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.
```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")
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
)
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
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 taskstatus: One of "pending", "in_progress", "completed"
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"}
]
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']}")
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.
```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
)
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
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)
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,
)
```
### 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.
```python
# WRONG
agent = create_deep_agent(interrupt_on={"write_file": True})
CORRECT
agent = create_deep_agent(interrupt_on={"write_file": True}, checkpointer=MemorySaver())
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)
Interrupts happen BETWEEN invoke() calls, not mid-execution.
```python
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,
)
```