
deep-agents-core
โ 108by langchain-ai ยท part of langchain-ai/skills-benchmarks
INVOKE THIS SKILL when building ANY Deep Agents application. Covers create_deep_agent(), harness architecture, SKILL.md format, and configuration options.
This is the playbook your agent receives when the skill activates โ you don't need to read it to use the skill, but it's here to audit before installing.
- 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 |
@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.
```typescript
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);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.
```typescript
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
SKILL.md Format
Skills use **progressive disclosure** - agents only load content when relevant.Directory Structure
skills/
โโโ my-skill/
โโโ SKILL.md # Required: main skill file
โโโ examples.py # Optional: supporting files
โโโ 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.| 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 |
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.
```typescript
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" } });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.
```python
# WRONG
agent = create_deep_agent(interrupt_on={"write_file": True})
# CORRECT
agent = create_deep_agent(interrupt_on={"write_file": True}, checkpointer=MemorySaver())// 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.
```python
# WRONG
agent = create_deep_agent(backend=lambda rt: StoreBackend(rt))
# CORRECT
agent = create_deep_agent(backend=lambda rt: StoreBackend(rt), store=InMemoryStore())// 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.
```python
# 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)// 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>
```markdown
# 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...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.
```markdown
# 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
---CORRECT: Provide skills explicitly
agent = create_deep_agent( skills=["/main-skills/"], subagents=[{"name": "helper", "skills": ["/helper-skills/"], ...}] )
</python>
</fix-subagent-skills>npx skills add langchain-ai/skills-benchmarks --skill "deep-agents-core" --full-depthRun this in your project โ your agent picks the skill up automatically.
No common issues documented yet. If you hit a problem, the repository's GitHub Issues page is the best place to look.
Licensed under MITโ you can use, modify, and redistribute it under that license's terms.
View the full license file on GitHub โ