LangChain

LangChain is the most widely adopted framework for building applications powered by large language models. It provides a standardized interface for connecting LLMs to external data sources, tools, and APIs through composable chains and autonomous agents. With native integrations for Ollama, llama.cpp, and any OpenAI-compatible endpoint, LangChain is the primary way developers build sophisticated AI applications on top of local models.

Key Features

Composable chains. LangChain’s core abstraction lets you pipe LLM calls together with data transformations, API calls, and conditional logic. Build multi-step workflows where the output of one model call feeds into the next, with prompt templates, output parsers, and retry logic handled automatically.

Agent framework. Define tools that an LLM can invoke autonomously — web search, database queries, code execution, file operations — and let the model decide which tools to use and in what order. LangChain’s agent system handles the reasoning loop, tool dispatch, and error recovery.

RAG pipeline support. LangChain provides end-to-end retrieval-augmented generation with document loaders for 80+ file formats, text splitters, embedding model integrations, and vector store connectors for ChromaDB, FAISS, Weaviate, Pinecone, and others. Build a local knowledge base that grounds model responses in your specific documents.

Local model integrations. The ChatOllama and ChatLlamaCpp classes connect directly to local inference backends. Any tool running an OpenAI-compatible API — Ollama, vLLM, LM Studio, Mullama — works through the ChatOpenAI class by changing the base URL. No cloud API keys required.

Python and TypeScript. LangChain ships as both a Python package (langchain) and a JavaScript/TypeScript package (langchain/js), covering the two most common languages for AI application development. Both share the same architectural concepts and API patterns.

When to Use LangChain

Use LangChain when building AI applications that go beyond simple chat — retrieval-augmented generation, multi-step agents, tool-using assistants, or data processing pipelines. It is the right choice when you need structured orchestration of LLM calls with external data and tools, especially if you want your application to work with both local and cloud models interchangeably.

Ecosystem Role

LangChain sits in the application layer above inference engines. It does not run models itself but orchestrates calls to engines like Ollama, vLLM, or llama.cpp. Pair it with a local inference backend and a vector database like ChromaDB to build fully private AI applications with no external API dependencies.