Apohara-ContextForge

Multi-agent AI pipelines waste up to 70% of their context window re-encoding shared information from scratch. ContextForge eliminates this at the infrastructure layer — without changing models or prompts. ContextForge is a shared context compiler that sits between your orchestration layer and your vLLM/LMCache inference backend. It implements six peer-reviewed research papers (arXiv 2024–2026) as production-grade Python modules: • TokenDance Master-Mirror storage: 10.81x KV-cache compression across 12-agent committees (arXiv:2604.03143) • JCR Safety Gate: prevents critic-agent context corruption with INV-15 enforcement, 0 violations (arXiv:2601.08343) • KVCOMM cross-context protocol: 7.8x TTFT improvement via shared KV-cache communication (arXiv:2510.12872) • Speculative Coordinator: cross-agent draft-and-verify decoding, >70% acceptance rate • Visual KV Cache: 5x fewer vision encoder calls in multimodal pipelines • LSH + FAISS semantic deduplication: 79.85% token savings on live demo Results: 310 unit tests passing (0 failures), 15/15 benchmark scenarios PASS, live Gradio dashboard deployable via single Docker command on AMD MI300X ROCm hardware. Target customers: Any team running agentic AI at scale — LLM API costs drop immediately, no code changes required. Revenue model: usage-based SaaS per million tokens optimized, plus enterprise on-prem licensing for AMD MI300X clusters.