
deep-agents-orchestration
β 108by langchain-ai Β· part of langchain-ai/skills-benchmarks
INVOKE THIS SKILL when using subagents, task planning, or human approval in Deep Agents. Covers SubAgentMiddleware, TodoList for planning, and HITL interrupts.
This is the playbook your agent receives when the skill activates β you don't need to read it to use the skill, but it's here to audit before installing.
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:- SubAgentMiddleware: Delegate work via
tasktool to specialized agents - TodoListMiddleware: Plan and track tasks via
write_todostool - 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 |
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")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')// 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/"], ...}]
)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) |
Each todo item has:
content: Description of the taskstatus: One of"pending","in_progress","completed"</todolist-tool>
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" } });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 preservedHuman-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 |
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
});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
);- 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>
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() });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);npx skills add https://github.com/langchain-ai/skills-benchmarks --skill deep-agents-orchestrationRun this in your project β 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 MITβ you can use, modify, and redistribute it under that license's terms.