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InMemoryRecordManager treats an explicitly provided empty list for group_ids as if it were None because of truthiness checks. This causes update() to skip length validation and store records with group_id=None, and list_keys() to return all keys instead of an empty result when group_ids=[] is passed. The async methods have the same issue.
The on_tool_error callback does not receive the arbitrary keyword arguments (e.g., tenant, trace_id) that were passed to tool.invoke, while on_tool_start and on_tool_end do. This inconsistency causes loss of critical context during error handling.
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`.
In langchain-core, `merge_configs` calls `ensure_config` on each input, which injects `DEFAULT_RECURSION_LIMIT = 25` when absent. This makes an explicitly provided `recursion_limit=25` indistinguishable from the injected default, causing it to be silently removed from the merged config.
Three security regression tests in langchain-core's prompt loading tests unconditionally create symlinks, causing OSError [WinError 1314] on default Windows environments. This makes the core test suite fail for contributors without elevated privileges or Developer Mode, and the suite has no Windows CI to catch it.
The parse_json_markdown function in langchain_core.utils.json uses a case-sensitive regex to identify fenced code blocks, causing parsing failures when models output fences with uppercase 'JSON' or 'Json'. This affects users of JsonOutputParser and other utilities relying on this function.
Blob.as_string() uses the default 'utf-8' encoding, which does not strip a UTF-8 byte order mark (BOM). Files written by Windows tools (Notepad, PowerShell, etc.) often include a BOM, and this BOM is returned as part of the string, contaminating downstream processing such as Markdown heading parsing.
The convert_to_openai_messages utility in langchain-core omits AIMessage.invalid_tool_calls from the converted OpenAI message, while the corresponding ToolMessage replies are retained, leading to orphaned tool_call_id references in exported conversations.
The UsageMetadataCallbackHandler in langchain_core only records token usage when the response's response_metadata contains a 'model_name' key. If model_name is absent, usage_metadata is silently discarded, defeating the purpose of the callback and causing silent data loss in usage tracking for custom or third-party chat models.
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.
When converting a finalized AIMessage into the v3 protocol event stream using message_to_events(), the generated message-finish event does not include the AIMessage.additional_kwargs field. This causes provider-specific data to be lost during cache replay, making replayed execution inconsistent with the original live streaming path.
XMLOutputParser.parse() raises OutputParserException for valid XML where a non-declaration element has an encoding attribute and the opening tag is followed by a newline. The regex used to remove the XML declaration is too broad and matches such element tags, causing the parser to remove the opening tag and produce malformed XML.
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.
ContextThreadPoolExecutor.map() calls len(iterables[0]) to preallocate contexts, which raises TypeError for generators and other unsized iterables, even though the standard ThreadPoolExecutor.map() accepts them. The proposed fix delegates directly to super().map(), relying on the existing submit() override for context propagation.
When an input raises an exception not in exceptions_to_handle, batch/abatch re-raise immediately, skipping the cleanup loop that closes root callback runs, causing them to remain pending forever in tracers like LangSmith.
In langchain-core's test_convert_to_openai_data_block, the Responses API file/url case sets up test data but never calls the conversion function or asserts the result, leaving the branch untested. The fix adds the missing call and assertion.
PromptTemplate.from_template("Value: {}") is accepted silently, reports an empty input variable, and then raises IndexError on every .format() call. It should be rejected at creation time with a clear ValueError, consistent with the all-digit `{0}` case.
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'.
The `limit` parameter is treated as falsy when `0`, causing all keys to be returned instead of an empty list for both `list_keys` and `alist_keys`.
The issue requests adding documentation for integrating DeadendCallbackHandler, a third-party runtime circuit breaker, into LangChain agents to block prompt injection and RCE payloads at the on_tool_start phase before tool execution.