Token-Efficient Hybrid Router for Fireworks AI

Token-Efficient Hybrid Routing Agent for Fireworks AI is a lightweight, Docker-based AI pipeline that automatically classifies incoming tasks into one of eight categories — factual knowledge, mathematical reasoning, sentiment classification, text summarization, named entity recognition, code debugging, logical reasoning, and code generation — and routes each task to the cheapest capable Fireworks AI model. Instead of relying on a secondary LLM call for classification, the agent uses a zero-token regex-based classifier that analyzes prompt patterns without consuming any API tokens. An empirical routing table, built from real benchmark runs across all allowed models, maps each category to its most token-efficient provider: kimi-k2p7-code handles seven categories where it consistently delivers fewer prompt tokens, while minimax-m3 is reserved for NER tasks thanks to its clean JSON output at just 256 tokens. The agent applies category-specific system prompts, response-format constraints (JSON mode for sentiment and NER), and tight max-token caps to eliminate wasted generation. Across eight representative test tasks, the pipeline consumes approximately 4,769 total tokens and completes in under 40 seconds inside a 152 MB Docker image — making it both fast to deploy and aggressively cost-optimized for production AI orchestration.