Individual skill
agents-langchain
Framework for building LLM-powered applications with agents, chains, and RAG. Supports multiple providers (OpenAI, Anthropic, Google), 500+ integrations, ReAct agents, tool calling, memory management, and vector store retrieval. Use for building chatbots, question-answering systems, autonomous agents, or RAG applications. Best for rapid prototyping and production deployments.
cli-tool/components/skills/ai-research/agents-langchain/SKILL.md
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- anthropic Repository topic
- anthropic-claude Repository topic
- claude Repository topic
- claude-code Repository topic
- agent-skills Repository topic
- ai-agents Repository topic
- claude-code-plugins Repository topic
- cli Repository topic
- mcp Repository topic
- registry Repository topic
- agents Declared skill tag
- langchain Declared skill tag
- rag Declared skill tag
- tool-calling Declared skill tag
- react Declared skill tag
- memory-management Declared skill tag
- vector-stores Declared skill tag
- llm-applications Declared skill tag
- chatbots Declared skill tag
- production Declared skill tag
- engineering Derived category
- engineering-web Derived category
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# LangChain - Build LLM Applications with Agents & RAG
The most popular framework for building LLM-powered applications.
## When to use LangChain
**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
