Mem0 vs Supermemory
Two of the top projects in Memory, side by side: who leads on each score part and who each one suits, then setup, license, activity and what each review found.
Mem0
Long-term memory layer that stores and retrieves user facts for LLM agents
Supermemory
Memory and context engine for AI agents, with user profiles and hybrid search
Verdict
Mem0 ranks higher, 89 to 86. Of the 4 score parts we measure for both, Mem0 leads on 1, Supermemory on 1 and 2 are even.
- AdoptionMem0 leads by 25 points, 96 to 71: a wide gap.
- FreshnessEven at 100.
- MaintenanceSupermemory leads by 18 points, 93 to 75: a wide gap.
- Agent-readyEven at 70.
Pick Mem0 if you are
Developers adding persistent per-user memory to agents or chatbots
- Python (pip) and npm SDKs plus a CLI, so it embeds in existing agents
- Hybrid retrieval: semantic, BM25 keyword and entity linking fused in one search
Pick Supermemory if you are
Developers adding long-term memory to agents, apps and AI coding tools
- Hybrid search returns RAG documents and user memories in a single query
- Handles knowledge updates and contradictions, and auto-forgets expired facts
| What we compare | Mem0 | Supermemory |
|---|---|---|
| Score parts, out of 100 | ||
| Adoption | 96, widely used | 71, popular |
| Freshness | 100, active | 100, active |
| Maintenance | 75, fair | 93, healthy |
| Agent-ready | 70, partly | 70, partly |
| Facts from GitHub and the README | ||
| Stars | 68.2k | 31.5k |
| License | Apache-2.0 (permissive) | MIT (permissive) |
| Last commit | Oct 2026 | Oct 2026 |
| Last release | Sep 2026 | Oct 2026 |
| Language | Python | TypeScript |
| Docker | No | No |
| GPU | Optional | Optional |
| arm64 or Apple Silicon | Not stated | Mentioned |
Mem0
Mem0 extracts facts from conversations with an LLM, stores them per user, session or agent, and retrieves them with combined semantic, BM25 keyword and entity matching. It ships as a Python and npm library, a self-hosted server with a dashboard and API keys, a CLI, and a managed cloud platform. The published benchmark numbers come from the managed platform, not the open-source SDK.
Who it is for: Developers adding persistent per-user memory to agents or chatbots
Strengths
- Python (pip) and npm SDKs plus a CLI, so it embeds in existing agents
- Hybrid retrieval: semantic, BM25 keyword and entity linking fused in one search
- Evaluation framework is open-sourced so benchmark numbers can be reproduced
- Supports several LLMs and embedders; defaults are configurable
Weaknesses
- Benchmarks reflect the managed platform; OSS results may differ
- Needs an LLM; default is OpenAI gpt-5-mini and text-embedding-3-small
- BM25 and entity features need the optional mem0ai[nlp] extra and a spaCy model
- Self-hosted dashboard is listed as limited; advanced features are cloud-only
- GPU optional
- Needs LLM provider (default OpenAI), Embedding model (default OpenAI text-embedding-3-small), Docker Compose (self-hosted server), spaCy en_core_web_sm (optional NLP extra)
- Models: OpenAI gpt-5-mini (default), text-embedding-3-small (default), other LLMs and embedders via configuration
- port 3000
Supermemory
Supermemory extracts facts from conversations, tracks updates and contradictions, expires stale information, and returns a per-user profile plus relevant memories through one API. It also covers RAG, connectors (Google Drive, Gmail, Notion, OneDrive, GitHub) and file extraction. It can be used via the hosted API, an MCP server and editor plugins, or a local server on port 6767.
Who it is for: Developers adding long-term memory to agents, apps and AI coding tools
Strengths
- Hybrid search returns RAG documents and user memories in a single query
- Handles knowledge updates and contradictions, and auto-forgets expired facts
- Local server keeps data in ~/.supermemory and can use Ollama with local embeddings
- Plugins for Claude Code, Cursor, Codex, OpenCode, plus Vercel AI SDK, LangChain, Mastra wrappers
Weaknesses
- Benchmark #1 claims are self-reported in the README, with details on a separate research page
- Hosted MCP server and plugins point at supermemory.ai cloud by default
- Local mode URL ingestion depends on a hosted reader service
- Local-versus-Enterprise feature differences are only documented on a separate page
- GPU optional
- Needs LLM provider (OpenAI, Anthropic, Gemini, Groq, or Ollama), Docker (optional)
- Models: OpenAI, Anthropic, Gemini, Groq, Ollama
- port 6767
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