Cross-agent skill quality gate for SKILL.md files. Validates frontmatter, scores description discoverability, checks file references, enforces three-tier token budgets, and flags compatibility issues across Claude Code, VS Code/Copilot, Codex, and Cursor.

Open-source edge engine to control API request budgets and enforce fair usage.

Runtime containment kernel for LLM agents. Enforces budget, step, retry, and circuit-breaker limits before the model call.

Coding agents forget your repo. mcp-brain is the missing memory layer — repo-aware, team-aware, lifecycle-aware. 63% Hit@10, zero LLM cost. Works with any MCP client.

Don't go into production without these - 3 auto-triggering Claude Code skills for cost and drift prevention. 6 months of practitioner notes.

Build optimal LLM context windows. Priority-based assembly, token budgeting, smart truncation (HEAD/TAIL/MIDDLE). Zero mandatory dependencies. Drop into any RAG pipeline in 3 lines.

Context engineering toolkit for LLMs — pack, cache, debug, red-team, and orchestrate context windows. Council of Experts, adversarial testing, immune system, context compiler, drift detection, multi-agent entanglement. TypeScript + Python.

Optimal context window selection for LLM coding tools. Treats context as a constrained optimization problem, not retrieval. Beats RAG, grep, and LLM-triage baselines on real GitHub issues.

Open source AI cost tracking. Know exactly what your AI costs — per feature, per user, per project.

AI agent harness engineering tool — visualize CLAUDE.md, AGENTS.md, skills, and agent config structure

Core library: scoring, selection, and caching for the Context Engine

Enforce real-time token budgets and spending limits for OpenAI, Anthropic Claude, and Google Gemini API calls in Node.js

Local-first cost firewall for AI agents. Blocks runaway loops before they burn your API budget

🚀 Optimize AI context retrieval with OrionGraphDB, a powerful engine that respects token budgets and delivers diverse, relevant information seamlessly.

Governance layer for token-scoped authority and policy enforcement.

Track AI API costs per task, model, and project. Stop guessing your spend.

A context database for AI agents. Multi-channel retrieval (semantic, lexical, structural) with MMR selection and token budget management. Built in Rust. Apache 2.0.

Smart retry for LLM API calls — rate limits, model fallback, token budgets

Open-source platform for deterministic, token-aware context selection for AI agents and LLMs

Deterministic context planner for coding agents — scores, budgets, and explains what files to load before your agent runs.