Caveman
Caveman is an open-source project that aims to reduce the number of tokens used by AI agents, making them more efficient and cost-effective. It achieves this by using a proxy that shrinks the input tokens read by the agent, resulting in 33.2% fewer input tokens across whole sessions, with the same answers. The project also includes a skill that compresses the output of the agent, reducing the number of tokens used. Caveman has been cited by Adobe Research and has been featured on GitHub Trending, Hacker News, and Product Hunt. The project is licensed under Apache-2.0 and is available on GitHub.
- Category
- LLM
- Pricing
- Open Source
Problem solved
The problem that Caveman solves is the inefficiency of AI agents in using tokens, which can result in increased costs and reduced performance. By reducing the number of tokens used, Caveman helps to make AI agents more efficient and cost-effective, making them more accessible to a wider range of users.
Use cases
- Reducing token usage in AI agents
- Improving the efficiency of AI agents
- Making AI agents more cost-effective
Audience
- Developers
- Data Scientists
- ML Engineers
Pros
- Reduces token usage by 33.2%
- Improves efficiency of AI agents
- Cost-effective
Cons
- May not work for all types of AI agents
- Requires technical expertise to implement
Official website: https://github.com/JuliusBrussee/caveman
Canonical LambdaBase page: https://www.lambdabase.com/tools/caveman