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NOTE: This project started as Robocorp Action Server, and is currently being migrated under Sema4.ai organization. You will still likely find links to Robocorp resources. It's all the same company!

Build MCP Tools or AI Actions that connect AI Agents with the real-world - all in 🐍 Python.

Sema4.ai is the easiest way to extend the capabilities of AI agents, assistants and copilots with custom actions, written in Python. Create and deploy tools, skills, loaders and plugins that securely connect any AI Assistant platform to your data and applications.

Sema4.ai Action Server makes your Python scripts compatible with Agents using protocols such as MCP, OpenAI's custom GPTs, LangChain and OpenGPTs by automatically creating and exposing an API based on function declaration, type hints and docstrings. Just create your @tool (or @action) and start!

🏃‍♂️ Quickstart

There are two main ways using the Action Server: with the command line, or with our VS Code extension. This section gets you going!

pip install sema4ai-action-server

Alternatively, it's also possible to download the Action Server as a standalone binary for a specific platform:

brew update
brew install sema4ai/tools/action-server

# Download Sema4.ai Action Server
curl -o action-server.exe https://cdn.sema4.ai/action-server/releases/latest/windows64/action-server.exe

# Download Sema4.ai Action Server
curl -o action-server https://cdn.sema4.ai/action-server/releases/latest/linux64/action-server
chmod a+x action-server

# Add to PATH or move to a folder that is in PATH
sudo mv action-server /usr/local/bin/

Bootstrap a new project from a template. You’ll be prompted for the name of the project:

action-server new

Navigate to the freshly created project folder and start the server:

cd my-project
action-server start

👉 You should now have an Action Server running locally at: http://localhost:8080, to open the web UI.

👉 The MCP endpoint is available at: http://localhost:8080/mcp.

👉 Using the --auto-reload -flag for developing the Action Server will automatically reload your tools/actions when you change them during development.

Head over to Action Server docs for more.

What makes a Python function an MCP Tool or ⚡️Action?

1️⃣ package.yaml file that describes the MCP/Action Package you are working on, and defines up your Python environment and dependencies:

spec-version: v2

name: Package name
description: Package description
documentation: https://github.com/...

dependencies:
  conda-forge:
    - python=3.12.10
    - uv=0.6.11
  pypi:
    - sema4ai-actions=1.3.15
    - sema4ai-mcp=0.0.1
    - pytz=2024.1

pythonpath:
  - src
  - tests

dev-dependencies:
  pypi:
    - pytest=8.3.3

dev-tasks:
  test: pytest tests

packaging:
  exclude:
    - ./.git/**
    - ./.vscode/**
    - ./devdata/**
    - ./output/**
    - ./venv/**
    - ./.venv/**
    - ./.DS_store/**
    - ./**/*.pyc
    - ./**/*.zip
    - ./**/.env
    - ./**/__MACOSX
    - ./**/__pycache__
    - ./**/.git
    - ./node_modules/**

2️⃣ @tool decorator or @action decorator that determines the tool or action entry point and Type hints and docstring to let AI agents know what the Tool/Action does in natural language

Note: any function decorated as @action imported from sema4ai.actions is also available as a @tool imported from sema4ai.mcp and vice-versa (besides, there are other custom decorators for other functionalities such as @resource, @prompt for mcp and @query for actions).

from sema4ai.mcp import tool

@tool
def greeting(name: str) -> str:
    """
    Greets the user

    Args:
        name (str): The user name

    Returns:
        str: Final user greeting
    """

Once you have started the Action Server, point the client to the Action Server /mcp endpoint (example: http://localhost:8080/mcp).

Note: in production, the Action Server should be put under a reverse proxy that controls SSL and authentication.

For testing with a GPTs actions, it's possible to start the Action Server with the --expose flag.

Once you have started the Action Server with --expose flag, you’ll get a URL available to the public, along with the authentication token. The relevant part of the output from the terminal looks like this, of course with your own details:

...
Uvicorn running on http://localhost:8080 (Press CTRL+C to quit)
🌍 URL: https://seventy-six-helpless-dragonflies.sema4ai.link
🔑 Add following header api authorization header to run actions: { "Authorization": "Bearer xxx_xxx" }

👉 Example video in Youtube 👈

Adding the Action Server-hosted AI Action to your custom GPT is super simple: basically just navigate to “Actions” section of the GPT configuration, add the link to import the actions, and Add Authentication with Authentication method set to “API key” and Auth Type to “Bearer”.

TIP: Use the @action(is_consequential=False) flag to avoid the user needing to accept the action execution separately each time on your GPT.

Why use Sema4.ai AI Actions

• ❤️ “when it comes to automation, the (ex)Robocorp suite is the best one” /u/disturbing_nickname
• ❤️ “(ex)Robocorp seems to be a good player in this domain” /u/thankred
• ❤️ “Since you know Python, check out (ex)Robocorp. Their product is crazy good.” /u/Uomis

Sema4.ai stack is hands down the easiest way to give AI agents more capabilities. It’s an end-to-end stack supporting every type of connection between AI and your apps and data. You are in control where to run the code and everything is built for easiness, security, and scalability.

• 🔐 Decouple AI and Actions that touches your data/apps - Clarity and security with segregation of duties between your AI agent and code that touches your data and apps. Build @tool or @action and use from multiple AI frameworks.
• 🏎️ Develop Actions faster with Sema4.ai's robocorp automation libraries - Robocorp libraries and the Python ecosystem lets you act on anything - from data to API to Browser to Desktops.
• 🕵️ Observability out of the box - Log and trace every @tool or @action run automatically without a single print statement. Pro tip: connect LangSmith traces with Action logs!
• 🤯 No-pain Python environment management - Don't do this. Sema4.ai manages a full Python environment for your actions with ease.
• 🚀 Deploy with zero config and infra - One step deployment, and you'll be connecting your @tool to MCP clients or @action to AI apps like Langchain and OpenAI GPTs in seconds.

Inspiration

Check out these example projects for inspiration.

• 🐣 Simplest possible AI Action
• 🤡 Get a random joke or jokes per theme. Showcases how easy it is to work with APIs.
• 📝 Add files to agent conversation

Build more @actions and be awesome! We'd love to hear and see what have you built. Join our Slack community to share your work.

Contributing and issues

⭐️ First, please star the repo - your support is highly appreciated!

• 🚩 Issues – our GitHub Issues is kept up to date with bugs, improvements, and feature requests
• 🙋 Help - you are welcome to join our Community Slack if you experience any difficulty getting setup
• 🌟 Contribution and recognition – Start here, PR's are welcome!
• 🔐 Refer to our Security policy for details

Contributors