mempalace 3.3.1

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

[!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.

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

pip install mempalace
mempalace init ~/projects/myapp

Quickstart

# Mine content into the palace
mempalace mine ~/projects/myapp                    # project files
mempalace mine ~/chats/ --mode convos              # conversation exports

# 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
pip install -e ".[dev]"
# see benchmarks/README.md for dataset download commands
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.

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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
• 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.