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LangChain Agents and RAG

Guides the agent to write code using LangChain for LLM agents, tool calling, memory and RAG pipelines.

Source Download skill
Repository
Orchestra-Research/AI-Research-SKILLs
Stars
14,195
Licence
MIT
Files
4 files

What it does

Once installed, the agent knows how to help you build applications on LangChain, a popular open-source Python framework for apps powered by large language models. It can write code that connects models from OpenAI, Anthropic or Google, sets up agents that call tools such as search or calculators, adds conversation memory, and builds retrieval-augmented generation (RAG) pipelines that answer questions from your own documents. The skill itself is a set of instructions and code examples; it does not run LangChain on its own.

Great for

  • Python developers prototyping chatbots or question-answering tools over documents
  • Teams comparing LLM providers who want one interface to swap models
  • Engineers who want a starting template for a tool-using agent

Skip it if

  • Non-programmers who want a finished app without writing code (the skill produces code you must install and run)
  • Projects needing the newest LangChain API: the examples use older classes such as LLMChain, ConversationBufferMemory and RetrievalQA, which may be deprecated, so check current docs
  • Sensitive data pipelines without review, since agents calling tools and loading web pages can take unintended actions or leak content

Try asking

  • “Build a Python agent with Claude that can call a weather tool and a web search tool”
  • “Create a RAG pipeline that loads my documentation site into Chroma and answers questions with sources”
  • “Add conversation memory to my chatbot so it remembers earlier turns”
  • “Show me how to get structured JSON output from an LLM using a Pydantic schema”
Step-by-step guide: how to use LangChain Agents and RAGInstall, first run, examples, tips and FAQs

Install in 3 steps

  1. 1

    Download the skill

    The ZIP contains the 4 original files, the MIT licence and an attribution note.

    langchain.zip
  2. 2

    Add it to your agent

    Claude Code (all projects)

    mkdir -p ~/.claude/skills && unzip langchain.zip -d ~/.claude/skills/

    Claude Code / Agent SDK (this project only)

    mkdir -p .claude/skills && unzip langchain.zip -d .claude/skills/

    Claude apps (web/desktop): Settings → Capabilities → Skills → Upload skill, and choose the ZIP.

  3. 3

    Ask for the task

    No special command needed — the agent reads the skill's description and loads it when your request matches. Try one of the prompts above.

Get better results

  • Pin library versions before running examples, since LangChain APIs change often and some examples use deprecated classes.
  • Keep API keys in environment variables, not in code, and review any tools the agent writes that execute code (the calculator example uses eval, which is unsafe on untrusted input).
  • Ask for a small working example first, then extend it; check the reference files for integration and RAG details.

What's inside

  • references/agents.md
  • references/integration.md
  • references/rag.md
  • SKILL.md

Redistributed unmodified from Orchestra-Research/AI-Research-SKILLs under the MIT licence. The assessment was drafted with AI from the skill's files on 2026-10-29. Skills can instruct your agent to run code — read SKILL.md and any scripts before installing.