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](https://github.com/langchain-ai/deepagents).
## Quickstart
```bash
npm install deepagents
# or
pnpm add deepagents
# or
yarn add deepagents
```
```typescript
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](https://docs.langchain.com/langsmith/home).
## Customization
Add tools, swap models, and customize prompts as needed:
```typescript
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](https://docs.langchain.com/oss/javascript/deepagents/overview) for full configuration options.
## LangGraph Native
`createDeepAgent` returns a compiled [LangGraph](https://docs.langchain.com/oss/javascript/langgraph/overview) 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
---
## Documentation
- [docs.langchain.com](https://docs.langchain.com/oss/javascript/deepagents/overview) - Concepts and guides
- [Examples](/examples) - Working agents and patterns
- [LangChain Forum](https://forum.langchain.com) - 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](https://github.com/langchain-ai/deepagentsjs?tab=security-ov-file) for more information.
