FastMemory: Remove Hallucinations from Your AI’s Dictionary - Newsletter # 25th March 2026
FastMemory: Remove Hallucinations from Your AI’s Dictionary
Today marks a major milestone for FastBuilder.AI. We are officially releasing FastMemory as an open-source project for the entire AI community.
Standard RAG (Retrieval-Augmented Generation) has a fundamental flaw: it treats your knowledge like a "pile of snippets." When an agent queries a standard vector DB, it gets "roads" but no "buildings"—no functional context, no rules of engagement, and no structural logic.
FastMemory is the solution. It transforms flat text into a structured, agent-navigable functional memory graph using our proprietary CBFDAE taxonomy.
🗺️ The Google Maps Analogy
Imagine navigating a city with a map that only shows streets. You see a "road to a bed," but you don't know if it’s a hospital, a hotel, or a house. Your AI agent faces the same struggle with flat vector graphs.
FastMemory provides the "Buildings" (Functional Components) and the "Rules of Entry" (Access and Events). It ensures your AI doesn't just "guess"—it traverses a deterministic logic graph.
🧠 Why Developers Love It
- CBFDAE Ontology: Classify data into Components, Blocks, Functions, Data, Access, and Events.
- Zero Hallucinations: Recursive subtree targeting provides the AI with sibling functions and contextual boundaries.
- Rust-Powered Speed: Built for high-speed community detection and low-latency retrieval.
- MCP Native: Plug directly into Claude, Gemini, or any agentic IDE loop.
🚀 Get Started on GitHub
FastMemory is ready for your next project. Clone it, build it, and give your AI the memory it deserves.
Build
Compile the Rust binary or use the Cargo crate.
Ingest
Transform any text or doc into a memory graph.
Orchestrate
Query via REST API or stdio MCP.
Accurate and fast memory is here. Let's build the future together.
Best,
The FastBuilder.AI Team
