Hello World: Introducing the HEBBS Blog

Welcome to the HEBBS Blog.

HEBBS is a memory engine purpose-built for AI agents. It gives agents the ability to remember, recall, consolidate, and forget: the same cognitive primitives that make biological memory useful. Instead of stuffing everything into a context window or querying a generic vector database, HEBBS provides structured memory operations designed for how agents actually work.

What HEBBS Does

At its core, HEBBS offers four recall strategies (semantic, temporal, graph, and hybrid) so an agent can retrieve the right memory for the right situation. Memories aren’t static: they consolidate over time (turning raw observations into higher-level insights), decay when they stop being useful, and maintain lineage so you always know where an insight came from.

It’s a single binary with an embedded storage engine. No external databases. No infrastructure to manage. Drop it in and your agent has memory.

What We’ll Write About

This blog will cover:

• Engineering deep dives: how we build HEBBS at the systems level. Storage engines, indexing strategies, recall algorithms, and the performance work that makes it fast.
• Product updates: new features, releases, and what’s on the roadmap.
• AI agent patterns: how memory changes what agents can do, and the design patterns that emerge when agents can actually remember.
• Benchmarks and research: how HEBBS performs against alternatives, and the research behind our design decisions.

We’re building in public and writing as we go. If you’re working on AI agents and care about memory, this is the place to follow.

Stay tuned.