Local-first AI memory distributed as a Python CLI/library plus an MCP server. Stores conversation and project history as verbatim text — it explicitly does not summarize, extract, or paraphrase — and retrieves it with semantic search over a structured index where people/projects are 'wings', topics are 'rooms', and original content lives in 'drawers' so searches can be scoped rather than run flat. Bundles a temporal entity-relationship knowledge graph with validity windows.
- Storage
- Verbatim drawers indexed in a pluggable vector backend (ChromaDB by default; sqlite_exact for local exact-vector checks, plus opt-in Qdrant and pgvector backends behind a single storage contract). A temporal entity-relationship knowledge graph is backed by local SQLite. Nothing leaves the machine unless an external backend is explicitly configured.
- Retrieval
- Semantic search scoped by wing/room, with an optional hybrid pipeline (keyword boosting, temporal-proximity boosting, preference-pattern extraction) and an optional LLM-reranker over the top-20 retrieved sessions. The raw semantic path requires no API key, no cloud, and no LLM.
- Self-host
- Self-host: trivial
- License
- MIT
- Pricing
- Open-source (MIT) Python package, free to self-host; raw semantic retrieval needs no API key or cloud. Docs at mempalaceofficial.com; no paid tier identified. · Free / OSS
- GitHub stars
- 56,697
- Last release
- 2026-06-23
- Last commit
- 2026-06-28
- First catalogued
- 2026-06-28
Strengths
- Verbatim, no-extraction storage — the original text is never paraphrased away, so retrieval is auditable against the source
- Structured palace index (wings/rooms/drawers) lets searches be scoped instead of run against a flat corpus, plus a temporal SQLite knowledge graph
- Pluggable vector backend (ChromaDB default; sqlite_exact, Qdrant, pgvector) and a 35-tool MCP server; raw semantic recall runs with no API key or cloud
Watch out
- README headlines self-reported retrieval benchmarks (96.6% R@5 raw / 98.4% held-out on LongMemEval, plus LoCoMo/ConvoMem/MemBench) — record those via harvest-benchmarks with selfReported:true, not on this card
- Very high star count (~57k) accumulated fast for a young project (created 2026-04); the README itself warns of impostor domains, so treat third-party mirrors with care
- Hybrid/rerank quality figures are vendor-reproducible but not independently audited
Best for
- Local-first agent memory where verbatim, source-traceable recall and scoped semantic search matter more than fact extraction
Benchmark results
No sourced results yet.
Sources
- MemPalace README (vendor)
- GitHub API repo metadata (stars, MIT license, v3.5.0 release) (third-party)
Last verified 2026-06-28 · updated by discover-frameworks