local/node_python_ai_task

Node + Python AI Debugging Task (Terminal Bench)

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Built with support from Erutalia (erutalia.com) — a software company specializing in AI, Graph Systems, Custom Software Development, CRA solutions, and scalable backend architectures.

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Overview

This task simulates a real-world DevOps / Backend engineering scenario where an AI agent must debug and fix a broken microservice.

The system consists of:

• A Node.js API server
• A Python-based processing module
• Inter-process communication between Node and Python
• A rate-limited endpoint

The application is intentionally broken and requires multi-step reasoning to fix.

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Objective

The goal is to:

1. Fix the /analyze endpoint:

   • Ensure correct communication between Node.js and Python
   • Return accurate JSON responses

2. Ensure the server runs correctly on port 3000

3. Implement a working rate limiter:

   • Endpoint: /ping
   • Limit: 5 requests per 10 seconds per client
   • Exceeding limit should return HTTP 429

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Key Challenges

• Debugging inter-process communication (Node <-> Python)
• Fixing incorrect execution methods
• Understanding middleware behavior in Express
• Implementing and verifying rate limiting logic
• Ensuring correct API responses under constraints

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Project Structure

node_python_ai_task/ ├── task.toml ├── instruction.md ├── environment/ ├── solution/ └── tests/

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How to Run

Oracle (solution validation)

harbor run -p "./node_python_ai_task" -a oracle

AI Agent (Groq model)

harbor run -p "./node_python_ai_task" -a terminus-2 --model groq/moonshotai/kimi-k2-instruct-0905 -k 10 -n 10

Evaluation Criteria

Functional correctness (API responses) Proper rate limiting behavior System stability Ability of AI agent to reason and fix issues

Notes

• The environment does NOT contain the solution
• Tests are hidden from the agent
• The agent must infer and fix issues independently

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