LangChain 概览

Agent = Model + Harness. LangChain provides create_agent: a minimal, highly configurable harness. The harness is everything around the model loop: the prompt, the tools, and any middleware that shapes behavior. Start with the primitives and compose exactly what your use case needs. Supports OpenAI, Anthropic, Google, and more (opens in a new tab).

LangChain vs. LangGraph vs. Deep Agents

Start with Deep Agents (opens in a new tab) for a "batteries-included" agent with features like automatic context compression, a virtual filesystem, and subagent-spawning. Deep Agents are built on LangChain agents (opens in a new tab) which you can also use directly.

Use LangChain (opens in a new tab) (create_agent) for a highly customizable harness, easily tailored to your use case and data.

Use LangGraph (opens in a new tab), our low-level orchestration framework, for advanced needs combining deterministic and agentic workflows.

Use LangSmith to trace, debug, and evaluate agents built with any of these frameworks. Follow the tracing quickstart to get set up. We recommend you also set up LangSmith Engine which monitors your traces, detects issues, and proposes fixes.

Create an agent

This example demonstrates how to create a simple LangChain agent with a custom tool:

# pip install -qU langchain "langchain[openai]"
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's the weather in San Francisco?"}]}
)
print(result["messages"][-1].content_blocks)

Google Gemini

# pip install -qU langchain "langchain[google-genai]"
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="google_genai:gemini-2.5-flash-lite",
    tools=[get_weather],
    system_prompt="You are a helpful assistant",
)
 
result = agent.invoke(
    {"messages": [{"role": "user", "content": "What's the weather in San Francisco?"}]}
)
print(result["messages"][-1].content_blocks)

Claude (Anthropic)

# pip install -qU langchain "langchain[anthropic]"
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="claude-sonnet-4-6",
    tools=[get_weather],
    system_prompt="You are a helpful assistant",
)
 
result = agent.invoke(
    {"messages": [{"role": "user", "content": "What's the weather in San Francisco?"}]}
)
print(result["messages"][-1].content_blocks)

OpenRouter

# pip install -qU langchain langchain-openrouter
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="openrouter:anthropic/claude-sonnet-4-6",
    tools=[get_weather],
    system_prompt="You are a helpful assistant",
)
 
result = agent.invoke(
    {"messages": [{"role": "user", "content": "What's the weather in San Francisco?"}]}
)
print(result["messages"][-1].content_blocks)

Fireworks

# pip install -qU langchain langchain-fireworks
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="fireworks:accounts/fireworks/models/qwen3p5-397b-a17b",
    tools=[get_weather],
    system_prompt="You are a helpful assistant",
)
 
result = agent.invoke(
    {"messages": [{"role": "user", "content": "What's the weather in San Francisco?"}]}
)
print(result["messages"][-1].content_blocks)

Baseten

# pip install -qU langchain langchain-baseten
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="baseten:zai-org/GLM-5.2",
    tools=[get_weather],
    system_prompt="You are a helpful assistant",
)
 
result = agent.invoke(
    {"messages": [{"role": "user", "content": "What's the weather in San Francisco?"}]}
)
print(result["messages"][-1].content_blocks)

Ollama

# pip install -qU langchain langchain-ollama
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="ollama:devstral-2",
    tools=[get_weather],
    system_prompt="You are a helpful assistant",
)
 
result = agent.invoke(
    {"messages": [{"role": "user", "content": "What's the weather in San Francisco?"}]}
)
print(result["messages"][-1].content_blocks)

Azure

# pip install -qU langchain "langchain[openai]"
import os
from langchain.agents import create_agent
from langchain.chat_models import init_chat_model
 
def get_weather(city: str) -> str:
    """Get weather for a given city."""
    return f"It's always sunny in {city}!"
 
model = init_chat_model(
    "azure_openai:gpt-5.5",
    azure_deployment=os.environ["AZURE_OPENAI_DEPLOYMENT_NAME"],
)
agent = create_agent(
    model=model,
    tools=[get_weather],
    system_prompt="You are a helpful assistant",
)
 
result = agent.invoke(
    {"messages": [{"role": "user", "content": "What's the weather in San Francisco?"}]}
)
print(result["messages"][-1].content_blocks)

AWS Bedrock

# pip install -qU langchain langchain-aws
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}!"
 
# US cross-region inference profile; use global.anthropic.claude-sonnet-4-6 for worldwide routing.
agent = create_agent(
    model="bedrock_converse:us.anthropic.claude-sonnet-4-6",
    tools=[get_weather],
    system_prompt="You are a helpful assistant",
)
 
result = agent.invoke(
    {"messages": [{"role": "user", "content": "What's the weather in San Francisco?"}]}
)
print(result["messages"][-1].content_blocks)

HuggingFace

# pip install -qU langchain "langchain[huggingface]"
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="huggingface:microsoft/Phi-3-mini-4k-instruct",
    tools=[get_weather],
    system_prompt="You are a helpful assistant",
)
 
result = agent.invoke(
    {"messages": [{"role": "user", "content": "What's the weather in San Francisco?"}]}
)
print(result["messages"][-1].content_blocks)

See the Installation instructions (opens in a new tab) and Quickstart guide (opens in a new tab) to get started building your own agents and applications with LangChain.

Use LangSmith to trace requests, debug agent behavior, and evaluate outputs. Set LANGSMITH_TRACING=true and your API key to get started.

Core benefits

Standard model interface (opens in a new tab):Use one interface for chat models, embeddings, and more across providers. Switch models with minimal code changes and keep your application portable as requirements evolve.
Highly configurable harness (opens in a new tab):Start with create_agent as a minimal harness and add capabilities incrementally through middleware. Compose only what your use case needs, from guardrails and retries to routing and custom tool policies.
Built on top of LangGraph (opens in a new tab):LangChain's agents are built on top of LangGraph. This allows us to take advantage of LangGraph's durable execution, human-in-the-loop support, persistence, and more.
Debug with LangSmith:Inspect traces, tool calls, state transitions, and latency in one place. Find failure modes, evaluate quality, and improve agent behavior with execution data.

Models Quickstart