🚀 Site Reliability Engineer (SRE) Agent 🕵️

Welcome to the SRE Agent project! This open-source AI agent is here to assist your debugging, keep your systems healthy, and make your DevOps life a whole lot easier. Plug in your Kubernetes cluster, GitHub repo, and Slack, and let the agent do the heavy lifting—diagnosing, reporting, and keeping your team in the loop.

🌟 What is SRE Agent?

SRE Agent is your AI-powered teammate for monitoring application and infrastructure logs, diagnosing issues, and reporting diagnostics after errors. It connects directly into your stack, so you can focus on building, not firefighting.

🤔 Why Did We Build This?

We wanted to learn best practices, costs, security, and performance tips for AI agents in production. Our journey is open-source—check out our Production Journey Page and Agent Architecture Page for the full story.

We've been writing blogs and sharing our learnings along the way. Check out our blog for insights and updates.

Contributions welcome! Join us and help shape the future of AI-powered SRE.

✨ Features

• 🕵️‍♂️ Root Cause Debugging – Finds the real reason behind app and system errors
• 📜 Kubernetes Logs – Queries your cluster for logs and info
• 🔍 GitHub Search – Digs through your codebase for bugs
• 💬 Slack Integration – Notifies and updates your team
• 🚦 Diagnose from Anywhere – Trigger diagnostics with a simple endpoint

Powered by the Model Context Protocol (MCP) for seamless LLM-to-tool connectivity.

🛠️ Prerequisites

• Docker
• A .env file in your project root (see below)
• An app deployed on AWS EKS (Elastic Kubernetes Service)

⚡ Getting Started

Ready to see the agent in action? Let's get you set up.

1️⃣ Connect to Your Kubernetes Cluster

Currently, we support EKS clusters.

1. Go to your AWS access portal and grab your access keys:

2. Choose Option 2 and copy credentials into ~/.aws/credentials:

   [default]
   aws_access_key_id=ABCDEFG12345
   aws_secret_access_key=abcdefg123456789
   aws_session_token=abcdefg123456789....=

2️⃣ Credentials Setup

You'll need some environment variables. Use our template .env.example and the helper script:

python credential_setup.py

More details: credentials

3️⃣ Fire Up the Agent

Spin up all the services with Docker Compose:

docker compose up --build

🚢 Deploy with ECR images

See ECR Setup for details.

docker compose -f compose.ecr.yaml up

Note: AWS credentials must be in your ~/.aws/credentials file.

You'll see logs like this when everything's running:

orchestrator-1   |    FastAPI   Starting production server 🚀
orchestrator-1   |
orchestrator-1   |              Searching for package file structure from directories with
orchestrator-1   |              __init__.py files
kubernetes-1     | ✅ Kubeconfig updated successfully.
kubernetes-1     | 🚀 Starting Node.js application...
orchestrator-1   |              Importing from /
orchestrator-1   |
orchestrator-1   |     module   📁 app
orchestrator-1   |              ├── 🐍 __init__.py
orchestrator-1   |              └── 🐍 client.py
orchestrator-1   |
orchestrator-1   |       code   Importing the FastAPI app object from the module with the following
orchestrator-1   |              code:
orchestrator-1   |
orchestrator-1   |              from app.client import app
orchestrator-1   |
orchestrator-1   |        app   Using import string: app.client:app
orchestrator-1   |
orchestrator-1   |     server   Server started at http://0.0.0.0:80
orchestrator-1   |     server   Documentation at http://0.0.0.0:80/docs
orchestrator-1   |
orchestrator-1   |              Logs:
orchestrator-1   |
orchestrator-1   |       INFO   Started server process [1]
orchestrator-1   |       INFO   Waiting for application startup.
orchestrator-1   |       INFO   Application startup complete.
orchestrator-1   |       INFO   Uvicorn running on http://0.0.0.0:80 (Press CTRL+C to quit)
kubernetes-1     | 2025-04-24 12:53:00 [info]: Initialising Kubernetes manager {
kubernetes-1     |   "service": "kubernetes-server"
kubernetes-1     | }
kubernetes-1     | 2025-04-24 12:53:00 [info]: Kubernetes manager initialised successfully {
kubernetes-1     |   "service": "kubernetes-server"
kubernetes-1     | }
kubernetes-1     | 2025-04-24 12:53:00 [info]: Starting SSE server {
kubernetes-1     |   "service": "kubernetes-server"
kubernetes-1     | }
kubernetes-1     | 2025-04-24 12:53:00 [info]: mcp-kubernetes-server is listening on port 3001
kubernetes-1     | Use the following url to connect to the server:
kubernetes-1     | http://localhost:3001/sse {
kubernetes-1     |   "service": "kubernetes-server"
kubernetes-1     | }

This means all the services — Slack, GitHub, the orchestrator, the prompt and the MCP servers have started successfully and are ready to handle requests.

🧑‍💻 Using the Agent

Trigger a diagnosis with a simple curl command:

curl -X POST http://localhost:8003/diagnose \
  -H "accept: application/json" \
  -H "Authorization: Bearer <token>" \
  -d "text=<service>"

• Replace <token> with your dev bearer token (from .env)
• Replace <service> with the name of your target Kubernetes service

The agent will do its thing and report back in your configured Slack channel 🎉

🩺 Checking Service Health

A /health endpoint is available on the orchestrator service:

curl -X GET http://localhost:8003/health

• 200 OK = All systems go!
• 503 Service Unavailable = Something's up; check the response for details.

🚀 Deployments

Want to run this in the cloud? Check out our deployment examples:

• EKS Deployment

📚 Documentation

Find all the docs you need in the docs folder:

• Creating an IAM Role
• ECR Setup Steps
• Agent Architecture
• Production Journey
• Credentials
• Security Testing

🙏 Acknowledgements & Attribution

Big thanks to:

• Suyog Sonwalkar for the Kubernetes MCP server
• Anthropic's Model Context Protocol team for the Slack and GitHub MCP servers

📖 Blogs

Check out our blog posts for insights and updates:

• Bringing Agentic AI into the Real World
• How We’re Building an Autonomous SRE with FastMCP