> ## Documentation Index
> Fetch the complete documentation index at: https://langchain-5e9cc07a-preview-addsan-1770744332-3d759b5.mintlify.site/llms.txt
> Use this file to discover all available pages before exploring further.

# Deep Agents overview

> Build agents that can plan, use subagents, and leverage file systems for complex tasks

Deep agents are the easiest way to start building agents and applications powered by LLMs—with builtin capabilities for task planning, file systems for context management, subagent-spawning, and long-term memory.
You can use deep agents for any task, including complex, multi-step tasks.

[`deepagents`](https://pypi.org/project/deepagents/) is a standalone library built on top of [LangChain](/oss/python/langchain/)'s core building blocks for agents. It uses the [LangGraph](/oss/python/langgraph/) runtime for durable execution, streaming, human-in-the-loop, and other features.

The `deepagents` library contains:

* **Deep Agents SDK**: A package for building agents that can handle any task
* **Deep Agents CLI**: A coding tool built on top of the `deepagents` package

[LangChain](/oss/python/langchain/) is the framework that provides the core building blocks for your agents.
To learn more about the differences between LangChain, LangGraph, and Deep Agents, see [Frameworks, runtimes, and harnesses](/oss/python/concepts/products).

## <Icon icon="wand-magic-sparkles" /> Create a deep agent

```python theme={null}
# pip install -qU deepagents
from deepagents import create_deep_agent

def get_weather(city: str) -> str:
    """Get weather for a given city."""
    return f"It's always sunny in {city}!"

agent = create_deep_agent(
    tools=[get_weather],
    system_prompt="You are a helpful assistant",
)

# Run the agent
agent.invoke(
    {"messages": [{"role": "user", "content": "what is the weather in sf"}]}
)
```

See the [Quickstart](/oss/python/deepagents/quickstart/) and [Customization guide](/oss/python/deepagents/customization/) to get started building your own agents and applications with deep agents.

## When to use the Deep Agents

Use the **Deep Agents SDK** when you want to build agents that can:

* **Handle complex, multi-step tasks** that require planning and decomposition
* **Manage large amounts of context** through file system tools
* **Delegate work** to specialized subagents for context isolation
* **Persist memory** across conversations and threads

For building simpler agents, consider using LangChain's [`create_agent`](/oss/python/langchain/agents) or building a custom [LangGraph](/oss/python/langgraph/overview) workflow.

Use the **Deep Agents CLI** when you want to use an interactive deep agent on the command-line for coding or other tasks:

* **Customize** agents with skills and memory.
* **Teach** agents as you use them about your preferences, common patterns, and custom project knowledge.
* **Execute code** on your machine or in sandboxes.

## Core capabilities

<Card title="Planning and task decomposition" icon="timeline">
  Deep agents include a built-in [`write_todos`](/oss/python/langchain/middleware/built-in#to-do-list) tool that enables agents to break down complex tasks into discrete steps, track progress, and adapt plans as new information emerges.
</Card>

<Card title="Context management" icon="scissors">
  File system tools ([`ls`](/oss/python/deepagents/harness#file-system-access), [`read_file`](/oss/python/deepagents/harness#file-system-access), [`write_file`](/oss/python/deepagents/harness#file-system-access), [`edit_file`](/oss/python/deepagents/harness#file-system-access)) allow agents to offload large context to in-memory or filesystem storage, preventing context window overflow and enabling work with variable-length tool results.
</Card>

<Card title="Subagent spawning" icon="people-group">
  A built-in `task` tool enables agents to spawn specialized subagents for context isolation. This keeps the main agent's context clean while still going deep on specific subtasks.
</Card>

<Card title="Long-term memory" icon="database">
  Extend agents with persistent memory across threads using LangGraph's [Memory Store](/oss/python/langgraph/persistence#memory-store). Agents can save and retrieve information from previous conversations.
</Card>

## Get started

<CardGroup cols={2}>
  <Card title="SDK Quickstart" icon="rocket" href="/oss/python/deepagents/quickstart">
    Build your first deep agent
  </Card>

  <Card title="Customization" icon="sliders" href="/oss/python/deepagents/customization">
    Learn about customization options for the SDK
  </Card>

  <Card title="Sandboxes" icon="cube" href="/oss/python/deepagents/sandboxes">
    Execute code in isolated environments
  </Card>

  <Card title="CLI" icon="terminal" href="/oss/python/deepagents/cli">
    Use the Deep Agents CLI
  </Card>

  <Card title="Reference" icon="arrow-up-right-from-square" href="https://reference.langchain.com/python/deepagents/">
    See the `deepagents` API reference
  </Card>
</CardGroup>

***

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

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