Internal Knowledge Decay Detection Agent 🕵️‍♂️📚

An autonomous Agentic AI system that monitors internal documentation, evaluates its relevance using LLMs, and flags "stale" or outdated knowledge.

🚀 Overview

Engineering teams often suffer from "Knowledge Decay"—documentation that becomes misleading over time. This tool automates the maintenance process by:

1. Scanning repositories (Local or GitHub) for Markdown files.
2. Evaluating content using OpenAI (GPT-4) to detect signs of staleness.
3. Notifying the team via console alerts (extensible to Slack/Email).

🏗 Architecture

The system follows a modular Agentic workflow:

• Scanner Agent (src/scanner.py): Handles file I/O and GitHub API traversal.
• Evaluator Agent (src/evaluator.py): Uses Prompt Engineering to judge document freshness.
• Notifier Agent (src/notifier.py): Aggregates results and generates alerts.
• Orchestrator (main.py): Manages the data flow between agents.

🛠 Tech Stack

• Language: Python 3.10+
• AI: OpenAI API (GPT-4o / GPT-3.5)
• Integration: PyGithub (GitHub API)
• Environment: python-dotenv for security

⚙️ Setup

1. Clone the repository

   git clone [https://github.com/YOUR_USERNAME/knowledge-decay-agent.git](https://github.com/YOUR_USERNAME/knowledge-decay-agent.git)
   cd knowledge-decay-agent

2. Create Virtual Environment

   python -m venv venv
   source venv/bin/activate  # Windows: venv\Scripts\activate

3. Install Dependencies

   pip install -r requirements.txt

4. Configure Credentials Create a .env file in the root directory:

   OPENAI_API_KEY=sk-...
   GITHUB_TOKEN=ghp-...

🏃 Usage

Scan a Local Directory:

python main.py --target "docs/"