Open-source Prompt Firewall — deflect up to 95% of redundant LLM traffic before it leaves your infrastructure. Documents: https://isartor-ai.github.io/Isartor/index.html

A high-performance, multi-agent observability engine designed for the Model Context Protocol (MCP). It provides a non-blocking, transparent proxy layer that implements deterministic token attribution, real-time context-window alerting, and heuristic-driven static analysis to optimize LLM metadata overhead at scale.

Right-size every Claude task: advisory slash commands that recommend the cheapest model and effort level whose cost of error is acceptable.

Open-source, self-hosted Fastest AI security gateway for LLM and agent apps: guardrails, agentic PDP, semantic caching, MCP gateway.

Cost-aware subagent delegation workflow for Cursor: model-pinned dispatch, fail-closed premium gate, and process-driven QA

3,300 practical techniques to cut LLM token usage by up to 90% — 300 core principles + 3,000 context-specific applications.

Builds prompt-cost discipline into Claude Code: measure every prompt, surface avoidable spend, report org-wide. Skill + UserPromptSubmit hook + MCP rollup with cited dollar/Wh/CO2e receipts.

Tamper-evident, stranger-verifiable receipts for LLM cost-savings — anyone can recompute your caching/routing savings math offline, no trust in your dashboard required. Pure stdlib, zero-dependency.

Sample code for two-tier LLM inference (cascading) on Amazon Bedrock, using a single Bedrock Converse API surface. NVIDIA Nemotron Nano handles routine support-ticket classification on every request; Anthropic Claude Sonnet handles cases the routing logic flags as harder. Includes a bake-off harness, sample data, and tests. Next.js + TypeScript.

A lightweight OpenClaw model router that reduces LLM costs by switching requests to cheaper models using custom rules.

Semantic cache for LLM calls with no torch, no server, no API key and no GPU. One file, CPU static embeddings. Benchmarked on 5,000 real question pairs: 70% of rephrasings served from cache, 28% false matches.

Production-ready tiered LLM cascade router — cuts API costs by 65%

Helix gives you agents that actually behave in production: hard budget limits, semantic caching that cuts API costs by 40-70%, persistent memory, multi-agent teams, YAML-based task pipelines, and a 5-scorer eval suite.

llmcfo

FinOps LLM

Claude Code skill that optimizes Claude Sonnet 5 for lower token usage, fewer wasted tool calls, and cheaper agentic execution

Cut Claude Code costs by 50–90% — free checklists + the complete 15-chapter system

Sovereign Execution Layer for Autonomous Agents | gVisor-Hardened Governance | FinOps-Optimized Inference Routing.

LLM routing layer that classifies prompt complexity and routes to the cheapest capable model across OpenAI, Anthropic, and Gemini — with async quality verification and a self-improving classifier. 33.8% cost savings on a 492-prompt load test.

AI cost governance toolkit for Claude Code. Monitor token usage, prevent runaway AI agent costs, optimize prompts, and enforce intelligent spending limits for enterprise AI workflows.