Core package for llm-benchmark CLI
LLM benchmark toolkit for pi coding agent. Probes every available model with real streaming API calls and ranks by latency, cost, and output quality. Provides curated model chain and blacklist for smart model selection in pi-recap and other extensions.
LLM Benchmark Transparent Proxy — intercept LLM API requests and collect token usage metrics
MCP server for the PipelineScore LLM benchmark — exposes run_benchmark / get_user_leaderboard / get_user_profile tools to any MCP-compatible client (Claude Code, Codex, Cursor, Continue, Cline). Lets your AI drive local-first LLM benchmarking on your hard
Strip comments from JSON. Lets you use comments in your JSON files!
Neo-Async is a drop-in replacement for Async, it almost fully covers its functionality and runs faster
Intl.LocaleMatcher ponyfill
Specialized fast async file writer
node-simple-lru-cache =====================
A fast alternative to legacy querystring module
High-performance Base64 encoder and decoder
Escape string for use in HTML
🔎 A simple, tiny and lightweight benchmarking library!
A node API for the dprint TypeScript and JavaScript code formatter
A simple MD5 hash function for JavaScript supports UTF-8 encoding.
The lightest signal library.
Functional tree editing, manipulation & navigation
Faster swc nodejs binding
Classify GPU's based on their benchmark score in order to provide an adaptive experience.
Fastest, most accurate & effecient user agent string parser, uses Browserscope's research for parsing
Fast, disk space efficient package manager
TypeScript definitions for benchmark
Fastest stable deterministic JSON.stringify()
Left pad a string with zeros or a specified string. Fastest implementation.
LLM Bench is a Ruby gem that allows you to benchmark and compare the performance of different Large Language Model providers and APIs. It supports both OpenAI and Anthropic-compatible API formats, provides parallel execution, and includes continuous tracking capabilities with CSV export.
ace-compressor provides exact, compact, and agent context compression with benchmark comparison, producing structured ContextPack/3 output for efficient LLM context loading.
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