Igor Lins e Silva
035fe6d658
Addresses issues found while reviewing the initial phase-2 implementation against real data: **Bug: uncertain bucket starved from the LLM.** `discover_entities` was dropping the regex-uncertain bucket whenever real git/manifest signal existed — which is exactly when `--llm` is most useful for cleaning up prose noise. The uncertain candidates never reached the refinement step. Fixed: only drop when `llm_provider is None`. **Context collection: word boundaries, not substring.** `_collect_contexts` used substring matching on lower-cased lines, so the name "Go" matched "good", "going", "forgot". Switched to a `(?<!\w)…(?!\w)` regex so short names only match at token boundaries. **Authoritative-source detection replaces confidence threshold.** Previously the refinement step skipped entries with `confidence >= 0.95` to avoid second-guessing manifest-backed projects. That threshold was fragile — the regex detector produces 0.99 confidence for things like `code file reference (5x)` on framework names (OpenAPI, etc.), so those skipped the LLM despite being regex-only noise. New helpers `_is_authoritative_person` / `_is_authoritative_project` look at the actual signal strings (commits, package.json, etc.) to decide. **Now also refines regex-derived people.** After #1148's high-pronoun-signal fix, the regex detector can promote non-people to the `people` bucket (e.g. a capitalized common noun that happened to appear near pronouns). The LLM now gets a chance to clean those up, while git-authored people are still skipped. **Robust JSON extraction.** Small local models routinely wrap JSON output in prose ("Sure, here's the classification: {…}"). The previous code-fence stripper failed on that. `_extract_json_candidates` now does balanced-bracket extraction with string-aware quote handling, so it recovers JSON from: - raw responses - markdown fenced blocks - JSON embedded inside surrounding text - multiple candidate objects/arrays **Prompt guidance for frameworks vs user projects.** Added an explicit instruction: frameworks, runtimes, APIs, cloud services, and third-party vendors (Angular, OpenAPI, Terraform, Bun, Google, etc.) are TOPIC unless the context clearly says it's the user's own codebase. Directly addresses a false-positive pattern observed during dev runs. **Defensive mtime.** `convo_scanner._safe_mtime` catches OSError during `stat()` — permission changes, filesystem races, broken symlinks — and sorts the affected file to the end of the newest-first order rather than crashing the scan. **Cosmetic:** merged two adjacent f-strings on the same line in `backends/chroma.py` and `llm_client.py` (no behaviour change). 15 new tests cover the OSError fallback, word-boundary matching, JSON extraction variants, authoritative-source helpers, refining high- confidence regex projects, and end-to-end LLM refinement preserving the uncertain bucket.
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.