Why Hebbrix

Honest comparisons. No spin.

Four approaches to AI agent memory, each with its own trade-offs. Here's what each one does well, including where Hebbrix falls short.

vs Mem0 vs Zep vs Letta vs RAG alone

quick reference

Feature comparison at a glance

Feature

Hebbrix

Mem0

Zep

Letta

Memory tiers (STM / MTM / LTM)✓ Versioned lifecycleBasicTemporal graphIn-context

Hybrid search layers✓ Adaptive hybrid2 to 32 to 3Vector only

Knowledge graph (auto)✓ Auto-extracted✓ Graph memory (Mem0g)✓ Temporal✕

Outcome Memory learning✓ Delayed outcome evidencePartialPartialPartial

Temporal memory lifecycle✓ Validity + supersession✕Manual expiry✕

Retrieval latency✓ 750ms p95 objectiveVariesVariesVaries

Multi-tenancy (collections)✓ Built-in✓✓Manual

OpenAI drop-in compatible✓ Same URLSDK onlySDK only✕ Full framework

Open source✕ Managed only✓ Apache 2.0✓ Open core✓ Apache 2.0

SOC 2 / HIPAAIn progress✓✓✕

comparison 01

Hebbrix vs Mem0

Mem0 is a widely-adopted open-source memory layer with a large GitHub following. Both store memories. The difference shows up in what happens to those memories over time.

Where Mem0 wins

→Open source (Apache 2.0) with a large, active community

→SOC 2 Type II and HIPAA compliance, ready for enterprise

→BYOK and on-premise deployment for security-sensitive teams

→More mature, with a longer track record in production

Where Hebbrix wins

✓Outcome Memory with decision receipts, delayed results, propensity, and uncertainty

✓Versioned current and historical memory with temporal provenance

✓Adaptive hybrid retrieval instead of vectors plus basic extraction

✓Calibrated scores, facet coverage, and explicit degraded-mode metadata

Bottom line: Both store memories. Mem0 is more mature and open-source. Hebbrix is the better fit if you want temporal memory plus explicit learning from reported action outcomes.

comparison 02

Hebbrix vs Zep

Zep pioneered temporal knowledge graphs for AI memory. Both care a lot about context, but they come at it from opposite directions.

Where Zep wins

→Temporal knowledge graphs that track how facts evolve and change over time

→Deep native LangGraph integration, first-class if you're on that stack

→SOC 2 Type II and HIPAA compliance

→Entity timeline tracking, so you know who said what and when

Where Hebbrix wins

✓Outcome Memory with causal decision and delayed-result evidence

✓Adaptive hybrid retrieval with calibrated confidence metadata

✓Operational memory tiers plus explicit temporal validity and history

✓Usage-based pricing with no per-seat enterprise tiers

Bottom line: Two approaches to context. Both support temporal graph workflows; Hebbrix additionally packages adaptive retrieval and explicit action-outcome learning.

comparison 03

Hebbrix vs Letta

Letta (formerly MemGPT) is a full agent framework where the LLM manages its own memory. Hebbrix is a memory API. A framework versus a service is a fundamentally different choice.

Where Letta wins

→A full open-source agent framework, not just memory but the whole agent

→The LLM manages its own context window, for maximum autonomy

→Self-hostable with Docker and PostgreSQL, so you keep full control

→An active research community with UC Berkeley origins and a strong paper track

Where Hebbrix wins

✓Framework-agnostic, so it works with LangChain, CrewAI, Dify, or raw code

✓Deterministic memory quality that doesn't depend on which LLM you're using

✓Adaptive hybrid retrieval instead of vector-only archival search

✓No infrastructure to manage, just API calls, and we handle the rest

Bottom line: Letta is a full agent framework. Hebbrix is infrastructure. If you want to adopt a whole new architecture, choose Letta. If you want to add memory to agents you're already building, choose Hebbrix.

comparison 04

Hebbrix vs RAG alone

RAG is great for documents. Conversations, preferences, and relationships call for a different kind of memory. Most teams end up wanting both, not one or the other.

What RAG does well

→Retrieving information from large, static document collections

→Grounding answers in specific, citable source material

→A well-understood pattern with mature tooling like LlamaIndex and LangChain

→Self-hostable, with no vendor dependency

What agent memory adds

✓Remembers conversations, user preferences, and relational context, not just documents

✓Can learn from explicit delayed action outcomes, with no invented feedback

✓A knowledge graph that connects entities across every conversation, not just within documents

✓Tracks current and superseded facts so changing truth stays auditable

Bottom line: Documents versus experiences. RAG retrieves from a knowledge base. Agent memory remembers what happened. You can run both, since Hebbrix connects to your existing docs connectors.

the honest take

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. No SOC 2 attestation is published here; request current evidence. Compare each option against your requirements.

Mem0

The most mature option, with the largest community. If you want open-source with a managed cloud option, SOC 2 compliance today, and you don't need automatic learning, it's a solid choice we respect.

Zep

Pioneered temporal knowledge graphs. If tracking how things change over time is central to your use case, Zep is purpose-built for it in a way no other tool matches.

Letta

A different philosophy, where the LLM manages its own memory. Maximum autonomy, fully open-source, and self-hostable. If you want to own the whole stack, Letta is the right call.

Hebbrix

We're newer. We focused on temporal memory, adaptive hybrid retrieval, and explicit Outcome Memory with delayed results and uncertainty. The bet we're making is that agent memory should learn from measured outcomes without hiding the evidence. If that sounds right for you, give us a try.

Try the one that learns

Free tier, usage-based pricing, and no lock-in. See whether cognitive memory fits your agents.

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