LLM

Key Features

• •Composable abstractions for prompts, LLMs, chat models, tools, memory, agents, and chains (Python and JavaScript) [https://python.langchain.com/docs/get_started/introduction/].
• •Retrieval-Augmented Generation (RAG) tooling including document loaders, text splitters, embeddings, vector store integrations, and retrievers [https://python.langchain.com/docs/tutorials/rag/].
• •Agents and tools framework to let LLMs call external tools/APIs and make multi‑step decisions [https://python.langchain.com/docs/tutorials/agents/].
• •Integration layer for many model providers (OpenAI, Anthropic, Google, Azure, open‑source models, etc.) via standardized interfaces [https://python.langchain.com/docs/integrations/llms/].
• •Production infrastructure components such as LangSmith for tracing, evaluation, and debugging of LLM applications [https://www.langchain.com/langsmith].

Pricing

Core LangChain libraries (Python and JS) are open‑source under the MIT License and free to use [https://github.com/langchain-ai/langchain/blob/master/LICENSE]. LangSmith (the hosted observability and evaluation platform) uses a paid/enterprise SaaS model; detailed pricing is not publicly listed and typically requires contacting sales [https://www.langchain.com/langsmith].

Alternatives