b06cf2452e
Five fixes for issues called out by the gemini-code-assist[bot] review.
Each gets a regression test that locks in the correction.
1. CRITICAL: marker-cleanup watcher used POSIX `wait` on a sibling pid
(save hook). bash `wait` only works on direct children of the
calling shell — the `( wait $MINE_PID ... ) &` subshell runs as a
sibling of MINE_PID, so wait fails immediately and the pending
marker is deleted within milliseconds, defeating the concurrency
guard. Replace with `while kill -0 $MINE_PID; do sleep 1; done`,
which queries pid existence regardless of parent-child relationship.
Test: test_save_hook_marker_watcher_uses_kill_polling.
2. Bare `mempalace` console-script invocation in the save hook fails
when the venv's bin/ is not on the hook's PATH (e.g. uv tool
install in some configurations, manually managed virtualenvs).
Switch to `"$MEMPAL_PYTHON_BIN" -m mempalace mine ...` so the
resolved interpreter runs the package directly via
mempalace/__main__.py. Tests:
test_save_hook_uses_python_module_invocation,
test_save_hook_missing_mempalace_python_module_does_not_crash.
3. Same issue in the wake hook's inner Python helper. Switch
`['mempalace', 'wake-up', ...]` to `[sys.executable, '-m',
'mempalace', 'wake-up', ...]` — sys.executable is the same
interpreter that resolved MEMPAL_PYTHON in lib/common.sh.
Test: test_wake_hook_uses_sys_executable_module_invocation.
4. The Python parser in lib/common.sh wrapped `json.load` in
`try/except` and silently fell back to `data = {}`. The script
then printed the `__MEMPAL_PARSE_OK__` sentinel even on parse
failure, so the bash sentinel-check on the caller side
(`[ "$_marker" != "__MEMPAL_PARSE_OK__" ]`) never triggered the
defense-in-depth `input parse failed` branch. Remove the
try/except so the exception propagates, Python exits non-zero,
and the sentinel is omitted on bad JSON. The traceback still
lands in antigravity_last_python_err.log for debugging.
Test: test_common_sh_parser_omits_sentinel_on_malformed_json.
5. `mempal_save_interval()` failed to strip leading zeros from
MEMPAL_SAVE_INTERVAL. Values like "08" or "09" then crashed the
modulo step `$((COUNT % INTERVAL))` because bash arithmetic
parses tokens starting with `0` as octal, and 8/9 are not valid
octal digits ("value too great for base"). Strip leading zeros
while preserving the literal "0" (which is then floored to 15).
Test: test_save_hook_handles_leading_zero_save_interval (4 cases).
Plus one cosmetic fix in install.sh: removed a no-op `(cd "$OLDPWD"
2>/dev/null || cd .) >/dev/null 2>&1` line in mempal_absolutize().
The subshell cd doesn't affect the parent shell, and the installer
never cd's in the main shell anyway, so $PWD is already correct.
Verification:
* 9 new regression tests, all 65 antigravity tests pass
* full repo: 2323 passed (was 2314), 3 skipped, 1 unrelated warning
* ruff check + ruff format --check both clean across 139 files
* bash -n clean on all four shell files
* clean reinstall to ~/.gemini/config/plugins/mempalace/ succeeds
* idempotent re-run produces zero file writes (cmp-gated)
* both hooks return {} exit 0 with synthetic camelCase stdin
Co-authored-by: Cursor <cursoragent@cursor.com>
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||
|---|---|---|
| .agents/plugins | ||
| .antigravity-plugin | ||
| .claude-plugin | ||
| .codex-plugin | ||
| .devcontainer | ||
| .github | ||
| assets | ||
| benchmarks | ||
| docs | ||
| examples | ||
| hooks | ||
| integrations/openclaw | ||
| landing | ||
| mempalace | ||
| tests | ||
| tools | ||
| website | ||
| .gitignore | ||
| .pre-commit-config.yaml | ||
| .python-version | ||
| AGENTS.md | ||
| CHANGELOG.md | ||
| CLAUDE.md | ||
| CONTRIBUTING.md | ||
| LICENSE | ||
| MISSION.md | ||
| README.md | ||
| ROADMAP.md | ||
| SECURITY.md | ||
| openarena-claim.txt | ||
| pyproject.toml | ||
| uv.lock | ||
README.md
[!CAUTION]
🚨 CRITICAL SECURITY WARNING: BEWARE OF SCAMS
MemPalace has NO other official websites.
The ONLY official sources are:
- This GitHub repository
- The PyPI package
- The docs at mempalaceofficial.com
ANY other domain (including
.tech,.net, or other.comvariants) 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.
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
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, Antigravity, 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
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.
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.
Requirements
- Python 3.9+
- A vector-store backend (ChromaDB by default)
- ~300 MB disk for the embedding model. Onboarding (
python -m mempalace.onboarding) offersembeddinggemma-300m(multilingual, 100+ languages, recommended) orall-MiniLM-L6-v2(English-only, ~30 MB). See the docstring atmempalace/embedding.pyfor 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.