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Mnemix vs Mem0
The mindshare leader in bolt-on agent memory. 54k stars, $24M raised, 21+ framework integrations, 19 vector backends — chat/session memory, not voice or caller enrichment.
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Mem0's developer experience for chat is genuinely good: a memory-as-a-service layer you wrap around LangChain, CrewAI, AutoGen, or your own agent loop, with unmatched vector-store choice. The moment you swap stdin for a Vapi webhook, you're outside their lane — no phone-native entry path, no Twilio/Trestle/Baylio enrichment, 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) |
| Agent framework integrations | 21+ (LangChain, CrewAI, AutoGen, Mastra, …) | Voice platforms + MCP (Vapi, Retell, Bland, LiveKit, Twilio) |
| 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% | Published once measured |
| LongMemEval (independent) | 49% (community replication) | Published once measured |
| Compliance | SOC 2 Type 2, HIPAA | SOC 2 + GDPR on the roadmap |
| 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 on LangChain, CrewAI, AutoGen, or similar; 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 bolt-on memory API is excellent for multi-session personalization 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-ai/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://mcp.mnemix.ai. 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. When Mnemix publishes its own LongMemEval result, the full methodology and harness ship with it — so independent replication is one command away.
- Mem0 ships 21+ framework integrations. Does Mnemix?
- Different lanes. Mem0's integration surface targets agent frameworks — LangChain, CrewAI, AutoGen, Mastra, and others — as a bolt-on memory layer for chat agents. Mnemix targets voice platforms and telephony: Vapi, Retell, Bland, LiveKit, Twilio, plus MCP for agent tooling. Mem0 wraps your agent loop; Mnemix resolves the caller and recalls same-number memory before your voice agent speaks.
- 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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