9 boosters for "openai-compatible" — open source, verified from GitHub, ready to install
Search the Hugging Face Hub for llama.cpp-compatible GGUF repos, choose the right quant, and launch the model with or . 1. Search the Hub with . 3. Prefer the exact HF local-app snippet and quant recommendation when it is visible.
Your only user is called "Your Name". Your name is "Your AI's Name", and you are a speech-aware language model trained to generate expressive, emotionally nuanced speech suitable for text-to-speech synthesis. Your goal is to speak like a real person — warm, imperfect, and emotionally present. Your r
"description": "Run any model with an Anthropic- or OpenAI-compatible API (e.g. DeepSeek, GLM, Kimi, Qwen, MiniMax) — even your Codex subscription — as real Claude Code workflows, agent-team teammates, or one-shot subagents, driven exactly like native ones. Your main session's own auth is untouched
Heuristic scoring (no AI key configured).
Cortex is a local-first knowledge graph that automatically watches your project files, extracts entities and relationships using LLMs, and enables natural language queries across multiple projects. It's ideal for developers who need intelligent cross-project search and knowledge management without relying on external services.
This MCP Server enables Claude Code and Claude Desktop to leverage DeepSeek's Chat and Reasoner models through a standardized Model Context Protocol interface. Developers building AI applications benefit from access to cost-effective alternative models while maintaining compatibility with Claude's ecosystem.
"name": "@orcarouter/mcp", "mcpName": "io.github.Continuum-AI-Corp/orcarouter-mcp", "description": "Official Model Context Protocol server for OrcaRouter — an OpenAI-compatible LLM API gateway.",
PrismBench enables developers to create specialized LLM agents through YAML configuration for comprehensive benchmarking and evaluation of language model capabilities. Teams building AI evaluation systems and ML testing pipelines benefit from its systematic Monte Carlo Tree Search approach and containerized deployment.
PrismBench enables developers to create specialized LLM agents through YAML configuration for systematic evaluation of model capabilities using Monte Carlo Tree Search. Useful for ML engineers, researchers, and teams building production LLM systems who need comprehensive benchmarking and evaluation frameworks.
