Token optimization 3

Platform for versioning, sharing, and optimizing AI coding skills across teams ModelBound is a platform designed to version, share, and optimize AI behavior across development teams by creating portable Skills and Agents. Built on the open Agent Skills standard (SKILL.md), it acts as a central repository and management system for AI context, bundling prompts, rules, and instructions that integrate seamlessly with popular AI-powered IDEs and coding tools like Claude Code, Cursor, Kiro, Windsurf, and GitHub Copilot. It enables engineering teams to standardize how AI tools interact with codebases while offering tools for context auto-optimization to reduce token costs.

AI-native memory platform for stateful agents and continual context Honcho is an AI-native memory platform for building stateful AI agents. It stores and indexes messages, continuously reasons about interactions through its Neuromancer models, and returns curated context through a simple context() method. Honcho helps agents maintain continuity, understand user preferences, reduce unnecessary token usage, and support advanced reasoning across users, agents, NPCs, groups, and other entities. It supports model-agnostic integrations, unlimited retrieval, token budgets, peer scoping, background dreaming, and on-demand reasoning.

Local CLI that reduces coding-agent token waste JFrog Boost is a free, local-first command-line tool that compresses noisy terminal output, MCP responses, file contents, and other tool data before it reaches coding agents such as Cursor, Claude Code, Codex, GitHub Copilot, and OpenCode. It reduces token usage while preserving important information such as errors, failures, summaries, and relevant context. Boost uses reversible compression and retrieval, allowing agents or developers to restore the original output when needed. It also provides graph-based code retrieval, file optimization, observability, telemetry, and enterprise-focused privacy.