Open_Ultra_SAST/docs/memory.md

Memory

One store, keyed by repository and pin, holds what plane runs and the decision engine learned. The code is src/openultrasast/plane/memory.py; the same layout serves both backends. How the stored rows feed detection is on Memory and detection.

What is stored

index.jsonl                                one row per ingested (run, task): sha256 of its rows, row count
facts/<sha256>.json                        repository facts by content (identical facts stored once)
repos/<host>__<owner>__<name>/<pin>.jsonl  the rows of one repository + 40-hex pin, sorted by id
excerpts/<sha256>.txt                      code excerpts, by content
embeddings/<model>/<excerpt sha>.json      an excerpt's embedding vector, per embedding model
responses/<request sha>.json               cached model responses
programs/<program id>.json                 compiled decision-engine programs

Rows. Every row carries id, kind, repo, pin, run, task, population, split and image (ROW_FIELDS); id is the sha256 of (kind, run, task, subject), so a repeated row replaces itself. The kinds (KINDS) and who writes them:

Kind	Written by	Holds
facts	remember task	the facts entry of a case (points at facts/<sha256>.json; reused by memory key)
verdict	remember task	per candidate: each verify pass's answer, the final outcome, whether a tie-break ran, cost
unit_cost	remember task	per verify pass and file: usd, calls and token usage
alert	remember task	a quick-scan or engine alert on the vulnerable or fixed pin (loop Runs)
coverage	remember task	per language and pin: whether any rule could have fired there
features	remember task, from the features task	one feature record per candidate and family
proposal_outcome	ousast improve --memory (not with --dry-run), and the loop's improve step	what happened to each memory proposal in a round
example	ousast learn memory build	a labelled case: label + feature record + excerpt sha (no rationale)
label, decision, experiment, arm_outcome, experiment_result	reserved for later decision-engine tasks	declared in KINDS; not written by the code on main yet

Blobs (put_blob) are content-addressed: a 64-hex name under a fixed prefix. Excerpts are written by learn memory build (learn/examples.py), embeddings by learn memory embed (learn/embeddings.py, keyed by excerpt sha so an excerpt is embedded once), responses by the program's caller (learn/program.py, keyed by the sha256 of model, parameters digest, messages, temperature, sample and format, so a rerun replays at no cost), and compiled programs by learn compile (learn/compile.py).

Write path

flowchart LR
    tasks["Plane tasks: facts, verify a/b/c, agree, alerts, features"] --> rem["remember task: memory.jsonl"]
    rem --> recv["Delivered to the host with the run's artifacts"]
    recv --> ingest["ingest after ousast plane run, or ousast plane remember RUN"]
    loop["Loop improve step: proposal_outcome"] --> ingest
    improve["ousast improve --memory: proposal_outcome"] --> put
    build["ousast learn memory build: labels + features + excerpts"] --> put["put_rows, put_blob"]
    embed["ousast learn memory embed"] --> put
    compile["ousast learn compile, evaluate"] --> put
    ingest --> idx{"index.jsonl: same run, task and sha256?"}
    idx -- "yes" --> skip["Skipped"]
    idx -- "no" --> put
    put --> store[("Memory store")]

• ingest validates every row first: a malformed file fails before anything is written.
• Rows are merged into their repository + pin object by id. Every object is written whole and the index last, so an interrupted ingest leaves nothing a repeat would not repair.
• Each row object carries tags (TAG_FIELDS: repo, pin, kind, family, run, population, split) listing the distinct values of its rows (* when they exceed 256 characters).
• The plane's store failures never change a run's result: an ingest can be repeated by hand (cli.py, _plane_memory).

Read path

flowchart LR
    store[("Memory store")] --> seed["seed before a Run"]
    seed --> reuse["Facts reused by memory key: repo, pin, candidates, runner image"]
    seed --> snap["memory-snapshot: rows after the train-on-test guard"]
    store --> examples["learn: load example rows, excerpts, embeddings"]
    examples --> retr["Retrieval of similar labelled cases"]
    store --> responses["Response cache: replay at no cost"]
    store --> imp["ousast improve --memory: alert, verdict, coverage rows"]
    imp --> guard["Train-on-test guard"]
    guard --> rules["Rules M1, M2 to rule-status proposals"]
    rules --> gate{"Unchanged validator and gate"}

• Seed (memory.seed): a Run task whose openultrasast.io/memory-key matches stored facts gets them and is marked done; a changed pin, candidate set or runner image finds nothing and recomputes. The loop's memory-snapshot is seeded the same way, because a sandboxed task cannot read the store.
• Retrieval (learn/retrieve.py): see Memory and detection.
• improve --memory (improve/memory.py): rows from holdout pairs, from the gated manifest's own cases and from any --qualify-population are dropped before any rule sees them. See Architecture.

Backends: FileStore and the S3 store

OUSAST_MEMORY selects the backend (open_store):

	FileStore	S3Store (any S3-compatible store; RustFS is the tested server)
Selected by	file:///path, or nothing (default $OUSAST_RESULTS/plane/memory)	s3://<bucket>[/<prefix>], or s3:// for the bucket in S3_BUCKET
Needs	a local disk; refuses to write below 1 GiB free	the s3 extra (boto3); S3_ENDPOINT, AWS_ACCESS_KEY_ID, AWS_SECRET_ACCESS_KEY (optional S3_REGION, AWS_SESSION_TOKEN) from .env or the environment
Filtered reads	read and filter locally	S3 Select pushdown (SelectObjectContent over JSON Lines); kind filter by object tag
Object version cited by provenance	the file's content hash	the bucket's object version id
Bucket setup	none	done once by an admin, verified at every start (RustFS setup)

Verify at startup, no fallback

The S3 store never configures its bucket. Each time an S3Store is opened for a bucket and prefix (once per process), verify_bucket() checks, using reads plus one small probe object at <prefix>/_probe/select.jsonl:

1. versioning is Enabled (provenance cites object versions);
2. an enabled lifecycle rule expires <prefix>/runs/ after a number of days, as a plain prefix filter, and no enabled rule expires the whole store;
3. S3 Select answers a probe query over JSON Lines;
4. the probe's object tags are readable.

Anything missing is named, with the admin command that fixes it, in one MemoryStoreError, and the store does not start. There is no local or fetch-and-filter fallback: a filtered read that S3 Select cannot answer raises a MemoryStoreError instead of returning "no rows". The exact messages are listed on RustFS setup.

Queryable fields per kind (in progress)

RustFS's Select infers an object's JSON schema from its leading rows, so a where field missing there fails with EvaluatorBindingDoesNotExist even when a later row has it (measured 2026-10-02; memory.py, S3Store._select). On main today the store turns that failure into a MemoryStoreError naming the field, never into an empty answer. A fixed set of queryable fields per row kind, so that every filtered field is present in every row of its kind, is in progress and not on main as of this page.