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Mem0

Listed at https://mem0.ai

Overall Rank #18 ⭐ Consider
✅ Open-source (Apache-2.0, 61k+ stars) + Cloud SaaS; self-host works in China without proxy | 🌍 International

💰 Token Pricing

TypePriceNote
Input Hobby 免费:10,000 memories + 1,000 retrieval API calls/月;Starter $19/月:50,000 memories + 5,000 calls per million tokens
Output Pro $249/月:Unlimited memories + 50,000 retrieval API calls + Graph Memory + 多项目;Enterprise 合同制 per million tokens
💡 Free Credits: Hobby free tier: 10,000 memories + 1,000 retrieval API calls/month (unlimited end users), community support

🤖 Supported Models (50)

OpenAI GPT-4o / GPT-4-Turbo / GPT-3.5Anthropic Claude 3.5 Sonnet / Haiku / OpusGoogle Gemini 1.5 Pro / FlashMistral Large / Mixtral / Mistral-7BGroq Llama 3.1 70B / MixtralOllama (any local model)AWS Bedrock (Claude / Llama / Titan)Azure OpenAI

✨ Pros

  • ✓De-facto AI Agent memory layer (61,493 GitHub stars, Apache-2.0, mem0ai/mem0)
  • ✓Clear 4-tier pricing: Hobby free → Starter $19 → Pro $249 → Enterprise
  • ✓8+ LLM providers supported (OpenAI/Anthropic/Gemini/Mistral/Groq/Ollama/Bedrock/Azure OpenAI)
  • ✓Graph Memory (Pro tier and up) for entity-relation modeling beyond pure vector retrieval
  • ✓Complete SDKs: Python, TypeScript, Java, Go, Ruby — same API for self-host and Cloud
  • ✓Local deployment supported: open-source + Docker — runs on China-hosted Kubernetes without proxy

⚠️ Cons

  • ×Hobby tier only 1,000 retrieval calls/month — production typically needs Starter or Pro
  • ×Pro tier $249/mo is expensive — same price point as Braintrust Pro but different positioning (memory vs evals)
  • ×Graph Memory only on Pro and up — Hobby/Starter users get pure vector retrieval only
  • ×No SSO/RBAC on free Hobby — Enterprise contract required for self-host with audit/SSO
  • ×Retrieval quality depends on LLM-based fact extraction — ~200-500ms per memory vs pure vector search
  • ×China-hosted Cloud SaaS access stability not yet verified (self-host bypasses entirely)

🎯 Best For

AI agent apps needing long-term memory (customer support bots, personalized assistants, multi-turn conversation systems); compliance-sensitive teams wanting self-host to avoid vendor lock-in; agent framework authors needing unified memory abstraction across multiple LLM providers