LangChain-OpenAI 1.3.5 adds explicit prompt caching support
Developers can now manually control prompt caching for OpenAI models in LangChain.
LangChain has released langchain-openai==1.3.5, a minor but impactful update to its popular OpenAI integration library. The headline feature is explicit prompt caching (via PR #38762), which allows developers to manually define cache keys for frequently used prompts. This moves beyond automatic caching to give fine-grained control over when and how responses are reused — critical for applications with heavy prompt repetition, like chatbots or RAG pipelines.
The release also includes a refresh of model profile data (PR #38774) to keep up with OpenAI's latest model offerings. The package, part of the larger LangChain ecosystem (141k stars on GitHub), streamlines building LLM-powered applications. This update is particularly useful for production deployments aiming to reduce API costs and latency without sacrificing flexibility.
- Explicit prompt caching support (PR #38762) for manual cache key management
- Model profile data refresh to align with current OpenAI model versions
- Patch release (1.3.5) available on PyPI, following 1.3.4
Why It Matters
Gives developers finer control over OpenAI API costs and latency in production LangChain apps.