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Some tool operations may be sensitive and require human approval before execution. Deep agents support human-in-the-loop workflows through LangGraph’s interrupt capabilities. You can configure which tools require approval using the interrupt_on parameter.

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Basic configuration

The interrupt_on parameter accepts a dictionary mapping tool names to interrupt configurations. Each tool can be configured with:
  • True: Enable interrupts with default behavior (approve, edit, reject allowed)
  • False: Disable interrupts for this tool
  • {"allowed_decisions": [...]}: Custom configuration with specific allowed decisions
from langchain_core.tools import tool
from deepagents import create_deep_agent
from langgraph.checkpoint.memory import MemorySaver

@tool
def delete_file(path: str) -> str:
    """Delete a file from the filesystem."""
    return f"Deleted {path}"

@tool
def read_file(path: str) -> str:
    """Read a file from the filesystem."""
    return f"Contents of {path}"

@tool
def send_email(to: str, subject: str, body: str) -> str:
    """Send an email."""
    return f"Sent email to {to}"

# Checkpointer is REQUIRED for human-in-the-loop
checkpointer = MemorySaver()

agent = create_deep_agent(
    model="anthropic:claude-sonnet-4-20250514",
    tools=[delete_file, read_file, send_email],
    interrupt_on={
        "delete_file": True,  # Default: approve, edit, reject
        "read_file": False,   # No interrupts needed
        "send_email": {"allowed_decisions": ["approve", "reject"]},  # No editing
    },
    checkpointer=checkpointer  # Required!
)

​
Decision types

The allowed_decisions list controls what actions a human can take when reviewing a tool call:
  • "approve": Execute the tool with the original arguments as proposed by the agent
  • "edit": Modify the tool arguments before execution
  • "reject": Skip executing this tool call entirely
You can customize which decisions are available for each tool: 
interrupt_on = {
    # Sensitive operations: allow all options
    "delete_file": {"allowed_decisions": ["approve", "edit", "reject"]},
    
    # Moderate risk: approval or rejection only
    "write_file": {"allowed_decisions": ["approve", "reject"]},
    
    # Must approve (no rejection allowed)
    "critical_operation": {"allowed_decisions": ["approve"]},
}

​
Handle interrupts

When an interrupt is triggered, the agent pauses execution and returns control. Check for interrupts in the result and handle them accordingly. 
import uuid
from langgraph.types import Command

# Create config with thread_id for state persistence
config = {"configurable": {"thread_id": str(uuid.uuid4())}}

# Invoke the agent
result = agent.invoke({
    "messages": [{"role": "user", "content": "Delete the file temp.txt"}]
}, config=config)

# Check if execution was interrupted
if result.get("__interrupt__"):
    # Extract interrupt information
    interrupts = result["__interrupt__"][0].value
    action_requests = interrupts["action_requests"]
    review_configs = interrupts["review_configs"]

    # Create a lookup map from tool name to review config
    config_map = {cfg["action_name"]: cfg for cfg in review_configs}

    # Display the pending actions to the user
    for action in action_requests:
        review_config = config_map[action["name"]]
        print(f"Tool: {action['name']}")
        print(f"Arguments: {action['args']}")
        print(f"Allowed decisions: {review_config['allowed_decisions']}")
    
    # Get user decisions (one per action_request, in order)
    decisions = [
        {"type": "approve"}  # User approved the deletion
    ]
    
    # Resume execution with decisions
    result = agent.invoke(
        Command(resume={"decisions": decisions}),
        config=config  # Must use the same config!
    )

# Process final result
print(result["messages"][-1]["content"])

​
Multiple tool calls

When the agent calls multiple tools that require approval, all interrupts are batched together in a single interrupt. You must provide decisions for each one in order. 
config = {"configurable": {"thread_id": str(uuid.uuid4())}}

result = agent.invoke({
    "messages": [{
        "role": "user",
        "content": "Delete temp.txt and send an email to admin@example.com"
    }]
}, config=config)

if result.get("__interrupt__"):
    interrupts = result["__interrupt__"][0].value
    action_requests = interrupts["action_requests"]
    
    # Two tools need approval
    assert len(action_requests) == 2
    
    # Provide decisions in the same order as action_requests
    decisions = [
        {"type": "approve"},  # First tool: delete_file
        {"type": "reject"}    # Second tool: send_email
    ]
    
    result = agent.invoke(
        Command(resume={"decisions": decisions}),
        config=config
    )

​
Edit tool arguments

When "edit" is in the allowed decisions, you can modify the tool arguments before execution: 
if result.get("__interrupt__"):
    interrupts = result["__interrupt__"][0].value
    action_request = interrupts["action_requests"][0]

    # Original args from the agent
    print(action_request["args"])  # {"to": "everyone@company.com", ...}

    # User decides to edit the recipient
    decisions = [{
        "type": "edit",
        "edited_action": {
            "name": action_request["name"],  # Must include the tool name
            "args": {"to": "team@company.com", "subject": "...", "body": "..."}
        }
    }]

    result = agent.invoke(
        Command(resume={"decisions": decisions}),
        config=config
    )

​
Subagent interrupts

Each subagent can have its own interrupt_on configuration that overrides the main agent’s settings: 
agent = create_deep_agent(
    tools=[delete_file, read_file],
    interrupt_on={
        "delete_file": True,
        "read_file": False,
    },
    subagents=[{
        "name": "file-manager",
        "description": "Manages file operations",
        "system_prompt": "You are a file management assistant.",
        "tools": [delete_file, read_file],
        "interrupt_on": {
            # Override: require approval for reads in this subagent
            "delete_file": True,
            "read_file": True,  # Different from main agent!
        }
    }],
    checkpointer=checkpointer
)
When a subagent triggers an interrupt, the handling is the same - check for __interrupt__ and resume with Command.

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Best practices

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Always use a checkpointer

Human-in-the-loop requires a checkpointer to persist agent state between the interrupt and resume: 
from langgraph.checkpoint.memory import MemorySaver

checkpointer = MemorySaver()
agent = create_deep_agent(
    tools=[...],
    interrupt_on={...},
    checkpointer=checkpointer  # Required for HITL
)

​
Use the same thread ID

When resuming, you must use the same config with the same thread_id: 
# First call
config = {"configurable": {"thread_id": "my-thread"}}
result = agent.invoke(input, config=config)

# Resume (use same config)
result = agent.invoke(Command(resume={...}), config=config)

​
Match decision order to actions

The decisions list must match the order of action_requests: 
if result.get("__interrupt__"):
    interrupts = result["__interrupt__"][0].value
    action_requests = interrupts["action_requests"]
    
    # Create one decision per action, in order
    decisions = []
    for action in action_requests:
        decision = get_user_decision(action)  # Your logic
        decisions.append(decision)
    
    result = agent.invoke(
        Command(resume={"decisions": decisions}),
        config=config
    )

​
Tailor configurations by risk

Configure different tools based on their risk level: 
interrupt_on = {
    # High risk: full control (approve, edit, reject)
    "delete_file": {"allowed_decisions": ["approve", "edit", "reject"]},
    "send_email": {"allowed_decisions": ["approve", "edit", "reject"]},
    
    # Medium risk: no editing allowed
    "write_file": {"allowed_decisions": ["approve", "reject"]},
    
    # Low risk: no interrupts
    "read_file": False,
    "list_files": False,
}
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