eino-examples/adk/human-in-the-loop/6_plan-execute-replan

Human-in-the-Loop: Plan-Execute-Replan Pattern with Review-and-Edit

This example demonstrates the Plan-Execute-Replan multi-agent pattern combined with the Review-and-Edit human-in-the-loop pattern.

It showcases how to build a planning agent system where users can review and modify booking parameters before critical actions (like flight and hotel bookings) are executed.

How It Works

1. Plan-Execute-Replan Architecture: The system consists of three specialized agents:

   • Planner: Creates an initial plan based on user requirements
   • Executor: Executes each step of the plan using available tools
   • Replanner: Adjusts the plan based on execution results

2. Review-Edit Tools: The book_flight and book_hotel tools are wrapped in InvokableReviewEditTool. This allows users to:

   • Approve the booking as-is
   • Modify the booking parameters (e.g., change dates, room type)
   • Reject the booking entirely

3. Workflow:

   • User requests a travel plan (e.g., "Plan a 3-day trip to Tokyo")
   • Planner creates a multi-step plan
   • Executor attempts to book flights → interrupt for review
   • User reviews/edits booking details → resume
   • Executor attempts to book hotel → interrupt for review
   • User reviews/edits → resume
   • Plan completes with all bookings confirmed

4. Iterative Execution: The plan-execute-replan pattern supports multiple iterations, allowing the replanner to adjust the plan based on execution results or user modifications.

Practical Example

Here's what the review-and-edit flow looks like:

========================================
User Query: Plan a 3-day trip to Tokyo starting from New York on 2025-10-15.
I need to book flights and a hotel. Also recommend some must-see attractions.
========================================

name: Planner
path: [{PlanExecuteAgent} {Planner}]
answer: Creating travel plan...

name: Executor
path: [{PlanExecuteAgent} {Executor}]
tool name: book_flight
arguments: {"from":"New York","to":"Tokyo","date":"2025-10-15","passengers":1,"preferred_time":"morning"}

========================================
REVIEW REQUIRED
========================================
Tool: book_flight
Arguments: {"from":"New York","to":"Tokyo","date":"2025-10-15","passengers":1,"preferred_time":"morning"}
----------------------------------------
Options:
  - Type 'ok' to approve as-is
  - Type 'n' to reject
  - Or enter modified JSON arguments
----------------------------------------
Your choice: ok

========================================
Resuming execution...
========================================

name: Executor
path: [{PlanExecuteAgent} {Executor}]
tool response: {"booking_id":"FL-2025-10-15-12345","airline":"Japan Airlines",...}

name: Executor
path: [{PlanExecuteAgent} {Executor}]
tool name: book_hotel
arguments: {"city":"Tokyo","check_in":"2025-10-15","check_out":"2025-10-18","guests":1,"room_type":"standard"}

========================================
REVIEW REQUIRED
========================================
Tool: book_hotel
Arguments: {"city":"Tokyo","check_in":"2025-10-15","check_out":"2025-10-18","guests":1,"room_type":"standard"}
----------------------------------------
Your choice: {"city":"Tokyo","check_in":"2025-10-15","check_out":"2025-10-18","guests":1,"room_type":"deluxe"}

========================================
Resuming execution...
========================================

name: Executor
path: [{PlanExecuteAgent} {Executor}]
tool response: {"booking_id":"HT-2025-10-15-67890","hotel_name":"Tokyo Grand Hyatt","room_type":"deluxe",...}

This trace demonstrates:

• Planning Phase: Planner creates a structured travel plan
• Flight Booking Review: User approves flight booking as-is
• Hotel Booking Edit: User modifies room type from "standard" to "deluxe"
• Flexible Input: Users can approve, reject, or provide modified JSON

How to Configure Environment Variables

Before running the example, you need to set up the required environment variables for the LLM API. You have two options:

Option 1: OpenAI-Compatible Configuration

export OPENAI_API_KEY="{your api key}"
export OPENAI_BASE_URL="{your model base url}"
# Only configure this if you are using Azure-like LLM providers
export OPENAI_BY_AZURE=true
# 'gpt-4o' is just an example, configure the model name provided by your LLM provider
export OPENAI_MODEL="gpt-4o-2024-05-13"

Option 2: ARK Configuration

export MODEL_TYPE="ark"
export ARK_API_KEY="{your ark api key}"
export ARK_MODEL="{your ark model name}"

Alternatively, you can create a .env file in the project root with these variables.

How to Run

Ensure you have your environment variables set (e.g., for the LLM API key). Then, run the following command from the root of the eino-examples repository:

go run ./adk/human-in-the-loop/6_plan-execute-replan

You will see the planner creating a travel plan, and when bookings are attempted, you'll be prompted to review and optionally edit the booking parameters.

Workflow Diagram

graph TD
    A[User Request] --> B[Planner Agent];
    B --> C[Create Plan];
    C --> D[Executor Agent];
    D --> E{book_flight Tool};
    E --> F[Interrupt: Review Required];
    F --> G{User Review};
    G -- "ok" --> H[Resume: Original Args];
    G -- "edit" --> I[Resume: Modified Args];
    G -- "n" --> J[Resume: Rejected];
    H --> K[Flight Booked];
    I --> K;
    J --> L[Replanner Adjusts];
    K --> M{book_hotel Tool};
    M --> N[Interrupt: Review Required];
    N --> O{User Review};
    O -- "ok/edit/n" --> P[Process Response];
    P --> Q[Hotel Booked or Replanned];
    Q --> R[Final Response];
    L --> D;