Content
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Fastest way to build, prototype and deploy AI Agents with tools <mark><i>securely</i></mark>
</p>
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<a href="https://docs.celesto.ai">Docs</a> |
<a href="https://github.com/celestoai/agentor/tree/main/docs/examples">Examples</a>
</p>
[](https://celesto.ai)
[](https://pypi.org/project/agentor/)
[](https://github.com/CelestoAI/agentor/actions/workflows/test.yml)
[](https://opensource.org/licenses/Apache-2.0)
[](https://discord.gg/KNb5UkrAmm)
## Agentor
Agentor is an open-source framework that makes it easy to build Agentic systems with secure integrations across email, calendars, CRMs, and more.
Agentor is how you write agents. [Celesto AI](https://celesto.ai/) is how you run them safely in production.
## Features
| Feature | Description | Docs
|---------------------------|------------------------------------------|-----------------------|
| 🚀 LiteMCP | The only **full FastAPI compatible** MCP Server with decorator API | [Link](https://docs.celesto.ai/agentor/tools/LiteMCP)
| 🦾 A2A Protocol | Multi-agent communication | [Link](https://docs.celesto.ai/agentor/agent-to-agent)
| ☁️ Fast Agent deployment| One click (serverless) deployment | [Link](https://celesto.ai)
| 🔐 Secure integrations | Multi-tenancy and fine-grained authorization | [Link](https://docs.celesto.ai/agentor/tools/auth)
| 🔍 Tool Search API | Reduced tool context bloat | [Link](https://docs.celesto.ai/agentor/tools/tool-search)
## 🚅 Quick Start
### Installation
The recommended method of installing `agentor` is with pip from PyPI.
```bash
pip install agentor
```
<details>
<summary>More ways...</summary>
You can also install the latest bleeding edge version (could be unstable) of `agentor`, should you feel motivated enough, as follows:
```bash
pip install git+https://github.com/celestoai/agentor@main
```
</details>
## 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:
```python
from agentor.tools import WeatherAPI
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=[WeatherAPI()]
)
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:
```bash
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.
To deploy using Celesto, run:
```bash
celesto deploy
```
Once deployed, your agent will be accessible via a REST endpoint, for example:
```bash
https://api.celesto.ai/deploy/apps/<app-name>
```
## Create an Agent from Markdown
Bootstrap an Agent directly from a markdown file with metadata for name, tools, model, and temperature:
```markdown
---
name: WeatherBot
tools: [get_weather]
model: gpt-4o-mini
temperature: 0.3
---
You are a concise weather assistant.
```
Load it with:
```python
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](https://modelcontextprotocol.io) using LiteMCP. You can run it inside a FastAPI application or as a standalone MCP server.
```python
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](https://docs.celesto.ai/agentor/tools/LiteMCP)
## 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.
```python
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](https://docs.celesto.ai/agentor/agent-to-agent)
## 🤝 Contributing
We'd love your help making Agentor even better! Please read our [Contributing Guidelines](.github/CONTRIBUTING.md) and [Code of Conduct](.github/CODE_OF_CONDUCT.md).
## 📄 License
Apache 2.0 License - see [LICENSE](LICENSE) for details.
