Operator-ready prompt for reuse, tuning, and workspace runs.
This item is set up for developers who want to inspect the original language, fork it into Workspace, and adapt the evidence model without losing the source prompt structure.
Implementation handoffs, eval setup, and prompt tuning where you need the original structure intact.
Inspect first, copy once, then fork into Workspace when you want variants, notes, and model settings attached to the same run.
Swap domain facts, examples, and any hard-coded entities for your own context.
Tighten the evidence or verification requirement if this is headed toward production.
Decide which failure mode you want to evaluate first before you branch the prompt.
This prompt already carries implementation detail, tool context, and a final-output instruction. Keep that structure intact when you tune it, or your comparison runs get noisy fast.
Open this prompt inside Workspace when you want a live iteration loop.
Copy for quick reuse, or run it in Workspace to keep prompt variants, model settings, and prompt-history changes in one place.
Structured source with 9 active lines to adapt.
Already linked to a challenge workflow.
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Prompt content
Original prompt text with formatting preserved for inspection and clean copy.
Create a real-time voice interface using Retell AI that allows a human developer to review the proposed refactorings and give verbal approval or request modifications. The `ApprovalAgent` in your LangGraph should transition to a 'waiting_for_voice_input' state, and upon receiving verbal confirmation via Retell AI, update the state to proceed with code application. ```python
# Assume Retell AI webhook endpoint /retell-webhook is set up
from flask import Flask, request, jsonify
import threading
import queue app = Flask(__name__)
voice_input_queue = queue.Queue() @app.route('/retell-webhook', methods=['POST'])
def retell_webhook(): data = request.json # Process Retell AI events, push confirmation to queue if data.get('event') == 'call_ended' and 'yes' in data.get('transcript', '').lower(): voice_input_queue.put('approved') return jsonify({'status': 'ok'}) def start_flask_server(): app.run(port=5000, debug=False) def approval_node(state: AgentState): print("---AWAITING VOICE APPROVAL---") # Logic to poll voice_input_queue for approval # Update state['next_action'] based on approval return state # Start Flask server in a separate thread
# threading.Thread(target=start_flask_server, daemon=True).start()
```Adaptation plan
Keep the source stable, then branch your edits in a predictable order so the next prompt run is easier to evaluate.
Hold the task contract and output shape stable so generated implementations remain comparable.
Update libraries, interfaces, and environment assumptions to match the stack you actually run.
Test failure handling, edge cases, and any code paths that depend on hidden context or secrets.
Copy once for a pristine source snapshot, then move the prompt into Workspace when you want variants, run history, and side-by-side tuning without losing the original.
Prompt diagnostics
Quick signals for how structured this prompt already is and where adaptation work is likely to happen first.
This prompt already mixes executable detail with instructions, so the safest path is to tune examples and interfaces before you rewrite the overall scaffold.
LangChain A2A Code Refactoring with OpenAI o4-mini & AutoGPT
Inspired by headlines about rebuilding AI foundations like xAI, this challenge focuses on constructing a sophisticated multi-agent system using LangChain and LangGraph for automated code analysis, refactoring, and quality assurance. The system will leverage a network of specialized agents communicating via A2A (Agent-to-Agent) protocols to iteratively improve code quality and optimize performance. Agents will engage in detailed code reviews, identify technical debt, suggest refactorings, and validate changes, simulating a high-performance development team. Developers will design a robust LangGraph workflow to manage agent states, coordinate tasks, and enable dynamic decision-making. The system will integrate external tools for real-time information retrieval and workflow automation, culminating in a voice-controlled interface for human developers to interact with the refactoring process, providing real-time feedback and approvals. This project emphasizes modern agentic design, iterative improvement, and seamless tool integration.
Use the challenge page to recover the original task boundaries before you tune the prompt. That keeps your variants grounded in the same evaluation target instead of drifting into a different problem.