d84eef667a
## Summary Fixes #33970 `get_buffer_string` was only checking for the deprecated `function_call` field in `additional_kwargs`, which modern LLM providers no longer return. This fix updates the function to check for the modern `tool_calls` field first, falling back to `function_call` for legacy compatibility. ## Changes - Check `AIMessage.tool_calls` first (modern standard) - Fall back to `additional_kwargs["function_call"]` (legacy support) - Added 3 unit tests covering tool_calls, empty content, and precedence behavior ## Testing ```python # Before fix: tool_calls info was lost msg = AIMessage(content="Hi", tool_calls=[{"name": "search", ...}]) get_buffer_string([msg]) # "AI: Hi" (no tool info) # After fix: tool_calls are included get_buffer_string([msg]) # "AI: Hi[{\"name\": \"search\", ...}]" ``` - All existing `get_buffer_string` tests pass - Legacy `function_call` behavior preserved --- > [!NOTE] > This PR was developed with AI agent assistance (Factory/Droid). --------- Co-authored-by: Mason Daugherty <github@mdrxy.com>
🦜🍎️ LangChain Core
Looking for the JS/TS version? Check out LangChain.js.
To help you ship LangChain apps to production faster, check out LangSmith. LangSmith is a unified developer platform for building, testing, and monitoring LLM applications.
Quick Install
pip install langchain-core
🤔 What is this?
LangChain Core contains the base abstractions that power the LangChain ecosystem.
These abstractions are designed to be as modular and simple as possible.
The benefit of having these abstractions is that any provider can implement the required interface and then easily be used in the rest of the LangChain ecosystem.
⛰️ Why build on top of LangChain Core?
The LangChain ecosystem is built on top of langchain-core. Some of the benefits:
- Modularity: We've designed Core around abstractions that are independent of each other, and not tied to any specific model provider.
- Stability: We are committed to a stable versioning scheme, and will communicate any breaking changes with advance notice and version bumps.
- Battle-tested: Core components have the largest install base in the LLM ecosystem, and are used in production by many companies.
📖 Documentation
For full documentation, see the API reference. For conceptual guides, tutorials, and examples on using LangChain, see the LangChain Docs.
📕 Releases & Versioning
See our Releases and Versioning policies.
💁 Contributing
As an open-source project in a rapidly developing field, we are extremely open to contributions, whether it be in the form of a new feature, improved infrastructure, or better documentation.
For detailed information on how to contribute, see the Contributing Guide.