RadixMind is an agent whose memory lives in MongoDB — and decides what deserves the model's cache. Most agent-memory systems store everything and retrieve by similarity. RadixMind scores every piece of tool output for viability before it touches a prompt: high-value evidence (runbooks, configs) is pinned into a byte-stable prompt prefix that the model provider's cache can reuse; low-value noise (log floods, status pings) is archived to MongoDB and kept out of the model's way. The admission decision for every chunk — its score, its verdict, its reuse history — lives as a MongoDB document. MongoDB isn't just storage here; it's the control plane deciding what's allowed into the model's expensive working memory. We tested it on a simulated production incident: an agent investigating checkout-service 502 errors across 8 turns of real, seeded application data (logs, runbooks, deploy history) stored in Atlas. Run naively, the agent drags 90,750 tokens through the model with a 0.9% cache hit rate. Run through RadixMind, it reaches the identical correct diagnosis using 6,434 tokens — a 14× reduction — with a 75.9% cache hit rate and lower average latency. Every number is a live Fireworks AI API response, read back from Atlas. Built with MongoDB Atlas (collections, indexes, aggregation pipelines as the ledger and metrics store), a FastAPI gateway, and Fireworks AI for inference with automatic prefix caching. No fine-tuning, no RL — the learning lives in database rows, not gradients.
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