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Mnemix vs Mem0

The mindshare leader in bolt-on agent memory. claimed/unverified 54k-star snapshot, $24M raised, 21+ framework integrations, 19 vector backends — chat/session memory, not voice or caller enrichment.

Motion available.Caller identity wedge: phone ring to informed responseThe phone rings, enrichment fans out through Twilio Lookup, Trestle, and Baylio intent, then the agent's first response is already informed.Phone ringsEnrichment fans out in parallelAgent's first response is already informed+1 555 123 4567Twilio LookupTrestleBaylio intentAgentHi Mike, I see this is about yourfleet account...Real-world identity, intent, and history — joined to your agent's memory — at Cloudflare edge latency.

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60-second verdict

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 claimed/unverified LongMemEval figures include 49% independent/community replication and a 93.4% self-reported score. Choose Mnemix if you're building voice.

Side-by-side

DimensionMem0Mnemix
GitHub starsclaimed/unverified snapshot: 54,251n/a (closed alpha)
Agent framework integrations21+ (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 backends191 (Supabase pgvector)
Multilingual NER❌ English-only spaCy✅ libphonenumber + Trestle
LongMemEval (self-reported)claimed/unverified: 93.4%Published once measured
LongMemEval (independent)claimed/unverified: 49% (community replication)Published once measured
ComplianceNot asserted in public copyNo public compliance claim
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

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 do Mem0 LongMemEval figures vary across sources?
Claimed/unverified Mem0 LongMemEval figures include 49% community replication and a 93.4% self-reported score. 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, designed-for-sub-300ms voice 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, a claimed/unverified voice-status snapshot marked deprecated).

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