optillm and LLMstudio
These two tools are complements: OptiLLM focuses on optimizing inference for deployed LLMs, while LLMstudio provides a framework to bring LLM applications to production, including potentially integrating and leveraging such optimization proxies.
About optillm
algorithmicsuperintelligence/optillm
Optimizing inference proxy for LLMs
Implements 20+ inference-time optimization techniques—including MARS, CePO, chain-of-thought reflection, and Monte Carlo tree search—that layer multiple reasoning strategies to achieve 2-10x accuracy gains on math and coding tasks. Acts as an OpenAI API-compatible proxy that intercepts requests and automatically applies selected techniques based on model prefix (e.g., `moa-gpt-4o-mini`), requiring no model retraining or client-side changes. Supports 100+ models across OpenAI, Anthropic, Google, and other providers via LiteLLM, with multi-variant Docker images for full, proxy-only, or offline deployment scenarios.
About LLMstudio
TensorOpsAI/LLMstudio
Framework to bring LLM applications to production
Provides a unified proxy layer across OpenAI, Anthropic, and Google LLMs plus local models via Ollama, with smart routing and fallback mechanisms for reliability. Includes a web-based prompt playground UI, Python SDK, request monitoring/logging, and LangChain compatibility for seamless integration into existing projects. Supports batch calling and deploys as a server with separate proxy and tracker APIs.
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