Deep agents overview

Deep agents (deepagents) is a standalone library for building agents that can tackle complex, multi-step tasks. Built on LangGraph and inspired by applications like Claude Code, Deep Research, and Manus, deep agents come with planning capabilities, file systems for context management, and the ability to spawn subagents.

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When to use deep agents

Use deep agents when you need agents that can:

• Handle complex, multi-step tasks that require planning and decomposition
• Manage large amounts of context through file system tools
• Delegate work to specialized subagents for context isolation
• Persist memory across conversations and threads

For simpler use cases, consider using LangChain’s create_agent or building a custom LangGraph workflow.

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Core capabilities

Planning and task decomposition Deep agents include a built-in write_todos tool that enables agents to break down complex tasks into discrete steps, track progress, and adapt plans as new information emerges. Context management File system tools (ls, read_file, write_file, edit_file) allow agents to offload large context to memory, preventing context window overflow and enabling work with variable-length tool results. Subagent spawning 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. Long-term memory Extend agents with persistent memory across threads using LangGraph’s Store. Agents can save and retrieve information from previous conversations.

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Relationship to the LangChain ecosystem

Deep agents is built on top of:

• LangGraph - Provides the underlying graph execution and state management
• LangChain - Tools and model integrations work seamlessly with deep agents
• LangSmith - Observability and deployment through LangGraph Platform

Deep agents applications can be deployed on LangGraph Platform and monitored with LangSmith.

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Get started

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