MCPInstall

deep-agents-core

INVOKE THIS SKILL when building ANY Deep Agents application. Covers createdeepagent(), harness architecture, SKILL.md format, and configuration options.

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Install

Works with every agent the skills CLI supports.

npx skills add https://github.com/langchain-ai/langchain-skills --skill deep-agents-core
  1. 01

    Install

    Run the command in your project. The CLI asks which agents to install for.

  2. 02

    Describe the task

    Ask your agent for something this skill covers. It loads deep-agents-core automatically when relevant.

  3. 03

    Review the result

    The agent follows the SKILL.md steps, scripts and references bundled with the skill.

Security audits

Independent scans of the skill's instructions and bundled files.

Gen Agent Trust Hub PassSAFE ·This skill provides architectural guidance and technical documentation for the Deep Agents framework by LangChain. It includes examples for configuring agents with middleware, persistence, and filesystem access as part of its core functionality. While the framework handles file operations and dynamic loading, these patterns are presented as intended features for developers building agents.
Socket PassNo alerts
Snyk PassLOW ·Risk: LOW · No issues
Runlayer PassNONE ·1 file scanned · No issues
ZeroLeaks PassNONE ·Score: 93/100 · 2 sections analyzed

SKILL.md

The instructions your agent reads when the skill is triggered.

<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 WhenUse LangChain's create_agent When
Multi-step tasks requiring planningSimple, single-purpose tasks
Large context requiring file managementContext fits in a single prompt
Need for specialized subagentsSingle agent is sufficient
Persistent memory across sessionsEphemeral, single-session work
</when-to-use> <middleware-selection>
If you need to...MiddlewareNotes
Track complex tasksTodoListMiddlewareDefault enabled
Manage file contextFilesystemMiddlewareConfigure backend
Delegate workSubAgentMiddlewareAdd custom subagents
Add human approvalHumanInTheLoopMiddlewareRequires checkpointer
Load skillsSkillsMiddlewareProvide skill directories
Access memoryMemoryMiddlewareRequires 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>
SkillsMemory (AGENTS.md)
On-demand loadingAlways loaded at startup
Task-specific instructionsGeneral preferences
Large documentationCompact context
SKILL.md in directoriesSingle 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>

FAQ

What does the deep-agents-core skill do?

INVOKE THIS SKILL when building ANY Deep Agents application. Covers createdeepagent(), harness architecture, SKILL.md format, and configuration options.

How do I install deep-agents-core?

Run npx skills add https://github.com/langchain-ai/langchain-skills --skill deep-agents-core in your project. The skills CLI detects the agents you use and copies the skill into the right folder. For Claude Code you can also place the skill folder at ~/.claude/skills/deep-agents-core/ (personal) or .claude/skills/deep-agents-core/ (project).

Which AI agents can use deep-agents-core?

Any agent that supports the open Agent Skills format (a folder with a SKILL.md file), including Claude Code, Codex, Cursor, GitHub Copilot, Gemini CLI, Windsurf, Cline, OpenCode and more.

Is deep-agents-core safe to install?

Security partners have reviewed it: Gen Agent Trust Hub passed; Socket passed; Snyk passed; Runlayer passed; ZeroLeaks passed. As with any skill, read the SKILL.md and bundled scripts before installing.

Who maintains deep-agents-core?

It is published by langchain-ai in langchain-ai/langchain-skills, and it is an official skill from the maker of the technology it covers. It has 15,990 recorded installs.

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