GeezerVison is showing docs.langchain.com — LangChain overview - Docs by LangChain 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 Managed Deep Agents for a batteries-included harness (context compression, virtual filesystem, subagents) when you want to run production agents without having to manage infrastructure. Alternatively, for maximum flexibility while using the same Deep Agents harness, use the Deep Agents SDK and host agents yourself, or run a custom Agent Server. Deep Agents is 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:
# 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-6.1-sol",
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-6.1-sol",
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)
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 interfaceUse 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 harnessStart 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 LangGraphLangChain’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 LangSmithInspect traces, tool calls, state transitions, and latency in one place. Find failure modes, evaluate quality, and improve agent behavior with execution data.
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