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Ali Ibrahim
Ali Ibrahim

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Announcing slimcontext: A Lightweight, Model-Agnostic Chat History Compression Utility 🚀

#programming #javascript #ai #npm

I'm excited to announce the release of my first npm package, slimcontext! It's a lightweight, model-agnostic chat history compression utility designed to keep your AI agent's conversations sharp and efficient.

The Problem

We've all been there. You're building an AI Agent, and as the conversation gets longer, the model starts to "forget" key details and performance degrades. While large context windows are great, they're not a silver bullet. Feeding a massive, unprocessed prompt to your model can still be slow and inefficient. The key is to be smart about what you include in the context.

The Solution

That's where slimcontext comes in. It helps you programmatically compress conversation histories to keep them concise while preserving vital context. It's designed to be simple and flexible, allowing you to "Bring Your Own Model" (BYOM).

Key Features

  • Trim Strategy: Token-aware trimming based on your model's maximum tokens and a specified threshold.
  • Summarize Strategy: Token-aware summarization of older messages using your own chat model.
  • Framework Agnostic: Plug in any model wrapper that implements a minimal invoke() interface.
  • Optional LangChain Adapter: Includes a one-call helper for easily compressing BaseMessage histories.

Get Started

You can install slimcontext from npm:

npm install slimcontext
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To see it in action, check out the complete "before and after" examples using OpenAI in the GitHub repository. These demonstrate how slimcontext can significantly reduce your token count while maintaining the flow of conversation.

I've also written a blog post that goes into more detail about the different message history summarization strategies that slimcontext can help you with. You can check it out here: Message History Summarization Strategies.

Check it out & Contribute!

I'd love for you to try out slimcontext and let me know what you think! Any feedback is welcome.

This is my first package, and I'm excited to see how it can help others. Contributions are very welcome! If you have ideas for new features or improvements, feel free to open an issue or a pull request.

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