Smart context management for LLMs - never forget what matters.

Installation

cargo add forgetless

Or add to your Cargo.toml:

[dependencies]
forgetless = "0.1"

Optional Features

# With HTTP server
cargo add forgetless --features server

# With GPU acceleration (macOS)
cargo add forgetless --features metal

# With GPU acceleration (NVIDIA)
cargo add forgetless --features cuda

Server Binary

cargo install forgetless --features server
forgetless-server  # Runs on http://localhost:8080

Basic Usage

use forgetless::{Forgetless, Config};

#[tokio::main]
async fn main() -> Result<(), Box<dyn std::error::Error>> {
    let result = Forgetless::new()
        .config(Config::default().context_limit(128_000))
        .add(&large_content)
        .run()
        .await?;

    println!("{}", result.content);
    Ok(())
}

Core Features

• Smart Compression: Intelligent content prioritization and compression to fit any token budget
• Multi-Format Support: PDF, images, text, code - all handled seamlessly with automatic extraction
• Priority System: Critical/High/Medium/Low/Minimal priority levels for fine-grained control
• Query-Based Filtering: LLM-powered relevance scoring to keep only what matters
• Vision Processing: Image analysis and description with GPU acceleration (Metal/CUDA)
• Semantic Chunking: Syntax-aware chunking for code and semantic boundaries for text
• Embedding Support: Vector-based similarity scoring for semantic retrieval
• Agent Memory: Cognitive-inspired memory architecture (Working/Episodic/Semantic)
• HTTP Server: REST API for language-agnostic integration

Documentation

For detailed documentation and examples, visit:

• Getting Started
• API Reference

Contributing

Contributions are welcome! Please feel free to submit a Pull Request.

1. Fork the repository
2. Create your feature branch (git checkout -b feature/amazing-feature)
3. Commit your changes (git commit -m 'Add some amazing feature')
4. Push to the branch (git push origin feature/amazing-feature)
5. Open a Pull Request

License

This project is licensed under the MIT License - see the LICENSE file for details.

Contact

Project Link: https://github.com/pzzaworks/forgetless