agents-langchain

Topics in the source

• LangChain - Build LLM Applications with Agents & RAG
• When to use LangChain
• Quick start
• Installation
• Basic LLM usage
• Create an agent (ReAct pattern)
• Core concepts
• 1. Models - LLM abstraction

The most popular framework for building LLM-powered applications. Use LangChain when: Building agents with tool calling and reasoning (ReAct pattern) Implementing RAG (retrieval-augmented generation) pipelines Need to swap LLM providers easily (OpenAI, Anthropic, Google) Creating chatbots with conversation memory Rapid prototyping of LLM applications Production deployments with LangSmith observability Metrics: 119,000+ GitHub stars 272,000+ repositories use LangChain 500+ integrations (models, vector stores, tools) 3,800+ contributors Use alternatives instead: LlamaIndex: RAG-focused, better for document Q&A LangGraph: Complex stateful workflows, more control Haystack: Production search pipelines Semantic Kernel: Microsoft ecosystem ReAct (Reasoning + Acting) pattern: 1. Start simple - Use createagent() for most cases 2. Enable streaming - Better UX for long responses 3. Add error handling - Tools can fail, handle gracefully 4. Use LangSmith - Essential for debugging agents 5. Optimize chunk size - 500-1000 chars for RAG 6. Version prompts - Track changes in production 7. Cache embeddings - Expensive, cache when possible 8. Monitor costs - Track token usage with LangSmith | Operati

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