deep-agents-orchestration

Agent Skills › langchain-ai/langchain-skills › deep-agents-orchestration

deep-agents-orchestration

GitHub

Deep Agents编排技能，涵盖子代理任务委派、TodoList规划及人机协作审批。指导如何配置专用工具子代理、隔离复杂工作流及管理敏感操作的人工审核。

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

Trigger Scenarios

需要委托特定任务给专业子代理使用TodoList进行任务规划与追踪执行敏感操作前需人工审批

Install

npx skills add langchain-ai/langchain-skills --skill deep-agents-orchestration -g -y

More Options

Non-standard path

npx skills add https://github.com/langchain-ai/langchain-skills/tree/main/config/skills/deep-agents-orchestration -g -y

Use without installing

npx skills use langchain-ai/langchain-skills@deep-agents-orchestration

指定 Agent (Claude Code)

npx skills add langchain-ai/langchain-skills --skill deep-agents-orchestration -a claude-code -g -y

安装 repo 全部 skill

npx skills add langchain-ai/langchain-skills --all -g -y

预览 repo 内 skill

npx skills add langchain-ai/langchain-skills --list

SKILL.md

Frontmatter

{
    "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."
}

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().

Subagents (Task Delegation)

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

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.

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")

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")

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
)

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')

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

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/"], ...}]
)

TodoList (Task Planning)

Use TodoList When	Skip TodoList When
Complex multi-step tasks	Simple single-action tasks
Long-running operations	Quick operations (< 3 steps)

write_todos(todos: list[dict]) -> None

Each todo item has:

content: Description of the task
status: One of "pending", "in_progress", "completed"

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"}
# ]

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" } });

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']}")

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

Human-in-the-Loop (Approval Workflows)

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

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
)

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
});

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)

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
);

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,
)

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,
);

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,
)

### 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)

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())

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() });

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)

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);

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,
    )