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
Install Agentor
Install Agentor using pip: 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)
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)
POST /chat - Send messages to the agentGET /.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:
Install the Celesto CLI
The CLI is included with Agentor: 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()
Deploy
Deploy your agent with a single command: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:
from agentor import Agentor
agent = Agentor(
name="My Agent",
model="gpt-4o-mini", # or gpt-4o, gpt-4-turbo
tools=["get_weather"]
)
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: