Skip to content

How Memnex compares

Memory is a crowded space. This page is an honest orientation, not a marketing pitch — pick the tool that fits your shape.

Side-by-side

Memnex Mem0 Zep Letta (formerly MemGPT) OpenAI Memory
Primary use case Cross-channel multi-agent memory for production support / commerce Per-app memory for a single agent Per-session and long-term memory for chat agents Stateful agent runtime with self-managed memory ChatGPT user-level memory
Cross-channel by design ✅ first-class partial (via user_id) partial partial ❌
Multi-tenant w/ row-level security ✅ Postgres RLS ❌ single-tenant ❌ single-tenant ❌ single-tenant n/a (managed)
MCP-native ✅ tools + resources + prompts ❌ Python/JS SDK ❌ Python/JS SDK ❌ Python SDK ❌ proprietary
GDPR forget ✅ signed receipt across all stores manual delete partial manual delete n/a
Regulated PII masking at write ✅ regex + Presidio partial ❌ ❌ n/a
Audit ledger (HMAC-signed) ✅ ❌ ❌ ❌ ❌
Storage layer pluggability ✅ Redis / Postgres / Qdrant via protocols hosted-first hosted-first LanceDB / Postgres n/a
Generative LLM on hot path ❌ optional ✅ for fact extraction ✅ for summarization ✅ for memory edits proprietary
Self-hostable ✅ Apache-2.0 ✅ ✅ ✅ ❌
Conflict detection w/ entity overlap ✅ ❌ ❌ ❌ ❌
Hallucination trace ✅ memory_trace ❌ ❌ ❌ ❌
Hosted SaaS option planned ✅ ✅ ✅ ✅

Last reviewed: 2026-04-25. Some of this changes monthly — check upstream docs before making a procurement decision.

When to pick what

Pick Memnex if

  • You run multiple agents on multiple channels (voice + WhatsApp + web + …) and they need to share state.
  • You're a B2B SaaS — multi-tenant isolation is a hard requirement, not a nice-to-have.
  • Your customers are in regulated industries (healthcare, finance, India fintech) and ask for audit trails or GDPR-style forget.
  • You already use MCP-compatible agent runtimes (Claude Desktop, Cursor, LangGraph) and don't want to write integration code.

Pick Mem0 if

  • You have one agent, one channel, and want the smallest possible API.
  • You're early-stage and optimizing for time-to-first-write, not compliance.

Pick Zep if

  • You want session-scoped memory plus longer-term consolidation, baked into a chat-shaped abstraction.
  • You're OK with their hosted product as the primary path.

Pick Letta if

  • You want the "agent OS" model where the agent itself manages its memory via tool calls (the MemGPT pattern).
  • You want a runtime, not just a memory layer.

Pick OpenAI Memory if

  • You're building inside ChatGPT and want zero infrastructure.
  • You don't need cross-tenant isolation (everyone is "your user" via OpenAI account).

What Memnex is NOT

To save you time:

  • Not a chatbot framework. Bring your own agent (LangGraph / LangChain / CrewAI / Anthropic SDK / your runtime). Memnex is the memory layer.
  • Not a vector database. It uses one (Qdrant) under the hood, but the surface is "facts about users", not "vectors and metadata."
  • Not a transcript store. If you write every conversation turn, retrieval quality collapses. Memnex stores durable facts, not raw history.
  • Not a CRM. Stores facts the agent needs to do its job, not your sales pipeline.