蒸留蔵 — distill-kura

A long-term memory for agents that is distilled, not accumulated. Recall works by meaning, writing is gated by evidence, and one server can hold several separate memories — one per agent mode — so switching mode switches what the agent remembers.

Ships as a DeepSeek Harness plugin, an MCP server for any other host, an HTTP service, and a Python library. Standard library only; no vector database, no embeddings, no framework.

        ┌── recall ──────────────────────────────────────────────┐
        │  question → whole index in one prompt → picked slugs   │
        │           → walk [[links]] → the neighbourhood         │  ~0.4 s
        └────────────────────────────────────────────────────────┘
        ┌── distil ──────────────────────────────────────────────┐
        │  journal → classed evidence → candidates → GATE        │
        │  → new? → composed → draft → judged → poured           │
        └────────────────────────────────────────────────────────┘

Why this exists

Two failures kill an agent's long-term memory, and they kill it from opposite sides.

Retrieval by keyword misses the thing you needed. A question about "SSD inference chips" shares no word with a memory titled "running the 2.6T model off an SSD tier" — yet they are the same subject. Word search returns nothing; the agent answers from nowhere. The fix here is not embeddings but recognition: the entire index (one line per memory, written as a recognition trigger) goes into one prompt, and a small model names what bears on the question. An index of ~500 memories is around 6k tokens — a few percent of a modern context window, and it sits in the prefix cache.

Writing everything poisons the store. An agent asserts something; a naive distiller records the assertion as a fact; the next agent reads it back as ground truth and repeats it with more confidence. That loop is self-reinforcing, and prompt instructions do not stop it — measured, not assumed. So the write path is gated by deterministic Python: every candidate memory must carry quotes that exist character-for-character in the raw material, tagged with where they came from.

A quote that is not found verbatim is discarded. A candidate with no surviving quote is thrown away. A number with no [TOOL] behind it is stripped. Text crediting the human with a decision, when no [USER] quote survived, is refused at the last gate. Ideas are welcome — they go to a seed file, never to the store, and graduate only when later evidence confirms them.

Quick start

git clone https://github.com/lna-lab/distill-kura && cd distill-kura
pip install -e .                       # or just run: python3 -m distill_kura.cli

cp kura.example.toml kura.toml         # edit: one model endpoint is enough to start
kura init main --path ~/kura/main      # create an empty store
kura serve                             # http://127.0.0.1:8085

curl -s -X POST localhost:8085/recall -H 'content-type: application/json' \
     -d '{"question":"what did we decide about the archive disk?","hops":1}'

Wear the index, so the agent always knows what is known:

kura weave                             # build the three-layer cloth
kura prefill                           # the block to put in the system prompt

Feed it your agent transcripts:

kura distill run      # drink a batch → candidates → gate → drafts
kura distill drafts   # look at what it wants to write
kura distill drain    # the scribe re-reads each draft cold: pour / fix / toss
kura distill night    # stay resident and do it whenever things go quiet

Nothing enters the store until drain (or a hand-run pour). Drafts carry their evidence in an HTML comment, so you can always see why a memory exists.

The resident map

Recall-by-tool answers "what do you know about X?" — but only once the agent has decided to ask. It never answers the question the agent does not think to ask: is there anything here at all? An agent that cannot see the map does not know what it is missing, so it guesses, and a confident guess about your household is precisely the failure this project exists to prevent.

So the index is also worn: a standing block in the system prompt, on every turn.

kura weave      # re-weave the index into the three-layer cloth
kura prefill    # print the block a host should inject

Three layers, because detail only pays for recent things

A blind A/B test — 20 questions, fat index vs slimmed index, scored without knowing which was which — settled the shape:

The doctrine lines were byte-identical in both indexes, and the slim index still won that band: a lighter surround makes the standing lines work better. Detail is not the source of insight. It earns its place only where things are still moving.

Trigger lines are written by the scribe model and cached in a ledger keyed on the description and the budget, so a re-weave in the steady state costs nothing. With no model reachable the loom trims mechanically instead — a memory system must not go blank because a GPU is down.

Age is not mtime. cp -r, a restore or a checkout resets every timestamp, the whole index turns "fresh", nothing is trimmed, and the mechanism has silently switched itself off. So the loom prefers a date written inside the memory, and distrusts any mtime that a fifth of the store shares with one calendar day.

Where it goes, and why that is a cache decision

- id: kura
  name: distill-kura
  config: { store: eq, promptOrder: -50 }   # before the persona

A prefix cache is lost from the first changed byte onward — measured on one local server: an identical 4,029-token preamble reprices from 0.68 s to 0.14 s, appending at the end stays 0.14 s, and one word added at the front costs the whole cache (0.66 s). The persona commonly carries a clock, so it changes every minute; the map is the largest block in the prompt and changes a few times a day. The big stable thing goes in front of the thing that ticks.

The block itself therefore contains no date, no clock, no counter — and build() refuses a header that does, at build time rather than through mysteriously slow turns three weeks later.

It never hands over half a map

A truncated map is the worst artifact available: it looks complete, and every memory below the cut appears not to exist. weave will shorten the fresh window to fit, but it will never drop a line — and if no setting reaches the budget it says so, keeps the better map, and tells you where the weight is.

Getting it into a host

The MCP instructions field is a MAY in the spec, and a 9,000-token index cannot travel through a 2KB cap regardless, so this project does not pretend otherwise.

Modes: more than one kura

A single memory that serves both "help me build this" and "help me think this through" serves neither well: the recall that helps you debug is noise in a conversation about what to do next. So a store is a directory, and a mode maps to a store.

[stores.maker]
path = "~/kura/maker"
label = "maker mode — building things"

[stores.eq]
path = "~/kura/eq"
label = "EQ mode — talking things through"

[modes]
maker = "maker"
eq    = "eq"

Every route takes a selector, so one process serves them all:

curl -s -X POST localhost:8085/recall -d '{"question":"...","mode":"eq"}'
curl -s localhost:8085/index?store=maker
curl -s localhost:8085/s/eq/doctor          # path form, for clients that only vary a base URL

The stores share no memories, no index, and no distiller watermark. Switching mode genuinely changes what is remembered — not the same memory in a different voice.

The room is chosen before the conversation. A mode is what the host sends — a DSH preset, KURA_STORE in an MCP environment, -s on the CLI — and it is the whole session's home. Nothing in this project reads a message and decides which store it belongs to; a conversation that drifts from building into feeling stays where it started, and the host may offer another room for the next session. An unknown selector is an error at the door, never a quiet fall to the default.

One room, many tags. A memory lives in exactly one store and may carry several tags that describe its character (decision, landmine, emotion-carried, …). Tags are words, not weights: nothing ranks by them, nothing counts them, and a Develop memory tagged emotion-carried is still a Develop memory. There is no command that moves or copies a memory to another store, and a mode change affects only future sessions. The same topic raised in two rooms yields two memories, each distilled from that room's own evidence — Research's "what we learned" and Develop's "what we did" are different facts, and nothing crosses the boundary to deduplicate them.

A wide room recalls a little softer. Narrow stores with fixed charters recognise sharply. A store that accepts anything — a USER room that follows the person rather than a purpose — is expected to be looser, and in exchange is the one whose understanding may grow: a profile.md beside its charter, in sentences, read after the charter, drafted from its own memories and applied by a person. Five such rooms, with their charters and a config, are in examples/rooms/.

Independent as routing, not as confidentiality. The server has no authentication, so any process that can reach its port can name any store it holds. Binding an agent keeps a model in its lane; it does not keep a process out. One trust level per process — docs/TRUST.md is short and worth reading before a private store goes in. It also covers the two boundaries that are easy to miss: two stores drinking from one journal root, and two stores behind one model endpoint.

With DeepSeek Harness

DSH switches persona and tools by agent preset. distill-kura switches memory by store. Bind them and one preset change moves the whole self:

# .agent-presets/eq/agent.cordis.yml
- id: kura-eq
  name: distill-kura
  config:
    url: http://127.0.0.1:8085
    store: eq            # this preset's memory
    readonly: true       # the CLIENT's own switch: do not even offer a write tool
    # (the store's own `write_policy` is the authority; this just keeps the tool
    #  out of the model's hands. Naming a store already binds the preset.)

One dependency, and why it is a peer. The plugin imports defineTool from @deepseek-ai/dsh-tools. A profile-local second copy can split the package's module-local Symbol identity — even at the same version — and make the first tool call fail on undefined.prepare. The plugin therefore declares the package as a "*" peer so the profile supplies its copy without a version mismatch. A stale physical duplicate can still require deduplication; see the install checks in examples/dsh-presets/.

Leave allowSwitch at its default and the agent also gets kura_use, so it can move between kura mid-conversation without a preset change. Tools: kura_recall, kura_read, kura_doctor, kura_list, kura_use, and kura_remember (only when the store is writable). Full wiring, including the MCP bridge and the isolate realm rule for service rows, is in examples/dsh-presets/.

Persona is the host's business, not ours. This project never renders or injects a persona; it only records, per store, which persona file belongs with it, readable at GET /profile?store=eq so the two halves can be kept in step by whoever owns the preset. Agent instructions likewise stay with the host's AGENTS.md mechanism — see AGENTS.md in this repo for the conventions an agent working on this codebase should follow.

With any MCP host

{ "mcpServers": { "kura": {
    "command": "python3", "args": ["-m", "distill_kura.mcp"],
    "env": { "KURA_URL": "http://127.0.0.1:8085", "KURA_STORE": "eq", "KURA_READONLY": "1" }
}}}

Leave KURA_STORE unset for free mode: the tools take an optional store argument and kura_use switches for the session.

Models: one by default, upgrade a role at a time

Three roles, not three machines:

Declare only [models.thinker] and one model does all three — the model you talk to is also the editor that writes and judges your memories. That is the default and it is a fair one: a capable GPU model does the editor's work well enough in its idle minutes, and kura tend stops it the moment you come back (see "Unattended" below).

The upgrade path is to give the editor its own seat — a bigger model, an online API, or a CPU model that does not compete for the GPU at all, so maintenance can go on while you are talking. The house this was built in runs a 1-trillion-parameter MoE on CPU at about 3 tokens/second as the editor: slow, but it never touches the seat the conversation uses, and the memories it wrote over five days are a third of the store today. Upgrade either of the other roles independently — a bigger local model, or an online API (any OpenAI-compatible /chat/completions; the key is read from an environment variable you name, never stored in the config):

[models.thinker]                       # always-on, local, small
url = "http://127.0.0.1:8000/v1"
model = "local-small"

[models.scribe]                        # upgrade just the writing
url = "https://api.example.com/v1"
model = "big-model"
api_key_env = "EXAMPLE_API_KEY"

Two things this handles for you: reasoning-effort dialects differ per model family (reasoning_effort, thinking_effort, enable_thinking), so all of them are sent — an unknown one is ignored by the template, while a model left on deep-thinking by default can spend its whole budget reasoning and return nothing. And the charter text is placed byte-identically at the head of every role's prompt, so on a slow local model the three roles share one cached prefix instead of paying three prefills.

A slow editor needs the prefix. The charter sits byte-identically at the head of every call, so a 3 tok/s CPU editor pays its prefill once per silence, not once per draft; on llama.cpp, keep --cache-reuse 0 off the table for recurrent models and let the server keep its slots warm (--slot-save-path). The editor's calls are the ones that wait an hour (timeout=3600) on purpose.

If the thinker is down, recall does not go silent — it falls back to word overlap and labels the answer how=words, which the tools surface as ⚠ degraded. Quiet degradation is worse than degradation.

Unattended: kura tend

The distiller, the pourer and the loom are meant to run in the quiet hours, and the watcher that decides when that is needs no model:

kura distill catchup -s maker   # first: start from today, do not drink a year of history
kura tend -s maker              # stays resident; one process per store
kura tend -s maker --once       # one tick, for a scheduler or a test

Run catchup once when you point a distiller at a journal it has never seen — otherwise its first act is to drink the whole history, which for a year-old journal is days of model time spent re-learning what the store may already know. It only moves the marks forward, so it can never lose progress.

"Quiet" is the newest journal file's mtime. After idle_min (10) of silence it drains waiting drafts (the editor reads each one cold: pour / fix / toss), or runs one distilling pass when there are none; when something was poured it re-weaves the resident map once; and it tidies the index once per silence. A track that had nothing to do exits 2 and rests for backoff_min (20), so an empty journal does not spin. It counts work — poured, tossed, fixed, drafted — never launches. Every track's output is kept in _still/tend.log. And it writes a heartbeat that kura doctor reads (tending.alive), because a watcher that dies quietly is the one failure a watcher must not have.

When the journal changes, a running track is stopped: the editor is usually the same GPU you are about to talk to. With the editor on a separate seat — a CPU model, another machine — set yield_on_return = false under [distill] and a verdict in flight is left to finish. This is the watcher the house ran its CPU editor with for five days, rebuilt with the lessons it taught; docs/OPERATING.md has the systemd unit.

What this project does not ship: an autonomous research loop that reads papers and grows the store on its own. The house has one; it needs a model that can be left alone for an hour per question, and its results are not evidence in the sense the gate uses. It stays on the house side of the line.

What a memory looks like

One file, one fact.

---
name: archive-on-slow-disk
description: the archive lives on the slow disk; the fast one stays scratch
metadata:
  type: project          # user | feedback | project | reference
  tags: ["decision", "landmine"]
  evidence_manifest: sha256:…
belongs_because: this store keeps how the machine is laid out and why
keep: which disk, and the reason
may_fade: the df figures from that afternoon
---

The archive goes on the slow disk. The fast disk is scratch space.

**Why:** the other way round burns write endurance for nothing.
**How to apply:** check which disk a target directory is on before writing there.
Related: [[disk-layout]]

And one line in MEMORY.md:

- [Archive on the slow disk](https://github.com/lna-lab/distill-kura/blob/HEAD/archive-on-slow-disk.md) — the archive lives on the slow disk; the fast one stays scratch

That line is the only thing read every single time. It is a recognition trigger, not a summary: proper nouns, numbers, ⚠️ landmines, the conclusion reached. If a line could be swapped with another memory's line and still read fine, it is not doing its job — kura distill tidy finds the mechanically detectable cases and rewrites them.

The four lines under metadata/at the top are curation, not facts: tags are words about the memory's character — several is normal, and a memory written before they existed simply has none — and the three sentences say why it belongs in this store, what meaning must outlive any later thinning, and what detail need not. The distiller proposes them against the store's charter; tags that claim something about the human (entrusted, emotion-carried, recurred) are checked against the quotes and the check is recorded in the manifest. recurred is written once, by the distiller, when the human brings a topic up again from another session — it is a property, not a counter, and there is no number behind it.

kura doctor reports counts, dead links, islands (memories nothing links to), index drift, tag lines it cannot read, manifests a memory points at that are gone, the state of the learned profile, and the store's capacity in four units side by side — memories, index tokens, body tokens, bytes — with limit and pressure left None. It is the eye the metabolism needs. What happens when a shelf is full is not decided yet: see docs/DESIGN.md §8.

The HTTP surface

Any route accepts ?store= / ?mode=, a store/mode field in the body, or the /s/<name>/… path prefix. No authentication: bind to loopback, or put something in front of it.

Design notes worth reading before you change things

• docs/DESIGN.md — why recognition beats search, what the gate buys, and the failure that motivated each mechanism.
• docs/OPERATING.md — running it resident, schedulers and exit codes, backups, what to watch.
• docs/TRUST.md — what a store boundary is and is not, write policies, and the two boundaries that are easy to miss (shared journals, shared models). Read it before a private store goes in.

A few decisions that look odd until you hit the thing they prevent:

• Reserve before drinking. The distiller claims a stretch of journal before reading it, under a lock, and watermarks only ever move forward. Two distillers each writing back their own snapshot erased each other's progress and re-drank the same water a dozen times.
• Watermarks are per-adapter units. Byte offsets for append-only transcripts, sequence numbers for archives that get rewritten (a byte offset into a recompressed file is a lie).
• Echo suppression. A quote that already exists in the store is not new material — it is the store reading itself back through a tool result. Without this, a memory system rediscovers and re-records its own contents forever.
• The last gate is a model, not a human. If a person must approve every draft, the system has quietly made that person its bottleneck, and drafts pile up forever. Nothing in the loop may require someone who is not always present.
• kura distill run exits 2 when there was nothing to do. A scheduler must be able to tell "did work" from "found nothing", or a watchdog spins on an empty queue and starves the steps that need the idle time.

Measuring it, instead of claiming it

Two questions get answered with one number and should not be.

How much smaller? store_ratio = tokens in the memories and index / tokens of raw journal actually consumed. What was lost? That is a different measurement, and a store that keeps one memory in a hundred scores beautifully on the first while being useless.

kura bench compress                       # what this store cost, from the distiller's own metrics
kura bench compress --tokenizer-command "./count-tokens"   # exact, not estimated
kura bench retention --questions bench/fixtures/questions.json

Measured here, with the shipped fixtures and the built-in estimator:

The second one is not a bug. On material where nothing is filler, distilling does not compress — each memory adds its why and how to apply, and the store comes out slightly larger than the transcript. The ratio is a property of the corpus, not of this tool, which is why there is no headline number here and why the command reports what it counted with.

Retention is scored model-free: each planted fact carries a marker that must appear in what recall returns, so the score is reproducible on someone else's machine. Distractors invert — a fact marked must_not_store costs a point if the store kept it, because a memory system is judged by what it declines as much as by what it keeps.

score 1.0 (10/10)   decision 1/1  number 2/2  negation 1/1  reversal 1/1
                    conditional 1/1  landmine 1/1  returning 1/1  distractor 2/2

That is ten planted facts in a synthetic fixture, distilled by a local Qwen3.8-27B (NVFP4) as brain and scribe with max_items = 8, coverage_passes = 2, and scored with the same model as thinker. A different model will give a different score: the score measures a pipeline-plus-model, and the fixture exists so the model is the only thing that varies. It measures whether a fact is findable, not whether the answer reads well — judging prose needs a model, and then the benchmark stops being reproducible.

kura distill run writes one line per batch to _still/metrics.jsonl, which is where the raw side comes from. The canonical side counts only memories whose evidence manifest points at a recorded batch — dividing a whole store by the raw material of a few batches is a number in the wrong direction by an order of magnitude, and the first version of this command did exactly that. Memories that predate manifests are reported as unattributed, not silently included. The raw side is always the distiller's estimate at drink time, so with --tokenizer-command the ratio is labelled mixed.

What this runs against

"OpenAI-compatible" is narrower than "any provider." A vendor's native API needs an OpenAI-compatible gateway in front of it; its own URL will not do. A strict service also rejects unknown top-level fields, so set dialect = "openai" (or "generic") — the default "vllm" sends chat_template_kwargs, which local servers want and a strict one 400s on. The client retries once with a plain body and records why a call failed rather than collapsing every cause into a silent None.

Tests

python3 -m pytest tests -q                              # 145 tests, no model required
cd dsh-plugin && npm test                               # 24 more for the plugin

The gate is tested adversarially: every case is a way a real model actually tried to smuggle something past it. test_containment.py is written the same way — every case is an escape attempt, not a happy path — because it guards a hole that was real: a store used to answer for any file whose path you could spell. The end-to-end test runs a full distil→drain cycle against a scripted model server on a real socket.

License

MIT.