Token-Oriented Object Notation for Go – JSON for LLMs at half the token cost

Local dashboard for visualizing usage across seven AI coding agents - sessions, token costs, cache performance, tool calls, and daily breakdowns

Token burn reducer and focus keeper for Claude Code, Codex, Gemini CLI, Cline, Windsurf, Aider, Cursor, Copilot, and more: session-aware read hints, 130+ bash output filters, compact manifest injection, image shrinking, and much more.

Build a private evaluation dataset to optimize your organization's token costs.

An MCP (Model Context Protocol) server that provides real-time LLM token pricing data for 60+ AI models across 15 providers.

OpenLLM Monitor 📊 is a plug-and-play, real-time observability dashboard 🔍 for monitoring and debugging LLM API calls across OpenAI 🤖, Ollama 🦙, OpenRouter 🌐, and more. It tracks tokens 🧮, latency ⏱️, cost 💸, retries 🔁, and lets you replay prompts 🔄. Fully open-source 🌍 and self-hostable 🛠️.

阿里云百炼大模型 Token 账单解析工具 | Alibaba Cloud Bailian LLM Token Billing Parser - Parse, analyze and summarize daily AI model token costs

A retrieval-augmented generation pipeline in Python with a rigorous offline evaluation harness. Chunks and embeds documents, retrieves by vector similarity, and generates grounded answers — with pluggable LLM providers (including a deterministic local fake for tests) and metrics for retrieval quality and answer faithfulness. No API key required.

Compare LLM API pricing from your terminal. Supports 300+ models across all major providers. https://x.com/saqibameen

Generate a local dashboard from Codex CLI / Claude Code / Cursor agent logs

Token cost comparison: why higher-level languages win for LLM-assisted coding

Source model capabilities and pricing from OpenRouter for cost-aware development without hardcoded data tables

Time estimation MCP server for AI agents: PERT, COCOMO II, Monte Carlo, sprint forecasting, token-to-time mapping, cost estimation, and schedule risk tools.

Token Price Estimation for LLMs

An Investigation Across Two MCP Servers, Two Frontier Models, and 20 Controlled Benchmark Runs

Cognitive timeline — AEF evidence consumer and memory distillation

Analyze the token cost of MCP server tool definitions. Find out how much context your MCP servers consume per LLM call.

Benchmark harness: CLI vs MCP (eager/deferred) vs Code Mode. 5 tasks × 4 configs × 10 runs = 200 data points. Token counts from Anthropic API usage object.

Measure the token costs of AI browser automation to reveal hidden inefficiencies and optimize interactions for better performance and resource use.