deep-agents

# 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](https://docs.langchain.com/oss/langchain/overview) core building blocks and the [LangGraph](https://docs.langchain.com/oss/langgraph/overview) 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](https://docs.langchain.com/oss/langchain/overview) agents instead—lighter weight - For custom graph-based orchestration with explicit control flow, use [LangGraph](https://docs.langchain.com/oss/langgraph/overview) directly - Deep Agents is the **highest-level abstraction**—it trades flexibility for convenience ## Install ```bash # Python pip install deepagents # JavaScript/TypeScript npm install deepagents langchain @langchain/core ``` ## Quick reference ### Create a deep agent ```python # 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 ```bash # 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](https://docs.langchain.com/oss/python/deepagents/overview)—What Deep Agents is and how it compares to LangChain and LangGraph - [Quickstart](https://docs.langchain.com/oss/python/deepagents/quickstart)—Build your first deep agent - [Customization](https://docs.langchain.com/oss/python/deepagents/customization)—Configure models, tools, and behavior - [Context engineering](https://docs.langchain.com/oss/python/deepagents/context-engineering)—Manage context for complex tasks - [Subagents](https://docs.langchain.com/oss/python/deepagents/subagents)—Delegate work to child agents - [Sandboxes](https://docs.langchain.com/oss/python/deepagents/sandboxes)—Run code in isolated environments - [Code](https://docs.langchain.com/oss/deepagents/code/overview)—Deep Agents Code, the terminal agent interface - [Deploy](https://docs.langchain.com/langsmith/managed-deep-agents-overview)—Deploy to production ## API reference For SDK class and method details, use the [LangChain API Reference](https://reference.langchain.com) 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