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<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.
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> [!CAUTION]
> **Beware of impostor sites.** MemPalace has no other official websites. The **only** official sources are this **[GitHub repository](https://github.com/MemPalace/mempalace)**, the **[PyPI package](https://pypi.org/project/mempalace/)**, and the docs at **[mempalaceofficial.com](https://mempalaceofficial.com)**. Any other domain (including `.tech`, `.net`, or other `.com` variants) is an impostor and may distribute malware. Details and timeline: [docs/HISTORY.md](docs/HISTORY.md).
> [!IMPORTANT]
> **Claude Code sessions expire in 30 days without auto-save hooks wired.** [Read this →](https://github.com/MemPalace/mempalace/discussions/1388)
>
> Need the shortest recovery/setup path? Use the [Claude Code retention setup checklist](https://mempalaceofficial.com/guide/claude-code-retention.html).
---
## 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`](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](https://mempalaceofficial.com/concepts/the-palace.html).
---
## Install
### Agent-guided setup
Install the MemPalace skills first, then ask your coding agent to set up
MemPalace. The setup skill detects your system, installs the Python package,
configures MCP, and asks whether you want a private local palace, a shared-brain
hub, or a client connected to an existing hub:
```bash
npx skills add MemPalace/mempalace
```
The repository exposes three skills: `mempalace` for guided installation and
operations, `mempalace-recall` for search-before-answer recall, and
`mempalace-task` for logstream delegation. Installing a skill does not by
itself install the MemPalace CLI or MCP server; the setup skill guides the
agent through those system changes and verifies the live connection.
During guided setup the agent can offer weekly stable-release checks. They are
disabled by default, contact only PyPI when enabled, and never install updates
automatically. Cached availability appears in scoped `mempalace_status` fields
for the serving runtime and, when a local proxy is present, its client runtime,
allowing the agent to explain the release and request authorization before showing an exact
upgrade plan. Setup records whether the runtime came from `uv tool`, `pipx`, or
`pip` so the plan never proposes an upgrade command for the wrong installation.
### Direct CLI setup
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`](https://docs.astral.sh/uv/) — `uv tool install` puts
the `mempalace` CLI in an isolated environment on your PATH:
```bash
uv tool install mempalace
mempalace init ~/projects/myapp
```
[`pipx`](https://pipx.pypa.io/) 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:
```bash
python -m venv .venv && source .venv/bin/activate
pip install mempalace
```
### Android / Termux
Native Termux installation is not currently supported because compiled
dependencies such as ChromaDB and ONNX Runtime publish Linux wheels, not
Android wheels. Android ARM64 users can run the regular Linux packages in an
isolated Debian PRoot container instead. See the
[Termux installation guide](website/guide/termux.md) for the tested setup and
an argv-preserving launcher.
### 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:
```bash
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:
```bash
# MCP server over stdio — note the `-i` flag (JSON-RPC needs stdin)
docker run -i --rm -v mempalace-data:/data ghcr.io/mempalace/mempalace
# Run any CLI command instead. The container only sees what you mount, so
# mount the directory you want to mine — read-only is enough, mining never
# writes to the source.
docker run --rm -v mempalace-data:/data -v /path/to/project:/work:ro \
ghcr.io/mempalace/mempalace mine /work
docker run --rm -v mempalace-data:/data ghcr.io/mempalace/mempalace search "why GraphQL"
```
The first command that needs embeddings downloads the model into `/data`
(~80 MB for the default `minilm`, ~300 MB for `embeddinggemma`). It is a
one-off as long as the volume persists, but it does mean the first call is
slow and needs network — worth knowing before assuming a hung container.
Wire it into an MCP client (e.g. Claude Code) as a stdio server. Mount
anything you want the server to be able to mine — it cannot reach your
transcripts otherwise:
```json
{
"mcpServers": {
"mempalace": {
"command": "docker",
"args": [
"run", "-i", "--rm",
"-v", "mempalace-data:/data",
"-v", "/absolute/path/to/.claude/projects:/transcripts:ro",
"ghcr.io/mempalace/mempalace"
]
}
}
}
```
Use a real absolute path there — `~` and `$HOME` are not expanded by every
MCP client. Paths are container paths from then on: mine `/transcripts`, not
`~/.claude/projects`.
**Mount permissions on Linux.** The image runs as uid 1000 and bind mounts
keep their host ownership, so a mounted directory has to be readable by that
uid — an ordinary `0755` checkout is fine, a `0700` directory is not, and the
failure surfaces as `PermissionError: [Errno 13]` rather than anything about
Docker. Docker Desktop maps uids on macOS and Windows, so this only bites on
Linux. Do **not** work around it with `--user`: `/data` is owned by uid 1000
inside the image, so another uid cannot write the palace at all.
`docker compose run --rm mcp` works too (see `docker-compose.yml`), and
`deploy/docker-compose.server.yml` stands up the team server. To build the
image yourself instead of pulling — required for the GPU variant, which is not
published:
```bash
docker build -t mempalace . # CPU
docker build --build-arg EXTRAS="extract,spellcheck" -t mempalace .
docker build -f Dockerfile.gpu -t mempalace:gpu . # CUDA; run with --gpus all
```
The GPU image is x86_64-only: `onnxruntime-gpu` publishes no aarch64 Linux
wheels, so that last build fails on an ARM host (including Apple Silicon) with
a dependency-resolution error rather than an obvious one.
Note that a build from a clone uses whatever branch you checked out; `develop`
is the default branch, so pull the published image if you want the released
version.
## Storage backends
ChromaDB is the default and needs no configuration. MemPalace also ships a
pluggable backend contract, exercised across deliberately different substrates
so the contract is never accidentally shaped around one vendor. Every
non-default backend is opt-in.
| Backend | Mode | Install | Namespaces | Lexical | Configure with |
| ------- | ---- | ------- | :--------: | :-----: | -------------- |
| `chroma` _(default)_ | Local (embedded) | bundled | – | ✓ | – |
| `sqlite_exact` | Local (exact NumPy) | bundled | – | ✓ | – |
| `rust_exact` | Local (native vectors) | wheel / compiled | – | ✓ | – |
| `milvus` | Local (Lite) · Server opt-in | `mempalace[milvus]` | ✓ | ✓ | `MEMPALACE_MILVUS_URI` |
| `qdrant` | Server (REST) | bundled | ✓ | ✓ | `MEMPALACE_QDRANT_URL` |
| `pgvector` | Server (Postgres) | `mempalace[pgvector]` | ✓ | ✓ | `MEMPALACE_PGVECTOR_DSN` |
Select with `--backend <name>`, `MEMPALACE_BACKEND=<name>`, or
`"backend": "<name>"` in `config.json`. `rust_exact` uses the exact same `sqlite_exact.sqlite3` file on disk as `sqlite_exact` with zero data migration. See [native installation and vector CLI usage](crates/README.md) for the separately distributed wheel and executables.
### Native vector search
`rust_exact` and the standalone `mempalace-native` CLI scan the same `sqlite_exact` database with a native Rust engine. The `rust_exact` adapter falls back to the Python backend for complex filters, requests for returned embeddings, and installs without the native extension; the `mempalace-native` executable is Rust-only and has no Python fallback. No benchmark figures are published for this release; `mempalace-native bench --db <sqlite_exact.sqlite3>` measures it on your own data. See [`crates/`](crates/) for the core workspace, PyO3 bindings, and native CLI.
## Quickstart
```bash
# 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
# Score how well organized the palace is (read-only, safe while the MCP server runs)
mempalace audit
```
For Claude Code, Gemini CLI, [Antigravity](https://mempalaceofficial.com/guide/antigravity.html),
MCP-compatible tools, and local models, see
[mempalaceofficial.com/guide/getting-started](https://mempalaceofficial.com/guide/getting-started.html).
---
## Benchmarks
All numbers below are reproducible from this repository with the commands
in [`benchmarks/BENCHMARKS.md`](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`](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:**
```bash
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
```
---
## 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](https://mempalaceofficial.com/concepts/knowledge-graph.html).
## MCP server
47 MCP tools cover palace reads/writes, knowledge-graph operations,
cross-wing navigation, drawer management, agent diaries, and agent
coordination (logstream events + artifact handoffs). Installation
and the full tool list:
[mempalaceofficial.com/reference/mcp-tools](https://mempalaceofficial.com/reference/mcp-tools.html).
## 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](https://mempalaceofficial.com/concepts/agents.html).
## Auto-save hooks
Auto-save hooks for **Claude Code, Codex CLI, and Cursor IDE** save
periodically and before context compression:
- Claude Code + Codex →
[mempalaceofficial.com/guide/hooks](https://mempalaceofficial.com/guide/hooks.html)
- Cursor IDE (adds session-start recall and a transcript snapshot before
compaction) →
[mempalaceofficial.com/guide/cursor-hooks](https://mempalaceofficial.com/guide/cursor-hooks.html)
If you are installing under time pressure, start with the
[Claude Code retention setup checklist](https://mempalaceofficial.com/guide/claude-code-retention.html):
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.
---
## Requirements
- Python 3.10+
- A vector-store backend (ChromaDB by default)
- ~300 MB disk for the embedding model. Pick it with `mempalace init --embedding-model`: `embeddinggemma` (embeddinggemma-300m, multilingual, 100+ languages, recommended) or `minilm` (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).
For GPU setup, see the [model/device support table](website/guide/configuration.md#embedding-models-and-devices).
On Apple Silicon, CPU is expected for MiniLM and EmbeddingGemma ONNX: MiniLM
disables CoreML, while EmbeddingGemma excludes it from automatic selection to
avoid invalid vectors. Experimental [EmbeddingGemma 2](docs/embeddinggemma2.md)
supports PyTorch MPS with health checks and CPU fallback. Changing models on an
existing palace requires re-embedding; changing only the device does not.
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](CONTRIBUTING.md).
## License
MIT — see [LICENSE](LICENSE).
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