Stash

1. Executive Summary

Stash is a Go memory service — Postgres with pgvector, an MCP server, one docker compose up, and an Ollama path for a fully local install — with a hosted version alongside.

It has six first-class epistemic entity types, which is more than almost anything in this atlas: facts, hypotheses, contradictions, causal links, goals and failures, each with its own table and its own consolidation pass.

The hypothesis table is the mechanism worth the report. A hypothesis carries a status in proposed / testing / confirmed / rejected, a verification_plan, a method, source_fact_ids, a confidence, and — on resolution — either a confirmed_fact_id or a rejection_reason, with tested_at, confirmed_at and rejected_at.

So a claim the system inferred but has not established is not stored as a fact. It is in a different table, with a plan for how it might be settled, and when it is settled the record says which fact confirmed it or why it was thrown out. The background pass reports autoConfirmed, autoRejected, updated, llmCalls.

Most systems here have one bucket and a confidence number, so an inference and an observation sit side by side and the only difference is a float. Stash makes it a table boundary: facts are what recall reads, and a hypothesis has to earn its way across.

And contradiction detection is keyed on structure, not similarity — section 7.

2. Mental Model

Facts are (entity, property, value) triples in a namespace. A new fact is checked against existing facts about the same property of the same entity. Inferences become hypotheses with verification plans. Goals accumulate failures with lessons. Background passes consolidate all of it.

%% caption: contradiction detection returns zero rather than guessing when the entity or property is missing, and a hypothesis lives in its own table that recall never reads
flowchart TD
    R["remember"] --> F["facts: entity, property, value,<br/>confidence, valid_until"]
    F --> CD{"DetectContradictions:<br/>WHERE namespace_id = $1<br/>AND entity = $2 AND property = $3<br/>AND id != $4 AND valid_until IS NULL"}
    CD -->|"candidates found"| REA["reasoner adjudicates"]
    REA --> RES["detected, autoResolved"]
    CD -->|"no entity or property"| SKIP["returns 0, 0 — no guessing"]
    B["batch of facts"] --> CL["DetectCausalLinks — reasoner extracts<br/>cause → effect pairs, self-links skipped"]
    CL --> CLT["causal links"]
    H["inference"] --> HY["hypotheses: status proposed,<br/>verification_plan, method, source_fact_ids"]
    HY --> CON{"consolidateHypothesisEvidence<br/>over status IN (proposed, testing)"}
    CON -->|confirmed| OK["status = confirmed,<br/>confirmed_at, confirmed_fact_id"]
    CON -->|rejected| NO["status = rejected,<br/>rejected_at, rejection_reason"]
    CON -->|"still open"| HY
    G["goals: status active / completed"] --> FA["failures: content, reason, lesson"]
    D["decay run"] --> DR["FactsDecayed, FactsExpired"]
    Q["recall"] --> F
    HY -.->|"a hypothesis is not a fact —<br/>different table, not read by recall"| Q

3. Architecture

internal/ splits into brain (the epistemic core), reasoner (the LLM adjudicator, with an OpenAI-compatible implementation), embedder, db, queries, models, config, bootstrap, observability.

internal/brain/ has one file per concept — fact, hypothesis, contradiction, causal, goal, failure, episode, decay, recall, context, namespace, and three consolidation files (consolidate, consolidate_hypothesis, consolidate_goal, consolidate_failure). The vocabulary of the domain is the file layout, which makes the design legible in a directory listing.

7,900 lines of Go. docker compose up brings Postgres, pgvector, migrations and the MCP server with background consolidation in one command, and docs/LOCAL_OLLAMA.md documents a no-cloud path with "private embeddings and reasoner".

4. Essential Implementation Paths

Hypothesise — internal/brain/hypothesis.go (the confirm update :250), internal/brain/consolidate_hypothesis.go (the open-hypothesis query :11-16).

Contradict — internal/brain/contradiction.go (DetectContradictions :18-50, the entity/property/namespace query :23-27, the missing-key guard :19-21).

Link — internal/brain/causal.go (DetectCausalLinks :14-30, the self-link skip :26-28).

Learn from failure — internal/brain/failure.go (failureColumns with reason and lesson :13), consolidate_failure.go.

5. Memory Data Model

A fact has an entity, a property, a value, a content, a confidence, a valid_until and a deleted_at. Structuring a memory as a triple rather than a sentence is what makes everything else in this system possible — contradiction detection has a key to group on, decay has a scope, and valid_until closes a fact without deleting it.

failures carry content, reason and lesson as separate columns. Separating what happened, why it happened and what to do differently is a distinction most systems collapse into one text field, and only the third is worth injecting into a future prompt.

hypotheses carry the full lifecycle described in section 1.

6. Retrieval Mechanics

Vector recall over facts within a namespace. namespace_id = $1 is the first condition on the brain package's queries — including the contradiction scan — so a stored key reaches every read, earning scope_enforced.

The structural point is that recall reads facts. A hypothesis is not retrievable as knowledge because it lives elsewhere; there is no status filter to forget, because the separation is a table boundary rather than a predicate. That is a stronger form of the same guarantee, and it is why this report awards trust_state on the hypothesis lifecycle rather than on a flag.

7. Write Mechanics

Contradiction detection is keyed on (entity, property), not on similarity:

SELECT id, content, value, confidence FROM facts
WHERE namespace_id = $1 AND entity = $2 AND property = $3
  AND id != $4 AND deleted_at IS NULL AND valid_until IS NULL

Two facts contradict when they assign different values to the same property of the same entity. Embedding similarity cannot make that distinction — "Alice works at Acme" and "Alice left Acme" are near neighbours, and so are "Alice works at Acme" and "Bob works at Acme" — so a similarity-keyed detector conflates about the same thing with says the opposite. Keying on the triple's subject and predicate produces a candidate set that is actually contradictory-or-not, and only then does the reasoner judge.

The guard above it is equally right: if entity or property is missing or empty, the function returns (0, 0, nil) — no candidates rather than a guess. A detector that fell back to similarity when the structure was absent would produce its worst results exactly where it knew least.

valid_until IS NULL in the same query means a closed fact is not a contradiction candidate, so superseding a fact does not generate a contradiction against its own replacement.

Causal-link detection skips link.CauseFactID == link.EffectFactID, because a reasoner asked for cause-and-effect pairs will occasionally return a fact causing itself.

8. Agent Integration

An MCP server, docker compose up as the whole install, a getting-started guide that walks init / remember / recall and says to verify, and a documented fully-local path on Ollama. A hosted version exists at a linked service.

The README also carries a sponsor section for an inference platform, with configuration for pointing Stash at it — disclosed inline rather than buried, which is the right handling.

9. Reliability, Safety, and Trust

Two marks: trust state and scope enforced.

Trust state is the hypothesis lifecycle: proposed and testing are states in which a claim exists and is not treated as true, rejected records why, and confirmed records which fact settled it. Both resolution paths keep their evidence, which is the part most status enums omit — a rejected hypothesis with no rejection_reason teaches nothing and will be re-proposed.

Tombstone — no, though it is closer than most: a rejected hypothesis is a durable record of a claim that failed, and nothing keys on it to prevent the same claim being proposed again.

Bitemporal, audit log, human review, negative eval — no. valid_until closes a fact and no separate transaction-time dimension was found.

The risk is that the reasoner is the arbiter of everything and is unevaluated. It adjudicates contradictions, extracts causal links, and confirms or rejects hypotheses — three judgements, each of which writes into the store that the next judgement reads. autoResolved and autoConfirmed counts are returned, so the rate is observable at runtime; the accuracy is not measured anywhere in the tree. For contradictions the entity/property key bounds the damage, since the reasoner only sees genuinely comparable pairs. For hypotheses there is no such bound.

10. Tests, Evals, and Benchmarks

No paper, no benchmark, no committed results, and internal/brain/brain_test.go is the only test file found in the brain package.

That is thin for a system with six epistemic entity types and three reasoner-driven judgements, and the shape of the missing test is obvious in each case: a fixture pair that is a contradiction and one that merely shares an entity, a hypothesis whose evidence should confirm and one whose evidence should reject, a causal batch containing a self-link. All three are cheap because the functions take plain slices and return counts.

The consolidation functions returning (autoConfirmed, autoRejected, updated, llmCalls, errs) is the right runtime instrumentation — llmCalls beside the outcomes means the cost of a consolidation run is visible — and it is not a substitute for knowing whether the outcomes were right.

I ran nothing.

11. For Your Own Build

Steal

• Give hypotheses their own table. A claim you inferred but have not established is not a fact, and a table boundary enforces that more reliably than a status column recall has to remember to filter on.
• Store the verification plan with the hypothesis. verification_plan and method mean the record says how it could be settled, not just that it is open.
• Record both resolutions with their evidence. confirmed_fact_id on one side, rejection_reason on the other. A rejected hypothesis with no reason will be re-proposed.
• Detect contradiction on (entity, property), not on embedding similarity. Similarity cannot tell "says the opposite" from "is about the same thing"; a triple's subject and predicate can.
• Return no candidates when the key is missing. if entity or property is empty: return 0, 0, nil — a detector that falls back to similarity does its worst work exactly where it knows least.
• Exclude closed facts from the contradiction scan. valid_until IS NULL stops a superseded fact contradicting its own replacement.
• Skip the self-link. A reasoner asked for cause-and-effect pairs will return a fact causing itself.
• Split a failure into content, reason and lesson. Only the lesson belongs in a future prompt.
• Name your files after the domain. fact, hypothesis, contradiction, causal, goal, failure, decay — the design is legible from ls.
• Report llmCalls beside the outcome counts. The cost of a background pass should be as visible as its result.

Avoid

• Do not let one reasoner adjudicate three things with no evaluation. Contradictions, causal links and hypothesis resolution all write into the store the next judgement reads, and only the contradiction path has a structural bound on what it is asked.
• Do not ship six epistemic entity types on one test file. Each judgement has an obvious two-fixture test — the positive case and the near-miss — and the functions are already shaped to take them.
• Do not let a rejected hypothesis be re-proposable. The rejection is durable and nothing consults it on the next inference pass.

Fit

A strong fit if you want an opinionated, structured memory service rather than a library — triples rather than sentences, a real consolidation worker, and one command to a running Postgres. The Ollama path means the whole thing can be local.

internal/brain/ is worth reading whatever you build. The file list is a good answer to "what are the parts of a memory system", and contradiction.go's twenty lines are the clearest statement in this atlas of why structure beats similarity for detecting disagreement.

12. Open Questions

• How accurate is the reasoner's adjudication? Rates are reported; accuracy is not measured.
• What promotes a hypothesis to testing? The consolidation query reads both states; the transition was not traced.
• Does anything consult a rejected hypothesis before proposing again? The rejection is stored with its reason.
• What sets valid_until? It is respected in the contradiction scan; the writer was not traced.

Appendix: File Index

Hypotheses — internal/brain/hypothesis.go (the status filter :103, the confirm update with confirmed_fact_id :250), internal/brain/consolidate_hypothesis.go (the open-hypothesis query with its full column list :11-24)

Contradiction — internal/brain/contradiction.go (ErrContradictionNotFound :13, the doc comment on the entity/property scope :15-17, the missing-key guard :19-21, the candidate query :23-27)

Causal links — internal/brain/causal.go (DetectCausalLinks :14-19, the self-link skip :26-28)

Other entities — internal/brain/fact.go, goal.go (status counts :138, :188), failure.go (failureColumns with reason and lesson :13), episode.go, decay.go (DecayResult :9-12), recall.go, context.go, namespace.go, consolidate.go, consolidate_goal.go, consolidate_failure.go

Reasoner — internal/reasoner/reasoner.go, openai.go

Documentation — README.md, docs/GETTING_STARTED.md, docs/LOCAL_OLLAMA.md

History

2026-08-09 — d34ed430… — first reading. Screened before reading; the tree was read, never built or run, and the hosted service was not used.