LangChain

Use it for

• — Building conversational AI agents and chatbots
• — Retrieval-augmented generation (RAG) systems
• — Document analysis and summarization
• — Multi-step reasoning and workflow automation
• — Code analysis and generation
• — Integrating multiple LLM providers
• — Production LLM observability and debugging
• — Agent evaluation and testing
• — Complex tool-calling workflows
• — Customer support automation

Get the most out of it

1. 01Start with the free LangChain framework and LangSmith Developer tier to prototype without cost; upgrade only when you need production-grade observability beyond 5,000 monthly traces
2. 02Monitor trace volume carefully in LangSmith Plus—each LLM call within a pipeline counts toward your quota; budget for overage charges ($2.50 per 1,000 traces) once you move to production
3. 03Leverage LangGraph alongside LangChain for complex agent orchestration with stateful persistence, human-in-the-loop approvals, and multi-agent delegation patterns
4. 04Use LangSmith's evaluation and regression testing features to capture production traces as test cases and maintain quality standards as you iterate on agent prompts and logic
5. 05Budget separately for LLM API costs (OpenAI, Anthropic, etc.) and LangGraph Cloud compute charges—chain-of-thought reasoning and multi-step agents multiply your model API costs compared to single direct calls