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This guide will help you develop, deploy, and invoke your first Agent in 5 minutes.

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Table of Contents

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Overview

The complete workflow consists of 4 steps:
  1. ✅ Integrate SDK - Add decorators to your Agent code
  2. ✅ Configure Agent - Generate configuration files using CLI
  3. ✅ Deploy Agent - One-click deployment to the cloud
  4. ✅ Invoke Agent - Call your Agent using SDK or CLI

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Prerequisites

Ensure you have the following prerequisites:
  • ✅ Python 3.9+ and Node.js 20+ installed
  • ✅ Beta version Python SDK and Node.js CLI installed (see Installation Guide)
  • ✅ Novita AI API Key obtained (from the Console)
  • ✅ Docker installed

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Step 1: Integrate SDK into Your Agent

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1.1 Create Agent Code

Create app.py in your project directory: 
from novita_sandbox.agent_runtime import AgentRuntimeApp

# Create Agent Runtime application instance
app = AgentRuntimeApp()

# Define Agent entry point with decorator
@app.entrypoint
def my_agent(request: dict) -> dict:
    """
    Agent entry function
    
    Args:
        request: Request data, which typically contains fields like prompt
        
    Returns:
        Response data dictionary
    """
    prompt = request.get("prompt", "")
    
    # Agent business logic
    # You can call LLMs, use Agent frameworks, or implement any custom logic here
    result = f"Received message: {prompt}"
    
    return {"result": result}

# Local run entry point
if __name__ == "__main__":
    app.run()

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1.2 Prepare Dependencies File

Ensure your project root has a requirements.txt file with the required dependencies: 
novita-sandbox>=1.1.0b1
# Your other dependencies...

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1.3 Local Testing

Test locally before deployment: 
# Start the Agent service
python app.py
In another terminal, test the endpoints: 
# Test health check
curl http://localhost:8080/ping

# Test Agent invocation
curl -X POST http://localhost:8080/invocations \
  -H "Content-Type: application/json" \
  -d '{"prompt": "Hello, Agent!"}'
Expected output: Health check should return: 
{"status": "Healthy"}
Agent invocation should return: 
{"result": "Received message: Hello, Agent!"}

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Step 2: Generate Configuration Files with CLI

Use CLI to interactively configure your Agent: 
# Make sure environment variables are configured
export NOVITA_API_KEY=your-novita-api-key

# Run configuration command
npx novita-sandbox-cli agent configure
Follow the CLI prompts to configure the required information. When complete, the CLI will generate the following files in your project directory:
  • novita.Dockerfile - Docker build file
  • .dockerignore - Docker ignore file
  • .novita-agent.yaml - Agent configuration file

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Step 3: Deploy to Novita AI with One-click

⚠️ Important: After successful deployment, an agent_id will be generated. This is the unique identifier for invoking your Agent—make sure to save it.

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3.1 Deploy Command

Deploy with one-click using CLI: 
npx novita-sandbox-cli agent launch

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3.2 View Deployment Results

After successful deployment, the .novita-agent.yaml file will update the status field: 
status:
  phase: deployed
  agent_id: agent-xxxx  # ⭐ This is your Agent's unique identifier
  last_deployed: '2025-10-23T10:35:00Z'
  build_id: build_xyz789
Record the agent_id—you’ll need it for subsequent invocations.

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Step 4: Invoke Agent via CLI or SDK

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Option 1: Quick Test with CLI

Quickly test your Agent using CLI: 
npx novita-sandbox-cli agent invoke "Hello, Agent!"
Note: The CLI automatically reads the Agent ID from the status.agent_id field in .novita-agent.yaml. Expected output: 
{"result": "Received message: Hello, Agent!"}
Use the SDK to invoke your Agent in backend services:

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Example Code

import asyncio
import json
import os
from novita_sandbox.agent_runtime import AgentRuntimeClient
from dotenv import load_dotenv

# Load environment variables
load_dotenv()

# Create Agent Runtime client
client = AgentRuntimeClient(
    api_key=os.getenv("NOVITA_API_KEY")
)

async def main():
    # Get from status.agent_id in .novita-agent.yaml after deployment
    agent_id = "agent-xxxx"
    
    # Prepare request data
    payload = json.dumps({
        "prompt": "Hello, Agent! Tell me something about AI."
    }).encode()
    
    # Invoke Agent
    print(f"🚀 Invoking agent: {agent_id}")
    response = await client.invoke_agent_runtime(
        agentId=agent_id,
        payload=payload,
        timeout=300
    )
    
    print(f"✅ Response: {response}")

if __name__ == "__main__":
    asyncio.run(main())

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FAQ

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Q1: How do I get the Agent ID?

After successful deployment, the Agent ID is saved in the status.agent_id field of the .novita-agent.yaml file: 
status:
  agent_id: agent-xxxxx  # Here

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Q2: What if deployment fails?

  1. Check dependencies file: Ensure requirements.txt contains all dependencies
  2. View detailed logs: Use the --verbose flag
  3. Check network connection: Ensure you can access the Novita AI Sandbox domain (e.g., sandbox.novita.ai)
  4. Verify API Key: Confirm NOVITA_API_KEY is correct
# Show detailed logs
npx novita-sandbox-cli agent launch --verbose

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Q3: How do I update a deployed Agent?

Modify your code and redeploy: 
# Option 1: Create new version (Recommended)
npx novita-sandbox-cli agent configure --agent-version 1.1.0
npx novita-sandbox-cli agent launch

# Option 2: Update existing version
npx novita-sandbox-cli agent launch --update-existing

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Q4: Works locally but fails after deployment?

Possible causes:
  1. Environment variables not passed to sandbox instance
  2. Dependency package version inconsistencies
  3. File path issues
Solution: Pass environment variables to the sandbox instance via the envVars parameter: 
response = await client.invoke_agent_runtime(
    agentId=agent_id,
    payload=payload,
    envVars={
        "NOVITA_API_KEY": os.getenv("NOVITA_API_KEY"),
        "MODEL_NAME": "deepseek/deepseek-v3-0324"
    }
)