Deep Agents Core
LangChainOfficialPublished by LangChain
- Skill
- Tool integration
- Agent tooling
- MIT
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AutoGineer summary
Written by AutoGineer from the skill’s own files, not by the author
Deep Agents is an opinionated framework built on LangChain and LangGraph designed to simplify the creation of robust, multi-step AI agents. It provides a structured harness that includes built-in middleware for task planning, filesystem interaction, subagent delegation, persistent memory, and human-in-the-loop approval workflows. The framework emphasizes configuration over implementation, allowing developers to define agent capabilities through standardized SKILL.md files and modular backend configurations.
What it does
- Provides a unified create_deep_agent function for agent initialization.
- Supports on-demand skill loading via a standardized SKILL.md format.
- Enables persistent memory across sessions using Store and checkpointer backends.
- Facilitates complex task management through built-in TodoList and SubAgent middleware.
- Allows for human-in-the-loop approval workflows for sensitive operations.
- Offers flexible storage backends including filesystem and store-based implementations.
When to use it
- When building agents that require multi-step task planning and tracking.
- When your application needs persistent memory across multiple user sessions.
- When you need to delegate complex tasks to specialized subagents.
- When managing large amounts of context that require file-based storage.
- When you need to implement human approval steps for specific agent actions.
Advantages
- Reduces boilerplate by providing pre-configured middleware.
- Promotes modularity through the use of on-demand skill loading.
- Ensures consistent agent behavior with standardized configuration patterns.
- Supports both local filesystem and memory-store backends for flexibility.
- Simplifies the integration of human-in-the-loop workflows.
Usage examples
- “Create a basic deep agent with a custom weather tool.”
- The agent is initialized using create_deep_agent with the custom tool and a system prompt, ready to handle user queries with persistent thread context.
- “Configure a deep agent with subagents, skills, and memory persistence.”
- The agent is set up with specialized subagents, a filesystem backend for skills, and a checkpointer/store for maintaining state across interactions.
- “How do I add a new skill to my agent?”
- Create a directory in your skills folder with a SKILL.md file containing the required YAML frontmatter and instructions, then point the agent to that directory.
- “How can I require human approval for file writes?”
- Configure the agent with a checkpointer and set interrupt_on to include write_file, ensuring the agent pauses for approval before executing the action.
Install
Download the skill as a ZIP:
https://autogineer.com/agent-skills/langchain-ai/deep-agents-core/downloadUnzip it into the cross-client skills folder of your project:
mkdir -p .agents/skills && unzip deep-agents-core.zip -d .agents/skills
Documentation by LangChain
<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.
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:
Planning:
write_todos- Track multi-step tasksFilesystem:
ls,read_file,write_file,edit_file,glob,grepDelegation:
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: templatesSKILL.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>
| 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.
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>
Files
- ATTRIBUTION.mdAdded by AutoGineer894 B
- LICENSELicence file added by AutoGineer1.0 KiB
- SKILL.md12.0 KiB
SKILL.md
<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.
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:
Planning:
write_todos- Track multi-step tasksFilesystem:
ls,read_file,write_file,edit_file,glob,grepDelegation:
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: templatesSKILL.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>
| 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.
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>
Requirements and compatibility
Works with: Claude Code, Claude apps, OpenAI Codex, GitHub Copilot, Cursor, Gemini CLI, Kiro, OpenCode, Goose, Amp, Roo Code, JetBrains Junie, Factory Droid, Devin (ex-Windsurf), Mistral Vibe, OpenHands, Letta
“Works with” means the tool reads the open Agent Skills format; a skill written for one tool may use features others lack.
Needs: Needs Python
Security
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Attribution and licence
- Published by
- LangChain
- Publisher
- LangChain (Official)
- Source
- langchain-ai/langchain-skills · config/skills/deep-agents-core · commit 126c384
- Licence
- MIT License (redistribution allowed)
- LICENSE (added by AutoGineer from LICENSE)
- ATTRIBUTION.md (written by AutoGineer)
- Synced
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Download ZIP
The ZIP holds the skill’s files, its licence files and an ATTRIBUTION.md. Download deep-agents-core.zip