Building the memory layer
for AI agents.
AI agents are getting smarter at reasoning, planning, and tool use. But they still forget everything the moment a session ends. We're fixing that. Hebbrix gives agents persistent, intelligent memory that gets better the more they use it.
AI agents forget. That's a solvable problem.
Context windows truncate. In-memory buffers vanish on restart. Vector databases require weeks of configuration and break at scale. The result: every conversation feels like the first one, and users get frustrated repeating themselves.
We built Hebbrix because we experienced this pain building production agents. The tools didn't exist, so we built them — a memory API that handles persistence, intelligent search, and continuous learning in three lines of code.
What's under the hood
A two-line API hides serious engineering. Here's what Hebbrix actually does.
3-tier cognitive memory
Short-term, medium-term, long-term. Memories promote and decay based on usage — modeled after how human memory actually works.
5-layer hybrid search
Semantic vectors + BM25 + knowledge graph + importance + recency. Finds what matters, not just what's recent.
Automatic knowledge graph
Store natural text. Entities and relationships are extracted automatically. No schema to define, no pipeline to maintain.
Self-improving retrieval
6 RL quality checks run after every interaction. Good memories get reinforced. Noisy ones fade. The system gets smarter over time.
Collections & multi-tenancy
Isolate memories per user, team, or scope. Maps cleanly to any application data model without custom sharding logic.
Sub-50ms retrieval
All five search layers run in parallel. Production-grade latency with no warm-up, no caching tricks, no compromises.
Built for developers, by developers
Python & TypeScript SDKs
Type-safe, async-ready, framework-agnostic.
OpenAI-compatible API
Change one URL. Memory works automatically.
Framework integrations
LangChain, LangGraph, CrewAI, and more.
Free tier
Generous limits. No credit card to start.
How we think about this
Memory should be invisible infrastructure
Just like you don't think about DNS when loading a webpage, AI agents shouldn't need custom pipelines to remember things. The complexity lives in Hebbrix, not in your code.
Cognitive science, not just vector math
Human memory has tiers, decay curves, and reinforcement loops. We modeled ours the same way — because that's what actually works at production scale.
Developer experience is the product
If it takes more than three lines to integrate, we've failed. The API should feel obvious, the docs should answer real questions, and the errors should tell you what to fix.
Give your agents a memory upgrade
Free tier. No credit card. Start building in five minutes.
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