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The Multi-Agent Workbench shipped end-to-end (commits1771063dfthrough7c89eb625). This commit turns the plan doc into a proper history + post- mortem and captures the deferred Team-Workbench as its own forward plan so the architectural breadcrumbs don't rot. docs/plans/multi-agent-workbench.md: - Status bumped to ✅ Shipped; every phase checkbox flipped. - Open-questions section rewritten with the decisions that were actually made (name-unique via store write-time check, per-source system principalIds, policy fully migrated, scene binding default- empty with smart suggestion). - New "Shipping-Historie" table mapping each phase to its commit, the number of files touched, and the test outcome. - New "Lessons Learnt + Follow-Up Ideen" with: * What went better than expected (L3 Actor cutover, getOrCreate instead of unique index, displayName caching) * Thin spots worth revisiting (avatar not on Actor, missing token counter for budget, no missions list on agent detail, no drag-reassign, scene binding doesn't drive filters yet) * Five deferred follow-up projects (team features, agent memory self-update, agent-to-agent messaging, meta-planner, per-agent encryption domains) docs/plans/team-workbench.md (NEW): - Full forward-looking plan for the deferred Team-Workbench. - Two use-cases (human multi-user vs multi-agent sharing team context) with the observation that they share the same infra. - Decision candidates table (still open — meant as T0 RFC fodder, not baked in). - Architecture sketch with data-model deltas over the current single-user shape. - Encryption subsection dedicated to the hardest problems: team-key wrapping per member (reuses Mission-Grant pattern), member-removal rotation (lazy vs eager), Zero-Knowledge-mode incompatibility. - T0..T6 phasing (~7 weeks for a clean first-pass). - Section "Wie Multi-Agent dafür den Weg geebnet hat" enumerating the four invariants the shipped Phase 0-7 deliberately preserved to make this plan cheap when it lands. docs/plans/README.md (NEW): - Index doc with the AI/Workbench roadmap as an ASCII flow so future contributors can locate themselves in the sequence without reading three 400-line plans first. docs/future/AI_AGENTS_IDEAS.md: - Header marks Point 1 (encrypted tables) as shipped via the Mission Grant plan; points 2-8 stay relevant. Cross-link to all three plan docs so this stays the go-to backlog. services/mana-ai/CLAUDE.md: - Design-context header expanded to link to all four related docs (arch §20-22, both shipped plans, forward team plan, ideas backlog). No code changes. Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
141 lines
6.2 KiB
Markdown
141 lines
6.2 KiB
Markdown
# News Research — Module Plan
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## Status (2026-04-15)
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Planung. Noch kein Code. Geschwistermodul zu `news/`, teilt Low-Level-Primitives, eigene Pipeline.
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## Ziel
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Ein **Research-Tool**, das zu einem Thema passende RSS-Feeds findet, sie filtert, die Artikel nach Relevanz durchsucht und das Ergebnis als **KI-Kontext-Paket** ausliefert. Agent-facing, nicht consumer-facing.
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**Abgrenzung zum bestehenden `news/`-Modul:**
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- `news/` = kuratierter Consumer-Feed, persistent, verschlüsselt, synced, Lern-Reaktionen.
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- `news-research/` = Discovery + Query, ephemer pro Session, eigenes Scoring (Relevanz statt Recency).
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- **Brücke**: "Save article" → landet in der bestehenden `newsArticles`-Tabelle (type='saved'). Einzige Reading-List bleibt dort.
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- **Brücke**: "Pin feed" → persistiert einen entdeckten Feed in `customFeeds` (Preferences-Erweiterung) — damit ist gleichzeitig die eigene RSS-Abo-Funktion abgedeckt, die zuvor im `news/`-Modul fehlte.
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## Shared Primitives zuerst
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Vor dem Modul: **`packages/shared-rss`** (neues Workspace-Package) extrahieren.
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- `parseFeed(url | xml)` — RSS/Atom Parser (wandert aus `services/news-ingester`)
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- `extractArticle(url, html?)` — Readability-Fallback (wandert aus `apps/api/src/modules/news/routes.ts`)
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- `discoverFeeds(siteUrl)` — neu: `<link rel="alternate" type="application/rss+xml">`-Discovery + Sitemap-Heuristik
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- `validateFeed(url)` — Health-Check (fetchbar, parsebar, min. N Items)
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- Typen: `FeedMeta`, `FeedItem`, `ExtractedArticle`
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`news-ingester` und der `news/extract`-Endpoint werden migriert, damit nur eine Parser-Implementation existiert.
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## Discovery-Strategie (einfache Variante)
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Kein Feedly/RSS.app. Alles in-house, zwei Quellen:
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1. **Direkte URL**: User pastet Site-URL → Server holt HTML → sucht `<link rel="alternate" type="application/rss+xml">` → validiert → gibt Feed-Metadaten zurück.
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2. **Thema-Suche**: Query `"klimawandel" rss` oder ähnlich an eine Websuche (reuse was bei `mana-search`/crawler vorhanden ist), Top-N Ergebnisse filtern → für jedes Top-Result Discovery-Schritt 1 ausführen.
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Phase 2 kann später externe APIs hinzufügen.
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## Modul-Struktur
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```
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apps/mana/apps/web/src/lib/modules/news-research/
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├── types.ts # ResearchSession, DiscoveredFeed, ResearchArticle
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├── stores/
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│ ├── session.svelte.ts # Aktuelle Research-Session (ephemer, kein Dexie)
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│ └── pinned.svelte.ts # customFeeds-Verwaltung (persistiert über news-Preferences)
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├── engine/
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│ ├── relevance.ts # BM25 / Keyword-Scoring Artikel vs. Query
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│ └── filter.ts # Thema-Filter Feed + Artikel
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├── api.ts # Client für /api/v1/news-research/*
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└── components/ # UI
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```
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```
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apps/api/src/modules/news-research/routes.ts
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├── POST /discover # { query | siteUrl } → DiscoveredFeed[]
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├── POST /validate # { url } → FeedMeta + Sample-Items
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├── POST /search # { feeds[], query, filters } → ScoredArticle[]
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└── POST /extract # { url } → ExtractedArticle (reuse shared-rss)
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```
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Kein eigener Background-Service. Alles on-demand request/response. Session läuft im Browser-Memory + SessionStorage.
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## Data Model
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**Ephemer (nur Session):**
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```ts
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type ResearchSession = {
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id: string
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query: string
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topics: string[]
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discoveredFeeds: DiscoveredFeed[]
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selectedFeedIds: string[]
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results: ScoredArticle[]
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createdAt: number
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}
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```
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**Persistent — Erweiterung von `newsPreferences`:**
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```ts
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customFeeds: Array<{
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id: string
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url: string
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title: string
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topic?: NewsTopic
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pinnedAt: number
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}>
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```
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Kein neues Dexie-Table. Reading-List-Saves gehen in `newsArticles` wie bisher.
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## UI-Flow
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1. `/news-research` Landing: Query-Feld + Site-URL-Feld.
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2. **Discovery-Ergebnis**: Liste gefundener Feeds (Name, Item-Count, Sample-Topics). User hakt relevante an, kann pinnen.
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3. **Filter**: Thema-Chips, Keyword-Filter, Zeitraum.
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4. **Ergebnis**: Artikel-Liste sortiert nach Relevanz. Actions pro Artikel: `Save` (→ `newsArticles`), `Open`, `Add to Context`.
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5. **Context-Paket**: Button "Export as AI Context" → erzeugt einen strukturierten Markdown-/JSON-Block, der in eine Mission/Chat übernommen werden kann.
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## KI-Integration
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- Neues LLM-Tool `research_news(query, options)` in `tools.ts` (Geschwister zu `save_news_article`).
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- Rückgabe: Context-Paket (Top-N Artikel mit Excerpt + URL + Publish-Date).
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- Agent kann eigenständig eine Research-Session fahren, ohne UI.
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## Milestones
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### M1 — `packages/shared-rss` extrahieren
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- [ ] Neues Workspace-Package anlegen
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- [ ] Parser aus `news-ingester` verschieben, Service migrieren
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- [ ] `extractArticle` aus `apps/api` verschieben, Endpoint migrieren
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- [ ] `discoverFeeds` neu implementieren (rel=alternate)
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- [ ] `validateFeed` neu
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### M2 — API Routes
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- [ ] `apps/api/src/modules/news-research/routes.ts` mit `/discover`, `/validate`, `/extract`
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- [ ] Websuche-Integration für Query-basierte Discovery
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- [ ] `/search` — lädt Feeds parallel, wendet Relevanz-Scoring an
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### M3 — Frontend-Modul
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- [ ] Modulstruktur + types
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- [ ] Session-Store, Pinned-Store
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- [ ] `/news-research` Route + UI (Discovery → Filter → Ergebnis)
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- [ ] Save-Action in `newsArticles` (reuse `articlesStore.saveFromUrl`)
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- [ ] "Export as AI Context"
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### M4 — `customFeeds` als Abo-Funktion
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- [ ] `newsPreferences.customFeeds` Field + Encryption-Allowlist
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- [ ] UI in `news/` zur Verwaltung
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- [ ] Optional: `feed-cache` lädt auch `customFeeds` und merged im globalen Feed-Engine
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### M5 — KI-Tool
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- [ ] `research_news` LLM-Tool
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- [ ] Context-Paket-Format dokumentieren
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- [ ] Mission-Beispiel in `missions/debug.ts` oder neue Mission
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## Offene Fragen
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- **Websuche-Backend**: `mana-search` nutzen oder direkt SearXNG/Brave-API? → Entscheidung vor M2.
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- **Relevanz-Scoring**: Reicht lokales BM25 über Excerpts, oder Embedding-basiert via `mana-llm`? → Lokal starten, Embeddings als Phase 2.
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- **Feed-Caching serverseitig**: Feeds pro Query erneut fetchen (Kaltstart) oder kurzlebiger Redis-Cache? → Ohne Cache starten, messen.
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- **Topic-Taxonomie**: Die 7 News-Topics übernehmen oder frei (Tags)? → Frei, mit Vorschlägen aus News-Topics.
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