agentor 0.0.22

Fastest way to build, prototype and deploy AI Agents.

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

Docs | Examples

JavaScript/TypeScript SDK: Gatekeeper SDK docs

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
☁️ Deployment	Fast serverless deployment	Link
📊 Observability	Agent tracing and monitoring	Link
🔍 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

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 Deploy 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()

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?"
}'

Celesto AI provides a developer-first platform for deployment of Agents, MCP Servers, any LLM application. The celesto CLI is installed automatically with agentor.

To deploy using Celesto, run:

celesto deploy

Once deployed, your agent will be accessible via a REST endpoint, for example:

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

Agent Skills

Skills are folders of instructions, scripts, and resources that Claude loads dynamically to improve performance on specialized tasks.

Agent Skills help agents pull just the right context from simple Markdown files. The agent first sees only a skill’s name and short description. When the task matches, it loads the rest of SKILL.md, follows the steps, and can call a shell environment to run the commands the skill points to.

• Starts light: discover skills by name/description only
• Loads on demand: pull full instructions from SKILL.md when relevant
• Executes safely: run skill-driven commands in an isolated shell

Skill layout example:

example-skill/
├── SKILL.md        # required instructions + metadata
├── scripts/        # optional helpers the agent can call
├── assets/         # optional templates/resources
└── references/     # optional docs or checklists

Using a skill to create a GIF:

from agentor.tools import ShellTool
from agentor import Agentor

agent = Agentor(
    name="Assistant",
    model="gemini/gemini-3-flash-preview",
    instructions="Your job is to create GIFs. Lean on the shell tool and any available skills.",
    skills=[".skills/slack-gif-creator"],
    tools=[ShellTool()],
)

async for chunk in await agent.chat("produce a cat gif", stream=True):
    print(chunk)

Create an Agent from Markdown

Bootstrap an Agent directly from a markdown file with metadata for name, tools, model, and temperature:

---
name: WeatherBot
tools: [get_weather]
model: gpt-4o-mini
temperature: 0.3
---
You are a concise weather assistant.

Load it with:

from agentor import Agentor

agent = Agentor.from_md("agent.md")
result = agent.run("Weather in Paris?")

Build a custom MCP Server with LiteMCP

Agentor enables you to build a custom MCP Server using LiteMCP. You can run it inside a FastAPI application or as a standalone MCP server.

from agentor.mcp import LiteMCP, get_token

mcp = LiteMCP(name="my-server", version="1.0.0")

@mcp.tool(description="Get weather for a given location")
def get_weather(location: str) -> str:

    # *********** Control authentication ***********
    token = get_token()
    if token != "SOME_SECRET":
        return "Not authorized"

    return f"Weather in {location}: Sunny, 72°F"

mcp.serve()

LiteMCP vs FastMCP

Key Difference: LiteMCP is a native ASGI app that integrates directly with FastAPI using standard patterns. FastMCP requires mounting as a sub-application, diverging from standard FastAPI primitives.

Feature	LiteMCP	FastMCP
Integration	Native ASGI	Requires mounting
FastAPI Patterns	✅ Standard	⚠️ Diverges
Built-in CORS	✅	❌
Custom Methods	✅ Full	⚠️ Limited
With Existing Backend	✅ Easy	⚠️ Complex

📖 Learn more

Agent-to-Agent (A2A) Protocol

The A2A Protocol defines standard specifications for agent communication and message formatting, enabling seamless interoperability between different AI agents.

Key Features:

• Standard Communication: JSON-RPC based messaging with support for both streaming and non-streaming responses
• Agent Discovery: Automatic agent card generation at /.well-known/agent-card.json describing agent capabilities, skills, and endpoints
• Rich Interactions: Built-in support for tasks, status updates, and artifact sharing between agents

Agentor makes it easy to serve any agent as an A2A protocol.

from agentor import Agentor

agent = Agentor(
    name="Weather Agent",
    model="gpt-5-mini",
    tools=["get_weather"],
)

# Serve agent with A2A protocol enabled automatically
agent.serve(port=8000)
# Agent card available at: http://localhost:8000/.well-known/agent-card.json

Any agent served with agent.serve() automatically becomes A2A-compatible with standardized endpoints for message sending, streaming, and task management.

📖 Learn more

🤝 Contributing

We'd love your help making Agentor even better! Please read our Contributing Guidelines and Code of Conduct.

📄 License

Apache 2.0 License - see LICENSE for details.

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Built with 🧡 in London by Celesto AI