deep-agents-core（深层 Agent 核心）

• Task Planning: TodoListMiddleware for breaking down complex tasks
• Context Management: Filesystem tools with pluggable backends
• Task Delegation: SubAgent middleware for spawning specialized agents
• Long-term Memory: Persistent storage across threads via Store
• Human-in-the-loop: Approval workflows for sensitive operations
• Skills: On-demand loading of specialized capabilities

The agent harness provides these capabilities automatically - you configure, not implement.

@tool def get_weather(city: str) -> str: """Get the weather for a given city.""" return f"It is always sunny in {city}"

agent = create_deep_agent(model="claude-sonnet-4-5-20250929", tools=[get_weather], system_prompt="You are a helpful assistant")

config = {"configurable": {"thread_id": "user-123"}} result = agent.invoke({"messages": [{"role": "user", "content": "What's the weather in Tokyo?"}]}, config=config)

</python>
<typescript>
Create a basic deep agent with a custom tool and invoke it with a user message.

import { createDeepAgent } from "deepagents"; import { tool } from "@langchain/core/tools"; import { z } from "zod";

const getWeather = tool( async ({ city }) => It is always sunny in ${city}, { name: "get_weather", description: "Get weather for a city", schema: z.object({ city: z.string() }) } );

const agent = await createDeepAgent({ model: "claude-sonnet-4-5-20250929", tools: [getWeather], systemPrompt: "You are a helpful assistant" });

const config = { configurable: { thread_id: "user-123" } }; const result = await agent.invoke({ messages: [{ role: "user", content: "What's the weather in Tokyo?" }] }, config);

agent = create_deep_agent(name="my-assistant", model="claude-sonnet-4-5-20250929", tools=[custom_tool1, custom_tool2], system_prompt="Custom instructions", subagents=[research_agent, code_agent], backend=FilesystemBackend(root_dir=".", virtual_mode=True), interrupt_on={"write_file": True}, skills=["./skills/"], checkpointer=MemorySaver(), store=InMemoryStore())

</python> <typescript> Configure a deep agent with all available options including subagents, skills, and persistence.

import { createDeepAgent, FilesystemBackend } from "deepagents";
import { MemorySaver, InMemoryStore } from "@langchain/langgraph";

const agent = await createDeepAgent({
  name: "my-assistant",
  model: "claude-sonnet-4-5-20250929",
  tools: [customTool1, customTool2],
  systemPrompt: "Custom instructions",
  subagents: [researchAgent, codeAgent],
  backend: new FilesystemBackend({ rootDir: ".", virtualMode: true }),
  interruptOn: { write_file: true },
  skills: ["./skills/"],
  checkpointer: new MemorySaver(),
  store: new InMemoryStore()
});

1. Planning: write_todos - Track multi-step tasks
2. Filesystem: ls, read_file, write_file, edit_file, glob, grep
3. Delegation: task - Spawn specialized subagents

SKILL.md Format

Directory Structure

skills/
└── my-skill/
    ├── SKILL.md        # Required: main skill file
    ├── examples.py     # Optional: supporting files
    └── templates/      # Optional: templates

SKILL.md Format

---
name: my-skill
description: Clear, specific description of what this skill does
---

# Skill Name

## Overview
Brief explanation of the skill's purpose.

## When to Use
Conditions when this skill applies.

## Instructions
Step-by-step guidance for the agent.

agent = create_deep_agent(backend=FilesystemBackend(root_dir=".", virtual_mode=True), skills=["./skills/"], checkpointer=MemorySaver())

result = agent.invoke({"messages": [{"role": "user", "content": "Use the python-testing skill"}]}, config={"configurable": {"thread_id": "session-1"}})

</python>
<typescript>
Set up an agent with skills directory and filesystem backend for on-demand skill loading.

import { createDeepAgent, FilesystemBackend } from "deepagents"; import { MemorySaver } from "@langchain/langgraph";

const agent = await createDeepAgent({ backend: new FilesystemBackend({ rootDir: ".", virtualMode: true }), skills: ["./skills/"], checkpointer: new MemorySaver() });

const result = await agent.invoke({ messages: [{ role: "user", content: "Use the python-testing skill" }] }, { configurable: { thread_id: "session-1" } });

store = InMemoryStore()

# Load skill content into store

## skill_content = """--- name: python-testing description: Best practices for Python testing with pytest

# Python Testing Skill

..."""

store.put(namespace=("filesystem",), key="/skills/python-testing/SKILL.md", value=create_file_data(skill_content))

agent = create_deep_agent(backend=lambda rt: StoreBackend(rt), store=store, skills=["/skills/"])

</python> </ex-skills-with-store-backend>

<boundaries>

What Agents CAN Configure

• Model selection and parameters
• Additional custom tools
• System prompt customization
• Backend storage strategy
• Which tools require approval
• Custom subagents with specialized tools

What Agents CANNOT Configure

• Core middleware removal (TodoList, Filesystem, SubAgent always present)
• The write_todos, task, or filesystem tool names
• The SKILL.md frontmatter format

</boundaries>

<fix-checkpointer-for-interrupts> <python> Interrupts require a checkpointer.

# WRONG
agent = create_deep_agent(interrupt_on={"write_file": True})

# CORRECT
agent = create_deep_agent(interrupt_on={"write_file": True}, checkpointer=MemorySaver())

// CORRECT const agent = await createDeepAgent({interruptOn: {write_file: true}, checkpointer: new MemorySaver()});

</typescript>
</fix-checkpointer-for-interrupts>

<fix-store-for-memory>
<python>
StoreBackend requires a Store instance for persistent memory across threads.

WRONG

agent = create_deep_agent(backend=lambda rt: StoreBackend(rt))

CORRECT

agent = create_deep_agent(backend=lambda rt: StoreBackend(rt), store=InMemoryStore())

// CORRECT const agent = await createDeepAgent({backend: (config) => new StoreBackend(config), store: new InMemoryStore()});

</typescript> </fix-store-for-memory>

<fix-thread-id-for-conversations> <python> Use consistent thread_id to maintain conversation context across invocations.

# WRONG: Each invocation is isolated
agent.invoke({"messages": [{"role": "user", "content": "Hi"}]})
agent.invoke({"messages": [{"role": "user", "content": "What did I say?"}]})

# CORRECT
config = {"configurable": {"thread_id": "user-123"}}
agent.invoke({"messages": [...]}, config=config)
agent.invoke({"messages": [...]}, config=config)

// CORRECT const config = {configurable: {thread_id: "user-123"}}; await agent.invoke({messages: [...]}, config); await agent.invoke({messages: [...]}, config);

</typescript>
</fix-thread-id-for-conversations>

<fix-frontmatter-required>

WRONG: Missing frontmatter in SKILL.md

My Skill

This is my skill...

CORRECT: Include YAML frontmatter

name: my-skill description: Python testing best practices with pytest fixtures and mocking

My Skill

This is my skill...

# CORRECT: Use FilesystemBackend for local skills

agent = create_deep_agent(backend=FilesystemBackend(root_dir=".", virtual_mode=True), skills=["./skills/"])

</python> </fix-backend-for-skills>

<fix-specific-skill-descriptions> Use specific descriptions to help agents decide when to use a skill.

# WRONG: Vague description
---
name: helper
description: Helpful skill
---

# CORRECT: Specific description
---
name: python-testing
description: Python testing best practices with pytest fixtures, mocking, and async patterns
---

CORRECT: Provide skills explicitly

agent = create_deep_agent(skills=["/main-skills/"], subagents=[{"name": "helper", "skills": ["/helper-skills/"],...}])

</python>
</fix-subagent-skills>