Honest Comparisons
Why Hebbrix?
Four platforms give AI agents persistent memory. Each takes a different approach. Pick the comparison that matters to you.
Hebbrix vs Mem0
Mem0 is the most popular open-source memory layer with 47K+ GitHub stars. Both store memories. The difference is in how they learn from them.
Where Mem0 wins
- Open source (Apache 2.0) with massive community
- SOC 2 Type II and HIPAA compliance
- BYOK and on-premise deployment options
- More mature, battle-tested in production
Where Hebbrix wins
- Automatic reinforcement learning that improves retrieval
- 3-tier memory with natural Ebbinghaus decay
- 5-layer hybrid search (vector + BM25 + graph + importance + recency)
- Memory promotion and quality scoring built in
Both store memories. One learns from them.
Full comparisonHebbrix vs Zep
Zep pioneered temporal knowledge graphs for AI memory. Both care deeply about context, but they approach it from opposite directions.
Where Zep wins
- Temporal knowledge graphs that track how facts change over time
- Deep native LangGraph integration
- SOC 2 Type II and HIPAA compliance
- Entity timeline tracking and evolving context
Where Hebbrix wins
- Cognitive 3-tier memory architecture (STM, MTM, LTM)
- Automatic learning that improves without manual feedback
- Natural memory decay instead of manual expiry rules
- Token-based pricing instead of per-seat enterprise tiers
Two philosophies on temporal context.
Full comparisonHebbrix vs Letta
Letta (formerly MemGPT) is a full agent framework where the LLM manages its own memory. Hebbrix is a standalone memory API. A framework vs. a service.
Where Letta wins
- Full open-source agent framework (not just memory)
- LLM autonomously manages its own context window
- Self-hostable with Docker and PostgreSQL
- Active research community from UC Berkeley origins
Where Hebbrix wins
- Works with any framework (LangChain, CrewAI, Dify, custom code)
- Deterministic memory quality that doesn't depend on which LLM you use
- 5-layer hybrid search vs. vector-only archival search
- No infrastructure to manage. API calls only.
A framework vs. an API. Different paths to the same goal.
Full comparisonHebbrix vs RAG Alone
RAG is great for documents. But conversations, preferences, and relationships need a different kind of memory. You probably need both.
What RAG does well
- Retrieving information from large document collections
- Answering questions grounded in specific source material
- Well understood pattern with mature tooling
- Works great for static knowledge bases
What agent memory adds
- Remembers conversations, preferences, and user context
- Learns and improves from every interaction automatically
- Knowledge graph connects entities across all interactions
- Memory decays and promotes naturally, stays relevant over time
Documents vs experiences. You need both.
Full comparisonThe honest take
The most mature option with the largest community. If you want open-source with a managed option and don't need automatic learning, it's solid.
Pioneered temporal knowledge graphs. If tracking how things change over time is central to your use case, Zep is purpose-built for that.
A fundamentally different philosophy where the LLM manages its own memory. Maximum autonomy, but requires adopting a full framework.
We're newer. We focused on automatic reinforcement learning, cognitive memory tiers, and a 5-layer search. If you want memory that gets smarter on its own, that's our bet.
We're not going to pretend we're better at everything. We don't have an open-source edition. We're newer than Mem0 and Zep. But on automatic learning and cognitive architecture, we think we're building something genuinely different.
Try the one that learns
Free tier. Token-based pricing. No lock-in. See if cognitive memory fits your agents.