730a3676f8
**Description:** *Closes #[33883](https://github.com/langchain-ai/langchain/issues/33883)* Chat model cache keys are generated by serializing messages via `dumps(messages)`. The optional `BaseMessage.id` field (a UUID used solely for tracing/threading) is included in this serialization, causing functionally identical messages to produce different cache keys. This results in repeated API calls, cache bloat, and degraded performance in production workloads (e.g., agents, RAG chains, long conversations). This change normalizes messages **only for cache key generation** by stripping the nonsemantic `id` field using Pydantic V2’s `model_copy(update={"id": None})`. The normalization is applied in both synchronous and asynchronous cache paths (`_generate_with_cache` / `_agenerate_with_cache`) immediately before `dumps()`. ```python normalized_messages = [ msg.model_copy(update={"id": None}) if getattr(msg, "id", None) is not None else msg for msg in messages ] prompt = dumps(normalized_messages) --------- Co-authored-by: Mason Daugherty <mason@langchain.dev> 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.