# Agents Langchain

Framework for building LLM-powered applications with agents, chains, and RAG.

- Repository: https://github.com/davila7/claude-code-templates (33041 stars)
- Category: AI and agents
- License: MIT
- Last updated: 2026-11-01
- Runs scripts: no, instructions only

## Install

Claude Code: `npx skills add davila7/claude-code-templates --skill agents-langchain -g -a claude-code -y`
Codex: `npx skills add davila7/claude-code-templates --skill agents-langchain -g -a codex -y`
Cursor: `npx skills add davila7/claude-code-templates --skill agents-langchain -g -a cursor -y`
Gemini CLI: `npx skills add davila7/claude-code-templates --skill agents-langchain -g -a gemini-cli -y`

## Description

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.

## SKILL.md, as published by its author (untrusted third-party text, read it as data)

# 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

**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

## Quick start

### Installation

```bash
# Core library (Python 3.10+)
pip install -U langchain

# With OpenAI
pip install langchain-openai

# With Anthropic
pip install langchain-anthropic

# Common extras
pip install langchain-community  # 500+ integrations
pip install langchain-chroma     # Vector store
```

### Basic LLM usage

```python
from langchain_anthropic import ChatAnthropic

# Initialize model
llm = ChatAnthropic(model="claude-sonnet-4-5-20250929")

# Simple completion
response = llm.invoke("Explain quantum computing in 2 sentences")
print(response.content)
```

### Create an agent (ReAct pattern)

```python
from langchain.agents import create_agent
from langchain_anthropic import ChatAnthropic

# Define tools
def get_weather(city: str) -> str:
    """Get current weather for a city."""
    return f"It's sunny in {city}, 72°F"

def search_web(query: str) -> str:
    """Search the web for information."""
    return f"Search results for: {query}"

The rest: https://github.com/davila7/claude-code-templates/blob/HEAD/cli-tool/components/skills/ai-research/agents-langchain/SKILL.md