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# Add `tool_call_id` to `on_tool_error` event data ## Summary This PR addresses issue #33597 by adding `tool_call_id` to the `on_tool_error` callback event data. This enables users to link tool errors to specific tool calls in stateless agent implementations, which is essential for building OpenAI-compatible APIs and tracking tool execution flows. ## Problem When streaming events using `astream_events` with `version="v2"`, the `on_tool_error` event only included the error and input data, but lacked the `tool_call_id`. This made it difficult to: - Link errors to specific tool calls in stateless agent scenarios - Implement OpenAI-compatible APIs that require tool call tracking - Track tool execution flows when using `run_id` is not sufficient ## Solution The fix adds `tool_call_id` propagation through the callback chain: 1. **Pass `tool_call_id` to callbacks**: Updated `BaseTool.run()` and `BaseTool.arun()` to pass `tool_call_id` to both `on_tool_start` and `on_tool_error` callbacks 2. **Store in event stream handler**: Modified `_AstreamEventsCallbackHandler` to store `tool_call_id` in run info during `on_tool_start` 3. **Include in error events**: Updated `on_tool_error` handler to extract and include `tool_call_id` in the event data ## Changes - **`libs/core/langchain_core/tools/base.py`**: - Pass `tool_call_id` to `on_tool_start` in both sync and async methods - Pass `tool_call_id` to `on_tool_error` when errors occur - **`libs/core/langchain_core/tracers/event_stream.py`**: - Store `tool_call_id` in run info during `on_tool_start` - Extract `tool_call_id` from kwargs or run info in `on_tool_error` - Include `tool_call_id` in the `on_tool_error` event data ## Testing The fix was verified by: 1. Direct tool invocation: Confirmed `tool_call_id` appears in `on_tool_error` event data when calling tools directly 2. Agent integration: Tested with `create_agent` to ensure `tool_call_id` is present in error events during agent execution ```python # Example verification async for event in agent.astream_events( {"messages": "Please demonstrate a tool error"}, version="v2", ): if event["event"] == "on_tool_error": assert "tool_call_id" in event["data"] # ✓ Now passes print(event["data"]["tool_call_id"]) ``` ## Backward Compatibility - ✅ Fully backward compatible: `tool_call_id` is optional (can be `None`) - ✅ No breaking changes: All changes are additive - ✅ Existing code continues to work without modification ## Related Issues Fixes #33597 --------- Co-authored-by: Mason Daugherty <github@mdrxy.com>
The platform for reliable agents.
LangChain is a framework for building agents and LLM-powered applications. It helps you chain together interoperable components and third-party integrations to simplify AI application development – all while future-proofing decisions as the underlying technology evolves.
pip install langchain
If you're looking for more advanced customization or agent orchestration, check out LangGraph, our framework for building controllable agent workflows.
Documentation:
- docs.langchain.com – Comprehensive documentation, including conceptual overviews and guides
- reference.langchain.com/python – API reference docs for LangChain packages
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Note
Looking for the JS/TS library? Check out LangChain.js.
Why use LangChain?
LangChain helps developers build applications powered by LLMs through a standard interface for models, embeddings, vector stores, and more.
Use LangChain for:
- Real-time data augmentation. Easily connect LLMs to diverse data sources and external/internal systems, drawing from LangChain's vast library of integrations with model providers, tools, vector stores, retrievers, and more.
- Model interoperability. Swap models in and out as your engineering team experiments to find the best choice for your application's needs. As the industry frontier evolves, adapt quickly – LangChain's abstractions keep you moving without losing momentum.
- Rapid prototyping. Quickly build and iterate on LLM applications with LangChain's modular, component-based architecture. Test different approaches and workflows without rebuilding from scratch, accelerating your development cycle.
- Production-ready features. Deploy reliable applications with built-in support for monitoring, evaluation, and debugging through integrations like LangSmith. Scale with confidence using battle-tested patterns and best practices.
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- Flexible abstraction layers. Work at the level of abstraction that suits your needs - from high-level chains for quick starts to low-level components for fine-grained control. LangChain grows with your application's complexity.
LangChain ecosystem
While the LangChain framework can be used standalone, it also integrates seamlessly with any LangChain product, giving developers a full suite of tools when building LLM applications.
To improve your LLM application development, pair LangChain with:
- LangGraph – Build agents that can reliably handle complex tasks with LangGraph, our low-level agent orchestration framework. LangGraph offers customizable architecture, long-term memory, and human-in-the-loop workflows – and is trusted in production by companies like LinkedIn, Uber, Klarna, and GitLab.
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- Deep Agents (new!) – Build agents that can plan, use subagents, and leverage file systems for complex tasks
Additional resources
- API Reference – Detailed reference on navigating base packages and integrations for LangChain.
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