Deep Agents Orchestration

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().

Subagents (Task Delegation)

Default subagent: "general-purpose" - automatically available with same tools/config as main agent.

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.

@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>

<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,
        )
    ]
)

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

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"}
# ]

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']}")

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
)

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
    )

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,
)

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())

// 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)

// 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,
    )