A local, SQLite-backed persistent memory for AI assistants that stores, searches, and curates short knowledge entries—no embeddings or external service required.
Store facts with remember, filing each entry on two axes: a free-text project tag and a kind (interface, pitfall, decision, measurement, workflow, history); it reports similar existing entries and flag-worthy new tags.
Supersede instead of accumulate by passing supersedes ids, which hide older entries without deleting them.
Search with recall using ranked full-text (FTS5) matching that handles inflected forms, with filters for kind and tag, optional full text, and optional inclusion of superseded entries.
Fetch full entries with show by id, capped at ~6,000 characters per call so the cost is predictable.
Browse with topics to list all projects or one project's newest title lines page by page—useful when you don't know the search terms.
Tidy up with consolidate, which finds groups of near-duplicate entries for you to merge manually.
Get a second opinion from check, an opt-in call to any OpenAI-compatible model that judges whether two entries contradict or merely progress.
Retire entries with forget, marking them obsolete with a reason (reversible via undo), and reclassify existing entries with classify.
Learn from use—repeated searches re-rank entries that answered before—and run read-only as a guest (--guest) over SSH, with ids prefixed by the memory's name.
Enables an optional fact-checking tool that uses an OpenAI-compatible model to judge whether memory entries contradict each other, supersede one another, or are independent.
Click on "Deploy Server".
Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state.
In the chat, type @ followed by the MCP server name and your instructions, e.g., "@frugal-memory remember that the release process requires a sign-off from QA"
That's it! The server will respond to your query, and you can continue using it as needed.
Here is a step-by-step guide with screenshots.
A persistent memory for AI assistants that is just a SQLite file — and costs about a fifth of what reading markdown notes does.
No vector store, no embeddings, no external service, no API key. Python,
SQLite with FTS5, stdio — the only dependency is the official mcp SDK.
Nothing leaves your machine unless you explicitly turn on the optional
fact check.
Measured over 275 real questions from 89 working sessions: 2,359 tokens
per answered question, against 11,319 for the usual one markdown file
per fact, found with grep and read whole. That baseline is the naive case
on purpose: it is what happens when the file is opened with Read, which
loads it whole. The careful path, grep -C with three lines of context,
turned out more expensive, not cheaper: 28 times the characters in the
median with a realistic search order (added after the run; 65 times under
the rule fixed before it), because a broad search over 1,756 files returns
hundreds of matching lines
(details).
Both compare against markdown notes, not against other memory servers.
The gap grows as the memory does, because every answer here is capped and
grep is not. All figures come from one installation, and the docs say
where one did not hold up.
An assistant's memory usually fails in one of two ways: it is a pile of markdown files that has to be read whole to be searched, or it is a vector database that answers that something matched but never why.
This one is a ranked full-text index over short entries, with two things bolted on that turn a note pile into a memory: entries are filed on two axes — where it belongs (project) and what kind of knowledge it is (pitfall, decision, measurement, …) — and when you write something down, the tool tells you which existing entries it overlaps with, so contradictions surface instead of quietly piling up.
Related MCP server: MCPMem
Capped, calculable answers — a search returns eight preview lines
of about 1.2 KB in total instead of 4.2 KB of full text; show then
fetches the entries you pick, about 6,000 characters per call unless
you ask for everything. The worst case is known before you ask.
It learns from its own failures — when a search is followed by fetching an entry, that pair is remembered. Ask the same thing again and the entry that answered last time comes first. Word-for-word repeats only, and the header says when it fired.
Two filing axes — a free-text project tag and a closed, enforced vocabulary of knowledge kinds. Chronicle entries are hidden from search by default, but the header always says how many were held back.
Duplicate and contradiction hints — remember reports similar
existing entries and which numbers changed, so you can supersede
instead of accumulate.
Nothing is ever deleted — superseded entries drop out of the default view and stay findable, with a note on why and by what. One switch brings them back if that was a mistake.
Browsing, not just searching — topics() lists projects and a
project's newest title lines for when you have forgotten the words to
search for. Capped like everything else: one page, and a line saying how
to get the rest.
Project-bound in use, not in search — the usual question is asked
inside one project, with tag=. That filter is hard, so when the best
match lies outside the project, recall names it instead of leaving you
with a plausible-looking list of the wrong entries.
Two opt-in extensions on the same engine — a literature archive that all projects share, and a lab notebook for measurement series. Each is its own file and its own server entry; leave them out and nothing changes (below).
Opt-in second opinion — optionally ask any OpenAI-compatible model whether two entries contradict each other or just supersede one another.
English by default, German as a second language — the search
matches inflected forms ("configured" finds "configuring").
MEMORY_LANG=de switches tool names, messages and kinds to German, and
the search to German stems and compound parts ("Rangliste" finds
"Ranglisten", "Katalysator" finds "Fusionskatalysator"). The figures
from real use below come from German notes
(A Note on Language). Measured, the stems rarely
change the answer: the plain any-term stage of the search already finds
what they would add
(details).
Python 3.11+
The mcp SDK (requirements.txt, that's the whole list)
SQLite with FTS5 — included in standard CPython builds
Tested on Linux, macOS and Windows with Python 3.11 and 3.14: the full test suites plus the installed package, on every push. Day-to-day use so far is on Linux only — reports from macOS and Windows are very welcome as issues.
Optional, for the fact check only: any OpenAI-compatible endpoint
From PyPI, as a command in its own environment:
pipx install frugal-memory # or: uv tool install frugal-memory
claude mcp add memory -s user -- frugal-memoryWith uv it also works without installing
first; uvx fetches the package on the first start:
claude mcp add memory -s user -- uvx frugal-memoryThe database is created on first use at
~/.local/share/frugal-memory/memory.db ($XDG_DATA_HOME if set).
frugal-memory-nachziehen is the upkeep script described in
DESIGN.md.
Coming from a folder of markdown notes, frugal-memory-import <folder>
moves them in, paragraph by paragraph
(IMPORT.md).
frugal-memory-skill installs the working rules for Claude Code (see below).
With uvx, all of them run as uvx --from frugal-memory frugal-memory-import ….
Or from a checkout of this repository:
python3 -m venv .venv
.venv/bin/pip install -r requirements.txt
claude mcp add memory -s user -- \
/path/to/frugal-memory/.venv/bin/python \
/path/to/frugal-memory/memory_server.pyHere the database is created as memory.db next to the script. Either way,
MEMORY_DB=/some/path.db puts it somewhere else.
-s user makes the server available in all projects; the entry ends up in
~/.claude.json. Restart Claude Code afterward — MCP servers are only
loaded at startup. The tools are then called mcp__memory__recall and
mcp__memory__remember.
For Claude Desktop, use ~/.config/Claude/claude_desktop_config.json
instead:
{
"mcpServers": {
"memory": {
"command": "/path/to/frugal-memory/.venv/bin/python",
"args": ["/path/to/frugal-memory/memory_server.py"]
}
}
}One rule matters more than any setting: one fact per call, not one
document. recall returns whole entries — store a 180 KB file as a
single entry and you get it back as a single hit, having gained nothing.
The tools alone don't tell an agent the working rules: search before
reading files, recall → show, exactly one kind per entry, supersede
instead of accumulate, retry in the other language. Two layers carry them:
The short form comes with the server. On connecting it sends about
600 characters of instructions (English, or German with MEMORY_LANG=de),
which Claude Code adds to every session that has the server. Guest mode
(--guest) sends none, since it cannot store anything.
The full rules are a skill. It ships with the repo and the PyPI package; one command installs it:
uvx --from frugal-memory frugal-memory-skill # or frugal-memory-skill after pip install
cp -r skills/frugal-memory ~/.claude/skills/ # or from a cloneClaude Code loads the skill's full text only when the memory comes up;
until then only its one-line description sits in context. An existing
SKILL.md that differs is left alone unless you add --force.
A third layer is up to you: a few lines in CLAUDE.md, for what neither
layer can say — mainly that the server, not Claude Code's own memory
files, is where knowledge goes. An optional block to copy is in
docs/CLAUDE-MD.md.
Each machine keeps its own memory, and each numbers its entries from 1. A session on machine A can read the memory of machine B over SSH — the server runs on B, next to its file; the database itself never crosses the network:
claude mcp add memory-b -s project -- \
ssh b /path/to/frugal-memory/.venv/bin/python \
/path/to/frugal-memory/memory_server.py --guest--guest (German: --gast) means three things:
Read-only. The file is opened with mode=ro, and only recall,
show and topics are offered. The writing tools are gone, and so are
consolidate and check: upkeep belongs to the owner, who can act on it. A guest's searches do
not feed the owner's search feedback either, and a guest never migrates
the schema.
Every id carries the memory's name: b#72, not #72. The name is the
host name in lower case; MEMORY_NAME overrides it.
A named id is checked, in both modes. show("b#72") asked of any
memory other than b is refused instead of quietly returning that
memory's own #72. Bare ids (72, #72) mean the memory being asked.
The guest speaks the language of the memory it reads, unless
MEMORY_LANG says otherwise. -s project keeps the three extra tool
definitions out of sessions that do not need them. The tools are then called mcp__memory-b__recall and so on.
The same server starts in two more modes. Each has its own file, its own
tools and its own entry in Claude Code; the memory itself is never opened
in them. Both follow MEMORY_LANG like the memory does. They grew out
of daily use with several projects at once and have not been measured
the way the memory has — they are offered because they made that work
noticeably easier, not because a number says so.
--archive)Papers, datasets and prior work, shared by all projects. The memory holds what you found out; the archive holds what others published, and its value lies in the find another project already made.
claude mcp add archive -s user -- uvx frugal-memory --archiveSame eight tools, other kinds: finding, method, survey,
resource (code, dataset), gap (searched, nowhere measured), lead
(unchecked) and rejected (checked and rejected — e.g. a citation a
language model made up). Leads and rejected entries are hidden from
search, like the chronicle in the memory.
The same source is recognized by its id, not its wording. When
remember sees an arXiv number, DOI or link that is already in the
archive, it says so — two projects rarely describe a paper in the same
words. It also asks for the source and the evidence level (full text,
abstract, unconfirmed) when they are missing.
Every id carries the name archive#12 (archiv#12 in German mode),
so it is never mistaken for a memory id.
File: archive.db next to the memory's, or ARCHIVE_DB. Several
machines can share one archive by starting it over SSH on the machine
that holds the file (without --guest, so all of them can write).
--messbuch)Measurement series as a structure instead of loose entries: question, criterion, recipe, numbers, verdict, and what supersedes what. Spread over a dozen memory entries, a series loses exactly the things that matter — which one is still running without an evaluation, and whether the criterion stood before the result.
claude mcp add messbuch -s user -- uvx frugal-memory --messbuchseries() lists open series with what needs a look: no entry for N
days — is the process still running?, measurements without result,
no criterion. A table across all series of one topic is
series(key="vision/lr-*", table=True).
new_series before the first run. The criterion gets a timestamp;
after the first measurement it can only change with a reason, and the
change stays visible. Without a criterion the only possible verdict is
observation.
recipe records the command, code version, seed and machine;
measurements one value per row; result the verdict against the
criterion and what follows from it. Nothing is deleted; wrong values
are superseded.
Turn it on before your first series. Entering old series after the
fact works (backfill=, started= keep their real dates), but
collecting them from scattered notes costs far more tokens than
writing each one down when it happens.
File: messbuch.db, or MESSBUCH_DB. Memory ids written into the
notebook must carry the memory's name (laptop#452): the notebook may
be shared by machines whose memories all count from 1.
| store an entry; reports similar existing ones and what it would supersede |
| ranked full-text search, preview by default; under |
| full text of specific entries, about 6000 characters per call; what doesn't fit is listed with the call that fetches it |
| browse: which projects exist, or one project's title lines, newest first, a page at a time |
| mark as outdated — never deletes; |
| assign the knowledge kind of existing entries |
| find groups of entries that say much the same thing |
| opt-in: have a language model judge contradiction vs. newer state |
Full reference with parameters and behavior: docs/TOOLS.md.
With MEMORY_LANG=de the same tools come with German names and parameters,
see A Note on Language.
Measured on one real dataset that grew from ~900 to ~1,900 entries along the way — evidence from one installation, not a benchmark:
4.8× cheaper than reading markdown files, in real tokens, over 275 real questions (2,359 vs. 11,319 per question). The gap grows with the dataset and with file size, because reading a file is all-or-nothing: one question against a 188 KB project file costs 94,152 tokens.
89.8 % Recall@10 against the public LoCoMo dataset with a session as the unit, untuned (2026-10-19; the first run in September gave 82.3 %, and 45.1 % with a single dialogue turn as the unit).
73 % smaller returns from the preview, with a calculable worst case.
−23 % expected characters per question from the feedback loop, at 84 % vs. 73 % first-round hit rate, measured over 164 questions from 30 real sessions in a time-ordered replay — a pair only ever helps later questions, never itself. 19 questions gained, none lost.
53 % → 64 % hit rate on 47 real reformulation chains from a one-line ranking fix — found by measuring where real search sessions had failed, not by guessing. On today's dataset the same fix also costs 3 of the 117 questions that already worked, so the net is +2 of 164.
93 % of the dataset belongs to a project (1,777 of 1,907 entries),
and that is how the tool is used. Search itself does not care: general
knowledge is found as well as project knowledge (92 % vs. 96 %, a tie
under the 10 % rule). What the project filter buys is precision for
short questions — 97 % vs. 85 % found at about three words. What it
costs is everything outside the project: 40 questions about general
knowledge, asked under 22 project tags, found 0 of 880. recall now
names the better match outside the tag in 82 % of those cases, and in
70 % that line already is the entry sought; on real project questions
it fires in 7–16 %.
−83 % for a project's title index, −34 % for fetching eight full
texts, from capping topics and show. On 110 project questions the
cap cost not one answer.
Where a measurement did not survive a larger sample, it says so:
docs/MEASUREMENTS.md, with the raw reports in
messung/. The ranking figure above was corrected twice —
see The Ranking Fix, Corrected Twice.
all eight tools in detail | |
the two axes, the morphology, the database schema, and what was left out on purpose | |
what was measured, and the known limitations | |
the opt-in fact check: setup, what it can and cannot do | |
importing an existing folder of notes, and the damage that did | |
an optional block to copy into your | |
the eight underlying measurement reports (mostly German) |
English is the default since 0.3.0: tool names, messages and the kind vocabulary are English, and the search uses a small English suffix stemmer.
German mode: start the server with MEMORY_LANG=de. If your notes are
in German, this is the better choice, and it is the mode all figures from
real use in this README were measured in. (LoCoMo, a set of English
conversations, gave 89.8 % in English mode and 89.7 % in German mode.) Then
the tools are remember(text, tags, art, ersetzt),
recall(query, limit, art, marke, voll, mit_veraltet), zeige(ids, voll),
themen(marke, limit, seite, alle), vergessen(ids, grund, rueckgaengig),
einordnen(ids, art), verdichten(marke, art, schwelle, gruppen) and
pruefe(ids),
the kinds are schnittstelle, fallstrick, entscheidung, messwert,
arbeitsweise, verlauf, gemischt,
every message is German,
the search stems German and splits German compounds.
Existing memories keep their language. Without MEMORY_LANG, the server
speaks the language the file was built in — a new file starts in English,
and a memory from before 0.3.0 stays German. So updating changes nothing
unless you set the variable. The stem index is what ties a file to its
language: the file records it, and the server refuses to open it with the
other one. To switch an existing memory, back it up and run
MEMORY_LANG=en frugal-memory-nachziehen once (or MEMORY_LANG=de …; from a
checkout: python3 nachziehen.py). Kinds are stored under their German
names in both modes and translated at the border; English kind names are
accepted in German mode too.
The two extensions follow the same switch: in German mode the archive's
kinds are befund, methode, ueberblick, werkzeug, luecke, spur
and verworfen, and the notebook's tools are reihen, reihe_anlegen,
reihe_aendern, rezept, messwerte and ergebnis. The archive follows
its own file like the memory does; the notebook stores the same data in
both languages, so it follows MEMORY_LANG alone and is English without it.
Not covered by the switch: the fact-check prompt stays German (it is calibrated word for word, and adding one sentence once halved its accuracy; only its verdicts are shown in English), and so do the import script and the measurement reports. The English stemmer has not been measured on real questions the way the German search has.
| MCP server, eight tools, search cascade, schema, migrations |
| stemmers: a small English one (default) and a German one with compound splitting ( |
| the English tool surface (the default) |
| the lab notebook ( |
| its English tools and messages |
| English messages, keyed by the German text |
| recomputes the derived columns and the word and tag directories ( |
| opt-in second opinion via any OpenAI-compatible model (off unless configured) |
| measures the fact check against gold labels from the dataset |
| the hand-built and hand-found contradiction pairs |
| import a folder of markdown notes ( |
| regression net, one case per pitfall (no pytest) |
| the tests of the two extensions |
| working rules for the agent, as a Claude Code skill (in the package too) |
| copies the skill to |
| just |
| package definition for PyPI ( |
| entry in the official MCP registry |
| the database — created on first use, never committed |
messung/ keeps the eight measurement reports. The scripts that
produced them and their raw data are not included: they only run against
the author's own dataset, and the raw data is a verbatim transcript of
real working sessions.
Bug reports, questions and experiences from your own setup are welcome as a GitHub issue - including "it did not work for me", which is the report this project can least get any other way.
MIT — see LICENSE.
Has a small language model judge how the named entries relate.
Use it on a group from consolidate, or on two entries that seem to disagree, to ask whether a CONTRADICTION is behind it, PROGRESS (a newer state), or nothing (INDEPENDENT). Not authoritative: measured 67 % correct on 70 German pairs, so the result says where to look, not what is true.
Returns one line per pair. Each pair is asked in both directions; only what comes back the same both times is reported, otherwise "disputed". At most 5 pairs per call, roughly 10 seconds each.
Opt-in: needs MEMORY_FC_URL and MEMORY_FC_MODEL (any OpenAI-compatible endpoint; MEMORY_FC_KEY if it wants a key). Without them this tool does nothing and says so. When set up, up to 200 characters of each note are sent there.
Args: ids: Two or more entry ids, e.g. "249,255".
| Name | Required | Description | Default |
|---|---|---|---|
| ids | Yes |
| Name | Required | Description |
|---|---|---|
| result | Yes |
Sets the kind of knowledge for existing entries.
Use it in passing on entries recall shows as "unsorted"; the backlog need not be sorted in one sitting. Writes only the kind - text, tags, ids and superseded status stay as they are - and is undone by classifying again.
Returns the ids changed, names unknown ids, and how many live entries are still unsorted.
Args: ids: One or more entry ids, e.g. "376,481"; a range "376-380" means every id in between. kind: Exactly one of: interface, pitfall, decision, measurement, workflow, history. "unsorted" undoes the classification.
| Name | Required | Description | Default |
|---|---|---|---|
| ids | Yes | ||
| kind | Yes |
| Name | Required | Description |
|---|---|---|
| result | Yes |
Finds groups of entries that say largely the same thing.
Use it for tidying up. Read-only, changes nothing: merging is a judgment call - replace a group by one new entry via remember(..., supersedes="..."). Compares shared word stems weighted by rarity, not meaning; identical copies are always found. A few seconds for thousands of entries.
Returns how many entries and pairs were compared, then the groups, largest first, each with its best similarity and its entries (id, kind, date, title) - or that nothing reached the threshold.
Args: tag: Only consider entries from this project (empty = all). Sub-tags are included, as in recall. kind: Only consider entries of this kind/these kinds, comma-separated. Default "all"; "history" alone is the most rewarding case. threshold: Share of the weighted stems two entries must have in common, 0 to 1. Default 0.5; 0.8 shows only near-copies. groups: Maximum number of groups reported (default 5).
| Name | Required | Description | Default |
|---|---|---|---|
| tag | No | ||
| kind | No | all | |
| groups | No | ||
| threshold | No |
| Name | Required | Description |
|---|---|---|
| result | Yes |
Hides entries that are wrong or obsolete and have no replacement.
If a newer entry replaces them, use remember(..., supersedes=...) instead. Nothing is deleted: show and recall(..., include_superseded=True) still find them, undo=True brings them back.
Returns the ids changed and names unknown ones.
Args: ids: One or more entry ids, e.g. "12,34". reason: Why - shown later next to the superseded entry. undo: True brings superseded entries back.
| Name | Required | Description | Default |
|---|---|---|---|
| ids | Yes | ||
| undo | No | ||
| reason | No |
| Name | Required | Description |
|---|---|---|
| result | Yes |
Searches memory by words, best matches first.
Use it first, before searching files or the web for something noted before. It matches words, not meaning: inflected forms count ("configured" -> "configuring"), synonyms don't - if nothing fits, retry in the other language your notes may use. No search terms? Browse with topics.
Returns a preview per hit (id, kind, date, title line). Read the ones you need with show("376,481"), or set full=True for a narrow question.
Args: query: Search term(s), FTS5 syntax allowed (e.g. "sqlite AND fts5", "proj*"). limit: Maximum number of hits (default 8). kind: Restrict to kinds of knowledge, e.g. "pitfall,decision". Possible: interface, pitfall, decision, measurement, workflow, history, unsorted, all. Default: everything except history - otherwise the chronicle drowns out every search for a detail. tag: Restrict to a project, e.g. "spaceship". Several tags separated by comma or space are OR-combined. Sub-tags are included: "lighthouse" also matches "lighthouse-project", but "mcp-memory-server" does not match "mcp-memory". topics() lists which tags exist. full: Full text instead of preview (default: no), capped like show. include_superseded: Also show superseded entries (default: no).
| Name | Required | Description | Default |
|---|---|---|---|
| tag | No | ||
| full | No | ||
| kind | No | ||
| limit | No | ||
| query | Yes | ||
| include_superseded | No |
| Name | Required | Description |
|---|---|---|
| result | Yes |
Stores one fact permanently in memory.
Use it for knowledge a later session needs: a decision with its reason, a pitfall, a measured number, an interface. Not for secrets, guesses or short-lived details. Search with recall first; if an entry covers the fact, supersede that one.
Returns the new id, existing entries that resemble it (candidates to supersede) and a note when the tag is new - often a misspelling.
Args: text: The fact. A text that belongs to two kinds is two entries. tags: The project, i.e. WHERE the knowledge belongs (spaces or commas between several). kind: WHICH KIND of knowledge: interface (signature, file name, command, data format), pitfall (the obvious approach is wrong because ...), decision (with rationale), measurement (a number plus its criterion), workflow (how to work with this user), history (chronicle, hidden from recall's default view). Empty = unsorted. supersedes: Ids this entry replaces, e.g. "12,34". Hidden, not deleted.
| Name | Required | Description | Default |
|---|---|---|---|
| kind | No | ||
| tags | No | ||
| text | Yes | ||
| supersedes | No |
| Name | Required | Description |
|---|---|---|
| result | Yes |
Returns the named entries in full text.
Use it after recall or topics, once you know which entries you need - it takes ids, not search terms. Superseded entries are included: whoever asks for the id means that one.
Returns about 6000 characters per call, in the order asked. Whatever doesn't fit comes back as a title line plus the call that fetches it; unknown ids are named as such.
Args: ids: One or more entry ids, e.g. "376,481". Most wanted first. full: Everything asked for at once, without the cap (default: no).
| Name | Required | Description | Default |
|---|---|---|---|
| ids | Yes | ||
| full | No |
| Name | Required | Description |
|---|---|---|
| result | Yes |
Browses memory: the board of all tags, or the title index of one project.
Use it when search terms are missing - a project picked up after months, or "what happened here lately". To find a specific fact, recall is faster.
Returns the tag board (entries per tag and kind) or, with a tag, that project's title lines, newest first, one page at a time. Follow up with show("...") or recall(query, tag="...").
Args: tag: Project, e.g. "spaceship". Empty = tag board. limit: Rows per page (default 40 for the board, 30 for the title index). page: Page of the title index (default 1 = the newest). all: Whole title index at once, grouped by kind.
| Name | Required | Description | Default |
|---|---|---|---|
| all | No | ||
| tag | No | ||
| page | No | ||
| limit | No |
| Name | Required | Description |
|---|---|---|
| result | Yes |
Recent tool additions, removals, and schema changes observed during successful MCP inspections.
v0.3.0checkclassifyconsolidateeinordnenforgetprueferecall8 fields changedInput schema / properties / artRemoved value: -{
- "default": "",
- "type": "string"
-}Input schema / properties / fullAdded value: +{
+ "default": false,
+ "type": "boolean"
+}Input schema / properties / include_supersededAdded value: +{
+ "default": false,
+ "type": "boolean"
+}Input schema / properties / kindAdded value: +{
+ "default": "",
+ "type": "string"
+}Input schema / properties / markeRemoved value: -{
- "default": "",
- "type": "string"
-}Input schema / properties / mit_veraltetRemoved value: -{
- "default": false,
- "type": "boolean"
-}Input schema / properties / tagAdded value: +{
+ "default": "",
+ "type": "string"
+}Input schema / properties / vollRemoved value: -{
- "default": false,
- "type": "boolean"
-}remember4 fields changedInput schema / properties / artRemoved value: -{
- "default": "",
- "type": "string"
-}Input schema / properties / ersetztRemoved value: -{
- "default": "",
- "type": "string"
-}Input schema / properties / kindAdded value: +{
+ "default": "",
+ "enum": [
+ "interface",
+ "pitfall",
+ "decision",
+ "measurement",
+ "workflow",
+ "history"
+ ],
+ "type": "string"
+}Input schema / properties / supersedesAdded value: +{
+ "default": "",
+ "type": "string"
+}showthementopicsverdichtenvergessenzeigev0.2.3vergessen1 field changedInput schema / properties / rueckgaengigAdded value: +{
+ "default": false,
+ "type": "boolean"
+}v0.1.0einordnenprueferecallrememberthemenverdichtenvergessenzeigeScored across 8 tools
Each tool has a distinct role: remember writes, recall searches by words, show fetches by id, topics browses when no terms exist, classify/forget mutate metadata, and consolidate/check form a two-step duplicate-analysis workflow. The only mild adjacency is recall vs topics vs show, but the descriptions explicitly state when to use which.
Seven of eight names are single lowercase verbs (remember, recall, show, classify, forget, consolidate, check), giving a predictable pattern. 'topics' is a noun, a minor deviation, though its browsing purpose makes the name natural.
Eight tools is well within the sweet spot and each maps to a real operation (write, search, fetch, browse, classify, supersede, dedupe, judge). No tool feels redundant or like padding.
The lifecycle is well covered: create (remember), read (recall/show/topics), update-kind (classify), retire (forget/supersede), and maintenance (consolidate/check). The only gap is no direct text-editing operation — replacement is done via remember(supersedes=...), which is a workaround rather than a dead end.
Persistent memory for AI agents. Search and store durable facts, preferences and decisions.
Persistent memory for AI agents. Search, store, and recall across sessions.
Persistent memory for AI assistants: store, search, and connect knowledge across conversations.
Persistent memory for AI agents. Semantic search, memory graph, W3C DID identity.
