Official agent skill

Deep Agents Orchestration

by langchain-ai in langchain-ai/langchain-skills

INVOKE THIS SKILL when using subagents, task planning, or human approval in Deep Agents.

OfficialMITAuto-check passedAgent Workflows

Install Deep Agents Orchestration

skills CLI
$ npx skills add langchain-ai/langchain-skills --skill deep-agents-orchestration -a claude-code

Project install by default; add -g for ~/.claude/skills/.

GitHub CLI
$ gh skill install langchain-ai/langchain-skills deep-agents-orchestration --agent claude-code

Project scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).

Manual copy
$ git clone --depth 1 https://github.com/langchain-ai/langchain-skills.git skills-src && mkdir -p .claude/skills && cp -r skills-src/config/skills/deep-agents-orchestration .claude/skills/deep-agents-orchestration && rm -rf skills-src

Use ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.

Claude Code skills documentation · loads skills from .claude/skills/

Facts

Skill name
deep-agents-orchestration
GitHub stars
1.3k
Token cost
~3.4k tokens
SKILL.md length
515 words
Files
1
Skills in repo
22
Repo updated
First seen
Licence
MIT

At a glance

INVOKE THIS SKILL when using subagents, task planning, or human approval in Deep Agents.

  • Works in 3 steps: SubAgentMiddleware: Delegate work via… → TodoListMiddleware: Plan and track tasks… → HumanInTheLoopMiddleware: Require…
  • Tasks that involve Subagents
  • SKILL.md covers Subagents (Task Delegation), TodoList (Task Planning) and Human-in-the-Loop (Approval…
  • Instructions only: no scripts, shell commands, URLs or credentials in SKILL.md

What it does

Deep Agents Orchestration is an agent skill from langchain-ai/langchain-skills, published by the product's own GitHub organization. INVOKE THIS SKILL when using subagents, task planning, or human approval in Deep Agents. Covers SubAgentMiddleware, TodoList for planning, and HITL interrupts.

Its SKILL.md is about 3.4k tokens, which your agent loads only when the skill is triggered. It is a single SKILL.md file with no bundled scripts.

It sits in Agent Workflows, covering Subagents and Planning. It works with Python and TypeScript. The licence is MIT.

When your agent uses it

  • Tasks that involve Subagents
  • Tasks that involve Planning

Example prompts

  • “/deep-agents-orchestration”

Requirements

  • Python 3

Workflow steps

3 steps, taken from the first numbered list in SKILL.md.

  1. SubAgentMiddleware: Delegate work via task tool to specialized agents
  2. TodoListMiddleware: Plan and track tasks via write_todos tool
  3. HumanInTheLoopMiddleware: Require approval before sensitive operations

What it can do on your machine

Read from SKILL.md and the folder at commit 16a992f. It shows what the files ask for, not the result of running them.

  • Tool permissions

    Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.

    From allowed-tools in the SKILL.md frontmatter.

  • Runs code

    No scripts in the folder and no shell commands in SKILL.md (its code samples are python and typescript).

    From the folder's file list and the shell code blocks in SKILL.md.

  • Network

    No URLs in SKILL.md.

    From URLs in SKILL.md, links to its own repository left out.

  • Credentials

    Names no API keys, tokens, secrets or passwords.

    From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.

Context cost

Deep Agents Orchestration loads about 3.4k tokens when it runs. Until then it costs about 46 tokens; SKILL.md has 515 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~46
When it runs · the whole SKILL.md, loaded when a task matches
~3.4k

Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.

Safety

Auto-check passed

The automated check found no risky patterns in SKILL.md.

Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); files beside SKILL.md are not scanned.

SKILL.md

The full file from langchain-ai/langchain-skills at commit 16a992f, republished under its MIT licence (© langchain-ai). 515 words, ~3,434 tokens.

Download SKILL.mdSave it as .claude/skills/deep-agents-orchestration/SKILL.md (or your agent's skills folder).
name
deep-agents-orchestration
description
INVOKE THIS SKILL when using subagents, task planning, or human approval in Deep Agents. Covers SubAgentMiddleware, TodoList for planning, and HITL interrupts.
<overview>
Deep Agents include three orchestration capabilities:
  1. SubAgentMiddleware: Delegate work via task tool to specialized agents
  2. TodoListMiddleware: Plan and track tasks via write_todos tool
  3. HumanInTheLoopMiddleware: Require approval before sensitive operations

All three are automatically included in create_deep_agent(). </overview>


Subagents (Task Delegation)

<when-to-use-subagents>
Use Subagents WhenUse Main Agent When
Task needs specialized toolsGeneral-purpose tools sufficient
Want to isolate complex workSingle-step operation
Need clean context for main agentContext bloat acceptable
</when-to-use-subagents>
<how-subagents-work>
Main agent has `task` tool -> creates fresh subagent -> subagent executes autonomously -> returns final report.

Default subagent: "general-purpose" - automatically available with same tools/config as main agent. </how-subagents-work>

<ex-custom-subagents>
<python>
Create a custom "researcher" subagent with specialized tools for academic paper search.
python
from deepagents import create_deep_agent
from langchain.tools import tool

@tool
def search_papers(query: str) -> str:
    """Search academic papers."""
    return f"Found 10 papers about {query}"

agent = create_deep_agent(
    subagents=[
        {
            "name": "researcher",
            "description": "Conduct web research and compile findings",
            "system_prompt": "Search thoroughly, return concise summary",
            "tools": [search_papers],
        }
    ]
)

# Main agent delegates: task(agent="researcher", instruction="Research AI trends")
</python>
<typescript>
Create a custom "researcher" subagent with specialized tools for academic paper search.
typescript
import { createDeepAgent } from "deepagents";
import { tool } from "@langchain/core/tools";
import { z } from "zod";

const searchPapers = tool(
  async ({ query }) => `Found 10 papers about ${query}`,
  { name: "search_papers", description: "Search papers", schema: z.object({ query: z.string() }) }
);

const agent = await createDeepAgent({
  subagents: [
    {
      name: "researcher",
      description: "Conduct web research and compile findings",
      systemPrompt: "Search thoroughly, return concise summary",
      tools: [searchPapers],
    }
  ]
});

// Main agent delegates: task(agent="researcher", instruction="Research AI trends")
</typescript>
</ex-custom-subagents>
<ex-subagent-with-hitl>
<python>
Configure a subagent with HITL approval for sensitive operations.
python
from deepagents import create_deep_agent
from langgraph.checkpoint.memory import MemorySaver

agent = create_deep_agent(
    subagents=[
        {
            "name": "code-deployer",
            "description": "Deploy code to production",
            "system_prompt": "You deploy code after tests pass.",
            "tools": [run_tests, deploy_to_prod],
            "interrupt_on": {"deploy_to_prod": True},  # Require approval
        }
    ],
    checkpointer=MemorySaver()  # Required for interrupts
)
</python>
</ex-subagent-with-hitl>
<fix-subagents-are-stateless>
<python>
Subagents are stateless - provide complete instructions in a single call.
python
# WRONG: Subagents don't remember previous calls
# task(agent='research', instruction='Find data')
# task(agent='research', instruction='What did you find?')  # Starts fresh!

# CORRECT: Complete instructions upfront
# task(agent='research', instruction='Find data on AI, save to /research/, return summary')
</python>
<typescript>
Subagents are stateless - provide complete instructions in a single call.
typescript
// WRONG: Subagents don't remember previous calls
// task research: Find data
// task research: What did you find?  // Starts fresh!

// CORRECT: Complete instructions upfront
// task research: Find data on AI, save to /research/, return summary
</typescript>
</fix-subagents-are-stateless>
<fix-custom-subagents-dont-inherit-skills>
<python>
Custom subagents don't inherit skills from the main agent.
python
# WRONG: Custom subagent won't have main agent's skills
agent = create_deep_agent(
    skills=["/main-skills/"],
    subagents=[{"name": "helper", ...}]  # No skills inherited
)

# CORRECT: Provide skills explicitly (general-purpose subagent DOES inherit)
agent = create_deep_agent(
    skills=["/main-skills/"],
    subagents=[{"name": "helper", "skills": ["/helper-skills/"], ...}]
)
</python>
</fix-custom-subagents-dont-inherit-skills>

TodoList (Task Planning)

<when-to-use-todolist>
Use TodoList WhenSkip TodoList When
Complex multi-step tasksSimple single-action tasks
Long-running operationsQuick operations (< 3 steps)
</when-to-use-todolist>
<todolist-tool>
write_todos(todos: list[dict]) -> None

Each todo item has:

  • content: Description of the task
  • status: One of "pending", "in_progress", "completed"
    </todolist-tool>
<ex-todolist-usage>
<python>
Invoke an agent that automatically creates a todo list for a multi-step task.
python
from deepagents import create_deep_agent

agent = create_deep_agent()  # TodoListMiddleware included by default

result = agent.invoke({
    "messages": [{"role": "user", "content": "Create a REST API: design models, implement CRUD, add auth, write tests"}]
}, config={"configurable": {"thread_id": "session-1"}})

# Agent's planning via write_todos:
# [
#   {"content": "Design data models", "status": "in_progress"},
#   {"content": "Implement CRUD endpoints", "status": "pending"},
#   {"content": "Add authentication", "status": "pending"},
#   {"content": "Write tests", "status": "pending"}
# ]
</python>
<typescript>
Invoke an agent that automatically creates a todo list for a multi-step task.
typescript
import { createDeepAgent } from "deepagents";

const agent = await createDeepAgent();  // TodoListMiddleware included

const result = await agent.invoke({
  messages: [{ role: "user", content: "Create a REST API: design models, implement CRUD, add auth, write tests" }]
}, { configurable: { thread_id: "session-1" } });
</typescript>
</ex-todolist-usage>
<ex-access-todo-state>
<python>
Access the todo list from the agent's final state after invocation.
python
result = agent.invoke({...}, config={"configurable": {"thread_id": "session-1"}})

# Access todo list from final state
todos = result.get("todos", [])
for todo in todos:
    print(f"[{todo['status']}] {todo['content']}")
</python>
</ex-access-todo-state>
<fix-todolist-requires-thread-id>
<python>
Todo list state requires a thread_id for persistence across invocations.
python
# WRONG: Fresh state each time without thread_id
agent.invoke({"messages": [...]})

# CORRECT: Use thread_id
config = {"configurable": {"thread_id": "user-session"}}
agent.invoke({"messages": [...]}, config=config)  # Todos preserved
</python>
</fix-todolist-requires-thread-id>

Show full SKILL.md (229 more words)Show less

Human-in-the-Loop (Approval Workflows)

<when-to-use-hitl>
Use HITL WhenSkip HITL When
High-stakes operations (DB writes, deployments)Read-only operations
Compliance requires human oversightFully automated workflows
</when-to-use-hitl>
<ex-hitl-setup>
<python>
Configure which tools require human approval before execution.
python
from deepagents import create_deep_agent
from langgraph.checkpoint.memory import MemorySaver

agent = create_deep_agent(
    interrupt_on={
        "write_file": True,  # All decisions allowed
        "execute_sql": {"allowed_decisions": ["approve", "reject"]},
        "read_file": False,  # No interrupts
    },
    checkpointer=MemorySaver()  # REQUIRED for interrupts
)
</python>
<typescript>
Configure which tools require human approval before execution.
typescript
import { createDeepAgent } from "deepagents";
import { MemorySaver } from "@langchain/langgraph";

const agent = await createDeepAgent({
  interruptOn: {
    write_file: true,
    execute_sql: { allowedDecisions: ["approve", "reject"] },
    read_file: false,
  },
  checkpointer: new MemorySaver()  // REQUIRED
});
</typescript>
</ex-hitl-setup>
<ex-approval-workflow>
<python>
Complete workflow: trigger an interrupt, check state, approve action, and resume execution.
python
from deepagents import create_deep_agent
from langgraph.checkpoint.memory import MemorySaver
from langgraph.types import Command

agent = create_deep_agent(
    interrupt_on={"write_file": True},
    checkpointer=MemorySaver()
)

config = {"configurable": {"thread_id": "session-1"}}

# Step 1: Agent proposes write_file - execution pauses
result = agent.invoke({
    "messages": [{"role": "user", "content": "Write config to /prod.yaml"}]
}, config=config)

# Step 2: Check for interrupts
state = agent.get_state(config)
if state.next:
    print(f"Pending action")

# Step 3: Approve and resume
result = agent.invoke(Command(resume={"decisions": [{"type": "approve"}]}), config=config)
</python>
<typescript>
Complete workflow: trigger an interrupt, check state, approve action, and resume execution.
typescript
import { createDeepAgent } from "deepagents";
import { MemorySaver, Command } from "@langchain/langgraph";

const agent = await createDeepAgent({
  interruptOn: { write_file: true },
  checkpointer: new MemorySaver()
});

const config = { configurable: { thread_id: "session-1" } };

// Step 1: Agent proposes write_file - execution pauses
let result = await agent.invoke({
  messages: [{ role: "user", content: "Write config to /prod.yaml" }]
}, config);

// Step 2: Check for interrupts
const state = await agent.getState(config);
if (state.next) {
  console.log("Pending action");
}

// Step 3: Approve and resume
result = await agent.invoke(
  new Command({ resume: { decisions: [{ type: "approve" }] } }), config
);
</typescript>
</ex-approval-workflow>
<ex-reject-with-feedback>
<python>
Reject a pending action with feedback, prompting the agent to try a different approach.
python
result = agent.invoke(
    Command(resume={"decisions": [{"type": "reject", "message": "Run tests first"}]}),
    config=config,
)
</python>
<typescript>
Reject a pending action with feedback, prompting the agent to try a different approach.
typescript
const result = await agent.invoke(
  new Command({ resume: { decisions: [{ type: "reject", message: "Run tests first" }] } }),
  config,
);
</typescript>
</ex-reject-with-feedback>
<ex-edit-before-execution>
<python>
Edit the proposed action arguments before allowing execution.
python
result = agent.invoke(
    Command(resume={"decisions": [{
        "type": "edit",
        "edited_action": {
            "name": "execute_sql",
            "args": {"query": "DELETE FROM users WHERE last_login < '2020-01-01' LIMIT 100"},
        },
    }]}),
    config=config,
)
</python>
</ex-edit-before-execution>
<boundaries>
### What Agents CAN Configure
  • Subagent names, tools, models, system prompts
  • Which tools require approval
  • Allowed decision types per tool
  • TodoList content and structure
What Agents CANNOT Configure
  • Tool names (task, write_todos)
  • HITL protocol (approve/edit/reject structure)
  • Skip checkpointer requirement for interrupts
  • Make subagents stateful (they're ephemeral)
    </boundaries>
<fix-checkpointer-required>
<python>
Checkpointer is required when using interrupt_on for HITL workflows.
python
# WRONG
agent = create_deep_agent(interrupt_on={"write_file": True})

# CORRECT
agent = create_deep_agent(interrupt_on={"write_file": True}, checkpointer=MemorySaver())
</python>
<typescript>
Checkpointer is required when using interruptOn for HITL workflows.
typescript
// WRONG
const agent = await createDeepAgent({ interruptOn: { write_file: true } });

// CORRECT
const agent = await createDeepAgent({ interruptOn: { write_file: true }, checkpointer: new MemorySaver() });
</typescript>
</fix-checkpointer-required>
<fix-thread-id-required-for-resumption>
<python>
A consistent thread_id is required to resume interrupted workflows.
python
# WRONG: Can't resume without thread_id
agent.invoke({"messages": [...]})

# CORRECT
config = {"configurable": {"thread_id": "session-1"}}
agent.invoke({...}, config=config)
# Resume with Command using same config
agent.invoke(Command(resume={"decisions": [{"type": "approve"}]}), config=config)
</python>
<typescript>
A consistent thread_id is required to resume interrupted workflows.
typescript
// WRONG: Can't resume without thread_id
await agent.invoke({ messages: [...] });

// CORRECT
const config = { configurable: { thread_id: "session-1" } };
await agent.invoke({ messages: [...] }, config);
// Resume with Command using same config
await agent.invoke(new Command({ resume: { decisions: [{ type: "approve" }] } }), config);
</typescript>
</fix-thread-id-required-for-resumption>
<fix-interrupt-checks-between-invocations>
<python>
Interrupts happen BETWEEN invoke() calls, not mid-execution.
python
result = agent.invoke({...}, config=config)       # Step 1: triggers interrupt
if "__interrupt__" in result:                      # Step 2: check for interrupt
    result = agent.invoke(                         # Step 3: resume
        Command(resume={"decisions": [{"type": "approve"}]}),
        config=config,
    )
</python>
</fix-interrupt-checks-between-invocations>

© langchain-ai, MIT. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file

Files

Just SKILL.md in config/skills/deep-agents-orchestration of langchain-ai/langchain-skills.

Open the folder on GitHubat commit 16a992f

Used in 1 other repository

We found 1 copy of this SKILL.md (exact, near-identical or edited) in other folders. This page covers the copy in langchain-ai/langchain-skills, which our catalogue first saw on October 7, 2026.

Compare with similar skills

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Categories

Questions about Deep Agents Orchestration

What does Deep Agents Orchestration do?

INVOKE THIS SKILL when using subagents, task planning, or human approval in Deep Agents. Deep Agents Orchestration is an agent skill from langchain-ai/langchain-skills, published by the product's own GitHub organization. INVOKE THIS SKILL when using subagents, task planning, or human approval in Deep Agents.

When should I use Deep Agents Orchestration?

Deep Agents Orchestration fits situations like: tasks that involve Subagents; tasks that involve Planning.

How do I install Deep Agents Orchestration in Claude Code?

Run `npx skills add langchain-ai/langchain-skills --skill deep-agents-orchestration -a claude-code`. Or copy the skill folder (config/skills/deep-agents-orchestration in langchain-ai/langchain-skills) into .claude/skills/deep-agents-orchestration in your project. Claude Code loads it when a task matches its description.

How do I install Deep Agents Orchestration in Codex?

Run `npx skills add langchain-ai/langchain-skills --skill deep-agents-orchestration -a codex`. Or copy the skill folder (config/skills/deep-agents-orchestration in langchain-ai/langchain-skills) into .agents/skills/deep-agents-orchestration in your project. Codex loads it when a task matches its description.

Can I use Deep Agents Orchestration in Cursor, Gemini CLI or GitHub Copilot?

Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add langchain-ai/langchain-skills --skill deep-agents-orchestration -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/deep-agents-orchestration, .gemini/skills/deep-agents-orchestration, .github/skills/deep-agents-orchestration and .opencode/skills/deep-agents-orchestration in your project.

What does Deep Agents Orchestration need to run?

SKILL.md names no scripts, command-line tools or credentials: Deep Agents Orchestration is instructions for the agent only. Our summary lists: Python 3.

Does Deep Agents Orchestration access the network?

SKILL.md contains no URLs. Any network use would come from the scripts or tools the agent runs. This is read from the text; nothing was executed.

Is Deep Agents Orchestration safe to install?

Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. Review the folder before installing.

What licence does Deep Agents Orchestration use?

Deep Agents Orchestration is published under the MIT licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does Deep Agents Orchestration use?

About 3.4k tokens (SKILL.md is roughly 14k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full.

What are the alternatives to Deep Agents Orchestration?

Skills that share tags, products or a category with Deep Agents Orchestration: Migrating Mastra To Pydantic AI (pydantic/pydantic-ai, 21k stars), Migrating Vercel AI SDK And Eve To Pydantic AI (pydantic/pydantic-ai, 21k stars), Pydantic AI Harness (pydantic/pydantic-ai, 21k stars) and MCP Server Builder (anthropics/skills, 180k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Deep Agents Orchestration?

langchain-ai (a GitHub organization, an official publisher) maintains it in langchain-ai/langchain-skills, which has 1,276 GitHub stars. The repository holds 22 skills in this directory. The repository was last updated on October 8, 2026.

Source: langchain-ai/langchain-skills on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.