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From langchain-skills
Builds Deep Agents applications using create_deep_agent(), harness architecture with middleware for task planning, context management, subagents, memory, and human-in-the-loop.
npx claudepluginhub langchain-ai/langchain-skills --plugin langchain-skillsHow this skill is triggered — by the user, by Claude, or both
Slash command
/langchain-skills:deep-agents-coreThe summary Claude sees in its skill listing — used to decide when to auto-load this skill
<overview>
Builds hierarchical AI agents using deep-agents TypeScript/npm package for multi-step task orchestration, file system context management, subagent delegation, and persistent memory.
Orchestrates subagents, task planning, and human-in-the-loop approval in Deep Agents. Covers SubAgentMiddleware, TodoListMiddleware, and HITL interrupts for delegated execution and workflow control.
Implements agents using the Deep Agents library: create_deep_agent, backends, subagents, middleware, and human-in-the-loop workflows.
Share bugs, ideas, or general feedback.
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)
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);
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()
)
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()
});
Every deep agent has access to:
write_todos - Track multi-step tasksls, read_file, write_file, edit_file, glob, greptask - Spawn specialized subagents
skills/
└── my-skill/
├── SKILL.md # Required: main skill file
├── examples.py # Optional: supporting files
└── templates/ # Optional: templates
---
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"}})
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" } });
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/"]
)
# WRONG
agent = create_deep_agent(interrupt_on={"write_file": True})
# CORRECT
agent = create_deep_agent(interrupt_on={"write_file": True}, checkpointer=MemorySaver())
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() });
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())
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() });
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)
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);
# 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...
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/"]
)
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
---
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/"], ...}]
)