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memware

Memory for AI agents that only remembers the latest truth.

memware is one SQLite file with two stores:

  • turns — immutable evidence. Every prompt and answer from past sessions, split into ~400-token passages and indexed with FTS5. Recall ranks passages and quotes only the matching ones; reading a session back returns whole turns. BM25 × recency × use, no model in the loop.
  • beliefs — a bi-temporal ledger of facts. A new value for the same (subject, relation) supersedes the old one. Recall only ever returns the currently valid belief; history is kept for audit and never reaches a prompt.

No daemon, no vector database, no LLM call at capture or read time. A 30-day corpus of a busy coding agent — 18k turns, 40k passages — indexes in about fourteen seconds into ~120 MB.

$ memware sync ~/.claude/projects --harness claude-code
{"added": 14348, "files": 1475}

$ memware assert "api" "listens on port" "8443" --source "session 3f2a, turn 41"
{"outcome": "superseded", "belief_id": 2, "incumbent_id": 1}

$ memware recall "which port does the api use" --what beliefs
api listens on port 8443            # 8080 is in the ledger, retired, and never surfaces

Why

Agent memory systems that rewrite what they remember degrade: continuous LLM consolidation can push utility below having no memory at all (Useful Memories Become Faulty When Continuously Updated by LLMs). And embeddings cannot tell a contradicted fact from a rephrased one — AUROC 0.59 — so vector stores serve stale facts 15–40% of the time on evolving knowledge (Temporal Validity in Retrieval Memory).

memware borrows four mechanisms from human memory research and keeps them deliberately small:

mechanism in the brain in memware
evidence ≠ belief hippocampus vs neocortex (complementary learning systems) turn table is append-only; belief table is separate
update on surprise reconsolidation driven by prediction error memware assert at the moment an agent notices a conflict
only the latest understanding reconsolidated traces overwrite in place deterministic supersession keyed on (subject, relation), ordered by event time
need-probability recall Anderson & Schooler 1991 / ACT-R activation bm25 × (1+age)^-d × (1 + w·ln(1+uses))

Full rationale and citations: docs/design.md.

Install

The Claude Code plugin's hooks call memware as a bare command, so the CLI must be on the PATH your shell uses — install it as a tool, not into a project virtualenv:

uv tool install "memware[mcp]"     # recommended
# or
pipx install "memware[mcp]"

Then confirm the shim resolves (if this prints nothing, the hooks will silently do nothing):

memware --version
which memware
Plain pip install

pip install "memware[mcp]" works for library and CLI use, but a plain pip install into a project or conda environment usually leaves memware off the PATH that Claude Code's hooks run under — use uv tool or pipx (above) for the plugin, or install into an environment that is always active. memware (core) omits the MCP server; drop [mcp] only if you do not want the MCP tools.

Use it from Claude Code

claude plugin marketplace add ericwalisko/memware
claude plugin install memware@memware
claude mcp add -s user memware -- memware-mcp   # optional tools; -s user = every project, not just this dir

Backfill your existing sessions (optional, once). The plugin only captures new sessions; index the transcripts already on disk so recall works over past work from day one:

memware backfill                 # indexes ~/.claude/projects (idempotent; ~5 s for a month)

Prefer a guided first run? memware setup walks through the backfill and backups together, asks whether derive may run automatically, and prints the operating guidance. Run it on a fresh install or after an upgrade. memware setup --yes accepts the defaults non-interactively and never switches derive on.

The belief ledger starts empty and is not backfilled — beliefs are derived, not stored in transcripts. It fills as you work (via the remember tool) and through memware derive, which mines the indexed transcripts for durable facts — see Deriving beliefs. Transcript recall is what backfill gives you immediately, and it is where most of the value is.

Requires the memware CLI on your PATH (see Install). Hooks: a SessionStart hook catches up any session whose SessionEnd was skipped (some environments force-kill Claude Code — a worktree manager may SIGKILL it — and a kill cannot run SessionEnd); a second one injects memware digest, a short block with this project's recent sessions and beliefs and a line pointing at recall; SessionEnd/PreCompact sync the transcript into the index; an optional UserPromptSubmit hook injects the handful of currently valid beliefs whose subject the prompt names (beliefs only — transcript search is on demand through the MCP tools). Set MEMWARE_DB to move the store. Start a session with MEMWARE_NO_CAPTURE=1 to keep it out of the index and the backup mirror: the hooks list its transcript, and every sync and backup skips what is listed. A session that runs no memware hook cannot be recognised that way. See docs/integrations.md and docs/keeping-memory-clean.md.

Deriving beliefs

memware derive --plan     # no network: every excerpt a run would send, and where
memware derive            # dry run: sends the excerpts to the model, prints the facts, files nothing
memware derive --apply    # files them; runs again later from where it stopped

derive reads every transcript turn indexed since its last run, has a model turn the sentences that look like facts into (subject, relation, value) triples, and files only the triples that pass a deterministic check: every word of the value must appear in the excerpt, so a model cannot introduce a fact the evidence does not contain. Derived beliefs carry reliability 0.5, below anything you stated yourself, so a contradiction lands in memware review rather than on top of your belief.

The default provider is the Claude Code CLI on your own subscription (claude -p, Haiku), so there is nothing to configure. --provider openai sends the extraction to any OpenAI-compatible endpoint instead (OPENAI_BASE_URL / OPENAI_MODEL / OPENAI_API_KEY).

A dry run is not offline: it sends the excerpts to that provider and skips only the write. --plan is the view that sends nothing. It lists every excerpt a run would send with its source pointer, then the session, excerpt, character and model-call counts and the destination, and it works before claude or OPENAI_* is set up. Read it before you turn derive on for transcripts that must not leave the machine.

You do not need an always-on machine. The plugin can run it for you on session start, at most once a day. memware setup is where you switch that on: it shows where the excerpts go before it asks. memware config derive.auto true sets the same switch directly. Other options — a macOS LaunchAgent that catches up after sleep, a systemd timer with Persistent=true, plain cron — are in docs/scheduling.md.

Use it from Hermes Agent

integrations/hermes/memware/ is a memory-provider plugin built on Hermes's MemoryProvider ABC — prompt-time belief prefetch, non-blocking turn capture, and memware_recall / memware_remember tools — sharing one store with Claude Code.

The supersession rule

same key, same value   → reinforce (reliability rises, use is counted)
same key, newer value  → supersede: incumbent gets valid_to = new.valid_from
same key, older value  → filed as history; the timeline stays consistent
weaker challenger      → parked as a candidate and sent to review

Ordering is decided by valid_from (when the evidence says it became true), never by insertion order — so a backfill converges to the same state in any order, twice, or in batches. Three policies: auto (last writer by event time), gate_conflicts (default: a less reliable challenger goes to review), await_confirmation.

Recall is keyword search; the agent supplies the meaning

The index is FTS5/BM25 — fast, model-free, and literal. The recall tool therefore takes several phrasings and fuses them by reciprocal rank, so a tool-calling agent puts its own reasoning into retrieval at call time (synonyms, related concepts, the literal value it expects), the same way it would issue a few grep or web-search queries:

recall(queries=["which port does the api listen on", "api port", "8443", "gateway listen port"])

Byte-identical hits collapse to a single slot, so a prompt captured on many days — a scheduled job's own preamble, say — never crowds out distinct evidence; the turns stay in the store and a session still reads back whole.

Prompt-time injection (the hooks) stays deterministic and only injects beliefs whose subject the prompt names.

Backups and the wipe trap

Transcripts are deleted by the OS after ~30 days, so an aged session lives only in the store — back it up, and never wipe-and-re-backfill (backfill only re-indexes transcripts still on disk). memware guards this: migrations snapshot first, and backfill warns if a backup is larger than the store. Once a destination is set, backups happen automatically at session boundaries — the SessionStart hook takes a throttled snapshot (at most once every ~20h), and a clean SessionEnd does too. No cron; immune to a laptop sleeping through a scheduled time, and — because the start hook always runs — also to a session being force-killed (a worktree/pane manager that SIGKILLs Claude Code never runs SessionEnd).

memware setup                              # guided: index sessions, pick a folder, take a first backup
memware backup                             # tiered snapshot (1/3/7/14-day) + transcript mirror
memware restore --latest                   # after a wipe, restore — do not re-backfill
memware nuke                               # delete everything, typed-confirmation guarded

Full guide: docs/backup.md.

Keeping evaluations out of the evidence

Full guide: docs/keeping-memory-clean.md.

Headless runs write transcripts too, unless you pass --no-session-persistence to claude -p. Set MEMWARE_NO_CAPTURE=1 in any run you do not want indexed: the plugin's hooks list its transcript so no sync indexes it and no backup mirrors it, and the Hermes provider captures nothing. That needs a memware hook to run in the session, so also put [memware-eval] in evaluation prompts, and use memware-eval --corpus ROOT --db scratch.db --beliefs-from ~/.memware/memware.db to judge retrieval against a store that excludes them. memware prune --containing TEXT un-indexes runs that already slipped in; copies already mirrored to a backup folder have to be deleted there by hand. For a durable filter that every sync and backup honours — including runs that predate a marker — list content signatures in ~/.memware/ignore-markers.txt (or MEMWARE_IGNORE_MARKERS); any transcript whose head contains one is never indexed or mirrored.

Reviewing contested supersessions

memware does not ship a UI. It ships a contract — ReviewBackend with publish() and collect() — plus two implementations: JSONL outbox/inbox files and a plain HTTP endpoint. Wire it to whatever you already use to make decisions.

memware review sync                       # outbox ~/.memware/review-outbox.jsonl
echo '{"review_id": 7, "decision": "approve"}' >> ~/.memware/review-inbox.jsonl
memware review sync                       # applied

Evaluation

memware-eval scores retrieval against a question set: does the right evidence surface, and does the stale value stay hidden? It needs no model, so results are reproducible. The protocol for end-to-end comparisons — agent alone vs agent + memware — is in docs/eval.md.

Editor and shell integration

memware ships no editor plugins — --plain (tab-separated, id-first) and --json are the integration surface, and everything is a copy-paste recipe on top of them. Shell completions come from memware completions zsh|bash|fish (needs the [shell] extra: uv tool install "memware[mcp,shell]"). --plain pipes cleanly to fzf/awk/cut:

memware recall "which port does the api use" --plain | fzf --delimiter='\t' --with-nth=10

Emacs, Vim, Neovim, an $EDITOR bulk-edit round-trip, and completion install steps are in docs/editor-integration.md.

Accessibility

memware emits no colour at all (so NO_COLOR is honoured by construction), and no information is ever carried by colour. Default output is screen-reader-friendly — labeled, one field per line, blank line between records; --plain and --json are the stable machine formats; and --ascii (auto-on in a non-UTF-8 locale) avoids glyphs a screen reader or terminal might mangle. Full statement: docs/accessibility.md.

Status

Alpha. The schema may change before 1.0; the ledger semantics will not.

License

MIT. See LICENSE.

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