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e8c8ddd48d feat: add CI/CD pipeline with cross-compilation support 2026-03-04 09:00:36 +01:00
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# CLAUDE.md # CLAUDE.md
This file provides guidance to Claude Code (claude.ai/code) when working with code in this repository.
## Commands ## Commands
```bash ```bash
source ~/.nvm/nvm.sh && npm start # Dev with hot-reload # Development
source ~/.nvm/nvm.sh && npm start # Start Electron app with hot-reload
# Build & Package
source ~/.nvm/nvm.sh && npm run make # Build distributable packages source ~/.nvm/nvm.sh && npm run make # Build distributable packages
source ~/.nvm/nvm.sh && npm run package # Package without installers source ~/.nvm/nvm.sh && npm run package # Package without making installers
source ~/.nvm/nvm.sh && npm run lint # ESLint (.ts/.tsx)
source ~/.nvm/nvm.sh && npx drizzle-kit generate # Generate migration from schema # Lint
source ~/.nvm/nvm.sh && npm run lint # ESLint over .ts/.tsx files
# Database migrations (Drizzle)
source ~/.nvm/nvm.sh && npx drizzle-kit generate # Generate migration from schema changes
source ~/.nvm/nvm.sh && npx drizzle-kit push # Push schema directly (dev only) source ~/.nvm/nvm.sh && npx drizzle-kit push # Push schema directly (dev only)
``` ```
No test suite currently. There is no test suite currently.
## Architecture ## Architecture Overview
Adiuva is a local-first Electron desktop app. The three processes communicate via a custom tRPC v11 ↔ IPC bridge (the public `electron-trpc` package is incompatible with tRPC v11). Adiuva is a local-first Electron desktop app. The three Electron processes communicate via a custom tRPCIPC bridge (the public `electron-trpc` package is incompatible with tRPC v11, so a custom implementation is used).
### Process Boundaries
``` ```
Renderer (React 19) ──ipcLink──► Preload (contextBridge) ──IPC──► Main (tRPC router + SQLite) Renderer (React) ──ipcLink──► Preload (contextBridge) ──IPC──► Main (tRPC router + SQLite)
``` ```
### Main Process (`src/main/`) 1. **Main process** (`src/main/`) — Node.js, owns the database and all business logic
- `index.ts` — Window creation, app lifecycle
- `ipc.ts` — Custom handler that bridges `ipcMain` to tRPC procedures
- `router/index.ts` — All tRPC routers (clients, projects, tasks, checkpoints, notes, settings, ai)
- `db/index.ts` — Drizzle + better-sqlite3, WAL mode, singleton `getDb()`
- `db/schema.ts` — All table definitions (clients, projects, tasks, checkpoints, notes)
- `store.ts` — electron-store for persistent UI settings (e.g., `sidebarCollapsed`)
Owns the database and all business logic. 2. **Preload** (`src/preload/trpc.ts`) — Exposes `window.electronTRPC` with `sendMessage()` / `onMessage()`
| File | Purpose | 3. **Renderer** (`src/renderer/`) — React 19, never accesses Node APIs directly
|---|---| - `lib/ipcLink.ts` — Custom TRPCLink that routes calls through `window.electronTRPC`
| `index.ts` | Window creation, app lifecycle | - `lib/trpc.ts``createTRPCReact<AppRouter>()` typed client
| `ipc.ts` | Bridges `ipcMain` to tRPC procedures | - `index.tsx` — QueryClient + tRPC + Router providers
| `router/index.ts` | All tRPC sub-routers merged into `appRouter` | - All data access is through `trpc.*.*useQuery()` / `trpc.*.*.useMutation()`
| `db/index.ts` | Drizzle + better-sqlite3, WAL mode, singleton `getDb()` |
| `db/schema.ts` | Table definitions: clients, projects, tasks, checkpoints, notes, taskComments |
| `db/vectordb.ts` | LanceDB vector store for note embeddings |
| `store.ts` | electron-store for persistent UI settings |
### Preload (`src/preload/trpc.ts`)
Exposes `window.electronTRPC` with `sendMessage()` / `onMessage()`.
### Renderer (`src/renderer/`)
React 19 — never accesses Node APIs directly. All data through `trpc.*.useQuery()` / `trpc.*.useMutation()`.
| File | Purpose |
|---|---|
| `lib/ipcLink.ts` | Custom TRPCLink routing through `window.electronTRPC` |
| `lib/trpc.ts` | `createTRPCReact<AppRouter>()` typed client |
| `index.tsx` | QueryClient + tRPC + Router providers |
### Routing ### Routing
File-based via TanStack Router (`tsr.config.json` at root). Route tree auto-generated at `routeTree.gen.ts`. File-based routing via TanStack Router. Add a file to `src/renderer/routes/` and the route tree (`src/renderer/routeTree.gen.ts`) is auto-regenerated by the Vite plugin on next `npm start`. Routes:
- `__root.tsx` — Root layout wrapping everything in `AppShell`
Routes: `__root.tsx` (AppShell layout), `index`, `tasks`, `timeline`, `projects`, `notes.$noteId` - `index.tsx`, `tasks.tsx`, `timeline.tsx`, `projects.tsx`
### tRPC Routers
`health`, `settings`, `clients`, `projects`, `tasks`, `checkpoints`, `notes`, `taskComments`, `ai`
### Database ### Database
Schema in `src/main/db/schema.ts`, migrations in `src/main/db/migrations/`. DB created in Electron's `userData` as `adiuva.db`. On startup, `initDb()` runs non-destructive migrations. Schema lives in `src/main/db/schema.ts`. Migrations are in `src/main/db/migrations/`. The DB is created in Electron's `userData` directory as `adiuva.db`. On startup, `initDb()` runs non-destructive migrations (CREATE TABLE IF NOT EXISTS).
To add a table/column: edit `schema.ts` `drizzle-kit generate` `drizzle-kit push` (dev) or commit the migration. To add a new table or column: edit `schema.ts`, run `drizzle-kit generate`, then `drizzle-kit push` (dev) or commit the migration file.
### Adding a Feature (end-to-end) ### Adding a New Feature (end-to-end pattern)
1. **Schema**`src/main/db/schema.ts` 1. **Schema** Add table/columns to `src/main/db/schema.ts`
2. **Router** — Add sub-router in `src/main/router/index.ts`, merge into `appRouter` 2. **Router** — Add a tRPC sub-router in `src/main/router/index.ts`, merge it into `appRouter`
3. **Types**Flow automatically via `AppRouter` export 3. **Types**`AppRouter` is exported from `src/main/router/index.ts` and imported in `src/renderer/lib/trpc.ts` — types flow automatically
4. **UI** — Components in `src/renderer/components/<feature>/`, data via `trpc.*.useQuery()` 4. **UI** — Create components under `src/renderer/components/<feature>/`, use `trpc.*.*useQuery()` for data
## AI Subsystem (`src/main/ai/`) ### AI Subsystem (`src/main/ai/`)
LangGraph-based agentic system with pluggable LLM providers. LangGraph-based agentic system with pluggable LLM providers (OpenAI, Anthropic, GitHub Copilot).
### Orchestrator (`orchestrator.ts`) **Orchestrator** (`orchestrator.ts`): Classifies user intent → routes to one of three specialist agents:
- **Project agent** — project-scoped Q&A with tools: `read_project_notes`, `add_task`, `get_summary`, `suggest_checkpoints`, `suggest_tasks`
- **Knowledge agent** — cross-project semantic search via `vector_search_all`
- **General agent** — workspace-wide `add_task`
Classifies user intent → routes to a specialist agent: Tool-calling strategy differs by provider: OpenAI/Anthropic use LangChain `bindTools()` + ToolMessage loop (max 5 iterations); Copilot uses SDK-native tools (loop handled internally).
| Agent | Scope | Tools | **Streaming**: Orchestrator calls `sendStreamChunk(sender, token, done)` over IPC channel `'ai:stream'`. Renderer subscribes via `window.electronAI.onStreamChunk()` in `AIChatPanel.tsx`. `<tool_call>` blocks are filtered before sending to renderer.
|---|---|---|
| Project | Project-scoped Q&A | `read_project_notes`, `add_task`, `get_summary`, `suggest_checkpoints`, `suggest_tasks` |
| Knowledge | Cross-project search | `vector_search_all` |
| General | Workspace-wide | `add_task` |
All providers use LangChain `bindTools()` + ToolMessage loop (max 5 iterations). **Provider factory** (`llm.ts`): `gpt-4o-mini` (OpenAI), `claude-sonnet-4-20250514` (Anthropic), or ChatCopilot wrapper — all with `temperature: 0.3` and streaming enabled.
Also exports `dailyBrief()` for AI-generated daily summaries (`ai.dailyBrief` tRPC mutation). **Token storage** (`token.ts`) — two-tier fallback:
1. electron-store + `safeStorage` — encrypted at rest (preferred)
2. Plain electron-store — last resort (e.g. WSL with no keyring)
### Streaming **AI approval pattern**: Tasks and checkpoints have `isAiSuggested` (bool) and `isApproved` (bool) columns. AI-suggested items appear in the UI pending user approval before being treated as real records.
`sendStreamChunk(sender, token, done)` over IPC `'ai:stream'`. Renderer subscribes via `window.electronAI.onStreamChunk()` in `AIChatPanel.tsx`. `<tool_call>` blocks are filtered before display. ### Vector Embeddings (`src/main/db/vectordb.ts`)
### Providers (`llm.ts`) LanceDB stored in `{userData}/vectors/`. Table schema: `{ id, projectId, content, vector }`. Vectors are 1536-dimensional (`text-embedding-3-small`). Embeddings use a priority chain: Copilot CLI token → OpenAI token.
| Provider | Model | Notes | - Note create/update fires `upsertNoteEmbedding()` (fire-and-forget, errors swallowed)
|---|---|---| - `migrateNotesIfNeeded()` backfills existing notes on first startup
| OpenAI | `gpt-4o-mini` | Via LangChain | - `searchNotes(query, limit=5)` is called by the Knowledge agent tool
| Anthropic | `claude-sonnet-4-20250514` | Via LangChain |
| Copilot | `ChatCopilot` wrapper | `copilot.ts` / `chat-copilot.ts` |
All use `temperature: 0.3`, streaming enabled. Provider management in `provider.ts`. ### Key Config Notes
### Token Storage (`token.ts`) - Vite configs use `.mts` extension (not `.ts`) to avoid ESM/CJS conflicts with electron-forge's externalize-deps plugin
- `@/*` path alias resolves to `src/renderer/*` (TypeScript + Vite + shadcn/ui all share this alias)
Three-tier fallback (keytar service name: `'adiuva'`): - shadcn/ui style: **new-york**, base color: **neutral**
1. keytar (OS keychain) — preferred; `keytarFailed` flag skips after first failure - Icons: **lucide-react** throughout — do not introduce other icon libraries
2. electron-store + `safeStorage` — encrypted at rest - Tailwind 4 (not 3) — use CSS variable theming via `globals.css`, not `tailwind.config.js`
3. Plain electron-store — WSL fallback - Notes use Milkdown (`@milkdown/crepe`) as the markdown editor (`src/renderer/components/notes/MilkdownEditor.tsx`)
- Routes: `index`, `tasks`, `timeline`, `projects`, `notes.$noteId` (note ID is a URL param)
### Vector Embeddings (`db/vectordb.ts`)
LanceDB in `{userData}/vectors/`. Schema: `{ id, projectId, content, vector }` (1536-dim, `text-embedding-3-small` via `embeddings.ts`). Embedding priority: Copilot CLI token → OpenAI token.
- `upsertNoteEmbedding()` on note create/update (fire-and-forget)
- `migrateNotesIfNeeded()` backfills on first startup
- `searchNotes(query, limit=5)` used by Knowledge agent
### AI Approval Pattern
Tasks and checkpoints have `isAiSuggested` + `isApproved` columns. AI suggestions appear pending user approval (dashed borders in UI).
## Config Notes
- Vite configs use `.mts` (not `.ts`) — avoids ESM/CJS conflicts with electron-forge
- `@/*` path alias → `src/renderer/*` (TypeScript + Vite + shadcn/ui)
- **shadcn/ui**: new-york style, neutral base color
- **Icons**: lucide-react only — do not introduce other icon libraries
- **Tailwind 4** — CSS variable theming in `globals.css`, no `tailwind.config.js`
- **Notes editor**: Milkdown (`@milkdown/crepe`) at `src/renderer/components/notes/MilkdownEditor.tsx`
## Design Context ## Design Context
### Target User ### Users
Freelancers and solo professionals managing client work (projects, tasks, notes, timelines). Single workspace, no enterprise overhead. AI as force multiplier. Freelancers and solo professionals managing their own client work projects, tasks, notes, and timelines. They work alone and need a single workspace that keeps everything organized without the overhead of enterprise tools. The AI assistant is a force multiplier, helping them stay on top of their workload.
### Brand ### Brand Personality
**Calm, intelligent, warm.** Thoughtful companion, not flashy tool. Confident and understated, never loud or gamified. **Calm, intelligent, warm.** Adiuva is a thoughtful companion, not a flashy tool. It should feel like a well-organized desk — everything in its place, nothing competing for attention. The tone is confident and understated, never loud or gamified.
### Palette ### Aesthetic Direction
- **Visual tone**: Editorial, premium, content-first. Inspired by Notion's clean typography and warm neutrals, but with a distinct identity through the warm pinkish-white canvas and golden yellow accent
| | Canvas | Primary | Secondary | Borders | - **Light mode**: Soft and warm — pinkish-white (`#f4edf3`) canvas, golden yellow (`#fbc881`) primary, slate blue-gray (`#8a8ea9`) secondary, dusty lavender borders (`#c8c3cd`)
|---|---|---|---|---| - **Dark mode**: Stark monochrome — near-black canvas (`#0c0c0c`), crisp white text, dark gray surfaces (`#323232`). No color accent; primary is pure white
| **Light** | Pinkish-white `#f4edf3` | Golden yellow `#fbc881` | Slate blue-gray `#8a8ea9` | Dusty lavender `#c8c3cd` | - **Typography**: Geist (geometric sans-serif) at 400/500/600. Tight tracking on large headings (`-1px`). Body at `text-sm`, metadata at `text-xs`
| **Dark** | Near-black `#0c0c0c` | Pure white | — | Dark gray `#323232` | - **Corners**: 10px base radius, consistently rounded. Chat elements use `rounded-2xl`
- **Signature effects**: Glassmorphism on AI inputs/floating chat (`backdrop-blur-xl`, transparency). Spring physics animations (stiffness 400, damping 30). Subtle scale-and-fade transitions
### Typography - **Anti-references**: No gamification (badges, streaks, confetti). No corporate/enterprise density. Keep it mature and professional
Geist sans-serif, weights 400/500/600. Tight tracking (`-1px`) on headings. Body `text-sm`, metadata `text-xs`.
### Visual Language
- 10px border-radius, `rounded-2xl` for chat elements
- Glassmorphism on AI inputs (`backdrop-blur-xl`, transparency)
- Spring animations (stiffness 400, damping 30), scale-and-fade transitions
- No gamification (badges, streaks, confetti). Mature and professional
### Design Principles ### Design Principles
1. **Clarity over cleverness** — Clear hierarchy, generous whitespace, comfortable density
2. **AI as quiet partner** — Deeply integrated but never intrusive. Dashed borders for pending AI items, Sparkles icon as AI marker 1. **Clarity over cleverness** — Every element should communicate its purpose instantly. Prefer clear hierarchy and whitespace over decorative flourish. Information density should feel comfortable, not cramped.
3. **Warmth in restraint** — Warm palette feels approachable without being playful. Dark mode trades warmth for focus
4. **Motion with purpose** — Animations reinforce spatial relationships, never decorative 2. **AI as quiet partner** — The AI is deeply integrated (floating chat, suggestions) but never intrusive. AI-suggested items use dashed borders to signal "pending." The Sparkles icon is the consistent AI identity marker.
5. **Confidence through consistency** — CSS variable tokens, shadcn/ui primitives, Geist font. Predictable, keyboard-first
3. **Warmth in restraint** — The palette is deliberately warm (pinkish whites, golden yellows) to feel approachable without being playful. Dark mode trades warmth for focus. Let the content breathe.
4. **Motion with purpose** — Spring physics and glassmorphism create a sense of physicality and depth. Animations should feel natural and responsive, never decorative or slow. Every transition should reinforce spatial relationships.
5. **Confidence through consistency** — Use the established token system (CSS variables, shadcn/ui primitives, Geist font). The user should feel in control — predictable patterns, keyboard-first interactions, no surprises.

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{
"permissions": {
"allow": [
"Bash(git add AI_REFACTOR_PLAN.md)",
"Bash(git commit:*)"
]
}
}

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.gitea/workflows/build.yaml Normal file
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name: Release Electron App
run-name: Releasing ${{ gitea.ref_name }}
on:
push:
tags:
- 'v*'
jobs:
release-desktop:
runs-on: ubuntu-latest
container:
image: electronuserland/builder:wine
steps:
- name: Checkout Code
uses: actions/checkout@v4
- name: Install System Dependencies for Linux Makers
run: |
apt-get update
apt-get install -y fakeroot dpkg mono-complete
- name: Install Dependencies
run: npm install
- name: Set version from tag
run: npm version "${GITHUB_REF_NAME#v}" --no-git-tag-version --allow-same-version
- name: Make App (Linux)
run: npm run make -- --platform=linux --arch=x64
- name: Initialize Wine
run: |
export WINEDEBUG=-all
export DISPLAY=
wineboot --init
env:
WINEDEBUG: '-all'
- name: Make App (Windows)
run: npm run make -- --platform=win32 --arch=x64
env:
WINEDEBUG: '-all'
- name: Create Gitea Release
run: |
GITEA_URL="http://10.0.0.119:3000"
TAG="${GITHUB_REF_NAME}"
REPO="${GITHUB_REPOSITORY}"
TOKEN="${{ gitea.token }}"
# Check if release exists, create if not
RELEASE_ID=$(curl -sf \
-H "Authorization: token ${TOKEN}" \
"${GITEA_URL}/api/v1/repos/${REPO}/releases/tags/${TAG}" \
| grep -o '"id":[0-9]*' | head -1 | cut -d: -f2)
if [ -z "$RELEASE_ID" ]; then
RELEASE_ID=$(curl -sf \
-X POST \
-H "Authorization: token ${TOKEN}" \
-H "Content-Type: application/json" \
-d "{\"tag_name\":\"${TAG}\",\"name\":\"${TAG}\"}" \
"${GITEA_URL}/api/v1/repos/${REPO}/releases" \
| grep -o '"id":[0-9]*' | head -1 | cut -d: -f2)
fi
echo "Release ID: ${RELEASE_ID}"
echo "RELEASE_ID=${RELEASE_ID}" >> "$GITHUB_ENV"
- name: Upload Release Assets
shell: bash
run: |
GITEA_URL="http://10.0.0.119:3000"
REPO="${GITHUB_REPOSITORY}"
TOKEN="${{ gitea.token }}"
MAX_RETRIES=3
upload_file() {
local file="$1"
local name
name=$(basename "$file")
local encoded_name
encoded_name=$(printf '%s' "$name" | sed 's/ /%20/g')
local attempt=1
while [ $attempt -le $MAX_RETRIES ]; do
local filesize
filesize=$(stat -c%s "$file" 2>/dev/null || stat -f%z "$file" 2>/dev/null || echo "unknown")
echo "Uploading ${name} (${filesize} bytes, attempt ${attempt}/${MAX_RETRIES})..."
RESPONSE=$(curl -s -w "\n%{http_code}" \
--max-time 300 \
-X POST \
-H "Authorization: token ${TOKEN}" \
-H "Expect:" \
-F "attachment=@${file}" \
"${GITEA_URL}/api/v1/repos/${REPO}/releases/${RELEASE_ID}/assets?name=${encoded_name}")
HTTP_CODE=$(echo "$RESPONSE" | tail -1)
BODY=$(echo "$RESPONSE" | head -n -1)
if [ "$HTTP_CODE" -ge 200 ] 2>/dev/null && [ "$HTTP_CODE" -lt 300 ] 2>/dev/null; then
echo "✅ Uploaded ${name}"
return 0
fi
echo "⚠️ Upload failed (HTTP ${HTTP_CODE}), body: ${BODY}"
echo "Retrying in 5s..."
sleep 5
attempt=$((attempt + 1))
done
echo "❌ Failed to upload ${name} after ${MAX_RETRIES} attempts"
return 1
}
FAILED=0
while IFS= read -r -d '' file; do
upload_file "$file" || FAILED=1
done < <(find out/make -type f \( -name "*.exe" -o -name "*.zip" -o -name "*.deb" -o -name "*.rpm" \) -print0)
if [ $FAILED -eq 1 ]; then
echo "Some uploads failed"
exit 1
fi

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# AI Refactor Plan — Adiuva Electron App
> **Objective:** Transform the Electron app from a single-process AI integration into a local-first multi-agent client with plugin-based batch agents, multi-provider LLM support, E2E encrypted backup, granular permissions, and cloud backend integration.
>
> **Backend:** Lives in a separate repository. See `BACKEND_PLAN.md` for the API contract and backend implementation guide.
>
> **Protocol:** Execute steps sequentially. Each step is atomic and committable. Mark `[x]` when done.
---
## Phase 0 — API Contracts & Types
### Step 0.1 — Define backend API contract types
- [ ] Create `src/shared/api-types.ts` with all interfaces the Electron app needs to communicate with the backend:
- `ExecutionPlan`, `PlanStep`, `PlanAction` (action types: `create_record`, `update_record`, `delete_record`, `index_document`, `send_notification`)
- `ChatRequest` (message, context, execution_mode: `'direct'` | `'plan'`)
- `ChatResponse` (response, actions)
- `ChatContext` (user_profile, relevant_documents, recent_tasks, conversation_history)
- `AgentManifest` (name, description, permissions, schedule)
- `PermissionGrant` (plugin, permission type, resource path, granted_at)
- `BackupMetadata` (version, timestamp, checksum, chunk_count)
- `BillingTier` enum (`free`, `pro`, `power`, `team`)
- `AuthTokens` (access_token, refresh_token, expires_at)
- `UserProfile` (id, email, tier)
- [ ] Update `tsconfig.json` paths if needed to include `src/shared/`
- **Files:** `src/shared/api-types.ts`, `tsconfig.json`
- **Outcome:** Type-safe contracts for all backend communication. Backend repo mirrors these as Pydantic schemas.
---
## Phase 1 — LiteLLM Multi-Provider Client
### Step 1.1 — Create unified LLM client wrapper
- [ ] Create `src/main/llm/litellm-client.ts`:
- `LiteLLMClient` class with unified interface:
- `complete(messages: Message[], options?: CompletionOptions): Promise<CompletionResponse>`
- `stream(messages: Message[], options?: CompletionOptions): AsyncGenerator<string>`
- `embed(text: string): Promise<number[]>`
- `CompletionOptions`: model override, temperature, max_tokens, tools
- Provider-agnostic: internally maps to the correct provider SDK
- Fallback chain: tries primary provider, on failure tries secondary, logs each attempt
- Timeout handling: per-provider configurable timeouts
- [ ] Create `src/main/llm/providers.ts`:
- `ProviderConfig` interface: name, apiKey, model, endpoint (for Ollama), timeout, isLocal
- `ProviderRegistry`: manages configured providers, persists to electron-store
- `getActiveProvider()`, `setActiveProvider(name)`, `addProvider(config)`, `removeProvider(name)`
- `getFallbackChain(): ProviderConfig[]`
- Supported providers: OpenAI, Anthropic, Google (Gemini), Mistral, Groq, Ollama (local)
- [ ] Create `src/main/llm/embeddings.ts` (refactored):
- Support multiple embedding providers (OpenAI text-embedding-3-small, local ONNX with all-MiniLM-L6-v2)
- Auto-select: use local ONNX if available, fall back to API
- Same `embedText(text): Promise<number[]>` interface
- **Files:** `src/main/llm/litellm-client.ts`, `src/main/llm/providers.ts`, `src/main/llm/embeddings.ts`
- **Outcome:** Single LLM interface that all local components use. Supports 6+ providers with fallback.
### Step 1.2 — Migrate existing AI code to use new LLM client
- [ ] Update `src/main/ai/orchestrator.ts`:
- Replace direct `getLLM()` calls with `LiteLLMClient.complete()` / `LiteLLMClient.stream()`
- Keep local orchestration working with the new client (backend delegation comes in Phase 3)
- [ ] Update `src/main/ai/llm.ts`:
- Deprecate. Redirect `getLLM()` to instantiate via `LiteLLMClient` as a thin compatibility shim
- [ ] Update `src/main/ai/embeddings.ts` to delegate to `src/main/llm/embeddings.ts`
- [ ] Update `src/main/ai/token.ts`:
- Add `listStoredProviders(): Promise<string[]>` to enumerate which providers have tokens
- [ ] Ensure all existing AI features (chat, daily brief, tool calling) continue to work
- **Files:** `src/main/ai/orchestrator.ts`, `src/main/ai/llm.ts`, `src/main/ai/embeddings.ts`, `src/main/ai/token.ts`
- **Outcome:** Existing AI features work identically but go through the new unified LLM client.
---
## Phase 2 — Local Plugin System & Batch Agents
### Step 2.1 — Create plugin manifest system and permission manager
- [ ] Create `src/main/permissions/manifest-validator.ts`:
- `PluginManifest` interface: `name`, `description`, `version`, `permissions: PermissionRequest[]`, `schedule?: string` (cron), `entryPoint: string`
- `PermissionRequest`: `type` (read_folder, read_email, read_calendar, read_browser_history), `resource?: string` (path, account), `reason: string`
- `validateManifest(manifest): ValidationResult` — validates structure, checks for dangerous permissions
- [ ] Create `src/main/permissions/permission-manager.ts`:
- `PermissionManager` class (singleton):
- `grantPermission(pluginName, permission): void` — persists to SQLite
- `revokePermission(pluginName, permission): void`
- `checkPermission(pluginName, permission): boolean`
- `getPluginPermissions(pluginName): PermissionGrant[]`
- `getAllGrants(): PermissionGrant[]`
- `logAccess(pluginName, permission, resource, timestamp): void` — activity log
- `getActivityLog(pluginName?, limit?): ActivityLogEntry[]`
- Permission grants stored in a new `plugin_permissions` SQLite table
- Activity log stored in a new `plugin_activity_log` SQLite table
- [ ] Add `plugin_permissions` and `plugin_activity_log` tables to `src/main/db/schema.ts`
- [ ] Generate and apply migration
- **Files:** `src/main/permissions/manifest-validator.ts`, `src/main/permissions/permission-manager.ts`, `src/main/db/schema.ts`, `src/main/db/migrations/`
- **Outcome:** Granular, opt-in permission system for plugins. Every access is logged.
### Step 2.2 — Create worker pool and batch runner
- [ ] Create `src/main/workers/worker-pool.ts`:
- `WorkerPool` class:
- Manages a pool of Node.js `worker_threads`
- `runPlugin(manifest, context): Promise<PluginResult>` — spawns or reuses a worker, sends manifest + context, receives result
- Worker lifecycle: create, send message, receive result, terminate on timeout
- Max concurrent workers: configurable (default 4)
- Error isolation: worker crash doesn't affect main process
- [ ] Create `src/main/workers/batch-runner.ts`:
- `BatchRunner` class:
- `registerPlugin(manifest): void` — validates manifest, stores in registry
- `startScheduler(): void` — cron-based scheduler using `node-cron` or simple setInterval
- `runPlugin(name, triggerContext?): Promise<PluginResult>` — manual trigger
- `stopAll(): void` — graceful shutdown of all scheduled plugins
- Scheduler checks permissions before each run; skips if revoked
- Results logged to activity log
- [ ] Create `src/main/workers/plugin-worker.ts`:
- Worker thread entry point
- Receives plugin config + context via `parentPort.on('message')`
- Dynamically imports the plugin entry point
- Executes `run(context)` with sandboxed access (only permitted resources)
- Posts result back via `parentPort.postMessage()`
- **Files:** `src/main/workers/worker-pool.ts`, `src/main/workers/batch-runner.ts`, `src/main/workers/plugin-worker.ts`
- **Outcome:** Isolated plugin execution environment with scheduling, permissions enforcement, and error isolation.
### Step 2.3 — Implement batch agent plugins
- [ ] Create `src/plugins/email-scanner.ts`:
- Manifest: requires `read_email` permission
- Connects to IMAP via `imapflow` (account configured in settings)
- Scans for new emails since last run
- Uses `LiteLLMClient` to classify each email (has actionable task? extract title, priority, description)
- Returns extracted task metadata (never raw email content) for execution via backend or local playbook
- [ ] Create `src/plugins/file-watcher.ts`:
- Manifest: requires `read_folder` permission for each watched path
- Uses `chokidar` to watch approved directories
- On new/modified file: reads content, generates embedding, upserts into vector store
- Supports: .txt, .md, .pdf (text extraction), .docx (basic extraction)
- [ ] Create `src/plugins/calendar-sync.ts`:
- Manifest: requires `read_calendar` permission
- Parses ICS files or connects to CalDAV endpoint
- Detects scheduling conflicts
- Suggests reorganizations via LLM analysis
- Returns calendar events + conflict reports
- [ ] Create `src/plugins/browser-agent.ts`:
- Manifest: requires `read_browser_history` permission (explicit opt-in)
- Reads browser bookmarks and history from known browser paths (Chrome, Firefox, Edge)
- Indexes relevant entries into vector store
- Privacy-first: only indexes URLs and titles, not page content
- **Files:** `src/plugins/email-scanner.ts`, `src/plugins/file-watcher.ts`, `src/plugins/calendar-sync.ts`, `src/plugins/browser-agent.ts`
- **Outcome:** Four local batch agents running as isolated worker threads, using LiteLLM for analysis.
---
## Phase 3 — Backend Integration
### Step 3.1 — Create backend HTTP/WebSocket client
- [ ] Create `src/main/api/backend-client.ts`:
- `BackendClient` class:
- `baseUrl` configurable (default: production cloud URL, overridable for dev)
- `setAuthToken(jwt: string): void`
- `chat(request: ChatRequest): Promise<ChatResponse>` — POST /api/v1/chat
- `chatStream(request: ChatRequest): AsyncGenerator<string>` — WebSocket /api/v1/chat/stream
- `getPlaybooks(): Promise<ExecutionPlan[]>` — GET /api/v1/plans/playbook
- `uploadBackup(blob: Buffer, metadata: BackupMetadata): Promise<void>` — PUT /api/v1/backup
- `downloadBackup(): Promise<{ blob: Buffer, metadata: BackupMetadata }>` — GET /api/v1/backup
- Automatic retry with exponential backoff (max 3 attempts)
- Offline detection: returns cached playbook responses when offline
- `isOnline(): boolean` — connectivity check
- [ ] Create `src/main/api/plan-runner.ts`:
- `PlanRunner` class:
- `execute(plan: ExecutionPlan): Promise<PlanResult>` — executes plan steps locally
- Step handlers: `create_record` (inserts into SQLite), `update_record`, `delete_record`, `index_document` (upserts into vector store), `send_notification` (Electron notification API)
- Each step logs to activity log
- Supports `data_from_step` references (pipeline execution)
- Validates plan structure before execution
- **Files:** `src/main/api/backend-client.ts`, `src/main/api/plan-runner.ts`
- **Outcome:** Electron can communicate with the cloud backend and execute returned plans locally.
### Step 3.2 — Refactor orchestrator to delegate to backend
- [ ] Update `src/main/ai/orchestrator.ts`:
- When online: forward chat requests to backend via `BackendClient.chatStream()`
- Build `ChatRequest` from local context: query SQLite for user profile, relevant documents (from vector store), recent tasks, conversation history
- Stream backend response tokens to renderer via existing `ai:stream` IPC channel
- Execute any returned actions via `PlanRunner`
- When offline: fall back to local orchestration (existing LangGraph pipeline) with degraded capabilities
- Remove direct agent logic (project agent, knowledge agent, general agent tool definitions) — these now live on the backend
- Keep `buildProjectContext()` and `buildGlobalContext()` as context builders for the request payload
- [ ] Update `src/main/router/index.ts` `ai` sub-router:
- `chat` mutation: call refactored orchestrator (which now delegates to backend)
- Add `getPlaybooks` query: fetches cached playbooks
- Keep `dailyBrief` mutation: sends daily brief request to backend
- [ ] Add IPC handler for plan execution results
- **Files:** `src/main/ai/orchestrator.ts`, `src/main/router/index.ts`, `src/main/ipc.ts`
- **Outcome:** Chat intelligence lives on the backend; Electron is the execution layer.
### Step 3.3 — Implement Shared Memory (three-tier local memory)
- [ ] Create `src/main/database/shared-memory.ts`:
- **Short-term memory**: In-memory conversation buffer
- `ConversationBuffer` class: stores last N messages per session
- `addMessage(sessionId, role, content)`, `getHistory(sessionId, limit?) -> Message[]`
- Cleared on session end
- **Long-term KV store**: SQLite-backed key-value store
- New `agent_memory` table: `id`, `namespace` (agent name), `key`, `value` (JSON text), `updated_at`
- `AgentMemoryStore` class: `get(namespace, key)`, `set(namespace, key, value)`, `delete(namespace, key)`, `listKeys(namespace)`
- Used by agents to persist learned facts, user preferences
- **Vector store**: Already exists (LanceDB). Enhance with:
- Multi-collection support: separate tables for notes, emails, files, calendar
- `searchByCollection(collection, query, limit) -> SearchResult[]`
- [ ] Add `agent_memory` table to `src/main/db/schema.ts`
- [ ] Generate migration
- **Files:** `src/main/database/shared-memory.ts`, `src/main/db/schema.ts`, `src/main/db/migrations/`
- **Outcome:** Three-tier memory system supporting short-term conversation, long-term agent facts, and semantic search.
---
## Phase 4 — Security: E2E Backup & Offline Mode
### Step 4.1 — Implement E2E encrypted backup
- [ ] Create `src/main/backup/e2e-crypto.ts`:
- `generatePassphrase(): string` — BIP39-compatible 12-word recovery phrase
- `deriveKey(passphrase: string, salt: Buffer): Promise<Buffer>` — Argon2id key derivation (time cost 3, memory 64MB, parallelism 1)
- `encrypt(data: Buffer, key: Buffer): { ciphertext: Buffer, iv: Buffer, authTag: Buffer }` — AES-256-GCM
- `decrypt(ciphertext: Buffer, key: Buffer, iv: Buffer, authTag: Buffer): Buffer`
- Uses `node:crypto` for AES and `argon2` npm package for key derivation
- [ ] Create `src/main/backup/backup-manager.ts`:
- `BackupManager` class:
- `createBackup(passphrase: string): Promise<BackupBlob>` — Exports SQLite DB, encrypts, returns blob + metadata
- `restoreBackup(blob: Buffer, passphrase: string): Promise<void>` — Decrypts blob, replaces local DB, re-initializes
- `uploadBackup(passphrase: string): Promise<void>` — Creates backup, uploads via `BackendClient`
- `downloadAndRestore(passphrase: string): Promise<void>` — Downloads from backend, decrypts, restores
- Incremental backup: chunks DB into segments, encrypts each separately, tracks content hashes to skip unchanged chunks
- Metadata header: version, timestamp, checksum (SHA-256 of plaintext), chunk count
- **Files:** `src/main/backup/e2e-crypto.ts`, `src/main/backup/backup-manager.ts`
- **Outcome:** User data never leaves the device unencrypted. Backend stores only opaque blobs.
### Step 4.2 — Implement offline sync queue
- [ ] Create `src/main/backup/sync-queue.ts`:
- `SyncQueue` class:
- `enqueue(action: QueuedAction): void` — Adds action to persistent queue (SQLite table `sync_queue`)
- `processQueue(): Promise<void>` — Processes queued actions in FIFO order when online
- `getQueueSize(): number`
- `clearQueue(): void`
- Conflict resolution: last-write-wins with timestamps
- New `sync_queue` table: `id`, `action_type`, `payload` (JSON), `created_at`, `status` (pending/processing/failed), `retry_count`, `last_error`
- Auto-drain: watches connectivity, starts processing when online
- Failed actions: retry up to 3 times with exponential backoff, then mark as `failed` for user review
- [ ] Add `sync_queue` table to schema
- [ ] Integrate with `BackendClient`: when offline, chat/backup calls enqueue instead of failing
- **Files:** `src/main/backup/sync-queue.ts`, `src/main/db/schema.ts`, `src/main/api/backend-client.ts`
- **Outcome:** App works offline; queued actions sync automatically when connectivity returns.
---
## Phase 5 — Auth Integration & Database Encryption
### Step 5.1 — Integrate auth into Electron app
- [ ] Create `src/main/auth/auth-manager.ts`:
- `AuthManager` class:
- `login(email, password): Promise<void>` — Calls backend POST /api/v1/auth/login, stores JWT in secure storage (via token.ts)
- `register(email, password): Promise<void>` — Calls POST /api/v1/auth/register
- `logout(): void` — Clears stored JWT
- `getToken(): string | null` — Returns current JWT
- `refreshToken(): Promise<void>` — Auto-refresh before expiry
- `isAuthenticated(): boolean`
- `getCurrentTier(): BillingTier`
- Auto-refresh: checks token expiry every 5 minutes, refreshes if < 10 minutes remaining
- [ ] Add tRPC procedures: `auth.login`, `auth.register`, `auth.logout`, `auth.status`, `auth.tier`
- [ ] Wire `BackendClient` to use `AuthManager.getToken()` for all requests
- **Files:** `src/main/auth/auth-manager.ts`, `src/main/router/index.ts`, `src/main/api/backend-client.ts`
- **Outcome:** Electron app has full auth flow; backend requests are authenticated.
### Step 5.2 — Migrate from better-sqlite3 to SQLCipher
- [ ] Add `@journeyapps/sqlcipher` to dependencies (replaces `better-sqlite3`)
- [ ] Update `src/main/db/index.ts`:
- Replace `better-sqlite3` import with `@journeyapps/sqlcipher`
- On first launch: derive DB key from OS keychain or prompt user
- `initDb(password)`: opens DB with `PRAGMA key = 'password'`
- Migration path for existing unencrypted DBs: detect export create encrypted import delete old
- WAL mode still enabled after keying
- [ ] Update `src/main/index.ts`: pass password to `initDb()`
- [ ] Test that all existing Drizzle operations work with SQLCipher
- **Files:** `package.json`, `src/main/db/index.ts`, `src/main/index.ts`
- **Outcome:** All local data encrypted at rest with SQLCipher.
---
## Phase 6 — Renderer UI Updates
### Step 6.1 — Update Settings page for multi-provider config
- [ ] Add provider management UI to Settings:
- List of configured providers with status (active/inactive/error)
- Add provider form: name dropdown (OpenAI, Anthropic, Google, Mistral, Groq, Ollama), API key input, model selection, endpoint (for Ollama)
- Set primary and fallback providers
- Test connection button per provider
- [ ] Add auth section to Settings:
- Login/register form
- Current tier display with upgrade CTA
- Logout button
- [ ] Add backup section to Settings:
- Create/view recovery passphrase
- Manual backup trigger
- Backup history with restore points
- Auto-backup schedule toggle
- **Files:** `src/renderer/components/settings/` (new), route file
- **Outcome:** Users can manage AI providers, auth, and backups from Settings.
### Step 6.2 — Add Permission Dialog and Activity Log
- [ ] Create `src/renderer/components/permissions/PermissionDialog.tsx`:
- Modal shown when a plugin requests new permissions
- Lists requested permissions with reasons
- Per-permission approve/deny toggles
- Shows plugin manifest info (name, description, version)
- [ ] Create `src/renderer/components/permissions/ActivityLog.tsx`:
- Filterable table of all plugin activity
- Columns: timestamp, plugin name, action type, resource, status
- Filter by plugin, date range, action type
- Export as CSV
- [ ] Add tRPC procedures for permission management and activity log queries
- **Files:** `src/renderer/components/permissions/PermissionDialog.tsx`, `src/renderer/components/permissions/ActivityLog.tsx`, `src/main/router/index.ts`
- **Outcome:** Transparent permission system with full activity audit trail.
### Step 6.3 — Update AIChatPanel for backend-powered chat
- [ ] Update `src/renderer/hooks/useAIChat.ts`:
- Support WebSocket streaming from backend (when online)
- Fall back to IPC streaming (when offline, using local orchestrator)
- Add connection status indicator (online/offline/reconnecting)
- Support execution plan responses: show plan preview, allow user to approve/modify before execution
- [ ] Update `src/renderer/components/ai/AIChatPanel.tsx`:
- Add connection status badge
- Add tier indicator (shows current plan limitations)
- Plan approval UI: expandable plan steps with approve/reject buttons
- Enhanced error states: differentiate between offline, auth expired, rate limited, server error
- [ ] Update `src/renderer/components/ai/FloatingChat.tsx`:
- Same streaming changes as AIChatPanel
- Compact plan approval for inline context
- **Files:** `src/renderer/hooks/useAIChat.ts`, `src/renderer/components/ai/AIChatPanel.tsx`, `src/renderer/components/ai/FloatingChat.tsx`
- **Outcome:** Chat UI seamlessly handles both online (backend) and offline (local) modes.
---
## Phase 7 — Cleanup & Hardening
### Step 7.1 — Remove deprecated AI code
- [ ] Delete `src/main/ai/copilot.ts` (Copilot SDK replaced by LiteLLM)
- [ ] Delete `src/main/ai/chat-copilot.ts` (LangChain adapter no longer needed)
- [ ] Delete or archive `src/main/ai/llm.ts` (replaced by `src/main/llm/litellm-client.ts`)
- [ ] Remove `@github/copilot-sdk`, `@langchain/langgraph` from dependencies (if unused)
- [ ] Clean up `src/main/ai/provider.ts`: simplify to delegate to `src/main/llm/providers.ts`
- [ ] Remove `currentSender` module-level mutable state from orchestrator (proper context passing)
- [ ] Update `src/main/index.ts` startup: remove `import './ai/copilot'`, add `BatchRunner.startScheduler()`, add `AuthManager` init
- **Files:** Multiple files under `src/main/ai/`, `package.json`, `src/main/index.ts`
- **Outcome:** No dead code; clean, maintainable codebase.
### Step 7.2 — Add error handling and logging
- [ ] Implement structured logging in main process:
- Log levels: debug, info, warn, error
- Log destinations: console (dev), file (production, rotated)
- Correlation IDs for request tracing across IPC backend response
- [ ] Add error boundaries in renderer:
- Per-route error boundaries
- AI chat error boundary (graceful degradation)
- Plugin error boundary (shows which plugin failed)
- **Files:** `src/main/utils/logger.ts` (new), `src/renderer/components/ErrorBoundary.tsx` (new)
- **Outcome:** Production-ready error handling and observability.
### Step 7.3 — Electron integration tests
- [ ] Test BackendClient with mocked HTTP responses
- [ ] Test PlanRunner with sample execution plans
- [ ] Test SyncQueue offline online transition
- [ ] Test BackupManager encrypt decrypt round-trip
- [ ] Test PermissionManager grant check revoke cycle
- **Files:** `src/main/__tests__/` (new test directory)
- **Outcome:** Confidence that all Electron-side components work correctly.
---
## New Dependencies (package.json)
| Package | Purpose |
|---|---|
| `@journeyapps/sqlcipher` | Encrypted SQLite (replaces `better-sqlite3`) |
| `argon2` | Key derivation for E2E backup |
| `node-cron` | Batch agent scheduling |
| `chokidar` | File watching (FileWatcher plugin) |
| `imapflow` | IMAP client (EmailScanner plugin) |
| `onnxruntime-node` | Local embeddings (optional) |
---
## Execution Notes
- **Each step is independently committable** and produces working code.
- **Phases 1-2** (LLM client + plugins) are independent of the backend can start immediately.
- **Phase 3** (backend integration) requires the backend repo to have the `/api/v1/chat` endpoint ready.
- **Phase 5.2** (SQLCipher) is intentionally late to avoid encryption overhead during active schema changes.
- **The existing app continues to work** throughout the migration. Local orchestration is preserved until backend is ready (Step 3.2).

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@@ -1,358 +0,0 @@
# Backend Plan — Adiuva Cloud API
> **Separate repository.** This document defines the FastAPI backend that the Electron app communicates with.
>
> The backend owns: orchestration logic, chat agent intelligence, prompt IP, auth, billing, and backup blob storage.
> The backend NEVER persists user data. It receives context in requests, uses it for orchestration, and discards it.
---
## Project Structure
```
adiuva-backend/
├── app/
│ ├── __init__.py
│ ├── main.py # FastAPI entry + CORS + lifespan + router includes
│ ├── core/
│ │ ├── __init__.py
│ │ ├── agent_registry.py # Base classes + singleton registry
│ │ ├── orchestrator.py # LLM-based intent router
│ │ ├── execution_plan.py # Plan builder + cache
│ │ └── plugin_loader.py # Dynamic agent loading
│ ├── agents/
│ │ ├── __init__.py # Auto-registers all agents
│ │ ├── task_agent.py
│ │ ├── calendar_agent.py
│ │ ├── email_agent.py
│ │ └── analytics_agent.py
│ ├── api/
│ │ ├── __init__.py
│ │ ├── routes/
│ │ │ ├── __init__.py
│ │ │ ├── chat.py # POST /chat + WS /chat/stream
│ │ │ ├── plans.py # GET /plans/playbook
│ │ │ ├── backup.py # PUT/GET /backup
│ │ │ ├── auth.py # Register/login/refresh
│ │ │ └── billing.py # Checkout/webhook/subscription
│ │ └── middleware/
│ │ ├── __init__.py
│ │ ├── auth.py # JWT validation
│ │ ├── rate_limit.py # Tier-aware rate limiting
│ │ └── sanitizer.py # Strip prompt metadata from responses
│ ├── billing/
│ │ ├── __init__.py
│ │ ├── stripe_service.py # Stripe checkout + webhooks
│ │ └── tier_manager.py # Feature matrix per tier
│ └── config/
│ ├── __init__.py
│ └── settings.py # Pydantic BaseSettings (env-based)
├── tests/
│ ├── __init__.py
│ ├── conftest.py # Fixtures: test client, mock agents, mock LLM
│ ├── test_orchestrator.py
│ ├── test_agents.py
│ ├── test_auth.py
│ └── test_backup.py
├── alembic/ # DB migrations (auth/billing tables only)
│ ├── alembic.ini
│ └── versions/
├── requirements.txt
├── Dockerfile
├── docker-compose.yml # App + PostgreSQL + Redis (dev)
├── .env.example
└── README.md
```
---
## Step-by-Step Implementation
### Step 1 — Project scaffolding
- [ ] Initialize repo with the directory structure above
- [ ] Write `requirements.txt`:
```
fastapi>=0.115.0
uvicorn[standard]>=0.34.0
langchain>=0.3.0
langchain-openai>=0.3.0
pydantic>=2.10.0
python-jose[cryptography]>=3.3.0
stripe>=11.0.0
boto3>=1.35.0
slowapi>=0.1.9
sqlalchemy>=2.0.0
asyncpg>=0.30.0
alembic>=1.14.0
bcrypt>=4.2.0
python-dotenv>=1.0.0
httpx>=0.28.0
websockets>=14.0
pytest>=8.0.0
pytest-asyncio>=0.24.0
```
- [ ] Write `app/main.py`: FastAPI app with CORS (allow `app://`, `http://localhost:*`), lifespan (init DB pool, init agent registry), include all routers under `/api/v1`
- [ ] Write `app/config/settings.py`: `Settings(BaseSettings)` with fields: `DATABASE_URL`, `JWT_SECRET`, `JWT_ALGORITHM` (default HS256), `STRIPE_SECRET_KEY`, `STRIPE_WEBHOOK_SECRET`, `S3_BUCKET`, `S3_REGION`, `AWS_ACCESS_KEY_ID`, `AWS_SECRET_ACCESS_KEY`, `OPENAI_API_KEY`, `CORS_ORIGINS`, `ENV` (dev/prod)
- [ ] Write `Dockerfile`: Python 3.12 slim, multi-stage (builder + runtime), non-root user
- [ ] Write `docker-compose.yml`: app, postgres:16, optional redis
- [ ] Write `.env.example`
- **Outcome:** Runnable FastAPI skeleton (returns 404 on all routes).
### Step 2 — Pydantic schemas (API contracts)
- [ ] Create `app/schemas.py` (mirrors `src/shared/api-types.ts` from Electron repo):
- `ChatRequest`: `message: str`, `context: ChatContext`, `execution_mode: Literal['direct', 'plan']`
- `ChatContext`: `user_profile: dict`, `relevant_documents: list[str]`, `recent_tasks: list[dict]`, `conversation_history: list[dict]`
- `ChatResponse`: `response: str`, `actions: list[PlanAction]`
- `PlanAction`: `type: Literal['create_record', 'update_record', 'delete_record', 'index_document', 'send_notification']`, `table: str | None`, `data: dict | None`
- `ExecutionPlan`: `agent: str`, `steps: list[PlanStep]`
- `PlanStep`: `action: str`, `prompt_template: str | None`, `variables: dict | None`, `data_from_step: int | None`
- `BackupMetadata`: `version: int`, `timestamp: int`, `checksum: str`, `chunk_count: int`
- `BillingTier`: `Literal['free', 'pro', 'power', 'team']`
- `AuthTokens`: `access_token: str`, `refresh_token: str`, `expires_at: int`
- `UserProfile`: `id: str`, `email: str`, `tier: BillingTier`
- **Outcome:** All request/response models defined and validated.
### Step 3 — Agent Registry + base classes
- [ ] `app/core/agent_registry.py`:
- `BaseAgent(ABC)`:
- `user_id: str`, `shared_memory: dict`, `vector_store_context: list[str]`, `skills: list[str]`
- Abstract `get_name() -> str`, `get_description() -> str`
- `ChatAgent(BaseAgent)`:
- Abstract `async handle(query: str, context: dict) -> str`
- Abstract `get_tools() -> list` (LangChain tool definitions)
- Concrete `_tool_loop(llm, messages, tools, max_iter=5) -> str` — shared tool-calling loop
- `AgentRegistry` (singleton):
- `_agents: dict[str, ChatAgent]`
- `register(agent_class)` — decorator pattern
- `get(name) -> ChatAgent`
- `list_agents() -> list[dict]` — returns `[{name, description}]` for orchestrator prompt
- `async call_agent(name, query, context) -> str` — for inter-agent calls
- [ ] Unit tests: register, get, list, call_agent with mock
- **Outcome:** Pluggable agent framework.
### Step 4 — Orchestrator
- [ ] `app/core/orchestrator.py`:
- `async classify_intent(message, context, registry) -> str`:
- System prompt: "You are an intent classifier. Given the user message and context, decide which agent to route to. Available agents: {registry.list_agents()}. Respond with just the agent name."
- Uses gpt-4o-mini via LangChain for low latency
- Falls back to `task_agent` if no clear match
- `async route_single(agent_name, message, context) -> ChatResponse`:
- Instantiates agent from registry
- Calls `agent.handle(message, context)`
- Returns response + any actions the agent produced
- `async route_pipeline(agent_names, message, context) -> ChatResponse`:
- Executes agents in sequence
- Each agent receives `{...context, previous_results: [...]}`
- Final synthesis via LLM: "Summarize these agent results into a coherent response"
- `async orchestrate(request: ChatRequest) -> ChatResponse | ExecutionPlan`:
- Main entry point
- Classifies intent
- If `execution_mode == 'direct'`: route + return response
- If `execution_mode == 'plan'`: route + return execution plan with template IDs
- `async orchestrate_stream(request: ChatRequest) -> AsyncGenerator[str, None]`:
- Same as orchestrate but yields tokens for WebSocket streaming
- [ ] Integration tests with mocked LLM and mocked agents
- **Outcome:** Intelligent routing with single-agent and pipeline modes.
### Step 5 — Execution Plan generator
- [ ] `app/core/execution_plan.py`:
- `PromptTemplateRegistry`: dict of `template_id -> prompt_text`. Templates are server-side only — client receives IDs.
- `ExecutionPlanBuilder`:
- `add_step(action, params) -> self`
- `add_llm_step(template_id, variables) -> self`
- `add_data_step(action, data_from_step) -> self`
- `build() -> ExecutionPlan` — validates step references
- `PlanCache`:
- In-memory LRU (maxsize=1000)
- `cache_plan(key, plan)`, `get_plan(key)`, `get_all_playbooks() -> list[ExecutionPlan]`
- Playbooks are pre-built plans for common operations (e.g., "create task from email", "generate weekly report")
- **Outcome:** Plans are cacheable as playbooks. Prompt IP never leaves the server.
### Step 6 — Chat Agents
- [ ] `app/agents/task_agent.py` — `@registry.register`:
- Description: "Manages tasks: create, update, list, suggest"
- Tools: `create_task(title, description, priority, due_date)`, `update_task(id, updates)`, `list_tasks(filters)`, `suggest_tasks(notes_context)`
- System prompt: PM-oriented, validates task structure, infers priority from context
- `handle()`: LLM + tool loop via `_tool_loop()`, returns response text + list of actions performed
- [ ] `app/agents/calendar_agent.py` — `@registry.register`:
- Description: "Calendar management: events, conflicts, scheduling"
- Tools: `list_events(date_range)`, `detect_conflicts(events)`, `suggest_reschedule(conflict)`
- Works with event metadata passed in context (never raw calendar data stored)
- [ ] `app/agents/email_agent.py` — `@registry.register`:
- Description: "Email analysis: classify, extract actions, draft responses"
- Tools: `classify_email(metadata)`, `extract_action_items(metadata)`, `draft_response(thread_context)`
- Only processes metadata sent by client — never raw email bodies
- [ ] `app/agents/analytics_agent.py` — `@registry.register`:
- Description: "Workspace analytics: metrics, reports, trends"
- Tools: `calculate_metrics(task_data)`, `generate_report(period, data)`, `trend_analysis(data_points)`
- Crunches numbers from context, returns structured insights
- [ ] `app/agents/__init__.py`: imports all agent modules to trigger `@registry.register` decorators
- [ ] Unit tests per agent with mocked LLM
- **Outcome:** Four specialized agents, all registered and tested.
### Step 7 — API Routes
#### 7a — Chat endpoint
- [ ] `app/api/routes/chat.py`:
- `POST /api/v1/chat`:
- Request: `ChatRequest`
- Calls `orchestrate(request)` or `orchestrate()` + `build_plan()`
- Response: `ChatResponse` or `ExecutionPlan`
- `WebSocket /api/v1/chat/stream`:
- Client sends `ChatRequest` as first JSON frame
- Server yields token strings via `orchestrate_stream()`
- Final frame: JSON `ChatResponse` with `{"done": true, "response": "...", "actions": [...]}`
- Heartbeat ping every 30s to keep connection alive
#### 7b — Plans endpoint
- [ ] `app/api/routes/plans.py`:
- `GET /api/v1/plans/playbook`: Returns all playbooks available for the user's tier
- `GET /api/v1/plans/playbook/{plan_id}`: Returns a specific plan
#### 7c — Backup endpoint
- [ ] `app/api/routes/backup.py`:
- `PUT /api/v1/backup`: Accepts binary blob + metadata headers (`X-Backup-Version`, `X-Backup-Timestamp`, `X-Backup-Checksum`). Stores in S3 keyed by `{user_id}/{timestamp}`. Enforces tier limits:
- Free: 0 (no backup)
- Pro: 5 GB
- Power: 50 GB
- Team: unlimited
- `GET /api/v1/backup`: Returns latest blob for authenticated user. Supports `If-Modified-Since`.
- `GET /api/v1/backup/history`: Returns list of `BackupMetadata` (no blobs).
- `DELETE /api/v1/backup/{backup_id}`: Delete specific backup.
#### 7d — Auth endpoint
- [ ] `app/api/routes/auth.py`:
- `POST /api/v1/auth/register`: `{email, password}` → bcrypt hash → insert user → return `AuthTokens`
- `POST /api/v1/auth/login`: Validate credentials → return `AuthTokens`
- `POST /api/v1/auth/refresh`: Rotate refresh token → return new `AuthTokens`
- `GET /api/v1/auth/me`: Return `UserProfile` for current JWT
#### 7e — Billing endpoint
- [ ] `app/api/routes/billing.py`:
- `POST /api/v1/billing/checkout`: Creates Stripe checkout session → returns URL
- `POST /api/v1/billing/webhook`: Handles Stripe webhooks (subscription lifecycle)
- `GET /api/v1/billing/subscription`: Returns current subscription info
- `DELETE /api/v1/billing/subscription`: Cancels subscription
- **Outcome:** Complete REST + WebSocket API.
### Step 8 — Middleware
#### 8a — Auth middleware
- [ ] `app/api/middleware/auth.py`:
- FastAPI dependency: `get_current_user(token: str = Depends(oauth2_scheme)) -> UserProfile`
- Validates JWT signature, expiry, extracts `user_id` and `tier`
- Raises `401` on invalid/expired token
- Exempt routes: `/api/v1/auth/register`, `/api/v1/auth/login`, `/api/v1/billing/webhook`
#### 8b — Rate limiter
- [ ] `app/api/middleware/rate_limit.py`:
- Uses `slowapi` with `Limiter(key_func=get_user_id_from_jwt)`
- Tier-based limits:
- Free: 20 req/min
- Pro: 60 req/min
- Power: 120 req/min
- Team: 200 req/seat/min
- Custom 429 response with `Retry-After` header
#### 8c — Sanitizer
- [ ] `app/api/middleware/sanitizer.py`:
- Response middleware that scans response bodies
- Strips: system prompt fragments, agent internal reasoning, tool schemas, routing metadata
- Pattern-based detection + exact match against known prompt fingerprints
- Logs sanitization events for monitoring
- **Outcome:** Secure, rate-limited API with prompt IP protection.
### Step 9 — Billing & Tier management
- [ ] `app/billing/stripe_service.py`:
- `create_checkout_session(user_id, tier) -> str`
- `handle_webhook(payload, sig_header) -> None`: processes `checkout.session.completed`, `customer.subscription.updated`, `customer.subscription.deleted`, `invoice.payment_failed`
- `get_subscription(user_id) -> dict | None`
- `cancel_subscription(user_id) -> None`
- [ ] `app/billing/tier_manager.py`:
- `TierManager`:
- Feature matrix:
```python
FEATURES = {
'free': {'agents': 3, 'batch': False, 'providers': 1, 'backup_gb': 0},
'pro': {'agents': -1, 'batch': True, 'providers': -1, 'backup_gb': 5},
'power': {'agents': -1, 'batch': True, 'providers': -1, 'backup_gb': 50, 'byok': True},
'team': {'agents': -1, 'batch': True, 'providers': -1, 'backup_gb': -1, 'sso': True},
}
```
- `get_tier(user_id) -> BillingTier`
- `check_feature(user_id, feature) -> bool`
- `get_rate_limit(tier) -> int`
- **Outcome:** Stripe integration with tier-based feature gating.
### Step 10 — Database (auth/billing only)
- [ ] PostgreSQL schema via Alembic:
- `users`: `id UUID PK`, `email UNIQUE`, `password_hash`, `tier` (default 'free'), `stripe_customer_id`, `created_at`, `updated_at`
- `refresh_tokens`: `id UUID PK`, `user_id FK`, `token_hash`, `expires_at`, `created_at`
- `subscriptions`: `id UUID PK`, `user_id FK`, `stripe_subscription_id`, `tier`, `status`, `current_period_end`, `created_at`
- `backup_metadata`: `id UUID PK`, `user_id FK`, `s3_key`, `version`, `timestamp`, `checksum`, `size_bytes`, `created_at`
- [ ] Initial Alembic migration
- [ ] SQLAlchemy models in `app/models.py`
- **Outcome:** Auth and billing persistence. Zero user data stored.
### Step 11 — Testing & deployment
- [ ] `tests/conftest.py`: TestClient fixture, mock LLM fixture (`AsyncMock` returning canned responses), mock agent fixture, test DB (SQLite in-memory for speed)
- [ ] `tests/test_orchestrator.py`: classify_intent routing, single agent, pipeline, plan mode
- [ ] `tests/test_agents.py`: each agent with mocked tools
- [ ] `tests/test_auth.py`: register → login → access protected → refresh → expired token
- [ ] `tests/test_backup.py`: upload → download → history → delete, tier limit enforcement
- [ ] `Dockerfile` optimized for production (gunicorn + uvicorn workers)
- [ ] GitHub Actions CI: lint (ruff), test (pytest), build Docker image
- **Outcome:** Fully tested, deployable backend.
---
## API Contract Summary
| Method | Endpoint | Auth | Request | Response |
|--------|----------|------|---------|----------|
| POST | `/api/v1/auth/register` | No | `{email, password}` | `AuthTokens` |
| POST | `/api/v1/auth/login` | No | `{email, password}` | `AuthTokens` |
| POST | `/api/v1/auth/refresh` | No | `{refresh_token}` | `AuthTokens` |
| GET | `/api/v1/auth/me` | JWT | — | `UserProfile` |
| POST | `/api/v1/chat` | JWT | `ChatRequest` | `ChatResponse \| ExecutionPlan` |
| WS | `/api/v1/chat/stream` | JWT | `ChatRequest` (first frame) | Token stream + final JSON |
| GET | `/api/v1/plans/playbook` | JWT | — | `ExecutionPlan[]` |
| GET | `/api/v1/plans/playbook/:id` | JWT | — | `ExecutionPlan` |
| PUT | `/api/v1/backup` | JWT | Binary blob + headers | `{ok: true}` |
| GET | `/api/v1/backup` | JWT | — | Binary blob |
| GET | `/api/v1/backup/history` | JWT | — | `BackupMetadata[]` |
| DELETE | `/api/v1/backup/:id` | JWT | — | `{ok: true}` |
| POST | `/api/v1/billing/checkout` | JWT | `{tier}` | `{checkout_url}` |
| POST | `/api/v1/billing/webhook` | Stripe sig | Stripe event | `{ok: true}` |
| GET | `/api/v1/billing/subscription` | JWT | — | Subscription info |
| DELETE | `/api/v1/billing/subscription` | JWT | — | `{ok: true}` |
| GET | `/api/v1/health` | No | — | `{status, version}` |
---
## Stack
| Layer | Technology |
|-------|-----------|
| Framework | FastAPI + Uvicorn |
| LLM | LangChain + langchain-openai |
| Auth | PyJWT + bcrypt + OAuth2 |
| Billing | stripe-python |
| Storage | boto3 (S3) |
| Database | PostgreSQL + SQLAlchemy + Alembic |
| Rate limiting | slowapi |
| Testing | pytest + pytest-asyncio + httpx |
| Deployment | Docker → fly.io / Railway / AWS ECS |
---
## Development Rules
1. **NEVER persist user data.** The DB stores only auth, billing, and backup metadata. User context arrives in requests and is discarded after processing.
2. **NEVER expose prompts.** System prompts are composed server-side from fragments. Responses are sanitized before sending.
3. **Stateless request handling.** No server-side session state. All context comes from the client + JWT.
4. **Type hints everywhere.** All functions have full type annotations.
5. **Test every agent.** Each chat agent has unit tests with mocked LLM responses.
6. **Structured logging.** JSON logs with request ID correlation.

View File

@@ -7,12 +7,232 @@ import { AutoUnpackNativesPlugin } from '@electron-forge/plugin-auto-unpack-nati
import { VitePlugin } from '@electron-forge/plugin-vite'; import { VitePlugin } from '@electron-forge/plugin-vite';
import { FusesPlugin } from '@electron-forge/plugin-fuses'; import { FusesPlugin } from '@electron-forge/plugin-fuses';
import { FuseV1Options, FuseVersion } from '@electron/fuses'; import { FuseV1Options, FuseVersion } from '@electron/fuses';
import path from 'node:path';
import fs from 'node:fs';
import { execSync } from 'node:child_process';
// Packages externalized in vite.main.config.mts that must be installed at runtime.
// Keep this list in sync with the Vite external array.
const externalPackages = [
'better-sqlite3',
'@github/copilot-sdk',
'@langchain/core',
'@langchain/langgraph',
'@langchain/openai',
'@langchain/anthropic',
'vectordb',
'electron-squirrel-startup',
'electron-store',
];
const config: ForgeConfig = { const config: ForgeConfig = {
packagerConfig: { packagerConfig: {
asar: true, asar: {
unpack: '**/{*.node,*.dll,*.so,*.dylib}',
},
name: 'adiuva',
}, },
rebuildConfig: {}, rebuildConfig: {},
hooks: {
packageAfterCopy: async (_forgeConfig, buildPath, _electronVersion, platform, arch) => {
// The VitePlugin's ignore filter only copies .vite/ into the build.
// Externalized packages need to be installed into node_modules here.
// At this point, only .vite/ exists. The VitePlugin writes package.json
// in its own afterCopy hook (which may run after ours). Read from source.
const srcPjPath = path.resolve(__dirname, 'package.json');
const pjPath = path.resolve(buildPath, 'package.json');
const pj = JSON.parse(fs.readFileSync(srcPjPath, 'utf-8'));
// Keep only externalized packages in dependencies
const filtered: Record<string, string> = {};
for (const pkg of externalPackages) {
if (pj.dependencies?.[pkg]) {
filtered[pkg] = pj.dependencies[pkg];
}
}
pj.dependencies = filtered;
delete pj.devDependencies;
fs.writeFileSync(pjPath, JSON.stringify(pj, null, 2));
// Copy lockfile for reproducible installs
const lockSrc = path.resolve(buildPath, '..', '..', 'package-lock.json');
if (fs.existsSync(lockSrc)) {
fs.copyFileSync(lockSrc, path.resolve(buildPath, 'package-lock.json'));
}
// Install only the externalized runtime deps
console.log('[forge] Installing externalized dependencies...');
execSync('npm install --omit=dev', {
cwd: buildPath,
stdio: 'inherit',
env: { ...process.env, npm_config_nodedir: '' },
});
const targetKey = `${platform}-${arch}`;
// vectordb uses platform-specific optional deps (@lancedb/vectordb-<platform>-<arch>-*).
// npm install on Linux only pulls the Linux variant. Force-install the target's.
const platformNativePackages: Record<string, Record<string, string>> = {
'win32-x64': {
'@lancedb/vectordb-win32-x64-msvc': '',
},
'linux-x64': {
'@lancedb/vectordb-linux-x64-gnu': '',
},
'darwin-x64': {
'@lancedb/vectordb-darwin-x64': '',
},
'darwin-arm64': {
'@lancedb/vectordb-darwin-arm64': '',
},
};
const nativePkgs = platformNativePackages[targetKey];
if (nativePkgs) {
// Remove wrong-platform lancedb native packages
const nmPath = path.join(buildPath, 'node_modules', '@lancedb');
if (fs.existsSync(nmPath)) {
for (const entry of fs.readdirSync(nmPath)) {
if (entry.startsWith('vectordb-') && !Object.keys(nativePkgs).includes(`@lancedb/${entry}`)) {
fs.rmSync(path.join(nmPath, entry), { recursive: true, force: true });
console.log(`[forge] Removed non-target native package: @lancedb/${entry}`);
}
}
}
// Install correct platform packages
const pkgsToInstall = Object.keys(nativePkgs).join(' ');
console.log(`[forge] Installing platform-specific packages for ${targetKey}: ${pkgsToInstall}`);
execSync(`npm install ${pkgsToInstall} --omit=dev --no-save --force`, {
cwd: buildPath,
stdio: 'inherit',
});
}
// Remove cross-platform prebuilt binaries that don't match the target.
// Packages like @github/copilot ship prebuilds for all platforms;
// keeping foreign-arch .node files breaks rpmbuild's strip step.
const nodeModulesPath = path.join(buildPath, 'node_modules');
const findPrebuilds = (dir: string): string[] => {
const results: string[] = [];
if (!fs.existsSync(dir)) return results;
for (const entry of fs.readdirSync(dir, { withFileTypes: true })) {
const full = path.join(dir, entry.name);
if (entry.isDirectory()) {
if (entry.name === 'prebuilds') {
results.push(full);
} else {
results.push(...findPrebuilds(full));
}
}
}
return results;
};
for (const prebuildsDir of findPrebuilds(nodeModulesPath)) {
for (const entry of fs.readdirSync(prebuildsDir)) {
if (entry !== targetKey) {
const fullPath = path.join(prebuildsDir, entry);
fs.rmSync(fullPath, { recursive: true, force: true });
console.log(`[forge] Removed non-target prebuild: ${entry}`);
}
}
}
// @github/copilot ships @teddyzhu/clipboard-* platform packages outside
// of prebuilds/. Remove non-target variants to avoid bundling wrong binaries.
const clipboardDir = path.join(buildPath, 'node_modules', '@github', 'copilot', 'clipboard', 'node_modules', '@teddyzhu');
if (fs.existsSync(clipboardDir)) {
const targetClipboardMap: Record<string, string> = {
'win32-x64': 'clipboard-win32-x64-msvc',
'win32-arm64': 'clipboard-win32-arm64-msvc',
'linux-x64': 'clipboard-linux-x64-gnu',
'linux-arm64': 'clipboard-linux-arm64-gnu',
'darwin-x64': 'clipboard-darwin-x64',
'darwin-arm64': 'clipboard-darwin-arm64',
};
const wantedPkg = targetClipboardMap[targetKey];
for (const entry of fs.readdirSync(clipboardDir)) {
if (entry.startsWith('clipboard-') && entry !== wantedPkg) {
fs.rmSync(path.join(clipboardDir, entry), { recursive: true, force: true });
console.log(`[forge] Removed non-target clipboard package: @teddyzhu/${entry}`);
}
}
}
},
// ── Post-rebuild: fix native binaries for cross-compilation ──────
// Forge runs @electron/rebuild AFTER packageAfterCopy, which
// recompiles native addons for the BUILD platform (Linux).
// packageAfterPrune runs AFTER rebuild+prune, so we can safely
// replace the Linux .node files with the correct target prebuilts.
packageAfterPrune: async (_forgeConfig, buildPath, _electronVersion, platform, arch) => {
const targetKey = `${platform}-${arch}`;
const buildKey = `${process.platform}-${process.arch}`;
if (targetKey === buildKey) return; // native build — nothing to fix
console.log(`[forge:afterPrune] Cross-compile fixup: ${buildKey}${targetKey}`);
const electronVersion = JSON.parse(
fs.readFileSync(path.resolve(__dirname, 'node_modules', 'electron', 'package.json'), 'utf-8'),
).version;
// Replace native addons that @electron/rebuild compiled for the host.
const nativeModules = ['better-sqlite3'];
for (const mod of nativeModules) {
const modDir = path.join(buildPath, 'node_modules', mod);
if (!fs.existsSync(modDir)) continue;
// Remove the host-platform binary left by @electron/rebuild
const buildRelease = path.join(modDir, 'build', 'Release');
if (fs.existsSync(buildRelease)) {
fs.rmSync(buildRelease, { recursive: true, force: true });
console.log(`[forge:afterPrune] Cleaned host-platform build/Release for ${mod}`);
}
// Download the correct prebuilt for the TARGET platform
console.log(`[forge:afterPrune] Downloading ${mod} prebuilt for ${targetKey} (Electron ${electronVersion})...`);
execSync(
`npx --yes prebuild-install -r electron -t ${electronVersion} ` +
`--platform ${platform} --arch ${arch} --tag-prefix v --verbose`,
{ cwd: modDir, stdio: 'inherit' },
);
// Verify the binary exists and is for the correct platform.
const releaseDir = path.join(modDir, 'build', 'Release');
if (!fs.existsSync(releaseDir)) {
throw new Error(
`[forge] FATAL: build/Release/ not found for ${mod} after prebuild-install. ` +
`The native binary was not downloaded.`,
);
}
const nodeFiles = fs.readdirSync(releaseDir).filter((f) => f.endsWith('.node'));
if (nodeFiles.length === 0) {
throw new Error(
`[forge] FATAL: No .node files in build/Release/ for ${mod} after prebuild-install.`,
);
}
for (const f of nodeFiles) {
const buf = Buffer.alloc(4);
const fd = fs.openSync(path.join(releaseDir, f), 'r');
fs.readSync(fd, buf, 0, 4, 0);
fs.closeSync(fd);
// ELF magic: 0x7f 'E' 'L' 'F'
if (buf[0] === 0x7f && buf[1] === 0x45 && buf[2] === 0x4c && buf[3] === 0x46) {
throw new Error(
`[forge] FATAL: ${mod} build/Release/${f} is an ELF binary! ` +
`Cross-compilation failed — refusing to package a Linux .node for Windows.`,
);
}
// PE magic: 'M' 'Z' (0x4d 0x5a) — expected for win32
if (platform === 'win32' && !(buf[0] === 0x4d && buf[1] === 0x5a)) {
throw new Error(
`[forge] FATAL: ${mod} build/Release/${f} is not a PE (Windows) binary! ` +
`Magic bytes: ${buf.toString('hex')}. Refusing to package.`,
);
}
console.log(`[forge:afterPrune] Verified ${mod}/${f} — correct platform ✓`);
}
}
},
},
makers: [ makers: [
new MakerSquirrel({}), new MakerSquirrel({}),
new MakerZIP({}, ['darwin']), new MakerZIP({}, ['darwin']),

18
package-lock.json generated
View File

@@ -32,7 +32,6 @@
"electron-squirrel-startup": "^1.0.1", "electron-squirrel-startup": "^1.0.1",
"electron-store": "^8.2.0", "electron-store": "^8.2.0",
"framer-motion": "^12.34.2", "framer-motion": "^12.34.2",
"keytar": "^7.9.0",
"lucide-react": "^0.575.0", "lucide-react": "^0.575.0",
"radix-ui": "^1.4.3", "radix-ui": "^1.4.3",
"react": "^19.2.4", "react": "^19.2.4",
@@ -16007,17 +16006,6 @@
"node": ">= 12" "node": ">= 12"
} }
}, },
"node_modules/keytar": {
"version": "7.9.0",
"resolved": "https://registry.npmjs.org/keytar/-/keytar-7.9.0.tgz",
"integrity": "sha512-VPD8mtVtm5JNtA2AErl6Chp06JBfy7diFQ7TQQhdpWOl6MrCRB+eRbvAZUsbGQS9kiMq0coJsy0W0vHpDCkWsQ==",
"hasInstallScript": true,
"license": "MIT",
"dependencies": {
"node-addon-api": "^4.3.0",
"prebuild-install": "^7.0.1"
}
},
"node_modules/keyv": { "node_modules/keyv": {
"version": "4.5.4", "version": "4.5.4",
"resolved": "https://registry.npmjs.org/keyv/-/keyv-4.5.4.tgz", "resolved": "https://registry.npmjs.org/keyv/-/keyv-4.5.4.tgz",
@@ -18209,12 +18197,6 @@
"node": ">=10" "node": ">=10"
} }
}, },
"node_modules/node-addon-api": {
"version": "4.3.0",
"resolved": "https://registry.npmjs.org/node-addon-api/-/node-addon-api-4.3.0.tgz",
"integrity": "sha512-73sE9+3UaLYYFmDsFZnqCInzPyh3MqIwZO9cw58yIqAZhONrrabrYyYe3TuIqtIiOuTXVhsGau8hcrhhwSsDIQ==",
"license": "MIT"
},
"node_modules/node-api-version": { "node_modules/node-api-version": {
"version": "0.2.1", "version": "0.2.1",
"resolved": "https://registry.npmjs.org/node-api-version/-/node-api-version-0.2.1.tgz", "resolved": "https://registry.npmjs.org/node-api-version/-/node-api-version-0.2.1.tgz",

View File

@@ -14,7 +14,7 @@
"knip": "knip" "knip": "knip"
}, },
"keywords": [], "keywords": [],
"author": "rmusso", "author": "roberto",
"license": "MIT", "license": "MIT",
"devDependencies": { "devDependencies": {
"@electron-forge/cli": "^7.11.1", "@electron-forge/cli": "^7.11.1",
@@ -71,7 +71,6 @@
"electron-squirrel-startup": "^1.0.1", "electron-squirrel-startup": "^1.0.1",
"electron-store": "^8.2.0", "electron-store": "^8.2.0",
"framer-motion": "^12.34.2", "framer-motion": "^12.34.2",
"keytar": "^7.9.0",
"lucide-react": "^0.575.0", "lucide-react": "^0.575.0",
"radix-ui": "^1.4.3", "radix-ui": "^1.4.3",
"react": "^19.2.4", "react": "^19.2.4",

View File

@@ -2,27 +2,11 @@ import { safeStorage } from 'electron';
import { getStore } from '../store'; import { getStore } from '../store';
/** /**
* Token storage with three-tier fallback: * Token storage with two-tier fallback:
* 1. OS keychain via keytar (best — encrypted, per-user) * 1. Electron safeStorage + electron-store (encrypted at rest)
* 2. Electron safeStorage + electron-store (encrypted at rest) * 2. Plain electron-store (last resort — e.g. WSL with no keyring)
* 3. Plain electron-store (last resort — e.g. WSL with no keyring)
*/ */
let keytar: typeof import('keytar') | null = null;
let keytarFailed = false;
try {
// eslint-disable-next-line @typescript-eslint/no-require-imports
keytar = require('keytar') as typeof import('keytar');
} catch {
keytarFailed = true;
console.log('[Token] keytar native module unavailable');
}
function useKeytar(): boolean {
return keytar !== null && !keytarFailed;
}
function canUseSafeStorage(): boolean { function canUseSafeStorage(): boolean {
try { try {
return safeStorage.isEncryptionAvailable(); return safeStorage.isEncryptionAvailable();
@@ -31,8 +15,6 @@ function canUseSafeStorage(): boolean {
} }
} }
const SERVICE_NAME = 'adiuva';
// --- electron-store helpers (with optional safeStorage encryption) --- // --- electron-store helpers (with optional safeStorage encryption) ---
function readFromStore(providerName: string): string | null { function readFromStore(providerName: string): string | null {
@@ -74,41 +56,16 @@ function removeFromStore(providerName: string): void {
/** Read a stored token for the given provider. */ /** Read a stored token for the given provider. */
export async function getToken(providerName: string): Promise<string | null> { export async function getToken(providerName: string): Promise<string | null> {
if (useKeytar()) {
try {
return await keytar!.getPassword(SERVICE_NAME, providerName);
} catch (err) {
console.log('[Token] keytar runtime error, falling back:', (err as Error).message);
keytarFailed = true;
}
}
return readFromStore(providerName); return readFromStore(providerName);
} }
/** Store a token for the given provider. */ /** Store a token for the given provider. */
export async function setToken(providerName: string, token: string): Promise<void> { export async function setToken(providerName: string, token: string): Promise<void> {
if (useKeytar()) {
try {
await keytar!.setPassword(SERVICE_NAME, providerName, token);
return;
} catch (err) {
console.log('[Token] keytar runtime error, falling back:', (err as Error).message);
keytarFailed = true;
}
}
writeToStore(providerName, token); writeToStore(providerName, token);
} }
/** Delete a stored token for the given provider. */ /** Delete a stored token for the given provider. */
async function deleteToken(providerName: string): Promise<boolean> { async function deleteToken(providerName: string): Promise<boolean> {
if (useKeytar()) {
try {
return await keytar!.deletePassword(SERVICE_NAME, providerName);
} catch (err) {
console.log('[Token] keytar runtime error, falling back:', (err as Error).message);
keytarFailed = true;
}
}
removeFromStore(providerName); removeFromStore(providerName);
return true; return true;
} }

View File

@@ -7,7 +7,6 @@ export default defineConfig({
// Externalize native Node modules — they're rebuilt by electron-forge // Externalize native Node modules — they're rebuilt by electron-forge
external: [ external: [
'better-sqlite3', 'better-sqlite3',
'keytar',
'@github/copilot-sdk', '@github/copilot-sdk',
'@github/copilot', '@github/copilot',
// LangChain — externalize to avoid bundling Node.js-specific code // LangChain — externalize to avoid bundling Node.js-specific code