MemPalace vs agentmemory
Two of the top memory, side by side: score, setup, license, activity and what each review found.
MemPalace
Local verbatim memory for coding agents on ChromaDB with 45 MCP tools
agentmemory
Persistent memory server for coding agents, exposed over MCP and REST
| What we compare | MemPalace | agentmemory |
|---|---|---|
| Score parts, out of 100 | ||
| Adoption | 87, widely used | 48, known |
| Freshness | 100, active | 100, active |
| Maintenance | 84, healthy | 81, healthy |
| Easy to run | 50, easy | 83, very easy |
| Agent-ready | 70, partly | 30, minimal |
| Facts from GitHub and the README | ||
| Stars | 59.5k | 29.3k |
| License | MIT (permissive) | Apache-2.0 (permissive) |
| Last commit | Oct 2026 | Oct 2026 |
| Last release | Sep 2026 | Oct 2026 |
| Language | Python | TypeScript |
| Docker | Yes | Yes |
| GPU | Not needed | Not needed |
| arm64 or Apple Silicon | Mentioned | Mentioned |
MemPalace
MemPalace stores conversation history verbatim, never summarised, and retrieves it by scoped semantic search over a palace of wings (people, projects), rooms (topics) and drawers. It runs locally with Python 3.9+ and ChromaDB by default, exposes 45 MCP tools plus a CLI, mines Claude Code, Codex and Cursor transcripts via hooks, and needs no API key: 96.6% R@5 on LongMemEval without an LLM.
Who it is for: Coding-agent users who want local, inspectable, verbatim session memory
Strengths
- Verbatim storage; nothing is summarised or paraphrased
- 96.6% R@5 on LongMemEval with no LLM or API key; results reproducible from the repo
- Pluggable backends: ChromaDB, sqlite, Rust-native, Milvus, Qdrant, pgvector
- Multi-arch Docker image; auto-save hooks for Claude Code, Codex and Cursor
Weaknesses
- First run downloads an 80 to 300 MB embedding model; Docker needs network then
- No native Android/Termux; GPU image is x86_64-only and unpublished
- Docker image runs as uid 1000, so bind mounts must be readable by that uid
- README warns about impostor domains distributing malware
- no GPU
- Docker + Compose
- Models: local embeddings (MiniLM, EmbeddingGemma), OpenAI-compatible embedding endpoints, Ollama
agentmemory
agentmemory captures what a coding agent does across sessions, stores it as searchable memory, and injects relevant context at the start of the next session. It runs as a local Node.js server on the pinned iii engine and connects to agents through hooks, MCP, or REST, with 20 adapters listed. Keyless mode uses BM25 search; vector embeddings need a provider or the local Xenova/all-MiniLM-L6-v2 model.
Who it is for: Developers using coding agents who want memory kept across sessions
Strengths
- No external database; state lives in a local iii engine data directory
- Works with 20 listed agents through hooks, MCP, or REST
- Keyless BM25 mode works without any API key
- Local embeddings via EMBEDDING_PROVIDER=local after a one-time model download
Weaknesses
- Keyless mode has no vector search, so semantic queries can return nothing
- Pinned to iii-engine v0.22.1; refuses to attach to other engine versions
- Native Windows needs manual iii.exe install; WSL2 or Docker recommended
- Uses four local ports (3111, 3112, 3113, 49134)
- no GPU
- Docker + Compose
- Needs Node.js 20+, iii-engine v0.22.1
- Models: Xenova/all-MiniLM-L6-v2 (local embeddings)
- port 3113