# Omega × donto — making the integration even better (2026-06-07)

*A grounded review of how the Omega Discord bot actually uses donto, from the live logs (bot
stdout since the `dontoQuery` deploy ~01:00Z), the tool source (`recallMemories.ts`,
`dontoQuery.ts`), the system prompt, and the substrate search impl (`search.rs`) — plus live
probes of `/search`. Every finding below is evidence-backed, with the fix.*

---

## TL;DR

Omega **is** using donto on every relevant turn — but **only through one path**: the
`recallMemories` tool (memory `/recall` + substrate `/search`). The headline finding:

> **The new 12-lens `dontoQuery` reasoning tool has been invoked exactly 0 times.** It is
> deployed, BM25-selected, and loaded as a *core* tool on every turn — yet the model never
> calls it. It answers everything with `recallMemories`.

That's not a bug in `dontoQuery`; it's a **tool-overlap and prompting** problem (root cause
below). The three levers that make Omega *materially* better at donto:

1. **Disambiguate the two tools** so the model knows *when* to reason with the graph vs fetch text — this alone unlocks `dontoQuery`.
2. **Teach it to investigate, not one-shot** — every turn today is a single `recallMemories` call then an answer; no `search → entity_facts → cross_source → contradictions` chaining (the "take it further" goal).
3. **Fix substrate recall quality** — `plainto_tsquery` ANDs words (phrases collapse to zero), `ts_rank` scores are near-degenerate, and the semantic embedding fabric that already exists isn't in Omega's path.

---

## Method & data

- **Logs:** `docker logs omega-vm-omega-bot-1` — the bot prints every tool call's args + result and a per-turn tool-selection summary. Window: since the `dontoQuery` deploy (~4h of live traffic) + the full retained buffer for the "ever called?" check.
- **Source:** `apps/omega/packages/agent/src/tools/{recallMemories,dontoQuery}.ts`, `lib/systemPrompt.ts`, `toolRegistry/metadata.ts`; substrate `crates/donto-memory/src/api/routes/search.rs`.
- **Live probes:** `POST https://memories.apexpots.com/search` with single vs multi-word queries.

---

## What's already working

- **Donto is in the answer loop.** `recallMemories` is a CORE tool and the BM25 selector reliably surfaces it (and `dontoQuery`) for knowledge questions — e.g. `Top tools: memorize, dontoQuery, …` on a "what do you remember about X" turn.
- **Cross-source reach.** `recallMemories(scope:'all')` fans out to memory `/recall` **and** substrate `/search` in parallel (`Promise.allSettled`) and tags each row by source — so a single call already pulls from `ctx:genes/*`, `ctx:discord/*`, memory, and the 40.9M-statement substrate.
- **It grounds.** Real turns show it pulling genuinely relevant rows (e.g. `lablanche` → `ex:patrice-francois-lablanche hasFullName "Patrice François Lablanche"` from the val-mauritius corpus) and answering from them. The user's read — "working great with donto" — is fair for the *recall* path.

---

## Finding 1 — `dontoQuery` is deployed but **dormant** (0 invocations)

**Evidence.** Across the full retained log: `Tool called: dontoQuery` = **0**; `dontoQuery` tool-usage report blocks = **0**. In the last 6h every knowledge turn resolved to `recallMemories` (5 calls), even though the BM25 selector listed `dontoQuery` among the top tools and it was loaded as 1 of the 8 core tools every turn.

**Root cause — tool-purpose overlap.** `recallMemories`'s own description says:

> *"This reaches the ENTIRE donto knowledge graph, not just chat history: … the whole substrate — genealogy research, the ctx:genes/* corpora, and ~39M statements across every context."*

From the model's vantage, `recallMemories` **already does** "search donto." `dontoQuery`'s value (graph traversal, contradictions, identity, semantic neighbours, bitemporal) is real but *abstract*, and the only thing telling the model to prefer it is one subtle system-prompt line ("recall fetches text; dontoQuery lets you THINK with the graph"). A subtle distinction loses to an established habit + an overlapping, confident description **every time.**

**Consequence.** All of `dontoQuery`'s differentiated power — `contradictions`, `identity`, `predicate_alignment`, `semantic_neighbors`, `cross_source`, `bitemporal`, the `raw_sql` escape hatch — is sitting unused. Omega is doing keyword recall, not analysis.

---

## Finding 2 — single-shot lookup, not investigation

Every observed turn is **one** `recallMemories` call (occasionally two near-identical ones, e.g. `"two split problem"` then `"split problem"`) followed immediately by the answer. There is **no multi-step chase** — no "got an entity, now pull its facts, now see who else attests it, now check contradictions." The user's explicit goal was for Omega to *"think like that with the information and take it further when needed."* Today it takes one keyword swing and stops.

Two near-duplicate recall calls in a turn (just dropping a stop-word) is the model *trying* to dig but lacking a real traversal tool in-hand — exactly the gap `dontoQuery` fills, if it were used.

---

## Finding 3 — substrate recall quality

The substrate arm Omega leans on is `/search` (FTS), and it has three measurable weaknesses:

1. **Phrases AND to zero.** `search.rs` uses `plainto_tsquery('simple', $1)`, which **ANDs** every lexeme. Live probe:
   - `"love"` → **20** rows · `"infinite love"` → **6** · `"love verify real"` → **3** · `"love death meaning"` → **0**.
   The more natural-language the query, the fewer hits — the opposite of what you want. Omega partly works around this by querying single words, but that's fragile and loses precision.

2. **Degenerate ranking.** Recall results show the same score (`0.07599088549613953`) repeated across completely unrelated rows. `ts_rank('simple', <humanized IRI-segment projection>)` over short, structurally-similar text can't discriminate — so the "relevance score" is mostly noise, and the top-20 is effectively arbitrary among matches.

3. **No semantic arm in Omega's path, and noisy contexts.** The bot container sets **no** `DONTO_*`/embed env, so it uses the FTS defaults — the **hybrid vector recall** (bge-small + RRF) that the LongMemEval work proved load-bearing (FTS-only → hybrid lifted hit@10 0.85→0.98) is **not** reaching Omega's substrate search. Result: abstract queries surface low-signal rows (`"hate"` → `ex:hate-speech-content-N sameAs …` chains; `"rotors"` → car-maintenance chunks) and **test/quarantine/smoketest contexts** (`ctx:genealogy/smoketest`, `ctx:_quarantine/*`) leak into answers.

---

## Recommendations (prioritized)

### P0 — Unlock `dontoQuery` by disambiguating the two tools
This is the single highest-leverage change; it's pure prompting, no infra.

- **Rewrite `recallMemories`'s description to own ONE job: "what was *said/remembered*"** (chat history + memorized text). Drop the "reaches the ENTIRE knowledge graph / ~39M statements" claim — that overlap is exactly what starves `dontoQuery`.
- **Rewrite `dontoQuery`'s description to own "what is *true* / who is *who* / how things *relate*"** (entities, facts, relationships, contradictions, identity). Already good; make the boundary explicit: *"Use this, not recallMemories, for any question about a person/place/thing/claim or how they connect."*
- **Add explicit IF→THEN routing + a worked example to the system prompt**, e.g.:
  - *"who is X / what do we know about X / is X the same as Y / what conflicts about X / what's connected to X"* → **dontoQuery** (start `search`, then chain `entity_facts` → `cross_source` → `contradictions` → `identity`/`semantic_neighbors`).
  - *"what did I/we say about X / what happened in this channel / remind me"* → **recallMemories**.
  - One concrete 3-step `dontoQuery` transcript in the prompt so the model has a pattern to imitate.

### P0 — Fix `/search` query semantics
- Switch `plainto_tsquery` → **`websearch_to_tsquery`** (handles phrases, OR, quotes) **or** OR-join lexemes with a phrase-boost, so natural-language queries stop collapsing to zero.
- **Exclude test/quarantine contexts** from substrate results by default (`context NOT LIKE 'ctx:%test%' AND context NOT LIKE 'ctx:_quarantine%' AND context NOT LIKE '%smoketest%'`).

### P1 — Put the semantic arm in Omega's path
- Point Omega's recall at the **hybrid** recall (the embedding fabric already exists: pgvector + bge-small, RRF) — either set the embed-service env on the bot so `/recall` uses hybrid, or add a vector arm to `/search`. This is the proven precision lever (LongMemEval: hit@10 0.85→0.98).
- Add a lightweight **rerank + de-dup + time-decay** pass on the merged rows (OMEGA/Mastra-style cross-encoder rerank is the gap vs the leaders) so the top-k Omega reads is actually the best-k, not 20 tied rows.

### P1 — Make `dontoQuery` output *induce* chaining
`dontoQuery` returns raw rows. Wrap each result with a tiny **summary + suggested next lens** (e.g. after `search`: *"3 candidate entities; call `entity_facts` on `ex:…` to go deeper"*; after `entity_facts`: *"2 contradictions detected — try `contradictions`"*). Models follow affordances; this turns one-shot lookups into investigations without relying on prompt willpower alone.

### P1 — Nudge investigation in the system prompt
Add a short directive: *"For knowledge questions, don't answer from the first lookup. Resolve the entity, pull its facts, check who else attests it and what conflicts, then answer — cite the contexts."* Pair with the worked example from P0.

### P2 — Observability & hygiene
- Add an **Omega-usage panel to admin.donto.org** (mirror the embedding tracker): per-tool call counts, `dontoQuery` lens distribution, multi-step rate, empty-result rate. Right now adoption is invisible — we only found the 0-calls fact by grepping stdout.
- **De-duplicate** near-identical recall rows before returning (the `hasContentSnippet`/`listsSnippet` pairs and `sameAs` chains flood the top-k).
- Revisit `lens_name: likely_identity_v1` + `scope:'all'` defaults once the two tools are split.

---

## Exact change sketches

**`recallMemories.ts` description (narrow it):**
> *"Recall what was **said or remembered** — Omega's chat history and memorized notes for this holder/session. Use for 'what did I say about X', 'remind me', 'what happened in this channel'. For facts about a person/place/thing or how they relate, use **dontoQuery** instead."*

**System prompt routing block (replace the subtle distinction):**
```
KNOWLEDGE ROUTING
- "who is X" / "what do we know about X" / "is X the same as Y" / "what conflicts about X"
  / "what's connected to X"  → dontoQuery. Investigate: search → entity_facts → cross_source
  → contradictions → identity/semantic_neighbors. Don't stop at the first lookup.
- "what did I/we say about X" / "what happened here" / "remind me"  → recallMemories.
Example: user "who is Caroline Brown?" → dontoQuery search "caroline brown" → entity_facts
  ex:caroline-rose-brown → cross_source → contradictions → answer, citing contexts.
```

**`search.rs` (one-line query fix):**
```sql
-- was: plainto_tsquery('simple', $1)
WITH q AS (SELECT websearch_to_tsquery('simple', $1) AS tsq)
... AND context NOT LIKE 'ctx:%test%' AND context NOT LIKE 'ctx:_quarantine%'
```

---

## How we'll know it worked (measurement)

Re-pull the bot logs after the changes and track:
- **`dontoQuery` call-rate** on knowledge turns (target: from 0% → the majority of "who/what/how-related" questions).
- **Multi-step rate** — turns with ≥2 chained donto calls of *different* lenses (target: >0, today 0).
- **Empty/degenerate-result rate** on `/search` (phrases returning 0; tied scores).
- **Grounded-answer spot-check** — does the reply cite the contexts/entities it pulled?

---

## Appendix — evidence

- **0 `dontoQuery` calls:** `docker logs … | grep -cE "Tool called: dontoQuery|[0-9]+/[0-9]+: dontoQuery"` → 0 (full retained log).
- **Overlap:** `recallMemories.ts:42` — *"reaches the ENTIRE donto knowledge graph … ~39M statements across every context."*
- **plainto AND:** `search.rs:43,148` `plainto_tsquery('simple',$1)`; live `"love"`=20 → `"love death meaning"`=0.
- **Degenerate score:** recall rows repeat `"score": 0.07599088549613953` across unrelated subjects.
- **No semantic arm for the bot:** omega-bot container sets no `DONTO_*`/`EMBED*` env → FTS defaults.
- **Single-shot:** every observed turn = 1 (occasionally 2 near-duplicate) `recallMemories` calls, then answer.

---

*Generated 2026-06-07 from the live Omega bot + donto substrate. The P0 changes are prompting-only
(no infra) and should flip `dontoQuery` from dormant to default; P1 adds the proven semantic-recall
precision lever. Re-run the §measurement checks after deploying to confirm adoption.*