Cognitive Compute Mesh: A Unified Inference Federation & Production RAG Pipeline for MoFA

The current MoFA architecture tightly couples agents to a single inference backend (e.g., OpenAI, Ollama), making switching providers, handling failures, or optimizing cost and latency difficult without modifying agent code. This project introduces the Cognitive Compute Mesh (CCM): a distributed inference layer implemented as a new mofa-gateway crate that transparently sits behind the LLMCapability interface. Instead of acting as a central gateway, CCM enables a mesh architecture, where every node (local, cloud, or edge) participates in both computation and routing. The system is built around three core components: - A typed Inference Request Protocol (IRP) that standardizes communication across all providers - An intelligent routing layer that dynamically selects backends based on latency, cost, capability, and health - A production-grade RAG pipeline with hybrid retrieval (dense + BM25), reranking, and caching CCM ensures that agents become backend-agnostic, enabling seamless failover, cost optimization, and hybrid local-cloud execution without code changes. Deliverables: - mofa-gateway crate implementing LLMPlugin - Backend SDK for integrating new inference providers - Support for OpenAI, Anthropic, Ollama, and OminiX-MLX - Distributed routing with circuit breakers and health tracking - Full RAG pipeline with vector storage support - Documentation, benchmarks, and demo showing backend-agnostic agents This project aims to establish a protocol-level abstraction for AI inference, similar to how HTTP abstracted networking enabling “write once, run anywhere” for AI agents.