🤖 AI Automated Code Repair Agent

AI Automated Code Repair Agent is a professional, autonomous CLI tool that executes Python scripts, detects crashes, and uses an AI-driven workflow to analyze, repair, and securely verify fixes before recommending them. It packages everything you need—from intelligent error parsing to isolated Docker-based verification—so you can point it at a script and get reliable, reproducible results.

PyPI Package: ai-code-repair-agent

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✨ Core Features

• 🤖 Autonomous End-to-End Workflow: Run agent <script.py> and let the tool handle execution, crash detection, analysis, patching, and reporting.
• 🧠 AI-Powered Code Generation (Gemini): Sends rich error context to Google's Gemini LLM to propose logical, high-quality fixes—not just syntax band-aids.
• 🛡️ Secure Sandbox Verification: Builds an isolated Docker container on the fly, runs tests, and accepts only verified solutions.
• 🔮 Auto-Dependency & Environment Discovery: Inspects the target script to infer third‑party dependencies and auto‑generate requirements.txt and a Dockerfile.
• 🎯 Environment-Aware Diagnostics: Distinguishes code bugs from environment issues (e.g., ModuleNotFoundError) and recommends precise pip install commands when appropriate.
• 📦 Professional CLI Experience: Distributed via PyPI with a user-friendly agent configure command for easy setup and a built-in Docker prerequisite check.

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🧭 Decision Workflow

First-Time User Setup

graph TD
    A[New User] --> B[pip install ai-code-repair-agent];
    B --> C[agent configure];
    C --> D[Enters API Key];
    D --> E[✅ Agent is ready to use];

Main Repair Workflow

graph TD
    A[User executes `agent <script_path>`] --> B{Docker Running?};
    B -- No --> C[❌ End: Advise user to start Docker];
    B -- Yes --> D[ProcessRunner executes script];
    D --> E{Crash Detected?};
    E -- No --> F[✅ End: Success];
    E -- Yes --> G[ErrorParser analyzes traceback];
    G --> H{Error Type?};
    H -- ModuleNotFoundError --> I[👩‍🏫 Reporter advises `pip install` & exits];
    H -- Other Error --> J[ContextManager retrieves faulty function];
    J --> K[AICore sends context to Gemini LLM];
    K --> L[Receives proposed code fix];
    L --> M[🛡️ SandboxVerifier begins test];
    M --> N{Test Passed?};
    N -- Yes --> O[🎉 Reporter shows success & diff];
    N -- No --> P[❌ Reporter shows failure];

    style C fill:#e63946,stroke:#333,stroke-width:2px
    style I fill:#f9f,stroke:#333,stroke-width:2px

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🏗️ System Architecture

This project is modular for clarity, testability, and extensibility.

• agent/cli.py — Orchestrator & CLI (argparse), handles configure command, Docker prerequisite check, and full workflow.
• agent/process_runner.py — Executes target scripts (subprocess), captures stdout/stderr, determines crash state.
• agent/error_parser.py — Parses Python tracebacks into structured data via regex.
• agent/context_manager.py — Extracts faulty function context using ast for high-signal AI prompts.
• agent/ai_core.py — Prompts and calls Google Gemini, securely loading API key from a .env in the user's home directory.
• agent/sandbox_verifier.py — Uses Docker SDK to build sandbox environments, apply patches, and verify.
• agent/reporter.py — Provides human-friendly output with colorama and clean diffs.

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🛠️ Tech Stack

Category	Technology
Core Language	Python 3.9+
Execution	subprocess, tempfile, pathlib
Analysis	ast, inspect, re, traceback, difflib, stack-data
AI	Google Gemini via google-generativeai
Sandboxing	Docker & Docker SDK for Python; auto-generated Dockerfile and requirements.txt
CLI & UX	argparse, colorama, interactive agent configure command
Packaging	PyPI with pyproject.toml, versioned CLI entry point

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🚀 Getting Started

Prerequisites

• Python 3.9+
• Docker Desktop: Required for creating isolated sandboxes. Download Docker Desktop.

Installation & Configuration

pip install ai-code-repair-agent
agent configure

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🧪 Usage

agent path/to/your/buggy_script.py
agent path/to/script_with_args.py --input data.csv --output results.json

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🧩 Design Principles

• Safety First: AI-generated code is never trusted without sandbox verification.
• Minimal Friction: One-time setup, then zero-config runs.
• Clear Boundaries: Distinguishes environment issues from logic bugs.
• Modular & Extensible: Clean interfaces for easy customization and future enhancements.
• Reproducible: Dockerized tests ensure consistent behavior across environments.

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🔍 Example Module Layout

ai_code_repair_agent/
└─ agent/
   ├─ __init__.py
   ├─ cli.py              # CLI orchestrator, configure command, Docker check
   ├─ process_runner.py   # Run target script, capture outputs
   ├─ error_parser.py     # Parse tracebacks
   ├─ context_manager.py  # Extract faulty function via AST
   ├─ ai_core.py          # Prompt & call Gemini, load API key
   ├─ sandbox_verifier.py # Build Docker envs & verify
   └─ reporter.py         # Colorized results & diffs

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🧱 Security & Privacy

• No source exfiltration: Only error-specific context is sent to the AI.
• Ephemeral sandboxes: Containers are temporary and removed post-execution.
• Secrets management: API keys securely stored in a .env file in your home directory.

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📜 License

Distributed under the MIT License. See LICENSE for details.

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👤 Author

Darshan S

• GitHub: @darshan0106
• LinkedIn: Darshan Sivakumar