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Decision brief
MemPalace is an advanced open-source AI memory system that integrates with ChromaDB to optimize machine learning model memories and enhance data retrieval efficiency.
Good fit when
- When you need a highly benchmarked solution for managing AI model memories, MemPalace can provide superior performance due to its optimization features integrated specifically around ML model needs.
- If your application requires seamless integration with ChromaDB as the vector database component, MemPalace offers enhanced data retrieval efficiency tailored to these integrations.
Avoid when
- Avoid if requiring a proprietary system where full transparency or customization of the memory management layer may not be necessary, since MemPalace is open source and might involve deeper technical啃
- "如果你的应用场景对内存管理层的完全透明或定制化需求不高，因为MemPalace是开源的，可能需要更深的技术介入来满足特定需求。"
Observed Jul 11, 2026 · Source: enrich:decision_facts
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Maintenance and security
Full trust report- Maintenance
- Very active (1d since push)
- As of 5d
- Provenance
- Not a fork · Organization account
- As of 5d
- Security (OSV)
- No MCP manifest
- As of 1mo
Public GitHub metadata and optional OSV scans. Signals, not a guarantee. Trust methodology.
Install
pip install mempalaceHow it fits your stack(34)
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Evidence and technical details
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Overview
MemPalace is an advanced open-source AI memory system that provides solutions for managing and optimizing machine learning model memories. It integrates with ChromaDB as a vector database component to enhance data retrieval efficiency.
Capability facts
- Deploy
- Self-host
Source: dockerfile:Dockerfile · Aug 16, 2026
- Docker
- Dockerfile present
Source: dockerfile:Dockerfile · Aug 16, 2026
- CLI
- CLI entrypoint
Source: pyproject.toml:[project.scripts] · Aug 16, 2026
- MCP server
- No MCP server detected
Source: repo_scan · Aug 16, 2026
- Languages
- python
Source: github.language+pyproject.toml · Aug 16, 2026
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Compatibility
Sourced claims from the README excerpt - not unsourced marketing copy.
Source: README excerpt (regex_v1, Aug 16, 2026)
python -m venv .venv && source .venv/bin/activateSource link
Tags
README
Install
MemPalace ships a CLI, so install it in an isolated environment to avoid
PEP 668 errors on Debian/Ubuntu/Homebrew Pythons and to keep mempalace's
deps (chromadb, numpy, grpcio, …) from conflicting with anything
else in your global site-packages.
We recommend uv — uv tool install puts
the mempalace CLI in an isolated environment on your PATH:
uv tool install mempalace
mempalace init ~/projects/myapp
pipx works the same way if you prefer it:
pipx install mempalace.
Prefer plain pip only inside an activated virtualenv where you
explicitly want import mempalace available:
python -m venv .venv && source .venv/bin/activate
pip install mempalace
Docker
A container image is also available for running the MCP server or the CLI without a local Python toolchain. Multi-arch (amd64 + arm64), so it runs natively on Apple Silicon:
docker pull ghcr.io/mempalace/mempalace:latest
Everything persists under /data — palace, config, and the cached embedding
model — so mount a volume there and reuse it across runs:
---
## Requirements
- Python 3.9+
- A vector-store backend (ChromaDB by default)
- ~300 MB disk for the embedding model. Onboarding (`python -m mempalace.onboarding`) offers `embeddinggemma-300m` (multilingual, 100+ languages, recommended) or `all-MiniLM-L6-v2` (English-only, ~30 MB). See the docstring at [`mempalace/embedding.py`](mempalace/embedding.py) for details and migration notes.
- Optional — compute embeddings on a server instead of locally. Set `embedding_model: "openai-compat"` in `~/.mempalace/config.json` together with `embedding_api_url` / `embedding_api_model` (and `embedding_api_key` if the server needs auth) to use any OpenAI-compatible `/v1/embeddings` endpoint — LM Studio, llama.cpp, vLLM, Ollama's OpenAI shim, or a self-hosted server (e.g. a larger multilingual or GPU-served embedder). Each key is overridable via the matching `MEMPALACE_EMBEDDING_API_*` env var. When the endpoint is on your machine or LAN, no content leaves your network. Switching to it requires `mempalace repair rebuild-index` (different vector space).
No API key is required for the core benchmark path.
---
## License
MIT — see [LICENSE](LICENSE).
[version-shield]: https://img.shields.io/badge/version-3.7.1-4dc9f6?style=flat-square&labelColor=0a0e14
[release-link]: https://github.com/MemPalace/mempalace/releases
[python-shield]: https://img.shields.io/badge/python-3.9+-7dd8f8?style=flat-square&labelColor=0a0e14&logo=python&logoColor=7dd8f8
[python-link]: https://www.python.org/
[license-shield]: https://img.shields.io/badge/license-MIT-b0e8ff?style=flat-square&labelColor=0a0e14
[license-link]: https://github.com/MemPalace/mempalace/blob/main/LICENSE
[discord-shield]: https://img.shields.io/badge/discord-join-5865F2?style=flat-square&labelColor=0a0e14&logo=discord&logoColor=5865F2
[discord-link]: https://discord.com/invite/ycTQQCu6kn
For agents
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