Agentor

Fastest way to build and deploy long-running AI agents—with durability, observability, and security.

Docs | Examples

Features

Feature	Description	Docs
🚀 MCP & tool security	The only full FastAPI compatible MCP Server with decorator API	Link
🦾 Agent-to-agent	Multi-agent communication	Link
📊 Observability	Agent tracing and monitoring	Link
💾 Durable runs	Persist, resume, and fork agent runs	Durable runs docs
🔍 Tool Search API	Reduced tool context bloat	Link

🚅 Quick Start

Installation

The recommended method of installing agentor is with pip from PyPI.

pip install agentor

This installs the v0.1.0 line, built on Agentor's own agent engine — durable, forkable runs included.

Tools with heavy or vendor-specific dependencies ship as extras, so the base install stays small:

pip install "agentor[google]"   # GmailTool, CalendarTool
pip install "agentor[all]"      # every optional tool

Available extras: google, exa, git, github, slack, postgres, scrapegraph, all.

More ways...

You can also install the latest bleeding edge version (could be unstable) of agentor, should you feel motivated enough, as follows:

pip install git+https://github.com/celestoai/agentor@main

Build and Serve an Agent

Build an Agent, connect external tools or MCP Server and serve as an API in just a few lines of code:

from agentor.tools import GetWeatherTool
from agentor import Agentor

agent = Agentor(
    name="Weather Agent",
    model="gpt-5-mini",  # Use any LLM provider - gemini/gemini-2.5-pro or anthropic/claude-3.5
    tools=[GetWeatherTool()]
)
result = agent.run("What is the weather in London?")  # Run the Agent
print(result)

# Serve Agent with a single line of code
agent.serve()

Any OpenAI-compatible provider

Point base_url at any provider that speaks OpenAI's /chat/completions — OpenRouter, Groq, Together, Fireworks, DeepSeek, vLLM, Ollama, or Anthropic's and Gemini's compatible endpoints — with no extra dependency:

agent = Agentor(
    name="Assistant",
    model="openrouter/auto",
    base_url="https://openrouter.ai/api/v1",
    api_key=os.environ["OPENROUTER_API_KEY"],
)

Run the following command to query the Agent server:

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

agent.serve() gives you an ordinary ASGI app, so host it wherever you already run Python services.

Durable Runs

Give the agent a store and every run becomes an append-only event log that survives process death. Resume an interrupted run, or fork any persisted run — even a completed one — into a new, independent run that keeps the full trace, the model's reasoning included when the provider returns it:

from agentor import Agentor
from agentor.engine.store import FileStore