Affected version
Reports that share this version, grouped in one feed.
BaseCumulativeTransformOutputParser converts incoming AIMessage to BaseMessageChunk using chunk.model_dump(), but BaseMessageChunk lacks tool_calls, causing tool calls to be dropped. This results in empty stream/astream output for non-streaming chat models (e.g., disable_streaming="tool_calling") even though invoke works correctly.
A tool using `InjectedToolCallId` receives the wrong tool_call_id when the custom args_schema omits the injected field and the ToolCall args contain a key `tool_call_id`. The runtime ToolCall.id should always win, but currently the user-provided value from args is used, causing mismatch between tool body and ToolMessage.
The RunnableWithFallbacks methods mutate the caller-provided input dictionary by inserting the exception_key, violating expected immutability and causing unexpected side effects across all six entry points (invoke, ainvoke, batch, abatch, stream, astream).
An injected tool argument declared with a Pydantic alias (e.g., Field(alias="private_token")) is not stripped from the inputs and input_str passed to on_tool_start callbacks, leaking internal sentinel values to tracing backends.
RunnableParallel's async execution paths (ainvoke and _atransform) do not read the `max_concurrency` configuration key, causing all branches to run concurrently and potentially violating downstream rate limits. The sync path correctly uses a thread pool with the configured limit.
Report of numerous test failures in langchain-core 1.6.5 on FreeBSD 15.1 (Python 3.12), likely due to platform-specific environmental issues such as event loop policy or locale settings.
Streaming large tool call arguments through (prompt | model).stream() or with_structured_output(...).stream() triggers repeated parse_partial_json calls on every AIMessageChunk addition, causing quadratic CPU time as arguments grow.