eino-examples/adk/human-in-the-loop/3_feedback-loop

Human-in-the-Loop: Feedback Loop Pattern

This example demonstrates a sophisticated "human-in-the-loop" pattern: Feedback Loop. It showcases a multi-agent workflow where one agent generates content, another agent requests human feedback, and the system supports iterative refinement based on that feedback.

How It Works

This implementation uses a multi-agent architecture with two specialized agents working together:

1. Writer Agent: A specialized agent that generates creative content (poems in this example). It produces the initial draft and can incorporate feedback to create revised versions.

2. Review Agent: An agent that acts as a bridge between the Writer Agent and the human reviewer. It:

   • Captures the content generated by the Writer Agent
   • Triggers an interrupt to request human feedback
   • Handles the resume process with human feedback
   • Either passes the feedback to the Writer Agent or exits the loop if no edits are needed

Workflow Sequence

1. Initial Request: The user requests content creation (e.g., "write a short poem about potato")
2. Content Generation: The Writer Agent generates the initial content
3. Feedback Interrupt: The Review Agent captures the content and triggers an interrupt, presenting it to the human for feedback
4. Human Feedback: The human can either:
   • Provide specific feedback for improvements
   • Indicate "NO NEED TO EDIT" if the content is satisfactory
5. Resume and Iterate: The system resumes with the human's feedback:
   • If feedback is provided, the Writer Agent generates a revised version
   • If "NO NEED TO EDIT" is selected, the workflow completes
6. Loop Continuation: The process can repeat for multiple iterations of feedback and refinement

Key Features Demonstrated

• Multi-Agent Collaboration: Shows how different agents can work together in a coordinated workflow
• Stateful Interrupts: The Review Agent maintains state about the content being reviewed
• Iterative Refinement: Supports multiple rounds of human feedback and content improvement
• Flexible Exit Conditions: Allows the human to indicate when the content is satisfactory

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/3_feedback-loop

You will see:

1. The Writer Agent generating a poem about potatoes
2. A prompt asking for your feedback
3. Options to either provide specific feedback or type "NO NEED TO EDIT"
4. The system incorporating your feedback and generating revised content

Workflow Diagram

graph TD
    A[User: Content Request] --> B{Writer Agent};
    B --> C[Generates Content] --> D{Review Agent};
    D --> E[Interrupt: Request Feedback] --> F{Human Feedback};
    F -- "Provide Feedback" --> G[Targeted Resume: With Feedback] --> B;
    F -- "NO NEED TO EDIT" --> H[Targeted Resume: No Edit Needed] --> I[Workflow Complete];
    G --> C;

Practical Example

Here's a real trace from running the example, showing how the iterative feedback process works:

name: WriterAgent
path: [{Writer MultiAgent} {WriterAgent}]
answer: Hidden beneath earth's gentle fold,
A humble potato, golden and bold.

name: ReviewerAgent
path: [{Writer Multi-Agent} {WriterAgent} {ReviewerAgent}]
Original content to review:
`
Hidden beneath earth's gentle fold,
A humble potato, golden and bold.
`.
If you think the content is good as it is, please reply with "No need to edit".
Otherwise, please provide your feedback.

your input here: Make it more funny.

name: ReviewerAgent
path: [{Writer Multi-Agent} {WriterAgent} {ReviewerAgent}]
answer: Make it more funny.

name: WriterAgent
path: [{Writer Multi-Agent} {WriterAgent} {ReviewerAgent} {WriterAgent}]
answer: I'm just a spud, not a fancy tomato,
But mash me up, I'm a party potato!

name: ReviewerAgent
path: [{Writer Multi-Agent} {WriterAgent} {ReviewerAgent} {WriterAgent} {ReviewerAgent}]
Original content to review:
`
I'm just a spud, not a fancy tomato,
But mash me up, I'm a party potato!
`.
If you think the content is good as it is, please reply with "No need to edit".
Otherwise, please provide your feedback.

your input here: No need to edit

name: ReviewerAgent
path: [{Writer Multi-Agent} {WriterAgent} {ReviewerAgent} {WriterAgent} {ReviewerAgent}]
action: exit

This trace demonstrates:

• Initial generation: The Writer Agent creates a serious poem about potatoes
• Human feedback: The user requests a more humorous tone
• Iterative improvement: The Writer Agent incorporates the feedback and creates a funnier version
• Workflow completion: The user approves the revised version, ending the process

The path notation shows the hierarchical agent structure and how the workflow progresses through multiple iterations.

Implementation Details

Agent Architecture

• WriterAgent: Uses adk.NewChatModelAgent with specific instructions for poem writing
• ReviewAgent: Custom agent implementing the adk.Agent interface with interrupt/resume logic
• LoopAgent: Wraps both agents using adk.NewLoopAgent to create the multi-agent workflow

State Management

• The FeedbackInfo struct maintains:
  • OriginalContent: The content being reviewed
  • Feedback: Human feedback for improvements
  • NoNeedToEdit: Flag indicating content is satisfactory

Interrupt/Resume Logic

• Uses adk.StatefulInterrupt() to pause execution and request human input while preserving state
• Implements Resume() method to handle different resume scenarios
• Supports both feedback-based iteration and workflow completion

Use Cases

This pattern is ideal for:

• Content creation and refinement (writing, design, code generation)
• Quality assurance workflows
• Collaborative editing processes
• Any scenario requiring human judgment and iterative improvement

The implementation demonstrates how complex human-in-the-loop workflows can be built using the Eino framework's interrupt and resume capabilities.