The batteries-included agent harness.

Deep Agents is an agent harness. An opinionated, ready-to-run agent out of the box. Instead of wiring prompts, tools, and context management yourself, you get a working agent immediately and customize what you need.

What's included:

• Planning — write_todos for task breakdown and progress tracking
• Filesystem — read_file, write_file, edit_file, ls, glob, grep for working memory
• Sub-agents — task for delegating work with isolated context windows
• Smart defaults — built-in prompt and middleware that make these tools useful out of the box
• Context management — file-based workflows to keep long tasks manageable

Note

Looking for the Python package? See langchain-ai/deepagents.

Quickstart

npm install deepagents
# or
pnpm add deepagents
# or
yarn add deepagents

Important

deepagents declares the LangChain runtime packages as peer dependencies so your app controls their versions and everything resolves to a single shared copy. npm 7+ and pnpm 8+ install these automatically; Yarn users must add them explicitly:

yarn add @langchain/core @langchain/langgraph @langchain/langgraph-checkpoint @langchain/langgraph-sdk langchain langsmith

import { createDeepAgent } from "deepagents";

const agent = createDeepAgent();

const result = await agent.invoke({
  messages: [
    {
      role: "user",
      content: "Research LangGraph and write a summary in summary.md",
    },
  ],
});

The agent can plan, read/write files, and manage longer tasks with sub-agents and filesystem tools.

Tip

For developing, debugging, and deploying AI agents and LLM applications, see LangSmith.

Runtime Entrypoints

deepagents now publishes environment-specific entrypoints:

• deepagents - default Node.js/server entrypoint with the full API.
• deepagents/browser - recommended browser entrypoint (no Node-only exports).
• deepagents/node - optional explicit Node.js entrypoint (same full API as deepagents).

// Browser-safe usage
import { createDeepAgent, StateBackend } from "deepagents/browser";

// Node.js usage (recommended)
import { createDeepAgent, FilesystemBackend } from "deepagents";

// Optional explicit Node.js usage
// import { createDeepAgent, FilesystemBackend } from "deepagents/node";

Customization

Add tools, swap models, and customize prompts as needed:

import { ChatOpenAI } from "@langchain/openai";
import { createDeepAgent } from "deepagents";

const agent = createDeepAgent({
  model: new ChatOpenAI({ model: "gpt-5", temperature: 0 }),
  tools: [myCustomTool],
  systemPrompt: "You are a research assistant.",
});

See the JavaScript Deep Agents docs for full configuration options.

LangGraph Native

createDeepAgent returns a compiled LangGraph graph, so you can use streaming, Studio, checkpointers, and other LangGraph features.

Why Use It

• 100% open source — MIT licensed and extensible
• Provider agnostic — works with tool-calling chat models
• Built on LangGraph — production runtime with streaming and persistence
• Batteries included — planning, file access, sub-agents, and defaults out of the box
• Fast to start — install and run with sensible defaults
• Easy to customize — add tools/models/prompts when you need to

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Documentation

• docs.langchain.com - Concepts and guides
• Examples - Working agents and patterns
• LangChain Forum - Community discussion and support

Security

Deep Agents follows a "trust the LLM" model. The agent can do anything its tools allow. Enforce boundaries at the tool/sandbox level, not by expecting the model to self-police. See the security policy for more information.