INVOKE THIS SKILL when building ANY Deep Agents application. Covers create_deep_agent(), harness architecture, SKILL.md format, and configuration options.
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
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()
)
---
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/"]
)
# 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
---
