a6eb829701
When a langsmith `@traceable` function invokes a LangChain Runnable or LangGraph subgraph, the callback manager's `_configure` function injects the `@traceable` RunTree into the `LangChainTracer`'s `run_map` so that child runs can resolve their parent for trace nesting. However, since the RunTree was created outside the tracer's callback lifecycle, `_end_trace` never removes it. The entry persists in `run_map` indefinitely, retaining the full RunTree and its entire child tree. In applications with nested subgraph invocations (e.g. an outer investigation graph delegating to skill agent subgraphs, each compiled as their own `StateGraph`), this causes RunTree objects to accumulate linearly with every call. **Fix:** Track which `run_map` entries were injected externally via a shared `_external_run_ids` refcount dict on `_TracerCore`. When `_start_trace` adds a child under an external parent, it increments the count. When `_end_trace` finishes a child, it decrements — and evicts the external parent from `run_map` once the last child completes. The refcount (rather than a simple set) is necessary because a single external parent may have multiple sibling children in the callback chain (e.g. a `prompt | llm` `RunnableSequence`). Only truly external runs are tracked — the `_configure` guard `if run_id_str not in handler.run_map` prevents tracer-managed runs from being misclassified.
🦜🍎️ 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. You can also chat with the docs using Chat LangChain.
📕 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.