token-efficiency

Open-source AI coding agent focused on context curation and lower API cost. The repo describes hash-anchored edits, AST-based refactoring, parallel operations, and benchmark results against other agents.

A TypeScript library for deterministic context folding in long-running LLM agent sessions, designed to preserve prompts, avoid extra summarization calls, and keep provider caches hot.

Rudi is a Python tool for LLM memory management that replaces a growing chat transcript with a causal dependency graph, aiming to keep token usage flat across long sessions. The README shows benchmarking results, fold behavior, and example usage with get_slice, store_decisions, and run_turn.

A VS Code blog post explaining how GitHub Copilot’s agentic harness is being optimized to reduce token usage, latency, and cost through extended prompt caching, tool search, and WebSocket connections.

An open-source ID library that generates human-readable, token-efficient IDs for AI agents and LLM workflows, with parsing, validation, deterministic IDs, and alias mapping utilities.

GitHub Blog article on reducing token usage and API cost in agentic coding workflows, including logging token usage, removing unused MCP tools, replacing GitHub MCP with GitHub CLI, and measuring efficiency gains.

A blog post from Augment Code comparing its coding agent, Auggie, against Claude Code on Opus 4.7. It presents benchmark results, token usage, cost comparisons, and an explanation of the Context Engine and Prism router.