LangChain Framework for Building LLM Applications

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LangChain is an open-source framework for building applications powered by large language models. It provides the connective tissue between LLMs and the rest of your application stack.

What It Is

LangChain abstracts the complexity of working with LLMs into composable components. Instead of writing boilerplate for every LLM interaction, you chain together modular pieces: prompts, models, memory, tools, and output parsers.

Key Values

• Composability: Build complex workflows by connecting simple components
• Model-agnostic: Swap between OpenAI, Anthropic, local models, etc. with minimal code changes
• Production-ready: Built for real applications, not just prototypes

Key Features

• Chains: Sequence multiple LLM calls or operations together
• Agents: LLMs that can decide which tools to use and in what order
• Memory: Persist conversation state across interactions
• Retrieval (RAG): Connect LLMs to external data sources via vector stores
• Tools/Functions: Let LLMs interact with APIs, databases, and external systems
• Callbacks: Hook into the execution lifecycle for logging, tracing, debugging

Ecosystem Relevance

LangChain has become the de facto standard for LLM application development:

• LangSmith: Their observability platform for debugging, testing, and monitoring LLM apps
• LangGraph: Framework for building stateful, multi-actor applications with LLMs
• LangServe: Deploy LangChain apps as REST APIs
• Integrations: 700+ integrations with vector stores, document loaders, tools, and model providers

The framework dominates the Python LLM ecosystem (JavaScript/TypeScript version also available). Whether you’re building a chatbot, RAG system, or autonomous agent, LangChain is likely in the stack.