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* feat(agents): elide blocked write payloads from model-bound requests A write_file / str_replace call rejected by the read-before-write gate never runs, yet its payload (up to 80 KB for a non-append write, unbounded for append) stayed verbatim in every later model request: nothing in the chain rewrites AIMessage tool-call arguments, and ToolOutputBudgetMiddleware only budgets ToolMessage output. The gate demands a re-read plus a fresh call, so the model re-emits the content anyway and the original is pure dead weight. - ReadBeforeWriteMiddleware stamps `deerflow_write_block` on the blocked ToolMessage and, in wrap_model_call, replaces the paired call's payload fields (content / old_str / new_str) with a short deterministic placeholder in the model-bound request only. state["messages"], receipts, loop detection, and the run journal keep the original arguments; nothing is externalized to disk, since a file reference to content the model must re-derive after reading the target would only invite bypassing the gate. - New `tool_call_args` helper rewrites every provider surface together (structured tool_calls, raw additional_kwargs.tool_calls, tool_use content blocks, tool_call_chunks) so strict providers never see them disagree; the gate only supplies the policy (which calls, what placeholder). - `read_before_write.elide_blocked_payloads` (default on) and `read_before_write.elide_min_chars` (default 2000) configure it; the runtime builder passes the config through and the middleware declares it via release_policy_parameters. Co-Authored-By: Claude Fable 5.1 <noreply@anthropic.com> * docs(agents): condense middleware guide entry 11 to fit the guidance budget The agent-guidance CI check failed: the effective AGENTS.md chain for agents/middlewares was 99673 bytes against a 98304-byte hard limit. The chain already sat at 98459 on main, so the ReadBeforeWrite entry could not grow. Rewrite entry 11 so it states the same facts (gate, lock scope, fail-open, authorization scope, blocked-payload elision, shared tool_call_args helper) in 1229 bytes instead of 2640; the chain is now 98262 bytes. Co-Authored-By: Claude Fable 5.1 <noreply@anthropic.com> * fix(config): bump config_version for the read_before_write elision keys Review follow-ups on #5329: - `read_before_write.elide_blocked_payloads` / `elide_min_chars` are new user-settable YAML keys, i.e. a config schema change, so bump `config_version` 40 -> 41 in config.example.yaml; without it an existing config.yaml gets no outdated-config warning and `make config-upgrade` has nothing to signal. - Say in the `elide_min_chars` description (and the example comment) that the threshold and the placeholder's size figure are Python character counts, not tokens: the same value spans roughly 3-4x in real context cost between ASCII and CJK text. - The builder wiring test now asserts only the wired `elide_min_chars` value instead of the whole `ReadBeforeWriteConfig` dump, so future knobs do not have to edit an unrelated test. Co-Authored-By: Claude Fable 5.1 <noreply@anthropic.com> * chore(helm): bump chart config_version to 41 validate-chart's config_version drift check failed after config.example.yaml moved to 41 in ef9ee267. Bare bump of the chart's embedded `config:` block and the README example; the chart does not mirror the read_before_write section, so no field changes are needed. Co-Authored-By: Claude Fable 5.1 <noreply@anthropic.com> * fix(agents): rewrite Responses and v1 content-block arguments too Review finding on #5329 (P2): the content rewriter only handled Anthropic `tool_use` blocks. With `use_responses_api=true` and `output_version='responses/v1'`, AIMessage.content carries `function_call` blocks whose `arguments` still hold the full write payload, and langchain_openai's Responses input builder emits that block instead of the rewritten structured call whose `call_id` it already carries. Standard `v1` `tool_call` blocks likewise keep `extras.arguments`, which the v1->Responses translator prefers over the structured args. So the blocked payload was still sent on every later Responses API request. `tool_call_args` now rewrites every content dialect that carries its own copy of the arguments: Anthropic `tool_use` (input, drop partial_json), Responses `function_call` (arguments, matched by call_id, `fc_...` item id and status preserved), and v1 `tool_call` / `tool_call_chunk` (args plus `extras.arguments`). Tests assert against the real adapter serializers: `_construct_responses_api_input` for responses/v1, v1, and v0 messages, `_convert_message_to_dict` for chat completions, and Anthropic `_format_messages` for native and v1 content, plus an end-to-end probe through the gate's wrap_model_call. * fix(agents): pair blocked writes per call occurrence and defeat Responses chaining Two review findings on #5329: - Tool-call ids may repeat across assistant turns (DanglingToolCallMiddleware pairs them with per-id queues). The gate matched blocked results against a history-wide id set, so a successful write sharing an id with a later (or earlier) blocked one also lost its payload and was labelled as blocked. `_blocked_call_occurrences` now pairs ToolMessages with call occurrences the same FIFO-per-id way and the selector keys on (message, call id). - With `use_previous_response_id`, the OpenAI adapter sends only the messages after the last AIMessage carrying a `resp_` response id and lets the server rebuild the rest from its stored copy, which still holds the original arguments and cannot be edited; every later response chains back to it. `rewrite_messages_tool_call_args` now drops every `resp_` id from the model-bound copy whenever it rewrote anything, so the adapter replays the full rewritten history (the `use_previous_response_id=False` request shape). OpenAI bills chained input tokens as input either way, so replay costs no more; the state keeps its ids. Tests cover success-before-block and block-before-success histories through the Chat Completions serializer, and chaining through `ChatOpenAI._get_request_payload` with `use_previous_response_id=True`: unrewritten history chains and omits the call, rewritten history is replayed with the placeholder and no `previous_response_id`. --------- Co-authored-by: Claude Fable 5.1 <noreply@anthropic.com>
200 lines
10 KiB
Python
200 lines
10 KiB
Python
"""Rewrite AIMessage tool-call arguments on every provider surface at once.
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Middlewares that shrink or replace a historical tool call's arguments in the
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*model-bound request* (never in graph state) share one hazard: a LangChain
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``AIMessage`` carries the same arguments on up to four surfaces, and provider
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adapters do not all read the same one —
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- ``tool_calls``: the structured list most adapters prefer;
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- ``additional_kwargs["tool_calls"]``: the raw provider payload (OpenAI
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``function.arguments`` JSON string) some adapters fall back to;
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- ``content`` blocks that carry their own copy of the arguments: Anthropic
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``tool_use`` (``input`` + ``partial_json``), OpenAI Responses
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``function_call`` (``arguments`` string, matched by ``call_id``; the
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``fc_…`` item id is preserved), and LangChain standard-content
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``tool_call`` / ``tool_call_chunk`` (``args`` plus ``extras.arguments``);
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- ``tool_call_chunks`` on an ``AIMessageChunk``.
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Rewriting only one surface leaves the original payload reachable through the
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others and can hand a strict provider a request whose surfaces disagree. The
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content surfaces matter most: ``langchain_openai``'s Responses input builder
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emits a content ``function_call`` block *instead of* the structured call
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whose ``call_id`` it already carries, and prefers ``extras.arguments`` over
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the structured args when translating a v1 ``tool_call`` block, so a rewrite
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that touched ``tool_calls`` alone would still send the original payload.
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:func:`rewrite_tool_call_args` rewrites them together and returns a
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``model_copy`` (or the same object when nothing matched), so callers never
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mutate state and the result is identical across model calls. Policy — which
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calls, and what replaces their arguments — stays with the caller; see
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``read_before_write_middleware.elide_blocked_write_payloads`` for one.
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A rewrite also invalidates server-side continuation. With
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``use_previous_response_id`` the OpenAI adapter sends only the messages after
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the last AIMessage carrying a ``resp_…`` ``response_metadata["id"]`` and lets
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the server rebuild the rest from *its* stored copy of the conversation, which
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still holds the original arguments; stored responses cannot be edited, and
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every response produced after the rewritten call chains back to that history.
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So whenever anything was rewritten, :func:`rewrite_messages_tool_call_args`
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drops every ``resp_`` id from the model-bound copy and the adapter falls back
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to replaying the full rewritten history (the same request shape as
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``use_previous_response_id=False``; per OpenAI's docs chained input tokens are
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billed either way, so replay costs no more).
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"""
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from __future__ import annotations
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import json
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from collections.abc import Callable, Mapping, Sequence
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from typing import Any
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from langchain_core.messages import AIMessage
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#: Replacement args keyed by tool-call id.
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ArgsReplacements = Mapping[str, dict[str, Any]]
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#: ``(message, tool_call) -> new_args`` or ``None`` to leave the call alone.
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ReplacementSelector = Callable[[AIMessage, dict[str, Any]], dict[str, Any] | None]
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def rewrite_messages_tool_call_args(messages: list[Any], replacement_for: ReplacementSelector) -> list[Any] | None:
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"""Apply ``replacement_for(message, tool_call)`` to every AIMessage tool call in ``messages``.
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Returns a new list with the rewritten AIMessages, or ``None`` when no call
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was replaced. Untouched messages pass through by identity, except that once
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anything was rewritten every AIMessage loses its ``resp_`` response id (see
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the module docstring: the server-side history behind that id still holds
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the original arguments). Only calls with a non-empty string id are offered
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to the selector, since nothing else can be matched across surfaces.
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"""
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updated: list[Any] = []
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changed = False
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for message in messages:
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patched = message
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if isinstance(message, AIMessage) and message.tool_calls:
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replacements: dict[str, dict[str, Any]] = {}
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for tool_call in message.tool_calls:
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if not isinstance(tool_call, dict):
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continue
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call_id = tool_call.get("id")
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if not isinstance(call_id, str) or not call_id:
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continue
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new_args = replacement_for(message, tool_call)
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if new_args is not None:
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replacements[call_id] = new_args
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if replacements:
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patched = rewrite_tool_call_args(message, replacements)
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if patched is not message:
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changed = True
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updated.append(patched)
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if not changed:
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return None
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return [_without_response_chain_id(message) for message in updated]
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def _without_response_chain_id(message: Any) -> Any:
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"""Drop an OpenAI ``resp_`` response id so the adapter replays history instead of chaining to it."""
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if not isinstance(message, AIMessage):
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return message
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response_metadata = message.response_metadata or {}
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response_id = response_metadata.get("id")
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if not (isinstance(response_id, str) and response_id.startswith("resp_")):
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return message
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return message.model_copy(update={"response_metadata": {key: value for key, value in response_metadata.items() if key != "id"}})
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def rewrite_tool_call_args(message: AIMessage, replacements: ArgsReplacements) -> AIMessage:
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"""Return ``message`` with the args of every tool call in ``replacements`` (by id) rewritten on all surfaces.
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``message`` is never mutated; the same object comes back when no id matches.
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"""
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if not replacements:
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return message
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update: dict[str, Any] = {}
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tool_calls = message.tool_calls or []
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rewritten_calls = [dict(tool_call, args=new_args) if isinstance(tool_call, dict) and (new_args := _replacement_for_id(tool_call.get("id"), replacements)) is not None else tool_call for tool_call in tool_calls]
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if _any_replaced(rewritten_calls, tool_calls):
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update["tool_calls"] = rewritten_calls
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tool_call_chunks = getattr(message, "tool_call_chunks", None)
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if isinstance(tool_call_chunks, list):
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rewritten_chunks = [dict(chunk, args=_serialize(new_args)) if isinstance(chunk, dict) and (new_args := _replacement_for_id(chunk.get("id"), replacements)) is not None else chunk for chunk in tool_call_chunks]
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if _any_replaced(rewritten_chunks, tool_call_chunks):
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update["tool_call_chunks"] = rewritten_chunks
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additional_kwargs = message.additional_kwargs or {}
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raw_tool_calls = additional_kwargs.get("tool_calls")
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if isinstance(raw_tool_calls, list):
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rewritten_raw = [_rewrite_raw_tool_call(entry, replacements) for entry in raw_tool_calls]
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if _any_replaced(rewritten_raw, raw_tool_calls):
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update["additional_kwargs"] = {**additional_kwargs, "tool_calls": rewritten_raw}
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if isinstance(message.content, list):
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rewritten_content = [_rewrite_content_block(block, replacements) for block in message.content]
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if _any_replaced(rewritten_content, message.content):
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update["content"] = rewritten_content
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return message.model_copy(update=update) if update else message
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def _replacement_for_id(identifier: Any, replacements: ArgsReplacements) -> dict[str, Any] | None:
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"""Non-string ids (malformed provider payloads) never match, and never raise from a membership probe."""
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return replacements.get(identifier) if isinstance(identifier, str) else None
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def _any_replaced(rewritten: Sequence[Any], original: Sequence[Any]) -> bool:
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return any(new is not old for new, old in zip(rewritten, original, strict=True))
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def _serialize(args: dict[str, Any]) -> str:
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return json.dumps(args, ensure_ascii=False)
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def _rewrite_raw_tool_call(entry: Any, replacements: ArgsReplacements) -> Any:
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"""Rewrite one raw provider tool-call payload (OpenAI ``function.arguments`` JSON string, or flattened variants)."""
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if not isinstance(entry, dict):
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return entry
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new_args = _replacement_for_id(entry.get("id"), replacements)
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if new_args is None:
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return entry
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function = entry.get("function")
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if isinstance(function, dict):
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return {**entry, "function": {**function, "arguments": _serialize(new_args)}}
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if isinstance(entry.get("arguments"), str):
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return {**entry, "arguments": _serialize(new_args)}
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if isinstance(entry.get("args"), dict):
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return {**entry, "args": new_args}
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return entry
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def _rewrite_content_block(block: Any, replacements: ArgsReplacements) -> Any:
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"""Rewrite one content block that carries tool-call arguments; anything else passes through by identity."""
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if not isinstance(block, dict):
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return block
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block_type = block.get("type")
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if block_type == "tool_use":
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# Anthropic: ``partial_json`` is dropped so it cannot leak the old payload.
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new_args = _replacement_for_id(block.get("id"), replacements)
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if new_args is None:
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return block
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rewritten = {key: value for key, value in block.items() if key != "partial_json"}
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rewritten["input"] = new_args
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return rewritten
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if block_type == "function_call":
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# OpenAI Responses (``responses/v1``): matched by ``call_id``; the ``fc_…`` item id and status are kept.
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new_args = _replacement_for_id(block.get("call_id"), replacements)
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if new_args is None:
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return block
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return {**block, "arguments": _serialize(new_args)}
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if block_type in ("tool_call", "tool_call_chunk"):
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# LangChain standard content (``v1``): ``args`` is a dict on tool_call and a JSON string on
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# tool_call_chunk; ``extras.arguments`` (raw provider string) wins in the Responses translator.
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new_args = _replacement_for_id(block.get("id"), replacements)
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if new_args is None:
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return block
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rewritten = {**block, "args": new_args if block_type == "tool_call" else _serialize(new_args)}
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extras = block.get("extras")
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if isinstance(extras, dict) and "arguments" in extras:
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rewritten["extras"] = {**extras, "arguments": _serialize(new_args)}
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return rewritten
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return block
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