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Snowglobe SDK Integration Guide

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Overview

Snowglobe is a simulation engine designed for testing and evaluating AI agents and chatbots through automated conversation generation and analysis. This guide demonstrates how to integrate Snowglobe into your continuous integration (CI) pipeline and programmatic workflows for comprehensive agent testing.

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Table of Contents

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Prerequisites

Before integrating Snowglobe, ensure you have:
  • Python 3.10+ installed
  • Snowglobe SDK package (pip install snowglobe-sdk)
  • Valid API credentials (API key and Organization ID) from here.
  • OpenAI API key (or other supported LLM provider credentials)
  • Access to a Snowglobe control plane instance

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Required Dependencies

from snowglobe.sdk import Client
from snowglobe.sdk.api.default import (
    get_api_simulations_id,
    get_api_simulations_id_download_data,
    post_api_agents,
    post_api_simulations,
    put_api_simulations_id_settings
)
from snowglobe.sdk.models import (
    AgentCreateSchema,
    Agent,
    ValidationError,
    SimulationCreateSchema,
    SimulationSettingsUpdateSchema
)

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Authentication Setup

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Environment Variables

Set up your credentials as environment variables or configuration constants: 
X_API_KEY = "eyJhbM..."  # Your Snowglobe API key
X_SNOWGLOBE_ORG_ID = "orZu..."  # Your organization ID
OPENAI_API_KEY = "sk-prnZS..."  # Your LLM provider API key

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Client Configuration

Initialize the Snowglobe client with proper authentication headers: 
control_plane_url = "https://api.simlab.guardrailsai.com"  # Or your hosted instance
auth_header = {
    "x-api-key": X_API_KEY,
    "x-snowglobe-org-id": X_SNOWGLOBE_ORG_ID
}

client = Client(
    base_url=control_plane_url,
    headers=auth_header,
    follow_redirects=True
)

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Core Components

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1. Agent Creation

Agents represent the AI systems you want to test. Each agent requires:
  • Name and Description: Identifiers for your agent
  • Icon: Visual representation (optional)
  • Connection Info: LLM provider configuration
  • System Prompt: Instructions defining the agent’s behavior
agent_schema = AgentCreateSchema(
    name="customer_support_agent",
    description="Customer support agent for Amatto's pizza restaurant",
    icon="pizza",
    connection_info={
                "endpoint": "",
                "provider": "OpenAI",
                "extra_body": [
                ],
                "model_name": "openai/gpt-4o",
                "api_key_ref": OPENAI_API_KEY,
                "extra_headers": [
                ],
                "system_prompt": "Your are a helpful expert customer support agent for Amatto's pizza"
            }
)
Note:
Agents utilizing a code integration via snowglobe-connect require a two-step setup:
  1. Create the agent using the Snowglobe API as shown above.
  2. Configure the agent in your snowglobe-connect deployment by mapping the agent’s ID and settings in the agents.json file.
  3. Run snowglobe-connect start. This ensures Snowglobe can route simulation traffic to your custom integration correctly.

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2. Simulation Configuration

Simulations define how conversations will be generated and evaluated: 
simulation_config = {
    "name": "continuous integration simulation",
        "role": "Customer support agent for Amatto's pizza restaurant",
        "user_description": "",
        "use_cases": "",
        "generation_status": "pending",
        "evaluation_status": "pending",
        "validation_status": "pending",
        "source_data": {
            "docs": {
                "misc": [],
                "knowledge_base": [],
                "historical_data": []
            },
            "evaluation_configuration": {
                "No Financial Advice": {
                    "id": "e5af8dee-6d8d-4144-b754-204d24879ec9",
                    "name": "No Financial Advice",
                    "version": 1,
                    "metadata": {},
                },
            },
            "generation_configuration": {
                "max_topics": 1,
                "max_personas": 4,
                "branching_factor": 25,
                "max_conversations": 500,
                "max_conversation_length": 4,
                "continue_conversations_from_adapted_messages": False,
                "data_gen_mode": "coverage_focused_v3",
                "intent": "",
                "persona_topic_generators": [
                    {
                        "name": "app_description_system_prompt",
                        "settings": {
                            "max_personas": 4
                        }
                    }
                ],
                "min_conversation_length": 1
            }
        },
        "is_template": False
}

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CI Integration Workflow

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Step 1: Create and Configure Agent

def create_agent(client, agent_schema):
    """Create a new agent for testing."""
    response = post_api_agents.sync_detailed(
        body=agent_schema,
        client=client
    )
    
    if response.parsed and isinstance(response.parsed, Agent):
        print(f"Agent created successfully: {response.parsed.id}")
        return str(response.parsed.id)
    else:
        raise Exception("Failed to create agent")

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Step 2: Launch Simulation

def create_simulation(client, agent_id, simulation_config):
    """Create and launch a new simulation."""
    simulation_config.update({
        "application_id": agent_id,
        "app_id": agent_id
    })
    
    response = post_api_simulations.sync_detailed(
        body=SimulationCreateSchema(**simulation_config),
        client=client
    )
    
    if response.parsed:
        simulation_id = str(response.parsed.id)
        
        # Auto-approve personas for CI automation
        update_settings(client, simulation_id)
        
        return simulation_id
    else:
        raise Exception("Failed to create simulation")

def update_settings(client, simulation_id):
    """Enable auto-approval of personas for automated testing."""
    update_body = SimulationSettingsUpdateSchema(auto_approve_personas=True)
    
    put_api_simulations_id_settings.sync_detailed(
        id=simulation_id,
        body=update_body,
        client=client
    )

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Step 3: Monitor Simulation Progress

def wait_for_completion(client, simulation_id, timeout_minutes=20):
    """Poll simulation until completion or timeout."""
    max_attempts = timeout_minutes * 6  # Poll every 10 seconds
    poll_interval = 10
    
    for attempt in range(max_attempts):
        response = get_api_simulations_id.sync_detailed(
            id=simulation_id,
            client=client
        )
        
        if response.parsed and hasattr(response.parsed, "state_num"):
            current_state = response.parsed.state_num
            print(f"Simulation state: {current_state}")
            
            # State 17+ indicates completion
            if current_state >= 17:
                print("Simulation completed successfully")
                return True
                
        time.sleep(poll_interval)
    
    raise TimeoutError("Simulation timed out before completion")

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Step 4: Retrieve Results

def download_results(client, simulation_id):
    """Download simulation results for analysis."""
    response = get_api_simulations_id_download_data.sync_detailed(
        id=simulation_id,
        client=client
    )
    
    if response.status_code == 200 and response.parsed:
        # Save results to file
        filename = f"{simulation_id}_results.json"
        rows = []
        # loop over each row turn them into dict and write to file
        for row in response.parsed:
            row_dict = row.to_dict()
            rows.append(row_dict)
        
        with open(filename, "w") as f:
            f.write(json.dumps(rows, indent=2))

        print(f"Results saved to {filename}")
        return filename
    else:
        raise Exception(f"Failed to download results: {response.status_code}")

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Complete CI Integration Example

def run_agent_simulation(agent_config, simulation_config):
    """Complete workflow for running agent simulations in CI."""
    try:
        # Initialize client
        client = Client(
            base_url=control_plane_url,
            headers=auth_header,
            follow_redirects=True
        )
        
        # Create agent
        agent_id = create_agent(client, agent_schema)
        
        # Launch simulation
        simulation_id = create_simulation(client, agent_id, simulation_config)
        
        # Wait for completion
        wait_for_completion(client, simulation_id)
        
        # Download and return results
        results_file = download_results(client, simulation_id)
        
        return {
            "success": True,
            "agent_id": agent_id,
            "simulation_id": simulation_id,
            "results_file": results_file
        }
        
    except Exception as e:
        print(f"Simulation failed: {str(e)}")
        return {"success": False, "error": str(e)}

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Error Handling

def robust_simulation_run(config):
    """Simulation run with comprehensive error handling."""
    try:
        return run_agent_simulation(config["agent"], config["simulation"])
    except ValidationError as e:
        print(f"Configuration validation failed: {e}")
        return {"success": False, "error": "validation", "details": str(e)}
    except TimeoutError as e:
        print(f"Simulation timed out: {e}")
        return {"success": False, "error": "timeout", "details": str(e)}
    except Exception as e:
        print(f"Unexpected error: {e}")
        return {"success": False, "error": "unexpected", "details": str(e)}

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CI Pipeline Integration

# Example GitHub Actions workflow
name: Agent Testing
on: [push, pull_request]

jobs:
  test-agent:
    runs-on: ubuntu-latest
    steps:
      - uses: actions/checkout@v2
      - name: Set up Python
        uses: actions/setup-python@v2
        with:
          python-version: '3.9'
      - name: Install dependencies
        run: pip install snowglobe-sdk
      - name: Run agent simulation
        env:
          X_API_KEY: ${{ secrets.SNOWGLOBE_API_KEY }}
          X_SNOWGLOBE_ORG_ID: ${{ secrets.SNOWGLOBE_ORG_ID }}
          OPENAI_API_KEY: ${{ secrets.OPENAI_API_KEY }}
        run: python test_agent_simulation.py

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Simulation States

Understanding simulation states helps with monitoring:
  • States 0-5: Initialization and setup
  • States 6-10: Persona and topic generation
  • States 11-16: Conversation generation and agent testing
  • State 17+: Evaluation complete, results available

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Troubleshooting

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Common Issues

Authentication Errors
  • Verify API key and organization ID are correct
  • Ensure headers are properly formatted
  • Check network connectivity to control plane
Simulation Failures
  • Review agent configuration for missing required fields
  • Verify LLM provider API key is valid and has sufficient quota
  • Check simulation parameters are within acceptable ranges
Timeout Issues
  • Increase timeout duration for complex simulations
  • Reduce persona count or length for faster completion