Tokencompress Cuts AI Agent Token Use by 60-80% for Tool Outputs

TL;DR. Tokencompress offers a sub-2ms Go CLI and sidecar solution to prune raw tool outputs, significantly reducing AI agent context token consumption. - The tool filters large JSON payloads, verbose stack traces, and raw HTML before inputting to LLMs. - It achieves token reductions between 60% and 80%, lowering API costs and improving agent performance. - Tokencompress operates as a high-speed, deterministic filter for tools to model interactions.

• Tokencompress is a Go CLI and MCP sidecar designed to optimize AI agent context windows.
• It prunes raw tool outputs like JSON, logs, and HTML, which often bloat LLM prompts.
• The solution cuts LLM context token consumption by 60% to 80% without needing a second LLM summarization turn.
• Benchmarks show 87-93% token reduction for common data types like JSON arrays, Python stack traces, and HTML web scrapes.
• It addresses issues of high API costs, slow response times, and context rot in AI agents.
• Features include JSON truncation, log pruning, HTML cleaning, and duplicate loop detection.

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