compactHistory() now defaults to DEFAULT_COMPACT_MODEL =
'google/gemini-2.5-flash-lite' when the caller doesn't override. Lite
is ~3–5x cheaper than gemini-2.5-flash with near-identical
summarisation quality — summarisation doesn't need the same tier as
reasoning + tool-calling, and the compactor fires exactly when token
spend is highest, so the cheaper route saves exactly where it matters.
CompactHistoryOptions.model is now optional. All three consumers
(mana-ai tick, webapp Companion, webapp Mission runner) drop their
explicit gemini-2.5-flash override and let the default apply.
This is the pragmatic M2.5: no mana-llm changes. The "tier" abstraction
(X-Model-Tier header, env-routed aliases) from the Claude-Code report
makes sense only once multiple utility tasks need cheaper routing —
topic-detection, classification, command-injection checks. Today only
the compactor wants it, and a model constant is the simplest contract
that works.
2 new tests (default applied + override honoured). 79 shared-ai tests
green, all three consumers type-check clean. One pre-existing unrelated
type error in apps/mana/apps/web/src/lib/modules/wardrobe/queries.ts
(not touched by this commit).
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
Closes the loop on M2: when the compactor fires, the LLM needs to know
it's now seeing a <compact-summary> instead of raw turns so it
doesn't waste a turn asking about lost details or re-executing tools
whose responses are gone.
shared-ai:
- LoopState grows `compactionsDone: number` (cap-1 by current loop
policy, but shape kept as count for future multi-compact cycles).
- runPlannerLoop populates it on each reminder-channel call. New
loop test asserts [0, 1] sequence: round 1 before compaction,
round 2 after.
mana-ai:
- New producer `compactedReminder` — fires severity=info when
compactionsDone >= 1, wrapped in a German one-liner ("frag nicht
nach verlorenen Details").
- Injected FIRST in buildReminderChannel so the LLM frames the rest
of the round with "I'm looking at a summary" context. Metric
surface stays `{producer='compacted', severity='info'}`.
4 new reminder tests (3 pure producer + 1 composition-ordering) +
1 loop-wiring test. 77 shared-ai, 20 reminders.test.ts — green.
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
PlannerLoopInput grows an optional compactor:
compactor?: {
maxContextTokens: number;
threshold?: number; // default 0.92, matches Claude Code wU2
compact: (messages) => Promise<{ messages, compactedTurns }>;
}
Before each LLM call the loop checks whether promptTokens+completion
has crossed threshold × maxContextTokens. If yes AND we haven't
compacted this run yet, the callback runs, its returned messages
REPLACE the live history, and compactionsDone flips to 1 so a
runaway tool can't re-trigger.
Design choices:
- Fires at most ONCE per loop run. If the fresh (compacted)
history hits the threshold again in the same run, the LLM
round budget will hit first; better to terminate than to
recursively compact a summary.
- No reminder emitted automatically — the caller can wire
that via reminderChannel by reading compactionsDone from
LoopState (next PR; compactionsDone isn't exposed yet to
keep the state surface small).
- compactor callback is injectable, not hardcoded to
compactHistory() from compact.ts. Lets mana-ai route the
compactor LLM call to a cheaper model (Haiku) without
changing the loop.
- Zero maxContextTokens → skip silently (same contract as
shouldCompact()).
Also cleaned up the isParallelSafe non-null-assertion warning by
hoisting the predicate to a local with proper narrowing.
5 new loop tests: below-threshold no-op, single-fire replacement,
once-per-run idempotency, zero-cap bail, no-op when compactor
returns 0 turns. 76 shared-ai tests total, green.
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
The Claude-Code wU2 pattern: when token usage hits ~92% of the provider's
context budget, fold all pre-tail turns into a single structured summary
(Goal / Decisions / Tools Called / Current Progress) so subsequent
rounds see a synopsis instead of the raw log.
This commit ships ONLY the primitive. Wiring it into runPlannerLoop
(auto-trigger before the next LLM call when shouldCompact() fires)
is M2.2 so the surface stays small and testable.
New exports from @mana/shared-ai:
- shouldCompact(totalTokens, maxContextTokens, threshold?)
→ boolean; DEFAULT_COMPACT_THRESHOLD = 0.92, matching Claude Code.
Bails safely when maxContextTokens is missing (local models often
don't report usage).
- compactHistory(messages, { llm, model, keepRecent?, temperature? })
→ { messages, summary, compactedTurns, usage? }
Preserves: [0]=system, [1]=first user, [last N]=recent turns
(default 4). Everything between gets sent through the compact
agent with COMPACT_SYSTEM_PROMPT — a fixed 4-section Markdown
schema. Temperature default 0.2 because we want summarisation,
not creativity.
- parseCompactSummary / renderCompactSummary — round-trip helpers.
Parser is tolerant (missing sections → empty string) so a partial
compaction still produces a usable summary.
The summary replaces the middle as a single role='assistant' message
wrapped in <compact-summary> tags. Assistant role (not system) because
some providers reject arbitrary system messages deep in history.
Tests: 17 new across the 4 exports (trigger logic, Markdown round-trip,
structural preservation of anchors + tail, usage passthrough, custom
keepRecent). All 71 shared-ai tests green.
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
Two things:
1. AI tools (9) in the website module — writes go through the standard
proposal flow, reads run auto during planning.
- shared-ai/src/tools/schemas.ts: AI_TOOL_CATALOG entries with
defaultPolicy propose/auto.
- webapp modules/website/tools.ts: execute functions wired to the
existing stores. ModuleTool[] registered in data/tools/init.ts.
- Propose: create_website, apply_website_template, create_website_page,
add_website_block, update_website_block, publish_website
- Auto: list_websites, list_website_pages, list_website_blocks
Server-side mana-tool-registry integration (mana-mcp, mana-ai) is
a M5.x follow-up — webapp flow unblocks the missions-based use case.
2. Starter templates — clone into a fresh site with new UUIDs.
- templates/types.ts: SiteTemplate shape with localId / parentLocalId
so container→child references survive the clone.
- 4 templates: portfolio (4 pages), personal-linktree (1 page, 6 CTAs),
event (3 pages incl. RSVP form), blank (1 empty page). Deferred:
smb-corporate + product-landing (need team/pricing/testimonials
blocks, M6+).
- sitesStore.applyTemplate: walks template, bulk-inserts new rows,
remaps parent refs. Sets navConfig items from template pages.
- TemplatePicker component + /website/new route. Replaces the old
quick-create modal; ListView now links to /new. AppRegistry
context-menu action points there too.
AiProposalInbox integration deferred — the component doesn't exist in
the webapp yet (the plan mentions it aspirationally). defaultPolicy
'propose' is already set so writes stage correctly once the UI catches
up.
Validation:
- pnpm run validate:all: 6/6 gates green
- pnpm run check (web): 0 errors, 0 warnings
- apps/api + packages/shared-ai type-check: green
Plan: docs/plans/website-builder.md (M5 shipped)
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
Extends LoopState with a sliding window of the last N ExecutedCalls
(oldest-first), capped at LOOP_STATE_RECENT_CALLS_WINDOW = 5. The loop
maintains the window automatically; reminderChannel producers read it
without touching internal state.
This activates retryLoopReminder which was shape-only in faa472be9.
The guard now fires end-to-end: when round >= 3 and the tail-2 calls
both returned success:false, the LLM sees a "stop retrying, write a
summary instead" <reminder> on the next turn. The tail-2 check rather
than window-wide is deliberate — a flaky run with intermittent success
(F, F, F, OK, F) is not a retry loop, just flaky tools.
Why window=5: retry loops usually manifest within 2-3 consecutive
rounds; a 5-deep window gives room for burst-detection and
stale-tool heuristics without bloating the reminder channel. Cap
keeps the reminder producers O(5) regardless of loop length.
Tests: 3 new (sliding-window cap + slide + order in shared-ai, retry
composition + budget+retry chain + tail-only heuristic in mana-ai).
Total agent-loop tests now 74 across both packages.
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
Wires the M1 reminderChannel into the mana-ai mission runner with two
initial producers in services/mana-ai/src/planner/reminders.ts:
- tokenBudgetReminder — warns at 75% of the agent's daily cap, emits a
stronger "wrap up NOW" message at/above 100%. Uses pretick usage +
accumulated round usage so the warning tracks drift during a long
plan.
- retryLoopReminder — shape is in place (round≥3 + last 2 failures),
currently limited to the single lastCall LoopState exposes. Extends
cleanly once LoopState carries the full failure window.
buildReminderChannel composes active producers; the tick hoists
pretickUsage24h so the channel has the baseline. Each round the loop
re-evaluates the producers, so usage drift across rounds surfaces on
the NEXT turn.
Also exports LoopState + ReminderChannel from @mana/shared-ai top-level
so consumers don't need to reach into /planner.
Tests: 13 new bun tests covering thresholds, pretick+round summing,
composition, and per-round re-evaluation.
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
Three Claude-Code-inspired primitives for runPlannerLoop, derived from the
reverse-engineering reports in docs/reports/:
1. **Policy gate** (@mana/tool-registry) — evaluatePolicy() gates every tool
dispatch: denies admin-scope, denies destructive tools not in the user's
opt-in list, rate-limits per tool (30/60s default), flags prompt-injection
markers in freetext without blocking. Wired into mana-mcp with a
per-user rolling invocation log and POLICY_MODE env (off|log-only|enforce,
default log-only). mana-ai uses detectInjectionMarker only — tool dispatch
there is plan-only, so rate-limit/destructive checks don't apply yet.
2. **Reminder channel** (packages/shared-ai/src/planner/loop.ts) — new
reminderChannel callback in PlannerLoopInput. Called once per round with
LoopState snapshot (round, toolCallCount, usage, lastCall); returned
strings wrap in <reminder> tags and inject as transient system messages
into THIS LLM request only. Never pushed to messages[] — the Claude-Code
<system-reminder> pattern that keeps the KV-cache prefix stable.
3. **Parallel reads** (loop.ts) — isParallelSafe predicate enables
Promise.all dispatch when every tool_call in a round is parallel-safe,
in batches of PARALLEL_TOOL_BATCH_SIZE=10. Any non-safe call downgrades
the whole round to sequential. messages[] always appends in source
order, never completion order, so the debug log stays linear.
Default-off (undefined predicate) preserves pre-M1 behaviour.
Tests: 21 new in tool-registry (policy), 9 new in shared-ai (5 parallel,
4 reminder). All 74 green, type-check clean across 4 packages.
Design/plan: docs/plans/agent-loop-improvements-m1.md
Reports: docs/reports/claude-code-architecture.md,
docs/reports/mana-agent-improvements-from-claude-code.md
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
Five new entries in AI_TOOL_CATALOG (shared-ai/src/tools/schemas.ts):
list_articles auto Read-only listing with status +
query filter. Default hides
archived; 'all' includes them.
save_article propose URL → Readability → encrypted save.
Delegates to articlesStore.saveFromUrl
which already handles scope-aware
dedupe. Duplicates surface as
success:true with duplicate:true.
archive_article propose setStatus('archived') after
scoped existence check.
tag_article propose Case-insensitive dedupe over
globalTags; tagMutations.createTag
fills in when missing. Junction
write via articleTagOps.addTag.
add_article_highlight propose Snaps to the first verbatim
occurrence of `text` in the
decrypted article.content. Fails
cleanly when the snippet isn't
found — no orphan highlights.
Policy, client executor, and server planner derive automatically from
the catalog (see root CLAUDE.md §"AI Tool Catalog") so no manual
registration in policy.ts / services/mana-ai is needed.
Skipped from the M6 plan: <AiProposalInbox module="articles" />. The
component doesn't exist in the current codebase — after the
pendingProposals-table drop in Dexie v29 the inbox surface moved to
the mission-detail cross-module view, and articles proposals show up
there automatically. Documented in docs/plans/articles-module.md.
Also updated: plan doc now marks M1–M6 as DONE with commit refs and
the next-step pointer.
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
Closes the M7/M9/M10 plan items in one pass since they share patterns.
ListView (M7)
- 4 stats cards at the top: versendet YTD, Ø Öffnungsrate, Ø Klickrate,
Entwürfe. Same layout pattern as invoices for consistency.
- Status filter chips with live counts per status.
- Search across name + subject.
- Row now shows open-rate per-campaign when available.
- Settings gear in the header matches the invoices polish.
Dashboard widget (M10)
- BroadcastsWidget.svelte: 2x stats (sent YTD + avg open rate), next
scheduled link, last sent link with open-rate badge. Empty state
nudges toward creating a first campaign.
- Registered as 'broadcasts' in WIDGET_REGISTRY and the component map.
- Medium default size, no requiredBackend (reads from Dexie only;
stats are mirrored from the last DetailView poll so no server
round-trip for the widget).
AI tools (M9)
- 3 tools added to @mana/shared-ai's AI_TOOL_CATALOG:
- create_campaign_draft (propose) — generates HTML body from a
topic, lands as a draft; user picks audience + sends via UI
- list_campaigns (auto) — id/name/subject/status/recipients
- get_campaign_stats (auto) — rates as 0..1 floats
- broadcast/tools.ts: execute handlers with an HTML→CampaignContent
shim (stores both html and a minimal Tiptap JSON placeholder so
ListView renders without the editor having to remount). stripHtml
helper derives plaintext.
- Registered in data/tools/init.ts after library.
Suggest-style tools (suggest_subject_lines) deliberately omitted —
they're pure generative and don't need an executor. The LLM can
produce subject ideas without a tool call.
Verified:
- pnpm check: 0 broadcast errors (4 pre-existing errors in articles
module from parallel work, not mine)
- shared-ai test suite: 44/44 green (function-schema roundtrips the
expanded catalog cleanly)
- mana-ai drift guard: 41/41 green
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
Library module had no AI tool coverage post the M1 skeleton. Adds
four tools so the agent can curate the reading/watch list alongside
other modules:
- create_library_entry (propose) — books/movies/series/comics with
creators, year, status, rating, tags, genres. Default status
"planned" covers the most common flow ("add to watchlist").
- update_library_entry_status (propose) — status transitions
planned → active → completed (also paused / dropped). Auto-
stamps startedAt/completedAt on the matching transitions so the
existing Dexie projections (streaks, progress) fire correctly.
- rate_library_entry (propose) — 1-5 stars, thin wrapper over the
store's rate() method.
- list_library_entries (auto) — id/kind/title/status/rating/year,
filterable by kind + status.
Coverage table in apps/mana/CLAUDE.md updated (+library, +invoices
row that wasn't listed). Total now 67 tools / 21 modules.
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
The last open item from the plan. Missions can now draft invoices from
chat context, mark customer payments, and read status for autonomous
follow-up cadences.
Tool catalog (packages/shared-ai/src/tools/schemas.ts)
- create_invoice (propose) — clientName + lines[] + currency + due
- mark_invoice_paid (propose) — by id, optional back-dated paidAt
- list_invoices (auto) — with status + limit filter
- get_invoice_stats (auto) — open/overdue/YTD per currency
Had to widen the tool-parameter type vocabulary so create_invoice can
declare lines as a typed array. Touched three places:
- ToolSchema-side: the catalog's `type` string is already free-form so
'array' / 'object' just pass through
- ModuleTool-side (apps/mana/apps/web/src/lib/data/tools/types.ts): added
'array' | 'object' to the union so TS doesn't narrow the executor's
param signatures
- function-schema translator (packages/shared-ai): mapParamType +
JsonSchemaProperty both gained the two new types; the catalog-typo
guard test now uses 'fruit' as its sentinel (array no longer unknown)
Executor (apps/mana/apps/web/src/lib/modules/invoices/tools.ts)
- coerceLines accepts either a real array or a JSON-stringified array
(planners vary), skips malformed entries, converts major→minor units
- create_invoice pulls the generated number back from Dexie so the
success message shows "Entwurf 2026-0042 …" — the user recognises it
- mark_invoice_paid normalises YYYY-MM-DD → ISO so the store's timestamp
invariant (ISO throughout) stays intact
- list_invoices derives overdue on read (consistent with useAllInvoices),
returns major-unit amounts so the LLM reasons in user-facing numbers
- get_invoice_stats returns counts + open/overdue/YTD per currency
Registration: invoicesTools added to tools/init.ts. mana-ai drift guard
is happy (41/41 green); webapp + shared-ai type-check 0 errors; full
invoice test suite 59/59 green.
Closes: docs/plans/invoices-module.md §M8. All plan milestones now DONE.
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
Carries per-round token counts from the mana-llm response body
(prompt_tokens + completion_tokens) back through LlmCompletionResponse
→ PlannerLoopResult. The loop sums across rounds and exposes a single
aggregate on result.usage.
Lets mana-ai's tick re-activate per-agent daily-token budget tracking
— tokensUsed was stubbed to 0 in the migration commit (6) because the
loop didn't surface usage yet. Now recordTokenUsage + agentTokenUsage24h
get real numbers again, and the mana_ai_tokens_used_total Prometheus
counter is accurate.
Additive only: consumers without usage needs ignore the new field,
and providers that don't return usage produce zeros (not undefined —
the loop still exposes the object so downstream branches stay trivial).
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
The runPlannerLoop test file and the webapp's mission-runner test each
had their own inline scripted LLM mock — same interface, diverged
slightly. Consolidates into packages/shared-ai/src/planner/mock-llm.ts
and re-exports from the package root so any consumer can drive the
loop deterministically.
Both existing test files now use the shared client. 5 + 3 tests pass,
44 total in shared-ai still green.
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
The companion chat had its own ad-hoc 3-round tool-calling pipeline:
build a system prompt with tool descriptions, ask the LLM to emit
```tool JSON blocks, regex-extract, execute, feed back the result as
a synthetic user message. Same fragility class as the old text-JSON
planner — and now unnecessary since mana-llm speaks native function
calling.
Migrates companion/engine.ts to the shared runPlannerLoop, same as
the mission runner (commit 5a) and the server tick (commit 6). Tools
go to the LLM as proper function-schemas; tool_calls come back
structured; the executor runs them directly under USER_ACTOR.
Extends shared-ai/planner/loop.ts with an optional priorMessages[]
input field so the chat can preserve multi-turn history between
turns (missions don't need this and leave it empty).
Deletes the old llm-tasks/companion-chat.ts LlmTask wrapper. Nothing
else imported it.
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
After the mobile-app deletion unblocked \`@context/mobile\`, five more
pre-existing failures surfaced across shared packages and two services.
All were silent-masked by the postinstall \`|| true\` for months.
- **shared-ai**: \`planner/loop.ts\` imported \`ToolSchema\` from
\`../tools/function-schema\`, which only imports (not re-exports) the
type. Fixed to import from the source (\`../tools/schemas\`).
- **shared-logger**: \`typeof window !== 'undefined'\` blows up under
tsconfigs that don't include the DOM lib (e.g. uload-server's
\`bun-types\`-only config), because shared-logger is consumed via
source import. Replaced with a \`globalThis\`-indirected check that
compiles under any lib configuration.
- **shared-hono**: \`credits.ts\` returned \`res.json()\` directly as
\`Promise<T | null>\`. Modern \`@types/node\` / undici types return
\`unknown\` strictly — cast to \`T\` at the boundary so the generic
contract is explicit.
- **uload-server**: \`routes/analytics.ts\` + \`routes/email.ts\` still
imported \`AuthUser\` from a \`middleware/jwt-auth\` module that was
deleted during the migration to \`@mana/shared-hono\`. Replaced with
\`AuthVariables\` from shared-hono, which matches the actual context
shape set by \`authMiddleware()\`.
- **manavoxel/web**: \`guestSeed\` collection entries were wrapped in
arrow functions, but \`local-store\` expects \`T[]\` directly and
iterates \`seed.length\` — which on a function is 0. The "guest
seed" was silently dead; eager-evaluating \`generateGuestWorld()\`
once and sharing the result fixes both the type and the runtime.
Verified: \`pnpm run type-check\` from the repo root now exits 0 —
76/76 tasks successful, no failures. First fully green state since
well before the postinstall \`|| true\` was introduced.
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
Introduces the new planner pipeline both the webapp runner and the
mana-ai tick will swap onto in the next commits. Additive for now —
the legacy buildPlannerPrompt + parsePlannerResponse stay exported so
callers can migrate one at a time; they get removed once the last
consumer is gone.
- planner/loop.ts — runPlannerLoop orchestrates a multi-turn chat
against a caller-supplied LlmClient. Tool-calls from the LLM are
handed to an onToolCall callback and their results fed back as
tool-messages. Parallel tool-calls in one turn execute sequentially
to keep the message log linear for debugging. Stops on assistant
stop, empty tool_calls, or a hard max-rounds ceiling (default 5).
- planner/system-prompt.ts — new buildSystemPrompt. ~40-line German
system frame, no tool listing (the SDK-level tools field carries
the schemas now), no JSON format example, no "please return JSON"
plea. User frame renders mission + linked inputs + last 3
iteration summaries, same as before.
- Five test cases covering the loop: immediate stop, single tool
call with result feedback, parallel calls execute in order, tool
failures propagate as tool-messages the LLM can react to, and
maxRounds ceiling fires with the right stopReason.
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
Single bridge between the AI_TOOL_CATALOG shape and the wire format every
provider (Gemini, OpenAI-compat, Ollama ≥ 0.3) speaks for native tool
calling. Keeps the catalog as the source of truth — the runner never
reads catalog entries directly; it asks this converter for function-spec
shapes to hand the LLM.
- No _rationale or wrapper-tool injection: the runner doesn't need it
and the added schema noise would hurt planner quality.
- Throws on unknown parameter types so catalog typos (e.g. "array"
instead of "string") fail loudly instead of coercing silently.
- Preserves enum constraints; drops the enum key entirely when absent
so Gemini doesn't reject empty-enum function-declarations.
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
Completes the Quiz CRUD surface for the AI agent. Five new tools:
- update_quiz (propose) — rename/archive/pin + description/category
- update_quiz_question (propose) — text, type+options, explanation;
rejects a type swap without a matching optionsJson
- delete_quiz_question (propose) — symmetric to add_quiz_question
- get_quiz_questions (auto) — lets the planner see existing questions
before appending more (avoids duplicates)
- get_quiz_stats (auto) — attemptCount / avgScore / bestScore /
lastAttemptAt; enables adaptive missions like "analyze my weak spots
and generate harder questions"
delete_quiz deliberately left out — too destructive to leave in the
AI's hands when the user can delete manually in two clicks.
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
Quiz is now an AI-accessible module. The agent can mint empty quizzes
and append questions across all four types (single / multi / truefalse
/ text) via a single add_quiz_question tool whose optionsJson payload
shape is documented in the catalog description. list_quizzes (auto)
returns decrypted metadata so the planner can reference existing
quizzes when extending them. Enables missions like "baue ein Quiz aus
meinen Notizen zu Thema X" — planner reads via list_notes, proposes
create_quiz, then N × add_quiz_question.
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
- `app-registry/types.ts` now includes `tips` in the inline help shape,
matching `ModuleHelp` and what `AppPage.svelte` actually renders.
Drops 3 recurring type errors.
- `event-scout` template's `{ kind: 'daily' }` cadence now carries the
required `atHour` / `atMinute` fields (daily 08:00). Drops the 4th
type error — svelte-check is clean.
- `apps/mana/CLAUDE.md` gains a "Scene Scope" section documenting the
pattern: wire `filterBySceneScopeBatch` in the query AND render
`<ScopeEmptyState>` from the empty branch, so users always see why
the list is empty.
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
- Add discover_events (auto) and suggest_event (propose) to shared-ai
tool catalog. discover_events reads the discovery feed, suggest_event
creates a proposal to save a discovered event to the user's calendar.
- Add Event-Scout agent template with daily "Events der Woche" mission.
Policy: discover_events=auto, suggest_event=propose, all else denied.
- Add frontend tool implementations in events/tools.ts — discover_events
calls the feed API, suggest_event delegates to discoveryStore.saveEvent.
- Add feedback.ts — computes implicit user profile from save/dismiss
history (category affinity + source quality as 0–2x weight multipliers).
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
New module providing weather data for the DACH region via three sources:
- Open-Meteo (DWD ICON-D2 model) for current conditions and 7-day forecast
- DWD warnings endpoint for severe weather alerts
- Rainbow.ai / Open-Meteo fallback for minute-level rain nowcast
Includes API proxy with in-memory caching, Svelte 5 UI with location
picker, hourly/daily forecast, alert cards, and precipitation bar chart.
Two AI tools (get_weather, get_rain_forecast) enable the companion to
answer weather questions.
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
Six P2 items from the AI Workbench audit:
#7 Prompt ↔ loop budget sync:
System prompt now says "1 bis 5 Schritte pro Planungsrunde, bis zu 5
Planungsrunden" — matches MAX_REASONING_LOOP_ITERATIONS. Cross-ref
comment added to runner.ts.
#9 SceneHeader: useAgents() → useAgent(id):
Only loads the single bound agent instead of the full agent list.
Eliminates unnecessary Dexie churn on every scene header render.
#10 Unified scope filter:
New scope-filter.ts with filterByScopeTagMap() (batch, sync) and
filterByScopeAsync() (per-record). Both scope-context.ts (AI) and
scene-scope.svelte.ts (UI) now import from the shared module —
zero duplicated filter logic.
#11 Research dedup:
Research input ID changed from `news-research-${Date.now()}` to
`news-research-${mission.id}` — re-runs overwrite instead of
appending duplicates.
#12 Kontext injection policy clarified:
loadAgentKontextAsResolvedInput no longer falls back to the global
singleton. Comment + code aligned: kontext injection is explicit
(via input picker), not auto. Dead loadKontextAsResolvedInput
kept for potential future opt-in auto-inject feature.
Audit doc updated with all items marked DONE.
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
Add a guardrail system that runs alongside the Mission Runner pipeline
to catch obvious issues before they waste tokens or corrupt data.
Architecture (packages/shared-ai/src/guardrails/):
- types.ts: Guardrail, GuardrailResult, 4 phase interfaces
- builtin.ts: 4 built-in guardrails (always active):
- input-size-limit: blocks >100K chars of resolved input
- plan-step-limit: blocks plans with >25 steps (runaway planner)
- duplicate-destructive-tool: warns if undo_drink called 2x
- empty-required-params: blocks create_task without title
- runner.ts: runPrePlanGuardrails/runPostPlanGuardrails/runPreExecuteGuardrails
Wired into runner.ts at 3 checkpoints:
- Before deps.plan() — pre-plan check
- After plan received — post-plan check
- Before each stage() call — pre-execute check
Guardrails are synchronous, never hit the network, and produce
clear error messages when they block.
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
Connects the existing global tag system (@mana/shared-tags, 15+ module
junctions, TagSelector UI) to the AI agent model so different agents
can operate on different slices of the user's data.
Core additions:
1. Agent.scopeTagIds — optional array of global tag IDs. When set,
the agent sees only records tagged with at least one of those tags
(plus untagged records, which stay globally visible). Empty/undefined
= General-Agent, sees everything. Agent-editor grows a <TagSelector>
under "Bereiche (Tag-Scope)".
2. Per-agent kontext documents — new Dexie table `agentKontextDocs`
(v22, encrypted, synced). Each agent can have its own markdown
context doc, replacing the global singleton auto-inject. Runner
tries agent kontext first, falls back to global singleton when
the agent has no dedicated doc.
3. Ambient scope context — `withAgentScope(tagIds, fn)` sets a
module-level scope during the reasoning loop. Auto-tools read it
via `getAgentScopeTagIds()` and filter their result sets.
`filterByScope(records, getTagIds)` is the reusable filter
primitive (keeps untagged records, drops mismatched tagged ones).
4. Notes tag junction — `noteTags` table (v22) + `noteTagOps` via
`createTagLinkOps`. Notes was the only major module without
structured tag support. `list_notes` now calls `filterByScope`
so a scoped agent only sees notes tagged with its scope.
Flow: mission starts → runner resolves owning agent → reads
agent.scopeTagIds → wraps entire reasoning loop in withAgentScope →
list_notes (and future list_tasks etc.) auto-filter → planner sees
only scope-relevant records → proposes scoped edits.
Runner tests: 8/8. shared-ai type-check: clean.
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
Introduce AI_TOOL_CATALOG in @mana/shared-ai as the single source of truth
for all 29 tool schemas (17 propose + 12 auto). Both the webapp policy and
the server-side mana-ai planner now derive their tool lists from the catalog
instead of maintaining independent hardcoded copies.
- New: packages/shared-ai/src/tools/schemas.ts — catalog with ToolSchema type
- Rewrite: proposable-tools.ts — derived from catalog instead of hardcoded array
- Rewrite: services/mana-ai/src/planner/tools.ts — 277→30 lines (imports from catalog)
- Simplify: webapp policy.ts — derives AUTO/PROPOSE from catalog defaultPolicy
Adding a new tool now requires 2 files instead of 3-5:
1. Add schema to AI_TOOL_CATALOG (shared-ai)
2. Add execute function in the module's tools.ts (webapp)
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
Enable real-time token streaming during the planner "calling-llm" phase
so the user sees live progress ("empfange Plan… 128 tokens") instead of
a static spinner. The parser still receives the full text once complete —
no partial-JSON risk.
Changes:
- Extract shared SSE parser from playground into @mana/shared-llm/sse-parser
- remote.ts: use stream:true when onToken callback is provided
- AiPlanInput: add optional onToken field (shared-ai)
- ai-plan task: pass onToken through to backend.generate()
- runner.ts: throttled (500ms) phaseDetail updates during streaming
- Playground: refactored to use shared SSE parser
Also includes: AI agent architecture comparison report (docs/reports/)
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
Two major tool expansions — the Recherche-Agent and Today-Agent can
now research the web autonomously (no browser needed), and a future
Meeting-Prep agent can read + create contacts.
=== research_news (server-side execution) ===
The biggest addition: mana-ai can now call mana-api's news-research
endpoints (POST /discover + /search) directly, without a browser.
Infrastructure:
- services/mana-ai/src/planner/news-research-client.ts — full HTTP
client with discover→search pipeline. 15s/30s timeouts. Graceful
null on any failure (network, mana-api down, bad response) so the
tick never crashes from research errors.
- config.manaApiUrl added (default http://localhost:3060); wired in
docker-compose.macmini.yml as http://mana-api:3060 + depends_on
mana-api with service_healthy condition.
Pre-planning research step (cron/tick.ts):
- Before the planner prompt is built, the tick checks if the
mission's objective or conceptMarkdown matches research keywords
(same RESEARCH_TRIGGER regex the webapp uses). When it matches:
* NewsResearchClient.research(objective) runs discovery + search
* Results are injected as a synthetic ResolvedInput with id
'__web-research__' and a formatted markdown context block
* The Planner then sees real article URLs/titles/excerpts and can
reference them in create_note / save_news_article steps
* Log line: "pre-research: N feeds, M articles"
Tool registration:
- research_news added to AI_PROPOSABLE_TOOL_NAMES + mana-ai tools.ts
with params (query, language?, limit?). This lets the planner also
explicitly propose a research step as a PlanStep (in addition to
the pre-planning auto-injection).
=== create_contact ===
- Added to AI_PROPOSABLE_TOOL_NAMES + mana-ai tools.ts with params
(firstName required, lastName/email/phone/company/notes optional).
- Contacts are encrypted at rest; server planner can plan the step
but execution stays on the webapp (same as all propose tools).
Full server-side contact resolution via Key-Grant is a future
enhancement.
- get_contacts added to webapp AUTO_TOOLS so agents can inspect
existing contacts without nagging (read-only, auto-policy).
Module coverage now:
✅ todo (5) ✅ calendar (2) ✅ notes (5) ✅ places (4)
✅ drink (3) ✅ food (2) ✅ news (1) ✅ journal (1)
✅ habits (3) ✅ news-research (1) ✅ contacts (1)
11 modules, 28 tools total (17 propose, 11 auto).
Tests: mana-ai 41/41 (drift-guard passes), shared-ai type-check
clean, webapp svelte-check 0 errors, 0 warnings.
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
Closes the three biggest tool-coverage gaps so the shipped agent
templates can actually do their job end-to-end. Before this, the
Recherche-Agent couldn't create notes (only edit), the Today-Agent
couldn't create journal entries, and no habit-related tool was
server-proposable at all.
shared-ai (proposable-tools.ts):
- create_note (notes) — key unlock: Recherche-Agent now creates
per-source notes and the summary report.
- create_journal_entry (journal) — key unlock: Today-Agent proposes
a poem as a journal entry with optional mood.
- create_habit (habits) — agent can suggest new habits.
- log_habit (habits) — agent can log a habit completion for today.
Organized the list with per-module section comments for readability
now that we're at 15 proposable tools.
mana-ai (planner/tools.ts):
- 5 new tool definitions with full parameter schemas:
* create_note (title, content?)
* create_journal_entry (content, title?, mood? enum)
* create_habit (title, icon, color)
* log_habit (habitId, note?)
- Drift-guard contract test passes (41/41) — confirms the mana-ai
tool list is in sync with the shared-ai canonical set.
Webapp (policy.ts):
- get_habits added to AUTO_TOOLS (read-only; agent can inspect
which habits exist without nagging the user for approval).
- list_notes added to AUTO_TOOLS (was already used in the reasoning
loop but missing from the explicit auto-list; the planner default
fell through to 'propose' which was wasteful for a read op).
Module coverage after this change:
✅ todo (5 tools) ✅ calendar (2) ✅ notes (5 incl. create)
✅ places (4) ✅ drink (3) ✅ food (2)
✅ news (1) ✅ journal (1) ✅ habits (3)
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
Closes out T1 with three templates per category as discussed. The
gallery now renders agent-templates and workbench-templates as two
distinct labeled sections — the earlier implicit "everything's a
template for an agent" framing is gone.
Seed handlers (new):
- apps/mana/apps/web/src/lib/modules/habits/seed.ts — title-based
idempotency (there's no description column on LocalHabit). If a
non-deleted habit with the same title exists, the seed is skipped.
- apps/mana/apps/web/src/lib/companion/goals/seed.ts — title-based
idempotency on companionGoals where status !== 'abandoned'.
- Both pulled in via side-effect imports in missions/setup.ts so the
handler registry is populated before any apply.
New templates:
- 🏋️ Fitness (wellness) — scene body/habits/stretch/sleep + 3 habit
seeds (Täglich 30min Bewegung, 3× Woche Training, 2L Wasser) + 1
goal seed (3 Workouts pro Woche). No agent.
- 💻 Deep Work (work) — scene todo/calendar/notes/times + 2 habit
seeds (1 wichtigste Aufgabe pro Tag, 4h Deep Work pro Tag) + 1
goal seed (20h Deep Work pro Woche). No agent.
Gallery two-section layout:
- Title "Templates" (not "Agent-Templates") — broader framing.
- Section 1: "🤖 Agent-Templates" — filters ALL_TEMPLATES where
category ∈ {'ai','delight'}: Recherche-Agent, Kontext-Agent,
Today-Agent.
- Section 2: "🎨 Workbench-Templates" — filters to the rest:
Calmness, Fitness, Deep Work.
- Each section gets a short intro paragraph so users understand the
distinction before scanning the cards.
- Cards themselves unchanged; rendering extracted into a
{#snippet templateCard(t)} shared between both sections.
- Per-category arrays computed once at module-load time (const in
<script>); no per-render filter cost.
Result: each section has 3 templates, categorised by "does this
create an AI agent" rather than by use-case. Keeps the separation
honest — Agent-Templates set up autonomous work; Workbench-Templates
set up the user's own workspace.
Tests: shared-ai 26/26, webapp svelte-check 0 errors, 0 warnings.
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
First pass of the workbench-templates plan (docs/plans/workbench-
templates.md) — templates are no longer agent-centric but a general
"starter kit" bundle: optional agent + optional scene + optional
missions + optional per-module seeds. Pilot non-AI template "Calmness"
ships alongside.
Shape generalisation (packages/shared-ai/src/agents/templates/types.ts):
- AgentTemplate renamed to WorkbenchTemplate; all fields now optional
(agent, scene, missions, seeds). Back-compat AgentTemplate alias
kept so research/context/today keep compiling.
- Added `category: 'ai'|'wellness'|'work'|'lifeEvent'|'delight'` +
`icon` (for non-agent templates that have no avatar) + `version`
field (for future update-detection).
- New WorkbenchTemplateSeedItem shape: `{stableId?, data: unknown}`.
Module-specific seed payloads are typed at the handler side.
- Existing three AI templates nachgezogen: category='ai' (or
'delight' for today-agent), icon, version='1'.
Seed infrastructure:
- apps/mana/apps/web/src/lib/data/ai/agents/seed-registry.ts — in-
memory handler map keyed by module name; module-local seed.ts files
register themselves at import time.
- apps/mana/apps/web/src/lib/modules/meditate/seed.ts — first handler:
createPreset-based, idempotent via stableId embedded as HTML
comment in the preset description (T1 pragmatism; T2 adds a proper
column on the preset schema).
- data/ai/missions/setup.ts pulls `import '$lib/modules/meditate/seed'`
so the handler is registered before any template is applied.
Applicator upgrades (data/ai/agents/apply-template.ts):
- Agent step now optional — skipped cleanly when template has no
agent part.
- New step 4: seeds. Walks template.seeds, looks up the handler for
each module, aggregates per-item outcomes (created/skipped-exists/
failed) into result.seedOutcomes. Missing handler = warning, not
fatal. Crypto/encryption unchanged — seeds go through the same
module stores that module code already uses.
- Result shape gains `seedOutcomes: Record<string, SeedOutcome[]>`
so the gallery can show "3 new, 1 already there".
Calmness pilot (packages/shared-ai/src/agents/templates/calmness.ts):
- category='wellness', NO agent, scene with meditate/mood/journal/
sleep apps, two meditate preset seeds:
* 4-7-8 Atmung (breathing preset)
* Body-Scan 10min (bodyscan preset with 9 scan steps)
- Each seed has a stableId so re-apply is idempotent.
Gallery updates (routes/(app)/agents/templates/+page.svelte):
- Card avatar falls back to t.icon when no agent. "Agent" chip shows
only for agent-templates; "N Seeds" chip shows for templates with
seeds.
- Detail header shows "Workbench-Setup ohne AI-Agent" when no agent.
- New "Seeds" preview section: lists per-module counts + item names.
- Options section gains a "Seed-Daten in Module einpflegen" checkbox.
- Success panel shows seed summary: "3 Seeds neu, 1 bereits
vorhanden".
Tests: shared-ai 26/26, webapp svelte-check 0 errors, 0 warnings.
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
Debug log from a "tag 4 notes" mission showed the planner's second-round
response truncated mid-step: it was proposing one add_tag_to_note per
listed note but ran out of tokens halfway through note #2. Parser
rejected the malformed JSON → loop exited with 0 staged, user saw
nothing to approve.
Raising maxTokens to 4096 fits ~15-20 step objects, which covers the
batch-tagging / batch-save pattern the reasoning loop is designed for.
Also updating the system prompt so the planner actually knows about
the loop it's running inside: read-only tools are announced as
auto-executing with outputs visible next turn, and a new rule makes
explicit that batch jobs must emit all write-steps in one plan (because
staging a propose-tool ends the turn). Step count raised 1-5 → 1-10.
Prompt snapshot tests still pass (they check structure, not text).
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
First pass of the Multi-Agent discoverability UX. A new /agents/
templates route showcases pre-configured agents; clicking one creates
agent + scene + starter mission(s) as a single bundle. Addresses the
"blank form anxiety" + "user doesn't know what agents are for"
observations from the UX brainstorm.
Three templates for v1 (shared-ai/src/agents/templates/):
- 🔍 Recherche-Agent — reads sources one by one, writes a note per
source, summarizes into a report. Manual-cadence mission; all
writes propose so user curates.
- 🧭 Kontext-Agent — learns about the user via a weekly check-in.
Reads kontext/notes/goals, asks 2-3 questions, proposes a diff-
style context update. Weekly Sunday cadence.
- 🌅 Today-Agent — researches "on this day" history each morning,
writes a 4-8 line German poem, proposes a journal note. Daily 7am
cadence. A "delight" agent, not a productive one.
Each template packs (agent config, scene layout, starter mission):
- AgentTemplate type lives in @mana/shared-ai — pure data, no runtime
imports. Adding a new template = drop a file in templates/ and
extend ALL_TEMPLATES.
- Template-specific policies derive from the proposable-tool list so
drift-guard catches divergence from the canonical set.
- Starter missions default to startPaused=true — user sees the
mission ready-to-go and hits Play when ready. Prevents surprise
autonomous work on first apply.
Applicator (data/ai/agents/apply-template.ts):
- Creates agent → scene (if template defines one) → missions in
order. Agent failure = abort; scene/mission failures surface as
warnings in the result without blocking.
- Duplicate-name handling: falls through to findByName, returns
existing agent with wasExisting=true; scene is skipped in that
case to avoid clone-proliferation.
Gallery page /(app)/agents/templates/+page.svelte:
- Three large cards side-by-side (stacks on mobile) with avatar /
label / tagline / meta chips (Scene, N Missionen).
- Click opens detail panel with full description, scene preview
(app-ids + widths), mission preview (title / objective / cadence),
and override checkboxes (create scene, create missions, start
active vs paused).
- Success panel shows what landed with warnings inline; CTA back to
workbench.
Discoverability in /ai-agents module:
- Bar now has two buttons: "Aus Template" (primary, goto templates
route) + "Eigener Agent" (secondary, opens the existing blank-form
create mode).
- When only the default "Mana" agent exists, render a dashed promo
banner at the top linking to the template gallery. Disappears as
soon as the user has a second agent.
Tests: webapp svelte-check 0 errors, 0 warnings. shared-ai 26/26.
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
Makes the "read all notes and tag them #Natur/#Technologie/…" use case
fully functional. Four new ModuleTool entries in notes/tools.ts:
- list_notes(limit?, query?, includeArchived?) — auto, read-only. Returns
id + title + excerpt so the planner can reference concrete notes
without dumping full bodies.
- update_note(noteId, title?, content?) — proposable. Destructive full
overwrite. Docstring nudges toward append_to_note when applicable.
- append_to_note(noteId, content) — proposable, non-destructive. Handles
the trailing-newline separator so markdown stays clean.
- add_tag_to_note(noteId, tag) — proposable, idempotent, case-insensitive.
Strips leading #, replaces spaces with _, skips if already present.
Exactly the categorization primitive the user asked for.
All three writes are added to AI_PROPOSABLE_TOOL_NAMES so both the
webapp policy and mana-ai's boot-time drift guard agree (now 11 tools).
Mirrored in services/mana-ai/src/planner/tools.ts.
AiProposalInbox mounted on /notes so approvals land inline in the
notes module too (already appears in the mission-detail cross-module
inbox via the earlier commit).
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
Second phase of the Multi-Agent Workbench rollout (docs/plans/
multi-agent-workbench.md). Builds on Phase 1's identity-aware Actor.
Adds the Agent primitive — a named AI persona that owns Missions,
carries its own policy + memory, and (from Phase 3 on) drives the
Workbench lens. Everything is wired; a single user currently has one
"Mana" default agent until the UI (Phase 5) lets them create more.
Shared types (@mana/shared-ai):
- agents/types.ts: Agent, AgentState, DEFAULT_AGENT_ID/NAME constants
- policy/types.ts: AiPolicy + PolicyDecision (moved from webapp so
Agent.policy can reference it without a runtime dep on the web app)
- missions/types.ts: new optional Mission.agentId field
Webapp data layer:
- data/ai/agents/{types,store,queries,bootstrap}.ts
- Dexie schema v19 adds `agents` table (indexes on state, name,
[state+name]); sync registered under the existing ai app-id
- Encryption registry: agents.systemPrompt + agents.memory encrypted;
name/role/avatar/policy stay plaintext for search + UI rendering
- DuplicateAgentNameError thrown at write time (not a Dexie unique
index — bootstrap races between tabs would otherwise hit
ConstraintError; store now resolves via getOrCreateAgent)
- bootstrap.ts: ensureDefaultAgent + backfillMissionsAgentId. The
backfill runs once per device (localStorage sentinel) so missions
that pre-date the rollout get stamped with the default agent's id.
Called fire-and-forget from startMissionTick() during layout init.
Runner threading (already merged into d5c351d63 via Till's debug-log
commit that picked up my uncommitted edits):
- runner.ts + server-iteration-staging.ts now resolve mission.agentId
to the real Agent and build makeAgentActor with agent.name as
displayName. Missing-agent fallback keeps using LEGACY_AI_PRINCIPAL
so historical writes still attribute cleanly.
Tests: shared-ai 26/26, mana-ai 35/35, svelte-check 0 errors.
Agent store vitest suite is present but blocked by a pre-existing
\$lib alias resolution issue in the webapp vitest config that
predates this phase (proposals/store.test.ts is broken the same way
on HEAD). Will address separately.
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
New local-only Dexie table _aiDebugLog (v20, never synced) holds one
row per mission iteration with the full system+user prompt, raw LLM
response, latency, every ResolvedInput the planner saw, and pre-step
state (kontext-injected? web-research-ok-or-error?). Capped at 50
newest rows.
aiPlanTask always returns the captured prompt/response on AiPlanOutput.
debug; the runner persists it only when isAiDebugEnabled() — toggled
via a checkbox in the Mission detail header (defaults to on in DEV
builds, off in prod, override via localStorage 'mana.ai.debug').
New <AiDebugBlock> component renders below each iteration card:
expandable sections for Pre-Step, Resolved Inputs (each input
individually collapsible), System Prompt, User Prompt, Raw Response,
plus a "📋 JSON" copy-to-clipboard button for bug reports.
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
Foundation for the Multi-Agent Workbench roadmap
(docs/plans/multi-agent-workbench.md). Every event, record, and
sync_changes row now carries a principal identity + cached display
name in addition to the three-kind discriminator.
Shape change (source of truth in @mana/shared-ai):
Before: { kind: 'user' | 'ai' | 'system', ...kind-specific fields }
After: discriminated union on kind, with
- common: principalId, displayName
- 'user': principalId = userId
- 'ai': principalId = agentId + missionId/iterationId/rationale
- 'system': principalId = one of SYSTEM_* sentinel strings
('system:projection', 'system:mission-runner', etc.)
Key design calls (from the plan's Q&A):
- System sub-sources get distinct principalIds (not a shared 'system'
bucket) — lets Workbench filter + revert distinguish projection
writes from migration writes from server-iteration writes
- displayName cached on the record so renaming an agent doesn't
rewrite history
- normalizeActor() compat shim fills principalId/displayName on
legacy rows with 'legacy:*' sentinels so historical events never
crash the timeline
New exports:
- BaseActor / UserActor / AiActor / SystemActor (narrowed types)
- makeUserActor, makeAgentActor, makeSystemActor (factories with
typed return)
- SYSTEM_PROJECTION, SYSTEM_RULE, SYSTEM_MIGRATION, SYSTEM_STREAM,
SYSTEM_MISSION_RUNNER (principalId constants)
- LEGACY_USER_PRINCIPAL, LEGACY_AI_PRINCIPAL, LEGACY_SYSTEM_PRINCIPAL
- isUserActor / isFromMissionRunner predicates
Webapp:
- data/events/actor.ts now re-exports from shared-ai, keeps runtime
ambient-context (runAs, getCurrentActor) local
- bindDefaultUser(userId, displayName) lets the auth layer replace
the legacy placeholder with the real logged-in user actor at login
- Mission runner + server-iteration-staging stamp LEGACY_AI_PRINCIPAL
as the agentId placeholder — Phase 2 will thread the real agent
- Streaks projection uses makeSystemActor(SYSTEM_PROJECTION)
- All test fixtures migrated to factories
Service:
- mana-ai/db/iteration-writer.ts stamps makeSystemActor(
SYSTEM_MISSION_RUNNER) instead of the old { kind:'system',
source:'mission-runner' } shape. Phase 3 will switch this to an
agent actor per mission.
Tests: 26 shared-ai + 21 webapp vitest + 35 mana-ai — all green.
svelte-check: 0 errors, 0 warnings.
No behavior change; purely a type + shape upgrade. Old sync_changes
rows parse via the normalizeActor compat shim at read time.
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
Mission objectives matching /recherch|research|news|finde|suche|aktuelle|neueste/i
trigger a synchronous deep-research call (mana-search + mana-llm via the
existing /api/v1/research/start-sync pipeline) before the planner runs;
the summary plus top-8 source URLs are injected as a synthetic ResolvedInput
so the planner can stage save_news_article proposals against real URLs.
The kontext singleton is auto-attached to every mission's planner input
(decrypted client-side, gated on non-empty content + not already linked).
save_news_article is a new proposable tool routed through articlesStore
.saveFromUrl (Readability via /api/v1/news/extract/save). AiProposalInbox
mounted on /news so the user can approve/reject inline. mana-ai planner
tool list mirrors the new tool to keep the boot-time drift guard happy.
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
Replaces the single-line summary ("Planner failed: fetch …") with
full diagnostic detail: error name + message + last-active phase +
stack trace, all persisted onto the iteration itself. UI expands a
collapsed details block next to each failed iteration, so the user
can see *where* it broke ("TypeError in calling-llm") without opening
DevTools.
Paired with a one-click Retry button that re-runs the mission under
the same config — useful while debugging a flaky backend (GPU server
down, Gemini quota, etc.).
- `packages/shared-ai/src/missions/types.ts` — new
`MissionIteration.errorDetails: { name, message, phase?, stack? }`
- `finishIteration` accepts the field, deep-clones it, and also now
clears the transient phase markers (currentPhase/phaseStartedAt/
phaseDetail/cancelRequested) whenever an iteration finalises — keeps
the schema honest (phases are sub-state of \`running\` only).
- `runMission` tracks \`lastPhase\` via a new \`enterPhase\` helper that
wraps setIterationPhase. The catch handler populates errorDetails
with lastPhase + message + stack.
- ListView: \`<details>\` block under each failed iteration + Retry
button (disabled while another run is in-flight).
77/77 webapp tests still green; svelte-check clean.
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
Closes the "iteration is running, no feedback" black hole. The user now
sees, per running iteration:
⏳ Frage Planner · frage Planner an ⏱ 23s
[Abbrechen]
Phases (\`IterationPhase\`):
resolving-inputs → calling-llm → parsing-response →
staging-proposals → finalizing
The runner advances through these via \`setIterationPhase\` between each
await, writing currentPhase + phaseDetail + phaseStartedAt onto the
iteration. UI reads them via Dexie liveQuery — no polling.
Cancel:
- \`requestIterationCancel\` writes cancelRequested=true on the iteration
- runner polls \`isCancelRequested\` between every phase + per stage step
- cancellation finalises as \`failed\` with summary \`'cancelled by user'\`
- UI button is disabled + relabelled "Wird abgebrochen…" until the next
poll picks it up
Hard timeout: 90 s wall-clock per iteration via Promise.race against a
CancelledError. Wedged backends (e.g. flaky mana-llm) fail fast with
"timeout after 90s" instead of sitting in \`running\` forever.
Elapsed counter is a \$state variable ticking once a second, scoped to
the ListView component — Dexie isn't touched. Auto-cleaned on
component destroy.
shared-ai re-exports \`IterationPhase\` so server-side mana-ai can
inspect the same phase enum (no consumer there yet, but the type is
ready for the run-status endpoint planned in HEALTH page).
77/77 webapp tests still green; svelte-check clean.
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
Foundation for Phase 2+ of the Mission Key-Grant flow: lets mana-ai
execute missions that depend on encrypted inputs (notes/tasks/events/
journal/kontext) without needing an open browser tab. Opt-in per
mission, Zero-Knowledge users excluded.
- Canonical HKDF-SHA256 derivation (scope-bound via tables + recordIds
in the HKDF info string → scope changes invalidate the grant
cryptographically, not just via a runtime check)
- Mission.grant field on the shared Mission type
- Golden snapshot + drift-guard test so webapp wrap path and mana-auth
wrap endpoint can't silently diverge
- Ideas backlog at docs/future/AI_AGENTS_IDEAS.md
- Full rollout plan at docs/plans/ai-mission-key-grant.md
- COMPANION_BRAIN_ARCHITECTURE.md §21 captures the flow + privacy
guarantees + non-goals
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
Makes the webapp's AI policy and the server's tool allow-list physically
impossible to drift. Adds the missing entries the guard caught on first
run: `complete_tasks_by_title`, `visit_place`, `undo_drink` now have
parameter schemas server-side too.
- `packages/shared-ai/src/policy/proposable-tools.ts`
- `AI_PROPOSABLE_TOOL_NAMES` as `const` array + literal union type
- `AI_PROPOSABLE_TOOL_SET` for set-membership checks
- Webapp `DEFAULT_AI_POLICY` derives its `propose` entries from the
shared list via `Object.fromEntries(...)` — adding a tool there is now
a one-line change in `@mana/shared-ai`
- mana-ai `AI_AVAILABLE_TOOLS`: module-load assertion compares its
hardcoded names against `AI_PROPOSABLE_TOOL_SET` and throws with a
pointed error on drift (extras in one direction, missing in the
other). Service refuses to start on mismatch — better than silent
degradation.
- Bun test (`tools.test.ts`) runs the same contract plus sanity checks
(non-empty description, required params carry docs). Vitest policy
test adds the symmetric check on the webapp side.
All three runtimes now green: webapp 66/66, shared-ai 2/2,
mana-ai 9/9 Bun tests.
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
Completes the off-tab AI pipeline. mana-ai now writes produced plans
back to `sync_changes` as a server-sourced Mission iteration; the webapp
picks it up on next sync and translates each PlanStep into a local
Proposal via the existing createProposal flow. User sees the resulting
ghost cards in the matching module's AiProposalInbox with full mission
attribution.
Server (mana-ai v0.3):
- `db/connection.ts` — `withUser(sql, userId, fn)` RLS-scoped tx helper
mirroring the Go `withUser` pattern (SET LOCAL app.current_user_id)
- `db/iteration-writer.ts`
- `planToIteration(plan, id, now)` — shared-ai AiPlanOutput → inline
MissionIteration with `source: 'server'` + status='awaiting-review'
- `appendServerIteration(sql, input)` — INSERT sync_changes row with
op=update, data={iterations: [...]} + field_timestamps + actor
JSONB={kind:'system', source:'mission-runner'}
- `cron/tick.ts` — after parse success: build iteration, append to
mission.iterations, persist via appendServerIteration. Stats now
include `plansWrittenBack`.
Actor union:
- `packages/shared-ai/src/actor.ts` + webapp actor: `system.source` gains
`'mission-runner'` so the server's own writes are attributed correctly
and distinguishable from projection/rule writes
Webapp:
- `data/ai/missions/server-iteration-staging.ts`
- `startServerIterationStaging()` subscribes to aiMissions via Dexie
liveQuery; on each Mission update, walks iterations looking for
`source='server'` entries that haven't been staged yet
- For each such iteration: creates a Proposal per PlanStep under
`{kind:'ai', missionId, iterationId, rationale}` so policy + hooks
fire correctly
- Writes proposalIds back into plan[].proposalId + status='staged' so
other tabs and app restarts skip re-staging
- Idempotent: in-memory `processedIterations` Set + durable
proposalId marker
- Wired into (app)/+layout.svelte alongside startMissionTick
- 3 unit tests: translate server iteration → proposal, skip
already-staged, ignore browser iterations
Full pipeline now: user creates Mission in /companion/missions →
mana-ai tick picks it up → calls mana-llm → parses plan →
writes iteration → synced to webapp → staging effect creates
proposals → user approves in /todo (or any module) → task lands with
`{actor: ai, missionId, iterationId, rationale}` attribution.
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
Single source of truth for AI Workbench types shared between the webapp
(Vite/SvelteKit) and the server-side mana-ai Bun service. Prevents the
two runtimes from drifting on prompt shape or mission structure.
- `@mana/shared-ai` package:
- `actor.ts` — Actor union (user | ai | system) + helpers, mirrors the
webapp's runtime type so server-side consumers parse incoming actors
without re-declaring
- `missions/types.ts` — Mission, MissionCadence, MissionInputRef,
MissionIteration, PlanStep, MissionState. Adds optional
`iteration.source: 'browser' | 'server'` to distinguish foreground
vs server-produced iterations (groundwork for proposal write-back)
- `planner/prompt.ts` — `buildPlannerPrompt` pure function
- `planner/parser.ts` — `parsePlannerResponse` strict JSON validator
- Vitest smoke tests (2) cover prompt → parse round-trip + unknown-
tool rejection
- Webapp:
- `missions/types.ts` re-exports from shared-ai, keeps webapp-local
`MISSIONS_TABLE` constant + `planStepStatusFromProposal` bridge
- `missions/planner/{types,prompt,parser}.ts` become re-export stubs
so existing imports keep working unchanged
- Existing webapp tests (60) continue to pass — the wire code didn't
move, just its home
Next: mana-ai service imports buildPlannerPrompt/parsePlannerResponse
from shared-ai + wires mana-llm + writes iteration back as a
'source=server' row (tracked in services/mana-ai/CLAUDE.md).
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
