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) 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() }); Planning : write_todos - Track multi-step tasks Filesystem : ls , read_file , write_file , edit_file , glob , grep 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"}}) 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/"] )

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 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() }); 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() }); 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); ```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/"] ) 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/"], ...}] )