DeltaMemory – Persistent cognitive memory for production AI agents
Knowledge graph compression (3,714x token ratio) is impressive, but 'persistent agent memory' is crowded territory.
An agentic skills framework & software development with fastmemory methodology that works.
Louvain clustering for agent memory, but AI agent frameworks are already saturated.
Enterprise AI developers building agentic workflows
LangChain · AutoGen · CrewAI
While RAG has become the standard for "adding knowledge" to LLMs, it often fails at scale due to semantic noise and the destruction of logical boundaries during chunking. Superfast treats memory as an architectural layer. It utilizes Louvain community detection to mathematically derive functional clusters, giving agents a deterministic "Logic Layer" that persists across sessions.
We’ve maintained the strict TDD and Socratic discipline of the original framework but scaled it for environments like Microsoft Fabric and AWS Glue where "token waste" is a primary bottleneck.
Check it out here: https://github.com/FastBuilderAI/superfast
Knowledge graph compression (3,714x token ratio) is impressive, but 'persistent agent memory' is crowded territory.
Deterministic graph memory vs. embeddings is clever, but fragmentation of AI memory ecosystems is already painful.
Claims brain-like cognition with zero LLM calls, but zero evidence of actual learning.
Bundled 7MB embedder means zero network calls or model downloads for agent memory.
Tree-sitter dependency graph saves 5,000-20,000 tokens per agent query vs exploration.
Vocabulary governance solves agent memory cold-start better than raw GraphRAG.