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langchain
Python framework for building LLM agents and apps from interoperable components
LangChain is a Python library that gives a standard interface to chat models, embeddings, vector stores and tools, so providers can be swapped with little code change. Models load through init_chat_model with a provider:model string. For lower-level agent orchestration it points to LangGraph, and for tracing and evals to LangSmith.
Strengths
- Common interface across model providers, embeddings and vector stores
- Large integrations library covering tools, retrievers and vector stores
- Companions: LangGraph for orchestration, Deep Agents for planning and subagents
- MIT license; JS/TS counterpart available as LangChain.js
Weaknesses
- Observability, evals and managed deployment sit in LangSmith, a separate product
- Controllable stateful agent workflows need LangGraph, not core LangChain
- README gives no hardware requirements or list of supported providers
- Abstraction layer is another API to learn on top of provider SDKs
- Needs Model provider API or other model backend
- Models: OpenAI (README example), Other providers via integrations