Affected version
Reports that share this version, grouped in one feed.
When using langchain-openai with model o3 or o4-mini, passing a non-empty stop parameter results in a 400 error from OpenAI. The library does not validate unsupported parameters for these models.
When `from __future__ import annotations` is used, stringized type annotations are not resolved in the correct namespace for `RunnableLambda` and `RunnableGenerator`, causing `PydanticUserError` or empty tool argument schemas during `as_tool()` conversion, even though direct invocation works.
When streaming a chat completion, a tool-call delta chunk that includes a null `function` field causes an `AttributeError` inside `_convert_delta_to_message_chunk`. The public `ChatOpenAI.stream()` API terminates unexpectedly instead of ignoring the empty field or preserving the partial tool call.
Streaming with XMLOutputParser silently loses data when the root XML element uses a valid Unicode name (e.g., Chinese tags). The parser's start-tag detection regex only matches ASCII letters, colon, and underscore, causing it to skip Unicode roots and either return empty output or omit the root hierarchy.
The Tee context manager in langchain-core does not close the underlying source generator if one or more child iterators are never started, causing resource leaks.
The helper function _get_data_content_block_types() reconstructs its result from get_args and get_type_hints on every call, even though the result is immutable at runtime. This is called repeatedly by is_data_content_block, leading to ~23ms CPU per ChatOpenAI payload for a 204-message history, blocking the event loop in async applications.
langchain-core 1.6.6 contains two type contract inconsistencies: import_attr accepts package=None but raises TypeError due to always using relative imports, and AIMessage/AIMessageChunk.content_blocks return list[str] when content is a list of strings, violating the declared return type list[ContentBlock].
In langchain-core v3 streaming, tool-call argument deltas are emitted as full snapshots of accumulated state, leading to O(n^2) event sizes for large arguments. This causes severe performance degradation and memory overhead in LangGraph Server and any consumer of the event stream.
The default `text_format='string'` path in `convert_to_openai_messages` directly accesses `block["type"]` while collapsing content blocks, causing a `KeyError` for legitimate Bedrock Converse content blocks that lack a `type` key. The same blocks pass through correctly when `text_format='block'` is used, violating the promise of `pass_through_unknown_blocks=True`.
When using ChatOpenAI with use_responses_api=True and output_version='v1', only the first chunk (response.created) carries the provider ID resp_...; subsequent content chunks get id=None and are filled with lc_run--<run_id>. The final merged AIMessage prefers the provider ID, causing duplicate messages in LangGraph stream_mode='messages' and frontends that reconcile by ID.
The public ChatOpenAI.stream() API raises AttributeError when an OpenAI streaming chunk contains a tool-call delta whose `function` field is null. The parser assumes `rtc["function"]` is a dict and calls `.get` on None, causing a crash. Even though the repro uses a mocked response, the public API should handle malformed or unexpected provider payloads gracefully.
Docstring examples in langchain-core reference modules 'langchain_core.output_parser', 'langchain_core.runnable', and 'langchain_core.documents' which do not exist, causing ModuleNotFoundError or ImportError when copied. The correct modules are 'langchain_core.output_parsers', 'langchain_core.runnables', and 'langchain_core.documents.base'.
ChatOpenAI with extra_body={"enable_thinking": true} and bind_tools(..., tool_choice="get_weather") sends a named tool-choice object to Alibaba DashScope, which rejects it with InternalError.Algo.InvalidParameter. LangChain should validate this incompatible combination before sending the request.