langchain-ai/deep-agents-core

<overview> Deep Agents are an opinionated agent framework built on LangChain/LangGraph with built-in middleware:

• 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. </overview>

<when-to-use>

| 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 |

</when-to-use> <middleware-selection>

| 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 |

</middleware-selection> <ex-basic-agent> <python> Create a basic deep agent with a custom tool and invoke it with a user message.

from deepagents import create_deep_agent
from langchain.tools import tool

@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);

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

from deepagents import create_deep_agent
from deepagents.backends import FilesystemBackend
from langgraph.checkpoint.memory import MemorySaver
from langgraph.store.memory import InMemoryStore

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()
});

</typescript> </ex-full-configuration> <built-in-tools> Every deep agent has access to:

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 </built-in-tools>

---

SKILL.md Format

<skill-md-format> Skills use **progressive disclosure** - agents only load content when relevant.

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.

</skill-md-format> <skills-vs-memory>

| 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 |

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

from deepagents import create_deep_agent
from deepagents.backends import FilesystemBackend
from langgraph.checkpoint.memory import MemorySaver

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" } });

</typescript> </ex-skills-with-filesystem-backend> <ex-skills-with-store-backend> <python> Load skill content into a Store backend for environments without filesystem access.

from deepagents import create_deep_agent
from deepagents.backends import StoreBackend
from deepagents.backends.utils import create_file_data
from langgraph.store.memory import InMemoryStore

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())

</python> <typescript> Interrupts require a checkpointer.

// WRONG
const agent = await createDeepAgent({ interruptOn: { write_file: true } });

// 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())

</python> <typescript> StoreBackend requires a Store instance for persistent memory across threads.

// WRONG
const agent = await createDeepAgent({ backend: (config) => new StoreBackend(config) });

// 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)

</python> <typescript> Use consistent thread_id to maintain conversation context across invocations.

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

// 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...

</fix-frontmatter-required> <fix-backend-for-skills> <python> Skills require a proper backend to load from the filesystem.

# WRONG: Skills won't load without proper backend
agent = create_deep_agent(skills=["./skills/"])

# 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
---

</fix-specific-skill-descriptions> <fix-subagent-skills> <python> Skills are not inherited by subagents - provide them explicitly.

# WRONG: Custom subagents don't inherit skills
agent = create_deep_agent(
    skills=["/main-skills/"],
    subagents=[{"name": "helper", ...}]  # No skills
)

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

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