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Mnemix vs Mem0
The mindshare leader in OSS memory. 54k stars, $24M raised, 19 vector backends — but no voice, no enrichment.
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Mem0's developer experience for chat is genuinely good and their vector-store breadth is unmatched. The moment you swap stdin for a Vapi webhook, you're outside their lane — and their independent LongMemEval result was 49% vs the 93.4% they self-report. Choose Mnemix if you're building voice.
Side-by-side
| Dimension | Mem0 | Mnemix |
|---|---|---|
| GitHub stars | 54,251 | n/a (closed alpha) |
| Funding | $24M (YC, Peak XV, Basis Set) | Bootstrapped |
| Voice integrations | ❌ | ✅ Twilio, Vapi, Retell, Bland |
| Caller-ID enrichment | ❌ | ✅ Twilio Lookup + Trestle + Baylio |
| Bi-temporal memory | ❌ | ✅ (4 timestamps per fact) |
| Edge runtime | ❌ Python server | ✅ Cloudflare Workers |
| Vector backends | 19 | 1 (Supabase pgvector) |
| Multilingual NER | ❌ English-only spaCy | ✅ libphonenumber + Trestle |
| LongMemEval (self-reported) | 93.4% | Coming May 2026 (public methodology) |
| LongMemEval (independent) | 49% (community replication) | Coming May 2026 |
| Compliance | SOC 2 Type 2, HIPAA | GDPR day 1; SOC 2 Year 1 |
| Starter price | $19 to $249 cliff (13.1x) | Hobby $0; Starter+ contact sales |
When you'd pick Mem0
When you'd pick Mem0: you're building a chat product, you've already standardized on a specific vector backend (one of their 19), your team's Python infra is in good shape, and you don't need caller-ID enrichment. Mem0's developer experience for chat is excellent and their mindshare means you'll find Stack Overflow answers fast.
When you'd pick Mnemix
When you'd pick Mnemix: your product makes phone calls. You need a caller resolved through Twilio Lookup + Trestle + Baylio before the first audio packet. You're on Cloudflare or want to be. You'd rather have one well-tuned memory layer than 19 backends to reason about. You want the bench numbers and methodology to ship together, not separately.
FAQ
- Can I migrate from Mem0 to Mnemix without rewriting my agent?
- Yes. Mnemix exposes a Mem0-compatible adapter at @mnemix/client/compat-mem0 — same .add(), .search(), .get_all() surface. The differences land in two places: caller identity becomes a first-class field, and you point at https://mnemix-api.sayeed965.workers.dev. Migration is typically a one-line client swap plus an enrichment opt-in.
- What about Mem0's 19 vector backends? Mnemix has one.
- Mem0's 19 backends are a strength for non-voice teams who already run Pinecone/Weaviate/etc. and want a memory layer on top. Mnemix optimizes for voice latency at the edge — that means Supabase pgvector co-located with the rest of the data plane, with bring-your-own backend planned at higher tiers.
- Why does Mem0's independent LongMemEval differ from their self-reported 93.4%?
- Community replication of Mem0's LongMemEval has consistently landed near 49% rather than 93.4%. The gap appears to come from how the original benchmark configured the retriever and judge. We're publishing Mnemix's full LongMemEval methodology and harness in May 2026 specifically so this kind of gap doesn't happen with our numbers.
- Is Mnemix really the only voice-native option?
- Voice-native means: caller-ID resolution before the first audio packet, sub-300ms recall budget, and first-class integrations with Vapi/Retell/Bland/LiveKit/Twilio. By that definition, yes — Mem0, Zep, Supermemory, Cognee, Letta, LangMem, and OpenAI Assistants are all general-purpose memory with voice as an afterthought (or in Letta's case, deprecated outright).
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