MemPalace vs Mem0
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.
MemPalace
Local verbatim memory store for AI agents with semantic search and MCP
Mem0
Long-term memory layer that stores and retrieves user facts for LLM agents
Verdict
MemPalace ranks higher, 90 to 89. Of the 4 score parts we measure for both, MemPalace leads on 1, Mem0 on 1 and 2 are even.
- AdoptionMem0 leads by 3 points, 96 to 93: close to even.
- FreshnessEven at 100.
- MaintenanceMemPalace leads by 8 points, 83 to 75.
- Agent-readyEven at 70.
Pick MemPalace if you are
Developers giving coding agents persistent, local long-term memory
- Stores original text verbatim; raw mode needs no LLM or API key
- Pluggable backends: Chroma, SQLite exact, Rust exact, Milvus, Qdrant, pgvector
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
| What we compare | MemPalace | Mem0 |
|---|---|---|
| Score parts, out of 100 | ||
| Adoption | 93, widely used | 96, widely used |
| Freshness | 100, active | 100, active |
| Maintenance | 83, healthy | 75, fair |
| Agent-ready | 70, partly | 70, partly |
| Facts from GitHub and the README | ||
| Stars | 60.2k | 68.2k |
| License | MIT (permissive) | Apache-2.0 (permissive) |
| Last commit | Oct 2026 | Oct 2026 |
| Last release | Oct 2026 | Sep 2026 |
| Language | Python | Python |
| Docker | Yes | No |
| GPU | Optional | Optional |
| arm64 or Apple Silicon | Mentioned | Not stated |
MemPalace
MemPalace stores conversation history and project files as verbatim text, without summarizing, and retrieves it by semantic search scoped into wings (people, projects), rooms (topics) and drawers (original content). It defaults to ChromaDB with a pluggable backend interface, and exposes 47 MCP tools plus a SQLite temporal knowledge graph. The README reports 96.6% R@5 on LongMemEval in raw mode with no LLM.
Who it is for: Developers giving coding agents persistent, local long-term memory
Strengths
- Stores original text verbatim; raw mode needs no LLM or API key
- Pluggable backends: Chroma, SQLite exact, Rust exact, Milvus, Qdrant, pgvector
- Benchmarks reproducible from the repo with committed per-question results
- Auto-save hooks for Claude Code, Codex CLI and Cursor; 47 MCP tools
Weaknesses
- Native Termux install unsupported; Android needs a Debian PRoot container
- Claude Code sessions expire in 30 days unless auto-save hooks are wired
- First embedding call downloads a model of about 80-300 MB
- GPU optional
- Docker + Compose
- Needs Python 3.10+, ChromaDB (default), Milvus (optional), Qdrant (optional), Postgres pgvector (optional)
- Models: minilm, embeddinggemma, OpenAI-compatible embeddings endpoints
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
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