SLOP (Simple Language Open Protocol) is a specification for AI APIs designed to simplify interaction with AI services. It defines a set of RESTful endpoints and conventions for communication, enabling developers to integrate with any AI service using standard HTTP requests and JSON data. The protocol is modular, allowing implementations to adopt only the endpoints they need, from basic chat to complex multi-agent orchestration.

How It Works

SLOP is built on the principle of using familiar web technologies: REST, JSON, and standard HTTP methods (GET, POST, PUT, DELETE). It standardizes common AI interaction patterns into six optional endpoints: /chat, /tools, /memory, /resources, /pay, and /info. This approach avoids introducing new technologies or complex frameworks, making AI integration as straightforward as any other web API. The protocol also supports advanced features like WebSocket and Server-Sent Events (SSE) for real-time communication.

Quick Start & Requirements

To get started, clone the repository and run the provided JavaScript example:

git clone https://github.com/agnt-gg/slop
cd slop
cd examples/javascript
npm install
npm start

This will start a local SLOP server at http://localhost:3000. You can test it with curl or use the provided slop-inspector utility.

Highlighted Details

• Defines a modular set of 6 RESTful endpoints for AI interaction.
• Supports standard HTTP, WebSockets, and Server-Sent Events (SSE) for communication.
• Enables multi-agent capabilities through routing, memory, and tool usage.
• Includes examples for chat, tools, memory, resources, payment, and info endpoints.

Maintenance & Community

SLOP is an open-source protocol launched under the MIT license by @NathanWilbanks of AGNT.gg. A Discord server is available for community discussion: https://discord.com/invite/nwXJMnHmXP.

Licensing & Compatibility

SLOP is released under the MIT License, which permits commercial use and integration with closed-source applications.

Limitations & Caveats

SLOP is a protocol specification, not a ready-to-use library or framework. Implementations are required to build the actual server logic and integrate with specific AI models. The provided examples are basic and may require further development for production use.