Open Source Token Optimization Tools
Token optimization reduces the number of tokens used to process prompts, conversation history, retrieved information, and tool outputs in large language model workflows. By removing redundancy, shortening content, or prioritizing relevant context, it can lower inference costs and help systems work within context limits. Careful optimization aims to preserve the information needed for accurate responses rather than simply making inputs shorter.
Open source tools in this area include context compressors, history-pruning components, and proxies that optimize content between applications and language models. When choosing a tool, consider how it handles factual details, which models and integrations it supports, its resource requirements, license, documentation, and maintenance activity. These tools can be useful to developers building AI applications, agent workflows, or retrieval-augmented systems, as well as teams seeking more predictable usage and costs.
2 repositories · updated September 16, 2026
ctx-gate: LLM Context Gateway for Efficient Token Usage
ctx-gate is an LLM-agnostic context optimization proxy that reduces token consumption in AI interactions. It intelligently prunes conversation history and tool outputs, ensuring critical facts are retained without altering your workflow. Compatible with Anthropic and OpenAI APIs, ctx-gate helps developers manage LLM costs and maintain prompt fidelity.
tooltrim: Drastically Reduce LLM Agent Tool Output Tokens, Improve Accuracy
tooltrim provides drop-in compression for LLM agent tool outputs, drastically cutting tokens while often improving answer accuracy. This provider-agnostic solution offers content-aware compression, faithfulness benchmarks, and seamless integration with popular frameworks or as an OpenAI-compatible proxy.