
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- 01
Install
Run the command in your project. The CLI asks which agents to install for.
- 02
Describe the task
Ask your agent for something this skill covers. It loads deep-agents-core automatically when relevant.
- 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.
SKILL.md
The instructions your agent reads when the skill is triggered.
- 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 |
| 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 |
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)
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);
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()
)
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 </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.
| 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 |
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"}})
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" } });
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/"]
)
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>
# WRONG
agent = create_deep_agent(interrupt_on={"write_file": True})
# CORRECT
agent = create_deep_agent(interrupt_on={"write_file": True}, checkpointer=MemorySaver())
// WRONG
const agent = await createDeepAgent({ interruptOn: { write_file: true } });
// CORRECT
const agent = await createDeepAgent({ interruptOn: { write_file: true }, checkpointer: new MemorySaver() });
# WRONG
agent = create_deep_agent(backend=lambda rt: StoreBackend(rt))
# CORRECT
agent = create_deep_agent(backend=lambda rt: StoreBackend(rt), store=InMemoryStore())
// WRONG
const agent = await createDeepAgent({ backend: (config) => new StoreBackend(config) });
// CORRECT
const agent = await createDeepAgent({ backend: (config) => new StoreBackend(config), store: new InMemoryStore() });
# 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)
// 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);
# 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...
# 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/"]
)
# 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
---
# 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/"], ...}]
)
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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