Your LLM can reason better without any fine-tuning! optillm is an OpenAI API-compatible proxy that implements 20+ optimization techniques to improve LLM accuracy on reasoning tasks without training or fine-tuning. The concept: Instead of one API call, optillm makes multiple calls using different techniques and combines the results. You're trading compute for accuracy - more API calls, higher cost, slower response, but better results. How it works: optillm sits between your OpenAI client and the LLM API. You control which technique by prepending a slug to the model name. With Mixture of Agents, optillm makes 3 parallel API calls with different approaches, synthesizes them, and returns the best answer. The tradeoff: A query that takes 1 API call and 2 seconds now takes 4 calls and 5 seconds. Token cost goes up 4x. But accuracy jumps significantly on reasoning tasks. Results show the gains. Mixture of Agents using gpt-4o-mini matches GPT-4 on Arena-Hard-Auto. PlanSearch achieves 20% higher pass@5 on LiveCodeBench. Available techniques: • Mixture of Agents: Multiple models critique each other • Monte Carlo Tree Search: Explores decision trees • PlanSearch: Searches candidate plans before executing • Best of N: Generates multiple responses, picks best • Chain-of-Thought with Reflection: Structured thinking and output • Self-Consistency: Multiple reasoning paths Works with 100+ models via LiteLLM. You can combine techniques in pipelines or run them parallel. The insight: Spend more computation at query time to get better results without training. Works for benchmarks, offline tasks, critical queries. Not for real-time production. I've shared the link to the Github Repo in the comments!