BeClaude

langchain

New
19.9kSmitheryGeneralby davila7

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.

First seen 6/25/2026

Summary

The LangChain skill enables developers to build LLM-powered applications with agents, chains, and RAG, supporting over 500 integrations across multiple providers.

  • It simplifies rapid prototyping and production deployment of chatbots, Q&A systems, autonomous agents, and retrieval-augmented generation workflows.

Install & Usage

1
Open your MCP config
~/.claude.json
2
Add the server config

Add the configuration to "mcpServers": { "langchain": { "command": "...", "args": [] } }

3
Restart Claude Code
/mcp

Use Cases

Build a conversational chatbot with memory that remembers user context across sessions.
Create a RAG system that retrieves documents from a vector store and answers questions based on them.
Develop an autonomous ReAct agent that uses tools like web search or APIs to complete complex tasks.
Chain multiple LLM calls together to process data step-by-step, such as summarizing then translating text.
Integrate with external APIs and databases to fetch real-time information and act on it.
Rapidly prototype a multi-provider LLM application switching between OpenAI, Anthropic, and Google models.

Usage Examples

1

/langchain Create a conversational agent with memory that can search the web and answer questions about current events.

2

/langchain Build a RAG pipeline using a PDF document store and answer 'What are the key findings in this report?'

3

/langchain Set up a chain that first summarizes a long article, then translates the summary into French.

View source on GitHub
deploymentapiagentai-&-mlcoding

Security Audits

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Frequently Asked Questions

What is langchain?

The LangChain skill enables developers to build LLM-powered applications with agents, chains, and RAG, supporting over 500 integrations across multiple providers. It simplifies rapid prototyping and production deployment of chatbots, Q&A systems, autonomous agents, and retrieval-augmented generation workflows.

How to install langchain?

To install langchain: open your mcp config (~/.claude.json), then add the config to "mcpServers": { "langchain": { "command": "...", "args": [] } }. Finally, /mcp in Claude Code.

What is langchain best for?

langchain is a mcp categorized under General. It is designed for: deployment, api, agent, ai-&-ml, coding. Created by davila7.

What can I use langchain for?

langchain is useful for: Build a conversational chatbot with memory that remembers user context across sessions.; Create a RAG system that retrieves documents from a vector store and answers questions based on them.; Develop an autonomous ReAct agent that uses tools like web search or APIs to complete complex tasks.; Chain multiple LLM calls together to process data step-by-step, such as summarizing then translating text.; Integrate with external APIs and databases to fetch real-time information and act on it.; Rapidly prototype a multi-provider LLM application switching between OpenAI, Anthropic, and Google models..