Langchain

LangChain

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-3.6-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