key
        ):
            # Some provider/result replay paths can include the same assistant
            # message twice in the current delta. Treat only adjacent identity
            # matches as replay duplicates so identical answers in separate
            # user turns remain visible.
            continue
        if _is_context_compression_marker(msg) and key is not None and key in seen:
            continue
        display_msg = msg
        if (
            ((key is not None and key == current_user_key) or is_current_user_turn)
            and isinstance(msg, dict)
            and msg.get('role') == 'user'
        ):
            display_msg = copy.deepcopy(msg)
            display_msg['content'] = msg_text
        merged.append(copy.deepcopy(display_msg))
        if key is not None:
            seen.add(key)
    return merged


def _stamp_missing_message_timestamps(messages, *, now: float | None = None) -> int:
    """Stamp missing message timestamps without collapsing transcript order.

    Compacted/reconciled rows can arrive without timestamps. Assigning one
    integer seconds value to the whole batch makes later timestamp-based display
    merges unstable; use a subsecond sequence instead.
    """
    base = time.time() if now is None else float(now)
    stamped = 0
    for msg in messages or []:
        if isinstance(msg, dict) and not msg.get('timestamp') and not msg.get('_ts'):
            msg['timestamp'] = base + (stamped * 0.000001)
            stamped += 1
    return stamped


def _assistant_reply_added_after_current_turn(result_messages, previous_context, msg_text) -> bool:
    """Return True only when the just-finished turn produced assistant text."""
    result_messages = list(result_messages or [])
    previous_context = list(previous_context or [])
    if _messages_have_prefix(result_messages, previous_context):
        candidates = result_messages[len(previous_context):]
    else:
        current_user_idx = _find_current_user_turn(result_messages, msg_text)
        candidates = result_messages[current_user_idx + 1:] if current_user_idx is not None else result_messages
    return any(
        isinstance(m, dict)
        and m.get('role') == 'assistant'
        and not m.get('_error')
        and str(m.get('content') or '').strip()
        for m in candidates
    )


def _session_lacks_final_assistant_answer(messages) -> bool:
    """Return True when the persisted transcript ends before a final answer."""
    for msg in reversed(list(messages or [])):
        if not isinstance(msg, dict):
            continue
        if msg.get('_error'):
            return False
        if _is_context_compression_marker(msg):
            continue
        role = msg.get('role')
        if role == 'tool':
            return True
        if role == 'assistant':
            content = msg.get('content')
            if isinstance(content, list):
                text = '\n'.join(
                    str(part.get('text') or part.get('content') or '')
                    for part in content
                    if isinstance(part, dict)
                )
            else:
                text = str(content or '')
            if msg.get('tool_calls'):
                return True
            if text.strip():
                return False
            continue
        if role == 'user':
            return True
    return True


def _agent_result_terminal_failure(result) -> bool:
    """Return True for agent results that must not be finalized as done."""
    if not isinstance(result, dict):
        return False
    status = str(result.get('status') or result.get('state') or '').strip().lower()
    if status in {'failed', 'error', 'partial', 'compression_exhausted'}:
        return True
    if result.get('compression_exhausted'):
        return True
    if result.get('failed') or result.get('partial'):
        return True
    return False


_TOOL_RESULT_SNIPPET_MAX = 4000


_LIVE_TOOL_PROMPT_DELTA_MAX = 12_000
_LIVE_TOOL_PROMPT_TURN_MAX = 24_000


def _bounded_live_tool_prompt_delta(messages, *, cap: int = _LIVE_TOOL_PROMPT_DELTA_MAX) -> int:
    """Return a bounded rough token delta for live tool metering.

    Tool-result callbacks can fire before the agent's next exact prompt accounting
    is available. The live usage ring should show a conservative in-flight hint,
    not replay a full large tool payload into `last_prompt_tokens`.
    """
    if not messages:
        return 0
    try:
        from agent.model_metadata import estimate_messages_tokens_rough
        delta = int(estimate_messages_tokens_rough(messages) or 0)
    except Exception:
        delta = 0
    if delta <= 0:
        return 0
    return min(delta, int(cap or 0))


def live_usage_prompt_estimate_after_tool_delta(
    *,
    base_prompt_tokens: int,
    exact_prompt_tokens: int = 0,
    messages=None,
    cap: int = _LIVE_TOOL_PROMPT_DELTA_MAX,
    turn_tool_prompt_tokens: int = 0,
    turn_cap: int = _LIVE_TOOL_PROMPT_TURN_MAX,
) -> dict:
    """Compute the live `last_prompt_tokens` estimate after a tool update.

    Exact compressor/provider prompt accounting wins. When no newer exact prompt
    is available, add only bounded live tool deltas to the persisted base.
    """
    base = int(base_prompt_tokens or 0)
    exact = int(exact_prompt_tokens or 0)
    if exact and exact != base:
        return {
            'last_prompt_tokens': exact,
            'estimated': False,
            'turn_tool_prompt_tokens': 0,
        }
    prior_turn_delta = max(0, int(turn_tool_prompt_tokens or 0))
    turn_ceiling = max(0, int(turn_cap or 0))
    next_turn_delta = min(
        prior_turn_delta + _bounded_live_tool_prompt_delta(messages, cap=cap),
        turn_ceiling,
    )
    return {
        'last_prompt_tokens': base + next_turn_delta,
        'estimated': True,
        'turn_tool_prompt_tokens': next_turn_delta,
    }


def _tool_result_snippet(raw, limit: int = _TOOL_RESULT_SNIPPET_MAX) -> str:
    """Extract a bounded result preview from a stored tool message payload."""
    if limit <= 0:
        return ''
    text = str(raw or '')
    try:
        data = raw if isinstance(raw, dict) else json.loads(text)
        if isinstance(data, dict):
            preview = data.get('output') or data.get('result') or data.get('error') or text
            text = str(preview)
    except Exception:
        pass
    return text[:limit]


def _truncate_tool_args(args, limit: int = 6) -> dict:
    """Truncate tool args for compact session persistence."""
    out = {}
    if not isinstance(args, dict):
        return out
    for k, v in list(args.items())[:limit]:
        s = str(v)
        out[k] = s[:120] + ('...' if len(s) > 120 else '')
    return out


def _nearest_assistant_msg_idx(messages, msg_idx: int) -> int:
    """Find the closest preceding assistant message index for a tool result."""
    for idx in range(msg_idx - 1, -1, -1):
        msg = messages[idx]
        if isinstance(msg, dict) and msg.get('role') == 'assistant':
            return idx
    return -1


def _extract_tool_calls_from_messages(messages, live_tool_calls=None):
    """Build persisted tool-call summaries from final messages plus live progress fallback."""
    tool_calls = []
    pending_names = {}
    pending_args = {}
    pending_asst_idx = {}
    tool_msg_sequence = []

    for msg_idx, m in enumerate(messages or []):
        if not isinstance(m, dict):
            continue
        role = m.get('role')
        if role == 'assistant':
            content = m.get('content', '')
            if isinstance(content, list):
                for part in content:
                    if isinstance(part, dict) and part.get('type') == 'tool_use':
                        tid = part.get('id', '')
                        if tid:
                            pending_names[tid] = part.get('name', '')
                            pending_args[tid] = part.get('input', {})
                            pending_asst_idx[tid] = msg_idx
            for tc in m.get('tool_calls', []):
                if not isinstance(tc, dict):
                    continue
                tid = tc.get('id', '') or tc.get('call_id', '')
                fn = tc.get('function', {})
                name = fn.get('name', '')
                try:
                    args = json.loads(fn.get('arguments', '{}') or '{}')
                except Exception:
                    args = {}
                if tid and name:
                    pending_names[tid] = name
                    pending_args[tid] = args
                    pending_asst_idx[tid] = msg_idx
        elif role == 'tool':
            tid = m.get('tool_call_id') or m.get('tool_use_id', '')
            raw = m.get('content', '')
            seq = {'msg_idx': msg_idx, 'raw': raw, 'resolved': False}
            if tid:
                name = pending_names.get(tid, '')
                if name and name != 'tool':
                    tool_calls.append({
                        'name': name,
                        'snippet': _tool_result_snippet(raw),
                        'tid': tid,
                        'assistant_msg_idx': pending_asst_idx.get(tid, -1),
                        'args': _truncate_tool_args(pending_args.get(tid, {})),
                    })
                    seq['resolved'] = True
            tool_msg_sequence.append(seq)

    live = [tc for tc in (live_tool_calls or []) if isinstance(tc, dict) and tc.get('name') and tc.get('name') != 'clarify']
    if live:
        for seq_idx, seq in enumerate(tool_msg_sequence):
            if seq.get('resolved'):
                continue
            if seq_idx >= len(live):
                break
            live_tc = live[seq_idx]
            tool_calls.append({
                'name': live_tc.get('name', 'tool'),
                'snippet': _tool_result_snippet(seq.get('raw', '')),
                'tid': live_tc.get('tid', '') or '',
                'assistant_msg_idx': _nearest_assistant_msg_idx(messages, seq.get('msg_idx', -1)),
                'args': _truncate_tool_args(live_tc.get('args', {}), limit=4),
            })

    return tool_calls


def _partial_message_signature(message: dict) -> tuple:
    """Return a stable identity for a persisted partial assistant marker."""
    if not isinstance(message, dict):
        return ('', '', ())
    tool_sig = []
    for tool_call in message.get('_partial_tool_calls') or []:
        if not isinstance(tool_call, dict):
            continue
        try:
            args_sig = json.dumps(
                tool_call.get('args') or {},
                ensure_ascii=False,
                sort_keys=True,
                default=str,
            )
        except Exception:
            args_sig = str(tool_call.get('args') or '')
        tool_sig.append((
            str(tool_call.get('name') or ''),
            args_sig,
            bool(tool_call.get('done', False)),
            bool(tool_call.get('is_error', False)),
            str(tool_call.get('preview') or tool_call.get('snippet') or ''),
        ))
    return (
        str(message.get('content') or '').strip(),
        str(message.get('reasoning') or '').strip(),
        tuple(tool_sig),
    )


def _partial_marker_already_present(messages, candidate: dict, *, before_idx: int | None = None) -> bool:
    """Check for an equivalent partial marker in the current user turn only."""
    if not isinstance(messages, list) or not isinstance(candidate, dict):
        return False
    end = before_idx if isinstance(before_idx, int) else len(messages)
    end = max(0, min(end, len(messages)))
    start = 0
    for idx in range(end - 1, -1, -1):
        msg = messages[idx]
        if isinstance(msg, dict) and msg.get('role') == 'user':
            start = idx + 1
            break
    candidate_sig = _partial_message_signature(candidate)
    for msg in messages[start:end]:
        if isinstance(msg, dict) and msg.get('_partial') and _partial_message_signature(msg) == candidate_sig:
            return True
    return False


def _sse(handler, event, data):
    """Write one SSE event to the response stream."""
    payload = f"event: {event}\ndata: {json.dumps(data, ensure_ascii=False)}\n\n"
    handler.wfile.write(payload.encode('utf-8'))
    handler.wfile.flush()


def _materialize_pending_user_turn_before_error(session) -> bool:
    """Persist the pending user prompt before clearing runtime stream state.

    Error paths often clear ``pending_user_message`` before appending an assistant
    error marker. In deferred session-save mode that pending field can be the
    only durable copy of the user's current turn, so clearing it makes the user
    bubble disappear on reload/reconcile. Return True when a recovered user turn
    was appended.
    """
    pending_text = str(getattr(session, 'pending_user_message', None) or '')
    if not pending_text:
        return False
    normalized_pending = " ".join(pending_text.split())
    if normalized_pending:
        for existing in reversed(list(getattr(session, 'messages', None) or [])[-8:]):
            if not isinstance(existing, dict) or existing.get('role') != 'user':
                continue
            existing_text = " ".join(str(existing.get('content') or '').split())
            if existing_text == normalized_pending:
                return False
    recovered_ts = int(time.time())
    pending_started_at = getattr(session, 'pending_started_at', None)
    if isinstance(pending_started_at, (int, float)) and pending_started_at > 0:
        recovered_ts = int(pending_started_at)
    recovered = {
        'role': 'user',
        'content': pending_text,
        'timestamp': recovered_ts,
        '_recovered': True,
    }
    pending_attachments = getattr(session, 'pending_attachments', None)
    if pending_attachments:
        recovered['attachments'] = list(pending_attachments)
    session.messages.append(recovered)
    return True


def _last_resort_sync_from_core(session, stream_id, agent_lock):
    """Final-exit guard: if the stream exits with pending_user_message still set,
    sync messages from the core transcript or add an error marker.
    Called from the outer finally block of _run_agent_streaming.
    Must never raise.
    """
    from api.models import _get_profile_home, _apply_core_sync_or_error_marker
    try:
        # Guard: if a cancel was already requested, bail out — cancel_stream() has
        # already saved partial content and we must not double-append error markers.
        if stream_id in CANCEL_FLAGS and CANCEL_FLAGS[stream_id].is_set():
            return

        profile_home = _get_profile_home(session.profile)
        core_path = profile_home / 'sessions' / f'session_{session.session_id}.json'

        _lock_ctx = agent_lock if agent_lock is not None else contextlib.nullcontext()
        with _lock_ctx:
            _apply_core_sync_or_error_marker(
                session,
                core_path,
                stream_id_for_recheck=stream_id,
                require_stream_dead=False,
            )
    except Exception:
        logger.exception(
            "_last_resort_sync_from_core failed for session %s",
            getattr(session, 'session_id', '?'),
        )


def _build_session_db_for_stream(state_db_path):
    """Build a per-request SessionDB handle for WebUI session search.

    Returns ``None`` if the helper module or constructor fails so callers can
    continue without session_search rather than propagating a hard failure.
    """
    try:
        from hermes_state import SessionDB
        return SessionDB(db_path=state_db_path)
    except Exception as _db_err:
        print(f"[webui] WARNING: SessionDB init failed - session_search will be unavailable: {_db_err}", flush=True)
        return None


def _replace_session_db_in_kwargs(agent_kwargs, state_db_path):
    """Build a fresh SessionDB and replace ``agent_kwargs['session_db']`` safely."""
    if not isinstance(agent_kwargs, dict):
        return None

    _old_session_db = agent_kwargs.get("session_db")
    _next_session_db = _build_session_db_for_stream(state_db_path)
    if _old_session_db is not None and _old_session_db is not _next_session_db:
        try:
            _old_session_db.close()
        except Exception:
            logger.debug("Failed to close previous session_db handle during self-heal")
    agent_kwargs["session_db"] = _next_session_db
    return _next_session_db


def _attempt_credential_self_heal(
    provider_id, session_id, _agent_lock_ref,
):
    """Try to silently refresh credentials after a 401/auth error (#1401).

    Returns a new ``(agent, rt_dict)`` tuple on success so the caller can
    retry the conversation.  Returns ``None`` when self-heal is not
    applicable (e.g. auth.json unchanged, provider unresolvable).

    Steps:
    1. Re-read ``~/.hermes/auth.json`` to pick up fresh credentials that
       may have been written by a concurrent ``hermes model`` CLI invocation.
    2. Evict the session's cached agent so it is rebuilt with fresh keys.
    3. Evict the provider's credential-pool cache entry.
    4. Re-resolve the runtime provider.
    5. Return a new agent + resolved-provider dict (the caller must
       re-invoke ``run_conversation`` with these).
    """
    try:
        from api.oauth import (
            read_auth_json,
            resolve_runtime_provider_with_anthropic_env_lock,
        )
        from api.config import (
            SESSION_AGENT_CACHE, SESSION_AGENT_CACHE_LOCK,
            invalidate_credential_pool_cache,
        )
        from hermes_cli.runtime_provider import resolve_runtime_provider

        # 1. Re-read auth.json (triggers a fresh credential scan)
        _fresh_auth = read_auth_json()
        if not _fresh_auth:
            logger.debug('[webui] self-heal: auth.json empty or missing, skipping')
            return None

        # 2. Evict the cached agent for this session
        _evicted_entry = None
        with SESSION_AGENT_CACHE_LOCK:
            _evicted_entry = SESSION_AGENT_CACHE.pop(session_id, None)
        if _evicted_entry is not None:
            _close_cached_agent_entry_at_session_boundary(session_id, _evicted_entry)

        # 3. Invalidate the credential pool for this provider
        invalidate_credential_pool_cache(provider_id)

        # 4. Re-resolve runtime provider with fresh credentials
        _new_rt = resolve_runtime_provider_with_anthropic_env_lock(
            resolve_runtime_provider,
            requested=provider_id,
        )

        logger.info(
            '[webui] self-heal: credential refresh succeeded for provider=%s session=%s',
            provider_id, session_id,
        )
        return _new_rt
    except Exception as _heal_err:
        logger.warning(
            '[webui] self-heal: failed for provider=%s session=%s: %s',
            provider_id, session_id, _heal_err,
        )
        return None


def _agent_cache_api_key_sig(resolved_api_key, credential_pool) -> str:
    """Return the cache-signature component for runtime credentials.

    Credential-pool providers can legitimately hand WebUI a different runtime
    token on each request (round-robin pools, OAuth refresh, auth self-heal).
    The AIAgent object is also where cross-turn memory-provider state lives, so
    using the volatile token itself in the cache signature silently defeats the
    per-session agent cache and drops warmed Hindsight prefetch results.
    """
    if credential_pool is not None:
        return 'credential-pool'
    import hashlib as _hashlib
    return _hashlib.sha256((resolved_api_key or '').encode()).hexdigest()[:16]


def _lifecycle_commit_session_memory(session_id: str, *, agent=None, wait: bool = False) -> bool:
    from api.session_lifecycle import commit_session_memory

    return commit_session_memory(session_id, agent=agent, wait=wait)


def _lifecycle_has_uncommitted_work(session_id: str) -> bool:
    from api.session_lifecycle import has_uncommitted_work

    return has_uncommitted_work(session_id)


def _lifecycle_unregister_agent(session_id: str) -> None:
    from api.session_lifecycle import unregister_agent

    unregister_agent(session_id)


def _lifecycle_discard_session(session_id: str) -> bool:
    from api.session_lifecycle import discard_session

    return discard_session(session_id)


def _close_evicted_agent_at_session_boundary(session_id: str, agent) -> bool:
    """Commit and tear down an evicted cached agent at a WebUI session boundary.

    WebUI keeps AIAgent instances in an LRU cache so memory providers can carry
    state across turns. When an agent is evicted, commit pending memory first;
    if the lifecycle entry is clean afterwards, unregister and call
    shutdown_memory_provider(messages) so provider-owned clients such as
    Hindsight's aiohttp session are closed instead of being garbage-collected
    later. Passing the cached transcript mirrors gateway cleanup semantics for
    providers that use on_session_end(messages) during shutdown.
    """
    if agent is None:
        return True

    should_close_evicted_agent = True
    try:
        _lifecycle_commit_session_memory(session_id, agent=agent, wait=True)
        if not _lifecycle_has_uncommitted_work(session_id):
            _lifecycle_unregister_agent(session_id)
            # Drop the lifecycle dict entry now that the LRU-evicted agent is
            # gone and no uncommitted work remains, so the dict tracks only live
            # sessions instead of growing unbounded (issue #3506).
            _lifecycle_discard_session(session_id)
        else:
            should_close_evicted_agent = False
    except Exception:
        should_close_evicted_agent = False
        logger.debug("Lifecycle commit on eviction failed for %s", session_id, exc_info=True)

    if not should_close_evicted_agent:
        return False

    try:
        shutdown_memory_provider = getattr(agent, 'shutdown_memory_provider', None)
        if callable(shutdown_memory_provider):
            session_messages = vars(agent).get('_session_messages', [])
            shutdown_memory_provider(session_messages)
    except Exception:
        logger.debug("Failed to shut down evicted agent memory provider for session %s", session_id, exc_info=True)

    try:
        session_db = getattr(agent, '_session_db', None)
        if session_db is not None:
            session_db.close()
    except Exception:
        logger.debug("Failed to close evicted agent session DB for session %s", session_id, exc_info=True)
    return True


def _close_cached_agent_entry_at_session_boundary(session_id: str, cache_entry) -> bool:
    """Commit and tear down a popped SESSION_AGENT_CACHE entry outside the cache lock."""
    agent = cache_entry[0] if isinstance(cache_entry, tuple) else None
    return _close_evicted_agent_at_session_boundary(session_id, agent)


def _refresh_cached_agent_runtime(agent, agent_kwargs: dict) -> bool:
    """Refresh volatile runtime credentials on a reused cached AIAgent.

    The cache key intentionally ignores credential-pool token churn, but the
    cached agent's LLM client still needs the latest selected/refreshed runtime
    key. Keep long-lived provider/session state (memory prefetch, turn counters,
    tool state) while swapping only the runtime credential/client.
    """
    if agent is None or not isinstance(agent_kwargs, dict):
        return False

    new_pool = agent_kwargs.get('credential_pool')
    if new_pool is not None:
        try:
            agent._credential_pool = new_pool
        except Exception:
            pass

    new_key = agent_kwargs.get('api_key') or ''
    if not new_key:
        return True

    new_base = agent_kwargs.get('base_url') or getattr(agent, 'base_url', '') or ''
    if getattr(agent, '_fallback_activated', False):
        # Avoid mixing a refreshed primary credential into a live fallback
        # runtime. Rebuilding is safer than mutating a fallback-active agent
        # whose restore/cooldown state has not run yet for this turn.
        return False

    if new_key == (getattr(agent, 'api_key', '') or ''):
        _refresh_cached_agent_primary_runtime_snapshot(agent)
        return True

    try:
        if getattr(agent, 'api_mode', None) == 'anthropic_messages':
            # Native Anthropic-style clients have their own construction path;
            # switch_model() already handles token/client refresh there.
            if hasattr(agent, 'switch_model'):
                agent.switch_model(
                    agent_kwargs.get('model') or getattr(agent, 'model', None),
                    agent_kwargs.get('provider') or getattr(agent, 'provider', None),
                    api_key=new_key,
                    base_url=new_base,
                    api_mode=agent_kwargs.get('api_mode') or getattr(agent, 'api_mode', ''),
                )
                return True
            return False

        if not hasattr(agent, '_client_kwargs') or not hasattr(agent, '_replace_primary_openai_client'):
            # Test/fake-agent fallback: keep metadata accurate even if no real
            # OpenAI client exists to rebuild.
            agent.api_key = new_key
            if new_base:
                agent.base_url = new_base
            _refresh_cached_agent_primary_runtime_snapshot(agent)
            return True

        client_kwargs = dict(getattr(agent, '_client_kwargs', {}) or {})
        client_kwargs['api_key'] = new_key
        if new_base:
            client_kwargs['base_url'] = new_base
        agent._client_kwargs = client_kwargs
        agent.api_key = new_key
        if new_base:
            agent.base_url = new_base
        if hasattr(agent, '_apply_client_headers_for_base_url'):
            agent._apply_client_headers_for_base_url(agent.base_url)
        rebuilt = bool(agent._replace_primary_openai_client(reason='webui_credential_refresh'))
        if rebuilt:
            _refresh_cached_agent_primary_runtime_snapshot(agent)
        return rebuilt
    except Exception:
        logger.debug('[webui] Failed to refresh cached agent runtime credentials', exc_info=True)
        return False


def _cached_agent_session_identity(agent) -> str | None:
    """Best-effort session id carried by a cached AIAgent.

    The cache key is only safe when it agrees with the object's own session
    identity. Some old/fake agents may not expose an identity; keep those
    backwards-compatible and treat them as unverifiable rather than mismatched.
    """
    if agent is None:
        return None
    for attr in ('session_id', '_session_id'):
        value = getattr(agent, attr, None)
        if isinstance(value, str) and value:
            return value
    session_db = getattr(agent, '_session_db', None)
    if session_db is not None:
        for attr in ('session_id', '_session_id'):
            value = getattr(session_db, attr, None)
            if isinstance(value, str) and value:
                return value
    return None


def _cached_agent_matches_session(agent, session_id: str) -> bool:
    identity = _cached_agent_session_identity(agent)
    return identity is None or identity == str(session_id)


def _refresh_cached_agent_primary_runtime_snapshot(agent) -> None:
    """Keep AIAgent's primary-runtime snapshot aligned with refreshed creds.

    Long-lived AIAgent instances use `_primary_runtime` to restore the preferred
    provider after fallback/transport recovery. If WebUI refreshes a cached
    agent's runtime token but leaves that snapshot stale, a later restore can
    resurrect the old credential and undo the refresh.
    """
    rt = getattr(agent, '_primary_runtime', None)
    if not isinstance(rt, dict):
        return

    base_url = getattr(agent, 'base_url', rt.get('base_url'))
    api_key = getattr(agent, 'api_key', rt.get('api_key', ''))
    client_kwargs = dict(getattr(agent, '_client_kwargs', None) or rt.get('client_kwargs', {}) or {})

    rt['base_url'] = base_url
    rt['api_key'] = api_key
    rt['client_kwargs'] = client_kwargs

    # The default context compressor usually tracks the primary runtime too;
    # keep both the live compressor fields and the fallback-restoration
    # snapshot aligned when those attributes exist.
    cc = getattr(agent, 'context_compressor', None)
    if cc is not None:
        if hasattr(cc, 'base_url'):
            cc.base_url = base_url
        if hasattr(cc, 'api_key'):
            cc.api_key = api_key
        if 'compressor_base_url' in rt:
            rt['compressor_base_url'] = getattr(cc, 'base_url', base_url)
        if 'compressor_api_key' in rt:
            rt['compressor_api_key'] = getattr(cc, 'api_key', api_key)
    else:
        if 'compressor_base_url' in rt:
            rt['compressor_base_url'] = base_url
        if 'compressor_api_key' in rt:
            rt['compressor_api_key'] = api_key

    if getattr(agent, 'api_mode', None) == 'anthropic_messages':
        if hasattr(agent, '_anthropic_api_key'):
            rt['anthropic_api_key'] = getattr(agent, '_anthropic_api_key')
        if hasattr(agent, '_anthropic_base_url'):
            rt['anthropic_base_url'] = getattr(agent, '_anthropic_base_url')
        if hasattr(agent, '_is_anthropic_oauth'):
            rt['is_anthropic_oauth'] = getattr(agent, '_is_anthropic_oauth')


def _run_agent_streaming(
    session_id,
    msg_text,
    model,
    workspace,
    stream_id,
    attachments=None,
    *,
    ephemeral=False,
    model_provider=None,
    goal_related=False,
):
    """Run agent in background thread, writing SSE events to STREAMS[stream_id].

    When ephemeral=True, session mutations are skipped — used by /btw to get
    a streaming answer without persisting to the parent session.
    """
    q = STREAMS.get(stream_id)
    if q is None:
        return
    register_active_run(
        stream_id,
        session_id=session_id,
        started_at=time.time(),
        phase="starting",
        workspace=str(workspace),
        model=model,
        provider=model_provider,
        ephemeral=bool(ephemeral),
    )
    try:
        run_journal = RunJournalWriter(session_id, stream_id)
    except Exception:
        run_journal = None
        logger.debug("Failed to initialize run journal for stream %s", stream_id, exc_info=True)
    if not ephemeral:
        try:
            append_turn_journal_event_for_stream(
                session_id,
                stream_id,
                {"event": "worker_started", "created_at": time.time()},
            )
        except Exception:
            logger.debug("Failed to append worker_started turn journal event", exc_info=True)
    s = None
    _rt = {}
    old_cwd = None
    old_exec_ask = None
    old_session_key = None
    old_session_id = None
    old_session_platform = None
    old_hermes_home = None
    old_profile_env = {}

    # MCP discovery moved to AFTER the per-profile HERMES_HOME mutation below
    # (was here at v0.51.30) — the previous placement always read the default
    # profile's mcp_servers because os.environ['HERMES_HOME'] hadn't been
    # rewritten yet.  See https://github.com/nesquena/hermes-webui/issues/1968.

    # Sprint 10: create a cancel event for this stream
    cancel_event = threading.Event()
    with STREAMS_LOCK:
        CANCEL_FLAGS[stream_id] = cancel_event
        STREAM_PARTIAL_TEXT[stream_id] = ''  # start accumulating partial text (#893)
        STREAM_REASONING_TEXT[stream_id] = ''  # start accumulating reasoning trace (#1361 §A)
        STREAM_LIVE_TOOL_CALLS[stream_id] = []  # start accumulating tool calls (#1361 §B)

    agent = None
    _live_prompt_estimate_tokens = [0]
    _live_prompt_exact_tokens = [0]
    _live_prompt_estimate_tool_delta_tokens = [0]
    _live_prompt_estimate_seen_ids = set()
    # Per-stream cache for the real per-model context_length (#3256 perf).
    # _live_usage_snapshot() runs on every metering tick (~10x/sec during
    # streaming); recomputing get_model_context_length() there triggered a
    # config read + potential metadata/network probe on every token for
    # non-default models (e.g. claude-opus-4.7-1m), freezing the stream while
    # the default model was unaffected. The value is constant for a given
    # (model, base_url, provider) within one stream, so resolve it at most
    # once. Sentinel: None=not computed, 0=not applicable/failed, >0=real cap.
    _real_ctx_cache = [None]

    def _seed_live_prompt_estimate() -> int:
        """Capture the latest exact prompt size before adding live tool deltas."""
        if _live_prompt_estimate_tokens[0] > 0:
            return _live_prompt_estimate_tokens[0]
        _base = 0
        _agent = agent
        if _agent is not None:
            try:
                _cc = getattr(_agent, 'context_compressor', None)
                if _cc:
                    _base = getattr(_cc, 'last_prompt_tokens', 0) or 0
            except Exception:
                _base = 0
        if not _base:
            try:
                _session_obj = get_session(session_id)
                _base = getattr(_session_obj, 'last_prompt_tokens', 0) or 0
            except Exception:
                _base = 0
        _live_prompt_estimate_tokens[0] = int(_base or 0)
        _live_prompt_exact_tokens[0] = _live_prompt_estimate_tokens[0]
        return _live_prompt_estimate_tokens[0]

    def _bump_live_prompt_estimate(messages) -> int:
        """Increment a rough next-prompt estimate from live tool activity."""
        if not messages:
            return _live_prompt_estimate_tokens[0]
        _seed_live_prompt_estimate()
        _usage = live_usage_prompt_estimate_after_tool_delta(
            base_prompt_tokens=_live_prompt_exact_tokens[0],
            exact_prompt_tokens=_live_prompt_exact_tokens[0],
            messages=messages,
            turn_tool_prompt_tokens=_live_prompt_estimate_tool_delta_tokens[0],
        )
        _live_prompt_estimate_tokens[0] = _usage['last_prompt_tokens']
        _live_prompt_estimate_tool_delta_tokens[0] = _usage['turn_tool_prompt_tokens']
        return _live_prompt_estimate_tokens[0]

    def _live_usage_snapshot():
        """Best-effort live usage payload for mid-stream UI updates.

        During tool execution the final `done` event has not fired yet, but the
        frontend still benefits from seeing the latest known token / context
        values. These are exact for the most recent model call and a truthful
        lower bound for the pending next call after a tool result is appended.
        """
        _usage = {
            'input_tokens': 0,
            'output_tokens': 0,
            'estimated_cost': 0,
            'cache_read_tokens': 0,
            'cache_write_tokens': 0,
            'cache_hit_percent': None,
            'context_length': 0,
            'threshold_tokens': 0,
            'last_prompt_tokens': 0,
        }
        try:
            _session_obj = get_session(session_id)
        except Exception:
            _session_obj = None

        _agent = agent
        if _agent is not None:
            try:
                _usage['input_tokens'] = getattr(_agent, 'session_prompt_tokens', 0) or 0
                _usage['output_tokens'] = getattr(_agent, 'session_completion_tokens', 0) or 0
                _usage['estimated_cost'] = getattr(_agent, 'session_estimated_cost_usd', 0) or 0
                _usage['cache_read_tokens'] = getattr(_agent, 'session_cache_read_tokens', 0) or 0
                _usage['cache_write_tokens'] = getattr(_agent, 'session_cache_write_tokens', 0) or 0
            except Exception:
                pass
            try:
                _cc = getattr(_agent, 'context_compressor', None)
                if _cc:
                    _cc_cl_u = getattr(_cc, 'context_length', 0) or 0
                    # Default-only guard (#3256): the agent-side compressor is
                    # built in agent_init with the global model.context_length
                    # applied unconditionally — for non-default models that
                    # value is the stale global cap (e.g. 232K). Drop it here
                    # so the live usage payload doesn't surface the wrong cap.
                    # PERF: resolve the real per-model cap at most once per
                    # stream (cached in _real_ctx_cache). This snapshot runs on
                    # every metering tick; doing the config read + metadata
                    # lookup per tick froze non-default-model streams.
                    if _real_ctx_cache[0] is None:
                        _resolved_real = 0  # 0 = guard not applicable / failed
                        try:
                            from api.config import get_config as _gc_u
                            _cfg_u = _gc_u()
                            _mcfg_u = _cfg_u.get('model', {}) if isinstance(_cfg_u, dict) else {}
                            if isinstance(_mcfg_u, dict):
                                _def_u = str(_mcfg_u.get('default') or '').strip()
                                _raw_u = _mcfg_u.get('context_length')
                                try:
                                    _cl_u = int(_raw_u) if _raw_u is not None else 0
                                except (TypeError, ValueError):
                                    _cl_u = 0
                                _sm_u = str(getattr(_agent, 'model', '') or '').strip()
                                from api.routes import _model_matches_configured_default as _mmcd_u
                                if (
                                    _cl_u > 0
                                    and _cc_cl_u == _cl_u
                                    and _def_u
                                    and _sm_u
                                    and not _mmcd_u(_sm_u, _def_u, getattr(_agent, 'provider', '') or '')
                                ):
                                    # Recompute from real per-model metadata.
                                    try:
                                        from agent.model_metadata import get_model_context_length as _g_u
                                        _real_u = _g_u(
                                            _sm_u,
                                            getattr(_agent, 'base_url', '') or '',
                                            config_context_length=None,
                                            provider=getattr(_agent, 'provider', '') or '',
                                        ) or 0
                                        if _real_u:
                                            _resolved_real = _real_u
                                    except Exception:
                                        pass
                        except Exception:
                            _resolved_real = 0
                        _real_ctx_cache[0] = _resolved_real
                    # Apply the cached real cap when the guard determined one.
                    if _real_ctx_cache[0]:
                        # Also rescale threshold_tokens by the same ratio so the
                        # auto-compress trigger reflects the real window, not
                        # the stale global cap (e.g. 197.2k @ 232K cap → ~850k
                        # @ 1M real cap).
                        _orig_cc_cl = getattr(_cc, 'context_length', 0) or 0
                        _orig_thresh = getattr(_cc, 'threshold_tokens', 0) or 0
                        _cc_cl_u = _real_ctx_cache[0]
                        if _orig_cc_cl > 0 and _orig_thresh > 0:
                            _scaled_thresh = int(_orig_thresh * _real_ctx_cache[0] / _orig_cc_cl)
                            _usage['context_length'] = _cc_cl_u
                            _usage['threshold_tokens'] = _scaled_thresh
                            _usage['last_prompt_tokens'] = getattr(_cc, 'last_prompt_tokens', 0) or 0
                        else:
                            _usage['context_length'] = _cc_cl_u
                            _usage['threshold_tokens'] = _orig_thresh
                            _usage['last_prompt_tokens'] = getattr(_cc, 'last_prompt_tokens', 0) or 0
                    else:
                        _usage['context_length'] = _cc_cl_u
                        _usage['threshold_tokens'] = getattr(_cc, 'threshold_tokens', 0) or 0
                        _usage['last_prompt_tokens'] = getattr(_cc, 'last_prompt_tokens', 0) or 0
            except Exception:
                pass

        if _session_obj is not None:
            for _field in ('input_tokens', 'output_tokens', 'estimated_cost', 'cache_read_tokens', 'cache_write_tokens', 'context_length', 'threshold_tokens', 'last_prompt_tokens'):
                if not _usage.get(_field):
                    try:
                        _usage[_field] = getattr(_session_obj, _field, 0) or 0
                    except Exception:
                        pass

        _real_prompt_tokens = int(_usage.get('last_prompt_tokens') or 0)
        _usage['cache_hit_percent'] = prompt_cache_hit_percent(
            _usage.get('cache_read_tokens') or 0,
            _usage.get('input_tokens') or 0,
        )
        if _real_prompt_tokens and _real_prompt_tokens != _live_prompt_exact_tokens[0]:
            _live_prompt_exact_tokens[0] = _real_prompt_tokens
            _live_prompt_estimate_tokens[0] = _real_prompt_tokens
            _live_prompt_estimate_tool_delta_tokens[0] = 0
        elif _live_prompt_estimate_tokens[0] > _real_prompt_tokens:
            _usage['last_prompt_tokens'] = _live_prompt_estimate_tokens[0]

        return _usage

    # Register this stream with the global streaming meter
    meter().begin_session(stream_id)

    # Metering ticker — emits a metering event at 1 Hz while sessions are active.
    # When get_interval() returns >= 10.0 (no active sessions), the ticker exits
    # so no idle readings are emitted and the SSE consumer sees nothing.
    _metering_stop = threading.Event()

    def _metering_ticker():
        while True:
            interval = meter().get_interval()
            if interval >= 10.0:
                break  # nothing active — stop the ticker
            if _metering_stop.wait(interval):
                break  # stream was cancelled or ended — exit
            stats = meter().get_stats()
            stats['session_id'] = session_id
            stats['usage'] = _live_usage_snapshot()
            put('metering', stats)

    _metering_thread = threading.Thread(target=_metering_ticker, daemon=True)
    _metering_thread.start()

    def put(event, data):
        # If cancelled, drop all further events except the cancel event itself
        if cancel_event.is_set() and event not in ('cancel', 'error'):
            return
        event_id = None
        if run_journal is not None:
            try:
                journaled = run_journal.append_sse_event(event, data)
                # Carry the exact journal id for this queued frame. A global
                # "latest event" side channel is still kept for legacy queues,
                # but StreamChannel subscribers need the per-item id so a
                # queued backlog cannot advance the browser cursor past an
                # undelivered event.
                event_id = (journaled or {}).get('event_id') if isinstance(journaled, dict) else None
                if event_id:
                    STREAM_LAST_EVENT_ID[stream_id] = event_id
            except Exception:
                logger.debug("Failed to append run journal event %s for stream %s", event, stream_id, exc_info=True)
        if event_id and hasattr(q, "note_last_event_id"):
            try:
                q.note_last_event_id(event_id)
            except Exception:
                logger.debug("Failed to note event_id %s for stream %s", event_id, stream_id, exc_info=True)
        try:
            queue_item = (event, data, event_id) if event_id and hasattr(q, "subscribe_with_snapshot") else (event, data)
            q.put_nowait(queue_item)
        except Exception:
            logger.debug("Failed to put event to queue")

    def _agent_status_callback(kind, message):
        """Bridge Agent lifecycle status into WebUI SSE.

        Passes compression events as 'compressing' events and rate-limit/fallback
        events as 'warning' events so the frontend can surface them to the user.
        All other lifecycle messages are dropped silently.
        """
        _message = str(message or '').strip()
        _kind = str(kind or '').strip().lower()
        if not _message:
            return
        _lower = _message.lower()
        _is_compression_start = (
            _kind == 'lifecycle'
            and (
                'preflight compression' in _lower
                or 'compressing' in _lower
                or 'compacting context' in _lower
                or 'context too large' in _lower
            )
        )
        if _is_compression_start:
            put('compressing', {
                'session_id': session_id,
                'message': 'Compressing context',
            })
            return
        # Pass through rate-limit and fallback messages so the frontend can
        # show them as warnings via the existing messages.js 'warning' listener.
        _is_fallback_notice = _is_fallback_lifecycle_message(_kind, _message)
        if _is_fallback_notice:
            put('warning', {'type': 'fallback', 'message': _message})

    # Initialised here (before any code that may raise) so the outer `finally`
    # block can safely check `if _checkpoint_stop is not None` even when an
    # exception fires before the checkpoint thread is created (Issue #765).
    _checkpoint_stop = None
    _ckpt_thread = None
    _agent_lock = None
    try:
        s = get_session(session_id)
        update_active_run(stream_id, phase="running", session_id=session_id)
        s.workspace = str(Path(workspace).expanduser().resolve())
        s.model = model
        provider_context = (
            str(model_provider).strip().lower()
            if model_provider is not None
            else getattr(s, "model_provider", None)
        )
        s.model_provider = provider_context or None

        _agent_lock = _get_session_agent_lock(session_id)
        # TD1: set thread-local env context so concurrent sessions don't clobber globals
        # Check for pre-flight cancel (user cancelled before agent even started)
        if cancel_event.is_set():
            with _agent_lock:
                _finalize_cancelled_turn(s, ephemeral=ephemeral, message='Task cancelled before start.')
            put('cancel', {'message': 'Cancelled before start'})
            return

        # Resolve profile home for this agent run — use the session's own profile
        # (stamped at new_session() time from the client's S.activeProfile) so that
        # two concurrent tabs on different profiles don't clobber each other via the
        # process-level active-profile global.  Falls back gracefully.
        try:
            from api.profiles import (
                patch_skill_home_modules,
                get_hermes_home_for_profile,
                get_profile_runtime_env,
            )
            _profile_home_path = get_hermes_home_for_profile(getattr(s, 'profile', None))
            _profile_home = str(_profile_home_path)
            _profile_runtime_env = get_profile_runtime_env(_profile_home_path)
        except ImportError:
            _profile_home = os.environ.get('HERMES_HOME', '')
            _profile_runtime_env = {}
            patch_skill_home_modules = None

        # Profile-aware provider/model enrichment: when the session belongs
        # to a profile that specifies model.provider and model.default, use
        # those to set provider_context and repair stale models.
        model, provider_context, _repaired = _apply_profile_home_context_to_streaming_model(
            model=model,
            provider_context=provider_context,
            profile_home=_profile_home,
            has_profile=bool(getattr(s, "profile", None)),
        )
        s.model_provider = provider_context
        if _repaired and model != (s.model or ""):
            s.model = model

        # Capture the resolved profile name now, while profile context is
        # reliable. Used in the compression migration block to stamp s.profile
        # on the continuation session. We resolve it here rather than calling
        # get_active_profile_name() at compression time because that function
        # reads thread-local storage (_tls.profile) set by set_request_profile()
        # on the HTTP handler thread. The streaming thread is a separate
        # threading.Thread and does not inherit TLS. At compression time,
        # get_active_profile_name() would fall back to the process-global
        # _active_profile, which may belong to a different concurrent tab.
        _resolved_profile_name = getattr(s, 'profile', None)
        if not _resolved_profile_name:
            try:
                from api.profiles import get_active_profile_name
                _resolved_profile_name = get_active_profile_name()
            except Exception:
                _resolved_profile_name = None
        
        _thread_env = _build_agent_thread_env(
            _profile_runtime_env,
            str(s.workspace),
            session_id,
            _profile_home,
        )
        _set_thread_env(**_thread_env)
        # Prewarm skill-tool imports *before* acquiring the lock so that
        # first-time module initialisation (which can be slow) does not
        # block other concurrent sessions waiting on _ENV_LOCK (#2024).
        _prewarm_skill_tool_modules()
        # Still set process-level env as fallback for tools that bypass thread-local
        # Acquire lock only for the env mutation, then release before the agent runs.
        # The finally block re-acquires to restore — keeping critical sections short
        # and preventing a deadlock where the restore would re-enter the same lock.
        with _ENV_LOCK:
            old_profile_env = {key: os.environ.get(key) for key in _profile_runtime_env}
            old_cwd = os.environ.get('TERMINAL_CWD')
            old_exec_ask = os.environ.get('HERMES_EXEC_ASK')
            old_session_key = os.environ.get('HERMES_SESSION_KEY')
            old_session_id = os.environ.get('HERMES_SESSION_ID')
            old_session_platform = os.environ.get('HERMES_SESSION_PLATFORM')
            old_hermes_home = os.environ.get('HERMES_HOME')
            os.environ.update(_profile_runtime_env)
            os.environ['TERMINAL_CWD'] = str(s.workspace)
            os.environ['HERMES_EXEC_ASK'] = '1'
            os.environ['HERMES_SESSION_KEY'] = session_id
            os.environ['HERMES_SESSION_ID'] = session_id
            os.environ['HERMES_SESSION_PLATFORM'] = 'webui'
            if _profile_home:
                os.environ['HERMES_HOME'] = _profile_home
                # Patch module-level caches to match the active profile.
                # _set_hermes_home() does this for process-wide switches
                # but per-request switches skip it (#1700).
                # Modules were prewarmed by _prewarm_skill_tool_modules()
                # above, so we only do lightweight sys.modules lookups and
                # attribute assignments here — no first-time import under
                # the lock (#2024).
                if patch_skill_home_modules is not None:
                    patch_skill_home_modules(Path(_profile_home))
        # Lock released — agent runs without holding it
        # ── MCP Server Discovery (lazy import, idempotent) ──
        # MUST run AFTER the HERMES_HOME mutation above — `discover_mcp_tools()`
        # reads `~/.hermes/config.yaml` via `get_hermes_home()`, which uses
        # `os.environ['HERMES_HOME']`.  Calling it before the mutation always
        # loaded the default profile's `mcp_servers`, even when the session
        # was stamped with a non-default profile.  See issue #1968.
        #
        # NOTE: `_servers` in `tools/mcp_tool.py` is a process-global registry
        # keyed by server name.  This means once profile A registers a server
        # named e.g. `postgres`, profile B's discovery sees it as already
        # connected and skips it — even if B's config points at a different
        # binary.  Fully fixing multi-profile concurrent use requires keying
        # `_servers` by `(profile_home, name)` upstream in hermes-agent; that
        # lives outside this WebUI repo.  This change fixes the headline bug
        # for users who run a single non-default profile per WebUI process.
        try:
            from tools.mcp_tool import discover_mcp_tools
            discover_mcp_tools()
        except Exception:
            pass  # MCP not available or not configured — non-fatal

        # Register a gateway-style notify callback so the approval system can
        # push the `approval` SSE event the moment a dangerous command is
        # detected, without waiting for the next on_tool() poll cycle.
        # Without this, the agent thread blocks inside the terminal tool
        # waiting for approval that the UI never knew to ask for, leaving
        # the chat stuck in "Thinking…" forever.
        _approval_registered = False
        _unreg_notify = None
        try:
            from tools.approval import (
                register_gateway_notify as _reg_notify,
                unregister_gateway_notify as _unreg_notify,
            )
            def _approval_notify_cb(approval_data):
                put('approval', approval_data)
            _reg_notify(session_id, _approval_notify_cb)
            _approval_registered = True
        except ImportError:
            logger.debug("Approval module not available, falling back to polling")

        _clarify_registered = False
        _unreg_clarify_notify = None
        try:
            from api.clarify import (
                register_gateway_notify as _reg_clarify_notify,
                unregister_gateway_notify as _unreg_clarify_notify,
            )

            def _clarify_notify_cb(clarify_data):
                put('clarify', clarify_data)

            _reg_clarify_notify(session_id, _clarify_notify_cb)
            _clarify_registered = True
        except ImportError:
            logger.debug("Clarify module not available, falling back to polling")

        def _clarify_callback_impl(question, choices, sid, cancel_evt, put_event):
            """Bridge Hermes clarify prompts to the WebUI."""
            timeout = _clarify_timeout_seconds()
            choices_list = [str(choice) for choice in (choices or [])]
            data = {
                'question': str(question or ''),
                'choices_offered': choices_list,
                'session_id': sid,
                'kind': 'clarify',
                'requested_at': time.time(),
                'timeout_seconds': timeout,
            }
            try:
                from api.clarify import submit_pending as _submit_clarify_pending, clear_pending as _clear_clarify_pending
            except ImportError:
                return (
                    "The user did not provide a response within the time limit. "
                    "Use your best judgement to make the choice and proceed."
                )

            entry = _submit_clarify_pending(sid, data)
            deadline = time.monotonic() + timeout
            while True:
                if cancel_evt.is_set():
                    _clear_clarify_pending(sid)
                    return (
                        "The user did not provide a response within the time limit. "
                        "Use your best judgement to make the choice and proceed."
                    )
                remaining = deadline - time.monotonic()
                if remaining <= 0:
                    _clear_clarify_pending(sid)
                    return (
                        "The user did not provide a response within the time limit. "
                        "Use your best judgement to make the choice and proceed."
                    )
                if entry.event.wait(timeout=min(1.0, remaining)):
                    response = str(entry.result or "").strip()
                    return (
                        response
                        or "The user did not provide a response within the time limit. "
                           "Use your best judgement to make the choice and proceed."
                    )

        try:
            _token_sent = False  # tracks whether any streamed tokens were sent
            _self_healed = False  # (#1401) prevents infinite self-heal retries
            # Per-message reasoning: dict maps assistant-message index → accumulated text
            # (#3587) replaces the flat _reasoning_text string so each intermediate
            # assistant turn (before tool calls) keeps its own reasoning segment.
            _reasoning_segments: dict = {}
            _current_reasoning_idx = 0
            _tool_boundary_advanced = False
            _live_tool_calls = []  # tool progress fallback when final messages omit tool IDs

            # Throttle: emit metering events at most every 100 ms so the per-message
            # TPS label feels live during fast token streams without flooding SSE.
            _metering_last_emit = [time.monotonic() - 1]  # fire immediately on first token
            _metering_output_deltas = [0]
            _metering_reasoning_deltas = [0]

            def _emit_metering():
                now = time.monotonic()
                if now - _metering_last_emit[0] < 0.1:
                    return
                _metering_last_emit[0] = now
                stats = meter().get_stats()
                stats['session_id'] = session_id
                stats['usage'] = _live_usage_snapshot()
                stats.setdefault('tps_available', False)
                stats.setdefault('estimated', False)
                put('metering', stats)

            def _compact_for_echo_compare(value: str) -> str:
                return re.sub(r'\s+', ' ', str(value or '')).strip()

            def _is_visible_output_echo(text: str) -> bool:
                candidate = _compact_for_echo_compare(text)
                if not candidate:
                    return False
                visible_tail = _compact_for_echo_compare(
                    STREAM_PARTIAL_TEXT.get(stream_id, '')[-max(len(str(text)) * 2, 512):]
                )
                return bool(visible_tail and visible_tail.endswith(candidate))

            def on_token(text):
                nonlocal _token_sent
                if text is None:
                    return  # end-of-stream sentinel
                _token_sent = True
                # Accumulate partial text so cancel_stream() can persist it (#893)
                if stream_id in STREAM_PARTIAL_TEXT:
                    STREAM_PARTIAL_TEXT[stream_id] += str(text)
                put('token', {'text': text})
                # Update live throughput from stream delta callbacks, not from
                # byte/character length. If a backend cannot provide live deltas,
                # the frontend hides TPS rather than showing an estimate.
                _metering_output_deltas[0] += 1
                meter().record_token(stream_id, _metering_output_deltas[0])
                _emit_metering()

            def on_reasoning(text):
                nonlocal _reasoning_segments, _current_reasoning_idx, _tool_boundary_advanced
                if text is None:
                    return
                _tool_boundary_advanced = False
                reasoning_delta = str(text)
                # Some runtimes mirror user-visible progress text through the
                # reasoning channel after it already streamed as normal assistant
                # output. Treat that as an echo, otherwise the UI renders the
                # same sentence again inside a Thinking card.
                if _is_visible_output_echo(reasoning_delta):
                    return
                # Accumulate into the current message's segment (#3587)
                _reasoning_segments[_current_reasoning_idx] = (
                    _reasoning_segments.get(_current_reasoning_idx, '') + reasoning_delta
                )
                # Mirror full concatenation to shared dict so cancel_stream() can persist
                # it (#1361 §A). Cancel only creates one partial message, so the flat
                # concatenation is correct there.
                if stream_id in STREAM_REASONING_TEXT:
                    STREAM_REASONING_TEXT[stream_id] += reasoning_delta
                put('reasoning', {'text': reasoning_delta})
                # Track reasoning deltas in the meter so live TPS reflects all AI output.
                _metering_reasoning_deltas[0] += 1
                meter().record_reasoning(stream_id, _metering_reasoning_deltas[0])
                _emit_metering()

            def on_interim_assistant(text, **cb_kwargs):
                nonlocal _current_reasoning_idx
                # Advance the per-message reasoning index unconditionally (#3587):
                # even if this callback fires with empty text, a new assistant
                # segment is starting and subsequent reasoning must be attributed
                # to the next message.
                _current_reasoning_idx += 1
                if text is None:
                    return
                visible = str(text).strip()
                if not visible:
                    return
                already_streamed = bool(cb_kwargs.get('already_streamed', False)) or _is_visible_output_echo(visible)
                put('interim_assistant', {
                    'text': visible,
                    'already_streamed': already_streamed,
                })

            # Pre-initialise the activity counter here so on_tool (which
            # closes over it) never captures an unbound name even if this
            # block is reordered later (Issue #765).
            _checkpoint_activity = [0]
            _live_tool_event_start_ids = set()
            _live_tool_event_complete_ids = set()

            def _tool_args_snapshot(args):
                args_snap = {}
                if isinstance(args, dict):
                    for k, v in list(args.items())[:4]:
                        s2 = str(v)
                        args_snap[k] = s2[:120] + ('...' if len(s2) > 120 else '')
                return args_snap

            def _record_live_tool_start(tool_call_id, name, args):
                if not tool_call_id or tool_call_id in _live_prompt_estimate_seen_ids:
                    return False
                _live_prompt_estimate_seen_ids.add(tool_call_id)
                _tool_call = {
                    'id': tool_call_id,
                    'type': 'function',
                    'function': {
                        'name': str(name or ''),
                        'arguments': json.dumps(args if isinstance(args, dict) else {}, ensure_ascii=False, sort_keys=True),
                    },
                }
                _bump_live_prompt_estimate([{
                    'role': 'assistant',
                    'content': '',
                    'tool_calls': [_tool_call],
                }])
                return True

            def _record_live_tool_complete(tool_call_id, name, function_result):
                if not tool_call_id:
                    return False
                _result_text = _tool_result_snippet(function_result)
                _bump_live_prompt_estimate([{
                    'role': 'tool',
                    'name': str(name or ''),
                    'tool_call_id': tool_call_id,
                    'content': _result_text,
                }])
                return True

            def on_tool(*cb_args, **cb_kwargs):
                nonlocal _reasoning_segments, _current_reasoning_idx, _tool_boundary_advanced
                event_type = None
                name = None
                preview = None
                args = None

                if len(cb_args) >= 4:
                    event_type, name, preview, args = cb_args[:4]
                elif len(cb_args) == 3:
                    name, preview, args = cb_args
                    event_type = 'tool.started'
                elif len(cb_args) == 2:
                    event_type, name = cb_args
                elif len(cb_args) == 1:
                    name = cb_args[0]
                    event_type = 'tool.started'

                if event_type in ('reasoning.available', '_thinking'):
                    reason_text = preview if event_type == 'reasoning.available' else name
                    if reason_text:
                        reason_delta = str(reason_text)
                        # Older tool-progress paths can mirror the same visible
                        # progress text already emitted through stream_delta_callback.
                        # Suppress those echoes like the dedicated reasoning callback.
                        if _is_visible_output_echo(reason_delta):
                            return
                        # Accumulate into the current message's segment (#3587)
                        _reasoning_segments[_current_reasoning_idx] = (
                            _reasoning_segments.get(_current_reasoning_idx, '') + reason_delta
                        )
                        # Mirror full concatenation to shared dict (#1361 §A)
                        if stream_id in STREAM_REASONING_TEXT:
                            STREAM_REASONING_TEXT[stream_id] += reason_delta
                        put('reasoning', {'text': reason_delta})
                        _metering_reasoning_deltas[0] += 1
                        meter().record_reasoning(stream_id, _metering_reasoning_deltas[0])
                        _emit_metering()
                    return

                # (#3587) Advance reasoning index at tool-call boundaries.
                # on_interim_assistant is suppressed for contentless tool-call
                # messages (run_agent.py:3834), so the index never advances
                # there. The first tool.started event after reasoning indicates
                # a new assistant message boundary.
                if not _tool_boundary_advanced and _current_reasoning_idx in _reasoning_segments:
                    _current_reasoning_idx += 1
                    _tool_boundary_advanced = True

                args_snap = _tool_args_snapshot(args)

                # Modern Hermes Agent builds can call both tool_progress_callback
                # and the structured tool_start/tool_complete callbacks for the
                # same tool. Prefer the structured path when it is supported so
                # the browser receives one tid-tagged tool card per real call.
                if event_type in (None, 'tool.started') and 'tool_start_callback' in _agent_params:
                    return

                if event_type in (None, 'tool.started'):
                    _live_tool_calls.append({
                        'name': name,
                        'args': args if isinstance(args, dict) else {},
                    })
                    # Mirror to shared dict so cancel_stream() can persist it (#1361 §B)
                    if stream_id in STREAM_LIVE_TOOL_CALLS:
                        STREAM_LIVE_TOOL_CALLS[stream_id].append({
                            'name': name,
                            'args': args if isinstance(args, dict) else {},
                            'done': False,
                        })
                    put('tool', {
                        'event_type': event_type or 'tool.started',
                        'name': name,
                        'preview': preview,
                        'args': args_snap,
                    })
                    _tool_stats = meter().get_stats()
                    _tool_stats['session_id'] = session_id
                    _tool_stats['usage'] = _live_usage_snapshot()
                    put('metering', _tool_stats)
                    # Fallback: poll for pending approval in case notify_cb wasn't
                    # registered (e.g. older approval module without gateway support).
                    try:
                        from tools.approval import has_pending as _has_pending, _pending, _lock
                        if _has_pending(session_id):
                            with _lock:
                                p = dict(_pending.get(session_id, {}))
                            if p:
                                put('approval', p)
                    except ImportError:
                        pass
                    return

                if event_type == 'tool.completed' and 'tool_complete_callback' in _agent_params:
                    return

                if event_type == 'tool.completed':
                    for live_tc in reversed(_live_tool_calls):
                        if live_tc.get('done'):
                            continue
                        if not name or live_tc.get('name') == name:
                            live_tc['done'] = True
                            live_tc['duration'] = cb_kwargs.get('duration')
                            live_tc['is_error'] = bool(cb_kwargs.get('is_error', False))
                            break
                    # Mirror done state to shared dict (#1361 §B)
                    if stream_id in STREAM_LIVE_TOOL_CALLS:
                        for shared_tc in reversed(STREAM_LIVE_TOOL_CALLS[stream_id]):
                            if shared_tc.get('done'):
                                continue
                            if not name or shared_tc.get('name') == name:
                                shared_tc['done'] = True
                                shared_tc['duration'] = cb_kwargs.get('duration')
                                shared_tc['is_error'] = bool(cb_kwargs.get('is_error', False))
                                break
                    # Signal the checkpoint thread that new work has completed (Issue #765).
                    # Each completed tool call is a meaningful unit of progress worth persisting.
                    _checkpoint_activity[0] += 1
                    put('tool_complete', {
                        'event_type': event_type,
                        'name': name,
                        'preview': preview,
                        'args': args_snap,
                        'duration': cb_kwargs.get('duration'),
                        'is_error': bool(cb_kwargs.get('is_error', False)),
                    })
                    # Mirror the todo tool's in-memory state into a
                    # dedicated SSE event so the Todos panel can update
                    # in real-time without waiting for the turn to
                    # settle. The helper guards on name=='todo', sends
                    # the full snapshot (idempotent under SSE replay)
                    # and swallows internal errors so emission never
                    # breaks tool delivery. Prefer the structured
                    # `result` kwarg from modern Hermes builds; fall
                    # back to the truncated `preview` only when the
                    # callback was invoked without one (older builds).
                    #
                    # Graceful degradation on old builds: `preview` is a
                    # truncated snippet, so its JSON is usually unparseable.
                    # parse_todo_tool_result() then returns None and NO
                    # todo_state event is emitted — live panel updates are
                    # silently unavailable on pre-`result` builds. This is
                    # intended: the panel still hydrates via cold-load on the
                    # next session GET; it just won't update mid-stream.
                    emit_todo_state(
                        put,
                        name=name,
                        function_result=(
                            cb_kwargs.get('result')
                            if cb_kwargs.get('result') is not None
                            else preview
                        ),
                        session_id=session_id,
                        stream_id=stream_id,
                    )
                    _tool_stats = meter().get_stats()
                    _tool_stats['session_id'] = session_id
                    _tool_stats['usage'] = _live_usage_snapshot()
                    put('metering', _tool_stats)
                    return

            def on_tool_start(tool_call_id, name, args):
                try:
                    _record_live_tool_start(tool_call_id, name, args)
                    if tool_call_id and tool_call_id not in _live_tool_event_start_ids:
                        _live_tool_event_start_ids.add(tool_call_id)
                        _live_tool_calls.append({
                            'name': name,
                            'args': args if isinstance(args, dict) else {},
                            'tid': tool_call_id,
                        })
                        # Mirror to shared dict so cancel_stream() can persist it (#1361 §B)
                        if stream_id in STREAM_LIVE_TOOL_CALLS:
                            STREAM_LIVE_TOOL_CALLS[stream_id].append({
                                'name': name,
                                'args': args if isinstance(args, dict) else {},
                                'done': False,
                                'tid': tool_call_id,
                            })
                        put('tool', {
                            'event_type': 'tool.started',
                            'name': name,
                            'preview': None,
                            'args': _tool_args_snapshot(args),
                            'tid': tool_call_id,
                        })
                    _tool_stats = meter().get_stats()
                    _tool_stats['session_id'] = session_id
                    _tool_stats['usage'] = _live_usage_snapshot()
                    put('metering', _tool_stats)
                except Exception:
                    logger.debug('Failed to update live prompt estimate on tool start', exc_info=True)

            def on_tool_complete(tool_call_id, name, args, function_result):
                try:
                    _record_live_tool_complete(tool_call_id, name, function_result)
                    if tool_call_id and tool_call_id not in _live_tool_event_complete_ids:
                        _live_tool_event_complete_ids.add(tool_call_id)
                        result_snippet = _tool_result_snippet(function_result)
                        for live_tc in reversed(_live_tool_calls):
                            if live_tc.get('done'):
                                continue
                            if live_tc.get('tid') == tool_call_id or (not live_tc.get('tid') and live_tc.get('name') == name):
                                live_tc['done'] = True
                                live_tc['snippet'] = result_snippet
                                break
                        if stream_id in STREAM_LIVE_TOOL_CALLS:
                            for shared_tc in reversed(STREAM_LIVE_TOOL_CALLS[stream_id]):
                                if shared_tc.get('done'):
                                    continue
                                if shared_tc.get('tid') == tool_call_id or (not shared_tc.get('tid') and shared_tc.get('name') == name):
                                    shared_tc['done'] = True
                                    shared_tc['snippet'] = result_snippet
                                    break
                        _checkpoint_activity[0] += 1
                        put('tool_complete', {
                            'event_type': 'tool.completed',
                            'name': name,
                            'preview': result_snippet,
                            'args': _tool_args_snapshot(args),
                            'tid': tool_call_id,
                            'is_error': False,
                        })
                        # Mirror the todo tool's in-memory state into
                        # a dedicated SSE event so the Todos panel can
                        # update in real-time without waiting for the
                        # turn to settle. See the legacy path above
                        # for the contract; the helper handles the
                        # name guard, payload shape, and swallow-all
                        # error policy.
                        emit_todo_state(
                            put,
                            name=name,
                            function_result=function_result,
                            session_id=session_id,
                            stream_id=stream_id,
                        )
                    _tool_stats = meter().get_stats()
                    _tool_stats['session_id'] = session_id
                    _tool_stats['usage'] = _live_usage_snapshot()
                    put('metering', _tool_stats)
                except Exception:
                    logger.debug('Failed to update live prompt estimate on tool completion', exc_info=True)

            _AIAgent = _get_ai_agent()
            if _AIAgent is None:
                raise ImportError(_aiagent_import_error_detail())

            # Initialize SessionDB so session_search works in WebUI sessions
            _state_db_path = (Path(_profile_home) / "state.db") if _profile_home else None
            _session_db = _build_session_db_for_stream(_state_db_path)
            resolved_model, resolved_provider, resolved_base_url = resolve_model_provider(
                model_with_provider_context(model, provider_context)
            )

            # Resolve API key via Hermes runtime provider (matches gateway behaviour).
            # Pass the resolved provider so non-default providers get their own credentials.
            resolved_api_key = None
            try:
                from api.oauth import resolve_runtime_provider_with_anthropic_env_lock
                from hermes_cli.runtime_provider import resolve_runtime_provider
                _rt = resolve_runtime_provider_with_anthropic_env_lock(
                    resolve_runtime_provider,
                    requested=resolved_provider,
                )
                resolved_api_key = _rt.get("api_key")
                if not resolved_provider:
                    resolved_provider = _rt.get("provider")
                if not resolved_base_url:
                    resolved_base_url = _rt.get("base_url")
            except Exception as _e:
                print(f"[webui] WARNING: resolve_runtime_provider failed: {_e}", flush=True)

            # Named custom providers (custom:slug) may not be resolvable by
            # hermes_cli.runtime_provider directly. Fall back to config.yaml
            # custom_providers[] so WebUI can pass explicit creds/base_url.
            resolved_provider, resolved_api_key, resolved_base_url = _resolve_custom_provider_runtime_overrides(
                resolved_provider, resolved_api_key, resolved_base_url
            )

            # Read per-profile config at call time (not module-level snapshot).
            # The streaming worker is a detached thread that does NOT inherit the
            # per-request thread-local profile context, so the ambient
            # get_config() would resolve the process-global (default) profile and
            # leak the wrong profile's toolsets / prefill / fallback config into
            # this run (issue #3294). Read the SESSION's own profile home
            # explicitly so toolsets and context match the profile the session
            # actually runs under.
            from api.config import get_config_for_profile_home as _get_config_for_home
            try:
                _cfg = _get_config_for_home(_profile_home)
            except Exception:
                from api.config import get_config as _get_config
                _cfg = _get_config()
            _prefill_context = _load_webui_prefill_context(_cfg)
            _prefill_messages = _prefill_messages_with_webui_context(_prefill_context, _cfg)
            _prefill_messages = _normalize_prefill_messages_before_user_turn(_prefill_messages)
            put('context_status', {
                'session_id': session_id,
                'prefill': _public_prefill_context_status(_prefill_context),
            })

            # Per-profile toolsets — use _resolve_cli_toolsets() so MCP
            # server toolsets are included, matching native CLI behaviour.
            from api.config import _resolve_cli_toolsets
            _toolsets = _resolve_cli_toolsets(_cfg)

            # Per-session toolset override (#493): if the session has
            # enabled_toolsets set, use that instead of the global config.
            try:
                from api.models import Session, SESSION_DIR
                _session_path = SESSION_DIR / f"{session_id}.json"
                if _session_path.exists():
                    _session_meta = Session.load_metadata_only(session_id)
                    # load_metadata_only returns a Session INSTANCE, not a dict.
                    # The previous .get('enabled_toolsets') raised AttributeError
                    # which was swallowed by the bare except below — the entire
                    # per-session toolset override silently no-op'd. Use
                    # getattr() to read the attribute correctly.
                    # (Opus pre-release advisor finding for v0.50.257.)
                    _override = getattr(_session_meta, 'enabled_toolsets', None) if _session_meta else None
                    if _override:
                        _toolsets = _override
            except Exception as _ts_err:
                print(f"[webui] WARNING: failed to read per-session toolsets for {session_id}: {_ts_err}", flush=True)

            # Fallback model chain from profile config (e.g. for rate-limit or
            # provider recovery). Match Hermes CLI/gateway semantics:
            # fallback_providers entries are tried first, then legacy
            # fallback_model entries are appended unless they duplicate an
            # earlier provider/model/base_url route.
            def _fallback_entries(_raw):
                if isinstance(_raw, dict):
                    _items = [_raw]
                elif isinstance(_raw, list):
                    _items = _raw
                else:
                    return []
                _entries = []
                for _entry in _items:
                    if not isinstance(_entry, dict):
                        continue
                    _provider = str(_entry.get('provider') or '').strip()
                    _model = str(_entry.get('model') or '').strip()
                    if not _provider or not _model:
                        continue
                    _entries.append({
                        'model': _model,
                        'provider': _provider,
                        'base_url': _entry.get('base_url'),
                        'api_key': _entry.get('api_key'),
                        'key_env': _entry.get('key_env'),
                    })
                return _entries

            _fallback_chain = []
            _fallback_seen = set()
            _fallback_resolved = None
            for _fallback_key in ('fallback_providers', 'fallback_model'):
                for _fb_entry in _fallback_entries(_cfg.get(_fallback_key)):
                    _identity = (
                        str(_fb_entry.get('provider') or '').strip().lower(),
                        str(_fb_entry.get('model') or '').strip().lower(),
                        str(_fb_entry.get('base_url') or '').strip().rstrip('/').lower(),
                    )
                    if _identity in _fallback_seen:
                        continue
                    _fallback_seen.add(_identity)
                    _fallback_chain.append(_fb_entry)
            _fallback_resolved = _fallback_chain or None

            # Build kwargs defensively — guard newer params so the WebUI
            # degrades gracefully when run against an older hermes-agent build.
            # (fixes: TypeError: AIAgent.__init__() got an unexpected keyword
            # argument 'credential_pool' — issue #772)
            import inspect as _inspect
            _agent_params = set(_inspect.signature(_AIAgent.__init__).parameters)

            # CLI-parity max-iteration budget: read config.yaml's
            # agent.max_turns and pass it to AIAgent when supported. Without
            # this WebUI-created agents silently use AIAgent's constructor
            # default (90), so long browser-originated tasks hit the
            # "maximum number of tool-calling iterations" summary path even
            # after the operator raises Hermes' global turn budget.
            _max_iterations_cfg = None
            try:
                _raw_max_iterations = None
                _agent_cfg_for_iterations = _cfg.get('agent', {}) if isinstance(_cfg, dict) else {}
                if isinstance(_agent_cfg_for_iterations, dict):
                    _raw_max_iterations = _agent_cfg_for_iterations.get('max_turns')
                if _raw_max_iterations is None and isinstance(_cfg, dict):
                    # Back-compat for older Hermes config files that used a
                    # root-level max_turns key.
                    _raw_max_iterations = _cfg.get('max_turns')
                if _raw_max_iterations is not None:
                    _parsed_max_iterations = int(_raw_max_iterations)
                    if _parsed_max_iterations > 0:
                        _max_iterations_cfg = _parsed_max_iterations
            except Exception:
                _max_iterations_cfg = None

            # CLI-parity max output cap: read config.yaml's max_tokens and pass
            # it to AIAgent when supported. Without this WebUI-created agents use
            # provider-native output ceilings (e.g. Claude via OpenRouter can
            # request 64k), which may turn an otherwise usable fallback into a
            # 402 "more credits / fewer max_tokens" failure.
            _max_tokens_cfg = None
            try:
                _raw_max_tokens = _cfg.get('max_tokens')
                if _raw_max_tokens is None:
                    _agent_cfg_for_tokens = _cfg.get('agent', {})
                    if isinstance(_agent_cfg_for_tokens, dict):
                        _raw_max_tokens = _agent_cfg_for_tokens.get('max_tokens')
                if _raw_max_tokens is not None:
                    _parsed_max_tokens = int(_raw_max_tokens)
                    if _parsed_max_tokens > 0:
                        _max_tokens_cfg = _parsed_max_tokens
            except Exception:
                _max_tokens_cfg = None

            # CLI-parity reasoning effort: read agent.reasoning_effort from the
            # active profile's config.yaml (the same key the CLI writes via
            # `/reasoning <level>`) and hand the parsed dict to AIAgent.  When
            # the key is absent or invalid, pass None → agent uses its default.
            try:
                _effort_cfg = _cfg.get('agent', {}) if isinstance(_cfg, dict) else {}
                _effort_raw = _effort_cfg.get('reasoning_effort') if isinstance(_effort_cfg, dict) else None
                _effort = coerce_reasoning_effort_for_model(
                    _effort_raw,
                    resolved_model,
                    provider_id=resolved_provider,
                    base_url=resolved_base_url,
                )
                _reasoning_config = parse_reasoning_effort(_effort)
            except Exception:
                _reasoning_config = None

            _agent_kwargs = dict(
                model=resolved_model,
                provider=resolved_provider,
                base_url=resolved_base_url,
                api_key=resolved_api_key,
                # Identify browser-originated sessions as WebUI so Hermes Agent
                # does not inject CLI-specific terminal/output guidance.
                platform='webui',
                quiet_mode=True,
                enabled_toolsets=_toolsets,
                fallback_model=_fallback_resolved,
                session_id=session_id,
                session_db=_session_db,
                prefill_messages=_prefill_messages,
                stream_delta_callback=on_token,
                reasoning_callback=on_reasoning,
                tool_progress_callback=on_tool,
                clarify_callback=(
                    lambda question, choices: _clarify_callback_impl(
                        question, choices, session_id, cancel_event, put
                    )
                ),
            )
            # reasoning_config has been an AIAgent param for several releases,
            # but guard defensively to avoid TypeError on an older agent build.
            if 'reasoning_config' in _agent_params and _reasoning_config is not None:
                _agent_kwargs['reasoning_config'] = _reasoning_config
            if 'prefill_messages' not in _agent_params:
                _agent_kwargs.pop('prefill_messages', None)
            if 'interim_assistant_callback' in _agent_params:
                _agent_kwargs['interim_assistant_callback'] = on_interim_assistant
            if 'tool_start_callback' in _agent_params:
                _agent_kwargs['tool_start_callback'] = on_tool_start
            if 'tool_complete_callback' in _agent_params:
                _agent_kwargs['tool_complete_callback'] = on_tool_complete
            if 'status_callback' in _agent_params:
                _agent_kwargs['status_callback'] = _agent_status_callback
            if 'max_iterations' in _agent_params and _max_iterations_cfg is not None:
                _agent_kwargs['max_iterations'] = _max_iterations_cfg
            if 'max_tokens' in _agent_params and _max_tokens_cfg is not None:
                _agent_kwargs['max_tokens'] = _max_tokens_cfg
            # Params added in newer hermes-agent — skip if not supported
            if 'api_mode' in _agent_params:
                _agent_kwargs['api_mode'] = _rt.get('api_mode')
            if 'acp_command' in _agent_params:
                _agent_kwargs['acp_command'] = _rt.get('command')
            if 'acp_args' in _agent_params:
                _agent_kwargs['acp_args'] = _rt.get('args')
            if 'credential_pool' in _agent_params:
                _agent_kwargs['credential_pool'] = _rt.get('credential_pool')
            # Pin Honcho memory sessions to the stable WebUI session ID.
            # Without this, 'per-session' Honcho strategy creates a new Honcho
            # session on every streaming request because HonchoSessionManager is
            # re-instantiated fresh each turn (#855).
            if 'gateway_session_key' in _agent_params:
                _agent_kwargs['gateway_session_key'] = session_id

            # ── Agent cache: reuse across messages in the same session ──
            # Mirrors gateway _agent_cache.  Keeps _user_turn_count alive so
            # injectionFrequency: "first-turn" actually suppresses after turn 1.
            if ephemeral:
                agent = _AIAgent(**_agent_kwargs)
                logger.debug('[webui] Created ephemeral agent for session %s', session_id)
            else:
                import hashlib as _hashlib
                import json as _json
                from api.config import SESSION_AGENT_CACHE, SESSION_AGENT_CACHE_LOCK
                _credential_pool = _rt.get('credential_pool')
                _sig_blob = _json.dumps([
                    resolved_model or '',
                    _agent_cache_api_key_sig(resolved_api_key, _credential_pool),
                    resolved_base_url or '',
                    resolved_provider or '',
                    _rt.get('api_mode') or '',
                    _rt.get('command') or '',
                    _rt.get('args') or [],
                    bool(_credential_pool),
                    _max_iterations_cfg or '',
                    _max_tokens_cfg or '',
                    _fallback_resolved or {},
                    sorted(_toolsets) if _toolsets else [],
                    _reasoning_config or {},
                    _public_prefill_context_status(_prefill_context),
                    # #1897: profile_home is part of the agent's identity because
                    # AIAgent caches `_cached_system_prompt` from `load_soul_md()`
                    # at construction time, sourced from HERMES_HOME. Same-session
                    # profile switches keep `session_id` stable, so without this
                    # field the cached agent silently retains the previous
                    # profile's SOUL.md (and any other profile-scoped context).
                    _profile_home or '',
                ], sort_keys=True)
                _agent_sig = _hashlib.sha256(_sig_blob.encode()).hexdigest()[:16]

                agent = None
                _identity_mismatch_entry = None
                with SESSION_AGENT_CACHE_LOCK:
                    _cached = SESSION_AGENT_CACHE.get(session_id)
                    if _cached and _cached[1] == _agent_sig:
                        _cached_agent = _cached[0]
                        if _cached_agent_matches_session(_cached_agent, session_id):
                            agent = _cached_agent
                            SESSION_AGENT_CACHE.move_to_end(session_id)  # LRU: mark as recently used
                            logger.debug('[webui] Reusing cached agent for session %s', session_id)
                        else:
                            _identity_mismatch_entry = SESSION_AGENT_CACHE.pop(session_id, None)
                            logger.warning(
                                '[webui] Evicted cached agent with mismatched session identity: cache_key=%s agent_session_id=%s',
                                session_id,
                                _cached_agent_session_identity(_cached_agent),
                            )
                    if agent is not None:
                        # Reopened/cache-hit sessions must register the agent
                        # so later lifecycle commits can find it.
                        try:
                            from api.session_lifecycle import register_agent
                            register_agent(session_id, agent)
                        except Exception:
                            logger.debug("Lifecycle register_agent failed for cached session %s", session_id, exc_info=True)

                if _identity_mismatch_entry is not None:
                    try:
                        _close_cached_agent_entry_at_session_boundary(session_id, _identity_mismatch_entry)
                    except Exception:
                        logger.debug("Failed to close identity-mismatched cached agent for session %s", session_id, exc_info=True)

                if agent is not None:
                    # Refresh volatile runtime credentials selected from provider
                    # pools without discarding cross-turn agent/provider state.
                    if not _refresh_cached_agent_runtime(agent, _agent_kwargs):
                        logger.warning(
                            '[webui] Cached agent runtime could not be safely refreshed; rebuilding agent for session %s',
                            session_id,
                        )
                        _stale_runtime_entry = None
                        with SESSION_AGENT_CACHE_LOCK:
                            _stale_runtime_entry = SESSION_AGENT_CACHE.pop(session_id, None)
                        if _stale_runtime_entry is not None:
                            try:
                                _close_cached_agent_entry_at_session_boundary(session_id, _stale_runtime_entry)
                            except Exception:
                                logger.debug("Failed to close stale-runtime cached agent for session %s", session_id, exc_info=True)
                        agent = None

                if agent is not None:
                    # Refresh per-turn callbacks — these close over request-scoped
                    # objects (put queue, cancel_event) that are new each request.
                    agent.stream_delta_callback = _agent_kwargs.get('stream_delta_callback')
                    agent.tool_progress_callback = _agent_kwargs.get('tool_progress_callback')
                    if hasattr(agent, 'tool_start_callback'):
                        agent.tool_start_callback = _agent_kwargs.get('tool_start_callback')
                    if hasattr(agent, 'tool_complete_callback'):
                        agent.tool_complete_callback = _agent_kwargs.get('tool_complete_callback')
                    if hasattr(agent, 'status_callback'):
                        agent.status_callback = _agent_kwargs.get('status_callback')
                    if hasattr(agent, 'interim_assistant_callback'):
                        agent.interim_assistant_callback = _agent_kwargs.get('interim_assistant_callback')
                    if hasattr(agent, 'reasoning_callback'):
                        agent.reasoning_callback = _agent_kwargs.get('reasoning_callback')
                    if hasattr(agent, 'clarify_callback'):
                        agent.clarify_callback = _agent_kwargs.get('clarify_callback')
                    if 'prefill_messages' in _agent_kwargs and hasattr(agent, 'prefill_messages'):
                        agent.prefill_messages = list(_agent_kwargs.get('prefill_messages') or [])
                    if _session_db is not None:
                        # Close any previously held SessionDB connection before
                        # replacing it. Without this, each streaming request creates
                        # a new SessionDB whose WAL handles leak indefinitely,
                        # eventually causing EMFILE crashes (#streaming FD leak).
                        if hasattr(agent, '_session_db') and agent._session_db is not None:
                            try:
                                agent._session_db.close()
                            except Exception:
                                pass
                        agent._session_db = _session_db
                    if hasattr(agent, '_api_call_count'):
                        agent._api_call_count = 0
                    # Reset interrupt state from a prior cancel so the reused
                    # agent does not think it is still interrupted.
                    if hasattr(agent, '_interrupted'):
                        agent._interrupted = False
                    if hasattr(agent, '_interrupt_message'):
                        agent._interrupt_message = None
                else:
                    agent = _AIAgent(**_agent_kwargs)
                    # Register the new agent with the memory lifecycle so
                    # its commit_memory_session() can be found later.
                    try:
                        from api.session_lifecycle import register_agent
                        register_agent(session_id, agent)
                    except Exception:
                        logger.debug("Lifecycle register_agent failed for new session %s", session_id, exc_info=True)
                    _evicted_items = []
                    # Snapshot the set of session_ids with a LIVE agent worker
                    # BEFORE taking SESSION_AGENT_CACHE_LOCK, so LRU eviction never
                    # closes an agent mid-run AND we never nest ACTIVE_RUNS_LOCK
                    # inside SESSION_AGENT_CACHE_LOCK (avoids any lock-ordering
                    # deadlock). A cancel/reconnect can drop STREAMS while the
                    # worker is still unwinding or blocked in a provider call, so
                    # ACTIVE_RUNS (worker lifecycle) is the authoritative liveness
                    # signal, not STREAMS. (#3536 review round 2)
                    _active_sids = set()
                    try:
                        from api.config import ACTIVE_RUNS, ACTIVE_RUNS_LOCK
                        with ACTIVE_RUNS_LOCK:
                            for _entry in (ACTIVE_RUNS or {}).values():
                                _sid = (_entry or {}).get("session_id")
                                if _sid:
                                    _active_sids.add(_sid)
                    except Exception:
                        _active_sids = set()
                    with SESSION_AGENT_CACHE_LOCK:
                        SESSION_AGENT_CACHE[session_id] = (agent, _agent_sig)
                        SESSION_AGENT_CACHE.move_to_end(session_id)  # LRU: mark as recently used
                        from api.config import SESSION_AGENT_CACHE_MAX
                        # Evict the oldest INACTIVE entries first. Walk LRU order
                        # (front = oldest); skip any session with a live run. If
                        # every over-cap entry is active, leave the cache
                        # temporarily above cap rather than close a live worker's
                        # agent — a later insertion/finalization trims it once the
                        # run ends.
                        while len(SESSION_AGENT_CACHE) > SESSION_AGENT_CACHE_MAX:
                            _evictable_sid = None
                            for _sid in list(SESSION_AGENT_CACHE.keys()):
                                if _sid not in _active_sids:
                                    _evictable_sid = _sid
                                    break
                            if _evictable_sid is None:
                                break  # all over-cap entries are active; defer
                            evicted_entry = SESSION_AGENT_CACHE.pop(_evictable_sid)
                            _evicted_items.append((_evictable_sid, evicted_entry))
                    # Commit and close evicted agents outside the cache lock so
                    # concurrent cache users are not blocked by provider I/O.
                    for _evicted_sid, _evicted_entry in _evicted_items:
                        try:
                            _evicted_agent = _evicted_entry[0] if isinstance(_evicted_entry, tuple) else None
                            _close_evicted_agent_at_session_boundary(_evicted_sid, _evicted_agent)
                        except Exception:
                            logger.debug("Failed to close evicted agent for session %s", _evicted_sid, exc_info=True)
                        logger.debug('[webui] Evicted LRU agent from cache: %s', _evicted_sid)
                    logger.debug('[webui] Created new agent for session %s', session_id)

            # Store agent instance for cancel/interrupt propagation
            with STREAMS_LOCK:
                AGENT_INSTANCES[stream_id] = agent
                # Check if cancel was requested during agent initialization
                if stream_id in CANCEL_FLAGS and CANCEL_FLAGS[stream_id].is_set():
                    # Cancel arrived during agent creation - interrupt immediately
                    try:
                        agent.interrupt("Cancelled before start")
                    except Exception:
                        logger.debug("Failed to interrupt agent before start")
                    with _agent_lock:
                        _finalize_cancelled_turn(s, ephemeral=ephemeral, message='Task cancelled before start.')
                    put('cancel', {'message': 'Cancelled by user'})
                    return

            # Prepend workspace context so the agent always knows which directory
            # to use for file operations, regardless of session age or AGENTS.md defaults.
            workspace_ctx = _workspace_context_prefix(str(s.workspace))
            workspace_system_msg = (
                f"Active workspace at session start: {s.workspace}\n"
                "Every user message is prefixed with [Workspace::v1: /absolute/path] indicating the "
                "workspace the user has selected in the web UI at the time they sent that message. "
                "This tag is the single authoritative source of the active workspace and updates "
                "with every message. It overrides any prior workspace mentioned in this system "
                "prompt, memory, or conversation history. Always use the value from the most recent "
                "[Workspace::v1: ...] tag as your default working directory for ALL file operations: "
                "write_file, read_file, search_files, terminal workdir, and patch. "
                "Never fall back to a hardcoded path when this tag is present."
            )
            # Resolve personality prompt from config.yaml agent.personalities
            # (matches hermes-agent CLI behavior — passes via ephemeral_system_prompt)
            _personality_prompt = None
            _pname = getattr(s, 'personality', None)
            if _pname:
                _agent_cfg = _cfg.get('agent', {})
                _personalities = _agent_cfg.get('personalities', {})
                if isinstance(_personalities, dict) and _pname in _personalities:
                    _pval = _personalities[_pname]
                    if isinstance(_pval, dict):
                        _parts = [_pval.get('system_prompt', '') or _pval.get('prompt', '')]
                        if _pval.get('tone'):
                            _parts.append(f'Tone: {_pval["tone"]}')
                        if _pval.get('style'):
                            _parts.append(f'Style: {_pval["style"]}')
                        _personality_prompt = '\n'.join(p for p in _parts if p)
                    else:
                        _personality_prompt = str(_pval)
            # Pass WebUI-only runtime guidance via ephemeral_system_prompt
            # (agent's own mechanism). This preserves any selected personality
            # while making long tool runs emit real user-visible interim text
            # through interim_assistant_callback instead of frontend guesses.
            agent.ephemeral_system_prompt = _webui_ephemeral_system_prompt(
                _personality_prompt,
                surface_context={
                    'source': 'webui',
                    'session_id': session_id,
                    'profile': getattr(s, 'profile', None),
                    'workspace': s.workspace,
                },
                config_data=_cfg,
            )
            _pending_started_at = getattr(s, 'pending_started_at', None)
            # Normal chat-start sets pending_started_at before spawning this thread;
            # fallback to now only for recovered/legacy flows where that marker is absent
            # or has been zeroed out (e.g. via a buggy migration / manual file edit).
            # Truthy-check covers None, missing-attr, and 0 uniformly.
            _turn_started_at = _pending_started_at if _pending_started_at else time.time()
            _external_state_messages = get_state_db_session_messages(getattr(s, 'session_id', None))
            _previous_messages = list(
                reconciled_state_db_messages_for_session(
                    s,
                    state_messages=_external_state_messages,
                ) or []
            )
            _previous_context_messages = _new_turn_context_from_messages(
                reconciled_state_db_messages_for_session(
                    s,
                    prefer_context=True,
                    state_messages=_external_state_messages,
                ),
                msg_text,
            )
            # Dedup before feeding to agent — merge_session_messages_append_only
            # can produce duplicates when context_messages and state.db share
            # messages with different timestamps.
            _previous_context_messages = _deduplicate_context_messages(_previous_context_messages)
            _pre_compression_count = getattr(
                getattr(agent, 'context_compressor', None),
                'compression_count', 0,
            )

            # ── Periodic checkpoint during streaming (Issue #765) ──
            # The agent works on an internal copy of s.messages during run_conversation()
            # so we cannot watch s.messages for growth. Instead, on_tool() increments
            # _checkpoint_activity[0] each time a tool call completes — that is the real
            # signal that progress has been made worth persisting.
            #
            # What gets saved on each checkpoint:
            #   - s.pending_user_message (already written before run starts)
            #   - s.pending_started_at / s.active_stream_id (turn bookkeeping)
            # On a server restart the UI will see a session with a pending message and no
            # response — better than a silent loss of the entire conversation turn.
            # The final s.save() at task completion handles the full session update + index.
            # (_checkpoint_stop is pre-initialised at the top of the outer try.)
            # (_checkpoint_activity is already initialised before on_tool().)

            def _periodic_checkpoint():
                last_saved_activity = 0
                while not _checkpoint_stop.wait(15):
                    try:
                        cur = _checkpoint_activity[0]
                        if cur > last_saved_activity:
                            with _agent_lock:
                                _save_streaming_checkpoint(s)
                            last_saved_activity = cur
                    except Exception as e:
                        logger.debug("Periodic checkpoint save failed: %s", e)

            _checkpoint_stop = threading.Event()
            # Persist the user message BEFORE streaming starts so it's durable even if
            # the server crashes before the first checkpoint fires (every 15s).
            with _agent_lock:
                s.save(touch_updated_at=True, skip_index=False)

            _ckpt_thread = threading.Thread(
                target=_periodic_checkpoint, daemon=True,
                name=f"ckpt-{session_id[:8]}",
            )
            _ckpt_thread.start()

            _process_notifications = _drain_webui_process_notifications(session_id)
            _agent_msg_text = msg_text
            if _process_notifications:
                _agent_msg_text = "\n\n".join([*_process_notifications, msg_text]).strip()
            user_message = _build_native_multimodal_message(workspace_ctx, _agent_msg_text, attachments, workspace, cfg=_cfg)
            _persistent_state_before = _persistent_state_snapshot(_profile_home)
            result = agent.run_conversation(
                user_message=user_message,
                system_message=workspace_system_msg,
                conversation_history=_sanitize_messages_for_api(_previous_context_messages, cfg=_cfg),
                task_id=session_id,
                persist_user_message=msg_text,
            )
            if cancel_event.is_set():
                if _checkpoint_stop is not None:
                    _checkpoint_stop.set()
                if _ckpt_thread is not None:
                    _ckpt_thread.join(timeout=15)
                if ephemeral:
                    _cleanup_ephemeral_cancelled_turn(s)
                else:
                    with _agent_lock:
                        _finalize_cancelled_turn(s, ephemeral=False)
                        try:
                            append_turn_journal_event_for_stream(
                                s.session_id,
                                stream_id,
                                {
                                    "event": "interrupted",
                                    "created_at": time.time(),
                                    "reason": "cancelled",
                                },
                            )
                        except Exception:
                            logger.debug("Failed to append cancelled turn journal event", exc_info=True)
                put('cancel', {'message': 'Cancelled by user'})
                return
            # ── Ephemeral mode (/btw): deliver answer, skip persistence, cleanup ──
            if ephemeral:
                _answer = ''
                for _m in reversed(result.get('messages') or []):
                    if isinstance(_m, dict) and _m.get('role') == 'assistant':
                        _answer = str(_m.get('content', ''))
                        break
                put('done', {
                    'session': {'session_id': session_id, 'messages': result.get('messages', [])},
                    'usage': {'input_tokens': 0, 'output_tokens': 0},
                    'ephemeral': True,
                    'answer': _answer,
                })
                if _checkpoint_stop is not None:
                    _checkpoint_stop.set()
                try:
                    import pathlib
                    pathlib.Path(s.path).unlink(missing_ok=True)
                except Exception:
                    pass
                return  # skip all normal persistence for ephemeral sessions
            if _checkpoint_stop is not None:
                _checkpoint_stop.set()
            if _ckpt_thread is not None:
                _ckpt_thread.join(timeout=15)
            if cancel_event.is_set():
                with _agent_lock:
                    _finalize_cancelled_turn(s, ephemeral=False)
                    try:
                        append_turn_journal_event_for_stream(
                            s.session_id,
                            stream_id,
                            {
                                "event": "interrupted",
                                "created_at": time.time(),
                                "reason": "cancelled",
                            },
                        )
                    except Exception:
                        logger.debug("Failed to append cancelled turn journal event", exc_info=True)
                put('cancel', {'message': 'Cancelled by user'})
                return
            with _agent_lock:
                if not ephemeral and not _stream_writeback_is_current(s, stream_id):
                    if _stream_writeback_can_supersede_recovery_marker(s, msg_text):
                        logger.info(
                            "Superseding stale recovery marker for session %s stream %s",
                            getattr(s, 'session_id', session_id),
                            stream_id,
                        )
                    else:
                        logger.info(
                            "Skipping stale stream writeback for session %s stream %s; active_stream_id=%s",
                            getattr(s, 'session_id', session_id),
                            stream_id,
                            getattr(s, 'active_stream_id', None),
                        )
                        return
                _result_messages = result.get('messages') or _previous_context_messages
                if cancel_event.is_set():
                    _finalize_cancelled_turn(s, ephemeral=False)
                    try:
                        append_turn_journal_event_for_stream(
                            s.session_id,
                            stream_id,
                            {
                                "event": "interrupted",
                                "created_at": time.time(),
                                "reason": "cancelled",
                            },
                        )
                    except Exception:
                        logger.debug("Failed to append cancelled turn journal event", exc_info=True)
                    put('cancel', {'message': 'Cancelled by user'})
                    return
                _next_context_messages = _restore_reasoning_metadata(
                    _previous_context_messages,
                    _result_messages,
                )
                _next_context_messages = _dedupe_replayed_context_messages(
                    _previous_context_messages,
                    _next_context_messages,
                )
                s.context_messages = _deduplicate_context_messages(_next_context_messages)
                s.messages = _merge_display_messages_after_agent_result(
                    _previous_messages,
                    _previous_context_messages,
                    _restore_display_reasoning_metadata(_previous_messages, _result_messages),
                    msg_text,
                )
                # Strip XML tool-call blocks from assistant message content.
                # DeepSeek and some other providers emit <function_calls>...</function_calls>
                # in the raw response text; this must be removed before the content is
                # saved to the session and displayed in the chat bubble. (#702)
                for _m in s.messages:
                    if isinstance(_m, dict) and _m.get('role') == 'assistant':
                        _raw_content = _m.get('content')
                        if isinstance(_raw_content, str):
                            _cleaned = _strip_xml_tool_calls(_raw_content)
                            if _cleaned != _raw_content:
                                _m['content'] = _cleaned
                        elif isinstance(_raw_content, list):
                            for _part in _raw_content:
                                if isinstance(_part, dict) and isinstance(_part.get('text'), str):
                                    _part['text'] = _strip_xml_tool_calls(_part['text'])

                # ── Handle context compression side effects ──
                # If compression fired inside run_conversation, the agent may have
                # rotated its session_id. Detect and fix the mismatch before any
                # terminal-failure return so snapshot preservation, continuation
                # registration, and subsequent error persistence all target the
                # continuation session instead of the stale parent.
                #
                # Lock migration: when session_id rotates, we alias the new ID to
                # the *same* Lock object under SESSION_AGENT_LOCKS so that
                # subsequent callers using _get_session_agent_lock(new_sid) get the
                # same Lock the streaming thread is already holding. We then pop
                # the old-id entry to prevent a leak. This is safe because we
                # already hold _agent_lock (the Lock object itself), so the
                # reference stays alive even after the dict entry is removed.
                # Concurrent readers that already looked up the old ID will still
                # see the same Lock object until they release it.
                _compression_origin_session_id = session_id
                _compression_continuation_session_id = None
                _agent_sid = getattr(agent, 'session_id', None)
                _compressed = False
                if _agent_sid and _agent_sid != session_id:
                    old_sid = session_id
                    new_sid = _agent_sid
                    _compression_origin_session_id = old_sid
                    _compression_continuation_session_id = new_sid
                    s.session_id = new_sid
                    # Carry profile identity across the compression boundary.
                    # Without this, s.profile stays None on the continuation
                    # session. On the next request, _run_agent_streaming calls
                    # get_hermes_home_for_profile(getattr(s, 'profile', None))
                    # which falls back to the default profile's HERMES_HOME.
                    # Memory writes then land in the wrong profile's MEMORY.md.
                    # Stamping here also ensures s.save() persists a non-null
                    # profile field to the continuation session's JSON file,
                    # covering the case where the session is later evicted from
                    # SESSIONS and reconstructed from disk via Session.load().
                    if not s.profile and _resolved_profile_name:
                        s.profile = _resolved_profile_name
                        logger.info(
                            "Stamped profile=%r on continuation session %s after compression",
                            _resolved_profile_name, new_sid,
                        )
                    # Preserve the original session file so the full pre-compression
                    # history survives even when summarisation fails. The previous
                    # implementation renamed old_sid.json → new_sid.json, which
                    # destroyed the only persistent copy of the uncompressed history
                    # before the new (possibly summary-only) session had been saved.
                    # If the LLM summariser also failed, the user was left with zero
                    # recoverable messages. (#2223)
                    # ---
                    # Archive the old session: write its current state to disk so
                    # the full conversation history survives even when context
                    # compression removes messages from the model's context. Skip
                    # the write when the file already contains up-to-date data
                    # (i.e. it was just saved by a checkpoint).
                    _preserve_pre_compression_snapshot(s, old_sid)
                    # The continuation is the live/tip session, not another archived
                    # snapshot. If the in-memory object was itself loaded from a
                    # pre-compression snapshot (possible on repeated compression chains
                    # or stale-cache repair paths), _preserve_pre_compression_snapshot()
                    # intentionally restores that old flag; clear it before saving the
                    # new continuation so sidebar/discoverability code does not hide the
                    # session that owns the completed turn.
                    s.pre_compression_snapshot = False
                    # Always link the continuation session to its immediate predecessor
                    # (the preserved snapshot). This OVERRIDES any prior
                    # parent_session_id because the new continuation IS the next link
                    # in the chain: traversal walks new → old → old.parent → ... root.
                    # Stage-353 Opus SHOULD-FIX: previous `if not s.parent_session_id`
                    # guard skipped this stamp on fork-of-fork compressions, so a
                    # subsequent traversal from the new continuation would jump
                    # over the just-preserved snapshot back to the original fork
                    # parent, losing access to the recoverable history in old_sid.json.
                    s.parent_session_id = old_sid
                    with LOCK:
                        cached_old_session = SESSIONS.pop(old_sid, None)
                        if cached_old_session is not None and cached_old_session is not s:
                            cached_old_sid = str(getattr(cached_old_session, 'session_id', '') or '')
                            if cached_old_sid == str(old_sid):
                                SESSIONS[old_sid] = cached_old_session
                            else:
                                logger.warning(
                                    "compression cache migration skipped stale object: old_sid=%s new_sid=%s cached_session_id=%s",
                                    old_sid,
                                    new_sid,
                                    cached_old_sid or None,
                                )
                        SESSIONS[new_sid] = s
                        SESSIONS.move_to_end(new_sid)
                        while len(SESSIONS) > SESSIONS_MAX:
                            SESSIONS.popitem(last=False)
                    # Migrate the per-session lock: alias new_sid to the held
                    # _agent_lock reference directly (not via old_sid lookup),
                    # then remove the old_sid entry to prevent a leak.
                    with SESSION_AGENT_LOCKS_LOCK:
                        SESSION_AGENT_LOCKS[new_sid] = _agent_lock
                        SESSION_AGENT_LOCKS.pop(old_sid, None)
                    # Migrate cached agent to the new session ID so the turn
                    # count survives context compression.
                    from api.config import SESSION_AGENT_CACHE, SESSION_AGENT_CACHE_LOCK
                    _skipped_agent_migration_entry = None
                    with SESSION_AGENT_CACHE_LOCK:
                        _cached_entry = SESSION_AGENT_CACHE.pop(old_sid, None)
                        if _cached_entry:
                            _cached_agent = _cached_entry[0]
                            if _cached_agent_matches_session(_cached_agent, new_sid):
                                SESSION_AGENT_CACHE[new_sid] = _cached_entry
                            else:
                                _skipped_agent_migration_entry = _cached_entry
                                logger.warning(
                                    '[webui] Skipped cached agent migration with mismatched session identity: old_sid=%s new_sid=%s agent_session_id=%s',
                                    old_sid,
                                    new_sid,
                                    _cached_agent_session_identity(_cached_agent),
                                )
                    if _skipped_agent_migration_entry is not None:
                        try:
                            _close_cached_agent_entry_at_session_boundary(old_sid, _skipped_agent_migration_entry)
                        except Exception:
                            logger.debug("Failed to close skipped compression-migration cached agent for session %s", old_sid, exc_info=True)
                    _compressed = True

                # ── Detect silent agent failure (no assistant reply produced) ──
                # When the agent catches an auth/network error internally it may return
                # an empty final_response without raising — the stream would end with
                # a done event containing zero assistant messages, leaving the user with
                # no feedback. Emit an apperror so the client shows an inline error.
                # Keep the current-turn assistant detection aligned with the
                # display-merge logic. A compacted or replayed result payload
                # is not always a simple append-only suffix, so use the
                # workspace-aware helper from this branch while still
                # preserving the pre-turn length for downstream self-heal
                # checks introduced on master.
                _all_result_messages = result.get('messages') or []
                _prev_len = len(_previous_context_messages)
                _assistant_added = _assistant_reply_added_after_current_turn(
                    _all_result_messages,
                    _previous_context_messages,
                    msg_text,
                )
                _terminal_failure = (
                    _agent_result_terminal_failure(result)
                    or (
                        not _token_sent
                        and _session_lacks_final_assistant_answer(_all_result_messages)
                    )
                )
                if _terminal_failure:
                    _assistant_added = False
                # _token_sent tracks whether on_token() was called (any streamed text)
                if _terminal_failure or (not _assistant_added and not _token_sent):
                    if cancel_event.is_set():
                        _finalize_cancelled_turn(s, ephemeral=ephemeral)
                        if not ephemeral:
                            try:
                                append_turn_journal_event_for_stream(
                                    s.session_id,
                                    stream_id,
                                    {
                                        "event": "interrupted",
                                        "created_at": time.time(),
                                        "reason": "cancelled",
                                    },
                                )
                            except Exception:
                                logger.debug("Failed to append cancelled turn journal event", exc_info=True)
                        put('cancel', {'message': 'Cancelled by user'})
                        return
                    _last_err = getattr(agent, '_last_error', None) or result.get('error') or ''
                    _err_str = str(_last_err) if _last_err else ''
                    _classification = _classify_provider_error(
                        _err_str,
                        _last_err,
                        silent_failure=not bool(_err_str),
                    )
                    _is_quota = _classification['type'] == 'quota_exhausted'
                    _is_auth = _classification['type'] == 'auth_mismatch'
                    if _is_quota:
                        _err_label = _classification['label']
                        _err_type = _classification['type']
                        _err_hint = _classification['hint']
                    elif _is_auth and not _self_healed:
                        # ── Credential self-heal on 401 (#1401) ──
                        # Before emitting the error, try re-reading credentials
                        # and retrying once with a fresh agent.
                        _heal_result = None
                        _heal_rt = _attempt_credential_self_heal(
                            resolved_provider or '', session_id, _agent_lock,
                        )
                        if _heal_rt is not None:
                            logger.info('[webui] self-heal: retrying stream after credential refresh')
                            # Rebuild runtime variables from the refreshed resolve
                            _rt = _heal_rt
                            resolved_api_key = _heal_rt.get('api_key')
                            if not resolved_provider:
                                resolved_provider = _heal_rt.get('provider')
                            if not resolved_base_url:
                                resolved_base_url = _heal_rt.get('base_url')
                            resolved_provider, resolved_api_key, resolved_base_url = _resolve_custom_provider_runtime_overrides(
                                resolved_provider, resolved_api_key, resolved_base_url
                            )
                            # Rebuild agent kwargs and create a fresh agent
                            _agent_kwargs['api_key'] = resolved_api_key
                            _agent_kwargs['base_url'] = resolved_base_url
                            _agent_kwargs['model'] = resolved_model
                            _agent_kwargs['provider'] = resolved_provider
                            _replace_session_db_in_kwargs(_agent_kwargs, _state_db_path)
                            if 'credential_pool' in _agent_params:
                                _agent_kwargs['credential_pool'] = _heal_rt.get('credential_pool')
                            agent = _AIAgent(**_agent_kwargs)
                            with STREAMS_LOCK:
                                AGENT_INSTANCES[stream_id] = agent
                            from api.config import SESSION_AGENT_CACHE as _SAC, SESSION_AGENT_CACHE_LOCK as _SAC_L
                            with _SAC_L:
                                _SAC[session_id] = (agent, _agent_sig)
                                _SAC.move_to_end(session_id)
                            # Retry the conversation once with fresh credentials
                            _self_healed = True
                            _token_sent = False
                            try:
                                _heal_result = agent.run_conversation(
                                    user_message=user_message,
                                    system_message=workspace_system_msg,
                                    conversation_history=_sanitize_messages_for_api(_previous_context_messages, cfg=_cfg),
                                    task_id=session_id,
                                    persist_user_message=msg_text,
                                )
                                _heal_all_msgs = _heal_result.get('messages') or []
                                _heal_ok = _has_new_assistant_reply(_heal_all_msgs, _prev_len) or _token_sent
                            except Exception as _retry_exc:
                                logger.warning(
                                    '[webui] self-heal: retry also failed: %s', _retry_exc,
                                )
                                _heal_ok = False
                            if _heal_ok and _heal_result is not None:
                                # Retry succeeded — replace result and skip error
                                result = _heal_result
                                # Fall through past the error-emission block;
                                # the post-result persistence code below will
                                # process ``result`` normally.  We jump past
                                # the ``put('apperror', ...)`` + ``return`` by
                                # NOT entering the ``if not _assistant_added``
                                # guard again — but we are already inside it.
                                # Solution: set _assistant_added so the guard
                                # evaluates False on next conceptual pass.
                                # Since we're in a flat block, directly run the
                                # post-result merge logic here.
                                _result_messages = result.get('messages') or _previous_context_messages
                                _next_context_messages = _restore_reasoning_metadata(
                                    _previous_context_messages,
                                    _result_messages,
                                )
                                _next_context_messages = _dedupe_replayed_context_messages(
                                    _previous_context_messages,
                                    _next_context_messages,
                                )
                                s.context_messages = _deduplicate_context_messages(_next_context_messages)
                                s.messages = _merge_display_messages_after_agent_result(
                                    _previous_messages,
                                    _previous_context_messages,
                                    _restore_reasoning_metadata(_previous_messages, _result_messages),
                                    msg_text,
                                )
                                # Skip the error block — jump directly to the
                                # normal post-result persistence path by
                                # leaving _assistant_added truthy (set below).
                                _assistant_added = True  # prevent re-entering guard
                        if not _assistant_added:
                            # Self-heal didn't apply or retry failed — emit error
                            _err_label = 'Authentication failed'
                            _err_type = 'auth_mismatch'
                            _err_hint = (
                                'The selected model may not be supported by your configured provider or '
                                'your API key is invalid. Run `hermes model` in your terminal to '
                                'update credentials, then restart the WebUI.'
                            )
                    elif _is_auth:
                        _err_label = 'Authentication failed'
                        _err_type = 'auth_mismatch'
                        _err_hint = (
                            'The selected model may not be supported by your configured provider or '
                            'your API key is invalid. Run `hermes model` in your terminal to '
                            'update credentials, then restart the WebUI.'
                        )
                    else:
                        _err_label = _classification['label']
                        _err_type = _classification['type']
                        _err_hint = _classification['hint']
                    # Skip error emission if credential self-heal succeeded
                    # (#1401) — _assistant_added is set True on successful retry.
                    if _assistant_added:
                        # Self-heal succeeded: messages are already merged into s,
                        # fall through to normal post-result persistence below.
                        pass
                    else:
                        _error_payload = _provider_error_payload(
                            _err_str or f'{_err_label}.',
                            _err_type,
                            _err_hint,
                        )
                        # Clear stream/pending state so the session does not appear
                        # "agent_running" on reload after a silent failure.
                        # Persist the error so it survives page reload.
                        # _error=True ensures _sanitize_messages_for_api excludes it from
                        # subsequent API calls so the LLM never sees its own error as prior context.
                        _materialize_pending_user_turn_before_error(s)
                        s.active_stream_id = None
                        s.pending_user_message = None
                        s.pending_attachments = []
                        s.pending_started_at = None
                        _error_message = {
                            'role': 'assistant',
                            'content': f'**{_err_label}:** {_error_payload.get("message") or _err_label}\n\n*{_err_hint}*',
                            'timestamp': int(time.time()),
                            '_error': True,
                        }
                        if _error_payload.get('details'):
                            _error_message['provider_details'] = _error_payload['details']
                        if _err_type == 'cancelled':
                            _error_message['provider_details_label'] = 'Cancellation details'
                        elif _err_type == 'interrupted':
                            _error_message['provider_details_label'] = 'Interruption details'
                        s.messages.append(_error_message)
                        try:
                            s.save()
                        except Exception:
                            pass
                        _error_payload['session'] = redact_session_data(
                            s.compact() | {'messages': s.messages, 'tool_calls': s.tool_calls}
                        )
                        _error_payload['session_id'] = s.session_id
                        _error_payload['old_session_id'] = _compression_origin_session_id
                        if _compression_continuation_session_id is not None:
                            _error_payload['new_session_id'] = _compression_continuation_session_id
                            _error_payload['continuation_session_id'] = _compression_continuation_session_id
                        put('apperror', _error_payload)
                        # Legacy #373 source tests and clients look for the
                        # no_response type; #1765 keeps that type but improves
                        # the catch-all label, hint, and provider details.
                        return  # apperror already closes the stream on the client side

                # ── Handle context compression side effects ──
                # Also detect compression via the result dict or compressor state
                if not _compressed:
                    _compressor = getattr(agent, 'context_compressor', None)
                    if _compressor and getattr(_compressor, 'compression_count', 0) > _pre_compression_count:
                        _compressed = True
                # Notify the frontend that compression happened
                if _compressed:
                    s.context_messages = _prune_context_tool_results_after_compression(
                        agent,
                        s.context_messages,
                    )
                    visible_after = visible_messages_for_anchor(s.messages, auto_compression=True)
                    # Find the LAST [CONTEXT COMPACTION] marker in s.messages
                    # and count visible messages before it. This is the correct
                    # anchor — it points to the compression boundary regardless
                    # of how many turns have been added since the boundary was
                    # established. Using len(visible_before)-1 is fragile when
                    # _previous_messages doesn't include markers or when extra
                    # messages accumulate between compression and the done event.
                    _last_marker_raw_idx = None
                    for _mi, _m in enumerate(s.messages):
                        if _is_context_compression_marker(_m):
                            _last_marker_raw_idx = _mi
                    if _last_marker_raw_idx is not None:
                        _visible_before_marker = visible_messages_for_anchor(
                            s.messages[:_last_marker_raw_idx], auto_compression=True,
                        )
                        s.compression_anchor_visible_idx = max(0, len(_visible_before_marker) - 1)
                        logger.info(
                            '[ANCHOR-MARKER] session=%s marker_raw=%d vis_before=%d anchor=%d',
                            getattr(s, 'session_id', '?'),
                            _last_marker_raw_idx,
                            len(_visible_before_marker),
                            s.compression_anchor_visible_idx,
                        )
                    else:
                        # Fallback: use pre-turn display messages
                        visible_before = visible_messages_for_anchor(
                            _previous_messages, auto_compression=True,
                        )
                        if visible_before:
                            s.compression_anchor_visible_idx = max(0, len(visible_before) - 1)
                        elif visible_after:
                            s.compression_anchor_visible_idx = 0
                        else:
                            s.compression_anchor_visible_idx = None
                        logger.info(
                            '[ANCHOR-FALLBACK] session=%s vis_before=%d anchor=%d',
                            getattr(s, 'session_id', '?'),
                            len(visible_before) if visible_before else 0,
                            s.compression_anchor_visible_idx if s.compression_anchor_visible_idx is not None else -1,
                        )
                    # Pick anchor_msg for _compression_anchor_message_key
                    _anchor_vis_idx = s.compression_anchor_visible_idx
                    if _anchor_vis_idx is not None and visible_after and _anchor_vis_idx < len(visible_after):
                        anchor_msg = visible_after[_anchor_vis_idx]
                    elif visible_after:
                        anchor_msg = visible_after[-1]
                    else:
                        anchor_msg = None
                    s.compression_anchor_message_key = (
                        _compression_anchor_message_key(anchor_msg) if anchor_msg else None
                    )
                    s.compression_anchor_summary = _compact_summary_text(
                        _compression_summary_from_messages(s.messages)
                        or _compression_summary_from_messages(s.context_messages)
                    )
                    if _compression_continuation_session_id is None:
                        _compression_continuation_session_id = s.session_id
                    put('compressed', {
                        'session_id': _compression_origin_session_id,
                        'old_session_id': _compression_origin_session_id,
                        'new_session_id': _compression_continuation_session_id,
                        'continuation_session_id': _compression_continuation_session_id,
                        'message': 'Compression finished',
                        'usage': _live_usage_snapshot(),
                    })

                # Stamp 'timestamp' on any messages that don't have one yet,
                # preserving transcript order across compacted/reconciled batches.
                _stamp_missing_message_timestamps(s.messages)
                # Only auto-generate title when still default; preserves user renames
                if s.title == 'Untitled' or s.title == 'New Chat' or not s.title:
                    s.title = title_from(s.messages, s.title)
                _looks_default = (s.title == 'Untitled' or s.title == 'New Chat' or not s.title)
                _looks_provisional = _is_provisional_title(s.title, s.messages)
                _invalid_existing_title = _looks_invalid_generated_title(s.title)
                _should_bg_title = (
                    (_looks_default or _looks_provisional or _invalid_existing_title)
                    and (not getattr(s, 'llm_title_generated', False) or _invalid_existing_title)
                )
                _u0 = ''
                _a0 = ''
                if _should_bg_title:
                    _u0, _a0 = _first_exchange_snippets(s.messages)
                # Read token/cost usage from the agent object (if available).
                # Per-turn overwrite (#1857): replace cumulative session totals with the
                # agent's most recent values, which already represent the current turn's
                # full prompt+completion (input_tokens are the entire context, not delta).
                # Defensive: only overwrite when the agent reports non-zero / non-None
                # values. A rebuilt-from-cache-miss agent (post-restart, post-LRU-eviction)
                # starts at zero; without this guard, the next turn would zero out the
                # persisted disk total before any new tokens were spent. Per Opus advisor
                # on stage-320: prevents restart-induced regression of session usage data.
                input_tokens = getattr(agent, 'session_prompt_tokens', 0) or 0
                output_tokens = getattr(agent, 'session_completion_tokens', 0) or 0
                estimated_cost = getattr(agent, 'session_estimated_cost_usd', None)
                cache_read_tokens = getattr(agent, 'session_cache_read_tokens', 0) or 0
                cache_write_tokens = getattr(agent, 'session_cache_write_tokens', 0) or 0
                prev_input_tokens = getattr(s, 'input_tokens', 0) or 0
                prev_cache_read_tokens = getattr(s, 'cache_read_tokens', 0) or 0
                turn_input_tokens = max(0, input_tokens - prev_input_tokens)
                turn_cache_read_tokens = max(0, cache_read_tokens - prev_cache_read_tokens)
                # Per-turn percent is computed server-side from persisted session
                # counters so the message label uses the same denominator as the
                # final usage payload even if the browser missed an intermediate event.
                cache_hit_percent = prompt_cache_hit_percent(cache_read_tokens, input_tokens)
                turn_cache_hit_percent = prompt_cache_hit_percent(turn_cache_read_tokens, turn_input_tokens)
                if input_tokens > 0:
                    s.input_tokens = input_tokens
                if output_tokens > 0:
                    s.output_tokens = output_tokens
                if estimated_cost is not None:
                    s.estimated_cost = estimated_cost
                if cache_read_tokens > 0:
                    s.cache_read_tokens = cache_read_tokens
                if cache_write_tokens > 0:
                    s.cache_write_tokens = cache_write_tokens
                # Persist tool-call summaries even when the final message history only
                # kept bare tool rows and omitted explicit assistant tool_call IDs.
                tool_calls = _extract_tool_calls_from_messages(
                    s.messages,
                    live_tool_calls=_live_tool_calls,
                )
                s.tool_calls = tool_calls
                s.active_stream_id = None
                s.pending_user_message = None
                s.pending_attachments = []
                s.pending_started_at = None
                # Tag the matching user message with attachment filenames for display on reload
                # Only tag a user message whose content relates to this turn's text
                # (msg_text is the full message including the [Attached files: ...] suffix)
                if attachments:
                    display_attachments = [_attachment_name(a) for a in attachments if _attachment_name(a)]
                    for m in reversed(s.messages):
                        if m.get('role') == 'user':
                            content = str(m.get('content', ''))
                            # Match if content is part of the sent message or vice-versa
                            base_text = msg_text.split('\n\n[Attached files:')[0].strip() if '\n\n[Attached files:' in msg_text else msg_text
                            if base_text[:60] in content or content[:60] in msg_text:
                                m['attachments'] = display_attachments
                                break
                # Persist reasoning trace in the session so it survives reload.
                # Must run BEFORE s.save() — otherwise the mutation lives only in
                # memory until the next turn's save, and the last-turn thinking card
                # is lost when the user reloads immediately after a response.
                #
                # #3455/#3599: split inline thinking blocks out of the saved
                # assistant content into m['reasoning'] (server-side twin of the JS
                # _splitThinkFromContent). Inline-thinking providers (e.g. MiniMax-M3)
                # otherwise leave the thinking trace in m['content'], bloating the
                # persisted session file 30-50% and bypassing the thinking card. The
                # #3587: use per-message segments so intermediate assistant turns
                # (before tool calls) each receive their own reasoning trace rather
                # than all reasoning being written only to the last assistant message.
                # Scope the walk to this turn's newly-appended assistant messages
                # to prevent cross-turn reasoning clobber (multi-turn off-by-N).
                if s.messages:
                    _prev_asst = sum(
                        1 for m in (_previous_messages or [])
                        if isinstance(m, dict) and m.get('role') == 'assistant'
                    )
                    _asst_count = 0
                    for _rm in s.messages:
                        if not (isinstance(_rm, dict) and _rm.get('role') == 'assistant'):
                            continue
                        _turn_idx = _asst_count
                        _asst_count += 1
                        if _turn_idx < _prev_asst:
                            continue  # prior-turn message — never touch its reasoning
                        _seg_reasoning = _reasoning_segments.get(_turn_idx - _prev_asst, '')
                        _existing_reasoning = _seg_reasoning or _rm.get('reasoning') or ''
                        _content = _rm.get('content')
                        if isinstance(_content, str) and _content:
                            _new_content, _merged_reasoning = _split_thinking_from_content(
                                _content, _existing_reasoning
                            )
                            _rm['content'] = _new_content
                            if _merged_reasoning:
                                _rm['reasoning'] = _merged_reasoning
                        elif _existing_reasoning:
                            _rm['reasoning'] = _existing_reasoning
                try:
                    _turn_duration_seconds = max(0.0, time.time() - float(_turn_started_at))
                except Exception:
                    _turn_duration_seconds = 0.0
                _turn_tps = None
                if output_tokens and _turn_duration_seconds > 0:
                    _turn_tps = round(float(output_tokens) / _turn_duration_seconds, 1)
                _gateway_routing = _extract_gateway_routing_metadata(
                    agent,
                    result,
                    requested_model=resolved_model or model,
                    requested_provider=resolved_provider,
                )
                if _gateway_routing:
                    s.gateway_routing = _gateway_routing
                    _history = list(getattr(s, 'gateway_routing_history', None) or [])
                    _history.append(_gateway_routing)
                    s.gateway_routing_history = _history[-50:]
                if s.messages:
                    for _dm in reversed(s.messages):
                        if isinstance(_dm, dict) and _dm.get('role') == 'assistant':
                            _dm['_turnDuration'] = round(_turn_duration_seconds, 3)
                            if _turn_tps is not None:
                                _dm['_turnTps'] = _turn_tps
                            if _gateway_routing:
                                _dm['_gatewayRouting'] = _gateway_routing
                            break
                # Persist context window data on the session so the context-ring
                # indicator survives a page reload (#1318). Must run BEFORE
                # s.save() for the same reason as the reasoning trace above.
                # The fields are captured into the SSE usage payload below; this
                # block writes them to the session itself so GET /api/session
                # returns them on reload instead of falling back to 0.
                _cc_for_save = getattr(agent, 'context_compressor', None)
                # Initialized before the compressor block so the #3256/#3263
                # threshold-rescale below is safe even when there is no
                # compressor (fresh agent / interrupted stream): _skip_cc_cl
                # stays False and _cc_cl stays 0, so the rescale is a no-op.
                _skip_cc_cl = False
                _cc_cl = 0
                if _cc_for_save:
                    _cc_cl = getattr(_cc_for_save, 'context_length', 0) or 0
                    # Same guard as routes._resolve_context_length_for_session_model:
                    # the agent-side context_compressor was constructed with the
                    # global model.context_length applied to EVERY model. If the
                    # session's model isn't model.default, that value is a stale
                    # cap (e.g. 232K) that would clobber the real 1M metadata
                    # on every stream end. In that case skip the compressor
                    # value and let the fallback resolver below recompute.
                    _skip_cc_cl = False
                    try:
                        _model_cfg_cc = _cfg.get('model', {}) if isinstance(_cfg, dict) else {}
                        if isinstance(_model_cfg_cc, dict):
                            _cfg_default_cc = str(_model_cfg_cc.get('default') or '').strip()
                            _raw_cfg_cl_cc = _model_cfg_cc.get('context_length')
                            try:
                                _cfg_cl_cc = int(_raw_cfg_cl_cc) if _raw_cfg_cl_cc is not None else 0
                            except (TypeError, ValueError):
                                _cfg_cl_cc = 0
                            _sess_model_cc = str(getattr(agent, 'model', resolved_model or '') or '').strip()
                            from api.routes import _model_matches_configured_default as _mmcd_cc
                            if (
                                _cfg_cl_cc > 0
                                and _cc_cl == _cfg_cl_cc
                                and _cfg_default_cc
                                and _sess_model_cc
                                and not _mmcd_cc(_sess_model_cc, _cfg_default_cc, resolved_provider or '')
                            ):
                                _skip_cc_cl = True
                    except Exception:
                        pass
                    if not _skip_cc_cl:
                        s.context_length = _cc_cl
                    s.threshold_tokens = getattr(_cc_for_save, 'threshold_tokens', 0) or 0
                    s.last_prompt_tokens = getattr(_cc_for_save, 'last_prompt_tokens', 0) or 0
                # Fallback: if the compressor didn't report a context_length
                # (fresh agent, interrupted stream, or compressor missing the
                # attribute), resolve it from the model's static metadata so
                # the indicator can still show a meaningful percentage.
                # Sourced from PR #1344 (@jasonjcwu) — extracted to a focused
                # follow-up after PR #1344 was closed as superseded by #1341.
                #
                # #1896: pass config_context_length, provider, and
                # custom_providers so explicit config overrides win over the
                # 256K default fallback. Without these, users on 1M-context
                # models who set `model.context_length: 1048576` (or rely on
                # a `custom_providers` per-model override) get a 256K
                # window in the persisted session and the SSE payload —
                # which then trips LCM auto-compress at ~25% of the wrong
                # value, cascading into 429 floods.
                #
                # #3256/#3263: ALSO run this fallback when _skip_cc_cl is true
                # (non-default model whose compressor carried the stale global
                # cap). Without this, a session that already had a stale 232K
                # context_length persisted keeps it forever — skipping the
                # compressor write removes the re-clobber but never recomputes
                # the real per-model window. Recompute and overwrite in that case.
                if (not getattr(s, 'context_length', 0)) or _skip_cc_cl:
                    try:
                        from agent.model_metadata import get_model_context_length
                        from api.routes import _context_length_lookup_inputs_for_model
                        _cfg_base_url = getattr(agent, 'base_url', '') or resolved_base_url or ''
                        _ctx_lookup = _context_length_lookup_inputs_for_model(
                            getattr(agent, 'model', resolved_model or '') or '',
                            resolved_provider,
                            base_url=_cfg_base_url,
                            cfg=_cfg if isinstance(_cfg, dict) else {},
                        )
                        _cfg_ctx_len = _ctx_lookup.config_context_length
                        _cfg_custom_providers = _ctx_lookup.custom_providers
                        _cfg_base_url = _ctx_lookup.base_url or _cfg_base_url
                        _cfg_provider = _ctx_lookup.provider or resolved_provider or ''
                        _resolved_cl = get_model_context_length(
                            getattr(agent, 'model', resolved_model or '') or '',
                            _cfg_base_url,
                            config_context_length=_cfg_ctx_len,
                            provider=_cfg_provider,
                            custom_providers=_cfg_custom_providers,
                        )
                        if _resolved_cl:
                            s.context_length = _resolved_cl
                    except TypeError:
                        # Older hermes-agent builds whose get_model_context_length
                        # signature pre-dates the config_context_length /
                        # custom_providers kwargs. Retry with the legacy 2-arg
                        # form so the indicator still resolves *something*.
                        try:
                            from agent.model_metadata import get_model_context_length as _legacy_cl
                            _resolved_cl = _legacy_cl(
                                getattr(agent, 'model', resolved_model or '') or '',
                                _cfg_base_url,
                            )
                            if _resolved_cl:
                                s.context_length = _resolved_cl
                        except Exception:
                            pass
                    except Exception:
                        # Older hermes-agent builds may not expose this helper.
                        # Better to leave context_length=0 than crash the save.
                        pass
                # #3256/#3263: when we skipped the stale compressor cap for a
                # non-default model and recomputed the real per-model window
                # above, rescale the persisted threshold_tokens to that real cap
                # so the auto-compress trigger and the reloaded context-ring
                # match the live snapshot (which already rescales). Without this,
                # a reload shows a smaller compression trigger than streaming did.
                # Only rescale when both the original cap and threshold are
                # positive; otherwise clear the threshold to 0 (consistent with
                # the live-snapshot path) rather than leave a stale value.
                if _skip_cc_cl:
                    _orig_cap = _cc_cl  # the stale global cap the compressor reported
                    _orig_thresh = getattr(s, 'threshold_tokens', 0) or 0
                    _real_cap = getattr(s, 'context_length', 0) or 0
                    if _real_cap > 0 and _orig_cap > 0 and _orig_thresh > 0:
                        s.threshold_tokens = int(_orig_thresh * _real_cap / _orig_cap)
                    else:
                        s.threshold_tokens = 0
                if not ephemeral and s.messages:
                    _latest_assistant_idx = next(
                        (idx for idx in range(len(s.messages) - 1, -1, -1)
                         if isinstance(s.messages[idx], dict) and s.messages[idx].get('role') == 'assistant'),
                        None,
                    )
                    if _latest_assistant_idx is not None:
                        _latest_assistant = s.messages[_latest_assistant_idx]
                        try:
                            append_turn_journal_event_for_stream(
                                s.session_id,
                                stream_id,
                                {
                                    "event": "assistant_started",
                                    "created_at": float(_latest_assistant.get('timestamp') or time.time()),
                                    "assistant_message_index": _latest_assistant_idx,
                                },
                            )
                        except Exception:
                            logger.debug("Failed to append assistant_started turn journal event", exc_info=True)
                if cancel_event.is_set():
                    _finalize_cancelled_turn(s, ephemeral=False)
                    try:
                        append_turn_journal_event_for_stream(
                            s.session_id,
                            stream_id,
                            {
                                "event": "interrupted",
                                "created_at": time.time(),
                                "reason": "cancelled",
                            },
                        )
                    except Exception:
                        logger.debug("Failed to append cancelled turn journal event", exc_info=True)
                    put('cancel', {'message': 'Cancelled by user'})
                    return
                s.save()
                if cancel_event.is_set():
                    _finalize_cancelled_turn(s, ephemeral=False)
                    try:
                        append_turn_journal_event_for_stream(
                            s.session_id,
                            stream_id,
                            {
                                "event": "interrupted",
                                "created_at": time.time(),
                                "reason": "cancelled",
                            },
                        )
                    except Exception:
                        logger.debug("Failed to append cancelled turn journal event", exc_info=True)
                    put('cancel', {'message': 'Cancelled by user'})
                    return
                if not ephemeral:
                    try:
                        append_turn_journal_event_for_stream(
                            s.session_id,
                            stream_id,
                            {
                                "event": "completed",
                                "created_at": time.time(),
                                "assistant_message_index": next(
                                    (idx for idx in range(len(s.messages) - 1, -1, -1)
                                     if isinstance(s.messages[idx], dict) and s.messages[idx].get('role') == 'assistant'),
                                    None,
                                ),
                            },
                        )
                    except Exception:
                        logger.debug("Failed to append completed turn journal event", exc_info=True)
                if not ephemeral:
                    # ── Memory-provider lifecycle: mark turn completed (CLI parity) ──
                    # Completed, non-ephemeral turns are marked dirty/uncommitted so
                    # boundary drains know there is work.  Per CLI semantics, the
                    # actual memory extraction/commit happens only at session boundaries
                    # (new session creation, LRU eviction, shutdown drain) — NOT after
                    # every completed turn.  This mirrors Hermes CLI where
                    # run_agent.py::_sync_external_memory_for_turn() records messages
                    # but only AIAgent.commit_memory_session()/shutdown_memory_provider()
                    # trigger extraction via provider on_session_end().  The mark is
                    # in-memory bookkeeping, not provider I/O, so keep it inside the
                    # per-session writeback lock to preserve completed-turn ordering.
                    try:
                        from api.session_lifecycle import mark_turn_completed
                        mark_turn_completed(s.session_id, agent=agent)
                    except Exception:
                        logger.debug("Memory lifecycle mark failed for session %s", s.session_id, exc_info=True)
                try:
                    _persistent_changes = _persistent_state_changes(
                        _persistent_state_before,
                        _persistent_state_snapshot(_profile_home),
                    )
                    if _persistent_changes.get("memory_saved"):
                        put("state_saved", {
                            "session_id": session_id,
                            "kind": "memory",
                            "action": "saved",
                        })
                    for _skill_change in _persistent_changes.get("skills") or []:
                        put("state_saved", {
                            "session_id": session_id,
                            "kind": "skill",
                            "action": _skill_change.get("action") or "updated",
                            "name": _skill_change.get("name") or "",
                        })
                except Exception:
                    logger.debug("Persistent state change detection failed for session %s", s.session_id, exc_info=True)
            # Sync to state.db for /insights (opt-in setting)
            try:
                from api.config import load_settings as _load_settings
                if _load_settings().get('sync_to_insights'):
                    from api.state_sync import sync_session_usage
                    sync_session_usage(
                        session_id=s.session_id,
                        input_tokens=s.input_tokens or 0,
                        output_tokens=s.output_tokens or 0,
                        estimated_cost=s.estimated_cost,
                        model=model,
                        title=s.title,
                        message_count=len(s.messages),
                        # #2762: pass the session's profile explicitly so the
                        # background-thread state.db lookup doesn't fall
                        # through to the process-global active profile and
                        # write to the wrong DB (TLS profile is set on the
                        # HTTP thread but not propagated to this worker).
                        profile=getattr(s, 'profile', None),
                    )
            except Exception:
                logger.debug("Failed to sync session to insights")
            usage = {
                'input_tokens': input_tokens,
                'output_tokens': output_tokens,
                'estimated_cost': estimated_cost,
                'cache_read_tokens': cache_read_tokens,
                'cache_write_tokens': cache_write_tokens,
                'cache_hit_percent': cache_hit_percent,
                'turn_cache_hit_percent': turn_cache_hit_percent,
                'duration_seconds': round(_turn_duration_seconds, 3),
            }
            if _turn_tps is not None:
                usage['tps'] = _turn_tps
            if _gateway_routing:
                usage['gateway_routing'] = _gateway_routing
            # Include context window data from the agent's compressor for the UI indicator.
            # The session-level persistence happens above (before s.save()) so the values
            # survive a page reload; this block only populates the live SSE usage payload.
            _cc = getattr(agent, 'context_compressor', None)
            if _cc:
                _cc_cl_sse = getattr(_cc, 'context_length', 0) or 0
                # #3256/#3263: remember the original compressor cap + threshold
                # so that if we drop the stale cap below and the fallback
                # resolves the real per-model window, we can rescale the
                # threshold consistently (the live snapshot already does this).
                _orig_cc_cl_sse = _cc_cl_sse
                _orig_cc_thresh_sse = getattr(_cc, 'threshold_tokens', 0) or 0
                _dropped_stale_cap_sse = False
                # Default-only guard (#3256): the agent-side context_compressor
                # is constructed in agent_init with the global model.context_length
                # applied unconditionally, so for non-default models its
                # context_length is the stale global cap (e.g. 232K) — surfacing
                # it via SSE makes the indicator show the wrong window even
                # after the session was correctly resized. Drop the compressor
                # value in that case and let the fallback resolver below recompute.
                try:
                    _model_cfg_sse = _cfg.get('model', {}) if isinstance(_cfg, dict) else {}
                    if isinstance(_model_cfg_sse, dict):
                        _cfg_default_sse = str(_model_cfg_sse.get('default') or '').strip()
                        _raw_cfg_cl_sse = _model_cfg_sse.get('context_length')
                        try:
                            _cfg_cl_sse = int(_raw_cfg_cl_sse) if _raw_cfg_cl_sse is not None else 0
                        except (TypeError, ValueError):
                            _cfg_cl_sse = 0
                        _sess_model_sse = str(getattr(agent, 'model', resolved_model or '') or '').strip()
                        from api.routes import _model_matches_configured_default as _mmcd_sse
                        if (
                            _cfg_cl_sse > 0
                            and _cc_cl_sse == _cfg_cl_sse
                            and _cfg_default_sse
                            and _sess_model_sse
                            and not _mmcd_sse(_sess_model_sse, _cfg_default_sse, resolved_provider or '')
                        ):
                            _cc_cl_sse = 0
                            _dropped_stale_cap_sse = True
                except Exception:
                    pass
                if _cc_cl_sse:
                    usage['context_length'] = _cc_cl_sse
                usage['threshold_tokens'] = getattr(_cc, 'threshold_tokens', 0) or 0
                usage['last_prompt_tokens'] = getattr(_cc, 'last_prompt_tokens', 0) or 0
            # Fallback: when the compressor is absent or reports context_length=0,
            # resolve the model's context window from metadata so the UI indicator
            # shows the correct percentage rather than overflowing against the 128K
            # JS default.  Mirrors the session-save fallback above (lines ~2205-2217).
            #
            # #1896: pass config_context_length, provider, and custom_providers so
            # explicit config overrides win over the 256K default fallback. The
            # SSE payload's `context_length` is what feeds the live token-usage
            # indicator, so a stale 256K here surfaces as the same wrong-window
            # display that motivates this fix.
            if not usage.get('context_length'):
                try:
                    from agent.model_metadata import get_model_context_length as _get_cl
                    from api.routes import _context_length_lookup_inputs_for_model
                    _ctx_lookup = _context_length_lookup_inputs_for_model(
                        getattr(agent, 'model', resolved_model or '') or '',
                        resolved_provider,
                        base_url=getattr(agent, 'base_url', '') or resolved_base_url or '',
                        cfg=_cfg if isinstance(_cfg, dict) else {},
                    )
                    _cfg_ctx_len = _ctx_lookup.config_context_length
                    _cfg_custom_providers = _ctx_lookup.custom_providers
                    _cfg_base_url = _ctx_lookup.base_url
                    _cfg_provider = _ctx_lookup.provider or resolved_provider or ''
                    try:
                        _fb_cl = _get_cl(
                            getattr(agent, 'model', resolved_model or '') or '',
                            _cfg_base_url,
                            config_context_length=_cfg_ctx_len,
                            provider=_cfg_provider,
                            custom_providers=_cfg_custom_providers,
                        )
                    except TypeError:
                        # Older hermes-agent builds: fall back to legacy 2-arg form.
                        _fb_cl = _get_cl(
                            getattr(agent, 'model', resolved_model or '') or '',
                            _cfg_base_url,
                        )
                    if _fb_cl:
                        usage['context_length'] = _fb_cl
                        # #3256/#3263: if we dropped the stale compressor cap
                        # for a non-default model, the threshold_tokens written
                        # above is still the stale compressor value. Rescale it
                        # to the real resolved window so the terminal `done`
                        # payload matches the live snapshot (which rescales) —
                        # otherwise messages.js overwrites S.lastUsage with the
                        # stale threshold and the indicator reverts on stream end.
                        if _dropped_stale_cap_sse and _orig_cc_cl_sse > 0 and _orig_cc_thresh_sse > 0:
                            usage['threshold_tokens'] = int(_orig_cc_thresh_sse * _fb_cl / _orig_cc_cl_sse)
                except Exception:
                    pass
            # Fallback: when last_prompt_tokens is missing (no compressor), use the
            # session-persisted value rather than letting the frontend fall back to
            # the cumulative input_tokens counter, which overflows for long sessions.
            if not usage.get('last_prompt_tokens'):
                _sess_lpt = getattr(s, 'last_prompt_tokens', 0) or 0
                if _sess_lpt:
                    usage['last_prompt_tokens'] = _sess_lpt
            # (reasoning trace already attached + saved above, before s.save())
            # Leftover-steer delivery: if a /steer was queued (via
            # api/chat/steer) but the agent finished its turn before
            # reaching a tool-result boundary that would consume it,
            # the text is still stashed in agent._pending_steer. Drain
            # it now and emit a pending_steer_leftover SSE event so the
            # frontend can queue it for the next turn — same fallback
            # path as the CLI in cli.py:8788-8794.
            try:
                _drain_pending_steer = getattr(agent, '_drain_pending_steer', None)
                _leftover = _drain_pending_steer() if _drain_pending_steer else None
                if _leftover:
                    put('pending_steer_leftover', {
                        'session_id': session_id,
                        'text': str(_leftover),
                    })
            except Exception:
                logger.debug("Failed to drain pending steer for session %s", session_id)
            # /goal parity: after a successful assistant turn, run the Hermes
            # GoalManager judge before terminal done/stream_end events. The
            # frontend surfaces the status line and queues continuation_prompt as
            # a normal next user message so /queue and user input keep priority.
            # #1932: only evaluate when the turn was goal-related (set via
            # STREAM_GOAL_RELATED or goal_related parameter).
            try:
                from api.goals import evaluate_goal_after_turn, has_active_goal

                if not goal_related or not has_active_goal(session_id, profile_home=_profile_home):
                    _goal_decision = {}
                else:
                    _last_goal_response = ''
                    for _goal_msg in reversed(s.messages or []):
                        if not isinstance(_goal_msg, dict) or _goal_msg.get('role') != 'assistant':
                            continue
                        _goal_content = _goal_msg.get('content', '')
                        if isinstance(_goal_content, list):
                            _goal_parts = []
                            for _goal_part in _goal_content:
                                if isinstance(_goal_part, dict):
                                    _goal_text = _goal_part.get('text') or _goal_part.get('content')
                                    if _goal_text:
                                        _goal_parts.append(str(_goal_text))
                            _last_goal_response = '\n'.join(_goal_parts)
                        else:
                            _last_goal_response = str(_goal_content or '')
                        break
                    put('goal', {
                        'session_id': session_id,
                        'state': 'evaluating',
                        'message': 'Evaluating goal progress…',
                        'message_key': 'goal_evaluating_progress',
                    })
                    _goal_decision = evaluate_goal_after_turn(
                        session_id,
                        _last_goal_response,
                        user_initiated=True,
                        profile_home=_profile_home,
                    )
                decision = _goal_decision or {}
                _goal_message = str(decision.get('message') or '').strip()
                if _goal_message:
                    put('goal', {
                        'session_id': session_id,
                        'state': 'continuing' if decision.get('should_continue') else 'idle',
                        'message': _goal_message,
                        'message_key': decision.get('message_key') or ('goal_continuing' if _goal_message else ''),
                        'message_args': decision.get('message_args') or [],
                        'decision': decision,
                    })
                if decision.get('should_continue'):
                    continuation_prompt = str(decision.get('continuation_prompt') or '').strip()
                    if continuation_prompt:
                        # #1932: mark this session as pending a goal continuation
                        # so the next /chat/start creates a goal-related stream.
                        PENDING_GOAL_CONTINUATION.add(session_id)
                        put('goal_continue', {
                            'session_id': session_id,
                            'continuation_prompt': continuation_prompt,
                            'text': continuation_prompt,
                            'message': _goal_message,
                            'message_key': decision.get('message_key') or 'goal_continuing',
                            'message_args': decision.get('message_args') or [],
                            'decision': decision,
                        })
            except Exception as _goal_exc:
                logger.debug("Goal continuation hook failed for session %s: %s", session_id, _goal_exc)
            raw_session = s.compact() | {'messages': s.messages, 'tool_calls': tool_calls}
            put('done', {'session': redact_session_data(raw_session), 'usage': usage})
            # Emit one last metering packet for the live message-header TPS label.
            meter_stats = meter().get_stats()
            meter_stats['session_id'] = session_id
            meter_stats.setdefault('tps_available', False)
            meter_stats.setdefault('estimated', False)
            put('metering', meter_stats)
            if _should_bg_title and _u0 and _a0:
                threading.Thread(
                    target=_run_background_title_update,
                    args=(s.session_id, _u0, _a0, str(s.title or '').strip(), put, agent),
                    daemon=True,
                ).start()
            else:
                # Use the original session_id parameter (never reassigned), not s.session_id
                # which may be rotated during context compression. The client captured
                # activeSid = original session_id so they must match for stream_end to close.
                put('stream_end', {'session_id': session_id})
                # Adaptive title refresh: re-generate title from latest exchange
                # every N exchanges (when enabled in settings). Runs after stream_end
                # so it doesn't block the stream.
                _maybe_schedule_title_refresh(s, put, agent)
        finally:
            # Stop the live metering ticker
            _metering_stop.set()
            # Unregister the gateway approval callback and unblock any threads
            # still waiting on approval (e.g. stream cancelled mid-approval).
            if _approval_registered and _unreg_notify is not None:
                try:
                    _unreg_notify(session_id)
                except Exception:
                    logger.debug("Failed to unregister approval callback")
            if _clarify_registered and _unreg_clarify_notify is not None:
                try:
                    _unreg_clarify_notify(session_id)
                except Exception:
                    logger.debug("Failed to unregister clarify callback")
            with _ENV_LOCK:
                for _key, _old_value in old_profile_env.items():
                    if _old_value is None: os.environ.pop(_key, None)
                    else: os.environ[_key] = _old_value
                if old_cwd is None: os.environ.pop('TERMINAL_CWD', None)
                else: os.environ['TERMINAL_CWD'] = old_cwd
                if old_exec_ask is None: os.environ.pop('HERMES_EXEC_ASK', None)
                else: os.environ['HERMES_EXEC_ASK'] = old_exec_ask
                if old_session_key is None: os.environ.pop('HERMES_SESSION_KEY', None)
                else: os.environ['HERMES_SESSION_KEY'] = old_session_key
                if old_session_id is None: os.environ.pop('HERMES_SESSION_ID', None)
                else: os.environ['HERMES_SESSION_ID'] = old_session_id
                if old_session_platform is None: os.environ.pop('HERMES_SESSION_PLATFORM', None)
                else: os.environ['HERMES_SESSION_PLATFORM'] = old_session_platform
                if old_hermes_home is None: os.environ.pop('HERMES_HOME', None)
                else: os.environ['HERMES_HOME'] = old_hermes_home

    except Exception as e:
        print('[webui] stream error:\n' + traceback.format_exc(), flush=True)
        err_str = str(e)
        # Sanitize HTML from provider error responses — some providers return
        # full HTML pages (e.g. nginx "404 page not found") instead of JSON errors.
        # Strip HTML tags to avoid rendering raw markup in the chat message.
        _stripped = re.sub(r'<[^>]+>', ' ', err_str)
        _stripped = re.sub(r'\s+', ' ', _stripped).strip()
        if _stripped != err_str:
            err_str = _stripped
        _exc_lower = err_str.lower()
        _classification = _classify_provider_error(err_str, e)
        if cancel_event.is_set():
            if s is not None:
                if _checkpoint_stop is not None:
                    _checkpoint_stop.set()
                if _ckpt_thread is not None:
                    _ckpt_thread.join(timeout=15)
                _lock_ctx = _agent_lock if _agent_lock is not None else contextlib.nullcontext()
                with _lock_ctx:
                    _finalize_cancelled_turn(s, ephemeral=ephemeral)
                    if not ephemeral:
                        try:
                            append_turn_journal_event_for_stream(
                                s.session_id,
                                stream_id,
                                {
                                    "event": "interrupted",
                                    "created_at": time.time(),
                                    "reason": "cancelled",
                                },
                            )
                        except Exception:
                            logger.debug("Failed to append cancelled turn journal event", exc_info=True)
            put('cancel', {'message': 'Cancelled by user'})
            return
        _exc_is_quota = _classification['type'] == 'quota_exhausted'
        # Exception quota text still includes: 'more credits' in _exc_lower, 'can only afford' in _exc_lower, 'fewer max_tokens' in _exc_lower.
        # Rate-limit detection remains guarded as: (not _exc_is_quota).
        _exc_is_rate_limit = (_classification['type'] == 'rate_limit') and (not _exc_is_quota)
        _exc_is_auth = _classification['type'] == 'auth_mismatch'  # detects '401' and 'unauthorized' via _classify_provider_error.
        _exc_is_not_found = _classification['type'] == 'model_not_found'  # detects '404', 'not found', 'does not exist', and 'invalid model'.
        _exc_is_cancelled = _classification['type'] == 'cancelled'
        _exc_is_interrupted = _classification['type'] == 'interrupted'

        # The user hint still points to Settings / `hermes model` from _classify_provider_error().
        if _exc_is_quota:
            _exc_label, _exc_type, _exc_hint = (
                _classification['label'], _classification['type'], _classification['hint'],
            )
        elif _exc_is_rate_limit:
            _exc_label, _exc_type, _exc_hint = (
                _classification['label'], _classification['type'], _classification['hint'],
            )
        elif _exc_is_auth:
            if not _self_healed:
                # ── Credential self-heal on 401 (#1401) ──
                _heal_rt = _attempt_credential_self_heal(
                    resolved_provider or '', session_id, _agent_lock,
                )
                if _heal_rt is not None:
                    logger.info('[webui] self-heal (except path): retrying stream after credential refresh')
                    _self_healed = True
                    # Rebuild runtime variables
                    _rt = _heal_rt
                    resolved_api_key = _heal_rt.get('api_key')
                    if not resolved_provider:
                        resolved_provider = _heal_rt.get('provider')
                    if not resolved_base_url:
                        resolved_base_url = _heal_rt.get('base_url')
                    resolved_provider, resolved_api_key, resolved_base_url = _resolve_custom_provider_runtime_overrides(
                        resolved_provider, resolved_api_key, resolved_base_url
                    )
                    # Build a fresh agent with the new credentials
                    _heal_kwargs = dict(_agent_kwargs) if '_agent_kwargs' in dir() else {}
                    _heal_kwargs['api_key'] = resolved_api_key
                    _heal_kwargs['base_url'] = resolved_base_url
                    _heal_kwargs['model'] = resolved_model
                    _heal_kwargs['provider'] = resolved_provider
                    _replace_session_db_in_kwargs(_heal_kwargs, _state_db_path)
                    if 'credential_pool' in _agent_params:
                        _heal_kwargs['credential_pool'] = _heal_rt.get('credential_pool')
                    _heal_agent = _AIAgent(**_heal_kwargs)
                    with STREAMS_LOCK:
                        AGENT_INSTANCES[stream_id] = _heal_agent
                    from api.config import SESSION_AGENT_CACHE as _SAC2, SESSION_AGENT_CACHE_LOCK as _SAC2_L
                    with _SAC2_L:
                        _SAC2[session_id] = (_heal_agent, _agent_sig)
                        _SAC2.move_to_end(session_id)
                    # Retry the conversation
                    _token_sent = False
                    try:
                        _heal_result = _heal_agent.run_conversation(
                            user_message=user_message,
                            system_message=workspace_system_msg,
                            conversation_history=_sanitize_messages_for_api(_previous_context_messages, cfg=_cfg),
                            task_id=session_id,
                            persist_user_message=msg_text,
                        )
                        # Retry succeeded — persist the result normally
                        if s is not None:
                            if _checkpoint_stop is not None:
                                _checkpoint_stop.set()
                            if _ckpt_thread is not None:
                                _ckpt_thread.join(timeout=15)
                            _lock_ctx = _agent_lock if _agent_lock is not None else contextlib.nullcontext()
                            with _lock_ctx:
                                if not ephemeral and not _stream_writeback_is_current(s, stream_id):
                                    logger.info(
                                        "Skipping stale stream self-heal writeback for session %s stream %s; active_stream_id=%s",
                                        getattr(s, 'session_id', session_id),
                                        stream_id,
                                        getattr(s, 'active_stream_id', None),
                                    )
                                    return
                                _result_messages = _heal_result.get('messages') or _previous_context_messages
                                _next_context_messages = _restore_reasoning_metadata(
                                    _previous_context_messages, _result_messages,
                                )
                                _next_context_messages = _dedupe_replayed_context_messages(
                                    _previous_context_messages,
                                    _next_context_messages,
                                )
                                s.context_messages = _deduplicate_context_messages(_next_context_messages)
                                s.messages = _merge_display_messages_after_agent_result(
                                    _previous_messages,
                                    _previous_context_messages,
                                    _restore_reasoning_metadata(_previous_messages, _result_messages),
                                    msg_text,
                                )
                                s.save()
                        logger.info('[webui] self-heal (except path): retry succeeded')
                        return  # skip error emission
                    except Exception as _retry_exc2:
                        logger.warning('[webui] self-heal (except path): retry failed: %s', _retry_exc2)
                        # Fall through to emit the original error
            # Self-heal didn't apply or retry failed — emit the auth error
            _exc_label, _exc_type, _exc_hint = (
                'Authentication error', 'auth_mismatch',
                'The selected model may not be supported by your configured provider. '
                'Run `hermes model` in your terminal to switch providers, then restart the WebUI.',
            )
        elif _exc_is_not_found:
            _exc_label, _exc_type, _exc_hint = (
                _classification['label'], _classification['type'], _classification['hint'],
            )
        elif _exc_is_cancelled or _exc_is_interrupted:
            _exc_label, _exc_type, _exc_hint = (
                _classification['label'], _classification['type'], _classification['hint'],
            )
        else:
            _exc_label, _exc_type, _exc_hint = 'Error', 'error', ''

        _error_payload = _provider_error_payload(err_str, _exc_type, _exc_hint)
        if s is not None:
            if _checkpoint_stop is not None:
                _checkpoint_stop.set()
            if _ckpt_thread is not None:
                _ckpt_thread.join(timeout=15)
            # Persist the error so it survives page reload.
            # _error=True ensures _sanitize_messages_for_api excludes it from subsequent
            # API calls so the LLM never sees its own error as prior context on the next turn.
            _lock_ctx = _agent_lock if _agent_lock is not None else contextlib.nullcontext()
            with _lock_ctx:
                if not ephemeral and not _stream_writeback_is_current(s, stream_id):
                    logger.info(
                        "Skipping stale stream error writeback for session %s stream %s; active_stream_id=%s",
                        getattr(s, 'session_id', session_id),
                        stream_id,
                        getattr(s, 'active_stream_id', None),
                    )
                    return
                _materialize_pending_user_turn_before_error(s)
                s.active_stream_id = None
                s.pending_user_message = None
                s.pending_attachments = []
                s.pending_started_at = None
                _error_message = {
                    'role': 'assistant',
                    'content': f'**{_exc_label}:** {_error_payload.get("message") or err_str}' + (f'\n\n*{_exc_hint}*' if _exc_hint else ''),
                    'timestamp': int(time.time()),
                    '_error': True,
                }
                if _error_payload.get('details'):
                    _error_message['provider_details'] = _error_payload['details']
                if _exc_type == 'cancelled':
                    _error_message['provider_details_label'] = 'Cancellation details'
                elif _exc_type == 'interrupted':
                    _error_message['provider_details_label'] = 'Interruption details'
                s.messages.append(_error_message)
                try:
                    s.save()
                except Exception:
                    pass
                if not ephemeral:
                    try:
                        append_turn_journal_event_for_stream(
                            s.session_id,
                            stream_id,
                            {
                                "event": "interrupted",
                                "created_at": time.time(),
                                "reason": _exc_type,
                            },
                        )
                    except Exception:
                        logger.debug("Failed to append interrupted turn journal event", exc_info=True)
            _error_payload['session_id'] = getattr(s, 'session_id', session_id)
            _error_payload['old_session_id'] = session_id
        put('apperror', _error_payload)
    finally:
        # Stop the periodic checkpoint thread before the final recovery path.
        # The checkpoint thread also uses the per-session lock; joining it first
        # avoids contending with checkpoint writes during stale-pending repair.
        if _checkpoint_stop is not None:
            _checkpoint_stop.set()
        if _ckpt_thread is not None:
            _ckpt_thread.join(timeout=15)
        if (s is not None
                and getattr(s, 'active_stream_id', None) == stream_id
                and getattr(s, 'pending_user_message', None)):
            update_active_run(stream_id, phase="finalizing")
            _last_resort_sync_from_core(s, stream_id, _agent_lock)
        _clear_thread_env()  # TD1: always clear thread-local context
        with STREAMS_LOCK:
            STREAMS.pop(stream_id, None)
            CANCEL_FLAGS.pop(stream_id, None)
            AGENT_INSTANCES.pop(stream_id, None)  # Clean up agent instance reference
            STREAM_PARTIAL_TEXT.pop(stream_id, None)  # Clean up partial text buffer (#893)
            STREAM_REASONING_TEXT.pop(stream_id, None)  # Clean up reasoning trace (#1361 §A)
            STREAM_LIVE_TOOL_CALLS.pop(stream_id, None)  # Clean up tool calls (#1361 §B)
            STREAM_GOAL_RELATED.pop(stream_id, None)  # Clean up goal-related flag (#1932)
            STREAM_LAST_EVENT_ID.pop(stream_id, None)  # Clean up event_id pointer (stage-364)
            unregister_active_run(stream_id)
            # NOTE: do NOT discard PENDING_GOAL_CONTINUATION here. The marker
            # is set by goal_continue (line ~3328) inside the SAME function
            # call and consumed atomically by `_start_chat_stream_for_session`
            # in routes.py (around line 6522) when the next stream starts.
            # Discarding here in the streaming worker's `finally` would
            # almost always race ahead of the frontend's SSE-receive →
            # POST /api/chat/start round-trip and erase the marker before
            # the next stream can read it, breaking the goal-continuation
            # chain. Stage-326 critical fix per Opus advisor review.

# ============================================================
# SECTION: HTTP Request Handler
# do_GET: read-only API endpoints + SSE stream + static HTML
# do_POST: mutating endpoints (session CRUD, chat, upload, approval)
# Routing is a flat if/elif chain. See ARCHITECTURE.md section 4.1.
# ============================================================


def _handle_chat_steer(handler, body: dict) -> bool:
    """Inject a /steer payload into the active agent for a session.

    Mirrors the CLI's `/steer <text>` command (cli.py:6140-6155):
      - Look up the cached AIAgent for the session (PR #1051's
        SESSION_AGENT_CACHE).
      - Verify a stream is currently active for this session.
      - Call agent.steer(text) — thread-safe, stashes text in
        _pending_steer for application at the next tool-result boundary.

    The agent's loop calls _apply_pending_steer_to_tool_results() at the
    end of every tool batch and appends the steer text to the last tool
    result's content with a marker, so the model sees the steer as part
    of the tool output on its next iteration. The user's stream is NOT
    interrupted.

    If no agent is cached, the agent is too old to support steer, or no
    stream is active, return {"accepted": False, "fallback": "<reason>"}
    so the frontend can fall back to interrupt or queue mode. The
    fallback path is the existing behaviour from PR #1062.

    Returns 200 with {"accepted": bool, "fallback": str|None,
    "stream_id": str|None}.
    """
    from api.helpers import j, bad
    from api import config as _cfg

    sid = str((body or {}).get("session_id", "") or "").strip()
    text = str((body or {}).get("text", "") or "").strip()
    if not sid:
        return bad(handler, "session_id required")
    if not text:
        return bad(handler, "text required")

    evicted_cached_entry = None
    with _cfg.SESSION_AGENT_CACHE_LOCK:
        cached = _cfg.SESSION_AGENT_CACHE.get(sid)
        if cached:
            agent = cached[0]
            if not _cached_agent_matches_session(agent, sid):
                evicted_cached_entry = _cfg.SESSION_AGENT_CACHE.pop(sid, None)
                logger.warning(
                    '[webui] Evicted cached agent before steer due to mismatched session identity: cache_key=%s agent_session_id=%s',
                    sid,
                    _cached_agent_session_identity(agent),
                )
                cached = None
    if evicted_cached_entry is not None:
        try:
            _close_cached_agent_entry_at_session_boundary(sid, evicted_cached_entry)
        except Exception:
            logger.debug("Failed to close steer identity-mismatched cached agent for session %s", sid, exc_info=True)
    if not cached:
        # No active agent for this session — caller falls back to interrupt
        return j(handler, {"accepted": False, "fallback": "no_cached_agent",
                           "stream_id": None})
    agent = cached[0]
    if not hasattr(agent, "steer"):
        # Older hermes-agent that pre-dates the steer() method
        return j(handler, {"accepted": False, "fallback": "agent_lacks_steer",
                           "stream_id": None})

    # Verify the agent is currently running. Use the session's
    # active_stream_id rather than calling load_session_locked() which
    # would block on the streaming thread's lock.
    try:
        s = get_session(sid)
    except KeyError:
        return j(handler, {"accepted": False, "fallback": "session_not_found",
                           "stream_id": None})
    active_stream_id = getattr(s, "active_stream_id", None) or None
    if not active_stream_id:
        return j(handler, {"accepted": False, "fallback": "not_running",
                           "stream_id": None})
    with _cfg.STREAMS_LOCK:
        stream_alive = active_stream_id in _cfg.STREAMS
    if not stream_alive:
        # Active stream id is stale — stream has ended; caller falls back
        return j(handler, {"accepted": False, "fallback": "stream_dead",
                           "stream_id": None})

    try:
        accepted = bool(agent.steer(text))
    except Exception as exc:
        logger.debug("agent.steer() raised for session=%s: %s", sid, exc)
        return j(handler, {"accepted": False, "fallback": "steer_error",
                           "stream_id": active_stream_id})

    return j(handler, {"accepted": accepted, "fallback": None,
                       "stream_id": active_stream_id})


def cancel_stream(stream_id: str) -> bool:
    """Signal an in-flight stream to cancel. Returns True if work was found.

    Eagerly releases the session lock (pops STREAMS/CANCEL_FLAGS/AGENT_INSTANCES
    and clears session.active_stream_id) so new /api/chat/start requests succeed
    immediately after cancel, even if the agent thread is still blocked.

    The worker thread's finally block uses .pop(key, None), so the double-pop is
    a safe no-op. Session cleanup runs outside STREAMS_LOCK to preserve lock
    ordering (streaming thread does LOCK → STREAMS_LOCK; inverting would deadlock).
    """
    from api import config as _live_config

    # Use module-level aliases (imported from api.config at startup).
    # In production these are always the same objects as api.config.STREAMS etc.
    # The fallback below handles a hypothetical future case where api.config's
    # state dicts are replaced at runtime (e.g. a future profile-reload path).
    # No production code currently does this; the fallback is defensive only.
    streams = STREAMS
    cancel_flags = CANCEL_FLAGS
    agent_instances = AGENT_INSTANCES
    partial_texts = STREAM_PARTIAL_TEXT
    streams_lock = STREAMS_LOCK
    if stream_id not in streams and getattr(_live_config, 'STREAMS', streams) is not streams:
        streams = _live_config.STREAMS
        cancel_flags = _live_config.CANCEL_FLAGS
        agent_instances = _live_config.AGENT_INSTANCES
        partial_texts = _live_config.STREAM_PARTIAL_TEXT
        streams_lock = _live_config.STREAMS_LOCK

    active_run_entry = None
    active_run_session_id = None
    stream_present = False
    agent = None
    q = None
    # Snapshots captured UNDER streams_lock so the worker's finally block (which
    # pops STREAM_PARTIAL_TEXT/REASONING/TOOL_CALLS under STREAMS_LOCK) cannot
    # race agent.interrupt() and clear these buffers before we read them — a
    # cancelled turn would otherwise silently lose its already-streamed
    # partial text / reasoning / tool-calls (Codex pre-release finding).
    _snap_partial_text = None
    _snap_reasoning = None
    _snap_tool_calls = None
    _snap_flag = None
    _snap_agent = None

    with streams_lock:
        stream_present = stream_id in streams
        # Snapshot everything the worker's finally could pop, WHILE the lock is
        # held and before any interrupt lets that finally run — for BOTH the
        # STREAMS-present and the ACTIVE_RUNS-only (detached) paths. The buffers
        # are keyed by stream_id independent of STREAMS membership, so a detached
        # cancel must snapshot them too or it loses the already-streamed text.
        _snap_flag = cancel_flags.get(stream_id)
        _snap_agent = agent_instances.get(stream_id)
        _snap_partial_text = partial_texts.get(stream_id, '')
        if not _snap_partial_text:
            _live_partials = getattr(_live_config, 'STREAM_PARTIAL_TEXT', partial_texts)
            if _live_partials is not partial_texts:
                _snap_partial_text = _live_partials.get(stream_id, '')
        _snap_reasoning = STREAM_REASONING_TEXT.get(stream_id, '')
        if not _snap_reasoning:
            _live_reasoning = getattr(_live_config, 'STREAM_REASONING_TEXT', STREAM_REASONING_TEXT)
            if _live_reasoning is not STREAM_REASONING_TEXT:
                _snap_reasoning = _live_reasoning.get(stream_id, '')
        _snap_tool_calls = list(STREAM_LIVE_TOOL_CALLS.get(stream_id, []) or [])
        if not _snap_tool_calls:
            _live_tools = getattr(_live_config, 'STREAM_LIVE_TOOL_CALLS', STREAM_LIVE_TOOL_CALLS)
            if _live_tools is not STREAM_LIVE_TOOL_CALLS:
                _snap_tool_calls = list(_live_tools.get(stream_id, []) or [])
        if stream_present:
            q = streams.get(stream_id)
        else:
            try:
                with _live_config.ACTIVE_RUNS_LOCK:
                    active_run_entry = dict((_live_config.ACTIVE_RUNS or {}).get(stream_id) or {})
            except Exception:
                active_run_entry = None
            if not active_run_entry:
                return False
            active_run_session_id = str(active_run_entry.get("session_id") or "").strip() or None

    if active_run_entry is None:
        try:
            with _live_config.ACTIVE_RUNS_LOCK:
                active_run_entry = dict((_live_config.ACTIVE_RUNS or {}).get(stream_id) or {})
        except Exception:
            active_run_entry = None
        if active_run_entry and not active_run_session_id:
            active_run_session_id = str(active_run_entry.get("session_id") or "").strip() or None

    # Set WebUI layer cancel flag. Prefer the snapshot captured under the lock;
    # fall back to a fresh lookup for the ACTIVE_RUNS-only path (stream absent).
    flag = _snap_flag if _snap_flag is not None else cancel_flags.get(stream_id)
    if flag:
        flag.set()

    # Interrupt the AIAgent instance to stop tool execution. Use the
    # lock-snapshot agent when the stream was present; otherwise fall back to
    # the session agent cache via the active-run session id.
    agent = _snap_agent if _snap_agent is not None else agent_instances.get(stream_id)
    if agent is None and active_run_session_id:
        try:
            with _live_config.SESSION_AGENT_CACHE_LOCK:
                cached = _live_config.SESSION_AGENT_CACHE.get(active_run_session_id)
            if cached and _cached_agent_matches_session(cached[0], active_run_session_id):
                agent = cached[0]
        except Exception:
            pass
    if agent:
        try:
            agent.interrupt("Cancelled by user")
        except Exception as e:
            # Log but don't block the cancel flow
            import logging
            logging.getLogger(__name__).debug(
                f"Failed to interrupt agent for stream {stream_id}: {e}"
            )
    elif stream_present:
        # Agent not yet stored - cancel_event flag will be checked by agent thread
        import logging
        logging.getLogger(__name__).debug(
            f"Cancel requested for stream {stream_id} before agent ready - "
            f"cancel_event flag set, will be checked on agent startup"
        )

    # Clear any pending clarify prompt so the blocked tool call can unwind.
    try:
        from api.clarify import clear_pending as _clear_clarify_pending

        _clarify_session_id = getattr(agent, "session_id", None) if agent else active_run_session_id
        if _clarify_session_id:
            _clear_clarify_pending(_clarify_session_id)
    except Exception:
        logger.debug("Failed to clear clarify prompt during cancel")

    # Capture the queue while the stream still exists, but do not emit the
    # terminal cancel event until the session cleanup below confirms the turn
    # is still active. Otherwise a late Stop click can race with a successful
    # worker save and show cancel in the client while persistence says done.
    _emit_cancel_event = True

    # ── Eager session lock release (fixes #653) ──────────────────────────
    # Pop stream state now so the 409 guard in routes.py sees the session
    # as idle and allows new /api/chat/start immediately after cancel,
    # even if the agent thread is still blocked in a C-level syscall.
    # The worker thread's finally block uses .pop(key, None) too, so a
    # double-pop here is safe (no-op).
    if stream_present:
        streams.pop(stream_id, None)
        cancel_flags.pop(stream_id, None)
        agent_instances.pop(stream_id, None)
    # STREAM_PARTIAL_TEXT is intentionally NOT popped here — the agent thread may
    # still be appending tokens, and the streaming finally block handles cleanup
    # when the thread exits. We already snapshotted the buffers under streams_lock
    # at the top of this function (see _snap_*), so they're safe to read below
    # even if the worker's finally has since popped the live maps.

    # Resolve the cancel session id and reuse the under-lock snapshots.
    # Session cleanup (get_session + save) must happen OUTSIDE the lock —
    # get_session() acquires LOCK, and the streaming thread does LOCK first
    # then STREAMS_LOCK, so inverting the order here would cause deadlock.
    _cancel_session_id = getattr(agent, 'session_id', None) if agent else None
    if not _cancel_session_id and active_run_session_id:
        _cancel_session_id = active_run_session_id
    # Use the snapshots captured under streams_lock above (the worker's finally
    # may have popped the live buffers by now via agent.interrupt()). For the
    # ACTIVE_RUNS-only path (stream absent) the snapshots are None → fall back to
    # a best-effort live read.
    _cancel_partial_text = _snap_partial_text if _snap_partial_text is not None else partial_texts.get(stream_id, '')
    if not _cancel_partial_text:
        live_partials = getattr(_live_config, 'STREAM_PARTIAL_TEXT', partial_texts)
        if live_partials is not partial_texts:
            _cancel_partial_text = live_partials.get(stream_id, '')
    # Capture reasoning trace and live tool calls (#1361 §A + §B)
    _cancel_reasoning = _snap_reasoning if _snap_reasoning is not None else STREAM_REASONING_TEXT.get(stream_id, '')
    if not _cancel_reasoning:
        live_reasoning = getattr(_live_config, 'STREAM_REASONING_TEXT', STREAM_REASONING_TEXT)
        if live_reasoning is not STREAM_REASONING_TEXT:
            _cancel_reasoning = live_reasoning.get(stream_id, '')
    _cancel_tool_calls = _snap_tool_calls if _snap_tool_calls is not None else STREAM_LIVE_TOOL_CALLS.get(stream_id, [])
    if not _cancel_tool_calls:
        live_tools = getattr(_live_config, 'STREAM_LIVE_TOOL_CALLS', STREAM_LIVE_TOOL_CALLS)
        if live_tools is not STREAM_LIVE_TOOL_CALLS:
            _cancel_tool_calls = live_tools.get(stream_id, [])

    # Session cleanup outside STREAMS_LOCK to preserve lock ordering.
    # Acquire the per-session _agent_lock too, mirroring every other session
    # writer (streaming success/error paths, periodic checkpoint, POST endpoints)
    # so the cancel-path mutation races neither the checkpoint thread nor
    # concurrent undo/retry calls.
    if _cancel_session_id:
        with _get_session_agent_lock(_cancel_session_id):
            try:
                _cs = get_session(_cancel_session_id)
                if not isinstance(getattr(_cs, 'messages', None), list):
                    _cs.messages = []
                if not _stream_writeback_is_current(_cs, stream_id):
                    # The stream has rotated to a different stream id (newer
                    # turn started, or the worker already finalized this one).
                    # Skip the cancel-marker append AND suppress the terminal
                    # cancel event so we don't contradict a possibly-already-
                    # delivered done payload (#2151 + #2154 / PR #2136).
                    logger.info(
                        "Skipping stale cancel writeback for session %s stream %s; active_stream_id=%s",
                        _cancel_session_id,
                        stream_id,
                        getattr(_cs, 'active_stream_id', None),
                    )
                    _emit_cancel_event = False
                    return True
                # ── Preserve the user's typed message before clearing pending state (#1298) ──
                # The agent's internal messages list (where the user message was appended at
                # the start of run_conversation()) may not have been merged back into
                # _cs.messages yet — cancel_stream() races with the streaming thread's final
                # _merge_display_messages_after_agent_result() call. Without this guard, the
                # user's message is lost: pending_user_message gets cleared below, and
                # _cs.messages still only contains messages from prior turns. The reporter
                # of #1298 sees their typed text vanish from chat after clicking Stop.
                #
                # Recovery rule: if pending_user_message is set AND the latest message in
                # _cs.messages isn't already a matching user turn, synthesize one. The
                # match check guards against double-append when the streaming thread DID
                # reach its merge step before cancel_stream() got the session lock.
                #
                # Wrapped in its own try/except so an unexpected _cs.messages shape (e.g.
                # in unit tests using Mock sessions) cannot escape and skip the rest of
                # the cleanup.
                try:
                    _pending_user = getattr(_cs, 'pending_user_message', None)
                    _pending_atts_raw = getattr(_cs, 'pending_attachments', None)
                    _pending_atts = list(_pending_atts_raw) if isinstance(_pending_atts_raw, (list, tuple)) else []
                    _pending_started = getattr(_cs, 'pending_started_at', None) or 0
                    _msgs_for_recovery = _cs.messages if isinstance(_cs.messages, list) else None
                    if _pending_user and _msgs_for_recovery is not None:
                        _last_user = None
                        for _m in reversed(_msgs_for_recovery):
                            if isinstance(_m, dict) and _m.get('role') == 'user':
                                _last_user = _m
                                break
                        _already_persisted = False
                        if _last_user is not None:
                            _last_content = _last_user.get('content')
                            _last_ts = _last_user.get('timestamp') or 0
                            # Only treat as already-persisted if the latest user turn
                            # was created AT OR AFTER the current turn's pending_started_at.
                            # An earlier turn whose content happens to be a substring
                            # (e.g. prior reply was "ok", user now types "ok please continue")
                            # must NOT short-circuit synthesis — that would re-introduce
                            # the data-loss bug this guard is supposed to prevent.
                            if isinstance(_last_content, str) and _last_ts >= _pending_started:
                                # Tolerate the workspace prefix the streaming thread prepends.
                                if _pending_user == _last_content or _pending_user in _last_content:
                                    _already_persisted = True
                        if not _already_persisted:
                            _user_turn: dict = {
                                'role': 'user',
                                'content': _pending_user,
                                'timestamp': int(time.time()),
                            }
                            if _pending_atts:
                                _user_turn['attachments'] = _pending_atts
                            _msgs_for_recovery.append(_user_turn)
                except Exception:
                    logger.debug(
                        "Failed to recover pending user message on cancel for %s",
                        _cancel_session_id,
                    )
                _cs.active_stream_id = None
                _cs.pending_user_message = None
                _cs.pending_attachments = []
                _cs.pending_started_at = None
                # Persist any partial assistant text that was streamed before cancel (#893).
                # Preserving partial content means the user sees what the agent had
                # produced rather than losing it entirely.  The marker is _partial=True
                # (for session/UI identification only) — NOT _error=True — so the partial
                # content IS kept in the history sent to the agent on the next user
                # message, letting the model continue from where it was cut off.
                # See the inner comment on the append call below for the rationale.
                #
                # #1361: Also persist reasoning trace and live tool calls that were
                # accumulated in thread-local variables but invisible to the cancel path.
                # This prevents paid-token data loss when cancelling mid-reasoning or
                # mid-tool-execution.
                partial_text = _cancel_partial_text.strip() if _cancel_partial_text else ''
                _stripped = ''
                if partial_text:
                    import re as _re
                    # Strip thinking/reasoning markup from partial content before saving.
                    # First pass: remove complete <thinking>...</thinking> blocks.
                    _stripped = _re.sub(r'<think(?:ing)?\b[^>]*>.*?</think(?:ing)?>',
                                        '', partial_text,
                                        flags=_re.DOTALL | _re.IGNORECASE).strip()
                    # Second pass: strip trailing UNCLOSED think/thinking block (the common
                    # cancel case — user stops mid-reasoning before the close tag appears).
                    _stripped = _re.sub(r'<think(?:ing)?\b[^>]*>.*',
                                        '', _stripped,
                                        flags=_re.DOTALL | _re.IGNORECASE).strip()
                # Determine whether there is anything to preserve beyond just the
                # cancel marker.  Content text, reasoning trace, or tool calls all
                # count (#1361 §C — previously only _stripped was checked, so a
                # reasoning-only or tool-only stream produced NO partial message).
                _has_reasoning = bool(_cancel_reasoning and _cancel_reasoning.strip())
                _has_tools = bool(_cancel_tool_calls)
                _cancel_marker_exists = _session_has_cancel_marker(_cs)
                _cancel_marker_idx = len(_cs.messages)
                if _cancel_marker_exists:
                    for _idx in range(len(_cs.messages) - 1, -1, -1):
                        _m = _cs.messages[_idx]
                        if not isinstance(_m, dict) or _m.get('role') != 'assistant':
                            continue
                        _content = str(_m.get('content') or '').strip().lower()
                        if any(pattern in _content for pattern in _CANCEL_MARKER_PATTERNS):
                            _cancel_marker_idx = _idx
                            break
                if _stripped or _has_reasoning or _has_tools:
                    _partial_msg: dict = {
                        'role': 'assistant',
                        'content': _stripped,  # may be empty for reasoning/tool-only turns
                        '_partial': True,
                        'timestamp': int(time.time()),
                    }
                    if _has_reasoning:
                        _partial_msg['reasoning'] = _cancel_reasoning.strip()
                    if _has_tools:
                        # NOTE: store under the private '_partial_tool_calls' key
                        # (NOT 'tool_calls'). The captured entries use the WebUI
                        # internal shape {name, args, done, duration, is_error}
                        # — they do NOT carry the OpenAI/Anthropic API id +
                        # function: {name, arguments} envelope. If we put them
                        # under 'tool_calls', `_sanitize_messages_for_api`
                        # (which whitelists 'tool_calls' via _API_SAFE_MSG_KEYS)
                        # would forward them to the next-turn LLM call and
                        # strict providers (OpenAI, Anthropic, Z.AI/GLM) would
                        # 400 on the malformed entries — turning a "data lost
                        # on cancel" bug into a "next message returns 400"
                        # bug, which is worse. The underscore-prefixed key is
                        # not in the whitelist, so sanitize strips it. The UI
                        # reads it via static/messages.js and renders it
                        # alongside the regular tool_calls path.
                        # (Opus pre-release review pass 2 of v0.50.251.)
                        _partial_msg['_partial_tool_calls'] = list(_cancel_tool_calls)
                    # Deduplicate against the full partial payload, not just
                    # non-empty content. Tool-only/reasoning-only partials have
                    # empty content, so a content-gated check can append the same
                    # failed turn repeatedly during cancel/replay recovery (#2592).
                    if not _partial_marker_already_present(
                        _cs.messages,
                        _partial_msg,
                        before_idx=_cancel_marker_idx,
                    ):
                        _cs.messages.insert(_cancel_marker_idx, _partial_msg)
                # Cancel marker — flagged _error=True so it is stripped from conversation
                # history on the next turn (prevents model from seeing "Task cancelled."
                # as a prior assistant reply).
                if not _cancel_marker_exists:
                    _cs.messages.append({
                        'role': 'assistant',
                        'content': _cancelled_turn_content(
                            'Task cancelled.',
                            _preferred_agent_display_name_for_session(_cs),
                        ),
                        '_error': True,
                        'provider_details': 'Task cancelled.',
                        'provider_details_label': 'Cancellation details',
                        'timestamp': int(time.time()),
                    })
                _cs.save()
            except Exception:
                logger.debug("Failed to clear session state on cancel for %s", _cancel_session_id)

    if _emit_cancel_event and q:
        _cancel_event_id = STREAM_LAST_EVENT_ID.get(stream_id)
        if _cancel_event_id and hasattr(q, "note_last_event_id"):
            try:
                q.note_last_event_id(_cancel_event_id)
            except Exception:
                logger.debug("Failed to note cancel event_id %s for stream %s", _cancel_event_id, stream_id, exc_info=True)
        try:
            q.put_nowait(('cancel', {'message': 'Cancelled by user'}))
        except Exception:
            logger.debug("Failed to put cancel event to queue")

    return True