Two approaches to AI memory.
One might fit your project better.
Mem0 pioneered accessible AI memory with a clean API. Hebbrix takes a different path, one rooted in cognitive science. Here's how they actually compare.
Store and retrieve
Key-value memory with vector similarity search. You tell it what to remember, you ask for it back. Clean, straightforward, and easy to reason about.
Learn and understand
A cognitive memory system that mirrors how the human brain processes information. Memories form, strengthen, connect, and naturally decay based on relevance.
"This isn't about which is better. It's about which approach fits the way you're building."
Mem0 was one of the first to make AI memory accessible through a clean API, and they've done great work pushing the space forward. We respect what they've built. Hebbrix comes at the problem from a different angle: automatic learning, cognitive memory tiers, and knowledge graphs that form without you writing extraction logic. The result is a system that doesn't just store memories. It understands them.
Where the approaches diverge
Both platforms handle the basics well. These are the areas where the underlying philosophy creates real differences in what your agent can do.
Flat memory store
All memories stored at the same level. Retrieval relies on vector similarity to surface relevant results. Works well for simple lookup patterns.
Three-tier cognitive architecture
Short-term for recent context, medium-term for ongoing relationships, long-term for permanent knowledge. Memories promote or decay naturally, just like in the human brain.
Vector similarity search
Embeds queries and memories, then finds the closest matches. A solid starting point that works for many use cases.
Five-layer hybrid retrieval
Combines semantic vectors, BM25 keyword matching, knowledge graph traversal, importance scoring, and recency boosting. All in under 50ms. Your agent gets the right memory, not just the closest embedding.
No knowledge graph
Memories are independent entries. Connections between entities need to be managed in your application logic.
Automatic entity extraction and mapping
Store "Alex joined the product team and reports to Jordan" and Hebbrix automatically maps the entities and relationships. Ask about Jordan's team later, and it finds Alex through graph traversal, even without a direct mention.
Manual feedback
Memory quality depends on what you store and how you manage it. You control the quality loop through your own application logic.
Six automatic quality checks
After every interaction: self-consistency, hallucination detection via NLI, LLM-as-judge evaluation, answer quality heuristics, memory attribution scoring, and confidence calibration. Good memories get reinforced. Noisy ones fade. No thumbs-up buttons required.
At a glance
Choosing the right fit
Every project has different needs. Here's our honest take on when each platform shines.
Switching is straightforward
If you're already using Mem0, migrating to Hebbrix doesn't require a rewrite. The concepts map directly. The capabilities just go deeper.
Sign up and get your API key
Create a free account. Your API key is ready instantly.
Point your code to Hebbrix
Hebbrix's chat endpoint is OpenAI-compatible. Swap the base URL and API key. Your existing format works.
Store and search memories
Same mental model: store memories with one call, search with another. The API surface feels familiar.
Unlock what's new
Knowledge graphs, 3-tier memory, and auto-learning kick in automatically. No extra configuration needed.
What teams build with Hebbrix
Customer Support Agents
Agents that remember every interaction, resolve recurring issues faster, and never ask you to repeat yourself.
Personal AI Assistants
Assistants that learn your preferences over weeks and months, building a genuine understanding of how you work.
Research & Analysis
Agents that connect findings across sources via the knowledge graph, surfacing insights flat retrieval would miss.
Enterprise Multi-Agent
Teams of agents sharing knowledge through collections, each with its own scope but all contributing to shared understanding.
Workflow Automation
Agents that remember process patterns and adapt their behavior based on what's worked before.
Knowledge Management
Systems that automatically organize and connect institutional knowledge, making it searchable and always current.
Ready to compare?
The best way to compare is to try it. The free tier is generous enough to run a real proof of concept.