Foreman: self-hosted LLM gateway for cost control and privacy
Foreman is a self-hosted LLM gateway that routes requests between coding agents and multiple providers, keeping keys and traffic within the user's network. It enables cost tracking, model switching without code changes, and policy-based routing to cheaper models.
Entities
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Route LLM prompts to cheapest suitable model automatically
A tool that automatically routes LLM prompts to the most cost-effective model based on task complexity, preventing wasteful use of expensive models like GPT-4o for simple tasks such as formatting or classification. It helps developers reduce API costs without sacrificing quality.
LLM comparison dashboard for quality, latency, and cost
A dashboard that lets users test LLMs on their own data, comparing quality, latency, and cost side by side. It runs on Nebius Serverless and helps developers choose the best model for their specific use case rather than relying on leaderboards.
Self-hosted shared memory for AI agents with policy-controlled summaries
Luthn is an open-source, self-hosted shared memory space for AI agents. It keeps raw documents and sensitive records behind explicit boundaries, providing agents with only policy-approved summaries, references, and context packs. This solves the problem of agents needing shared context while avoiding privacy and access risks from copying raw data into every session.
Alexandria LLM API Gateway unifies multiple AI subscriptions into a single local endpoint
Alexandria is a local daemon that consolidates AI subscriptions (Claude Max, ChatGPT/Codex, SuperGrok, Gemini) into one OpenAI/Anthropic-compatible endpoint on 127.0.0.1:4100. It provides a credential vault, automatic token refresh, model routing, format translation, full trace capture, and usage/limit tracking, allowing any coding harness to authenticate and bill correctly.
LLM hardware recipe database with filters and community usage tracking
A community-driven database that lists which LLM models run on which hardware, with performance details. Users can filter by hardware, submit new recipes, and mark which recipes they actively use to show popularity. It solves the problem of finding compatible model-hardware combinations for LLM deployment.