Deep Agents Orchestration

LangChainOfficialPublished by LangChain

Skill
Tool integration
Agent tooling
MIT
No issues found

From the author

INVOKE THIS SKILL when using subagents, task planning, or human approval in Deep Agents. Covers SubAgentMiddleware, TodoList for planning, and HITL interrupts.

— the description by LangChain

AutoGineer summary

Written by AutoGineer from the skill’s own files, not by the author

Deep Agents provide a framework for managing complex agentic workflows through three core orchestration capabilities: SubAgentMiddleware for delegating tasks to specialized agents, TodoListMiddleware for structured task planning and tracking, and HumanInTheLoopMiddleware for requiring manual approval on sensitive operations. These features are integrated into the create_deep_agent() function, allowing developers to build robust, multi-step, and secure agent systems.

What it does

Delegates complex work to specialized subagents via the task tool.
Plans and tracks multi-step operations using the write_todos tool.
Implements human-in-the-loop approval for sensitive tool executions.
Supports approval, rejection with feedback, and editing of proposed actions.
Provides state persistence for todo lists and interrupted workflows via thread_id.
Enables custom subagent configuration with specific system prompts and tools.

When to use it

When a task requires specialized tools or isolation that a general-purpose agent cannot provide.
When managing long-running, multi-step operations that benefit from explicit planning.
When compliance or safety requires human oversight for high-stakes actions like database writes or deployments.
When you need to maintain context across complex, multi-stage agent interactions.

Advantages

Improves agent reliability by isolating complex tasks into specialized subagents.
Enhances transparency and control through structured planning and human approval steps.
Provides a consistent protocol for handling sensitive operations securely.
Allows for flexible intervention, including editing actions before they are executed.

Usage examples

“Create a researcher subagent to find academic papers.”: The agent is initialized with a custom 'researcher' subagent configured with a specific system prompt and academic search tools.
“Configure the agent to require approval before writing files.”: The agent is set up with interrupt_on={'write_file': True} and a checkpointer, pausing execution for human approval when a file write is proposed.
“How do I resume an agent after a human-in-the-loop interrupt?”: The agent resumes execution by invoking the agent with a Command object containing the approval decision using the same thread_id.
“How can I plan a multi-step task?”: The agent uses the write_todos tool to generate a list of pending, in-progress, and completed tasks, which can be tracked across invocations.

Install

Download the skill as a ZIP:
https://autogineer.com/agent-skills/langchain-ai/deep-agents-orchestration/download

Unzip it into the cross-client skills folder of your project:

mkdir -p .agents/skills && unzip deep-agents-orchestration.zip -d .agents/skills

Documentation by LangChain

<overview> Deep Agents include three orchestration capabilities:

SubAgentMiddleware: Delegate work via task tool to specialized agents
TodoListMiddleware: Plan and track tasks via write_todos tool
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 When	Use Main Agent When
Task needs specialized tools	General-purpose tools sufficient
Want to isolate complex work	Single-step operation
Need clean context for main agent	Context 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.

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.

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.

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.

# 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.

// 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.

# 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 When	Skip TodoList When
Complex multi-step tasks	Simple single-action tasks
Long-running operations	Quick 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.

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.

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.

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.

# 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>

Human-in-the-Loop (Approval Workflows)

<when-to-use-hitl>

Use HITL When	Skip HITL When
High-stakes operations (DB writes, deployments)	Read-only operations
Compliance requires human oversight	Fully automated workflows

</when-to-use-hitl>

<ex-hitl-setup> <python> Configure which tools require human approval before execution.

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.

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.

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.

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.

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.

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.

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.

# 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.

// 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.

# 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.

// 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.

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>

Files

ATTRIBUTION.mdAdded by AutoGineer912 B
LICENSELicence file added by AutoGineer1.0 KiB
SKILL.md13.4 KiB

ATTRIBUTION.md

Attribution

This folder is an unmodified copy of the Agent Skill "deep-agents-orchestration", distributed by AutoGineer (https://autogineer.com/agent-skills).

Source: https://github.com/langchain-ai/langchain-skills/tree/126c384d8552dcb476b226c244172186cc3f4328/config/skills/deep-agents-orchestration
Commit: 126c384d8552dcb476b226c244172186cc3f4328
Published by: LangChain (https://github.com/langchain-ai)
Publisher: LangChain (https://github.com/langchain-ai)
Licence: MIT License (https://github.com/langchain-ai/langchain-skills/blob/126c384d8552dcb476b226c244172186cc3f4328/LICENSE)
Licence evidence: repository licence LICENSE
Retrieved: 2026-11-07

The skill's own files are unmodified copies. AutoGineer is not affiliated with or endorsed by the author or the publisher.

Files added by AutoGineer

ATTRIBUTION.md: this file, written by AutoGineer
LICENSE: copied from LICENSE

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

No issues found by our static scan· No issues found

A static scan is not a guarantee. Skills are third-party content: review a skill before you let an agent use it.

Attribution and licence

Published by

LangChain

Publisher

LangChain (Official)

Source

langchain-ai/langchain-skills · config/skills/deep-agents-orchestration · commit 126c384

Licence

MIT License (redistribution allowed)

LICENSE (added by AutoGineer from LICENSE)
ATTRIBUTION.md (written by AutoGineer)

Synced

2026-11-09

AutoGineer is not affiliated with or endorsed by LangChain or LangChain.

Download ZIP

The ZIP holds the skill’s files, its licence files and an ATTRIBUTION.md. Download deep-agents-orchestration.zip