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{"outcome": "superseded", "belief_id": 2, "incumbent_id": 1}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 and
prints the operating guidance — safe on a fresh install and after upgrading from a pre-0.2
(no-backups) version; memware setup --yes accepts the defaults non-interactively.
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, or a derive job you schedule).
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:
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, and MEMWARE_NO_CAPTURE=1 for any session you do not want indexed. See
docs/integrations.md and docs/keeping-memory-clean.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
end (~once a day) — no cron, and immune to a laptop sleeping through a scheduled time.
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. Set MEMWARE_NO_CAPTURE=1 in any run you do not want
indexed (hooks, the Hermes provider and memware sync --from-hook all honour it), 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. For a durable filter that every sync 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.
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.
Status
Alpha. The schema may change before 1.0; the ledger semantics will not.
License
MIT. See LICENSE.
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