Awesome-LLM-Prompt-Optimization: a curated list of advanced prompt optimization and tuning methods in Large Language Models

Superpipe - optimized LLM pipelines for structured data

Implementation of the paper Fast Inference from Transformers via Speculative Decoding, Leviathan et al. 2023.

Nadir is a Python package designed to dynamically choose the best llm for your prompt by balancing complexity and cost and response time.

Optimize AI workflows with Arachne. Automatically assembles the perfect code context (Tree, Target, Deps, Semantic) to fit context windows without noise. Built for efficiency and scale.

Optimized fork of Superpowers — Retains full original features while adding automatic 3-tier workflow routing, integrated safety guards (OWASP-aligned), red-team adversarial testing with auto-fix pipeline, a built-in memory stack, and much more. Delivers faster, more reliable, hallucination-resistant coding sessions.

Save 47% on Manus AI credits automatically. Zero downsides. Pays for itself in ~27 prompts. Free MCP Server (PyPI) + $12 Manus Skill bundle with Fast Navigation (115x speed boost).

DSPEx - Declarative Self-improving Elixir | A BEAM-Native AI Program Optimization Framework

A Demo of Running Sleep-time Compute to Reduce LLM Latency

Declarative Self Improving Elixir - DSPy Orchestration in Elixir

⚡ Cut LLM inference costs 80% with Programmatic Tool Calling. Instead of N tool call round-trips, generate JavaScript to orchestrate tools in Vercel Sandbox. Supports Anthropic, OpenAI, 100+ models via AI Gateway. Novel MCP Bridge for external service integration.

Generate clean, AI-ready llms.txt files for your website or docs. Supports crawling, sitemaps, static builds, and framework-aware adapters (Next.js, Vite, Nuxt, Astro, Remix). Includes Markdown/MDX docs mode and robots.txt generator for LLM and search crawlers.

Hybrid adaptive memory system for Claude Code — Cheatsheet (positive patterns) + Immune (negative patterns)

A comprehensive toolkit for training and running lightweight adapters for GGUF-based language models (ERNIE, Llama, Mistral, Phi-3, etc.) without modifying the base model.

Opti-Oignon is a comprehensive optimization framework for local LLMs running on Ollama. It maximizes the performance of your local models through intelligent task routing based on a custom benchmark, RAG (Retrieval-Augmented Generation), and multi-model orchestration.

An application for LLM-based scheduling a roster prepared for my presentation at TTSH HINT.

End-to-End Python implementation of CompactPrompt (Choi et al., 2025): a unified pipeline for LLM prompt and data compression. Features modular compression pipeline with dependency-driven phrase pruning, reversible n-gram encoding, K-means quantization, and embedding-based exemplar selection. Achieves 2-4x token reduction while preserving accuracy.

Structured repository covering LLM foundations, fine-tuning workflows, optimization strategies, deployment patterns, evaluation methods, and Responsible AI considerations.

Adaptive semantic cache for LLMs with streaming support, ML-based thresholds, and real-time cost tracking. Built in Rust for sub-millisecond performance.

Bio-inspired optimization pipeline for Claude Code. 3 systems: Slime Mold (explore→prune) → PRISM (perspectives→compile) → Immune (scan→correct→learn). Domain-agnostic.