SRE Assistant Agent

A powerful Site Reliability Engineering (SRE) assistant built with Google's Agent Development Kit (ADK), featuring specialized agents for AWS cost analysis, Kubernetes operations, and operational best practices.

🚀 Quick Start

Prerequisites

• Docker and Docker Compose
• AI Provider API key (see AI Model Configuration below)
• (Optional) AWS credentials and Kubernetes config for respective features

1. Clone and Setup

git clone <your-repo-url>
cd sre-bot
Copy environment files and customize
cp .env.example .env
cp agents/.env.example agents/.env
cp slackbot/.env.example slackbot/.env

2. Configure Environment

Edit agents/.env with your AI provider credentials (see AI Model Configuration for details):

# Option 1: Google Gemini (Recommended)
GOOGLEAPIKEY=yourgoogleapikeyhere
GOOGLEAIMODEL=gemini-2.0-flash  # optional
Option 2: Anthropic Claude
ANTHROPICAPIKEY=youranthropicapikeyhere
ANTHROPIC_MODEL=claude-3-5-sonnet-20240620  # optional
Option 3: AWS Bedrock (requires AWS credentials)
BEDROCKINFERENCEPROFILE=arn:aws:bedrock:us-west-2:812201244513:inference-profile/us.anthropic.claude-opus-4-1-20250805-v1:0
Optional: AWS and Kubernetes configurations
AWSPROFILE=youraws_profile
KUBECONTEXT=yourkube_context

3. Start the Agent

# Build and start all services
docker compose build
docker compose up -d
Check if services are running
docker compose ps

4. Access the Interface

• Web Interface:
• API Server:
• Health Check:

🏗️ Architecture

The SRE bot follows a modular architecture with specialized sub-agents:

agents/sre_agent/
├── agent.py              # Main SRE agent orchestrator
├── serve.py              # FastAPI server with health checks
├── utils.py              # Shared utilities
└── sub_agents/
    └── aws_cost/         # AWS cost analysis module
        ├── agent.py      # Agent configuration
        ├── tools/        # Cost analysis tools
        └── prompts/      # Agent instructions

🛠️ Features

AWS Cost Analysis

• Retrieve and analyze AWS cost data for specific time periods
• Filter costs by services, tags, or accounts
• Calculate cost trends over time
• Provide average daily costs (including or excluding weekends)
• Identify the most expensive AWS accounts
• Compare costs across different time periods
• Generate cost optimization recommendations

Operational Excellence

• Infrastructure monitoring and troubleshooting
• Operational best practices and recommendations
• Performance optimization guidance
• Natural language interaction with technical systems

🔧 Development

Code Quality

# Run linting and formatting
ruff check .
ruff format .
ruff check . --fix
Run pre-commit hooks manually
pre-commit run --all-files

Local Development (Optional)

For rapid development and testing:

# Install dependencies
pip install -r agents/sre_agent/requirements.txt
pip install -r requirements-dev.txt
Use built-in ADK web interface for rapid bot testing
adk web --sessionserviceuri=postgresql://postgres:password@localhost:5432/srebot
Or use custom serve.py for API-only development
cd agents/sre_agent
python serve.py

📊 Docker Services

Available Services

• sre-bot-web: Web interface using ADK's built-in UI (port 8000)
• sre-bot-api: API-only server using custom serve.py (port 8001)
• slack-bot: Slack integration service (port 8002)
• postgres: PostgreSQL database for session persistence

Service Management

# Start specific services
docker compose up -d sre-bot-web    # Web interface
docker compose up -d sre-bot-api    # API server
docker compose up -d slack-bot      # Slack bot
View logs
docker compose logs [service-name]
Stop services
docker compose down

🔌 API Usage

Create a Session

curl -X POST http://localhost:8001/apps/sreagent/users/u123/sessions/s_123 \
  -H "Content-Type: application/json" \
  -d '{"state": {"key1": "value1"}}'

Send a Message

curl -X POST http://localhost:8001/run \
  -H "Content-Type: application/json" \
  -d '{
    "appname": "sreagent",
    "userid": "u123",
    "sessionid": "s123",
    "new_message": {
      "role": "user",
      "parts": [{"text": "How many pods are running in the default namespace?"}]
    }
  }'

💬 Slack Integration

Setup Slack Bot

• Configure Slack App (see detailed instructions below)
• Set environment variables in slack_bot/.env:

SLACKBOTTOKEN=xoxb-your-slack-bot-token
   SLACKSIGNINGSECRET=your-slack-signing-secret
   SLACKAPPTOKEN=xapp-your-slack-app-token

• Start the Slack bot:

docker compose up -d slack-bot

Creating the Slack App

• Go to and click "Create New App"
• Name it and choose a workspace
• Add Bot Token Scopes:

- app_mentions:read - View messages that mention the bot - chat:write - Send messages - channels:join - Join channels - chat:write.public - Send messages to channels the bot isn't in

• Install App to Workspace and get approval if needed
• Set up Event Subscriptions pointing to your ngrok URL
• Configure Slash Commands if desired

Example App Manifest

display_information:
  name: sre-bot
features:
  bot_user:
    display_name: sre-bot
    always_online: false
oauth_config:
  scopes:
    bot:
      - app_mentions:read
      - channels:join
      - channels:history
      - chat:write
      - chat:write.public
      - commands
      - reactions:read
settings:
  event_subscriptions:
    request_url: https://your-ngrok-url.ngrok-free.app/slack/events
    bot_events:
      - app_mention
  orgdeployenabled: false
  socketmodeenabled: false

📁 Environment Configuration

Service-Specific Environment Files

The SRE bot uses separate environment files for better organization:

• .env: Main Docker Compose configuration
• agents/.env: SRE Agent specific settings
• slack_bot/.env: Slack Bot configuration

Key Environment Variables

# Main Configuration (.env)
GOOGLEAPIKEY=yourgoogleapi_key
GOOGLEAIMODEL=gemini-2.0-flash
POSTGRES_PASSWORD=postgres
LOG_LEVEL=INFO
Agent Configuration (agents/.env)
PORT=8000
DB_HOST=localhost
DB_PORT=5432
Slack Bot Configuration (slack_bot/.env)
SLACKBOTTOKEN=xoxb-your-token
SLACKSIGNINGSECRET=your-secret
SREAGENTAPI_URL=http://sre-bot-api:8001

🤖 AI Model Configuration

The SRE bot supports multiple AI providers with automatic provider detection based on your environment variables. The system checks for API keys in priority order and configures the appropriate model.

Supported Providers

1. Google Gemini (Recommended)

Best for: Google Cloud users, fastest setup, most reliable

# Required
GOOGLEAPIKEY=yourgoogleapikeyhere
Optional (defaults shown)
GOOGLEAIMODEL=gemini-2.0-flash

Get API Key: Google AI Studio

2. Anthropic Claude

Best for: Advanced reasoning tasks, detailed analysis

# Required
ANTHROPICAPIKEY=youranthropicapikeyhere
Optional (defaults shown)
ANTHROPIC_MODEL=claude-3-5-sonnet-20240620

Get API Key: Anthropic Console

3. AWS Bedrock

Best for: AWS-native deployments, enterprise compliance

# Required
BEDROCKINFERENCEPROFILE=arn:aws:bedrock:us-west-2:812201244513:inference-profile/us.anthropic.claude-opus-4-1-20250805-v1:0
AWS credentials also required (one of the following):
AWSACCESSKEYID=youraccess_key
AWSSECRETACCESSKEY=yoursecret_key
OR
AWSPROFILE=youraws_profile

Setup: Configure AWS Bedrock access in your AWS account

Provider Selection Priority

The system automatically selects providers in this order:

• Google Gemini (if GOOGLEAPIKEY is set)
• Anthropic Claude (if ANTHROPICAPIKEY is set)
• AWS Bedrock (if BEDROCKINFERENCEPROFILE is set)

Configuration Examples

Minimal Google Setup

# agents/.env
GOOGLEAPIKEY=AIzaSyD4R5T6Y7U8I9O0P1A2S3D4F5G6H7J8K9L0

Anthropic with Custom Model

# agents/.env
ANTHROPICAPIKEY=sk-ant-api03-A1B2C3D4E5F6G7H8I9J0
ANTHROPIC_MODEL=claude-3-opus-20240229

Bedrock with Named Profile

# agents/.env
BEDROCKINFERENCEPROFILE=arn:aws:bedrock:us-east-1:123456789012:inference-profile/us.anthropic.claude-3-5-sonnet-20240620-v1:0
AWS_PROFILE=bedrock-user
AWS_REGION=us-east-1

Troubleshooting AI Configuration

No Provider Configured

ERROR: No AI provider configured!
Please configure one of the following providers...

Solution: Set at least one API key as shown above.

AWS Bedrock Credentials Missing

ERROR: BEDROCKINFERENCEPROFILE is set but AWS credentials are not configured

Solution: Configure AWS credentials via environment variables or AWS profiles.

Invalid API Key

ERROR: Authentication failed with provider

Solution: Verify your API key is correct and has necessary permissions.

Model Recommendations

| Use Case | Recommended Provider | Model | Why | |----------|---------------------|--------|------| | General SRE Tasks | Google Gemini | gemini-2.0-flash | Fast, reliable, good for operations | | Complex Analysis | Anthropic Claude | claude-3-5-sonnet-20240620 | Superior reasoning for complex problems | | Enterprise/AWS | AWS Bedrock | claude-3-opus-* | Enterprise compliance, AWS integration | | Cost-Sensitive | Google Gemini | gemini-2.0-flash | Most cost-effective for high-volume usage |

🔒 Security

• Store sensitive credentials in environment variables
• Use separate credentials for production vs development
• Follow principle of least privilege for AWS and Kubernetes access
• Never commit actual .env files to version control
• Review audit logs periodically

🐛 Troubleshooting

Common Issues

• Service Communication Issues:

docker compose ps                    # Check if all containers are running
   docker compose logs [service-name]  # Check specific service logs

• Database Connection Issues:

docker compose logs postgres         # Check PostgreSQL logs

• AI Model Configuration Issues:

docker compose logs sre-bot-api | grep -E "(ERROR|model|provider)"

Common errors: - No AI provider configured! → Set at least one API key - Bedrock requires valid AWS credentials → Configure AWS access - Authentication failed → Verify API key is valid - See AI Model Configuration for detailed setup

Health Checks

# Check overall health
curl http://localhost:8000/health
Kubernetes readiness/liveness probes
curl http://localhost:8000/health/readiness
curl http://localhost:8000/health/liveness

📚 Available Tools and Functions

AWS Cost Analysis Tools

• getcostfor_period - Get costs for specific date ranges
• getmonthlycost - Monthly cost summaries
• getcosttrend - Cost trend analysis
• getcostby_service - Service-level cost breakdown
• getcostby_tag - Tag-based cost analysis
• getmostexpensive_account - Identify highest-cost accounts

🤝 Contributing

• Follow the established code structure and patterns
• Use shared utilities from agents/sre_agent/utils.py
• Run code quality checks before committing:

ruff check . --fix
   ruff format .
   pre-commit run --all-files

• Test your changes with Docker Compose
• Update documentation as needed

📄 License

[Add your license here]

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Need help? Check the troubleshooting section above or review the service logs with docker compose logs [service-name].