lean-memory 0.1.3

Embedded, local-first agent memory with hybrid retrieval and ADD-only supersession.

lean-memory

Embedded, local-first agent memory. No server, no daemon, no mandatory cloud key.

Status (2026-07): working toward the first public launch (MCP-first). Roadmap and rationale: docs/superpowers/specs/2026-07-08-strategic-direction-design.md. Public benchmark runs (LongMemEval/LoCoMo) are deferred until after launch; the harness is complete (bench/phase2_*.py) and the engine flaws it exposed are fixed on this branch — see docs/phase2-learnings.md.

from lean_memory import Memory

mem = Memory(root="./data")

mem.add("user-42", "I work at Acme Corp.")
mem.add("user-42", "I now work at Globex.")          # supersedes Acme automatically

mem.search("user-42", "where does the user work?")   # → "I now work at Globex."

Facts are extracted from natural language, stored in a per-namespace SQLite file, and retrieved with hybrid dense+sparse search. Old facts are never deleted — they're superseded and queryable at any past point in time.

Install

pip install lean-memory

Runs fully offline out of the box. Optional extras unlock real model quality:

Extra	What it adds
lean-memory[models]	Real embedder + reranker (Qwen3-0.6B + Ettin-32M)
lean-memory[extract]	GLiNER2 candidate generation for richer extraction
lean-memory[llm]	Ollama-backed LLM typing pass
lean-memory[mcp]	MCP server bridge for Claude Desktop / Claude Code
lean-memory[examples]	Terminal demo agent (requires anthropic SDK)

Quickstart

from lean_memory import Memory

mem = Memory(root="./data")   # one SQLite file per namespace, stored under ./data/

# Store facts in natural language
mem.add("alice", "I work at Stripe.")
mem.add("alice", "I now work at Vercel.")   # supersedes Stripe automatically

# Retrieve — the superseded Stripe fact drops out; only the current one is returned
results = mem.search("alice", "what does Alice do for work?", k=3)
for hit in results:
    print(hit.fact.fact_text, hit.final_score)
# → I now work at Vercel. 0.89

# Point-in-time query — what was true at a specific moment?
mem.search("alice", "employer", as_of=1_700_000_000_000, is_latest_only=False)  # epoch ms

# Always close when done (flushes WAL)
mem.close()

Demo Agent

A terminal chatbot showing the full memory loop — add, retrieve, supersede, restart. The demo script lives in the repo (it is not installed with the package):

git clone https://github.com/Wuesteon/lean-memory && cd lean-memory
pip install -e '.[examples]'
export ANTHROPIC_API_KEY=sk-ant-...
python examples/chat.py                  # uses offline stubs by default
python examples/chat.py --namespace bob  # separate memory tenant, persists across restarts

No API key? The demo still runs — it echoes the retrieved memory context instead of calling Claude, so you can watch the engine work offline.

MCP Server — memory for Claude Code / Claude Desktop

Give any MCP agent persistent local memory: three tools (memory_add, memory_search, memory_clear), one SQLite file per namespace, nothing leaves your machine.

pip install 'lean-memory[mcp,models,extract]'

First run downloads three open models (~2.0 GB total: Qwen3-Embedding-0.6B

• Ettin-32M reranker for retrieval, plus GLiNER2-base (~0.8 GB) for real extraction — all ungated). Pre-warm once so your MCP client never waits on a download:

python -c "from lean_memory.embed.sentence_transformer import SentenceTransformerEmbedder; \
from lean_memory.retrieve.rerank import CrossEncoderReranker; \
SentenceTransformerEmbedder().embed_one('warm'); CrossEncoderReranker().score('warm', ['up']); \
from lean_memory.extract.gliner_extractor import Gliner2Generator; from lean_memory.types import Episode; \
Gliner2Generator().generate(Episode(namespace='w', raw='I work at Acme.', t_ref=0, source='user'))"

Claude Code:

claude mcp add lean-memory -- lean-memory-mcp

Claude Desktop — add to mcpServers (or copy examples/mcp_config.json):

{ "lean-memory": { "command": "lean-memory-mcp", "env": { "LM_DATA_ROOT": "~/.lean_memory" } } }

Data root: LM_DATA_ROOT (default ~/.lean_memory). Works offline-only too — the server opportunistically upgrades each backend that its extra is installed for ([models] → real embedder + reranker, [extract] → GLiNER2 extraction) and otherwise falls back to deterministic stub backends (fine for CI, semantically meaningless for real use — install [mcp,models,extract]).

What the optional [llm] extra buys. The canonical [mcp,models,extract] install has no LLM typing pass, so the ~15% of candidates that escalate — almost all of them inferential (derives) facts — are typed by a deterministic stub instead of a model. Assertional facts are unaffected; inference-type facts are effectively second-class on the default path. Adding [llm] (a local Ollama model) upgrades that escalated tier to real constrained typing. See ARCHITECTURE.md → Known Limitations.

Real Model Quality

The default backends are offline stubs — deterministic and dependency-free, but semantically meaningless. Swap in real models for production-quality retrieval:

pip install 'lean-memory[models]'

With Qwen3-Embedding-0.6B + Ettin-32M reranker, retrieval jumps from 1/5 to 4/5 on the internal benchmark with zero code changes.

For benchmark results, architecture decisions, and implementation status see ARCHITECTURE.md.

How It Works

Each mem.add() call runs a 4-pass hybrid extraction pipeline:

1. Rules — regex + dateparser for common predicates (works_at, lives_in, …)
2. GLiNER2 — open-vocabulary NER candidate generation (offline stub by default)
3. Router — recall-biased escalation: low-confidence, coreference, and inferential (derives) facts escalate to the LLM pass
4. LLM typing — constrained relation typing via a local Ollama model (stub by default)

Contradiction detection runs cheap-first (slot match → cosine → token subsumption → LLM). Conflicting facts are superseded, not deleted — the old fact stays with is_latest=False and a superseded_by pointer.

Retrieval fuses two-stage Matryoshka dense search (256-dim coarse KNN → 768-dim re-score) with BM25 sparse, applies RRF fusion, reranks with a cross-encoder, and scores with salience-decay (0.6·relevance + 0.2·recency + 0.2·importance).

Develop

git clone https://github.com/Wuesteon/lean-memory
cd lean-memory
python -m venv .venv && source .venv/bin/activate
pip install -e '.[dev]'
pytest -q    # full offline suite, no downloads

Project Layout

src/lean_memory/
  memory.py                   Memory facade — the public API
  types.py                    Episode / Fact / RetrievedFact types
  store/                      Store interface + SqliteStore (vec0 + FTS5)
  embed/                      Embedder interface, FakeEmbedder, SentenceTransformer
  extract/                    4-pass extraction pipeline
  retrieve/                   Reranker interface, retrieval pipeline
examples/
  chat.py                     Terminal demo agent
  mcp_config.json             Drop-in MCP client config
tests/                        offline test suite
bench/                        Retrieval quality + BET-2 ablation harnesses

License

Apache-2.0