Aegis Nexus

Welcome aboard the Aegis Nexus Shuttle, where we boldly go where no infrastructure team has gone before! 🚀

🌌 Overview

Aegis Nexus is an AI-powered multi-agent platform designed to transform your infrastructure management experience. Whether you're a CTO juggling Kubernetes dashboards or a CIO navigating compliance challenges, Aegis Nexus is your mission control for achieving zero-gravity peace of mind in your digital universe.

The backend follows a multi-step pipeline to transform a user question into actionable SRE insights. The high-level flow is:

1. Question – The user submits a question through the API or CLI.
2. Determine suitable action – The SRE agent analyzes the question to figure out what data is needed.
3. Select tools (LangGraph) – Using LangGraph, the agent chooses which monitoring tools or PromQL queries to use.
4. Generate PromQL – The agent formulates specific PromQL queries to retrieve metrics.
5. Run tools – Prometheus and other tools execute the queries and gather logs/metrics.
6. Process logs and metrics – Results are parsed and condensed into a technical summary.
7. Summarize – The LLM service provides a short natural language answer based on the technical findings.

🎬 Demo Video

Watch a quick demonstration of Aegis Nexus in action:

Project Structure

• Backend

  • FastAPI and LangGraph-based Python project managed with UV.
  • Includes agents, tools, and services for processing user questions and generating actionable SRE insights.

• Frontend

  • Next.js-based Vibe-coded interface.
  • Provides a modern and interactive user experience.

• Demo Grafana Prometheus

  • Chaos engineering project.
  • Generates logs using Prometheus, Grafana, Loki, and Litmus.
  • Kubernetes integration is a work in progress (WIP).

• Litmus Chaos Test

  • Focuses on chaos engineering experiments using Litmus.
  • Tests system resilience and generates chaos scenarios.

Installation

This project uses uv for fast Python package management.

Prerequisites

• Python 3.12+
• uv (install with: curl -LsSf https://astral.sh/uv/install.sh | sh)

Setup

To set up the project, clone the repository and install dependencies:

git clone <repository-url>
cd sre-agent-api
uv sync

Usage

FastAPI Server

To run the FastAPI application:

# Development mode (with auto-reload)
uv run uvicorn app.main:app --reload

# Production mode
uv run uvicorn app.main:app

This will start the server at http://127.0.0.1:8000.

API Endpoints

Ask SRE Questions

Send a POST request to the /sre/ask endpoint:

{
  "question": "What is the role of an SRE?"
}

Other Endpoints

• POST /sre/incident-response - Trigger incident response workflow
• GET /sre/health - Get system health report
• GET /sre/tools/demo - Run SRE tools demo
• GET /sre/tools/health - Check SRE tools health

Command Line Interface

The CLI supports multiple commands for interacting with the SRE agent:

Using UV Scripts (Recommended)

# Ask a question
uv run cli -q "What is the current CPU usage?"

# Or using the alias
uv run sre-cli -q "What is the current CPU usage?"

# Get system health report
uv run cli --health

# Run SRE tools demo
uv run cli --demo

# Check tools health
uv run cli --tools-health

# Trigger incident response
uv run cli --incident "HighCPU" "critical"

# Use a different agent
uv run cli -a sre -q "Show me recent alerts"

Using Direct Python Command

# Ask a question
uv run python cli.py -q "What is the current CPU usage?"

# Get system health report
uv run python cli.py --health

# Run SRE tools demo
uv run python cli.py --demo

# Check tools health
uv run python cli.py --tools-health

# Trigger incident response
uv run python cli.py --incident "HighCPU" "critical"

CLI Commands Reference

Command	Description	Example
-q, --question	Ask a question to the SRE agent	uv run cli -q "What is SRE?"
-a, --agent	Specify agent type (default: sre_agent)	uv run cli -a sre -q "hello"
--health	Get comprehensive system health report	uv run cli --health
--demo	Run SRE tools demonstration	uv run cli --demo
--tools-health	Check health status of all SRE tools	uv run cli --tools-health
--incident	Trigger incident response workflow	uv run cli --incident "AlertName" "severity"

Development

Running Tests

# Run all tests
uv run pytest

# Run specific test file
uv run pytest tests/test_sre_agent.py

# Run with verbose output
uv run pytest -v

Adding Dependencies

# Add production dependency
uv add package-name

# Add development dependency
uv add --dev package-name

Environment Variables

Create a .env.local file in the root directory based on the .env.example file:

# Copy example file
cp .env.example .env.local

# Edit with your actual API keys
LANGGRAPH_API_URL=your_langgraph_api_url_here
LANGGRAPH_API_KEY=your_langgraph_api_key_here
LLAMA_API_KEY=your_llama_api_key_here

Contributing

Contributions are welcome! Please open an issue or submit a pull request for any enhancements or bug fixes.

License

This project is licensed under the MIT License. See the LICENSE file for details.