TokenOptimizer
When to Use
- You have a coding task and want to send it to an LLM API with less context (fewer tokens, lower cost).
- You want automatic fallback from a cheap provider to a more capable one when credits run out.
- You want local LLM preprocessing to score relevance and compress context before it hits a paid API.
- You need to stay within a token budget while keeping the most important context.
Quick Start
# Optimize a prompt with default strategies
token_optimizer optimize --input "Fix the bug in auth" --context src/auth.rs
# Analyze cache potential for Anthropic
token_optimizer cache-optimize --task "Add feature" --context types.rs --static-indices "0"
# Launch interactive shell (auto-selects provider)
token_optimizer interactive
# Show current config
token_optimizer config show primaryCapabilities
| Capability | Description |
|---|---|
| StripWhitespace | Remove redundant whitespace, preserving code blocks |
| RemoveComments | Strip //, /* */, # comments from code |
| TruncateContext | Boundary-aware truncation using tiktoken token counts and priority-based boundary detection (code structure > paragraph > sentence > line > word) |
| Abbreviate | Shorten common programming terms in task text |
| LlmCompress | Compress context via local Ollama LLM |
| RelevanceFilter | Hybrid keyword + LLM relevance scoring; works without local LLM via keyword-only mode |
| ExtractSignatures | Keep only function/class/struct signatures |
| Deduplicate | Remove exact, whitespace-normalized, and near-duplicate context items |
| CachePrompting | Anthropic-compatible cache breakpoints for static content |
| Provider Fallback | Automatic primary -> fallback -> local provider pipeline |