We build tools to rescue tokens from inefficient formats
Every token matters in the age of Large Language Models. As AI systems become more powerful, they face a critical challenge: context rot. When LLMs process excessive input tokens, their performance degrades—critical information gets overlooked, responses slow down, and costs spiral.
SaveTokens exists to solve this fundamental problem. We believe developers shouldn't have to choose between data richness and efficiency. Our mission is to create tools that preserve context, reduce costs, and maximize LLM performance through intelligent data optimization.
Traditional data formats like JSON were designed for human readability and machine parsing—not for the token economy of AI systems. We're rethinking data serialization from first principles:
- Compress without losing information - Remove redundancy while preserving every detail
- Validate for integrity - Catch errors before expensive API calls
- Optimize for patterns - Adapt encoding to your specific data structures
Research shows that LLM performance can drop by 10-30% when critical information is buried in long contexts. The more tokens you send, the worse the model performs. This creates a vicious cycle:
- Inefficient formats waste tokens on structure
- Wasted tokens force you to send less data
- Less data means worse AI responses
- You're paying more for worse results
SaveTokens breaks this cycle.
We build open-source tools that rescue tokens from inefficient formats. Our first project, AXON, achieves 60-95% token reduction for LLM interactions while maintaining type safety and validation.
This isn't just about cost savings—it's about enabling more powerful AI applications by fitting more meaningful data within token budgets.
Our first tool in the fight against token waste. AXON is a data serialization format that achieves 60-95% token reduction for LLM interactions.
Key Features:
- 5 compression algorithms (RLE, Dictionary, Delta, Bit Packing, Varint)
- 13 validated types with schema support
- 6 adaptive encoding modes
- 93.51% test coverage, production-ready
- MIT licensed and open source
Learn More:
All SaveTokens tools are open source and free to use. We believe the AI community benefits when developers can build more efficiently without cost barriers.
Contribute:
- Star our repositories
- Report issues or suggest features
- Submit pull requests
- Share with other developers
SaveTokens is built for developers who care about:
- Cost efficiency in AI applications
- Performance optimization for LLMs
- Data integrity and type safety
- Open source collaboration
Join us in building the future of efficient LLM communication.
- Website: https://savetokens.dev
- GitHub Organization: https://github.com/savetokens
- AXON Project: https://github.com/savetokens/axon
MIT License - Free to use, modify, and distribute.
Rescue tokens from inefficient formats.