> ## Documentation Index
> Fetch the complete documentation index at: https://docs.langchain.com/llms.txt
> Use this file to discover all available pages before exploring further.

# LangChain overview

> LangChain is an open source framework with a prebuilt agent architecture and integrations for any model or tool—so you can build agents that adapt as fast as the ecosystem evolves

Build completely custom agents and applications powered by LLMs in under 10 lines of code, with integrations for [OpenAI, Anthropic, Google, and more](/oss/python/integrations/providers/overview).
LangChain provides a prebuilt agent architecture and model integrations to help you get started quickly and seamlessly incorporate LLMs into your agents and applications.

<Tip>
  **LangChain vs. LangGraph vs. Deep Agents**

  Start with [Deep Agents](/oss/python/deepagents/overview/) for a "batteries-included" agent with features like automatic context compression, a virtual filesystem, and subagent-spawning. Deep Agents are built on LangChain [agents](/oss/python/langchain/agents/) which you can also use LangChain directly.

  Use [LangGraph](/oss/python/langgraph/overview), our low-level orchestration framework, for advanced needs combining deterministic and agentic workflows.

  Use [LangSmith](/langsmith/home) to trace, debug, and evaluate agents built with any of these frameworks. Follow the [tracing quickstart](/langsmith/trace-with-langchain) to get set up.
</Tip>

## <Icon icon="wand" /> Create an agent

<CodeGroup>
  ```python OpenAI theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}}
  # 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-5.4",
      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)
  ```

  ```python Google Gemini theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}}
  # 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)
  ```

  ```python Claude (Anthropic) theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}}
  # 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)
  ```

  ```python OpenRouter theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}}
  # 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)
  ```

  ```python Fireworks theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}}
  # 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)
  ```

  ```python Baseten theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}}
  # 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",
      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)
  ```

  ```python Ollama theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}}
  # 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)
  ```

  ```python Azure theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}}
  # pip install -qU langchain "langchain[openai]"
  import os
  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="azure_openai:gpt-5.4",
      tools=[get_weather],
      system_prompt="You are a helpful assistant",
      azure_deployment=os.environ["AZURE_OPENAI_DEPLOYMENT_NAME"],
  )

  result = agent.invoke(
      {"messages": [{"role": "user", "content": "What's the weather in San Francisco?"}]}
  )
  print(result["messages"][-1].content_blocks)
  ```

  ```python AWS Bedrock theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}}
  # 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}!"

  agent = create_agent(
      model="anthropic.claude-3-5-sonnet-20240620-v1:0",
      model_provider="bedrock_converse",
      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)
  ```

  ```python HuggingFace theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}}
  # 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="microsoft/Phi-3-mini-4k-instruct",
      model_provider="huggingface",
      tools=[get_weather],
      system_prompt="You are a helpful assistant",
      temperature=0.7,
      max_tokens=1024,
  )

  result = agent.invoke(
      {"messages": [{"role": "user", "content": "What's the weather in San Francisco?"}]}
  )
  print(result["messages"][-1].content_blocks)
  ```
</CodeGroup>

See the [Installation instructions](/oss/python/langchain/install) and [Quickstart guide](/oss/python/langchain/quickstart) to get started building your own agents and applications with LangChain.

<Tip>
  Use [LangSmith](/langsmith/home) to trace requests, debug agent behavior, and evaluate outputs. Set `LANGSMITH_TRACING=true` and your API key to get started.
</Tip>

## <Icon icon="star" size={20} /> Core benefits

<Columns cols={2}>
  <Card title="Standard model interface" icon="refresh" href="/oss/python/langchain/models" arrow cta="Learn more">
    Different providers have unique APIs for interacting with models, including the format of responses. LangChain standardizes how you interact with models so that you can seamlessly swap providers and avoid lock-in.
  </Card>

  <Card title="Easy to use, highly flexible agent" icon="wand" href="/oss/python/langchain/agents" arrow cta="Learn more">
    LangChain's agent abstraction is designed to be easy to get started with, letting you build a simple agent in under 10 lines of code. But it also provides enough flexibility to allow you to do all the context engineering your heart desires.
  </Card>

  <Card title="Built on top of LangGraph" icon="https://mintcdn.com/langchain-5e9cc07a/nQm-sjd_MByLhgeW/images/brand/langgraph-icon.png?fit=max&auto=format&n=nQm-sjd_MByLhgeW&q=85&s=b997e1a7487d507a36556eedbfd99f81" href="/oss/python/langgraph/overview" arrow cta="Learn more" width="195" height="195" data-path="images/brand/langgraph-icon.png">
    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.
  </Card>

  <Card title="Debug with LangSmith" icon="https://mintcdn.com/langchain-5e9cc07a/nQm-sjd_MByLhgeW/images/brand/observability-icon-dark.png?fit=max&auto=format&n=nQm-sjd_MByLhgeW&q=85&s=ccbc183bca2a5e4ca78d30149e3836cc" href="/langsmith/observability" arrow cta="Learn more" width="200" height="200" data-path="images/brand/observability-icon-dark.png">
    Gain deep visibility into complex agent behavior with visualization tools that trace execution paths, capture state transitions, and provide detailed runtime metrics.
  </Card>
</Columns>

***

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  <Callout icon="terminal-2">
    [Connect these docs](/use-these-docs) to Claude, VSCode, and more via MCP for real-time answers.
  </Callout>

  <Callout icon="edit">
    [Edit this page on GitHub](https://github.com/langchain-ai/docs/edit/main/src/oss/langchain/overview.mdx) or [file an issue](https://github.com/langchain-ai/docs/issues/new/choose).
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