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LangChain provides create_agent: a minimal, highly configurable agent harness. Compose exactly the agent your use case needs from model, tools, prompt, and middleware.

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.

LangChain vs. LangGraph vs. Deep AgentsStart with Deep Agents for a batteries-included agent with features like automatic context compression, a virtual filesystem, and subagent-spawning. Deep Agents are built on LangChain agents which you can also use directly.Use LangChain (create_agent) for a highly customizable harness, easily tailored to your use case and data.Use LangGraph, 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:

OpenAI
Google Gemini
Claude (Anthropic)
OpenRouter
Fireworks
Baseten
Ollama
Azure
AWS Bedrock
HuggingFace
# 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)
# 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)
# 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)
# 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)
# 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)
# 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)
# 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)
# 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)
# 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)
# 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 and Quickstart guide 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

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.

Learn more

Highly configurable harness

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.

Learn more

Built on top of LangGraph

LangChains agents are built on top of LangGraph. This allows us to take advantage of LangGraphs durable execution, human-in-the-loop support, persistence, and more.

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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.

Learn more

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Core components

Middleware

Frontend

Advanced usage

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On this page

LangChain overview

Create an agent

Core benefits

Standard model interface

Highly configurable harness

Built on top of LangGraph

Debug with LangSmith