Deep Agents comes with a number of built-in components that help agents perform complex tasks.

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System prompt

Deep Agents comes with a detailed system prompt heavily inspired by Claude Code’s architecture. This prompt provides instructions for using the planning tool, file system tools, and sub agents effectively.

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Planning tool

A simple planning tool based on Claude Code’s TodoWrite functionality that helps agents create and track plans in their context, keeping them on track for complex tasks.

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File system tools

Four built-in tools that mock a file system using LangGraph’s state:
  • ls - List files and directories
  • read_file - Read file contents
  • write_file - Write to files
  • edit_file - Edit existing files
Files can be passed in and retrieved via the files key: 
# Pass files to the agent
result = agent.invoke({
    "messages": [...],
    "files": {"foo.txt": "file content", ...}
})

# Access files after execution
updated_files = result["files"]

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Sub-agents

Built-in support for calling specialized sub-agents with the following:
  • Context quarantine: Prevents polluting the main agent’s context
  • Custom instructions: Tailored prompts for specific tasks
  • Tool access control: Subagents can have different tool sets
A general-purpose sub-agent with the same instructions and tools as the main agent is always available.

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MCP

The deepagents library can be ran with MCP tools. This can be achieved by using the LangChain MCP Adapter library. For example: 
# pip install langchain-mcp-adapters

import asyncio
from langchain_mcp_adapters.client import MultiServerMCPClient
from deepagents import create_deep_agent

async def main():
    # Collect MCP tools
    mcp_client = MultiServerMCPClient(...)
    mcp_tools = await mcp_client.get_tools()

    # Create agent
    agent = create_deep_agent(tools=mcp_tools, ....)

    # Stream the agent
    async for chunk in agent.astream(
        {"messages": [{"role": "user", "content": "what is langgraph?"}]},
        stream_mode="values"
    ):
        if "messages" in chunk:
            chunk["messages"][-1].pretty_print()

asyncio.run(main())