deep-agents-core

★ 846

by langchain-ai · part of langchain-ai/langchain-skills

Foundation framework for building multi-step agents with built-in planning, memory, and skill delegation. Provides six core middleware options: task planning, filesystem context management, subagent delegation, persistent memory, human approval workflows, and on-demand skill loading Includes three always-present built-in tools: write_todos for task tracking, filesystem operations ( ls , read_file , write_file , edit_file , glob , grep ), and task for spawning specialized subagents Supports...

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🧩 One of 7 skills in the langchain-ai/langchain-skills package — works on its own, and pairs well with its siblings.

Inspect the full instructions your agent will receiveExpand

This is the exact playbook injected into your agent when the skill activates — shown here so you can audit it before installing. You don't need to read it to use the skill.

name: deep-agents-core description: "INVOKE THIS SKILL when building ANY Deep Agents application. Covers create_deep_agent(), harness architecture, SKILL.md format, and configuration options."

<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.

Copy & paste — that's it

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.

Copy & paste — that's it

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.

Copy & paste — that's it

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.

Copy & paste — that's it

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:

Planning: write_todos - Track multi-step tasks
Filesystem: ls, read_file, write_file, edit_file, glob, grep
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

Copy & paste — that's it

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

SKILL.md Format

Copy & paste — that's it

---
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.

Copy & paste — that's it

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.

Copy & paste — that's it

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.

Copy & paste — that's it

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.

Copy & paste — that's it

# 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.

Copy & paste — that's it

// 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.

Copy & paste — that's it

# 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.

Copy & paste — that's it

// 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.

Copy & paste — that's it

# 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.

Copy & paste — that's it

// 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>

Copy & paste — that's it

# 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.

Copy & paste — that's it

# 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.

Copy & paste — that's it

# 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.

Copy & paste — that's it

# 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>