context-optimizer

To reduce token waste in a multi-agent system, analyze debug logs and token profiling data to identify context growth and overlapping instructions. This process detects duplicate file reads and excessive instruction loads, allowing you to prune context and generate recommendations for subagent extraction and prompt deduplication.

Context optimization for multi-agent workflows is the process of analyzing debug logs and token profiling data to find inefficiencies and redundancies. It identifies duplicate file reads and overlapping instructions to reduce context size, lower token costs, and improve overall response times.

Parse Copilot debug logs by extracting structured data to assess context growth and latency. This log parsing and profiling mechanism identifies duplicate file reads and overlapping instructions, generating an analysis report that highlights key opportunities to narrow instructions and deduplicate prompts.

Yes, you can use token profiling to fit agent context within limits by analyzing debug logs to assess context growth. The profiling data identifies excessive instruction loads and redundancies, enabling you to prune context, extract subagents, and narrow instructions to stay within token boundaries.

The best way to identify duplicate file reads in agent debug logs is to parse the logs and extract structured profiling data. This redundancy detection process assesses context growth and pinpoints overlapping instructions, allowing you to apply prompt deduplication and subagent extraction for better efficiency.