MillaJ
bddba59ae3
Adds a brief [!IMPORTANT] callout at the top of the README pointing users to the urgent announcement at #1388. Claude Code auto-deletes local JSONL transcripts after 30 days; users without the auto-save hooks wired are losing transcript data off the rolling window. Ships 4 small standalone tools at tools/: - backup_claude_jsonls.sh — rsync ~/.claude/projects/ to a safe folder - render_jsonl.py — convert JSONL transcripts to readable text - find_orphan_claude_jsonls.sh — scan backup locations for orphan Claude Code transcripts (multi-line shape detection + topic preview) - save.md — Claude Code slash command for manual /save into MemPalace Tools verified by independent agent against v3.3.4 source. Read-only on user data. POSIX bash + Python stdlib only.
196 lines
7.4 KiB
Markdown
196 lines
7.4 KiB
Markdown
> [!CAUTION]
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> **Scam alert.** The only official sources for MemPalace are this
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> [GitHub repository](https://github.com/MemPalace/mempalace), the
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> [PyPI package](https://pypi.org/project/mempalace/), and the docs site at
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> **[mempalaceofficial.com](https://mempalaceofficial.com)**. Any other
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> domain — including `mempalace.tech` — is an impostor and may distribute
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> malware. Details and timeline: [docs/HISTORY.md](docs/HISTORY.md).
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> [!IMPORTANT]
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> **🚨 Claude Code sessions expire in 30 days w/out auto-save hooks wired!** **[Read this →](https://github.com/MemPalace/mempalace/discussions/1388)**
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<div align="center">
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<img src="assets/mempalace_logo.png" alt="MemPalace" width="240">
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# MemPalace
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Local-first AI memory. Verbatim storage, pluggable backend, 96.6% R@5 raw on LongMemEval — zero API calls.
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[![][version-shield]][release-link]
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[![][python-shield]][python-link]
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[![][license-shield]][license-link]
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[![][discord-shield]][discord-link]
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</div>
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---
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## What it is
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MemPalace stores your conversation history as verbatim text and retrieves
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it with semantic search. It does not summarize, extract, or paraphrase.
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The index is structured — people and projects become *wings*, topics
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become *rooms*, and original content lives in *drawers* — so searches
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can be scoped rather than run against a flat corpus.
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The retrieval layer is pluggable. The current default is ChromaDB; the
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interface is defined in [`mempalace/backends/base.py`](mempalace/backends/base.py)
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and alternative backends can be dropped in without touching the rest of
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the system.
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Nothing leaves your machine unless you opt in.
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Architecture, concepts, and mining flows:
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[mempalaceofficial.com/concepts/the-palace](https://mempalaceofficial.com/concepts/the-palace.html).
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---
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## Install
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```bash
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pip install mempalace
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mempalace init ~/projects/myapp
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```
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## Quickstart
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```bash
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# Mine content into the palace
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mempalace mine ~/projects/myapp # project files
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mempalace mine ~/.claude/projects/ --mode convos # Claude Code sessions (scope with --wing per project)
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# Search
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mempalace search "why did we switch to GraphQL"
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# Load context for a new session
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mempalace wake-up
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```
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For Claude Code, Gemini CLI, MCP-compatible tools, and local models, see
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[mempalaceofficial.com/guide/getting-started](https://mempalaceofficial.com/guide/getting-started.html).
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---
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## Benchmarks
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All numbers below are reproducible from this repository with the commands
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in [`benchmarks/BENCHMARKS.md`](benchmarks/BENCHMARKS.md). Full
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per-question result files are committed under `benchmarks/results_*`.
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**LongMemEval — retrieval recall (R@5, 500 questions):**
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| Mode | R@5 | LLM required |
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| Raw (semantic search, no heuristics, no LLM) | **96.6%** | None |
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| Hybrid v4, held-out 450q (tuned on 50 dev, not seen during training) | **98.4%** | None |
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| Hybrid v4 + LLM rerank (full 500) | ≥99% | Any capable model |
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The raw 96.6% requires no API key, no cloud, and no LLM at any stage. The
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hybrid pipeline adds keyword boosting, temporal-proximity boosting, and
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preference-pattern extraction; the held-out 98.4% is the honest
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generalisable figure.
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The rerank pipeline promotes the best candidate out of the top-20
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retrieved sessions using an LLM reader. It works with any reasonably
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capable model — we have reproduced it with Claude Haiku, Claude Sonnet,
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and minimax-m2.7 via Ollama Cloud (no Anthropic dependency). The gap
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between raw and reranked is model-agnostic; we do not headline a "100%"
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number because the last 0.6% was reached by inspecting specific wrong
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answers, which `benchmarks/BENCHMARKS.md` flags as teaching to the test.
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**Other benchmarks (full results in [`benchmarks/BENCHMARKS.md`](benchmarks/BENCHMARKS.md)):**
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| Benchmark | Metric | Score | Notes |
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| LoCoMo (session, top-10, no rerank) | R@10 | 60.3% | 1,986 questions |
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| LoCoMo (hybrid v5, top-10, no rerank) | R@10 | 88.9% | Same set |
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| ConvoMem (all categories, 250 items) | Avg recall | 92.9% | 50 per category |
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| MemBench (ACL 2025, 8,500 items) | R@5 | 80.3% | All categories |
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We deliberately do not include a side-by-side comparison against Mem0,
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Mastra, Hindsight, Supermemory, or Zep. Those projects publish different
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metrics on different splits, and placing retrieval recall next to
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end-to-end QA accuracy is not an honest comparison. See each project's
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own research page for their published numbers.
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**Reproducing every result:**
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```bash
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git clone https://github.com/MemPalace/mempalace.git
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cd mempalace
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pip install -e ".[dev]"
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# see benchmarks/README.md for dataset download commands
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python benchmarks/longmemeval_bench.py /path/to/longmemeval_s_cleaned.json
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```
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---
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## Knowledge graph
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MemPalace includes a temporal entity-relationship graph with validity
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windows — add, query, invalidate, timeline — backed by local SQLite.
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Usage and tool reference:
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[mempalaceofficial.com/concepts/knowledge-graph](https://mempalaceofficial.com/concepts/knowledge-graph.html).
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## MCP server
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29 MCP tools cover palace reads/writes, knowledge-graph operations,
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cross-wing navigation, drawer management, and agent diaries. Installation
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and the full tool list:
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[mempalaceofficial.com/reference/mcp-tools](https://mempalaceofficial.com/reference/mcp-tools.html).
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## Agents
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Each specialist agent gets its own wing and diary in the palace.
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Discoverable at runtime via `mempalace_list_agents` — no bloat in your
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system prompt:
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[mempalaceofficial.com/concepts/agents](https://mempalaceofficial.com/concepts/agents.html).
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## Auto-save hooks
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Two Claude Code hooks save periodically and before context compression:
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[mempalaceofficial.com/guide/hooks](https://mempalaceofficial.com/guide/hooks.html).
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For per-message recall on top of the file-level chunks the hooks produce,
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run `mempalace sweep <transcript-dir>` periodically — it stores one
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verbatim drawer per user/assistant message, idempotent and resume-safe.
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---
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## Requirements
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- Python 3.9+
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- A vector-store backend (ChromaDB by default)
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- ~300 MB disk for the default embedding model
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No API key is required for the core benchmark path.
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## Docs
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- Getting started → [mempalaceofficial.com/guide/getting-started](https://mempalaceofficial.com/guide/getting-started.html)
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- CLI reference → [mempalaceofficial.com/reference/cli](https://mempalaceofficial.com/reference/cli.html)
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- Python API → [mempalaceofficial.com/reference/python-api](https://mempalaceofficial.com/reference/python-api.html)
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- Full benchmark methodology → [benchmarks/BENCHMARKS.md](benchmarks/BENCHMARKS.md)
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- Release notes → [CHANGELOG.md](CHANGELOG.md)
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- Corrections and public notices → [docs/HISTORY.md](docs/HISTORY.md)
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## Contributing
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PRs welcome. See [CONTRIBUTING.md](CONTRIBUTING.md).
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## License
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MIT — see [LICENSE](LICENSE).
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<!-- Link Definitions -->
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[version-shield]: https://img.shields.io/badge/version-3.3.3-4dc9f6?style=flat-square&labelColor=0a0e14
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[release-link]: https://github.com/MemPalace/mempalace/releases
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[python-shield]: https://img.shields.io/badge/python-3.9+-7dd8f8?style=flat-square&labelColor=0a0e14&logo=python&logoColor=7dd8f8
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[python-link]: https://www.python.org/
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[license-shield]: https://img.shields.io/badge/license-MIT-b0e8ff?style=flat-square&labelColor=0a0e14
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[license-link]: https://github.com/MemPalace/mempalace/blob/main/LICENSE
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[discord-shield]: https://img.shields.io/badge/discord-join-5865F2?style=flat-square&labelColor=0a0e14&logo=discord&logoColor=5865F2
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[discord-link]: https://discord.com/invite/ycTQQCu6kn
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