Felipe Truman
99b820cb42
Addresses the six Copilot review comments on the initial commit. 1) #6 (critical) — mcp_server.py `_get_collection` bypassed ChromaBackend The MCP server creates its palace collection directly via `chromadb.PersistentClient.get_or_create_collection` in `_get_collection`, not through `ChromaBackend.get_collection`. That path was missing the `hnsw:num_threads=1` metadata, so the primary crash surface for #974 and #965 was untouched by the original patch. Fixed by passing `hnsw:num_threads=1` at the mcp_server create site too. Documented in a code comment that the setting is only honored at creation time — existing palaces created before this fix still need a `mempalace nuke` + re-mine to gain the protection. 2) #3 — mine_global_lock over-serialized mines across unrelated palaces Replaced the single global lock file `mine_global.lock` with a per-palace lock keyed by `sha256(os.path.abspath(palace_path))` (`mine_palace_<hash>.lock`). Mines against the same palace still collapse to a single runner (the correctness boundary), but mines against *different* palaces are now free to run in parallel. `mine_global_lock` is kept as a backward-compatible alias for `mine_palace_lock` so any external callers that imported the previous name keep working. 3) #1 — hook_precompact swallowed OSError but not subprocess.TimeoutExpired `subprocess.run(..., timeout=60)` raises `TimeoutExpired` on slow palaces. The previous `except OSError` clause didn't catch it, so the hook could raise and fail to emit any JSON decision — leaving the harness without a block/passthrough signal. Fixed by catching `(OSError, subprocess.TimeoutExpired)` together and always falling through to the block decision so the hook reliably emits a response. 4) #2 + #4 — tests - tests/test_hooks_cli.py: added `test_precompact_first_two_attempts_block`, `test_precompact_passes_through_after_cap`, and `test_precompact_counter_is_per_session` to lock in the #955 deadlock fix. - tests/test_palace_locks.py (new): covers `mine_palace_lock` single-acquire, reuse-after-release, cross-process serialization on the same palace, non-interference across different palaces, path normalization, and the `mine_global_lock` back-compat alias. 5) #5 — known limitation, documented but not auto-fixed Copilot suggested detecting collections missing `hnsw:num_threads=1` and calling `collection.modify(metadata=...)` to retrofit existing palaces. Verified against chromadb 1.5.7: `modify(metadata=...)` replaces metadata rather than merging, and re-passing `hnsw:space="cosine"` then raises `ValueError: Changing the distance function of a collection once it is created is not supported currently.` The HNSW runtime configuration (`configuration_json`) also does not expose `num_threads` in chromadb 1.5.x, so the flag appears to be read only at creation time. Rather than paper over the limitation with a best-effort `modify` that silently drops `hnsw:space`, documented in the mcp_server comment that pre-existing palaces need a `mempalace nuke` + re-mine to gain the protection. Fresh palaces are always protected. Testing - pytest tests/test_palace_locks.py tests/test_hooks_cli.py tests/test_backends.py tests/test_cli.py → **98 passed, 0 failed**. - Runtime validation with two concurrent `mempalace mine` calls: - Different palaces → both complete in parallel ✓ - Same palace → one completes, the other exits with "another `mine` is already running against <palace> — exiting cleanly." ✓
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.
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
pip install mempalace
mempalace init ~/projects/myapp
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.
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
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.
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.