deep-agents

Agent Building

Build batteries-included agents with planning, context management, subagent delegation, and sandboxed execution. Use for complex, multi-step tasks that need built-in capabilities.

QUICK START

How to use this skill

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Source SKILL.md: https://github.com/langchain-ai/docs/blob/HEAD/src/.mintlify/skills/deep-agents/SKILL.md

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Deep Agents

Deep Agents is the easiest way to start building agents powered by LLMs—with built-in capabilities for task planning, file systems for context management, subagent delegation, and long-term memory. It is an "agent harness" built on LangChain core building blocks and the LangGraph runtime.

When to use

Use Deep Agents when you need to:

Build agents fast with sensible defaults and minimal configuration
Handle complex, multi-step tasks that benefit from automatic planning
Manage context with a built-in virtual filesystem for large inputs
Delegate subtasks to specialized subagents
Run code safely in sandboxed execution environments
Use a terminal agent via Deep Agents Code

When NOT to use

For simple tool-calling agents without planning or subagents, use LangChain agents instead—lighter weight
For custom graph-based orchestration with explicit control flow, use LangGraph directly
Deep Agents is the highest-level abstraction—it trades flexibility for convenience

Install

# Python
pip install deepagents

# JavaScript/TypeScript
npm install deepagents langchain @langchain/core

Quick reference

Create a deep agent

# pip install deepagents langchain-anthropic
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(
    model="anthropic:claude-sonnet-4-6",
    tools=[get_weather],
    system_prompt="You are a helpful assistant",
)

result = agent.invoke(
    {"messages": [{"role": "user", "content": "What is the weather in SF?"}]}
)

Use Deep Agents Code

# Install Deep Agents Code
pip install deepagents-code

# Run an interactive terminal agent
deepagents

Built-in capabilities

Capability	Description
Planning	Automatic task decomposition for complex requests
File system	Virtual filesystem for reading, writing, and managing context
Subagents	Spawn child agents for parallel subtask execution
Context management	Automatic context compression for long conversations
Sandboxed execution	Run code in isolated environments (Modal, Runloop, Daytona)
Protocols	ACP, MCP, and A2A support for interoperability

Key documentation

Overview—What Deep Agents is and how it compares to LangChain and LangGraph
Quickstart—Build your first deep agent
Customization—Configure models, tools, and behavior
Context engineering—Manage context for complex tasks
Subagents—Delegate work to child agents
Sandboxes—Run code in isolated environments
Code—Deep Agents Code, the terminal agent interface
Deploy—Deploy to production

API reference

For SDK class and method details, use the LangChain API Reference site:

MCP server: https://reference.langchain.com/mcp

Related skills

langchain—Core building blocks that Deep Agents is built on
langgraph—Runtime that powers Deep Agents' durable execution
langsmith—Trace, evaluate, and deploy your deep agents