lean-memory 0.1.0 — first public release

[0.1.0] - 2026-07-12

First public release. lean-memory is an embedded, local-first agent-memory
engine: one SQLite file per namespace, hybrid dense+sparse retrieval with
rerank, and ADD-only supersession queryable at any past point in time
(as_of). No server, no daemon, no mandatory cloud key.

Added

• MCP server exposing memory as three tools (memory_add, memory_search,
  memory_clear) for Claude Code, Claude Desktop, and other MCP clients.
  Canonical install pip install 'lean-memory[mcp,models,extract]'
  opportunistically upgrades each backend whose extra is present (real embedder
  • reranker via [models], GLiNER2 extraction via [extract]) and otherwise
    falls back to deterministic offline stubs. Two-minute quickstart with
    copy-paste Claude Code / Claude Desktop config and a demo GIF.
• Memory.search(now=...) — recency decay now anchors to a caller-supplied
  timestamp, so the 0.2 recency term is no longer dead on historical corpora.
• Point-in-time queries via as_of (epoch ms) with is_latest_only=False.
• CI + release workflows (GitHub Actions): offline test matrix on
  ubuntu/macOS × Python 3.10/3.13, plus build-and-publish to PyPI on v* tag
  via Trusted Publishing.
• PyPI metadata: keywords, classifiers, and project URLs.

Changed

• Default embedder is now the ungated Qwen3-Embedding-0.6B (was a gated
  Gemma model that broke the [models] first run). Reranker default is
  Ettin-32M; both are pinned ungated and covered by regression tests.
• Escalation engine recalibrated on real conversational turns. Endpoint-
  scoped coreference/ellipsis detection replaces the whole-text pronoun scan
  (coreference escalations dropped from 65.6% to effectively nil on real
  turns), and the prior_entity trigger was retired (subject re-mention is
  normal discourse, measured at 52.8% of candidates). At the re-frozen
  (typing_threshold=0.4, conf_threshold=0.4) operating point, escalation on
  the real LongMemEval probe is 14.6% (was ~96% pre-fix), with the residual
  being irreducible inferential-edge (derives) escalations. BET-2 three-gate
  revalidation PASSes at this operating point.
• Extraction granularity calibrated — GLiNER candidate threshold set to
  0.4, cutting the extractor from ~8 facts/turn to ~3.7 so fact_text reads as
  facts rather than whole utterances.
• MCP server loads models lazily (first tool call rather than import) so a
  cold-cache spawn answers the MCP handshake immediately instead of blocking on
  a model download; search output is deduplicated.

Fixed

• Sparse BM25 retrieval arm now honors the as_of interval predicate.
• Known-entities handed to the router/typer are capped at the 100 most recent.

Install: pip install lean-memory · MCP quickstart: see the README

🤖 Generated with Claude Code