EvoScientist-Multi/EvoScientist/llm/patches.py

"""Monkey-patches and utilities for third-party LangChain provider quirks.

All patches follow the same pattern: wrap an existing method/function to
fix upstream bugs, applied at import time or on first use.

Patches:
    - _patch_anthropic_proxy_compat: ccproxy dict→Pydantic model mismatch
    - _patch_openai_compat_content: list content→string for strict APIs
    - _patch_ccproxy_codex_compat: ccproxy model fixes + langchain None guard
    - _patch_ccproxy_system_to_developer: system→developer role for ccproxy
    - _patch_openai_capture_reasoning_content: capture provider
      reasoning_content into AIMessage.additional_kwargs (module-level,
      applied at import)
    - _patch_openrouter_strip_responses_reasoning: drop OpenAI-Responses
      encrypted reasoning items (rs_* id) from outgoing OpenRouter messages
      (store=false → "Item with id rs_... not found")
    - _patch_langgraph_goto_none_crash: handle Command(goto=None)
      defensively in langgraph and langgraph-api (langgraph 1.2.9 workaround)

Utilities:
    - _is_ccproxy_codex: detect ccproxy Codex OAuth adapter
    - _flatten_message_content: convert content blocks to plain string
"""

from __future__ import annotations

import dataclasses
import functools
import hashlib
import os
import sys
from collections.abc import (
    AsyncIterator,
    Awaitable,
    Callable,
    Iterator,
    Mapping,
    Sequence,
)
from contextvars import ContextVar
from typing import Any, cast

from langchain_core.messages import AIMessage, BaseMessage


# ---------------------------------------------------------------------------
# Patch: langchain-anthropic (>=1.3.4) calls .model_dump() on
# context_management / container objects returned by the Anthropic SDK.
# Proxies like ccproxy may return plain dicts which lack that method.
# We wrap the class method to pre-convert dicts before the original runs.
# ---------------------------------------------------------------------------
def _patch_anthropic_proxy_compat() -> None:
    try:
        import types as _types

        from langchain_anthropic.chat_models import ChatAnthropic as _CA

        _orig = _CA._make_message_chunk_from_anthropic_event

        def _safe(self: Any, event: Any, *args: Any, **kwargs: Any) -> Any:
            for obj, attrs in [
                (event, ("context_management",)),
                (getattr(event, "delta", None), ("container",)),
            ]:
                if obj is None:
                    continue
                for attr in attrs:
                    val = getattr(obj, attr, None)
                    if isinstance(val, dict):
                        d = val.copy()
                        setattr(
                            obj,
                            attr,
                            _types.SimpleNamespace(model_dump=lambda d=d, **kw: d),
                        )
            return _orig(self, event, *args, **kwargs)

        _CA._make_message_chunk_from_anthropic_event = _safe
    except Exception:
        pass


_patch_anthropic_proxy_compat()


# ---------------------------------------------------------------------------
# Patch: ccproxy-api 0.2.7 Codex compatibility.
#
# 1) ResponseObject.output is required but upstream may omit it → 502.
#    Fix: make output default to [].
# 2) CodexMessage.role only allows "user"/"assistant" → 400 on system msgs.
#    Fix: widen to also accept "system" and "developer".
# 3) langchain-openai iterates response.output which can be None after the
#    proxy strips it.  Fix: guard in _construct_lc_result_from_responses_api.
# ---------------------------------------------------------------------------
def _patch_ccproxy_codex_compat() -> None:
    """Patch ccproxy-api models for Responses API compatibility."""
    # 1) Make ResponseObject.output optional (default=[])
    try:
        import ccproxy.llms.models.openai as _oai_mod

        _OrigResponse = _oai_mod.ResponseObject

        from pydantic import Field as _PydanticField

        class _PatchedResponseObject(_OrigResponse):  # type: ignore[misc]
            output: list = _PydanticField(default_factory=list)  # type: ignore[assignment]

            model_config = _OrigResponse.model_config.copy()

        _PatchedResponseObject.__name__ = "ResponseObject"
        _PatchedResponseObject.__qualname__ = "ResponseObject"
        _oai_mod.ResponseObject = _PatchedResponseObject  # type: ignore[misc]

        # Also patch modules that import ResponseObject directly
        for _mod_path in (
            "ccproxy.llms.formatters.openai_to_openai.responses",
            "ccproxy.llms.formatters.anthropic_to_openai.responses",
        ):
            try:
                import importlib

                _mod = importlib.import_module(_mod_path)
                if hasattr(_mod, "ResponseObject"):
                    _mod.ResponseObject = _PatchedResponseObject  # type: ignore[attr-defined]
            except Exception:
                pass
    except Exception:
        pass

    # 2) Widen CodexMessage.role to accept system/developer
    try:
        from typing import Annotated, Literal

        import ccproxy.plugins.codex.models as _codex_mod

        _OrigMessage = _codex_mod.CodexMessage

        from pydantic import Field as _Field

        class _PatchedCodexMessage(_OrigMessage):  # type: ignore[misc]
            role: Annotated[  # type: ignore[assignment]
                Literal["user", "assistant", "system", "developer"],
                _Field(description="Message role"),
            ]

        _PatchedCodexMessage.__name__ = "CodexMessage"
        _PatchedCodexMessage.__qualname__ = "CodexMessage"
        _codex_mod.CodexMessage = _PatchedCodexMessage  # type: ignore[misc]
    except Exception:
        pass

    # 3) Fix StreamingBufferService returning response.completed event
    #    whose output is None/empty, instead of using accumulated outputs.
    try:
        from ccproxy.llms.streaming.accumulators import ResponsesAccumulator

        _orig_get = ResponsesAccumulator.get_completed_response

        def _patched_get(self: Any) -> dict | None:
            result = _orig_get(self)
            if result is not None:
                output = result.get("output")
                if output is None:
                    # output field lost — force rebuild from accumulated items
                    return None
            return result

        ResponsesAccumulator.get_completed_response = _patched_get  # type: ignore[assignment]
    except Exception:
        pass

    # 4) Guard langchain-openai against None output (final safety net)
    try:
        import langchain_openai.chat_models.base as _base

        _orig_construct = _base._construct_lc_result_from_responses_api

        def _safe(response: Any, *args: Any, **kwargs: Any) -> Any:
            if response.output is None:
                response.output = []
            return _orig_construct(response, *args, **kwargs)

        _base._construct_lc_result_from_responses_api = _safe
    except Exception:
        pass


_patch_ccproxy_codex_compat()


# ---------------------------------------------------------------------------
# Utility: detect ccproxy's Codex adapter (as opposed to generic localhost).
# ---------------------------------------------------------------------------
def _is_ccproxy_codex() -> bool:
    """Return True if the OpenAI endpoint is ccproxy's Codex adapter.

    Checks for the ccproxy-specific markers set by ``setup_codex_env()``
    in ``ccproxy_manager.py``: the sentinel API key and the ``/codex/v1``
    path.  Plain localhost endpoints (vLLM, Ollama, etc.) are not affected.
    """
    base_url = os.environ.get("OPENAI_BASE_URL", "")
    api_key = os.environ.get("OPENAI_API_KEY", "")
    return (
        ("127.0.0.1" in base_url or "localhost" in base_url)
        and api_key == "ccproxy-oauth"
        and "/codex/" in base_url
    )


# ---------------------------------------------------------------------------
# Utility + Patch: Flatten list content to strings for OpenAI-compatible APIs.
# DeepSeek, SiliconFlow, etc. reject assistant messages whose content is a
# list rather than a string.  Image and file (PDF/document) blocks are
# preserved, not flattened away.
# ---------------------------------------------------------------------------
_SKIP_CONTENT_TYPES = frozenset({"thinking", "reasoning", "reasoning_content"})

# Media block types preserved when flattening (positive allowlist;
# thinking/reasoning still dropped).  Images + files (PDF/documents): both
# serialize on OpenAI-compatible APIs and capable models read them.  `video` is
# deliberately EXCLUDED (langchain-openai raises ValueError on it); `audio` is
# omitted (almost no model support).  is_data_content_block is unreliable
# (False for OpenAI image_url / Anthropic image-source).  Kept as separate
# image/file sets so the no-media fallback can gate per modality.
_IMAGE_CONTENT_TYPES = frozenset({"image", "image_url", "input_image"})
_FILE_CONTENT_TYPES = frozenset({"file", "input_file", "document"})
_MEDIA_CONTENT_TYPES = _IMAGE_CONTENT_TYPES | _FILE_CONTENT_TYPES


def _flatten_message_content(content: Any) -> str | list[Any] | Any:
    """Convert list-of-blocks content to a string, preserving media blocks.

    Thinking/reasoning blocks are dropped.  When a media block (image or file)
    is present, returns a list in the ORIGINAL order — consecutive text is
    joined into a single text block, media blocks kept as-is — so captions stay
    next to the attachment they describe.  Otherwise returns a plain string.
    Non-list input is returned unchanged.

    Args:
        content: Message content — a string, a list of content blocks, or
            another type.

    Returns:
        A plain string for text-only content, a list of blocks when media is
        present, or the input unchanged for non-list input.
    """
    if isinstance(content, str):
        return content
    if not isinstance(content, list):
        return content
    parts: list[str] = []
    ordered_blocks: list[Any] = []
    saw_media = False

    def _flush_text() -> None:
        if parts:
            ordered_blocks.append({"type": "text", "text": "\n\n".join(parts)})
            parts.clear()

    for block in content:
        if isinstance(block, dict):
            btype = block.get("type")
            if btype in _MEDIA_CONTENT_TYPES:
                # Keep media as-is (never mutate; upstream copy.copy is shallow)
                # and preserve its position relative to surrounding text.
                _flush_text()
                ordered_blocks.append(block)
                saw_media = True
                continue
            if btype in _SKIP_CONTENT_TYPES:
                continue
            text = block.get("text")
            if text:
                parts.append(text)
        elif isinstance(block, str):
            parts.append(block)
    if saw_media:
        _flush_text()
        return ordered_blocks
    return "\n\n".join(parts) if parts else ""


def _sanitize_messages(
    messages: list[Any],
    hoist_tool_media: bool = True,
    *,
    drop_reasoning_metadata: bool = False,
) -> list[Any]:
    """Flatten list content for OpenAI-compatible APIs, preserving media.

    Text/reasoning content is flattened to a string; image blocks are
    preserved.  Tool messages cannot carry media
    on OpenAI-compatible APIs (content must be a string), so when
    ``hoist_tool_media`` is set the media in a tool result is moved into a
    HumanMessage emitted after that turn's (possibly parallel) tool messages.
    Anthropic-routed providers accept tool-result media natively and pass
    ``hoist_tool_media=False`` to keep it inline.
    """
    import copy

    from langchain_core.messages import HumanMessage

    sanitize_tool_history = _has_assistant_tool_protocol(messages)
    if sanitize_tool_history:
        messages = _sanitize_openai_tool_history(messages)
    out: list[Any] = []
    pending_media: list[Any] = []  # media hoisted out of a run of tool messages

    def _flush() -> None:
        if pending_media:
            out.append(HumanMessage(content=list(pending_media)))
            pending_media.clear()

    for msg in messages:
        if drop_reasoning_metadata:
            additional_kwargs = getattr(msg, "additional_kwargs", None) or {}
            if set(additional_kwargs) & _NONPORTABLE_REASONING_METADATA:
                msg = copy.copy(msg)
                msg.additional_kwargs = {
                    key: value
                    for key, value in additional_kwargs.items()
                    if key not in _NONPORTABLE_REASONING_METADATA
                }
        is_tool = getattr(msg, "type", None) == "tool"
        if not is_tool:
            _flush()  # emit hoisted media before any non-tool message
        if not isinstance(msg.content, list):
            out.append(msg)
            continue
        flat = _flatten_message_content(msg.content)
        if hoist_tool_media and is_tool and isinstance(flat, list):
            text_blocks = [
                b for b in flat if isinstance(b, dict) and b.get("type") == "text"
            ]
            media_blocks = [
                b for b in flat if not (isinstance(b, dict) and b.get("type") == "text")
            ]
            tool_msg = copy.copy(msg)
            # Join ALL text runs (interleaved content can yield more than one)
            # so no text is lost; tool content must be a string on OpenAI-compat.
            tool_msg.content = (
                "\n\n".join(b["text"] for b in text_blocks)
                if text_blocks
                else "[media content provided in the following message]"
            )
            out.append(tool_msg)
            pending_media.extend(media_blocks)
        else:
            msg = copy.copy(msg)
            msg.content = flat
            out.append(msg)
    _flush()  # conversation may end with tool messages
    if sanitize_tool_history:
        _validate_openai_tool_history(out)
    return out


# Fallback for models that reject some media input (no vision / no file
# support): replace blocks of the unsupported types with a placeholder so the
# conversation can keep going instead of erroring every turn that re-sends them.
_UNSUPPORTED_MEDIA_PLACEHOLDER = (
    "[attachment omitted: this model does not support this input type]"
)
# Markers that identify WHICH modality an error rejects, so only the implicated
# type is remembered (not all media).  Generic markers map to all media.
_IMAGE_ERROR_MARKERS = ("image_url", "image input", "support image")
_FILE_ERROR_MARKERS = ("file input", "pdf input", "document input")
# `expected \`text\`` keeps the backticks — the bare "expected text" phrase is
# too broad (matches non-media schema errors like "... expected text").
_GENERIC_MEDIA_MARKERS = ("multimodal", "expected `text`")


def _media_types_in(messages: list[BaseMessage]) -> set[str]:
    """Set of preserved-media block types present across the messages."""
    found: set[str] = set()
    for msg in messages:
        content = getattr(msg, "content", None)
        if isinstance(content, list):
            for b in content:
                if isinstance(b, dict) and b.get("type") in _MEDIA_CONTENT_TYPES:
                    found.add(b["type"])
    return found


def _media_error_types(exc: Exception) -> set[str]:
    """Media modalities the error text implicates (empty if not media-specific).

    Used to remember ONLY the rejected modality, so e.g. a PDF rejection never
    disables images.
    """
    text = str(exc).lower()
    types: set[str] = set()
    if any(m in text for m in _IMAGE_ERROR_MARKERS):
        types |= _IMAGE_CONTENT_TYPES
    if any(m in text for m in _FILE_ERROR_MARKERS):
        types |= _FILE_CONTENT_TYPES
    if any(m in text for m in _GENERIC_MEDIA_MARKERS):
        types |= _MEDIA_CONTENT_TYPES
    return types


def _is_http_400(exc: Exception) -> bool:
    """True if the error carries an HTTP 400 status (bad request)."""
    status = getattr(exc, "status_code", None)
    if status is None:
        status = getattr(getattr(exc, "response", None), "status_code", None)
    return status == 400


def _strip_media_types(
    messages: list[BaseMessage], types: set[str]
) -> list[BaseMessage]:
    """Replace blocks of the given types with a placeholder text block.

    Each stripped block is replaced IN PLACE (consecutive ones collapse into one
    placeholder), so surrounding text/media keep their original positions and a
    model that rejects only one modality still receives the others in order.
    """
    import copy

    out: list[BaseMessage] = []
    for msg in messages:
        content = getattr(msg, "content", None)
        if not isinstance(content, list) or not any(
            isinstance(b, dict) and b.get("type") in types for b in content
        ):
            out.append(msg)
            continue
        kept: list[Any] = []
        last_was_placeholder = False
        for b in content:
            if isinstance(b, dict) and b.get("type") in types:
                if not last_was_placeholder:
                    kept.append(
                        {"type": "text", "text": _UNSUPPORTED_MEDIA_PLACEHOLDER}
                    )
                    last_was_placeholder = True
                continue
            kept.append(b)
            last_was_placeholder = False
        msg = copy.copy(msg)
        msg.content = kept
        out.append(msg)
    return out


class _OpenAICompatContent:
    """Apply OpenAI-compatible content normalization without owning a model."""

    def __init__(
        self,
        profile: Mapping[str, object] | None,
        hoist_tool_media: bool,
        *,
        drop_reasoning_metadata: bool = False,
    ) -> None:
        self.hoist_tool_media = hoist_tool_media
        self.drop_reasoning_metadata = drop_reasoning_metadata
        self.blocked: set[str] = set()
        if profile is not None:
            if profile.get("image_inputs") is False:
                self.blocked |= _IMAGE_CONTENT_TYPES
            if profile.get("pdf_inputs") is False:
                self.blocked |= _FILE_CONTENT_TYPES

    def _prepare(self, messages: list[BaseMessage]) -> list[BaseMessage]:
        prepared = (
            _strip_media_types(messages, self.blocked) if self.blocked else messages
        )
        return _sanitize_messages(
            prepared,
            self.hoist_tool_media,
            drop_reasoning_metadata=self.drop_reasoning_metadata,
        )

    def _stripped(
        self,
        messages: list[BaseMessage],
        suspects: set[str],
    ) -> list[BaseMessage]:
        return _sanitize_messages(
            _strip_media_types(messages, self.blocked | suspects),
            self.hoist_tool_media,
            drop_reasoning_metadata=self.drop_reasoning_metadata,
        )

    def invoke(
        self,
        call: Callable[..., Any],
        messages: list[BaseMessage],
        *args,
        **kwargs,
    ) -> Any:
        suspects = _media_types_in(messages) - self.blocked
        prepared = self._prepare(messages)
        if not suspects:
            return call(prepared, *args, **kwargs)
        try:
            return call(prepared, *args, **kwargs)
        except Exception as exc:
            culprit = _media_error_types(exc) & suspects
            if not culprit and not _is_http_400(exc):
                raise
            try:
                result = call(self._stripped(messages, suspects), *args, **kwargs)
            except Exception:
                raise exc from None
            self.blocked.update(culprit)
            return result

    async def ainvoke(
        self,
        call: Callable[..., Awaitable[Any]],
        messages: list[BaseMessage],
        *args,
        **kwargs,
    ) -> Any:
        suspects = _media_types_in(messages) - self.blocked
        prepared = self._prepare(messages)
        if not suspects:
            return await call(prepared, *args, **kwargs)
        try:
            return await call(prepared, *args, **kwargs)
        except Exception as exc:
            culprit = _media_error_types(exc) & suspects
            if not culprit and not _is_http_400(exc):
                raise
            try:
                result = await call(self._stripped(messages, suspects), *args, **kwargs)
            except Exception:
                # stripping didn't help — surface original, don't cache
                raise exc from None
            self.blocked.update(culprit)
            return result

    def stream(
        self,
        call: Callable[..., Iterator[Any]],
        messages: list[BaseMessage],
        *args,
        **kwargs,
    ) -> Iterator[Any]:
        suspects = _media_types_in(messages) - self.blocked
        prepared = self._prepare(messages)
        if not suspects:
            yield from call(prepared, *args, **kwargs)
            return
        started = False
        try:
            for chunk in call(prepared, *args, **kwargs):
                started = True
                yield chunk
            return
        except Exception as exc:
            culprit = _media_error_types(exc) & suspects
            if started or (not culprit and not _is_http_400(exc)):
                raise
            media_exc = exc
        retry_started = False
        try:
            for chunk in call(self._stripped(messages, suspects), *args, **kwargs):
                if not retry_started:
                    retry_started = True
                    self.blocked.update(culprit)
                yield chunk
        except Exception:
            if retry_started:
                raise
            raise media_exc from None
        if not retry_started:
            raise media_exc from None

    async def astream(
        self,
        call: Callable[..., AsyncIterator[Any]],
        messages: list[BaseMessage],
        *args,
        **kwargs,
    ) -> AsyncIterator[Any]:
        suspects = _media_types_in(messages) - self.blocked
        prepared = self._prepare(messages)
        if not suspects:
            async for chunk in call(prepared, *args, **kwargs):
                yield chunk
            return
        started = False
        try:
            async for chunk in call(prepared, *args, **kwargs):
                started = True
                yield chunk
            return
        except Exception as exc:
            culprit = _media_error_types(exc) & suspects
            if started or (not culprit and not _is_http_400(exc)):
                raise
            media_exc = exc
        retry_started = False
        try:
            async for chunk in call(
                self._stripped(messages, suspects), *args, **kwargs
            ):
                if not retry_started:
                    retry_started = True
                    self.blocked.update(culprit)
                yield chunk
        except Exception:
            if retry_started:
                raise
            raise media_exc from None
        if not retry_started:
            raise media_exc from None


def _patch_openai_compat_content(
    model: Any,
    hoist_tool_media: bool = True,
    *,
    drop_reasoning_metadata: bool = False,
) -> None:
    """Normalize content for OpenAI-compatible models lacking a native adapter.

    Ai4Sci extension: ``drop_reasoning_metadata`` strips hidden provider
    reasoning traces so a generic proxy never receives another provider's
    private chain-of-thought.
    """
    import functools

    profile = getattr(model, "profile", None)
    compat = _OpenAICompatContent(
        profile if isinstance(profile, Mapping) else None,
        hoist_tool_media,
        drop_reasoning_metadata=drop_reasoning_metadata,
    )

    orig_generate = getattr(model, "_generate", None)
    if orig_generate is None:
        return

    @functools.wraps(orig_generate)
    def _patched_generate(
        messages: list[BaseMessage], *args: Any, **kwargs: Any
    ) -> Any:
        return compat.invoke(orig_generate, messages, *args, **kwargs)

    model._generate = _patched_generate

    orig_agenerate = getattr(model, "_agenerate", None)
    if orig_agenerate is not None:

        @functools.wraps(orig_agenerate)
        async def _patched_agenerate(
            messages: list[BaseMessage], *args: Any, **kwargs: Any
        ) -> Any:
            return await compat.ainvoke(orig_agenerate, messages, *args, **kwargs)

        model._agenerate = _patched_agenerate

    # Also patch streaming paths — CLI/agent uses _stream/_astream, so without
    # these the content flattening is bypassed during normal streaming calls.
    orig_stream = getattr(model, "_stream", None)
    if orig_stream is not None:

        @functools.wraps(orig_stream)
        def _patched_stream(
            messages: list[BaseMessage], *args: Any, **kwargs: Any
        ) -> Any:
            yield from compat.stream(orig_stream, messages, *args, **kwargs)

        model._stream = _patched_stream

    orig_astream = getattr(model, "_astream", None)
    if orig_astream is not None:

        @functools.wraps(orig_astream)
        async def _patched_astream(
            messages: list[BaseMessage], *args: Any, **kwargs: Any
        ) -> Any:
            async for chunk in compat.astream(orig_astream, messages, *args, **kwargs):
                yield chunk

        model._astream = _patched_astream


# ---------------------------------------------------------------------------
# Patch: ccproxy Codex Responses API rejects "system" role messages.
# Convert SystemMessage to use "developer" role via langchain-openai's
# __openai_role__ mechanism.
# ---------------------------------------------------------------------------
def _patch_ccproxy_system_to_developer(model: Any) -> None:
    """Convert SystemMessage role from 'system' to 'developer' for ccproxy.

    ccproxy's Responses API endpoint rejects system role messages with
    400 "System messages are not allowed".  LangChain's ``langchain_openai``
    checks ``additional_kwargs["__openai_role__"]`` and uses that value as
    the message role when serializing to the API.

    Args:
        model: A LangChain chat model instance to patch in-place.
    """
    import copy
    import functools

    from langchain_core.messages import SystemMessage

    def _system_to_developer(messages: list[BaseMessage]) -> list[BaseMessage]:
        out: list[BaseMessage] = []
        for msg in messages:
            if isinstance(msg, SystemMessage):
                if msg.additional_kwargs.get("__openai_role__") != "developer":
                    msg = copy.copy(msg)
                    msg.additional_kwargs = {
                        **msg.additional_kwargs,
                        "__openai_role__": "developer",
                    }
            out.append(msg)
        return out

    orig_generate = getattr(model, "_generate", None)
    if orig_generate is None:
        return

    @functools.wraps(orig_generate)
    def _patched_generate(
        messages: list[BaseMessage], *args: Any, **kwargs: Any
    ) -> Any:
        return orig_generate(_system_to_developer(messages), *args, **kwargs)

    model._generate = _patched_generate

    orig_agenerate = getattr(model, "_agenerate", None)
    if orig_agenerate is not None:

        @functools.wraps(orig_agenerate)
        async def _patched_agenerate(
            messages: list[BaseMessage], *args: Any, **kwargs: Any
        ) -> Any:
            return await orig_agenerate(_system_to_developer(messages), *args, **kwargs)

        model._agenerate = _patched_agenerate

    orig_stream = getattr(model, "_stream", None)
    if orig_stream is not None:

        @functools.wraps(orig_stream)
        def _patched_stream(
            messages: list[BaseMessage], *args: Any, **kwargs: Any
        ) -> Any:
            return orig_stream(_system_to_developer(messages), *args, **kwargs)

        model._stream = _patched_stream

    orig_astream = getattr(model, "_astream", None)
    if orig_astream is not None:

        @functools.wraps(orig_astream)
        async def _patched_astream(
            messages: list[BaseMessage], *args: Any, **kwargs: Any
        ) -> Any:
            async for chunk in orig_astream(
                _system_to_developer(messages), *args, **kwargs
            ):
                yield chunk

        model._astream = _patched_astream


# ---------------------------------------------------------------------------
# Patch (module-level): langchain-openai's _convert_dict_to_message and
# _convert_delta_to_message_chunk discard provider-specific fields like
# `reasoning_content`. We monkey-patch them to capture reasoning_content
# into AIMessage.additional_kwargs so downstream code (incl. our passback
# patch) can find it. Benign for non-DeepSeek providers — they just don't
# return this field, so the patch is a no-op for them.
# ---------------------------------------------------------------------------
_openai_capture_patched = False


def _patch_openai_capture_reasoning_content() -> None:
    global _openai_capture_patched
    if _openai_capture_patched:
        return
    try:
        import langchain_openai.chat_models.base as _base

        _orig_dict_to_msg = _base._convert_dict_to_message
        _orig_delta_to_chunk = _base._convert_delta_to_message_chunk

        def _patched_dict_to_msg(_dict, *args, **kwargs):
            msg = _orig_dict_to_msg(_dict, *args, **kwargs)
            rc = _dict.get("reasoning_content") if isinstance(_dict, dict) else None
            if isinstance(rc, str) and rc and hasattr(msg, "additional_kwargs"):
                msg.additional_kwargs["reasoning_content"] = rc
            return msg

        def _patched_delta_to_chunk(_dict, *args, **kwargs):
            chunk = _orig_delta_to_chunk(_dict, *args, **kwargs)
            rc = _dict.get("reasoning_content") if isinstance(_dict, dict) else None
            if isinstance(rc, str) and rc and hasattr(chunk, "additional_kwargs"):
                # Per-chunk: stash this delta's reasoning_content on the chunk.
                # Cross-chunk accumulation is handled by AIMessageChunk.__add__
                # via merge_dicts (string values in additional_kwargs concatenate).
                chunk.additional_kwargs["reasoning_content"] = (
                    chunk.additional_kwargs.get("reasoning_content", "") + rc
                )
            return chunk

        _base._convert_dict_to_message = _patched_dict_to_msg
        _base._convert_delta_to_message_chunk = _patched_delta_to_chunk
        _openai_capture_patched = True
    except Exception:
        pass


_patch_openai_capture_reasoning_content()


# ---------------------------------------------------------------------------
# Patch (module-level): silence langgraph_api's OpenAPI schema-generation
# warnings for endpoints whose docstrings aren't valid YAML.
#
# Upstream ``langgraph_api.utils.SchemaGenerator.get_schema`` calls
# ``parse_docstring`` (inherited from Starlette's ``BaseSchemaGenerator``)
# on every registered endpoint. When the docstring is prose with stray
# ``:`` characters, ``yaml.safe_load`` raises and upstream logs the
# failure + full traceback at WARNING level. It then falls back to
# ``{"description": docstring}`` — the endpoint still ends up in the
# schema with its prose as the description, just without structured
# ``parameters``/``responses``/``tags`` fields.
#
# The fallback path is fine; the warning + traceback is just noise. And
# it's only triggered for our deploy because mounting any custom Starlette
# app (``EvoScientist/langgraph_dev/http.py``) makes upstream call
# ``update_openapi_spec`` at startup — which iterates EVERY route,
# including upstream's own endpoints whose prose docstrings predate the
# YAML convention.
#
# Fix: wrap ``parse_docstring`` itself and absorb ``yaml.YAMLError`` by
# returning the same fallback shape upstream's except branch produces.
# Non-YAML exceptions are deliberately left to propagate — upstream's
# ``get_schema`` already catches them and logs WARNING + traceback, so
# unexpected failures remain debuggable. Patching ``parse_docstring`` (a
# small, stable method) instead of ``get_schema`` (the larger loop body)
# minimizes our exposure to upstream churn.
# ---------------------------------------------------------------------------
_langgraph_schema_silenced_patched = False


def _patch_langgraph_schema_generator_silence_warnings() -> None:
    global _langgraph_schema_silenced_patched
    if _langgraph_schema_silenced_patched:
        return
    try:
        import langgraph_api.utils as _lgapi_utils
        import yaml

        _SchemaGenerator = _lgapi_utils.SchemaGenerator
        _orig_parse_docstring = _SchemaGenerator.parse_docstring

        def _patched_parse_docstring(self: Any, func: Any) -> dict[str, Any]:
            try:
                return _orig_parse_docstring(self, func)
            except yaml.YAMLError:
                return {"description": getattr(func, "__doc__", None) or ""}

        _SchemaGenerator.parse_docstring = _patched_parse_docstring
        _langgraph_schema_silenced_patched = True
    except Exception:
        # Patches are loader-safe: never crash the import. Silent failure
        # here just leaves the upstream warnings visible in deploy logs,
        # which is a benign fallback.
        pass


_patch_langgraph_schema_generator_silence_warnings()


# ---------------------------------------------------------------------------
# Patch (lazy, Anthropic protocol): strip foreign reasoning blocks from
# outgoing assistant messages.
#
# History produced on OpenAI-compatible providers (Qwen/DashScope, DeepSeek, …)
# can carry `reasoning_content` content blocks in AI messages. When a session
# switches to an Anthropic-protocol endpoint (native Anthropic, custom-anthropic,
# kimi-coding, minimax), langchain-anthropic's `_format_messages` passes unknown
# block types through verbatim (`else: content.append(block)`), and strict
# Anthropic-compatible backends (e.g. Moonshot's Kimi For Coding endpoint at
# api.kimi.com) reject the request with an opaque HTTP 400. Upstream already
# skips the OpenAI-Responses `reasoning` / `function_call` types the same way;
# `reasoning_content` is simply missing from that list. These blocks are display
# metadata — no Anthropic-protocol block type has this name — so stripping is
# always safe. Anthropic `thinking` blocks are preserved (Kimi backends require
# them on tool-call turns, even unsigned), but a missing `signature` is
# defaulted to "": Anthropic-compatible backends stream thinking without a
# signature_delta, so the aggregated block lacks the key, and strict edges
# (e.g. OpenRouter's Anthropic schema) reject replay with 400
# ("signature: expected string, received undefined"). Native Anthropic thinking
# always carries a signature string, so the default never fires there.
# ---------------------------------------------------------------------------
_anthropic_foreign_reasoning_patched = False
_FOREIGN_REASONING_BLOCK_TYPES = frozenset({"reasoning_content"})


def _normalize_anthropic_replay_messages(
    messages: Sequence[BaseMessage],
) -> Sequence[BaseMessage]:
    """Sanitize AI-message list content for Anthropic-protocol replay."""
    cleaned: list[BaseMessage] = []
    changed = False
    for message in messages:
        if isinstance(message, AIMessage) and isinstance(message.content, list):
            blocks: list[Any] = []
            block_changed = False
            for block in message.content:
                if isinstance(block, dict):
                    btype = block.get("type")
                    if btype in _FOREIGN_REASONING_BLOCK_TYPES:
                        block_changed = True
                        continue
                    if btype == "thinking" and not isinstance(
                        block.get("signature"), str
                    ):
                        block = {**block, "signature": ""}
                        block_changed = True
                blocks.append(block)
            if block_changed:
                message = message.model_copy(update={"content": blocks})
                changed = True
        cleaned.append(message)
    return cleaned if changed else messages


def _patch_anthropic_strip_foreign_reasoning() -> None:
    global _anthropic_foreign_reasoning_patched
    if _anthropic_foreign_reasoning_patched:
        return
    try:
        import langchain_anthropic.chat_models as _mod

        _orig = _mod._format_messages

        @functools.wraps(_orig)
        def _patched(messages: Sequence[BaseMessage]) -> Any:
            return _orig(_normalize_anthropic_replay_messages(messages))

        _mod._format_messages = _patched
        _anthropic_foreign_reasoning_patched = True
    except Exception:
        pass


# ---------------------------------------------------------------------------
# Patch (lazy, OpenRouter only): strip OpenAI-Responses encrypted reasoning
# items from outgoing assistant messages.
#
# OpenRouter's `/responses` beta is stateless — it does not propagate
# `store=true` / `previous_response_id` upstream. So when a prior turn's
# assistant message carries a reasoning item with an `rs_*` id (the encrypted
# Responses reasoning block), replaying it on the next turn fails with HTTP 400:
#   "Item with id 'rs_...' not found. Items are not persisted when `store` is
#    set to false. ... remove this item from your input."
# (observed against both Azure and OpenAI upstreams — so deepagents'
# azure-ignore is not sufficient). The only robust fix is to drop these items
# on passback, exactly as the upstream error instructs. Reasoning DISPLAY is
# unaffected: it happens when the item is generated, not on passback.
# Related: langchain-ai/langchain#37777.
# ---------------------------------------------------------------------------
_openrouter_reasoning_strip_patched = False


def _is_responses_reasoning_item(entry: Any) -> bool:
    """True for an OpenAI-Responses encrypted reasoning item (`rs_` id / data)."""
    if not isinstance(entry, dict):
        return False
    return str(entry.get("id") or "").startswith("rs_") or bool(entry.get("data"))


def _patch_openrouter_strip_responses_reasoning() -> None:
    global _openrouter_reasoning_strip_patched
    if _openrouter_reasoning_strip_patched:
        return
    try:
        import langchain_openrouter.chat_models as _mod

        _orig = _mod._convert_message_to_dict

        def _patched(message: Any) -> Any:
            result = _orig(message)
            details = (
                result.get("reasoning_details") if isinstance(result, dict) else None
            )
            if isinstance(details, list):
                kept = [e for e in details if not _is_responses_reasoning_item(e)]
                if kept:
                    result["reasoning_details"] = kept
                else:
                    result.pop("reasoning_details", None)
            return result

        _mod._convert_message_to_dict = _patched
        _openrouter_reasoning_strip_patched = True
    except Exception:
        pass


# ---------------------------------------------------------------------------
# Patch (lazy, OpenRouter only): default structured output to json_schema for
# Moonshot's always-thinking models (kimi-k3).
#
# Moonshot rejects a forced tool choice while thinking is enabled with HTTP 400:
#   "tool_choice 'specified' is incompatible with thinking enabled"
# and kimi-k3's thinking cannot be disabled, so the function_calling default of
# ChatOpenRouter.with_structured_output fails every structured-output call
# (LLMToolSelectorMiddleware included). These endpoints support
# response_format json_schema, which needs no tool_choice — route them there.
# Moonshot-specific: other mandatory-reasoning models keep function_calling.
# Gated on the instance's model_name, so copies behave correctly and other
# models keep the function_calling default.
# ---------------------------------------------------------------------------
_openrouter_structured_output_patched = False


def _patch_openrouter_structured_output() -> None:
    global _openrouter_structured_output_patched
    if _openrouter_structured_output_patched:
        return
    try:
        from langchain_openrouter import ChatOpenRouter

        _orig = ChatOpenRouter.with_structured_output

        def _patched(
            self: Any,
            schema: Any = None,
            *,
            method: str = "function_calling",
            **kwargs: Any,
        ) -> Any:
            if method == "function_calling":
                from .models import _OPENROUTER_JSON_SCHEMA_STRUCTURED_OUTPUT_MODELS

                if self.model_name in _OPENROUTER_JSON_SCHEMA_STRUCTURED_OUTPUT_MODELS:
                    method = "json_schema"
            return _orig(self, schema, method=method, **kwargs)

        ChatOpenRouter.with_structured_output = _patched
        _openrouter_structured_output_patched = True
    except Exception:
        pass


# ---------------------------------------------------------------------------
# Patch (lazy, Anthropic protocol): default structured output to json_schema
# for mandatory-thinking Kimi models (K3 / Kimi For Coding).
#
# These endpoints run with thinking always on and reject a forced tool choice
# with HTTP 400 ("tool_choice 'specified' is incompatible with thinking
# enabled") — ChatAnthropic's function_calling default forces tool_choice, so
# every structured-output call (LLMToolSelectorMiddleware included) fails.
# With thinking declared, langchain-anthropic falls back to tool_choice auto,
# but the model then calls the tool only sometimes (OutputParserException).
# These endpoints accept output_config.format (verified live via OpenRouter's
# Anthropic-compatible edge), which needs no tool_choice — route them there.
# Gated on the instance's model, so copies behave correctly and Claude models
# keep the function_calling default.
# ---------------------------------------------------------------------------
_anthropic_structured_output_patched = False


def _patch_anthropic_structured_output() -> None:
    global _anthropic_structured_output_patched
    if _anthropic_structured_output_patched:
        return
    try:
        from langchain_anthropic import ChatAnthropic

        _orig = ChatAnthropic.with_structured_output

        def _patched(
            self: Any,
            schema: Any = None,
            *,
            method: str = "function_calling",
            **kwargs: Any,
        ) -> Any:
            if method == "function_calling":
                from .models import _is_mandatory_thinking_kimi

                if _is_mandatory_thinking_kimi(self.model):
                    method = "json_schema"
            return _orig(self, schema, method=method, **kwargs)

        ChatAnthropic.with_structured_output = _patched
        _anthropic_structured_output_patched = True
    except Exception:
        pass


# ---------------------------------------------------------------------------
# Patch: forward CLI's live (model, model_provider) into deepagents'
# start_async_task / update_async_task tool calls so the deployed graph
# (running in a separate ``langgraph dev`` subprocess) re-resolves the
# chat model per run.
#
# Without this, async sub-agents stay on the model their graph was compiled
# with at langgraph dev boot — `/model` switches in the CLI never reach
# them because they live in another process.
#
# Mechanism: wrap deepagents' ``_build_start_tool`` and ``_build_update_tool``
# factories. Each wrapped factory calls the original with a proxied client
# cache that intercepts ``runs.create(...)`` calls only and injects
# ``config={"configurable": {"model": <cfg.model>, "model_provider": <cfg.provider>}}``.
# All other client methods (``threads.create``, ``runs.get``, ``runs.cancel``,
# ``runs.join_stream``) pass through unchanged. The deployed graph picks up
# ``configurable.model`` via ``ConfigurableModelMiddleware``.
#
# Reads ``_ensure_config()`` at tool-call time (not patch time) so a
# ``/model`` switch in the CLI is reflected on the very next async tool
# call without an agent rebuild.
#
# Upstream PR opportunity: passing ``config`` through ``client.runs.create``
# is generic functionality; worth contributing back to ``langchain-ai/deepagents``
# so this patch can be retired.
# ---------------------------------------------------------------------------
_model_passthrough_patched = False

# The launching run's per-run (model, provider), set by the async-task tools
# from ``runtime.config`` for the duration of a single ``runs.create`` call.
# ``_merge_runs_config_kwargs`` reads it as the highest-precedence source.
#
# Why a ContextVar rather than passing ``config=`` through each tool: the fix
# has to hold at every ``runs.create`` site (``start_async_task`` and
# ``update_async_task``), but only the tool functions can see the caller's
# per-run model (via ``runtime.config``, the config langgraph's ToolNode
# injects into tool calls). Threading it through a ContextVar
# lets the single merge point below inject it, so ``update_async_task`` (whose
# body we delegate to upstream unchanged) is covered without reimplementing it.
_caller_configurable: ContextVar[dict[str, str] | None] = ContextVar(
    "_evo_caller_configurable", default=None
)


def _extract_caller_configurable(config: Any) -> dict[str, str]:
    """Pull ``(model, model_provider)`` out of a launching run's ``config``.

    ``config`` is the tool's ``runtime.config`` (a ``RunnableConfig``-shaped
    dict). Returns a dict suitable for ``configurable``; empty when the caller
    carries no model override, so the config-default fallback still applies.
    ``model_provider`` is only forwarded alongside a model — a bare provider
    without a model is meaningless to the deployed graph's resolver.
    """
    configurable = (config or {}).get("configurable") or {}
    model = configurable.get("model")
    provider = configurable.get("model_provider")
    out: dict[str, str] = {}
    if isinstance(model, str) and model:
        out["model"] = model
        if isinstance(provider, str) and provider:
            out["model_provider"] = provider
    return out


def _read_cfg_configurable() -> dict[str, str]:
    """Read live ``(model, provider)`` from EvoScientist config.

    Returns a dict suitable for inserting under
    ``RunnableConfig.configurable``. Empty dict on any failure (so the
    patch degrades to a no-op rather than breaking async tool calls).
    """
    try:
        from EvoScientist.EvoScientist import _ensure_config

        cfg = _ensure_config()
    except Exception:
        return {}

    out: dict[str, str] = {}
    model = getattr(cfg, "model", None)
    provider = getattr(cfg, "provider", None)
    if isinstance(model, str) and model:
        out["model"] = model
    if isinstance(provider, str) and provider:
        out["model_provider"] = provider
    return out


def _merge_runs_config_kwargs(kwargs: dict) -> dict:
    """Merge the live model override into ``kwargs`` for ``runs.create``.

    Model source, in increasing precedence:

    1. ``_read_cfg_configurable()`` — the effective EvoScientist config. On the
       in-process backend this is the CLI's live per-run model; on the
       ``langgraph_server`` backend this proxy runs inside the dev-server
       process, where it reports the *server's* config-default model, not the
       CLI's per-run choice.
    2. ``_caller_configurable`` — the launching run's actual model, forwarded
       by the async-task tools from ``runtime.config``. This wins so that a
       sub-agent launched (or continued) while the caller runs on, say, a free
       model does not silently fall back to a billed config-default.

    Any unrelated caller-supplied ``config.configurable`` keys are preserved.
    """
    overrides = {**_read_cfg_configurable(), **(_caller_configurable.get() or {})}
    if not overrides:
        return kwargs
    existing = kwargs.get("config")
    if not isinstance(existing, dict):
        existing = {}
    existing_configurable = existing.get("configurable")
    if not isinstance(existing_configurable, dict):
        existing_configurable = {}
    merged_configurable = {**existing_configurable, **overrides}
    kwargs = dict(kwargs)
    kwargs["config"] = {**existing, "configurable": merged_configurable}
    return kwargs


class _SyncRunsProxy:
    """Wraps a sync ``RunsClient`` and injects config into ``create`` only."""

    def __init__(self, real: Any) -> None:
        object.__setattr__(self, "_real", real)

    def __getattr__(self, name: str) -> Any:
        return getattr(self._real, name)

    def create(self, **kwargs: Any) -> Any:
        return self._real.create(**_merge_runs_config_kwargs(kwargs))


class _AsyncRunsProxy:
    """Wraps an async ``RunsClient`` and injects config into ``create`` only."""

    def __init__(self, real: Any) -> None:
        object.__setattr__(self, "_real", real)

    def __getattr__(self, name: str) -> Any:
        return getattr(self._real, name)

    async def create(self, **kwargs: Any) -> Any:
        return await self._real.create(**_merge_runs_config_kwargs(kwargs))


class _ClientProxy:
    """Lightweight proxy that swaps ``client.runs`` for a runs proxy.

    Read-only forwarding: only ``__getattr__`` is overridden. Attribute
    *writes* on the proxy land on the proxy itself, NOT on the wrapped real
    client. Current deepagents only reads ``.threads`` / ``.runs``, so this
    is safe — but if a future caller tries ``client.foo = bar`` through the
    proxy, the write will be silently lost. ``object.__setattr__`` in
    ``__init__`` is used solely to avoid infinite recursion when seeding
    the internal slots.

    ``client.runs`` is a stable attribute set in
    ``langgraph_sdk.client.LangGraphClient.__init__`` (``self.runs =
    RunsClient(...)``) — not a property or lazy initializer — so the wrapped
    runs proxy is built once at ``__init__`` time and reused on every
    ``proxy.runs`` access. This avoids the previous behavior of selecting
    sync/async and instantiating a new ``_RunsProxy`` per attribute access.
    """

    def __init__(self, real: Any, *, is_async: bool) -> None:
        runs_proxy_cls = _AsyncRunsProxy if is_async else _SyncRunsProxy
        object.__setattr__(self, "_real", real)
        object.__setattr__(self, "_runs_proxy", runs_proxy_cls(real.runs))

    def __getattr__(self, name: str) -> Any:
        if name == "runs":
            return self._runs_proxy
        return getattr(self._real, name)


class _ClientCacheProxy:
    """Proxy a ``_ClientCache`` so callers receive config-injecting clients."""

    def __init__(self, real: Any) -> None:
        self._real = real

    def get_sync(self, name: str) -> Any:
        return _ClientProxy(self._real.get_sync(name), is_async=False)

    def get_async(self, name: str) -> Any:
        return _ClientProxy(self._real.get_async(name), is_async=True)


def _patch_deepagents_model_passthrough() -> None:
    """Wrap deepagents' async-launch tool factories to inject CLI model.

    Idempotent: re-invocation is a no-op once the patch is active. Safe to
    call from ``_maybe_swap_async_subagents`` on every CLI startup; both
    that hook and this patch turn on together when async sub-agents are
    enabled.
    """
    global _model_passthrough_patched
    if _model_passthrough_patched:
        return

    try:
        from deepagents.middleware import async_subagents as ds_mod
    except ImportError:
        return

    # Defensive ``getattr`` lookups mirror the rest of this file (lines 254,
    # 266, 279, 290, 339, 351, 362, 373, 473): a deepagents update that
    # renames or removes either private helper degrades to a no-op instead
    # of raising ``AttributeError`` at CLI startup.
    orig_build_start = getattr(ds_mod, "_build_start_tool", None)
    orig_build_update = getattr(ds_mod, "_build_update_tool", None)
    if orig_build_start is None or orig_build_update is None:
        return

    def _patched_build_start(
        agent_map: Any, clients: Any, tool_description: str
    ) -> Any:
        return orig_build_start(agent_map, _ClientCacheProxy(clients), tool_description)

    def _patched_build_update(agent_map: Any, clients: Any) -> Any:
        return orig_build_update(agent_map, _ClientCacheProxy(clients))

    ds_mod._build_start_tool = _patched_build_start
    ds_mod._build_update_tool = _patched_build_update
    _model_passthrough_patched = True


# ---------------------------------------------------------------------------
# Patch: LangGraph 1.2.9 _control_branch crashes with TypeError when
# Command.goto is None (e.g. from a resume command). We patch both the
# generator (map_cmd) and the consumer (_control_branch).
# ---------------------------------------------------------------------------
def _patch_langgraph_goto_none_crash() -> None:
    """Handle Command(goto=None) defensively in langgraph and langgraph-api.

    Either patch alone prevents the production crash; both together cover
    all code paths (a Command(goto=None) could in principle be created
    by code other than map_cmd).

    Matches upstream issue langchain-ai/langgraph#5656.
    """
    # ── 1. map_cmd — prevent goto=None from being created ────────────────
    try:
        import langgraph_api.command as _cmd_mod
        from langgraph.types import Command as _Command

        _orig_map_cmd = getattr(_cmd_mod, "map_cmd", None)
        if _orig_map_cmd and not hasattr(_orig_map_cmd, "_evosci_goto_none_fix"):

            @functools.wraps(_orig_map_cmd)
            def _patched_map_cmd(cmd: Any) -> _Command:
                result = _orig_map_cmd(cmd)
                if result.goto is None:
                    return dataclasses.replace(result, goto=[])
                return result

            _patched_map_cmd._evosci_goto_none_fix = True  # type: ignore[attr-defined]
            _cmd_mod.map_cmd = _patched_map_cmd
            # Rebind on already-loaded consumers that grabbed map_cmd via
            # ``from langgraph_api.command import map_cmd`` — those hold the
            # original reference and wouldn't see the module-level rebind.
            for _name in ("langgraph_api.stream", "langgraph_api.grpc.ops.threads"):
                _mod = sys.modules.get(_name)
                if _mod is not None and getattr(_mod, "map_cmd", None) is _orig_map_cmd:
                    _mod.map_cmd = _patched_map_cmd
    except ImportError:
        pass

    # ── 2. _control_branch — handle goto=None defensively ───────────────
    # Wrap (not reimplement) the upstream function so only the goto=None→[]
    # normalization is ours; all other routing logic delegates to the
    # original, avoiding drift as upstream evolves.
    try:
        import langgraph.graph.state as _state_mod
        from langgraph.types import Command as _Command

        _orig_ctrl = getattr(_state_mod, "_control_branch", None)
        if _orig_ctrl and not hasattr(_orig_ctrl, "_evosci_goto_none_fix"):

            def _fix_goto(cmd: Any) -> Any:
                if isinstance(cmd, _Command) and cmd.goto is None:
                    return dataclasses.replace(cmd, goto=[])
                return cmd

            @functools.wraps(_orig_ctrl)
            def _patched_ctrl(value: Any) -> Sequence[tuple[str, Any]]:
                if isinstance(value, (list, tuple)):
                    value = [_fix_goto(v) for v in value]
                else:
                    value = _fix_goto(value)
                return _orig_ctrl(value)

            _patched_ctrl._evosci_goto_none_fix = True  # type: ignore[attr-defined]
            _state_mod._control_branch = _patched_ctrl
    except (ImportError, AttributeError):
        pass


_patch_langgraph_goto_none_crash()


_NONPORTABLE_REASONING_METADATA = frozenset(
    {"reasoning_content", "reasoning_details"}
)


def _stable_tool_call_id(message: Any, message_index: int, call_index: int) -> str:
    seed = ":".join(
        (
            str(getattr(message, "id", "") or "message"),
            str(message_index),
            str(call_index),
        )
    )
    return "call_" + hashlib.sha256(seed.encode("utf-8")).hexdigest()[:24]


def _tool_message_match_index(
    tool_messages: list[Any],
    used_indexes: set[int],
    *,
    call_id: str,
    call_name: str,
) -> int | None:
    """Find the best unused result for one assistant tool call."""

    def _matches(index: int, *, require_id: bool, require_name: bool) -> bool:
        if index in used_indexes:
            return False
        message = tool_messages[index]
        result_id = str(getattr(message, "tool_call_id", "") or "")
        result_name = str(getattr(message, "name", "") or "")
        if require_id and result_id != call_id:
            return False
        if not require_id and result_id:
            return False
        return not require_name or not result_name or result_name == call_name

    if call_id:
        for require_name in (True, False):
            for index in range(len(tool_messages)):
                if _matches(index, require_id=True, require_name=require_name):
                    return index
        for require_name in (True, False):
            for index in range(len(tool_messages)):
                if _matches(index, require_id=False, require_name=require_name):
                    return index
        return None

    # A result-side identifier is more authoritative than a generated fallback.
    for require_name in (True, False):
        for index, message in enumerate(tool_messages):
            if index in used_indexes:
                continue
            result_id = str(getattr(message, "tool_call_id", "") or "")
            result_name = str(getattr(message, "name", "") or "")
            if result_id and (
                not require_name or not result_name or result_name == call_name
            ):
                return index
    for require_name in (True, False):
        for index in range(len(tool_messages)):
            if _matches(index, require_id=False, require_name=require_name):
                return index
    return None


def _copy_ai_message_with_tool_pairs(
    message: Any,
    message_index: int,
    tool_messages: list[Any],
) -> tuple[Any | None, list[Any]]:
    """Return a replay-safe assistant message and its matched tool results."""
    import copy

    copied = copy.copy(message)
    additional_kwargs = dict(getattr(message, "additional_kwargs", None) or {})
    # Parsed tool_calls are canonical. Raw copies can otherwise re-introduce an
    # invalid call after invalid_tool_calls has been cleared.
    additional_kwargs.pop("tool_calls", None)
    copied.additional_kwargs = additional_kwargs
    copied.invalid_tool_calls = []

    original_calls = list(getattr(message, "tool_calls", None) or [])
    used_results: set[int] = set()
    matched_calls: list[dict[str, Any]] = []
    matched_result_indexes: list[int] = []
    original_to_matched_call: dict[int, tuple[str, str]] = {}

    for call_index, original_call in enumerate(original_calls):
        call = dict(original_call)
        call_id = str(call.get("id") or "")
        call_name = str(call.get("name") or "").strip()
        # A missing name is structurally unreplayable. Never infer it from
        # arguments or retain its paired ToolMessage in provider history.
        if not call_name:
            continue
        call["name"] = call_name
        result_index = _tool_message_match_index(
            tool_messages,
            used_results,
            call_id=call_id,
            call_name=call_name,
        )
        # A historical client-side function call is only replayable together
        # with its result. Incomplete calls are discarded instead of asking the
        # provider to continue a broken tool turn.
        if result_index is None:
            continue
        if not call_id:
            result_id = str(
                getattr(tool_messages[result_index], "tool_call_id", "") or ""
            )
            call_id = result_id or _stable_tool_call_id(
                message, message_index, call_index
            )
        call["id"] = call_id
        matched_calls.append(call)
        matched_result_indexes.append(result_index)
        original_to_matched_call[call_index] = (call_id, call_name)
        used_results.add(result_index)

    copied.tool_calls = matched_calls
    if isinstance(copied.content, list):
        original_call_index = 0
        blocks: list[Any] = []
        for original_block in copied.content:
            if not isinstance(original_block, dict):
                blocks.append(original_block)
                continue
            block = dict(original_block)
            if block.get("type") in {"tool_call", "function_call"}:
                matched_call = original_to_matched_call.get(original_call_index)
                original_call_index += 1
                if matched_call is None:
                    continue
                call_id, call_name = matched_call
                # LangChain content blocks use id; the Responses converter later
                # maps it to call_id.
                block["id"] = call_id
                if "call_id" in block:
                    block["call_id"] = call_id
                block["name"] = call_name
                if isinstance(block.get("function"), dict):
                    block["function"] = {**block["function"], "name": call_name}
            blocks.append(block)
        copied.content = blocks

    matched_results: list[Any] = []
    result_to_call_id = {
        result_index: matched_calls[index]["id"]
        for index, result_index in enumerate(matched_result_indexes)
    }
    for result_index, result in enumerate(tool_messages):
        call_id = result_to_call_id.get(result_index)
        if call_id is None:
            continue
        copied_result = copy.copy(result)
        copied_result.tool_call_id = call_id
        matched_results.append(copied_result)

    had_tool_protocol = bool(original_calls) or bool(
        getattr(message, "invalid_tool_calls", None)
    )
    if not matched_calls and had_tool_protocol:
        replayable_content = _flatten_message_content(copied.content)
        if not replayable_content:
            return None, matched_results

    return copied, matched_results


def _sanitize_openai_tool_history(messages: list[Any]) -> list[Any]:
    """Copy history while retaining only complete, replayable tool turns."""

    normalized: list[Any] = []
    index = 0
    while index < len(messages):
        message = messages[index]
        message_type = getattr(message, "type", None)
        if message_type == "tool":
            # A tool result without its immediately preceding assistant call is
            # invalid for both Chat Completions and Responses APIs.
            index += 1
            continue
        if message_type != "ai":
            normalized.append(message)
            index += 1
            continue

        next_index = index + 1
        tool_messages: list[Any] = []
        while (
            next_index < len(messages)
            and getattr(messages[next_index], "type", None) == "tool"
        ):
            tool_messages.append(messages[next_index])
            next_index += 1
        copied, matched_results = _copy_ai_message_with_tool_pairs(
            message,
            index,
            tool_messages,
        )
        if copied is not None:
            normalized.append(copied)
        normalized.extend(matched_results)
        index = next_index

    return normalized


def _ensure_openai_tool_call_ids(messages: list[Any]) -> list[Any]:
    """Backward-compatible alias for replay-safe tool history normalization."""

    return _sanitize_openai_tool_history(messages)


def _has_assistant_tool_protocol(messages: list[Any]) -> bool:
    """Return whether history contains assistant-side tool protocol state."""

    for message in messages:
        if getattr(message, "type", None) != "ai":
            continue
        if getattr(message, "tool_calls", None) or getattr(
            message, "invalid_tool_calls", None
        ):
            return True
        additional_kwargs = getattr(message, "additional_kwargs", None) or {}
        if additional_kwargs.get("tool_calls"):
            return True
        content = getattr(message, "content", None)
        if isinstance(content, list) and any(
            isinstance(block, dict)
            and block.get("type") in {"tool_call", "function_call"}
            for block in content
        ):
            return True
    return False


def _validate_openai_tool_history(messages: list[Any]) -> None:
    """Raise when sanitized history still contains an invalid tool protocol."""

    available_call_ids: set[str] = set()
    for message in messages:
        message_type = getattr(message, "type", None)
        if message_type == "ai":
            if getattr(message, "invalid_tool_calls", None):
                raise ValueError("invalid_tool_calls must not be replayed")
            response_call_ids: set[str] = set()
            response_calls: dict[str, str] = {}
            for call in getattr(message, "tool_calls", None) or []:
                call_name = str(call.get("name") or "").strip()
                if not call_name:
                    raise ValueError("assistant tool call is missing a name")
                call_id = str(call.get("id") or "").strip()
                if not call_id:
                    raise ValueError("assistant tool call is missing an id")
                if call_id in response_call_ids or call_id in available_call_ids:
                    raise ValueError(
                        "assistant tool call id is duplicated while outstanding"
                    )
                response_call_ids.add(call_id)
                available_call_ids.add(call_id)
                response_calls[call_id] = call_name
            content = getattr(message, "content", None)
            content_call_ids: set[str] = set()
            if isinstance(content, list):
                for block in content:
                    if not isinstance(block, dict) or block.get("type") not in {
                        "tool_call",
                        "function_call",
                    }:
                        continue
                    block_id = str(
                    block.get("call_id") or block.get("id") or ""
                    ).strip()
                    block_name = block.get("name") or block.get("tool_name")
                    function = block.get("function")
                    if not block_name and isinstance(function, dict):
                        block_name = function.get("name")
                    block_name = str(block_name or "").strip()
                    if (
                        not block_id
                        or block_id in content_call_ids
                        or response_calls.get(block_id) != block_name
                    ):
                        raise ValueError(
                            "assistant content block does not match parsed tool call"
                        )
                    content_call_ids.add(block_id)
        elif message_type == "tool":
            call_id = str(getattr(message, "tool_call_id", "") or "")
            if not call_id or call_id not in available_call_ids:
                raise ValueError("tool result does not match a prior tool call")
            available_call_ids.remove(call_id)

    if available_call_ids:
        raise ValueError("assistant tool call is missing its tool result")


_openai_empty_sse_keepalive_patched = False


def _is_blank_sse_keepalive(event: Any) -> bool:
    """Return whether an SSE event has no JSON payload to parse."""

    data = getattr(event, "data", None)
    return data is None or (isinstance(data, str) and not data.strip())


def _patch_openai_empty_sse_keepalive() -> None:
    global _openai_empty_sse_keepalive_patched
    if _openai_empty_sse_keepalive_patched:
        return
    try:
        import functools

        from openai._streaming import AsyncStream as _AsyncStream
        from openai._streaming import Stream as _Stream

        original_async = _AsyncStream._iter_events
        if not getattr(original_async, "_evoscientist_skips_blank_sse", False):

            @functools.wraps(original_async)
            async def _filtered_async_events(self: Any) -> Any:
                async for event in original_async(self):
                    if not _is_blank_sse_keepalive(event):
                        yield event

            _filtered_async_events._evoscientist_skips_blank_sse = True  # type: ignore[attr-defined]
            _AsyncStream._iter_events = _filtered_async_events

        original_sync = _Stream._iter_events
        if not getattr(original_sync, "_evoscientist_skips_blank_sse", False):

            @functools.wraps(original_sync)
            def _filtered_sync_events(self: Any) -> Any:
                for event in original_sync(self):
                    if not _is_blank_sse_keepalive(event):
                        yield event

            _filtered_sync_events._evoscientist_skips_blank_sse = True  # type: ignore[attr-defined]
            _Stream._iter_events = _filtered_sync_events
        _openai_empty_sse_keepalive_patched = True
    except Exception:
        # The patch is only needed when the optional OpenAI SDK is available.
        pass


_deepagents_extracted_document_text_patched = False


def _patch_deepagents_extracted_document_text() -> None:
    global _deepagents_extracted_document_text_patched
    if _deepagents_extracted_document_text_patched:
        return
    try:
        from pathlib import Path as _Path

        import deepagents.middleware.filesystem as _filesystem

        from EvoScientist.document_extract import DOCUMENT_EXTENSIONS

        namespace = cast(dict[str, Any], vars(_filesystem))
        original = namespace["_get_file_type"]
        if getattr(original, "_evoscientist_document_text", False):
            _deepagents_extracted_document_text_patched = True
            return

        def _document_text_type(path: str) -> str:
            if _Path(path).suffix.lower() in DOCUMENT_EXTENSIONS:
                return "text"
            return original(path)

        _document_text_type._evoscientist_document_text = True  # type: ignore[attr-defined]
        namespace["_get_file_type"] = _document_text_type
        _deepagents_extracted_document_text_patched = True
    except Exception:
        pass