📊 项目概览 tool
🏗️ 技术架构
🧠 思维导图
📱 小红书卡片
💡 应用场景
🚀 快速启动
git clone https://github.com/MemPalace/mempalace.git
cd mempalace
pip install mempalace
mempalace search "why did we switch"📖 项目文档
> [!CAUTION]
> Scam alert. The only official sources for MemPalace are this
> GitHub repository, the
> PyPI package, and the docs site at
> mempalaceofficial.com. Any other
> domain — including mempalace.tech — is an impostor and may distribute
> malware. Details and timeline: docs/HISTORY.md.
<div align="center">
<img src="assets/mempalace_logo.png" alt="MemPalace" width="240">
MemPalace
Local-first AI memory. Verbatim storage, pluggable backend, 96.6% R@5 raw on LongMemEval — zero API calls.
[![][version-shield]][release-link]
[![][python-shield]][python-link]
[![][license-shield]][license-link]
[![][discord-shield]][discord-link]
</div>
---
What it is
MemPalace stores your conversation history as verbatim text and retrieves
it with semantic search. It does not summarize, extract, or paraphrase.
The index is structured — people and projects become *wings*, topics
become *rooms*, and original content lives in *drawers* — so searches
can be scoped rather than run against a flat corpus.
The retrieval layer is pluggable. The current default is ChromaDB; the
interface is defined in mempalace/backends/base.py
and alternative backends can be dropped in without touching the rest of
the system.
Nothing leaves your machine unless you opt in.
Architecture, concepts, and mining flows:
mempalaceofficial.com/concepts/the-palace.
---
Install
pip install mempalace
mempalace init ~/projects/myapp
Quickstart
# Mine content into the palace
mempalace mine ~/projects/myapp # project files
mempalace mine ~/.claude/projects/ --mode convos # Claude Code sessions (scope with --wing per project)
# Search
mempalace search "why did we switch to GraphQL"
# Load context for a new session
mempalace wake-up
For Claude Code, Gemini CLI, MCP-compatible tools, and local models, see
mempalaceofficial.com/guide/getting-started.
---
Benchmarks
All numbers below are reproducible from this repository with the commands
in benchmarks/BENCHMARKS.md. Full
per-question result files are committed under benchmarks/results_*.
LongMemEval — retrieval recall (R@5, 500 questions):
| Mode | R@5 | LLM required |
|---|---|---|
| Raw (semantic search, no heuristics, no LLM) | 96.6% | None |
| Hybrid v4, held-out 450q (tuned on 50 dev, not seen during training) | 98.4% | None |
| Hybrid v4 + LLM rerank (full 500) | ≥99% | Any capable model |
The raw 96.6% requires no API key, no cloud, and no LLM at any stage. The
hybrid pipeline adds keyword boosting, temporal-proximity boosting, and
preference-pattern extraction; the held-out 98.4% is the honest
generalisable figure.
The rerank pipeline promotes the best candidate out of the top-20
retrieved sessions using an LLM reader. It works with any reasonably
capable model — we have reproduced it with Claude Haiku, Claude Sonnet,
and minimax-m2.7 via Ollama Cloud (no Anthropic dependency). The gap
between raw and reranked is model-agnostic; we do not headline a "100%"
number because the last 0.6% was reached by inspecting specific wrong
answers, which benchmarks/BENCHMARKS.md flags as teaching to the test.
Other benchmarks (full results in benchmarks/BENCHMARKS.md):
| Benchmark | Metric | Score | Notes |
|---|---|---|---|
| LoCoMo (session, top-10, no rerank) | R@10 | 60.3% | 1,986 questions |
| LoCoMo (hybrid v5, top-10, no rerank) | R@10 | 88.9% | Same set |
| ConvoMem (all categories, 250 items) | Avg recall | 92.9% | 50 per category |
| MemBench (ACL 2025, 8,500 items) | R@5 | 80.3% | All categories |
We deliberately do not include a side-by-side comparison against Mem0,
Mastra, Hindsight, Supermemory, or Zep. Those projects publish different
metrics on different splits, and placing retrieval recall next to
end-to-end QA accuracy is not an honest comparison. See each project's
own research page for their published numbers.
Reproducing every result:
git clone https://github.com/MemPalace/mempalace.git
cd mempalace
pip install -e ".[dev]"
# see benchmarks/README.md for dataset download commands
python benchmarks/longmemeval_bench.py /path/to/longmemeval_s_cleaned.json
---
Knowledge graph
MemPalace includes a temporal entity-relationship graph with validity
windows — add, query, invalidate, timeline — backed by local SQLite.
Usage and tool reference:
mempalaceofficial.com/concepts/knowledge-graph.
MCP server
29 MCP tools cover palace reads/writes, knowledge-graph operations,
cross-wing navigation, drawer management, and agent diaries. Installation
and the full tool list:
mempalaceofficial.com/reference/mcp-tools.
Agents
Each specialist agent gets its own wing and diary in the palace.
Discoverable at runtime via mempalace_list_agents — no bloat in your
system prompt:
mempalaceofficial.com/concepts/agents.
Auto-save hooks
Two Claude Code hooks save periodically and before context compression:
mempalaceofficial.com/guide/hooks.
---
Requirements
- Python 3.9+
- A vector-store backend (ChromaDB by default)
- ~300 MB disk for the default embedding model
No API key is required for the core benchmark path.
Docs
- Getting started → [mempalaceofficial.com/guide/getting-started](https://mempalaceofficial.com/guide/getting-started.html)
- CLI reference → [mempalaceofficial.com/reference/cli](https://mempalaceofficial.com/reference/cli.html)
- Python API → [mempalaceofficial.com/reference/python-api](https://mempalaceofficial.com/reference/python-api.html)
- Full benchmark methodology → [benchmarks/BENCHMARKS.md](benchmarks/BENCHMARKS.md)
- Release notes → [CHANGELOG.md](CHANGELOG.md)
- Corrections and public notices → [docs/HISTORY.md](docs/HISTORY.md)
Contributing
PRs welcome. See CONTRIBUTING.md.
License
MIT — see LICENSE.
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[python-link]: https://www.python.org/
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[license-link]: https://github.com/MemPalace/mempalace/blob/main/LICENSE
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[discord-link]: https://discord.com/invite/ycTQQCu6kn