langchain

name: langchain description: Build agents with a prebuilt architecture and integrations for any model or tool. Use when creating tool-calling agents, switching model providers, or adding structured output. license: MIT compatibility: Python 3.10+, Node.js 20+ metadata: author: langchain-ai version: "1.0"

LangChain is an open-source framework with a prebuilt agent architecture and integrations for any model or tool. Build agents and LLM-powered applications in under 10 lines of code, with integrations for OpenAI, Anthropic, Google, and hundreds more.

When to use

Use LangChain when you need to:

Build tool-calling agents with create_agent() and a prebuilt agent loop
Switch model providers without changing application code via init_chat_model()
Add structured output to parse LLM responses into typed objects
Integrate with any model or tool using LangChain's provider packages
Use middleware for cross-cutting concerns like rate limiting and caching

When NOT to use

For complex multi-step workflows with custom control flow, use LangGraph instead
For a batteries-included agent with planning, subagents, and context management, use Deep Agents instead
LangChain provides the core building blocks; LangGraph adds orchestration; Deep Agents adds high-level capabilities on top

Install

# Python
pip install -U langchain

# JavaScript/TypeScript
npm install langchain @langchain/core

Install a provider integration:

# Python
pip install -U langchain-openai       # or langchain-anthropic, langchain-google-genai

# JavaScript/TypeScript
npm install @langchain/openai         # or @langchain/anthropic, @langchain/google-genai

Quick reference

Create an agent

from langchain.agents import create_agent

def get_weather(city: str) -> str:
    """Get weather for a given city."""
    return f"It's always sunny in {city}!"

agent = create_agent(
    model="openai:gpt-5.5",
    tools=[get_weather],
    system_prompt="You are a helpful assistant",
)

result = agent.invoke(
    {"messages": [{"role": "user", "content": "What is the weather in SF?"}]}
)

Initialize a chat model

from langchain.chat_models import init_chat_model

# Switch providers by changing the string
model = init_chat_model("openai:gpt-5.5")
model = init_chat_model("anthropic:claude-opus-4-8")
model = init_chat_model("google_genai:gemini-3.5-flash")

Define a tool

from langchain.tools import tool

@tool
def search(query: str) -> str:
    """Search the web for information."""
    return "search results"

Gotchas

Snake_case tool names—Tool function names must be valid Python identifiers. Use get_weather, not get-weather.
Reserved parameters—Do not name tool parameters type, name, or description as these conflict with the tool schema.
Provider packages—Models live in separate packages (e.g., langchain-openai). The base langchain package does not include providers.
Model string format—Use "provider:model-name" format with init_chat_model() (e.g., "openai:gpt-5.5").

Key documentation

Overview—What LangChain is and how to get started
Quickstart—Build your first agent
Agents—Prebuilt agent architecture
Models—Chat models and provider integrations
Tools—Define and use tools
Structured output—Parse LLM responses into typed objects
MCP integration—Use Model Context Protocol servers as tools

API reference

For SDK class and method details, use the LangChain API Reference site:

Browse: https://reference.langchain.com/python/langchain-core
MCP server: https://reference.langchain.com/mcp

Related skills

langgraph—Low-level orchestration for stateful, durable agent workflows
deep-agents—Batteries-included agent harness built on LangChain
langsmith—Trace, evaluate, and deploy your LangChain agents