deep-agents

v2026.09.24

Build hierarchical AI agents with the deepagents npm package. Use when creating orchestrators that plan multi-step tasks, delegate to child agents, or maintain persistent memory.

GitHub
安装命令
npx skhub add laurigates/deep-agents
Markdown
SKILL.md

Deep Agents

When to Use This Skill

Use this skill when...Use langgraph-agents instead when...
Building hierarchical agents with planning and subagent delegationYou need a single stateful graph without sub-agents
Managing large context via file-system memory across runsShort-lived state fits in checkpointed graph memory
Long-running, multi-step workflows modelled on Deep ResearchSimple LCEL chains suffice (use langchain-development)
Scaffolding from scratch (use /langchain:init first)The project is already initialised and only needs graph wiring

Core Expertise

Deep Agents (deepagents) is a TypeScript library for building sophisticated AI agents:

  • Built on LangGraph with planning and decomposition
  • File system context management (prevents token overflow)
  • Subagent delegation for focused exploration
  • Persistent memory across conversations
  • Modeled after Claude Code and Deep Research patterns

The package name on npm is deepagents (one word, unscoped). The source lives at langchain-ai/deepagentsjs.

Installation

# Install Deep Agents
npm install deepagents

# Add a model provider (pick the one matching your model)
npm install @langchain/openai  # or @langchain/anthropic, @langchain/google-genai

deepagents declares langsmith as a peer dependency (for tracing) and builds on @langchain/langgraph + @langchain/core, which are pulled in transitively.

Basic Agent Setup

createDeepAgent() returns a compiled LangGraph graph. The model can be a provider-prefixed string (e.g. "openai:gpt-5") or a model instance.

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

const model = new ChatOpenAI({
  model: "gpt-5",
  temperature: 0,
});

const agent = createDeepAgent({
  model,
  systemPrompt: `You are a research assistant.
    Break complex questions into steps using write_todos.
    Use read_file and write_file to manage context.`,
});

const result = await agent.invoke({
  messages: [{ role: "user", content: "Research X and summarize" }],
});

For browser or Node-explicit builds, import the backend-scoped entrypoints:

import { createDeepAgent, StateBackend } from "deepagents/browser";
import { createDeepAgent, FilesystemBackend } from "deepagents/node";

Built-in Tools

Deep Agents ships these tools automatically: write_todos, ls, read_file, write_file, edit_file, glob, grep, and task.

Planning Tools

// write_todos - Task decomposition (available automatically)

// The agent uses it to plan:
// write_todos([
//   { task: "Search for X", status: "pending" },
//   { task: "Analyze results", status: "pending" },
//   { task: "Write summary", status: "pending" },
// ])

File System Tools

// Built-in tools for context management

// ls        - List directory contents
// read_file - Read file content
// write_file - Write/create files
// edit_file - Modify existing files
// glob      - Match files by pattern
// grep      - Search file contents

// The agent stores intermediate results in files
// to prevent context overflow.

Subagent Delegation

// task - Spawn a focused subagent with an isolated context window

// The parent agent delegates:
// task({
//   description: "Research pricing models",
//   subagent_type: "research-agent",
// })

// The subagent runs independently and returns results.

Agentic Optimizations

ContextPattern
Large docsWrite to file, read sections as needed
Multi-stepUse write_todos to track progress
Focused workDelegate via the task tool
Long sessionsEnable checkpointing
Learned patternsStore via LangGraph store
DebugEnable LANGCHAIN_TRACING_V2

Quick Reference

Agent Methods

MethodDescription
.invoke(input, config)Run to completion
.stream(input, config)Stream execution
.batch(inputs, config)Parallel execution

Built-in Tools

ToolPurpose
write_todosPlan and track tasks
lsList directory
read_fileRead file contents
write_fileCreate/overwrite file
edit_fileModify file section
globMatch files by pattern
grepSearch file contents
taskDelegate to a subagent

Config Keys

KeyDescription
thread_idConversation ID
checkpoint_idResume point
recursion_limitMax iterations

Environment Variables

VariableDescription
LANGCHAIN_TRACING_V2Enable LangSmith
LANGCHAIN_API_KEYLangSmith key
LANGCHAIN_PROJECTProject name

For custom tools, persistence, full configuration options, multi-agent subagent patterns, context-management strategy, streaming, and the Claude Code comparison, see REFERENCE.md.

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版本
最新版本元数据

版本

v2026.09.24

发布时间

2026年9月24日

分类

未分类

许可证

MIT

源路径

langchain-plugin/skills/deep-agents

默认分支

main

最新提交

1668324

Tree SHA

b2d4cc3