• 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.

Use Deep Agents When	Use LangChain's create_agent When
Multi-step tasks requiring planning	Simple, single-purpose tasks
Large context requiring file management	Context fits in a single prompt
Need for specialized subagents	Single agent is sufficient
Persistent memory across sessions	Ephemeral, single-session work

If you need to...	Middleware	Notes
Track complex tasks	TodoListMiddleware	Default enabled
Manage file context	FilesystemMiddleware	Configure backend
Delegate work	SubAgentMiddleware	Add custom subagents
Add human approval	HumanInTheLoopMiddleware	Requires checkpointer
Load skills	SkillsMiddleware	Provide skill directories
Access memory	MemoryMiddleware	Requires Store instance

@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.
```typescript
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.
```typescript
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.

Skills	Memory (AGENTS.md)
On-demand loading	Always loaded at startup
Task-specific instructions	General preferences
Large documentation	Compact context
SKILL.md in directories	Single AGENTS.md file

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.
```typescript
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.
```python
# 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.
```python
# 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.
```python
# 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>
```markdown
# 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.
```markdown
# 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>