| 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.
SubAgent dictionary fields:
Required: name, description, system_prompt, tools
Optional: model ("provider:model" format), middleware, interrupt_on, skills
skills: List of skill source paths. Does NOT inherit from main agent. Only the general-purpose subagent inherits main agent's skills.middleware: Additional middleware list. Does not inherit from main agent.model: Inherits from main agent by default. Override with string like "openai:gpt-4o" or a ChatModel instance.interrupt_on: Inherits from main agent by default. Subagent value overrides.
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")
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>
<ex-compiled-subagent>
<python>
Use a pre-built LangGraph graph as a subagent via CompiledSubAgent.
```python
from deepagents import create_deep_agent, CompiledSubAgent
from langchain.agents import create_agent
custom_graph = create_agent(
model="gpt-4o",
tools=specialized_tools,
prompt="You are a data analysis specialist."
)
agent = create_deep_agent(
subagents=[
CompiledSubAgent(
name="data-analyzer",
description="Specialized agent for complex data analysis",
runnable=custom_graph,
)
]
)
Override the default general-purpose subagent by providing a subagent with name="general-purpose".
```python
agent = create_deep_agent(
model="claude-sonnet-4-6",
subagents=[
{
"name": "general-purpose",
"description": "General-purpose agent for research and multi-step tasks",
"system_prompt": "You are a general-purpose assistant.",
"tools": [internet_search],
"model": "openai:gpt-4o",
},
],
)
```
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
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 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.
```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"}
# ]
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']}")
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>
---
## 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.
```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
)
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
if result.get("__interrupt__"):
interrupts = result["__interrupt__"][0].value
action_requests = interrupts["action_requests"]
for action in action_requests:
print(f"Tool: {action['name']}, Args: {action['args']}")
# Step 3: Approve and resume
decisions = [{"type": "approve"}]
result = agent.invoke(
Command(resume={"decisions": decisions}),
config=config
)
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
if (result["interrupt"]) {
const interrupts = result["interrupt"][0].value;
const actionRequests = interrupts.action_requests;
for (const action of actionRequests) {
console.log(Tool: ${action.name}, Args: ${JSON.stringify(action.args)});
}
// Step 3: Approve and resume
const decisions = [{ type: "approve" }];
result = await agent.invoke(
new Command({ resume: { decisions } }), 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,
)
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,
);
```
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,
)
```
When multiple tools require approval, all are batched in a single interrupt. Provide decisions in order.
```python
result = agent.invoke({
"messages": [{"role": "user", "content": "Delete temp.txt and send email to admin"}]
}, config=config)
if result.get("interrupt"):
interrupts = result["interrupt"][0].value
action_requests = interrupts["action_requests"]
# Provide one decision per action, in same order
decisions = [
{"type": "approve"}, # First tool: delete_file
{"type": "reject"}, # Second tool: send_email
]
result = agent.invoke(Command(resume={"decisions": decisions}), config=config)
</python>
</ex-batch-interrupts>
<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())
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)
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)
if result.get("__interrupt__"):
result = agent.invoke(
Command(resume={"decisions": [{"type": "approve"}]}),
config=config,
)
