MemPalace vs OpenViking
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
OpenViking
Context database exposing agent memory, knowledge and skills as a filesystem
| What we compare | MemPalace | OpenViking |
|---|---|---|
| Score parts, out of 100 | ||
| Adoption | 87, widely used | 73, popular |
| Freshness | 100, active | 100, active |
| Maintenance | 84, healthy | 87, healthy |
| Easy to run | 50, easy | 67, easy |
| Agent-ready | 70, partly | 0, none |
| Facts from GitHub and the README | ||
| Stars | 59.5k | 39.6k |
| License | MIT (permissive) | AGPL-3.0 (copyleft) |
| Last commit | Oct 2026 | Oct 2026 |
| Last release | Sep 2026 | Oct 2026 |
| Language | Python | Python |
| 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- No native Android/Termux; GPU image is x86_64-only and unpublished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
OpenViking
OpenViking organises everything an agent knows as a viking:// virtual filesystem of resources, memories and skills, browsed with ls, tree, read and grep, with search scoped to a subtree. Each directory carries generated summaries so agents read full content only when needed. The server needs Python 3.10+ plus an embedding model and a VLM; plugins cover Claude Code, Codex, Cursor and OpenClaw.
Who it is for: Teams wanting transparent, file-like memory shared across coding agents
Strengths
- Memory is inspectable and editable as Markdown files under viking:// URIs
- LoCoMo accuracy 80 to 83% for OpenClaw, Hermes and Claude Code at far fewer tokens
- Python, Go and TypeScript SDKs plus HTTP API; multi-tenant accounts and ACLs
- Hosted Studio playground at openviking.ai/studio; Railway one-click deploy
Weaknesses
- AGPL-3.0 license
- Needs both an embedding model and a vision-language model from a provider
- Memory plugin installer is macOS/Linux only; Windows uses the beta desktop app
- Benchmarks were run with Volcengine Doubao models
- no GPU
- Docker + Compose
- Models: Volcengine, OpenAI, Codex OAuth, Kimi, GLM