mempalace 3.3.6

Give your AI a memory — mine projects and conversations into a searchable palace. No API key required.

[!CAUTION]

🚨 CRITICAL SECURITY WARNING: BEWARE OF SCAMS

MemPalace has NO other official websites.

The ONLY official sources are:

1. This GitHub repository
2. The PyPI package
3. The docs at mempalaceofficial.com

ANY other domain (including .tech, .net, or other .com variants) is an impostor and may distribute malware. Do not download executables from untrusted sites. Details and timeline: docs/HISTORY.md.

[!IMPORTANT] 🚨 Claude Code sessions expire in 30 days w/out auto-save hooks wired! Read this →

Need the shortest recovery/setup path? Use the Claude Code retention setup checklist.

MemPalace

Local-first AI memory. Verbatim storage, pluggable backend, 96.6% R@5 raw on LongMemEval — zero API calls.

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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.

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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

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.

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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
uv sync --extra dev   # or: pip install -e ".[dev]"
# see benchmarks/README.md for dataset download commands
uv run python benchmarks/longmemeval_bench.py /path/to/longmemeval_s_cleaned.json

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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.

If you are installing under time pressure, start with the Claude Code retention setup checklist: wire the hooks, back up existing JSONL transcripts, and backfill them with mempalace mine ~/.claude/projects/ --mode convos.

For per-message recall on top of the file-level chunks the hooks produce, run mempalace sweep <transcript-dir> periodically — it stores one verbatim drawer per user/assistant message, idempotent and resume-safe.

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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 for details and migration notes.

No API key is required for the core benchmark path.

Docs

• Getting started → mempalaceofficial.com/guide/getting-started
• CLI reference → mempalaceofficial.com/reference/cli
• Python API → mempalaceofficial.com/reference/python-api
• Full benchmark methodology → benchmarks/BENCHMARKS.md
• Release notes → CHANGELOG.md
• Corrections and public notices → docs/HISTORY.md

Contributing

PRs welcome. See CONTRIBUTING.md.

License

MIT — see LICENSE.