Quickstart

Build your first AI agent with Agentor in under 5 minutes

Get up and running with Agentor by building a simple weather agent and then exploring more advanced features.

Prerequisites

Before starting, make sure you have:

• Python 3.10 or higher installed
• An API key for an LLM provider (OpenAI, Anthropic, or Google)

Installation

1. Install Agentor

   Install Agentor using pip:

   pip install agentor
   
2. Set up your API key

   Set your LLM provider API key as an environment variable:

   # For OpenAI
   export OPENAI_API_KEY="your-api-key-here"
   
   # For Anthropic
   export ANTHROPIC_API_KEY="your-api-key-here"
   
   # For Google
   export GEMINI_API_KEY="your-api-key-here"
   

Build your first agent

Create a simple weather agent that can answer questions about the weather:

from agentor import Agentor

agent = Agentor(
    name="Weather Agent",
    model="gpt-4o-mini",
    tools=["get_weather"]
)

# Run the agent
result = agent.run("What is the weather in London?")
print(result)

The get_weather tool is a built-in tool that uses the WeatherAPI.com service. You’ll need to set the WEATHER_API_KEY environment variable to use it.

Run with streaming

See agent responses in real-time with streaming:

import asyncio
from agentor import Agentor

agent = Agentor(
    name="Weather Agent",
    model="gpt-4o-mini",
    tools=["get_weather"]
)

async def main():
    async for event in agent.stream_chat("What is the weather in Tokyo?"):
        print(event, flush=True)

asyncio.run(main())

Add custom instructions

Guide your agent’s behavior with custom instructions:

from agentor import Agentor

agent = Agentor(
    name="Weather Bot",
    model="gpt-4o-mini",
    instructions="You are a friendly weather assistant. Always include temperature in both Celsius and Fahrenheit.",
    tools=["get_weather"]
)

result = agent.run("How's the weather in Paris?")
print(result)

Use multiple tools

Combine multiple tools to create more capable agents:

from agentor import Agentor, function_tool

@function_tool
def calculate_temperature_diff(temp1: float, temp2: float) -> str:
    """Calculate the temperature difference between two values."""
    diff = abs(temp1 - temp2)
    return f"The temperature difference is {diff}°F"

agent = Agentor(
    name="Weather Analyzer",
    model="gpt-4o-mini",
    tools=["get_weather", calculate_temperature_diff]
)

result = agent.run("What's the temperature difference between London and Paris?")
print(result)

Serve as an API

Turn your agent into a REST API with a single line:

from agentor import Agentor

agent = Agentor(
    name="Weather Agent",
    model="gpt-4o-mini",
    tools=["get_weather"]
)

# Serve the agent on port 8000
agent.serve(port=8000)

This creates a FastAPI server with these endpoints:

• POST /chat - Send messages to the agent
• GET /.well-known/agent-card.json - A2A protocol agent card

Query the API

Use curl to interact with your agent API:

curl -X 'POST' \
  'http://localhost:8000/chat' \
  -H 'accept: application/json' \
  -H 'Content-Type: application/json' \
  -d '{
  "input": "What is the weather in London?"
}'

Deploy to production

Deploy your agent to Celesto AI’s serverless platform:

1. Install the Celesto CLI

   The CLI is included with Agentor:

   celesto --version
   
2. Create your agent file

   Save your agent code to a Python file (e.g., agent.py):

   from agentor import Agentor
   
   agent = Agentor(
       name="Weather Agent",
       model="gpt-4o-mini",
       tools=["get_weather"]
   )
   
   if __name__ == "__main__":
       agent.serve()
   
3. Deploy

   Deploy your agent with a single command:

   celesto deploy
   

   Your agent will be available at:

   https://api.celesto.ai/deploy/apps/<app-name>
   

Use different LLM providers

Agentor supports multiple LLM providers through LiteLLM:

OpenAI

from agentor import Agentor

agent = Agentor(
    name="My Agent",
    model="gpt-4o-mini",  # or gpt-4o, gpt-4-turbo
    tools=["get_weather"]
)

Configure model parameters

Fine-tune model behavior with ModelSettings:

from agentor import Agentor, ModelSettings

agent = Agentor(
    name="Creative Writer",
    model="gpt-4o",
    model_settings=ModelSettings(
        temperature=0.9,  # More creative
        max_tokens=2000,
        top_p=0.95
    ),
    tools=[]
)

result = agent.run("Write a short story about a robot learning to paint")
print(result)

Next steps

Now that you’ve built your first agent, explore more advanced features:

Building agents

Learn advanced agent patterns and best practices

Custom tools

Create custom tools for your agents

MCP servers

Build MCP servers with LiteMCP

Agent communication

Enable agent-to-agent communication with A2A protocol