Develarper: Token-Efficient AI Agent

Develarper is an engineered containerized AI agent constructed for the AMD Developer Hackathon Track 1. It utilizes a highly optimized four-layer hybrid routing core to minimize total remote Fireworks API token consumption while clearing the required accuracy threshold. The agent dynamically routes tasks across eight distinct capability domains using zero-token heuristic overrides, localized semantic caching, and a supervised PyTorch MLP classifier utilizing all-MiniLM-L6-v2 embeddings to achieve 100% routing accuracy. Low-complexity tasks (such as sentiment analysis, NER, short summarization, and factual questions) are processed entirely local within the strict 4 GB RAM sandbox using an embedded Qwen2.5-3B model via llama-cpp-python. The architecture implements a "local-first" code execution path that tests code generation and debugging locally, using AST parsing validation before failing back to remote infrastructure. High-order deduction, mathematics, and long-context summaries are dynamically escalated to specialized remote models like kimi-k2p7-code and minimax-m3. Token trimming algorithms are applied globally to strip conversational filler and strictly compress input and output footprints, ensuring maximum token efficiency and robust leaderboard positioning.