> ## Documentation Index
> Fetch the complete documentation index at: https://langchain-5e9cc07a-preview-patchr-1773857969-df0cef9.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

The easiest way to start building agents and applications powered by LLMs—with built-in 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.

We think of `deepagents` as an ["agent harness"](/oss/python/concepts/products#agent-harnesses-like-the-deep-agents-sdk). It is the same core tool calling loop as other agent frameworks, but with built-in tools and capabilities.

[`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**](/oss/python/deepagents/cli): A terminal coding agent built on the Deep Agents SDK

[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" /> 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.

<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>

## 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
* **Swap filesystem backends** to use in-memory state, local disk, durable stores, [sandboxes](/oss/python/deepagents/sandboxes), or [your own custom backend](/oss/python/deepagents/backends)
* **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 a coding agent on the command line, built on the Deep Agents SDK:

* **Run interactively or non-interactively** — use the CLI as a chat-style coding agent, or pipe tasks with `-n` for scriptable, headless execution.
* **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#virtual-filesystem-access), [`read_file`](/oss/python/deepagents/harness#virtual-filesystem-access), [`write_file`](/oss/python/deepagents/harness#virtual-filesystem-access), [`edit_file`](/oss/python/deepagents/harness#virtual-filesystem-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="Pluggable filesystem backends" icon="plug">
  The virtual filesystem is powered by [pluggable backends](/oss/python/deepagents/backends) that you can swap to fit your use case. Choose from in-memory state, local disk, LangGraph store for cross-thread persistence, [sandboxes](/oss/python/deepagents/sandboxes) for isolated code execution (Modal, Daytona, Deno), or combine multiple backends with composite routing. You can also implement your own custom backend.
</Card>

<Card title="Subagent spawning" icon="users-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="adjustments" href="/oss/python/deepagents/customization">
    Learn about customization options for the SDK
  </Card>

  <Card title="Backends" icon="plug" href="/oss/python/deepagents/backends">
    Choose and configure pluggable filesystem backends
  </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/overview">
    Use the Deep Agents CLI
  </Card>

  <Card title="Reference" icon="external-link" href="https://reference.langchain.com/python/deepagents/">
    See the `deepagents` API reference
  </Card>
</CardGroup>

***

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