deep-agents

Deep Agents

Deep Agents is the agent harness for complex, multi-step work: planning, a virtual filesystem for context, subagent delegation, and long-term memory. It is built on LangChain and the LangGraph runtime.

Start with Managed Deep Agents: a managed open-source harness for agents that plan, use tools, delegate to subagents, and work over long horizons. Alternatively, for maximum flexibility while using the same Deep Agents harness, use the Deep Agents SDK and host agents yourself, or run a custom Agent Server.

When to use

Use Deep Agents when you need to:

• 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

Default to Managed Deep Agents for new agents. Use the self-hosted SDK when the user needs to own the backend, store, checkpointer, or Agent Server.

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

Key documentation

• Managed Deep Agents—Build deep agents with managed deployment infrastructure
• Overview—Harness overview and Managed vs self-host decision
• SDK quickstart—The same deep agents harness, self-hosted
• 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

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