Quick Start

Setup your cluster

You need a Kubernetes cluster to run SREGym. For detailed setup instructions, see the Cluster Setup guide.

Running an Agent

Quick Start

To get started with the included Stratus agent:

1. Create your .env file:

cp .env.example .env

2. Open the .env file and configure your model and API key.

3. Run the benchmark:

python main.py

Model Selection

SREGym supports multiple LLM providers. Specify your model using the --model flag:

python main.py --model <model-id>

Available Models

Default: If no model is specified, gpt-4o is used by default.

Examples

OpenAI:

# In .env file
OPENAI_API_KEY="sk-proj-..."

# Run with GPT-4o
python main.py --model gpt-4o

Anthropic:

# In .env file
ANTHROPIC_API_KEY="sk-ant-api03-..."

# Run with Claude Sonnet 4
python main.py --model claude-sonnet-4

AWS Bedrock:

# In .env file
AWS_PROFILE="bedrock"
AWS_DEFAULT_REGION=us-east-2

# Run with Claude Sonnet 4.5 on Bedrock
python main.py --model bedrock-claude-sonnet-4.5

Note: For AWS Bedrock, ensure your AWS credentials are configured via ~/.aws/credentials and your profile has permissions to access Bedrock.

Monitoring with Dashboard

SREGym provides a dashboard to monitor the status of your evaluation. The dashboard runs automatically when you start the benchmark with python main.py and can be accessed at http://localhost:11451 in your web browser.

Next Steps

Now that you've run your first agent, you can:

• Learn about MCP Tools available for your agent
• Read the guide on Running Your Own Agent to create custom agents
• Check out Troubleshooting if you run into problems