TokenLite Safe Context Compression Strategy

Problem

When agents call tools, they often receive large non-code outputs: JSON logs, API responses, and batch results. Putting all of that into context consumes tokens, while naive truncation can discard failure states, exceptions, rare categories, schema variants, or numeric outliers that matter for later reasoning. The task is compression with an auditable retrieval path and hard safety boundaries.

How It Works

TokenLite treats the flow as evaluate, compress if safe, and retrieve only what is needed. tokenlite.py sense --input FILE --json returns a content field; if auto_compressed=true, the ccr_id enables later retrieval with retrieve using --grep, --line, or --field. Source code, user input, and short stack traces are protected: reason=protected:code returns byte-for-byte original content. For JSON arrays, TokenLite keeps visible records containing nested errors, failure states, rare categorical values, schema variants, and robust numeric outliers, then adds deterministic representative samples.

Boundaries

CCR is unencrypted local temporary storage, so it should not be used to cache secrets. Prefer --input over --text for multiline or large content to avoid shell-quoting corruption. Telemetry is off by default, and optional hooks modify user-level configuration, so they require explicit approval. Use it for shrinking large tool outputs, not for rewriting source code, persisting secrets, or automatic global hook installation.