Mantis, A self-hosted LLM gateway
Another LLM gateway when LiteLLM and Portkey already dominate the space.
The MCP-Native AI Gateway — Route requests to any AI provider through one universal endpoint. Intelligent auto-routing, 65+ models, self-hosted. Open source.
First gateway with native MCP server—connect Claude Code or Cursor in one command.
Teams needing unified AI provider access with cost optimization
LiteLLM · OpenRouter · Portkey
styx:auto — send "model": "styx:auto" and the gateway picks the right model based on prompt complexity. Simple questions go to cheap models ($0.15/1M tokens), complex code goes to frontier models. 9-signal classifier, zero config. MCP-native — first gateway with a built-in MCP server. Connect Claude Code or Cursor in one command: claude mcp add styx -- npx styx-mcp 65+ models with live pricing — prices auto-refresh every 24h from OpenRouter's public API. Self-hosted in 5 min — git clone, run setup.sh (interactive wizard), docker compose up.
Tech stack: Go (router/proxy, <10ms overhead), Python FastAPI (auth, billing), Next.js (dashboard). Apache 2.0. The auto-routing is the killer feature. Instead of hardcoding gpt-4o everywhere, your app sends styx:auto and the gateway classifies each request on 9 signals (prompt length, code presence, reasoning patterns, math, conversation depth, etc.) then routes to the optimal model. You also get styx:fast (always cheapest), styx:balanced, and styx:frontier (always best). Try it: https://github.com/timmx7/styx Would love feedback on the architecture and the auto-routing approach. Happy to answer questions.
Another LLM gateway when LiteLLM and Portkey already dominate the space.
Cache-aware LLM routing that doesn't burn prompts to save pennies.
Product Algebra routing plus an explicit 'dharma' pipeline (no-self regularization, entropy/mindfulness metrics, compassion and ethos scores) is a strikingly specific approach — it moves beyond cost/capability heuristics into cross-modal interaction scoring and reputation-driven incentives. There's real engineering here (1s perception loop, SQLite memory, Telegram UX, multi-provider SDK support), but the repo reads young and claim-heavy: I want reproducible benchmark artifacts, links from the code to the cited 439-model experiments, and clearer deployment/security guidance before trusting it for critical workloads.
One-command local AI stack bundling Ollama, Whisper, and MCP Gateway.
MCP integration with Claude beats reactive chat, but personal AI is crowded.
Drop-in OpenAI API gateway with failover—LiteLLM does this but this has a dashboard.