memstrand 0.2.1

Local-first memory for AI agents. Typed, scoped, and it never leaks one task into another.

Quickstart · Docs · Integrations

Why memstrand

Flat vector memory has three failure modes, and every agent hits all three:

• No types — a preference, a decision, and a guess all look the same to cosine similarity.
• No scope — a fact learned while testing Playwright leaks into an unrelated Mac disk-cleanup session. (This actually happened. It's why this exists.)
• No provenance — nobody knows whether a memory was said, inferred, or corrected, or how confident the agent was.

memstrand fixes all three: every memory is typed (13 categories), scoped (session/task/host, a hard filter — not a ranking signal), and carries provenance (how it was learned, confidence, expiry). One daemon (memstrand serve) shares all of that across Claude Code, Cursor, Open WebUI, and your own scripts — over MCP, REST, and an OpenAI-compatible proxy — backed by an embedded, on-disk store. No Docker, no cloud, no API keys.

Quickstart

pip install memstrand

from memstrand import Category, Memory, Provenance

mem = Memory.in_memory()

# A fact scoped to one task...
mem.remember(
    "Playwright installed as an agent-testing task dependency",
    category=Category.fact,
    provenance=Provenance.explicit_statement,
    task_id="playwright-test",
)

# ...and a global preference
mem.remember(
    "Chiran prefers a natural, unstylized look in AI portraits",
    category=Category.preference,
    provenance=Provenance.explicit_statement,
    confidence=0.95,
)

# An unrelated session asks about testing setup.
# The Playwright fact does NOT leak across the task boundary:
for hit in mem.recall("testing setup"):
    print(hit["category"], "-", hit["content"])
# preference - Chiran prefers a natural, unstylized look in AI portraits

# Same query WITH the task scope sees it:
for hit in mem.recall("testing setup", task_id="playwright-test"):
    print(hit["category"], "-", hit["content"])
# fact - Playwright installed as an agent-testing task dependency

Memory.in_memory() runs Qdrant in :memory: mode and the graph/session/ keyword stores in a temp SQLite file — nothing touches disk and nothing persists past the process. Embeddings are local ONNX (fastembed) by default, downloaded once on first use.

Make it your second brain

memstrand serve                                                    # one daemon: MCP + REST + web UI + OpenAI proxy
claude mcp add --transport http memstrand http://127.0.0.1:7777/mcp  # Claude Code
# Open WebUI -> Admin Settings -> Connections -> OpenAI API -> Base URL http://127.0.0.1:7777/v1
memstrand ingest ~/notes --watch                                   # keep your notes/Obsidian vault synced in

Full setup recipes (Ollama/llama.cpp/LM Studio, Cursor, launchd/systemd autostart, cron backups) are in docs/integrations.md.

Features

• 100% local, zero API keys — embedded Qdrant + SQLite, local ONNX embeddings by default
• Typed (13 categories) + full provenance (how it was learned, confidence, expiry)
• Hard scope isolation — session/task/host is a filter applied before ranking, not a signal
• Hybrid search (dense + BM25) with absolute, non-normalized relevance + optional reranking
• Learns from your sessions — Claude Code hooks + OpenAI-proxy distillation, not just manual writes
• Ingests notes, Obsidian vaults, and PDFs, with --watch for continuous sync
• Web UI for browsing, searching, and editing memories
• MCP + REST + OpenAI-compatible proxy, all from one daemon
• Contradictions and corrections are edges, never deletes — old memories stay queryable
• export / import / backup / restore for full portability

How it compares

How it works

flowchart LR
    Clients["Claude Code · Cursor · Open WebUI · scripts"] -->|MCP / REST / v1 proxy| Daemon["memstrand serve"]
    Daemon --> Vector[(Vector\nQdrant)]
    Daemon --> Graph[(Graph\nSQLite/Kuzu)]
    Daemon --> Keyword[(Keyword\nFTS5)]
    Daemon --> Session[(Session\nSQLite)]

Write (remember()) embeds once and writes vector+graph+keyword under one scope-aware content-hash id. Distill (improve()) extracts typed atoms from raw session turns via your local LLM and promotes them with supersedes/contradicts edges. Read (recall()) applies the hard scope filter first, then blends hybrid relevance with confidence, recency, provenance, and access frequency. Consolidate expires, decays, dedups, and replays on a schedule. Full detail, including the ranking formula and FSRS/Hebbian dynamics, in docs/architecture.md.

Docs

• docs/configuration.md — every MEMSTRAND_* environment variable
• docs/architecture.md — stores, write/distill/read/consolidate paths, ranking formula
• docs/python-api.md — the full Memory method surface
• docs/cli.md — every CLI command/flag, plus REST/MCP reference and security notes
• docs/integrations.md — Ollama/llama.cpp/LM Studio, Claude Code, Cursor, Open WebUI, autostart, backups, Obsidian

Acceptance test

Any memory system should pass these three before you trust it (rubric adapted from a Cognee ad, because it was right):

1. Remember me in a brand-new chat without re-pasting anything.
2. Search by meaning, not keywords.
3. Connect facts across sessions — and don't leak facts across unrelated tasks (the Playwright test).

examples/quickstart.py runs all three against a persistent store; tests/test_memory_flow.py runs the same rubric in CI.

Roadmap

• Typed schema, scoped retrieval, provenance ledger
• Session-commit distillation (extract → dedup → judge → promote)
• Graph neighbor hydration, Hebbian strengthen/decay, FSRS + replay consolidation
• One daemon: MCP + REST + web UI + OpenAI-compatible memory-injecting proxy
• Hybrid (dense + BM25) retrieval + optional cross-encoder reranking
• Markdown/Obsidian/PDF ingestion with --watch
• Claude Code hooks, export/import/backup/restore
• Automated migration tooling from 0.1.x's working-directory layout — see CHANGELOG.md (good first issue)
• Multi-user auth beyond a single shared bearer token (good first issue)

Contributions welcome — see CONTRIBUTING.md for dev setup, test philosophy, and how to add a store backend.

Acknowledgments

Standing on shoulders, with credit:

• Memanto (moorcheh-ai/memanto) — the 13-category typed schema, provenance, and confidence model. Their product is cloud-only; this is the schema reimplemented local-first.
• Cognee (topoteretes/cognee) — the pipeline patterns: fast session cache → distillation, joint graph+vector writes, contradictions as additive edges.
• ZenBrain (zensation-ai/zenbrain) — per-fact FSRS, deterministic write routing, replay consolidation, Hebbian edges.
• Mem0 — the ADD/UPDATE/DELETE/NONE extraction resolution pattern.

License

MIT — see LICENSE.

If memstrand saves you from re-explaining yourself to your agent, a ⭐ helps others find it.