Mem0 vs gbrain
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
gbrain
Long-term memory store for coding agents, with sources and corrections
Verdict
Mem0 ranks higher, 89 to 87. Of the 4 score parts we measure for both, Mem0 leads on 1, gbrain on 2 and 1 is even.
- AdoptionMem0 leads by 28 points, 96 to 68: a wide gap.
- FreshnessEven at 100.
- Maintenancegbrain leads by 20 points, 95 to 75: a wide gap.
- Agent-readygbrain leads by 15 points, 85 to 70: a wide gap.
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 gbrain if you are
Developers giving Claude Code, Codex or other agents persistent shared memory
- Keyless start: PGLite brain with keyword search, no server or Docker needed
- One brain shared across agents via stdio MCP or HTTP MCP with scoped OAuth tokens
| What we compare | Mem0 | gbrain |
|---|---|---|
| Score parts, out of 100 | ||
| Adoption | 96, widely used | 68, popular |
| Freshness | 100, active | 100, active |
| Maintenance | 75, fair | 95, healthy |
| Agent-ready | 70, partly | 85, ready |
| Facts from GitHub and the README | ||
| Stars | 68.2k | 31.4k |
| License | Apache-2.0 (permissive) | MIT (permissive) |
| Last commit | Oct 2026 | Nov 2026 |
| Last release | Sep 2026 | Nov 2026 |
| Language | Python | TypeScript |
| Docker | No | Yes |
| GPU | Optional | Not needed |
| 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
gbrain
GBrain keeps explicit facts and their sources in a local PGLite or Postgres brain and exposes them to agents such as Claude Code, Codex, Cursor and Hermes over stdio or HTTP MCP. It starts keyless with keyword search; semantic search, cited synthesis, a typed knowledge graph and an overnight enrichment cycle are optional. It is installed with Bun from GitHub, not npm.
Who it is for: Developers giving Claude Code, Codex or other agents persistent shared memory
Strengths
- Keyless start: PGLite brain with keyword search, no server or Docker needed
- One brain shared across agents via stdio MCP or HTTP MCP with scoped OAuth tokens
- One-command setup for Claude Code and Codex, with dry-run and remove options
- Optional cited synthesis that notes gaps, plus a typed graph for relationship queries
Weaknesses
- Not on npm; the unrelated npm package named gbrain can shadow the real binary
- Always-on OpenClaw/Hermes setup needs a server with 8GB+ RAM plus paid API usage
- Local files and shared database credentials are not isolated by sources
- Remote put_page does not extract graph edges inline; HTTP needs explicit maintenance
- RAM ≥ 8 GB
- no GPU
- Compose
- Needs Bun 1.4.0+, PGLite or Postgres, Tailscale (optional, for remote MCP)
- Models: Optional cloud embedding, reranking, extraction and synthesis providers (unspecified)
More in Memory
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