Serve agents as an API

You can connect an agent to other applications and services by exposing an API endpoint. Agentor makes it easy to create a production-ready server. agent.serve() returns an ordinary ASGI app running under uvicorn, so you host it the way you host any other Python service. It adds no authentication of its own.

Serve an Agent as API

Agents can be deployed as REST API server so you can query them from your applications or integrate them into your existing infrastructure. Agentor makes it easy to serve the Agents by providing a simple serve method.

from agentor.tools import GetWeatherTool
from agentor import Agentor

agent = Agentor(name="Weather Agent", model="gpt-5-mini", tools=[GetWeatherTool()])
agent.serve(port=8000)   

To query your Agent server:

import requests

URL = "http://localhost:8000/chat"

response = requests.post(
    URL,
    json={"input": "how are you?"},
    headers={"Content-Type": "application/json"}
)
print(response.content)

import requests

# Wherever you are hosting the agent. `serve()` adds no authentication of its
# own, so put it behind whatever your infrastructure already uses.
URL = "https://agents.example.com/chat"

response = requests.post(
    URL,
    json={"input": "how are you?"},
    headers={"Content-Type": "application/json"},
    timeout=(5, 120),
)
print(response.content)

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

Self-Hosted Deployment

Deploy agents on your own infrastructure using Docker or Kubernetes.

Docker
Kubernetes

Create a Dockerfile in your project:

FROM python:3.11-slim

WORKDIR /app

COPY requirements.txt .
RUN pip install --no-cache-dir -r requirements.txt

COPY . .

EXPOSE 8000

CMD ["python", "agent.py"]

Build and run your container:

docker build -t my-agent .
docker run -p 8000:8000 my-agent

Create a deployment manifest:

apiVersion: apps/v1
kind: Deployment
metadata:
  name: agent-deployment
spec:
  replicas: 3
  selector:
    matchLabels:
      app: my-agent
  template:
    metadata:
      labels:
        app: my-agent
    spec:
      containers:
      - name: agent
        image: my-agent:latest
        ports:
        - containerPort: 8000
        env:
        - name: OPENAI_API_KEY
          valueFrom:
            secretKeyRef:
              name: agent-secrets
              key: openai-api-key

Deploy to your cluster:

kubectl apply -f deployment.yaml

Environment Variables

Set required environment variables for your deployment:

string

required

Your LLM provider API key (OpenAI, Anthropic, etc.)

integer

default:"8000"

Port to run the agent server on

string

default:"INFO"

Logging level: DEBUG, INFO, WARNING, ERROR

Monitoring & Logs

Health Checks

Built-in health endpoint at /health for monitoring and load balancers.

For production deployments, we recommend setting up monitoring, logging, and auto-scaling based on your traffic patterns.

Get started

Runners

Caching

CLI

Performance

Security

Troubleshooting

Serve agents as an API

Serve an Agent as API

Self-Hosted Deployment

Environment Variables

Monitoring & Logs

Health Checks

​Serve an Agent as API

​Self-Hosted Deployment

​Environment Variables

​Monitoring & Logs

Health Checks

Serve an Agent as API

Self-Hosted Deployment

Environment Variables

Monitoring & Logs