Igor Lins e Silva
552e9927b7
Lands the read-side contract so third-party adapter authors (@Perseusxrltd, @JakobSachs, @adv3nt3, @zendesk-thittesdorf, @mfhens, @roip, @MrDys) have a stable target matching what RFC 001 §10 landed on the write side in #995. Scope (this PR): - mempalace/sources/base.py: BaseSourceAdapter ABC with kwargs-only ingest() / describe_schema() and default is_current() / source_summary() / close() (§1.1–1.2). Typed records: SourceRef, SourceItemMetadata, DrawerRecord, RouteHint, SourceSummary, AdapterSchema, FieldSpec (§1.3, §5.2). Error classes: SourceNotFoundError, AuthRequiredError, AdapterClosedError, TransformationViolationError, SchemaConformanceError (§2.7). Class-level identity contract: name / adapter_version / capabilities / supported_modes / declared_transformations / default_privacy_class (§2.1, §1.4, §1.5, §6). - mempalace/sources/transforms.py: reference implementations of the 13 reserved transformations (§1.4) — utf8_replace_invalid, newline_normalize, whitespace_trim, whitespace_collapse_internal, line_trim, line_join_spaces, blank_line_drop — as pure functions, plus identity shims for the six adapter-specific ones (strip_tool_chrome, tool_result_truncate, tool_result_omitted, spellcheck_user, synthesized_marker, speaker_role_assignment) that the conversations adapter will override when migrated. get_transformation(name) resolves by reserved name. - mempalace/sources/registry.py: entry-point discovery via importlib.metadata.entry_points(group="mempalace.sources") + explicit register()/unregister() surface (§3.1–3.2). resolve_adapter_for_source() implements the §3.3 priority order; crucially, no auto-detection on the read side (§3.3 is explicit about that — user intent never inferred from on-disk artifacts). - mempalace/sources/context.py: PalaceContext facade (§9) bundling the drawer/closet collections, knowledge graph, palace path, adapter identity, and progress hooks core passes into adapter.ingest(). upsert_drawer() applies the spec-mandated adapter_name/adapter_version stamps from §5.1. skip_current_item() signals laziness; emit() dispatches to hooks and swallows hook exceptions. - mempalace/knowledge_graph.py: add_triple() gains optional source_drawer_id and adapter_name kwargs (§5.5). Backwards-compatible column migration auto-adds the new columns on open of a pre-RFC 002 palace (PRAGMA table_info then ALTER TABLE ADD COLUMN), matching the pattern used for any new palace-side provenance fields. - pyproject.toml: mempalace.sources entry-point group declared. Empty on the first-party side for now — miners migrate in a follow-up; the group being present means third-party packages can begin registering today. Out of scope (explicit follow-ups): - miner.py → mempalace/sources/filesystem.py. Behavior-preserving rename that also moves READABLE_EXTENSIONS, detect_room(), detect_hall() into the adapter (§9). Larger refactor; lands separately. - convo_miner.py + normalize.py → mempalace/sources/conversations.py. The format-detection if-chain in normalize.py becomes per-format plugins; declared_transformations enumerates what the current pipeline already does to source bytes (§1.4 existing-code mapping). - Closet post-step wired into the conversations adapter (§1.7). - CLI --source flag + --mode deprecation alias (§3.3). - MCP mempalace_mine tool source parameter. - AbstractSourceAdapterContractSuite (§7.1–7.3): byte-preservation round- trip and declared-transformation round-trip tests. - Privacy-class floor enforcement (§6.2); depends on #389 for secrets_possible scanning. Tests: 1018 passed (up from ~990 on develop), +27 targeted tests covering the ABC instantiation rules, typed records, all reserved transformations, the registry register/get/unregister surface, PalaceContext upsert + skip + emit semantics, and both the new KG provenance kwargs and backwards- compatible legacy-schema migration. Refs: #989 (RFC 002 tracking), #990 (RFC 002 spec), #995 (RFC 001 §10 cleanup — sibling PR on the write side).
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 ~/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.
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.