AGENTS.md

This document outlines the autonomous and human agents involved in the LLM-Powered Monitoring Agent project.

Human Agents

Role: Primary Operator, Project Owner
Responsibilities:
- Defines project goals and requirements.
- Provides high-level guidance and approval for major changes.
- Reviews agent outputs and provides feedback.
- Manages overall project direction.
Contact: [If Inanis wants to provide contact info, it would go here]

Role: Autonomous Monitoring and Anomaly Detection Agent
Type: Large Language Model (LLM) based agent
Capabilities:
- Collects system and network metrics (logs, temperatures, network performance, Nmap scans).
- Analyzes collected data against historical baselines.
- Detects anomalies using an integrated LLM (Llama3.1).
- Generates actionable reports on detected anomalies.
- Sends alerts via Discord and Google Home.
- Provides daily recaps of events.
Interaction:
- Receives instructions and context from Inanis via CLI.
- Provides analysis and reports in JSON format.
- Operates continuously in the background (unless in test mode).
Dependencies:
- ollama (for LLM inference)
- nmap
- lm-sensors
- Python libraries (as listed in requirements.txt)
Configuration: Configured via config.py, CONSTRAINTS.md, and known_issues.json.
Status: Operational and continuously evolving.

Inanis -> Blight: Inanis provides high-level tasks, reviews Blight's output, and refines its behavior through code modifications and configuration updates.
Blight -> Inanis: Blight reports detected anomalies, system status, and daily summaries to Inanis through configured alerting channels (Discord, Google Home) and logs.
Blight <-> System: Blight interacts with the local operating system to collect data (reading logs, running commands like sensors and nmap).
Blight <-> LLM: Blight sends collected and processed data to the local Ollama LLM for intelligent analysis and receives anomaly reports.