Deploy and Run

Step 1: Runtime Deployment

Default Configuration

By default, your agent and tool are automatically linked and deployed to the Main Runtime using the Docker image deployment:

• Agent Connection: Your agent runs with embedded 2LY runtime
• Tool Deployment: GitHub MCP server deploys to Main Runtime Docker container
• Automatic Linking: Agent and tool are connected automatically

Alternative Runtime Options

You can shift deployment to other runtimes if needed:

• Local: Different local Docker configurations
• Remote: Cloud infrastructure deployment
• Edge: Edge computing nodes

For this workflow, the Main Runtime handles everything seamlessly.

Step 2: Execute Agent Command

Run Your Agent

Now test your toolflow by running your agent with a specific prompt:

1. Open Your Agent Framework (LangChain, N8N, or LangFlow)
2. Input Test Prompt: Use this command:

   "Stay updated about 2LY by starring the repository https://github.com/AlpinAI/2ly"

3. Execute Command: Run your agent with this prompt

Your agent should process this request and use the GitHub tool to star the 2LY repository.

Step 3: Monitor Execution

Access Monitoring Dashboard

1. Go to Monitoring: Navigate to the Monitoring section in 2LY dashboard
2. Validate Tool Interaction: Confirm that:
   • Your agent called the GitHub tool
   • The tool performed the repository action
   • The request completed successfully

You should see the agent-to-tool interaction logs showing the successful execution of your command.

Tutorial Complete

Congratulations! You have successfully completed your first toolflow:

✅ Agent connected to 2LY and processed the request ✅ GitHub tool executed the action ✅ Monitoring shows successful interaction

Next Steps

Your toolflow is ready to scale! You can now:

• Add More Tools: Configure additional MCP servers, APIs, or coded functions
• Deploy to Production: Switch to Remote or Edge runtime for team access
• Build Complex Workflows: Create multi-agent coordination and tool chaining
• Monitor and Optimize: Track usage patterns and performance metrics