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One set of CRUD functions.\n * FTS sync happens automatically on every insert.\n */\n\nimport { sqlite } from \"https://esm.town/v/std/sqlite/main.ts\";\n\n// ============ TYPES ============\n\nexport interface Entry {\n id: number;\n user_id: number;\n type: string;\n status: string;\n title: string | null;\n content: string;\n tags: string | null;\n metadata: Record | null;\n source_entry_id: number | null;\n next_run_at: string | null;\n schedule_description: string | null;\n created_at: string;\n updated_at: string;\n}\n\nexport interface User {\n id: number;\n email: string;\n name: string | null;\n system_prompt: string;\n created_at: string;\n updated_at: string;\n}\n\nexport interface SearchResult {\n type: string;\n id: number;\n snippet: string;\n rank: number;\n}\n\n// ============ USERS ============\n\nexport async function getOrCreateUser(\n email: string,\n name?: string,\n): Promise {\n const existing = await sqlite.execute({\n sql: `SELECT id, email, name, system_prompt, created_at, updated_at FROM users WHERE email = ?`,\n args: [email.toLowerCase()],\n });\n\n if (existing.rows.length > 0) {\n const row = existing.rows[0] as Record;\n if (name && row.name !== name) {\n await sqlite.execute({\n sql: `UPDATE users SET name = ?, updated_at = datetime('now') WHERE id = ?`,\n args: [name, row.id as number],\n });\n }\n return {\n id: row.id as number,\n email: row.email as string,\n name: (name || row.name) as string | null,\n system_prompt: (row.system_prompt as string) || \"\",\n created_at: row.created_at as string,\n updated_at: row.updated_at as string,\n };\n }\n\n const result = await sqlite.execute({\n sql: `INSERT INTO users (email, name) VALUES (?, ?)`,\n args: [email.toLowerCase(), name || null],\n });\n\n return {\n id: Number(result.lastInsertRowid),\n email: email.toLowerCase(),\n name: name || null,\n system_prompt: \"\",\n created_at: new Date().toISOString(),\n updated_at: new Date().toISOString(),\n };\n}\n\nexport async function getUser(userId: number): Promise {\n const result = await sqlite.execute({\n sql: `SELECT * FROM users WHERE id = ?`,\n args: [userId],\n });\n if (result.rows.length === 0) return undefined;\n const row = result.rows[0] as Record;\n return {\n id: row.id as number,\n email: row.email as string,\n name: row.name as string | null,\n system_prompt: (row.system_prompt as string) || \"\",\n created_at: row.created_at as string,\n updated_at: row.updated_at as string,\n };\n}\n\nexport async function updateSystemPrompt(\n userId: number,\n newInstructions: string[],\n): Promise {\n const user = await getUser(userId);\n if (!user) throw new Error(`User ${userId} not found`);\n\n // Append new instructions to existing system prompt\n let prompt = user.system_prompt.trim();\n for (const instruction of newInstructions) {\n const line = `- ${instruction.trim()}`;\n // Don't add duplicates\n if (!prompt.includes(instruction.trim())) {\n prompt += (prompt ? \"\\n\" : \"\") + line;\n }\n }\n\n await sqlite.execute({\n sql: `UPDATE users SET system_prompt = ?, updated_at = datetime('now') WHERE id = ?`,\n args: [prompt, userId],\n });\n\n return prompt;\n}\n\nexport async function getSystemPrompt(userId: number): Promise {\n const user = await getUser(userId);\n return user?.system_prompt || \"\";\n}\n\n// ============ ENTRIES (unified CRUD) ============\n\nexport async function addEntry(\n userId: number,\n opts: {\n type: string;\n title?: string;\n content: string;\n tags?: string;\n status?: string;\n metadata?: Record;\n sourceEntryId?: number;\n nextRunAt?: string;\n scheduleDescription?: string;\n },\n): Promise {\n const result = await sqlite.execute({\n sql: `INSERT INTO entries (user_id, type, status, title, content, tags, metadata, source_entry_id, next_run_at, schedule_description)\n VALUES (?, ?, ?, ?, ?, ?, ?, ?, ?, ?)`,\n args: [\n userId,\n opts.type,\n opts.status || \"active\",\n opts.title || null,\n opts.content,\n opts.tags || null,\n opts.metadata ? JSON.stringify(opts.metadata) : null,\n opts.sourceEntryId || null,\n opts.nextRunAt || null,\n opts.scheduleDescription || null,\n ],\n });\n const id = Number(result.lastInsertRowid);\n\n // Sync FTS\n await sqlite.execute({\n sql: `INSERT INTO entries_fts (rowid, title, content, tags) VALUES (?, ?, ?, ?)`,\n args: [id, opts.title || \"\", opts.content, opts.tags || \"\"],\n });\n\n return id;\n}\n\nexport async function getEntries(\n userId: number,\n opts?: {\n type?: string;\n status?: string;\n limit?: number;\n orderBy?: string;\n },\n): Promise {\n let sql = `SELECT * FROM entries WHERE user_id = ?`;\n const args: unknown[] = [userId];\n\n if (opts?.type) {\n sql += ` AND type = ?`;\n args.push(opts.type);\n }\n if (opts?.status) {\n sql += ` AND status = ?`;\n args.push(opts.status);\n }\n\n sql += ` ORDER BY ${opts?.orderBy || \"created_at DESC\"}`;\n sql += ` LIMIT ?`;\n args.push(opts?.limit || 50);\n\n const result = await sqlite.execute({ sql, args });\n return (result.rows as Record[]).map(parseEntryRow);\n}\n\nexport async function getEntry(entryId: number): Promise {\n const result = await sqlite.execute({\n sql: `SELECT * FROM entries WHERE id = ?`,\n args: [entryId],\n });\n if (result.rows.length === 0) return undefined;\n return parseEntryRow(result.rows[0] as Record);\n}\n\nexport async function updateEntryStatus(\n entryId: number,\n status: string,\n resultText?: string,\n) {\n const metadataUpdate = resultText\n ? `, metadata = json_set(COALESCE(metadata, '{}'), '$.result', ?)`\n : \"\";\n const args: unknown[] = resultText\n ? [status, resultText, entryId]\n : [status, entryId];\n\n await sqlite.execute({\n sql: `UPDATE entries SET status = ?, updated_at = datetime('now')${metadataUpdate} WHERE id = ?`,\n args,\n });\n}\n\nexport async function updateEntryMetadata(\n entryId: number,\n updates: Record,\n) {\n // Merge new fields into existing metadata\n const entry = await getEntry(entryId);\n if (!entry) return;\n\n const existing = entry.metadata || {};\n const merged = { ...existing, ...updates };\n\n await sqlite.execute({\n sql: `UPDATE entries SET metadata = ?, updated_at = datetime('now') WHERE id = ?`,\n args: [JSON.stringify(merged), entryId],\n });\n}\n\nexport async function rescheduleEntry(entryId: number, nextRunAt: string) {\n await sqlite.execute({\n sql: `UPDATE entries SET status = 'pending', next_run_at = ?, updated_at = datetime('now') WHERE id = ?`,\n args: [nextRunAt, entryId],\n });\n}\n\n// ============ TASK-SPECIFIC QUERIES ============\n\nexport async function getTasksDue(): Promise<\n (Entry & { user_email: string; user_name: string | null })[]\n> {\n // Compare using strftime to normalize both ISO 8601 (with T/Z) and SQLite\n // datetime formats into a consistent comparable string.\n // We use replace to strip T and Z so '2026-03-04T21:55:26Z' becomes '2026-03-04 21:55:26'\n // which is directly comparable to datetime('now') output.\n const now = new Date().toISOString().replace(\"T\", \" \").replace(\"Z\", \"\");\n const result = await sqlite.execute({\n sql: `SELECT e.*, u.email as user_email, u.name as user_name\n FROM entries e\n JOIN users u ON e.user_id = u.id\n WHERE e.type = 'task'\n AND e.status = 'pending'\n AND e.next_run_at IS NOT NULL\n AND REPLACE(REPLACE(e.next_run_at, 'T', ' '), 'Z', '') <= ?\n ORDER BY e.created_at ASC\n LIMIT 20`,\n args: [now],\n });\n return (result.rows as Record[]).map((row) => ({\n ...parseEntryRow(row),\n user_email: row.user_email as string,\n user_name: row.user_name as string | null,\n }));\n}\n\n// ============ ENTRY LINKS ============\n\nexport async function linkEntries(\n fromId: number,\n toId: number,\n relationship: string = \"related\",\n) {\n try {\n await sqlite.execute({\n sql: `INSERT OR IGNORE INTO entry_links (from_entry_id, to_entry_id, relationship) VALUES (?, ?, ?)`,\n args: [fromId, toId, relationship],\n });\n } catch {\n // Ignore duplicate link errors\n }\n}\n\nexport async function getLinkedEntries(\n entryId: number,\n): Promise<{ entry: Entry; relationship: string; direction: string }[]> {\n const result = await sqlite.execute({\n sql: `SELECT e.*, el.relationship,\n CASE WHEN el.from_entry_id = ? THEN 'outgoing' ELSE 'incoming' END as direction\n FROM entry_links el\n JOIN entries e ON (\n CASE WHEN el.from_entry_id = ? THEN el.to_entry_id ELSE el.from_entry_id END = e.id\n )\n WHERE el.from_entry_id = ? OR el.to_entry_id = ?`,\n args: [entryId, entryId, entryId, entryId],\n });\n return (result.rows as Record[]).map((row) => ({\n entry: parseEntryRow(row),\n relationship: row.relationship as string,\n direction: row.direction as string,\n }));\n}\n\n// ============ FULL-TEXT SEARCH ============\n\nexport async function searchEntries(\n userId: number,\n query: string,\n opts?: { type?: string; limit?: number },\n): Promise {\n const limit = opts?.limit || 20;\n let sql = `SELECT e.id, e.type, snippet(entries_fts, 1, '', '', '...', 32) as snippet, rank\n FROM entries_fts\n JOIN entries e ON entries_fts.rowid = e.id\n WHERE e.user_id = ? AND entries_fts MATCH ?`;\n const args: unknown[] = [userId, query];\n\n if (opts?.type) {\n sql += ` AND e.type = ?`;\n args.push(opts.type);\n }\n\n sql += ` ORDER BY rank LIMIT ?`;\n args.push(limit);\n\n const result = await sqlite.execute({ sql, args });\n return (result.rows as Record[]).map((row) => ({\n type: row.type as string,\n id: row.id as number,\n snippet: row.snippet as string,\n rank: row.rank as number,\n }));\n}\n\n// ============ USER CONTEXT ============\n\n/**\n * Get a full context bundle for a user — used before agent execution.\n * Returns memories, active tasks, recent ideas, recent interactions, and system prompt.\n */\nexport async function getUserContext(userId: number) {\n const [memories, activeTasks, recentIdeas, recentInteractions, user] =\n await Promise.all([\n getEntries(userId, { type: \"memory\", limit: 30 }),\n getEntries(userId, { type: \"task\", status: \"pending\", limit: 20 }),\n getEntries(userId, { type: \"idea\", limit: 10 }),\n getEntries(userId, {\n type: \"interaction\",\n limit: 10,\n orderBy: \"created_at DESC\",\n }),\n getUser(userId),\n ]);\n\n return {\n memories,\n activeTasks,\n recentIdeas,\n recentInteractions,\n systemPrompt: user?.system_prompt || \"\",\n };\n}\n\n// ============ ATTACHMENTS ============\n\nexport interface Attachment {\n id: number;\n entry_id: number;\n user_id: number;\n filename: string;\n mime_type: string;\n size_bytes: number;\n blob_key: string;\n is_text: boolean;\n created_at: string;\n}\n\nexport async function addAttachment(opts: {\n entryId: number;\n userId: number;\n filename: string;\n mimeType: string;\n sizeBytes: number;\n blobKey: string;\n isText: boolean;\n}): Promise {\n const result = await sqlite.execute({\n sql: `INSERT INTO attachments (entry_id, user_id, filename, mime_type, size_bytes, blob_key, is_text)\n VALUES (?, ?, ?, ?, ?, ?, ?)`,\n args: [\n opts.entryId,\n opts.userId,\n opts.filename,\n opts.mimeType,\n opts.sizeBytes,\n opts.blobKey,\n opts.isText ? 1 : 0,\n ],\n });\n return Number(result.lastInsertRowid);\n}\n\nexport async function getAttachmentsForEntry(\n entryId: number,\n): Promise {\n const result = await sqlite.execute({\n sql: `SELECT * FROM attachments WHERE entry_id = ? ORDER BY created_at ASC`,\n args: [entryId],\n });\n return (result.rows as Record[]).map(parseAttachmentRow);\n}\n\nexport async function getAttachmentsForUser(\n userId: number,\n opts?: { limit?: number },\n): Promise {\n const result = await sqlite.execute({\n sql: `SELECT * FROM attachments WHERE user_id = ? ORDER BY created_at DESC LIMIT ?`,\n args: [userId, opts?.limit || 50],\n });\n return (result.rows as Record[]).map(parseAttachmentRow);\n}\n\nfunction parseAttachmentRow(row: Record): Attachment {\n return {\n id: row.id as number,\n entry_id: row.entry_id as number,\n user_id: row.user_id as number,\n filename: row.filename as string,\n mime_type: row.mime_type as string,\n size_bytes: row.size_bytes as number,\n blob_key: row.blob_key as string,\n is_text: (row.is_text as number) === 1,\n created_at: row.created_at as string,\n };\n}\n\n// ============ HELPERS ============\n\nfunction parseEntryRow(row: Record): Entry {\n let metadata: Record | null = null;\n if (row.metadata) {\n try {\n metadata = JSON.parse(row.metadata as string);\n } catch {\n metadata = null;\n }\n }\n\n return {\n id: row.id as number,\n user_id: row.user_id as number,\n type: row.type as string,\n status: row.status as string,\n title: row.title as string | null,\n content: row.content as string,\n tags: row.tags as string | null,\n metadata,\n source_entry_id: row.source_entry_id as number | null,\n next_run_at: row.next_run_at as string | null,\n schedule_description: row.schedule_description as string | null,\n created_at: row.created_at as string,\n updated_at: row.updated_at as string,\n };\n}\n",[],[162,181],{"_106":191,"_97":192,"_163":193,"_165":164,"_166":148,"_167":194,"_169":170,"_108":195,"_102":168,"_172":186,"_174":196,"_175":22,"_176":-5,"_177":197,"_100":198},"b59dc3d2-181a-11f1-85c8-42dde27851f2","schema.ts","f44ed4c8-180f-11f1-b557-42dde27851f2",34,["D",1772663842032],"/**\n * Database schema for the email agent system.\n *\n * Unified model: everything is an \"entry\" with a type column.\n * Types include: memory, task, idea, interaction, person, meeting, project, etc.\n * New types can be added without schema changes — just use a new type string.\n *\n * Per-user system_prompt lives on the users table — the user's personal \"CLAUDE.md\"\n * that controls agent tone, style, and persistent instructions.\n *\n * entry_links creates a graph between entries (cross-referencing).\n * Single FTS5 table covers all entry types.\n */\n\nimport { sqlite } from \"https://esm.town/v/std/sqlite/main.ts\";\n\nexport async function initDatabase() {\n await sqlite.batch([\n // Users — each unique sender email is a user\n {\n sql: `CREATE TABLE IF NOT EXISTS users (\n id INTEGER PRIMARY KEY AUTOINCREMENT,\n email TEXT UNIQUE NOT NULL,\n name TEXT,\n system_prompt TEXT NOT NULL DEFAULT '',\n created_at TEXT DEFAULT (datetime('now')),\n updated_at TEXT DEFAULT (datetime('now'))\n )`,\n },\n\n // Unified entries table — all content types in one place\n {\n sql: `CREATE TABLE IF NOT EXISTS entries (\n id INTEGER PRIMARY KEY AUTOINCREMENT,\n user_id INTEGER NOT NULL,\n type TEXT NOT NULL,\n status TEXT DEFAULT 'active',\n title TEXT,\n content TEXT NOT NULL,\n tags TEXT,\n metadata TEXT,\n source_entry_id INTEGER,\n next_run_at TEXT,\n schedule_description TEXT,\n created_at TEXT DEFAULT (datetime('now')),\n updated_at TEXT DEFAULT (datetime('now')),\n FOREIGN KEY (user_id) REFERENCES users(id),\n FOREIGN KEY (source_entry_id) REFERENCES entries(id)\n )`,\n },\n\n // Entry cross-links — the relationship graph\n {\n sql: `CREATE TABLE IF NOT EXISTS entry_links (\n id INTEGER PRIMARY KEY AUTOINCREMENT,\n from_entry_id INTEGER NOT NULL,\n to_entry_id INTEGER NOT NULL,\n relationship TEXT DEFAULT 'related',\n created_at TEXT DEFAULT (datetime('now')),\n FOREIGN KEY (from_entry_id) REFERENCES entries(id),\n FOREIGN KEY (to_entry_id) REFERENCES entries(id),\n UNIQUE(from_entry_id, to_entry_id, relationship)\n )`,\n },\n\n // Attachments — files linked to entries, stored in blob storage\n {\n sql: `CREATE TABLE IF NOT EXISTS attachments (\n id INTEGER PRIMARY KEY AUTOINCREMENT,\n entry_id INTEGER NOT NULL,\n user_id INTEGER NOT NULL,\n filename TEXT NOT NULL,\n mime_type TEXT NOT NULL,\n size_bytes INTEGER NOT NULL,\n blob_key TEXT NOT NULL,\n is_text INTEGER DEFAULT 0,\n created_at TEXT DEFAULT (datetime('now')),\n FOREIGN KEY (entry_id) REFERENCES entries(id),\n FOREIGN KEY (user_id) REFERENCES users(id)\n )`,\n },\n\n // Indexes\n { sql: `CREATE INDEX IF NOT EXISTS idx_attachments_entry ON attachments(entry_id)` },\n { sql: `CREATE INDEX IF NOT EXISTS idx_attachments_user ON attachments(user_id)` },\n { sql: `CREATE INDEX IF NOT EXISTS idx_entries_user_type ON entries(user_id, type)` },\n { sql: `CREATE INDEX IF NOT EXISTS idx_entries_user_status ON entries(user_id, status)` },\n { sql: `CREATE INDEX IF NOT EXISTS idx_entries_pending_run ON entries(next_run_at) WHERE status = 'pending'` },\n { sql: `CREATE INDEX IF NOT EXISTS idx_entries_type_created ON entries(type, created_at DESC)` },\n { sql: `CREATE INDEX IF NOT EXISTS idx_entry_links_from ON entry_links(from_entry_id)` },\n { sql: `CREATE INDEX IF NOT EXISTS idx_entry_links_to ON entry_links(to_entry_id)` },\n\n // Single FTS5 table for all entries\n {\n sql: `CREATE VIRTUAL TABLE IF NOT EXISTS entries_fts USING fts5(\n title,\n content,\n tags,\n content_rowid=id\n )`,\n },\n ]);\n\n console.log(\"💾 Database schema initialized\");\n}\n\n/**\n * Migrate data from old tables (memories, tasks, ideas, interactions) into the\n * unified entries table. Safe to run multiple times — checks if migration is needed.\n *\n * Call this once after deploying the new schema, then remove the old tables.\n */\nexport async function migrateToUnifiedSchema() {\n // Check if old tables exist\n const oldTables = await sqlite.execute(\n `SELECT name FROM sqlite_master WHERE type='table' AND name IN ('memories', 'tasks', 'ideas', 'interactions')`,\n );\n\n if (oldTables.rows.length === 0) {\n console.log(\"📋 No old tables found — migration not needed\");\n return;\n }\n\n // Check if we've already migrated (entries table has data)\n const entryCount = await sqlite.execute(`SELECT COUNT(*) as cnt FROM entries`);\n if ((entryCount.rows[0] as Record).cnt as number > 0) {\n console.log(\"📋 Entries table already has data — skipping migration\");\n return;\n }\n\n // Add system_prompt column to users if it doesn't exist\n try {\n await sqlite.execute(`ALTER TABLE users ADD COLUMN system_prompt TEXT NOT NULL DEFAULT ''`);\n console.log(\"👤 Added system_prompt column to users\");\n } catch {\n // Column already exists\n }\n\n console.log(\"🔄 Starting migration to unified schema...\");\n\n // Migrate memories\n const memories = await sqlite.execute(`SELECT * FROM memories`);\n for (const row of memories.rows) {\n const m = row as Record;\n const result = await sqlite.execute({\n sql: `INSERT INTO entries (user_id, type, status, content, metadata, created_at, updated_at)\n VALUES (?, 'memory', 'active', ?, ?, ?, ?)`,\n args: [\n m.user_id,\n m.content,\n JSON.stringify({ category: m.category, legacy_source_interaction_id: m.source_interaction_id }),\n m.created_at,\n m.updated_at,\n ],\n });\n const id = Number(result.lastInsertRowid);\n await sqlite.execute({\n sql: `INSERT INTO entries_fts (rowid, title, content, tags) VALUES (?, '', ?, '')`,\n args: [id, m.content],\n });\n }\n console.log(` ✅ Migrated ${memories.rows.length} memories`);\n\n // Migrate tasks\n const tasks = await sqlite.execute(`SELECT * FROM tasks`);\n for (const row of tasks.rows) {\n const t = row as Record;\n const result = await sqlite.execute({\n sql: `INSERT INTO entries (user_id, type, status, title, content, metadata, next_run_at, schedule_description, created_at, updated_at)\n VALUES (?, 'task', ?, ?, ?, ?, ?, ?, ?, ?)`,\n args: [\n t.user_id,\n t.status,\n t.title,\n t.description || t.title,\n JSON.stringify({\n task_type: t.type,\n result: t.result,\n last_run_at: t.last_run_at,\n legacy_source_interaction_id: t.source_interaction_id,\n }),\n t.next_run_at,\n t.schedule_description,\n t.created_at,\n t.updated_at,\n ],\n });\n const id = Number(result.lastInsertRowid);\n await sqlite.execute({\n sql: `INSERT INTO entries_fts (rowid, title, content, tags) VALUES (?, ?, ?, '')`,\n args: [id, t.title, t.description || \"\"],\n });\n }\n console.log(` ✅ Migrated ${tasks.rows.length} tasks`);\n\n // Migrate ideas\n const ideas = await sqlite.execute(`SELECT * FROM ideas`);\n for (const row of ideas.rows) {\n const i = row as Record;\n const result = await sqlite.execute({\n sql: `INSERT INTO entries (user_id, type, status, content, tags, metadata, created_at)\n VALUES (?, 'idea', 'active', ?, ?, ?, ?)`,\n args: [\n i.user_id,\n i.content,\n i.tags,\n JSON.stringify({ legacy_source_interaction_id: i.source_interaction_id }),\n i.created_at,\n ],\n });\n const id = Number(result.lastInsertRowid);\n await sqlite.execute({\n sql: `INSERT INTO entries_fts (rowid, title, content, tags) VALUES (?, '', ?, ?)`,\n args: [id, i.content, i.tags || \"\"],\n });\n }\n console.log(` ✅ Migrated ${ideas.rows.length} ideas`);\n\n // Migrate interactions\n const interactions = await sqlite.execute(`SELECT * FROM interactions`);\n for (const row of interactions.rows) {\n const inter = row as Record;\n const result = await sqlite.execute({\n sql: `INSERT INTO entries (user_id, type, status, title, content, metadata, created_at)\n VALUES (?, 'interaction', 'active', ?, ?, ?, ?)`,\n args: [\n inter.user_id,\n inter.subject,\n inter.body,\n JSON.stringify({ direction: inter.direction }),\n inter.created_at,\n ],\n });\n const id = Number(result.lastInsertRowid);\n await sqlite.execute({\n sql: `INSERT INTO entries_fts (rowid, title, content, tags) VALUES (?, ?, ?, '')`,\n args: [id, inter.subject || \"\", inter.body],\n });\n }\n console.log(` ✅ Migrated ${interactions.rows.length} interactions`);\n\n console.log(\"🎉 Migration complete!\");\n}\n",[],[162,192],[162],{"_106":201,"_97":202,"_163":203,"_165":-5,"_166":148,"_167":185,"_169":170,"_108":204,"_102":33,"_172":173,"_174":-5,"_175":22,"_176":-5,"_177":205,"_100":244},"edffb3bc-180f-11f1-a610-42dde27851f2","lib","edffb48e-180f-11f1-a610-42dde27851f2",["D",1772659212112],[206,216,226,235],{"_106":207,"_97":208,"_163":209,"_165":203,"_166":148,"_167":210,"_169":170,"_108":211,"_102":212,"_172":186,"_174":213,"_175":22,"_176":-5,"_177":214,"_100":215},"58c31728-181c-11f1-90e1-42dde27851f2","agent.ts","2a0e16f0-1810-11f1-a610-42dde27851f2",50,["D",1772664545240],4,"/**\n * The Agent — executes tasks using Claude with an iterative tool-use loop.\n *\n * For each task, it:\n * 1. Loads the user's system prompt (their personal \"CLAUDE.md\")\n * 2. Loads memories, active tasks, recent ideas, recent conversations\n * 3. Loads text content from any attachments on the source entry\n * 4. Searches for relevant context via FTS\n * 5. Sends everything to Claude with tool definitions\n * 6. Loops: if Claude calls a tool, execute it and feed result back\n * 7. Repeats until Claude returns a final text response (or max iterations)\n * 8. Returns the result with a suggested subject line and optional file attachments\n */\n\nimport Anthropic from \"npm:@anthropic-ai/sdk@0.39.0\";\nimport {\n addEntry,\n getEntries,\n getLinkedEntries,\n getUserContext,\n searchEntries,\n getAttachmentsForEntry,\n updateEntryStatus,\n} from \"../db/queries.ts\";\nimport { getTextAttachmentContents } from \"./attachments.ts\";\n\nconst MAX_TOOL_ITERATIONS = 15;\n\nconst BASE_SYSTEM_PROMPT = `You are a helpful personal AI agent. You communicate via email with your user.\n\nYou have access to the user's stored context: their memories (things they've told you about themselves), their active tasks, their ideas, and recent conversation history.\n\nYou also have TOOLS you can call to gather more information, search the web, store new memories, and create follow-up tasks. Use them when needed — don't guess when you can look things up.\n\nWhen executing a task:\n1. Use the provided context to personalize your response\n2. If you need more information, use your tools (search memories, fetch URLs, look up entries)\n3. Be thorough but concise\n4. If the task requires real-time data (stock prices, weather, news, etc.), use the fetch_url tool to get it\n5. Format your response as a clear, readable email reply\n6. If you discover new facts about the user worth remembering, use store_memory to save them\n7. If the task would benefit from follow-up actions, use create_task to schedule them\n\n## Generating File Attachments\n\nIf a task requires creating a file (report, CSV, document, data export, etc.), include it in your response using this format:\n\n\ncol1,col2,col3\nval1,val2,val3\n\n\nYou can include multiple attachments. The content between the tags will be sent as an email attachment.\nGood use cases: CSV data exports, text reports, JSON data, markdown summaries, HTML reports.\nKeep attachment content focused and well-structured.\n\n## Tool Use Strategy\n\n- **search_memories**: Search the user's stored knowledge base. Good when the task references something the user told you before.\n- **get_entries**: Browse entries by type. Use to find all tasks, all people, all ideas, etc.\n- **get_linked_entries**: Follow relationships between entries. \"What was this entry connected to?\"\n- **store_memory**: When you learn a new fact about the user during task execution, save it.\n- **create_task**: Break complex work into follow-up tasks. Create future/recurring tasks as needed.\n- **fetch_url**: Fetch live data from the web. Use for news, prices, weather, documentation, etc.\n\nDon't over-use tools — if the context already has what you need, just answer. But don't under-use them either — if a web search or memory lookup would make your answer significantly better, do it.\n\nYour tone should be friendly, professional, and efficient — like a great personal assistant who knows the user well.`;\n\n// ============ TOOL DEFINITIONS ============\n\nconst TOOL_DEFINITIONS: Anthropic.Tool[] = [\n {\n name: \"search_memories\",\n description:\n \"Search the user's stored entries (memories, tasks, ideas, people, etc.) using full-text search. Returns matching snippets ranked by relevance.\",\n input_schema: {\n type: \"object\" as const,\n properties: {\n query: {\n type: \"string\",\n description: \"The search query — keywords or phrases to find in the user's stored entries.\",\n },\n type: {\n type: \"string\",\n description:\n \"Optional: filter by entry type (memory, task, idea, person, meeting, project, interaction). Omit to search all types.\",\n },\n limit: {\n type: \"number\",\n description: \"Max results to return (default 10, max 20).\",\n },\n },\n required: [\"query\"],\n },\n },\n {\n name: \"get_entries\",\n description:\n \"Get entries for the user filtered by type and/or status. Use this to browse categories of stored information (e.g., all pending tasks, all memories, all people).\",\n input_schema: {\n type: \"object\" as const,\n properties: {\n type: {\n type: \"string\",\n description: \"Entry type to filter by: memory, task, idea, person, meeting, project, interaction, etc.\",\n },\n status: {\n type: \"string\",\n description: \"Optional status filter: active, pending, in_progress, completed, failed, archived.\",\n },\n limit: {\n type: \"number\",\n description: \"Max entries to return (default 10, max 30).\",\n },\n },\n required: [],\n },\n },\n {\n name: \"get_linked_entries\",\n description:\n \"Get all entries linked to a specific entry. Returns related entries with their relationship type and direction. Use this to explore connections in the user's knowledge graph.\",\n input_schema: {\n type: \"object\" as const,\n properties: {\n entry_id: {\n type: \"number\",\n description: \"The ID of the entry to find links for.\",\n },\n },\n required: [\"entry_id\"],\n },\n },\n {\n name: \"store_memory\",\n description:\n \"Store a new memory/fact about the user that was discovered during task execution. Use this when you learn something new and important that should be remembered for future interactions.\",\n input_schema: {\n type: \"object\" as const,\n properties: {\n content: {\n type: \"string\",\n description: \"The fact or memory to store. Be concise and specific.\",\n },\n category: {\n type: \"string\",\n enum: [\"preference\", \"fact\", \"context\", \"general\"],\n description: \"Category of memory: preference (likes/dislikes), fact (verifiable info), context (situational), general.\",\n },\n tags: {\n type: \"string\",\n description: \"Optional comma-separated tags for searchability.\",\n },\n },\n required: [\"content\", \"category\"],\n },\n },\n {\n name: \"create_task\",\n description:\n \"Create a follow-up task for the user. Use this to schedule reminders, break complex work into steps, or set up recurring actions. The task will be processed by the task runner.\",\n input_schema: {\n type: \"object\" as const,\n properties: {\n title: {\n type: \"string\",\n description: \"Short title for the task.\",\n },\n content: {\n type: \"string\",\n description: \"Detailed description of what needs to be done.\",\n },\n task_type: {\n type: \"string\",\n enum: [\"instant\", \"future\", \"recurring\"],\n description: \"instant = do now, future = do at scheduled time, recurring = repeat on schedule.\",\n },\n schedule: {\n type: \"string\",\n description:\n \"Required for future/recurring tasks. Natural language schedule like 'tomorrow at 9am', 'every Monday at 8am', 'in 2 hours'.\",\n },\n },\n required: [\"title\", \"content\", \"task_type\"],\n },\n },\n {\n name: \"fetch_url\",\n description:\n \"Fetch content from a URL. Use this for live data: news articles, documentation, weather APIs, stock data, web pages, etc. Returns the text content of the page (HTML tags stripped for readability). Limited to 50KB of response text.\",\n input_schema: {\n type: \"object\" as const,\n properties: {\n url: {\n type: \"string\",\n description: \"The URL to fetch. Must be a complete URL including https://\",\n },\n accept: {\n type: \"string\",\n description: \"Optional Accept header value. Defaults to text/html. Use application/json for APIs.\",\n },\n },\n required: [\"url\"],\n },\n },\n];\n\n// ============ TOOL EXECUTION ============\n\nasync function executeTool(\n userId: number,\n toolName: string,\n toolInput: Record,\n opts?: { sourceEntryId?: number },\n): Promise {\n try {\n switch (toolName) {\n case \"search_memories\": {\n const query = toolInput.query as string;\n const type = toolInput.type as string | undefined;\n const limit = Math.min((toolInput.limit as number) || 10, 20);\n const results = await searchEntries(userId, query, { type, limit });\n if (results.length === 0) {\n return \"No matching entries found.\";\n }\n return results\n .map((r) => `[${r.type} #${r.id}] ${r.snippet}`)\n .join(\"\\n\");\n }\n\n case \"get_entries\": {\n const type = toolInput.type as string | undefined;\n const status = toolInput.status as string | undefined;\n const limit = Math.min((toolInput.limit as number) || 10, 30);\n const entries = await getEntries(userId, { type, status, limit });\n if (entries.length === 0) {\n return `No entries found${type ? ` of type \"${type}\"` : \"\"}${status ? ` with status \"${status}\"` : \"\"}.`;\n }\n return entries\n .map((e) => {\n const meta = e.metadata ? ` (${JSON.stringify(e.metadata)})` : \"\";\n return `[#${e.id} ${e.type}/${e.status}] ${e.title || \"(no title)\"}: ${e.content.slice(0, 200)}${meta}`;\n })\n .join(\"\\n\\n\");\n }\n\n case \"get_linked_entries\": {\n const entryId = toolInput.entry_id as number;\n const linked = await getLinkedEntries(entryId);\n if (linked.length === 0) {\n return `No linked entries found for entry #${entryId}.`;\n }\n return linked\n .map(\n (l) =>\n `[${l.direction} ${l.relationship}] #${l.entry.id} ${l.entry.type}: ${l.entry.title || l.entry.content.slice(0, 100)}`,\n )\n .join(\"\\n\");\n }\n\n case \"store_memory\": {\n const content = toolInput.content as string;\n const category = toolInput.category as string;\n const tags = toolInput.tags as string | undefined;\n const memoryId = await addEntry(userId, {\n type: \"memory\",\n content,\n tags,\n metadata: { category },\n sourceEntryId: opts?.sourceEntryId,\n });\n return `Memory stored (id: ${memoryId}): \"${content}\"`;\n }\n\n case \"create_task\": {\n const title = toolInput.title as string;\n const content = toolInput.content as string;\n const taskType = toolInput.task_type as string;\n const schedule = toolInput.schedule as string | undefined;\n\n // For future/recurring tasks, we need to parse the schedule\n let nextRunAt: string | undefined;\n if ((taskType === \"future\" || taskType === \"recurring\") && schedule) {\n // Dynamically import to avoid circular deps\n const { parseSchedule } = await import(\"./scheduler.ts\");\n const parsed = await parseSchedule(schedule);\n nextRunAt = parsed.nextRunAt;\n }\n\n const taskId = await addEntry(userId, {\n type: \"task\",\n title,\n content,\n status: \"pending\",\n metadata: { task_type: taskType },\n sourceEntryId: opts?.sourceEntryId,\n nextRunAt,\n scheduleDescription: schedule,\n });\n\n if (taskType === \"instant\") {\n return `Task created (id: ${taskId}): \"${title}\" — will be executed immediately after this conversation.`;\n }\n return `Task created (id: ${taskId}): \"${title}\" — scheduled${nextRunAt ? ` for ${nextRunAt}` : \"\"}.`;\n }\n\n case \"fetch_url\": {\n const url = toolInput.url as string;\n const accept = (toolInput.accept as string) || \"text/html\";\n\n // Safety: only allow http/https\n if (!url.startsWith(\"http://\") && !url.startsWith(\"https://\")) {\n return \"Error: Only http:// and https:// URLs are supported.\";\n }\n\n const controller = new AbortController();\n const timeout = setTimeout(() => controller.abort(), 15000); // 15s timeout\n\n try {\n const response = await fetch(url, {\n headers: {\n Accept: accept,\n \"User-Agent\": \"MemoryDo-Agent/1.0\",\n },\n signal: controller.signal,\n redirect: \"follow\",\n });\n\n clearTimeout(timeout);\n\n if (!response.ok) {\n return `HTTP ${response.status} ${response.statusText} fetching ${url}`;\n }\n\n const contentType = response.headers.get(\"content-type\") || \"\";\n let text = await response.text();\n\n // Truncate large responses\n const MAX_RESPONSE = 50_000;\n if (text.length > MAX_RESPONSE) {\n text = text.slice(0, MAX_RESPONSE) + \"\\n\\n[...truncated at 50KB...]\";\n }\n\n // Basic HTML tag stripping for readability (if HTML)\n if (contentType.includes(\"html\")) {\n // Remove script/style blocks entirely\n text = text.replace(//gi, \"\");\n text = text.replace(//gi, \"\");\n // Remove HTML tags\n text = text.replace(/<[^>]+>/g, \" \");\n // Collapse whitespace\n text = text.replace(/\\s+/g, \" \").trim();\n // Re-truncate after stripping\n if (text.length > MAX_RESPONSE) {\n text = text.slice(0, MAX_RESPONSE) + \"\\n\\n[...truncated at 50KB...]\";\n }\n }\n\n return `Content from ${url} (${contentType}):\\n\\n${text}`;\n } catch (fetchErr) {\n clearTimeout(timeout);\n if ((fetchErr as Error).name === \"AbortError\") {\n return `Error: Request to ${url} timed out after 15 seconds.`;\n }\n return `Error fetching ${url}: ${(fetchErr as Error).message}`;\n }\n }\n\n default:\n return `Unknown tool: ${toolName}`;\n }\n } catch (err) {\n return `Tool error (${toolName}): ${(err as Error).message}`;\n }\n}\n\n// ============ MAIN EXPORTS ============\n\nexport interface GeneratedAttachment {\n filename: string;\n mimeType: string;\n content: string;\n}\n\nexport interface AgentResult {\n response: string;\n suggestedSubject: string;\n attachments: GeneratedAttachment[];\n toolCallCount: number;\n iterationCount: number;\n}\n\nexport async function executeTask(\n userId: number,\n taskTitle: string,\n taskDescription: string,\n opts?: {\n additionalContext?: string;\n sourceEntryId?: number;\n },\n): Promise {\n const client = new Anthropic();\n\n // Load user context (includes system prompt)\n const context = await getUserContext(userId);\n\n // Build the system prompt: base + user's personal instructions\n let systemPrompt = BASE_SYSTEM_PROMPT;\n if (context.systemPrompt) {\n systemPrompt += `\\n\\n## User's Personal Instructions\\nThe user has set the following standing instructions. Always follow these:\\n${context.systemPrompt}`;\n }\n\n // Load text attachments from the source entry (if available)\n let attachmentContext: { filename: string; content: string }[] = [];\n if (opts?.sourceEntryId) {\n try {\n attachmentContext = await getTextAttachmentContents(opts.sourceEntryId);\n if (attachmentContext.length > 0) {\n console.log(`📎 Loaded ${attachmentContext.length} text attachment(s) for context`);\n }\n } catch (e) {\n console.log(\"📎 Attachment load skipped:\", (e as Error).message);\n }\n }\n\n // Search for relevant content based on task\n let searchResults: Awaited> = [];\n try {\n const searchQuery = `${taskTitle} ${taskDescription}`.slice(0, 200);\n searchResults = await searchEntries(userId, searchQuery, { limit: 10 });\n } catch (e) {\n console.log(\"🔍 FTS search skipped:\", (e as Error).message);\n }\n\n // Build initial context message\n let contextMessage = \"\";\n\n if (context.memories.length > 0) {\n contextMessage += \"\\n\";\n for (const m of context.memories) {\n const category = m.metadata?.category || \"general\";\n contextMessage += `- [${category}] ${m.content}\\n`;\n }\n contextMessage += \"\\n\\n\";\n }\n\n if (context.activeTasks.length > 0) {\n contextMessage += \"\\n\";\n for (const t of context.activeTasks) {\n const taskType = (t.metadata as Record)?.task_type || t.type;\n contextMessage += `- [${taskType}/${t.status}] ${t.title}: ${t.content}\\n`;\n }\n contextMessage += \"\\n\\n\";\n }\n\n if (context.recentIdeas.length > 0) {\n contextMessage += \"\\n\";\n for (const i of context.recentIdeas) {\n contextMessage += `- ${i.content} (tags: ${i.tags || \"none\"})\\n`;\n }\n contextMessage += \"\\n\\n\";\n }\n\n if (context.recentInteractions.length > 0) {\n contextMessage += \"\\n\";\n for (const inter of context.recentInteractions.slice(0, 5)) {\n const direction = (inter.metadata as Record)?.direction || \"unknown\";\n contextMessage += `[${direction} - ${inter.created_at}] Subject: ${inter.title || \"none\"}\\n${inter.content.slice(0, 500)}\\n---\\n`;\n }\n contextMessage += \"\\n\\n\";\n }\n\n // Include text attachment content\n if (attachmentContext.length > 0) {\n contextMessage += \"\\n\";\n for (const att of attachmentContext) {\n const truncated =\n att.content.length > 20000 ? att.content.slice(0, 20000) + \"\\n[...truncated...]\" : att.content;\n contextMessage += `--- ${att.filename} ---\\n${truncated}\\n\\n`;\n }\n contextMessage += \"\\n\\n\";\n }\n\n if (searchResults.length > 0) {\n contextMessage += \"\\n\";\n for (const r of searchResults) {\n contextMessage += `- [${r.type} #${r.id}] ${r.snippet}\\n`;\n }\n contextMessage += \"\\n\\n\";\n }\n\n if (opts?.additionalContext) {\n contextMessage += `\\n${opts.additionalContext}\\n\\n\\n`;\n }\n\n contextMessage += `\\nTitle: ${taskTitle}\\nDescription: ${taskDescription}\\n`;\n\n // ============ AGENTIC TOOL-USE LOOP ============\n\n const messages: Anthropic.MessageParam[] = [{ role: \"user\", content: contextMessage }];\n\n let totalToolCalls = 0;\n let iterations = 0;\n let finalResponseText = \"\";\n\n for (let i = 0; i < MAX_TOOL_ITERATIONS; i++) {\n iterations++;\n\n const response = await client.messages.create({\n model: \"claude-sonnet-4-20250514\",\n max_tokens: 4096,\n system: systemPrompt,\n tools: TOOL_DEFINITIONS,\n messages,\n });\n\n // Check if this is a final response (no tool use)\n if (response.stop_reason === \"end_turn\") {\n // Extract text from the response\n const textBlocks = response.content.filter((b) => b.type === \"text\");\n finalResponseText = textBlocks.map((b) => (b as Anthropic.TextBlock).text).join(\"\\n\");\n console.log(`🤖 Agent finished after ${iterations} iteration(s), ${totalToolCalls} tool call(s)`);\n break;\n }\n\n // Process tool use blocks\n if (response.stop_reason === \"tool_use\") {\n // Add the assistant's response (with tool_use blocks) to messages\n messages.push({ role: \"assistant\", content: response.content });\n\n // Execute each tool call and build tool_result blocks\n const toolResults: Anthropic.ToolResultBlockParam[] = [];\n\n for (const block of response.content) {\n if (block.type === \"tool_use\") {\n totalToolCalls++;\n console.log(`🔧 Tool call [${iterations}/${i + 1}]: ${block.name}(${JSON.stringify(block.input).slice(0, 200)})`);\n\n const result = await executeTool(\n userId,\n block.name,\n block.input as Record,\n { sourceEntryId: opts?.sourceEntryId },\n );\n\n console.log(` ↳ Result: ${result.slice(0, 150)}${result.length > 150 ? \"...\" : \"\"}`);\n\n toolResults.push({\n type: \"tool_result\",\n tool_use_id: block.id,\n content: result,\n });\n }\n }\n\n // Feed tool results back to Claude\n messages.push({ role: \"user\", content: toolResults });\n }\n\n // Safety: if we somehow got a stop_reason we don't handle, extract text and break\n if (response.stop_reason !== \"tool_use\" && response.stop_reason !== \"end_turn\") {\n const textBlocks = response.content.filter((b) => b.type === \"text\");\n finalResponseText = textBlocks.map((b) => (b as Anthropic.TextBlock).text).join(\"\\n\");\n console.log(`⚠️ Agent stopped with reason: ${response.stop_reason} after ${iterations} iterations`);\n break;\n }\n }\n\n // If we hit max iterations without a final response, extract what we have\n if (!finalResponseText) {\n console.log(`⚠️ Agent hit max iterations (${MAX_TOOL_ITERATIONS}). Extracting last response.`);\n // Ask Claude to wrap up\n messages.push({\n role: \"user\",\n content:\n \"You've reached the maximum number of tool calls. Please provide your final response now based on what you've gathered so far.\",\n });\n\n const wrapUpResponse = await client.messages.create({\n model: \"claude-sonnet-4-20250514\",\n max_tokens: 4096,\n system: systemPrompt,\n messages,\n });\n\n const textBlocks = wrapUpResponse.content.filter((b) => b.type === \"text\");\n finalResponseText = textBlocks.map((b) => (b as Anthropic.TextBlock).text).join(\"\\n\");\n }\n\n // Extract any generated attachments from the response\n const attachments = extractAttachments(finalResponseText);\n\n // Remove attachment tags from the email body text\n let responseText = finalResponseText\n .replace(/]*>[\\s\\S]*?<\\/attachment>/g, \"\")\n .replace(/\\n{3,}/g, \"\\n\\n\")\n .trim();\n\n if (attachments.length > 0) {\n responseText += `\\n\\n📎 ${attachments.length} file(s) attached: ${attachments.map((a) => a.filename).join(\", \")}`;\n }\n\n // Generate a subject line\n const subjectResponse = await client.messages.create({\n model: \"claude-sonnet-4-20250514\",\n max_tokens: 100,\n messages: [\n {\n role: \"user\",\n content: `Generate a short email subject line (max 60 chars) for this response. Return ONLY the subject line, nothing else.\\n\\nTask: ${taskTitle}\\nResponse preview: ${responseText.slice(0, 200)}`,\n },\n ],\n });\n\n const suggestedSubject =\n subjectResponse.content[0].type === \"text\"\n ? subjectResponse.content[0].text.trim().replace(/^[\"']|[\"']$/g, \"\")\n : `Re: ${taskTitle}`;\n\n return {\n response: responseText,\n suggestedSubject,\n attachments,\n toolCallCount: totalToolCalls,\n iterationCount: iterations,\n };\n}\n\n/**\n * Parse tags from agent response.\n * Format: content\n */\nfunction extractAttachments(text: string): GeneratedAttachment[] {\n const attachments: GeneratedAttachment[] = [];\n const regex = /([\\s\\S]*?)<\\/attachment>/g;\n\n let match;\n while ((match = regex.exec(text)) !== null) {\n const filename = match[1];\n const mimeType = match[2] || guessMimeType(filename);\n const content = match[3].trim();\n\n attachments.push({ filename, mimeType, content });\n console.log(`📎 Extracted attachment from response: ${filename} (${mimeType}, ${content.length} chars)`);\n }\n\n return attachments;\n}\n\nfunction guessMimeType(filename: string): string {\n const ext = filename.toLowerCase().split(\".\").pop();\n const types: Record = {\n csv: \"text/csv\",\n json: \"application/json\",\n txt: \"text/plain\",\n md: \"text/markdown\",\n html: \"text/html\",\n xml: \"application/xml\",\n yml: \"application/x-yaml\",\n yaml: \"application/x-yaml\",\n };\n return types[ext || \"\"] || \"application/octet-stream\";\n}\n",[],[202,208],{"_106":217,"_97":218,"_163":219,"_165":203,"_166":148,"_167":220,"_169":170,"_108":221,"_102":222,"_172":186,"_174":223,"_175":22,"_176":-5,"_177":224,"_100":225},"e8186e2e-181b-11f1-b9c4-42dde27851f2","attachments.ts","cf077a02-181a-11f1-81e1-42dde27851f2",49,["D",1772664356194],1,"/**\n * Attachment handling — inbound (receive & store) and outbound (generate & send).\n *\n * Inbound: email attachments → blob storage + attachments table\n * Outbound: generated files → blob storage + base64 for email\n *\n * Text-based files (txt, csv, json, md, html, xml) have their content\n * available for agent context. Binary files (images, PDFs, zips) are\n * stored but not fed to the agent as text.\n *\n * Blob keys follow the pattern: attachments/{userId}/{entryId}/{filename}\n */\n\nimport { blob } from \"https://esm.town/v/std/blob\";\nimport { addAttachment, getAttachmentsForEntry } from \"../db/queries.ts\";\nimport type { Attachment } from \"../db/queries.ts\";\n\n/** MIME types we consider \"text-readable\" — content can be fed to the agent */\nconst TEXT_MIME_TYPES = new Set([\n \"text/plain\",\n \"text/csv\",\n \"text/html\",\n \"text/xml\",\n \"text/markdown\",\n \"application/json\",\n \"application/xml\",\n \"application/csv\",\n \"text/tab-separated-values\",\n]);\n\n/** File extensions we consider text-readable (fallback when MIME is generic) */\nconst TEXT_EXTENSIONS = new Set([\n \".txt\",\n \".csv\",\n \".json\",\n \".md\",\n \".markdown\",\n \".html\",\n \".htm\",\n \".xml\",\n \".yml\",\n \".yaml\",\n \".toml\",\n \".ini\",\n \".cfg\",\n \".log\",\n \".tsv\",\n \".sql\",\n \".ts\",\n \".js\",\n \".py\",\n \".sh\",\n \".css\",\n]);\n\n/** Max text content to extract and store for agent context (100KB) */\nconst MAX_TEXT_CONTENT_BYTES = 100 * 1024;\n\nfunction isTextFile(filename: string, mimeType: string): boolean {\n if (TEXT_MIME_TYPES.has(mimeType)) return true;\n const ext = filename.toLowerCase().slice(filename.lastIndexOf(\".\"));\n return TEXT_EXTENSIONS.has(ext);\n}\n\nfunction blobKey(userId: number, entryId: number, filename: string): string {\n // Sanitize filename for use in blob key\n const safeName = filename.replace(/[^a-zA-Z0-9._-]/g, \"_\");\n return `attachments/${userId}/${entryId}/${safeName}`;\n}\n\n/**\n * Store an inbound email attachment in blob storage and record it in the DB.\n *\n * Val Town email attachments are File objects with:\n * - file.name: string\n * - file.type: string (MIME type)\n * - file.size: number\n * - file.text(): Promise\n * - file.arrayBuffer(): Promise\n */\nexport async function storeInboundAttachment(\n file: { name: string; type: string; size: number; text: () => Promise; arrayBuffer: () => Promise },\n userId: number,\n entryId: number,\n): Promise<{ attachmentId: number; textContent: string | null }> {\n const filename = file.name || \"unnamed\";\n const mimeType = file.type || \"application/octet-stream\";\n const isText = isTextFile(filename, mimeType);\n const key = blobKey(userId, entryId, filename);\n\n let textContent: string | null = null;\n\n if (isText && file.size <= MAX_TEXT_CONTENT_BYTES) {\n // Store as text in blob, and capture content for agent context\n textContent = await file.text();\n await blob.set(key, textContent);\n } else {\n // Store as binary\n const buffer = await file.arrayBuffer();\n const base64Content = btoa(\n String.fromCharCode(...new Uint8Array(buffer)),\n );\n // Store the base64 in blob (we'll decode when retrieving)\n await blob.set(key, base64Content);\n }\n\n const attachmentId = await addAttachment({\n entryId,\n userId,\n filename,\n mimeType,\n sizeBytes: file.size,\n blobKey: key,\n isText,\n });\n\n console.log(\n `📎 Stored attachment: ${filename} (${mimeType}, ${file.size} bytes, text=${isText})`,\n );\n\n return { attachmentId, textContent };\n}\n\n/**\n * Store an outbound attachment generated by the agent.\n * Returns the blob key and base64 content for email sending.\n */\nexport async function storeOutboundAttachment(opts: {\n filename: string;\n content: string;\n mimeType: string;\n userId: number;\n entryId: number;\n}): Promise<{ attachmentId: number; base64Content: string }> {\n const key = blobKey(opts.userId, opts.entryId, opts.filename);\n const isText = isTextFile(opts.filename, opts.mimeType);\n\n // Store in blob\n await blob.set(key, opts.content);\n\n const sizeBytes = new TextEncoder().encode(opts.content).length;\n\n const attachmentId = await addAttachment({\n entryId: opts.entryId,\n userId: opts.userId,\n filename: opts.filename,\n mimeType: opts.mimeType,\n sizeBytes,\n blobKey: key,\n isText,\n });\n\n // Base64 encode for email attachment\n const base64Content = btoa(opts.content);\n\n console.log(\n `📎 Created outbound attachment: ${opts.filename} (${opts.mimeType}, ${sizeBytes} bytes)`,\n );\n\n return { attachmentId, base64Content };\n}\n\n/**\n * Get the text content of text-based attachments for an entry.\n * Used to feed attachment content to the agent as context.\n */\nexport async function getTextAttachmentContents(\n entryId: number,\n): Promise<{ filename: string; content: string }[]> {\n const attachments = await getAttachmentsForEntry(entryId);\n const textAttachments = attachments.filter((a) => a.is_text);\n\n const results: { filename: string; content: string }[] = [];\n for (const att of textAttachments) {\n try {\n const content = await blob.get(att.blob_key);\n if (content) {\n const text = await content.text();\n results.push({ filename: att.filename, content: text });\n }\n } catch (e) {\n console.warn(`⚠️ Failed to read blob ${att.blob_key}:`, e);\n }\n }\n\n return results;\n}\n\n/**\n * Get attachment metadata for an entry (without downloading content).\n * Useful for listing what's attached.\n */\nexport async function getAttachmentSummaries(\n entryId: number,\n): Promise<{ filename: string; mimeType: string; sizeBytes: number; isText: boolean }[]> {\n const attachments = await getAttachmentsForEntry(entryId);\n return attachments.map((a) => ({\n filename: a.filename,\n mimeType: a.mime_type,\n sizeBytes: a.size_bytes,\n isText: a.is_text,\n }));\n}\n\n/**\n * Get a blob's raw content as base64 (for sending as email attachment).\n */\nexport async function getAttachmentBase64(attachment: Attachment): Promise {\n const content = await blob.get(attachment.blob_key);\n if (!content) throw new Error(`Blob not found: ${attachment.blob_key}`);\n\n if (attachment.is_text) {\n // Text files: encode the text as base64\n const text = await content.text();\n return btoa(text);\n } else {\n // Binary files: already stored as base64\n return await content.text();\n }\n}\n",[],[202,218],{"_106":227,"_97":228,"_163":229,"_165":203,"_166":148,"_167":230,"_169":170,"_108":231,"_102":168,"_172":186,"_174":232,"_175":22,"_176":-5,"_177":233,"_100":234},"2c92adb0-1819-11f1-99b3-42dde27851f2","classifier.ts","14b391d6-1810-11f1-9ab7-42dde27851f2",26,["D",1772663182608],"/**\n * Email classifier — uses Claude to analyze incoming emails and extract\n * a unified list of entries (any type), system instructions, and follow-up questions.\n *\n * Output is type-agnostic: each entry has a `type` field that can be anything\n * (memory, task, idea, person, meeting, etc.). No code changes needed for new types.\n */\n\nimport Anthropic from \"npm:@anthropic-ai/sdk@0.39.0\";\n\nexport interface ClassifiedEntry {\n type: string; // 'memory', 'task', 'idea', 'person', 'meeting', etc.\n title?: string;\n content: string;\n tags?: string;\n status?: string; // 'active', 'pending', etc.\n metadata?: Record; // type-specific fields\n schedule?: string; // natural language schedule for tasks: \"next Thursday\", \"every Monday\"\n}\n\nexport interface ClassificationResult {\n entries: ClassifiedEntry[];\n system_instructions: string[]; // persistent instructions for the agent (\"be casual\", \"call me Paul\")\n follow_up_questions: string[]; // natural follow-ups to include in the reply\n reply_needed: boolean;\n summary: string;\n}\n\nconst CLASSIFICATION_PROMPT = `You are an AI assistant that analyzes incoming emails to extract structured information.\n\nThe user often sends emails via voice transcription. Expect:\n- Misspellings, homophones (\"stone\" for \"some\", \"bio connect\" for \"I/O Connect\")\n- Run-on sentences without punctuation\n- Implicit intent rather than explicit commands\nInterpret what the user MEANS, not what they literally typed.\n\nGiven an email, extract a list of **entries** — each with a type. Be thorough — a single email may contain multiple entries across types.\n\n## Entry Types\n\n**memory** — Facts, preferences, or context about the user to remember long-term.\n - metadata: { \"category\": \"preference\" | \"fact\" | \"context\" | \"general\" }\n - Examples: \"I like chickens\", \"I work at Acme Corp\", \"My portfolio includes AAPL\"\n\n**task** — Something that needs to be done. Set the metadata.task_type to classify when:\n - metadata: { \"task_type\": \"instant\" | \"future\" | \"recurring\" }\n - instant: needs an immediate response/action NOW. E.g. \"What's the weather?\", \"Draft me an email\"\n - future: do at a specific future time. E.g. \"Remind me Thursday to call dentist\". Include schedule field.\n - recurring: do on a regular schedule. E.g. \"Every Monday, analyze stocks\". Include schedule field.\n - Set status to \"pending\" for all tasks.\n\n**idea** — ONLY capture ideas the user **explicitly** asks to store. Look for signals like:\n - \"I have an idea...\", \"Store this idea...\", \"Here's a thought I want to remember...\"\n - Do NOT infer ideas from general content. If something *could* be an idea but isn't framed as one, skip it.\n\n**person** — When the user mentions a specific person with details worth remembering.\n - metadata: { \"relationship\": \"...\", \"contact\": { ... }, \"connections\": [] }\n - Examples: \"My friend Sarah works at Google\", \"Bob's number is 555-1234\"\n\n**meeting** — When the user mentions a scheduled conversation or event.\n - metadata: { \"with\": [\"...\"], \"date\": \"...\", \"time\": \"...\", \"agenda\": [\"...\"] }\n - Include a schedule field with the meeting time for reminder scheduling.\n\n**project** — When the user discusses something they're actively building.\n - metadata: { \"repo\": \"...\", \"url\": \"...\", \"tech\": [\"...\"] }\n\nYou can use ANY type string — if something doesn't fit the above, invent an appropriate type.\n\n## System Instructions\n\nIf the user gives persistent instructions about how the agent should behave, extract them as system_instructions. These are things like:\n- \"Be more casual\" → \"Use casual, informal tone\"\n- \"Call me Paul\" → \"Address the user as Paul\"\n- \"Always use bullet points\" → \"Format responses using bullet points\"\n- \"Don't be so wordy\" → \"Keep responses concise and brief\"\n\nOnly extract these when the user is clearly giving a STANDING instruction, not a one-time request.\n\n## Follow-Up Questions\n\nSuggest 1-2 natural follow-up questions that would help deepen the conversation or capture more useful information. Keep them short and conversational. Examples:\n- After a task: \"Any deadline for this?\"\n- After a person mention: \"Want me to remember anything else about them?\"\n- After an idea: \"What problem does this solve?\"\n- After a memory: \"How does this connect to [something else you know about them]?\"\n\nOnly include follow-ups when they'd genuinely be useful. Don't force them.\n\n## Response Format\n\nRespond with ONLY valid JSON:\n{\n \"entries\": [\n {\n \"type\": \"memory\",\n \"content\": \"Likes chickens and cows\",\n \"metadata\": { \"category\": \"preference\" }\n },\n {\n \"type\": \"task\",\n \"title\": \"Check weather in London\",\n \"content\": \"Find the current weather in London and report back\",\n \"status\": \"pending\",\n \"metadata\": { \"task_type\": \"instant\" }\n },\n {\n \"type\": \"task\",\n \"title\": \"Call dentist\",\n \"content\": \"Remind to call the dentist about the appointment\",\n \"status\": \"pending\",\n \"metadata\": { \"task_type\": \"future\" },\n \"schedule\": \"next Thursday at 10am\"\n }\n ],\n \"system_instructions\": [],\n \"follow_up_questions\": [],\n \"reply_needed\": true,\n \"summary\": \"Brief one-line summary of what this email is about\"\n}\n\nIf a field has no items, return an empty array. Always include all top-level fields.`;\n\nexport async function classifyEmail(\n subject: string | null,\n body: string,\n userContext?: string,\n): Promise {\n const client = new Anthropic();\n\n let userMessage = \"\";\n if (userContext) {\n userMessage += `\\n${userContext}\\n\\n\\n`;\n }\n userMessage += `\\nSubject: ${subject || \"(no subject)\"}\\n\\n${body}\\n`;\n\n const response = await client.messages.create({\n model: \"claude-sonnet-4-20250514\",\n max_tokens: 2048,\n system: CLASSIFICATION_PROMPT,\n messages: [{ role: \"user\", content: userMessage }],\n });\n\n const text =\n response.content[0].type === \"text\" ? response.content[0].text : \"\";\n\n // Extract JSON from response (handle potential markdown code blocks)\n const jsonMatch = text.match(/\\{[\\s\\S]*\\}/);\n if (!jsonMatch) {\n console.error(\"Failed to parse classification response:\", text);\n return {\n entries: [\n {\n type: \"task\",\n title: \"Process email\",\n content: body,\n status: \"pending\",\n metadata: { task_type: \"instant\" },\n },\n ],\n system_instructions: [],\n follow_up_questions: [],\n reply_needed: true,\n summary: subject || \"Email received\",\n };\n }\n\n try {\n const parsed = JSON.parse(jsonMatch[0]);\n return {\n entries: parsed.entries || [],\n system_instructions: parsed.system_instructions || [],\n follow_up_questions: parsed.follow_up_questions || [],\n reply_needed: parsed.reply_needed ?? true,\n summary: parsed.summary || subject || \"Email received\",\n } as ClassificationResult;\n } catch (e) {\n console.error(\"JSON parse error:\", e, \"Raw:\", jsonMatch[0]);\n return {\n entries: [\n {\n type: \"task\",\n title: \"Process email\",\n content: body,\n status: \"pending\",\n metadata: { task_type: \"instant\" },\n },\n ],\n system_instructions: [],\n follow_up_questions: [],\n reply_needed: true,\n summary: subject || \"Email received\",\n };\n }\n}\n",[],[202,228],{"_106":236,"_97":237,"_163":238,"_165":203,"_166":148,"_167":239,"_169":170,"_108":240,"_102":222,"_172":186,"_174":241,"_175":22,"_176":-5,"_177":242,"_100":243},"2dfc5920-181a-11f1-9fb1-42dde27851f2","scheduler.ts","1d2551ec-1810-11f1-a610-42dde27851f2",33,["D",1772663614478],"/**\n * Schedule parser — converts natural language schedules into concrete timestamps.\n *\n * Two-tier approach:\n * 1. Try simple pattern matching first (fast, no API call, deterministic)\n * 2. Fall back to Claude for complex expressions\n *\n * All timestamps are returned in SQLite-compatible UTC format: \"YYYY-MM-DD HH:MM:SS\"\n * This avoids the ISO 8601 T/Z format mismatch with SQLite's datetime() function.\n */\n\nimport Anthropic from \"npm:@anthropic-ai/sdk@0.39.0\";\n\n/** Format a Date as \"YYYY-MM-DD HH:MM:SS\" (SQLite-compatible UTC) */\nfunction toSqliteUtc(date: Date): string {\n return date.toISOString().replace(\"T\", \" \").replace(/\\.\\d{3}Z$/, \"\");\n}\n\n/**\n * Try to parse simple relative time expressions without an LLM call.\n * Returns null if the expression is too complex.\n */\nfunction trySimpleParse(\n text: string,\n): { nextRunAt: string; isRecurring: boolean; interpreted: string } | null {\n const lower = text.toLowerCase().trim();\n const now = Date.now();\n\n // \"in X minutes/hours/days\"\n const relativeMatch = lower.match(\n /^in\\s+(\\d+)\\s*(minutes?|mins?|hours?|hrs?|days?)$/,\n );\n if (relativeMatch) {\n const amount = parseInt(relativeMatch[1]);\n const unit = relativeMatch[2];\n let ms = 0;\n if (unit.startsWith(\"min\")) ms = amount * 60 * 1000;\n else if (unit.startsWith(\"h\")) ms = amount * 60 * 60 * 1000;\n else if (unit.startsWith(\"d\")) ms = amount * 24 * 60 * 60 * 1000;\n\n const target = new Date(now + ms);\n return {\n nextRunAt: toSqliteUtc(target),\n isRecurring: false,\n interpreted: `In ${amount} ${unit} (${toSqliteUtc(target)} UTC)`,\n };\n }\n\n // \"in half an hour\" / \"in an hour\"\n if (lower === \"in half an hour\" || lower === \"in 30 minutes\" || lower === \"in 30 mins\") {\n const target = new Date(now + 30 * 60 * 1000);\n return {\n nextRunAt: toSqliteUtc(target),\n isRecurring: false,\n interpreted: `In 30 minutes (${toSqliteUtc(target)} UTC)`,\n };\n }\n if (lower === \"in an hour\" || lower === \"in 1 hour\") {\n const target = new Date(now + 60 * 60 * 1000);\n return {\n nextRunAt: toSqliteUtc(target),\n isRecurring: false,\n interpreted: `In 1 hour (${toSqliteUtc(target)} UTC)`,\n };\n }\n\n // \"tomorrow\" / \"tomorrow at HH:MM\"\n const tomorrowMatch = lower.match(\n /^tomorrow(?:\\s+at\\s+(\\d{1,2})(?::(\\d{2}))?\\s*(am|pm)?)?$/,\n );\n if (tomorrowMatch) {\n const target = new Date(now);\n target.setUTCDate(target.getUTCDate() + 1);\n let hours = tomorrowMatch[1] ? parseInt(tomorrowMatch[1]) : 9;\n const minutes = tomorrowMatch[2] ? parseInt(tomorrowMatch[2]) : 0;\n const ampm = tomorrowMatch[3];\n if (ampm === \"pm\" && hours < 12) hours += 12;\n if (ampm === \"am\" && hours === 12) hours = 0;\n target.setUTCHours(hours, minutes, 0, 0);\n return {\n nextRunAt: toSqliteUtc(target),\n isRecurring: false,\n interpreted: `Tomorrow at ${hours.toString().padStart(2, \"0\")}:${minutes.toString().padStart(2, \"0\")} UTC`,\n };\n }\n\n return null;\n}\n\nconst SCHEDULE_PROMPT = `You are a scheduling assistant. Given a natural language time expression and the current UTC datetime, return the next occurrence.\n\nRules:\n- If no time is specified, default to 09:00 UTC\n- For recurring schedules (\"every Monday\"), return the NEXT occurrence\n- For relative times (\"in 2 hours\"), calculate from now\n- For specific dates (\"next Thursday\"), calculate the date\n- Always return UTC times\n- Format the datetime as \"YYYY-MM-DD HH:MM:SS\" (NO T separator, NO Z suffix). Example: \"2026-03-05 14:30:00\"\n\nRespond with ONLY a JSON object:\n{\n \"next_run_at\": \"2026-01-15 09:00:00\",\n \"is_recurring\": true/false,\n \"interpreted_schedule\": \"Every Monday at 09:00 UTC\"\n}`;\n\nexport async function parseSchedule(\n scheduleText: string,\n): Promise<{ nextRunAt: string; isRecurring: boolean; interpreted: string }> {\n // Try simple parsing first — fast, deterministic, no API cost\n const simple = trySimpleParse(scheduleText);\n if (simple) {\n console.log(`⏰ Schedule parsed locally: \"${scheduleText}\" → ${simple.nextRunAt}`);\n return simple;\n }\n\n // Fall back to Claude for complex expressions\n console.log(`⏰ Schedule requires LLM: \"${scheduleText}\"`);\n const client = new Anthropic();\n const now = toSqliteUtc(new Date());\n\n const response = await client.messages.create({\n model: \"claude-sonnet-4-20250514\",\n max_tokens: 256,\n system: SCHEDULE_PROMPT,\n messages: [\n {\n role: \"user\",\n content: `Current UTC time: ${now}\\nSchedule expression: \"${scheduleText}\"`,\n },\n ],\n });\n\n const text =\n response.content[0].type === \"text\" ? response.content[0].text : \"\";\n const jsonMatch = text.match(/\\{[\\s\\S]*\\}/);\n\n if (!jsonMatch) {\n const fallback = toSqliteUtc(new Date(Date.now() + 60 * 60 * 1000));\n console.warn(`⏰ Schedule parse failed, falling back to +1hr: ${fallback}`);\n return {\n nextRunAt: fallback,\n isRecurring: false,\n interpreted: scheduleText,\n };\n }\n\n try {\n const parsed = JSON.parse(jsonMatch[0]);\n // Normalize the timestamp to SQLite format in case the LLM returns ISO\n let nextRunAt: string = parsed.next_run_at;\n nextRunAt = nextRunAt.replace(\"T\", \" \").replace(/Z$/, \"\").replace(/\\.\\d{3}$/, \"\");\n\n return {\n nextRunAt,\n isRecurring: parsed.is_recurring ?? false,\n interpreted: parsed.interpreted_schedule ?? scheduleText,\n };\n } catch {\n const fallback = toSqliteUtc(new Date(Date.now() + 60 * 60 * 1000));\n console.warn(`⏰ Schedule JSON parse failed, falling back to +1hr: ${fallback}`);\n return {\n nextRunAt: fallback,\n isRecurring: false,\n interpreted: scheduleText,\n };\n }\n}\n\n/**\n * Given a recurring task's schedule description, calculate the NEXT run time\n * after the current run. Used after a recurring task executes.\n */\nexport async function getNextRecurrence(\n scheduleDescription: string,\n): Promise {\n const result = await parseSchedule(scheduleDescription);\n return result.nextRunAt;\n}\n",[],[202,237],[202],{"_106":246,"_97":247,"_163":248,"_165":-5,"_166":148,"_167":103,"_169":170,"_108":249,"_102":250,"_172":251,"_174":252,"_175":22,"_176":-5,"_177":253,"_100":254},"6ead9e00-181c-11f1-9e7d-42dde27851f2","CLAUDE.md","ee2ddb2c-1817-11f1-944b-42dde27851f2",["D",1772664581983],5,"file","# Email Agent System\n\nAn email-based personal AI agent built on Val Town. Users interact entirely via email — sending messages, tasks, ideas, and memories to `memory-do@valtown.email`. The agent classifies, stores, executes, and replies.\n\n## Architecture\n\n```\nemail-agent/\n CLAUDE.md — This file. System instructions and conventions.\n main.ts — HTTP endpoint for status/admin/debugging (type: http)\n email-handler.ts — Email ingestion point (type: email, address: memory-do@valtown.email)\n task-runner.ts — Interval that processes pending/recurring tasks (type: interval)\n lib/\n classifier.ts — Claude-powered email classification → unified entries\n scheduler.ts — Natural language → ISO datetime conversion\n agent.ts — Task execution engine with per-user system prompt\n attachments.ts — Inbound/outbound attachment storage (blob + DB)\n db/\n schema.ts — SQLite schema (unified entries + FTS5 + entry links + migration)\n queries.ts — Unified CRUD, FTS search, user context builder\n```\n\n## How It Works\n\n### Email Flow (email-handler.ts)\n1. Email arrives at `memory-do@valtown.email`\n2. Sender is identified/created as a user\n3. Interaction is logged as an entry (type: `interaction`)\n4. Email is classified by Claude into **unified entries** — any type: memories, tasks, ideas, people, meetings, projects, or anything new\n5. System instructions are extracted and persisted to the user's personal system prompt\n6. All entries are stored in the unified `entries` table with FTS synced\n7. Entries are cross-linked to each other and to the source interaction\n8. Instant tasks execute immediately via the agent; results are emailed back\n9. Future/recurring tasks are queued with computed `next_run_at` timestamps\n10. Acknowledgment email is sent summarizing what was captured, with follow-up questions\n\n### Task Runner (task-runner.ts)\n- Runs on an interval (every 15 minutes)\n- Finds task entries where `next_run_at <= now` and `status = 'pending'`\n- Executes each via the agent with full user context (including per-user system prompt)\n- Emails results to the user\n- Recurring tasks get rescheduled to their next occurrence\n- Failed tasks still get rescheduled if recurring\n\n### HTTP Admin (main.ts)\n- `GET /` or `GET /stats` — system-wide counts, entries by type, link counts\n- `GET /user/:email` — full user profile with entries grouped by type and links\n- `GET /migrate` — one-time migration from old tables to unified schema\n\n## Database\n\nUses **project-scoped SQLite** (`std/sqlite/main.ts`). Do NOT change to `std/sqlite` or `std/sqlite/global.ts` — this project uses its own database, not the user's global one.\n\n### Unified Model\n\nEverything is an **entry** with a `type` column. No new tables needed for new content types — just use a new type string.\n\n### Tables\n\n- **users** — unique sender emails, optional name, **per-user system_prompt** (their personal \"CLAUDE.md\")\n- **entries** — all content: memories, tasks, ideas, interactions, people, meetings, projects, and any future type\n- **entry_links** — relationship graph between entries (cross-references, co-extraction, parent-child, etc.)\n\n### Entry Types (non-exhaustive — new types can be added without schema changes)\n\n| Type | Purpose | Key metadata fields |\n|---|---|---|\n| `memory` | Long-term facts/preferences | `{ category: \"preference\" \\| \"fact\" \\| \"context\" \\| \"general\" }` |\n| `task` | Actionable items | `{ task_type: \"instant\" \\| \"future\" \\| \"recurring\", result: \"...\" }` |\n| `idea` | Captured ideas (explicit only) | `{ status: \"seed\" \\| \"exploring\" \\| ... }` |\n| `interaction` | Raw email log | `{ direction: \"inbound\" \\| \"outbound\" }` |\n| `person` | People mentioned by the user | `{ relationship: \"...\", contact: { ... } }` |\n| `meeting` | Scheduled conversations | `{ with: [...], date: \"...\", agenda: [...] }` |\n| `project` | Active codebases/builds | `{ repo: \"...\", url: \"...\", tech: [...] }` |\n\n### Entry Fields\n\n- `id` — primary key\n- `user_id` — owner\n- `type` — the content type (any string)\n- `status` — lifecycle: `active`, `pending`, `in_progress`, `completed`, `failed`, `archived`\n- `title` — optional, used by tasks/ideas/meetings\n- `content` — the main body text (always present)\n- `tags` — comma-separated, searchable via FTS\n- `metadata` — JSON blob for type-specific fields\n- `source_entry_id` — which entry spawned this (creates a provenance chain)\n- `next_run_at` — for scheduled entries (tasks, meeting reminders)\n- `schedule_description` — natural language schedule for recurring entries\n- `created_at`, `updated_at` — timestamps\n\n### Entry Links (the Graph)\n\nThe `entry_links` table creates a relationship graph between entries:\n- `from_entry_id` → `to_entry_id` with a `relationship` label\n- Relationships: `related`, `created`, `co_extracted`, `parent`, `blocks`, `became`, etc.\n- Enables: \"This idea became this project\", \"These entries were extracted from the same email\", \"This person is connected to this meeting\"\n\n### FTS5\n\nSingle `entries_fts` table covers all entry types. Indexes `title`, `content`, and `tags`. Synced manually on every insert in `queries.ts`.\n\n### Indexes\n- `idx_entries_user_type` — entries by user + type\n- `idx_entries_user_status` — entries by user + status\n- `idx_entries_pending_run` — partial index for due task lookup\n- `idx_entries_type_created` — entries by type + creation date\n- `idx_entry_links_from/to` — link traversal\n\n## Per-User System Prompt\n\nEach user has a `system_prompt` column on the `users` table — their personal \"CLAUDE.md\" that controls how the agent behaves for them.\n\n### How it works:\n1. User emails: \"Be more casual and always use emojis. Call me Paul.\"\n2. Classifier extracts `system_instructions`: `[\"Use casual, informal tone with emojis\", \"Address user as Paul\"]`\n3. Email handler calls `updateSystemPrompt()` — appends new instructions, deduplicates\n4. Every future agent call loads this prompt and injects it into the system prompt as \"User's Personal Instructions\"\n5. User can see their prompt via the admin endpoint, or say \"show me my preferences\"\n\n### What it captures:\n- Tone/style preferences (\"be concise\", \"use bullet points\")\n- Name/addressing preferences (\"call me Captain\")\n- Format preferences (\"always include a summary section\")\n- Any standing instruction the user gives\n\n## Attachments\n\nFull bidirectional attachment support — receive files from users and send generated files back.\n\n### Inbound (Receiving)\n- Email attachments arrive as `File` objects in `email.attachments[]`\n- Each is stored in **blob storage** with key `attachments/{userId}/{entryId}/{filename}`\n- Metadata is tracked in the **`attachments`** table (filename, MIME type, size, blob key, is_text flag)\n- Text-readable files (txt, csv, json, md, html, xml, code files) have their content extracted and:\n - Fed to the **classifier** as part of the email body\n - Available to the **agent** when executing tasks via `getTextAttachmentContents()`\n- Binary files (images, PDFs, zips) are stored but content is not extracted\n\n### Outbound (Generating)\n- The agent can generate files by including `` tags in its response:\n ```\n \n col1,col2\n val1,val2\n \n ```\n- These are parsed from the response, stored in blob storage, and sent as email attachments\n- Both `email-handler.ts` (instant tasks) and `task-runner.ts` (scheduled tasks) handle outbound attachments\n- Outbound interaction entries record `attachment_count` and `attachment_names` in metadata\n\n### Storage\n- **Blob storage**: `@valtown/sdk` blob API — actual file content\n- **`attachments` table**: metadata linking blobs to entries and users\n- **Max inbound text extraction**: 100KB per file (configurable in `lib/attachments.ts`)\n\n### Tables\n```sql\nattachments (\n id, entry_id, user_id, filename, mime_type, size_bytes,\n blob_key, is_text, created_at\n)\n```\n\n## Environment Variables\n\n- **ANTHROPIC_API_KEY** — required. Used by all Claude API calls (classifier, scheduler, agent). Accessed implicitly by the Anthropic SDK.\n\n## Dependencies\n\n- `npm:@anthropic-ai/sdk@0.39.0` — Claude API client (pin the version)\n- `https://esm.town/v/std/sqlite/main.ts` — project-scoped SQLite\n- `https://esm.town/v/std/email` — Val Town email sending\n\n## Conventions\n\n### Code Style\n- TypeScript everywhere (.ts files only)\n- File and val names in **kebab-case** (e.g., `email-handler.ts`, `task-runner.ts`)\n- Interfaces for all data shapes crossing boundaries (API responses, DB rows, function params)\n- Always use parameterized queries (`args`) — never interpolate user input into SQL\n- Emoji prefixes in console.log for easy scanning: 📧 📝 🧠 📊 💾 💡 ⏰ 🔄 ⚡ ✅ ❌ 📤 🔧 📋 🧬 👤\n\n### Error Handling\n- Every email processing step is wrapped in try/catch\n- Task failures still send a notification email to the user\n- Recurring tasks get rescheduled even on failure\n- FTS search failures are non-fatal (empty tables can cause issues)\n- JSON parse failures in classifier/scheduler fall back to sensible defaults\n\n### LLM Calls\n- All Claude calls use `claude-sonnet-4-20250514` — keep this consistent\n- System prompts are defined as module-level constants (not inline)\n- Per-user system prompt is injected dynamically from the `users.system_prompt` column\n- Responses are parsed via regex (`/\\{[\\s\\S]*\\}/`) to extract JSON from potential markdown code blocks\n- Always have a fallback if JSON parsing fails\n\n### Database Patterns\n- `initDatabase()` is called at the top of every entry point — safe to call multiple times (`CREATE TABLE IF NOT EXISTS`)\n- FTS inserts happen immediately after main table inserts in `addEntry()`\n- Use `Promise.all` for independent parallel queries\n- `getUserContext()` is the canonical way to build a user context bundle for the agent\n- `metadata` is stored as JSON string, parsed in `parseEntryRow()`\n\n### Classifier Output\n- Returns a unified `entries[]` array — each entry has a `type` and optional `metadata`\n- `system_instructions[]` — persistent instructions extracted for the user's prompt\n- `follow_up_questions[]` — natural follow-ups included in the acknowledgment email\n- New entry types can be output by the classifier without any code changes\n- Voice input tolerance: the classifier prompt explicitly handles transcription errors\n\n## Working Style\n\n- **Don't break the email flow** — `email-handler.ts` is the critical path. Changes there should be tested carefully. A broken email handler means users get no response.\n- **Idempotent schema** — `initDatabase()` must always be safe to re-run. Use `IF NOT EXISTS` on everything.\n- **Keep classifier prompts tight** — The classification prompt directly controls what gets stored. Be precise about what counts as each type. Over-extraction creates noise.\n- **Ideas require explicit intent** — The classifier is specifically instructed to only capture ideas the user explicitly asks to store. Don't weaken this.\n- **Agent has full context** — When executing tasks, the agent gets memories, active tasks, recent ideas, recent conversations, FTS search results, AND the user's personal system prompt.\n- **Multiple Claude calls per task execution** — The agentic loop may make several calls (one initial + N tool-use rounds + 1 subject line). Log `toolCallCount` and `iterationCount` to monitor costs. The subject line is still a separate call.\n- **Voice input tolerance** — Users may send emails via voice transcription. Expect imperfect input. Interpret intent rather than demanding precision.\n- **Cross-link everything** — Every entry should be linked to its source and to related entries. The graph is what makes context retrieval powerful.\n- **Follow-up questions** — After processing, include 1-2 natural follow-ups in the reply to help deepen the conversation. Don't force them.\n\n## Agentic Tool-Use Loop\n\nThe agent (`lib/agent.ts`) uses an **iterative tool-use loop**, not a one-shot request-reply.\n\n### How it works:\n1. Build initial context (memories, tasks, ideas, conversations, attachments, FTS search)\n2. Send to Claude with **tool definitions** — Claude can call tools mid-execution\n3. **Loop**: If Claude returns `tool_use` blocks, execute each tool and feed results back\n4. **Repeat** until Claude returns a final `end_turn` response (or max 15 iterations)\n5. If max iterations hit, ask Claude to wrap up with what it has\n\n### Available Tools:\n| Tool | Purpose | DB function used |\n|---|---|---|\n| `search_memories` | Full-text search across all entry types | `searchEntries()` |\n| `get_entries` | Browse entries by type/status | `getEntries()` |\n| `get_linked_entries` | Traverse the entry relationship graph | `getLinkedEntries()` |\n| `store_memory` | Save a new memory discovered during task execution | `addEntry()` |\n| `create_task` | Create follow-up/sub-tasks (instant, future, recurring) | `addEntry()` + `parseSchedule()` |\n| `fetch_url` | Fetch live data from the web (news, APIs, weather, etc.) | `fetch()` |\n\n### Constants:\n- `MAX_TOOL_ITERATIONS = 15` — safety limit on the loop\n- `fetch_url` timeout: 15 seconds, response limit: 50KB\n- HTML responses are auto-stripped of tags for readability\n\n### AgentResult includes:\n- `response` — the final text response\n- `suggestedSubject` — email subject line (separate Claude call)\n- `attachments` — generated file attachments\n- `toolCallCount` — total number of tool calls made\n- `iterationCount` — total number of loop iterations\n\n## Known Limitations & Future Improvements\n\n- **No conversation threading** — Each email is processed independently. The agent has recent interaction history but doesn't maintain true conversation threads.\n- **No email HTML rendering** — Replies are plain text only.\n- **No auth on HTTP admin** — The endpoints are open. Add authentication if this becomes public-facing.\n- **No rate limiting** — No protection against flooding.\n- **No memory deduplication** — Same fact can be stored multiple times. Could use semantic similarity to deduplicate.\n- **Single Claude model** — All calls use the same model. Could use a lighter model for scheduling/subject lines.\n- **No system prompt reset** — User can add instructions but can't easily remove or reset them yet. Add \"reset my preferences\" as a recognized command.\n\n## Evolving the System\n\nThis agent should grow organically based on usage patterns. When you notice recurring needs:\n\n1. **New entry types need zero code changes** — just use a new type string in the classifier. The unified model handles it.\n2. **Refine classification** — If the classifier is miscategorizing things, tighten the prompt. Log examples of misclassifications.\n3. **New link relationships** — If new relationships emerge between entries, add relationship types to `entry_links`.\n4. **Proactive suggestions** — The agent should notice patterns and suggest improvements, like the journal system's \"Claude Tasks\" concept.\n5. **Keep this file updated** — When the system changes, update CLAUDE.md. This is the source of truth.\n\n## Migration\n\nThe system was migrated from separate tables (memories, tasks, ideas, interactions) to a unified `entries` table. The old tables still exist but are no longer used. The `/migrate` endpoint runs the migration. Old FTS tables (`memories_fts`, `tasks_fts`, `ideas_fts`, `interactions_fts`) are superseded by `entries_fts`.\n",[],[247],{"_106":256,"_97":257,"_163":258,"_165":-5,"_166":148,"_167":259,"_169":170,"_108":260,"_102":261,"_172":262,"_174":263,"_175":22,"_176":264,"_177":265,"_100":266},"5d341c1c-181c-11f1-9552-42dde27851f2","email-handler.ts","3ab7e648-1810-11f1-8d14-42dde27851f2",51,["D",1772664552688],12,"email","/**\n * Email Handler — the main ingestion point.\n *\n * When an email arrives:\n * 1. Identify/create the user from the sender's email\n * 2. Log the interaction as an entry\n * 3. Classify the email → unified entries + system instructions + follow-ups\n * 4. Store all entries, link them to the interaction\n * 5. Apply any system_instructions to the user's persistent prompt\n * 6. Execute instant tasks immediately and reply\n * 7. Queue future/recurring tasks for the task runner\n * 8. Send acknowledgment with follow-up questions\n */\n\nimport { email as sendEmail } from \"https://esm.town/v/std/email\";\nimport { initDatabase } from \"./db/schema.ts\";\nimport {\n addEntry,\n getEntries,\n getOrCreateUser,\n linkEntries,\n updateEntryStatus,\n updateSystemPrompt,\n} from \"./db/queries.ts\";\nimport { classifyEmail } from \"./lib/classifier.ts\";\nimport type { ClassifiedEntry } from \"./lib/classifier.ts\";\nimport { parseSchedule } from \"./lib/scheduler.ts\";\nimport { executeTask } from \"./lib/agent.ts\";\nimport type { GeneratedAttachment } from \"./lib/agent.ts\";\nimport { storeInboundAttachment, storeOutboundAttachment } from \"./lib/attachments.ts\";\n\n// Email type from Val Town (with attachments)\ninterface Email {\n from: string;\n to: string[];\n subject: string | null;\n text: string | null;\n html: string | null;\n attachments: {\n name: string;\n type: string;\n size: number;\n text: () => Promise;\n arrayBuffer: () => Promise;\n }[];\n}\n\nexport default async function (email: Email) {\n console.log(\n `📧 Email received from: ${email.from}, subject: ${email.subject}`,\n );\n\n await initDatabase();\n\n // 1. Get or create user\n const senderEmail = email.from;\n const user = await getOrCreateUser(senderEmail);\n console.log(`👤 User: ${user.email} (id: ${user.id})`);\n\n // 2. Log the inbound interaction as an entry\n const body = email.text || email.html || \"(empty email)\";\n const interactionId = await addEntry(user.id, {\n type: \"interaction\",\n title: email.subject,\n content: body,\n metadata: { direction: \"inbound\" },\n });\n console.log(`📝 Logged interaction: ${interactionId}`);\n\n // 2b. Store any attachments\n const attachmentTexts: string[] = [];\n if (email.attachments && email.attachments.length > 0) {\n console.log(`📎 Processing ${email.attachments.length} attachment(s)...`);\n for (const file of email.attachments) {\n try {\n const { textContent } = await storeInboundAttachment(\n file,\n user.id,\n interactionId,\n );\n if (textContent) {\n // Truncate very large text for classifier context\n const truncated = textContent.length > 10000\n ? textContent.slice(0, 10000) + \"\\n\\n[...truncated...]\"\n : textContent;\n attachmentTexts.push(\n `--- Attachment: ${file.name} (${file.type}) ---\\n${truncated}`,\n );\n } else {\n attachmentTexts.push(\n `--- Attachment: ${file.name} (${file.type}, ${file.size} bytes) [binary file, content not shown] ---`,\n );\n }\n } catch (err) {\n console.error(`📎 Failed to store attachment ${file.name}:`, err);\n }\n }\n console.log(\n `📎 Stored ${email.attachments.length} attachment(s), ${attachmentTexts.filter((t) => !t.includes(\"[binary\")).length} text-readable`,\n );\n }\n\n // 3. Build user context for the classifier\n const memories = await getEntries(user.id, {\n type: \"memory\",\n limit: 20,\n });\n const activeTasks = await getEntries(user.id, {\n type: \"task\",\n status: \"pending\",\n limit: 10,\n });\n\n let contextStr = \"\";\n if (memories.length > 0) {\n contextStr += \"Known facts about this user:\\n\";\n for (const m of memories) {\n contextStr += `- ${m.content}\\n`;\n }\n }\n if (activeTasks.length > 0) {\n contextStr += \"\\nActive tasks:\\n\";\n for (const t of activeTasks) {\n const taskType =\n (t.metadata as Record)?.task_type || \"task\";\n contextStr += `- [${taskType}] ${t.title}\\n`;\n }\n }\n\n // 4. Classify the email (include attachment text if available)\n let classifierBody = body;\n if (attachmentTexts.length > 0) {\n classifierBody += \"\\n\\n\" + attachmentTexts.join(\"\\n\\n\");\n }\n\n console.log(\"🧠 Classifying email...\");\n const classification = await classifyEmail(\n email.subject,\n classifierBody,\n contextStr || undefined,\n );\n console.log(`📊 Classification: ${classification.summary}`);\n console.log(` Entries: ${classification.entries.length}, System instructions: ${classification.system_instructions.length}, Follow-ups: ${classification.follow_up_questions.length}`);\n\n // 5. Apply system instructions to user's persistent prompt\n if (classification.system_instructions.length > 0) {\n const updatedPrompt = await updateSystemPrompt(\n user.id,\n classification.system_instructions,\n );\n console.log(\n `🧬 Updated system prompt for ${user.email}: ${updatedPrompt.slice(0, 100)}...`,\n );\n }\n\n // 6. Store all entries and track instant tasks for execution\n const instantTasks: { id: number; entry: ClassifiedEntry }[] = [];\n const storedEntries: { id: number; entry: ClassifiedEntry }[] = [];\n\n for (const entry of classification.entries) {\n // Determine if this is a scheduled entry\n let nextRunAt: string | undefined;\n let scheduleDescription: string | undefined;\n\n if (entry.schedule) {\n const schedule = await parseSchedule(entry.schedule);\n nextRunAt = schedule.nextRunAt;\n scheduleDescription = entry.schedule;\n console.log(\n `⏰ Scheduled: \"${entry.title}\" → ${nextRunAt} (${schedule.interpreted})`,\n );\n }\n\n const entryId = await addEntry(user.id, {\n type: entry.type,\n title: entry.title,\n content: entry.content,\n tags: entry.tags,\n status: entry.status || (entry.type === \"task\" ? \"pending\" : \"active\"),\n metadata: entry.metadata,\n sourceEntryId: interactionId,\n nextRunAt,\n scheduleDescription,\n });\n\n // Link to the source interaction\n await linkEntries(interactionId, entryId, \"created\");\n\n storedEntries.push({ id: entryId, entry });\n\n const taskType = entry.metadata?.task_type as string | undefined;\n if (entry.type === \"task\" && taskType === \"instant\") {\n instantTasks.push({ id: entryId, entry });\n }\n\n const emoji =\n entry.type === \"memory\"\n ? \"💾\"\n : entry.type === \"task\"\n ? \"⚡\"\n : entry.type === \"idea\"\n ? \"💡\"\n : entry.type === \"person\"\n ? \"👤\"\n : \"📋\";\n console.log(\n `${emoji} ${entry.type} stored: ${entry.title || entry.content.slice(0, 50)}`,\n );\n }\n\n // Cross-link entries that share tags or are related\n for (let i = 0; i < storedEntries.length; i++) {\n for (let j = i + 1; j < storedEntries.length; j++) {\n // Link entries from the same email to each other\n await linkEntries(storedEntries[i].id, storedEntries[j].id, \"co_extracted\");\n }\n }\n\n // 7. Execute instant tasks and build reply\n const replyParts: string[] = [];\n const allGeneratedAttachments: { attachment: GeneratedAttachment; taskEntryId: number }[] = [];\n\n for (const { id: taskId, entry: task } of instantTasks) {\n console.log(`⚡ Executing instant task: ${task.title}`);\n\n try {\n await updateEntryStatus(taskId, \"in_progress\");\n const result = await executeTask(\n user.id,\n task.title || \"Process request\",\n task.content,\n { sourceEntryId: interactionId },\n );\n await updateEntryStatus(taskId, \"completed\", result.response);\n replyParts.push(result.response);\n\n // Track any generated attachments\n for (const att of result.attachments) {\n allGeneratedAttachments.push({ attachment: att, taskEntryId: taskId });\n }\n\n console.log(`✅ Task completed: ${task.title} (${result.attachments.length} attachment(s), ${result.iterationCount} iterations, ${result.toolCallCount} tool calls)`);\n } catch (error) {\n const errMsg = error instanceof Error ? error.message : String(error);\n await updateEntryStatus(taskId, \"failed\", errMsg);\n replyParts.push(\n `I encountered an error while working on \"${task.title}\": ${errMsg}`,\n );\n console.error(`❌ Task failed: ${task.title}:`, errMsg);\n }\n }\n\n // 8. Build and send acknowledgment/reply\n if (classification.reply_needed || replyParts.length > 0) {\n let replyBody = \"\";\n\n if (replyParts.length > 0) {\n replyBody = replyParts.join(\"\\n\\n---\\n\\n\");\n } else {\n // Acknowledge receipt\n replyBody = `Got it! Here's what I've noted:\\n\\n`;\n\n const grouped: Record = {};\n for (const entry of classification.entries) {\n if (!grouped[entry.type]) grouped[entry.type] = [];\n grouped[entry.type].push(entry);\n }\n\n const typeEmoji: Record = {\n memory: \"📝\",\n task: \"⏰\",\n idea: \"💡\",\n person: \"👤\",\n meeting: \"📅\",\n project: \"🔧\",\n };\n\n for (const [type, entries] of Object.entries(grouped)) {\n const emoji = typeEmoji[type] || \"📋\";\n const label = type.charAt(0).toUpperCase() + type.slice(1) + (entries.length > 1 ? \"s\" : \"\");\n const items = entries\n .map((e) => {\n if (type === \"task\") {\n const taskType = e.metadata?.task_type || \"task\";\n return `${e.title} (${taskType}${e.schedule ? `: ${e.schedule}` : \"\"})`;\n }\n return e.title || e.content.slice(0, 80);\n })\n .join(\", \");\n replyBody += `${emoji} **${label}**: ${items}\\n\\n`;\n }\n\n replyBody += `I'm on it! You'll hear from me when tasks are ready.`;\n }\n\n // Add follow-up questions\n if (classification.follow_up_questions.length > 0) {\n replyBody += \"\\n\\n---\\n\";\n for (const q of classification.follow_up_questions) {\n replyBody += `\\n💭 ${q}`;\n }\n }\n\n // Note system instructions that were applied\n if (classification.system_instructions.length > 0) {\n replyBody += `\\n\\n🧬 *Updated my instructions: ${classification.system_instructions.join(\"; \")}*`;\n }\n\n // Build email attachments from generated files\n const emailAttachments: { content: string; filename: string; type: string; disposition: string }[] = [];\n for (const { attachment: att, taskEntryId } of allGeneratedAttachments) {\n try {\n const { base64Content } = await storeOutboundAttachment({\n filename: att.filename,\n content: att.content,\n mimeType: att.mimeType,\n userId: user.id,\n entryId: taskEntryId,\n });\n emailAttachments.push({\n content: base64Content,\n filename: att.filename,\n type: att.mimeType,\n disposition: \"attachment\",\n });\n } catch (err) {\n console.error(`📎 Failed to prepare attachment ${att.filename}:`, err);\n }\n }\n\n // Send reply (with attachments if any)\n try {\n const emailOpts: Record = {\n to: user.email,\n subject: `Re: ${email.subject || \"Your message\"}`,\n text: replyBody,\n replyTo: \"memory-do@valtown.email\",\n };\n if (emailAttachments.length > 0) {\n emailOpts.attachments = emailAttachments;\n console.log(`📎 Sending ${emailAttachments.length} attachment(s) with reply`);\n }\n await sendEmail(emailOpts as Parameters[0]);\n\n // Log outbound interaction\n await addEntry(user.id, {\n type: \"interaction\",\n title: `Re: ${email.subject || \"Your message\"}`,\n content: replyBody,\n metadata: {\n direction: \"outbound\",\n attachment_count: emailAttachments.length,\n attachment_names: emailAttachments.map((a) => a.filename),\n },\n sourceEntryId: interactionId,\n });\n console.log(`📤 Reply sent to ${user.email}`);\n } catch (error) {\n console.error(\"Failed to send reply:\", error);\n }\n }\n\n console.log(\"✅ Email processing complete\");\n}\n","memory-do",[],[257],{"_106":268,"_97":101,"_163":269,"_165":-5,"_166":148,"_167":270,"_169":170,"_108":271,"_102":185,"_172":272,"_174":273,"_175":22,"_176":264,"_177":274,"_100":275},"6ad14db6-1819-11f1-b5ab-42dde27851f2","222d82f2-17f4-11f1-9791-42dde27851f2",30,["D",1772663286967],"http","/**\n * Email Agent — HTTP endpoint for status, admin, and debugging.\n */\n\nimport { initDatabase } from \"./db/schema.ts\";\nimport { sqlite } from \"https://esm.town/v/std/sqlite/main.ts\";\n\nexport default async function (req: Request): Promise {\n await initDatabase();\n\n const url = new URL(req.url);\n\n // GET /stats or GET / — show system stats\n if (url.pathname === \"/stats\" || url.pathname === \"/\") {\n const [totalEntries, users] = await Promise.all([\n sqlite.execute(\"SELECT COUNT(*) as count FROM entries\"),\n sqlite.execute(\"SELECT COUNT(*) as count FROM users\"),\n ]);\n\n // Entry counts by type\n const typeCounts = await sqlite.execute(\n \"SELECT type, COUNT(*) as count FROM entries GROUP BY type ORDER BY count DESC\",\n );\n\n // Pending tasks\n const pendingTasks = await sqlite.execute(\n \"SELECT COUNT(*) as count FROM entries WHERE type = 'task' AND status = 'pending'\",\n );\n\n // Entry links\n const linkCount = await sqlite.execute(\n \"SELECT COUNT(*) as count FROM entry_links\",\n );\n\n // Type counts as an object\n const types: Record = {};\n for (const row of typeCounts.rows) {\n const r = row as Record;\n types[r.type as string] = r.count as number;\n }\n\n return new Response(\n JSON.stringify(\n {\n name: \"Email Agent\",\n status: \"running\",\n stats: {\n users: (users.rows[0] as Record).count,\n total_entries: (totalEntries.rows[0] as Record)\n .count,\n entries_by_type: types,\n pending_tasks: (pendingTasks.rows[0] as Record)\n .count,\n entry_links: (linkCount.rows[0] as Record).count,\n },\n email: \"memory-do@valtown.email\",\n },\n null,\n 2,\n ),\n { headers: { \"Content-Type\": \"application/json\" } },\n );\n }\n\n // GET /user/:email — show user details\n if (url.pathname.startsWith(\"/user/\")) {\n const email = decodeURIComponent(url.pathname.replace(\"/user/\", \"\"));\n const user = await sqlite.execute({\n sql: \"SELECT * FROM users WHERE email = ?\",\n args: [email],\n });\n\n if (user.rows.length === 0) {\n return new Response(JSON.stringify({ error: \"User not found\" }), {\n status: 404,\n headers: { \"Content-Type\": \"application/json\" },\n });\n }\n\n const userId = (user.rows[0] as Record).id as number;\n\n // Get all entries grouped by type\n const entries = await sqlite.execute({\n sql: \"SELECT * FROM entries WHERE user_id = ? ORDER BY created_at DESC\",\n args: [userId],\n });\n\n // Group entries by type\n const grouped: Record = {};\n for (const row of entries.rows) {\n const r = row as Record;\n const type = r.type as string;\n if (!grouped[type]) grouped[type] = [];\n grouped[type].push(r);\n }\n\n // Get entry links for this user's entries\n const links = await sqlite.execute({\n sql: `SELECT el.* FROM entry_links el\n JOIN entries e ON el.from_entry_id = e.id\n WHERE e.user_id = ?\n LIMIT 100`,\n args: [userId],\n });\n\n return new Response(\n JSON.stringify(\n {\n user: user.rows[0],\n entries_by_type: grouped,\n entry_links: links.rows,\n },\n null,\n 2,\n ),\n { headers: { \"Content-Type\": \"application/json\" } },\n );\n }\n\n // GET /migrate — run migration from old tables (one-time)\n if (url.pathname === \"/migrate\") {\n const { migrateToUnifiedSchema } = await import(\"./db/schema.ts\");\n await migrateToUnifiedSchema();\n return new Response(\n JSON.stringify({ status: \"migration complete\" }),\n { headers: { \"Content-Type\": \"application/json\" } },\n );\n }\n\n return new Response(\n JSON.stringify(\n {\n endpoints: {\n \"/\": \"System stats\",\n \"/stats\": \"System stats\",\n \"/user/:email\": \"User details (URL-encode the email)\",\n \"/migrate\": \"Run data migration from old tables (one-time)\",\n },\n },\n null,\n 2,\n ),\n { headers: { \"Content-Type\": \"application/json\" } },\n );\n}\n",[],[101],{"_106":277,"_97":278,"_163":279,"_165":-5,"_166":148,"_167":280,"_169":170,"_108":281,"_102":282,"_172":283,"_174":284,"_175":22,"_176":-5,"_177":285,"_100":286},"5e3bf792-181c-11f1-9156-42dde27851f2","task-runner.ts","448b65fa-1810-11f1-9ab7-42dde27851f2",52,["D",1772664554418],9,"interval","/**\n * Task Runner — interval that processes pending scheduled tasks.\n *\n * Runs every 15 minutes and:\n * 1. Finds task entries where next_run_at <= now and status = 'pending'\n * 2. Executes each task with full user context (including per-user system prompt)\n * 3. Emails the user with results\n * 4. For recurring tasks: calculates and sets next run time\n */\n\nimport { email as sendEmail } from \"https://esm.town/v/std/email\";\nimport { initDatabase } from \"./db/schema.ts\";\nimport {\n addEntry,\n getTasksDue,\n rescheduleEntry,\n updateEntryStatus,\n} from \"./db/queries.ts\";\nimport { executeTask } from \"./lib/agent.ts\";\nimport { getNextRecurrence } from \"./lib/scheduler.ts\";\nimport { storeOutboundAttachment } from \"./lib/attachments.ts\";\n\nexport default async function () {\n console.log(`⏰ Task runner started at ${new Date().toISOString()}`);\n\n await initDatabase();\n\n // Get all tasks that are due\n const dueTasks = await getTasksDue();\n console.log(`📋 Found ${dueTasks.length} tasks due for execution`);\n\n if (dueTasks.length === 0) {\n console.log(\"No tasks to process. Done.\");\n return;\n }\n\n for (const task of dueTasks) {\n const taskId = task.id;\n const userId = task.user_id;\n const taskTitle = task.title || \"Scheduled task\";\n const taskDescription = task.content;\n const userEmail = task.user_email;\n const scheduleDescription = task.schedule_description;\n const taskType = (task.metadata as Record)?.task_type as string | undefined;\n\n console.log(`🔧 Processing task ${taskId}: [${taskType}] ${taskTitle}`);\n\n try {\n // Mark as in progress\n await updateEntryStatus(taskId, \"in_progress\");\n\n // Execute the task (pass source_entry_id for attachment context)\n const sourceEntryId = task.source_entry_id || undefined;\n const result = await executeTask(userId, taskTitle, taskDescription, {\n sourceEntryId: sourceEntryId ?? undefined,\n });\n\n // Mark as completed\n await updateEntryStatus(taskId, \"completed\", result.response);\n\n // Prepare generated attachments for email\n const emailAttachments: { content: string; filename: string; type: string; disposition: string }[] = [];\n for (const att of result.attachments) {\n try {\n const { base64Content } = await storeOutboundAttachment({\n filename: att.filename,\n content: att.content,\n mimeType: att.mimeType,\n userId,\n entryId: taskId,\n });\n emailAttachments.push({\n content: base64Content,\n filename: att.filename,\n type: att.mimeType,\n disposition: \"attachment\",\n });\n } catch (err) {\n console.error(`📎 Failed to prepare attachment ${att.filename}:`, err);\n }\n }\n\n // Email the user with results (and attachments)\n try {\n const emailOpts: Record = {\n to: userEmail,\n subject: result.suggestedSubject,\n text: result.response,\n replyTo: \"memory-do@valtown.email\",\n };\n if (emailAttachments.length > 0) {\n emailOpts.attachments = emailAttachments;\n console.log(`📎 Sending ${emailAttachments.length} attachment(s) with task result`);\n }\n await sendEmail(emailOpts as Parameters[0]);\n\n // Log the outbound interaction\n await addEntry(userId, {\n type: \"interaction\",\n title: result.suggestedSubject,\n content: result.response,\n metadata: {\n direction: \"outbound\",\n attachment_count: emailAttachments.length,\n attachment_names: emailAttachments.map((a) => a.filename),\n },\n sourceEntryId: taskId,\n });\n console.log(\n `📤 Result emailed to ${userEmail}: ${result.suggestedSubject} (${emailAttachments.length} attachment(s))`,\n );\n } catch (emailError) {\n console.error(\n `Failed to email result for task ${taskId}:`,\n emailError,\n );\n }\n\n // If recurring, schedule the next run\n if (taskType === \"recurring\" && scheduleDescription) {\n const nextRunAt = await getNextRecurrence(scheduleDescription);\n await rescheduleEntry(taskId, nextRunAt);\n console.log(`🔄 Recurring task ${taskId} rescheduled for ${nextRunAt}`);\n }\n\n console.log(`✅ Task ${taskId} completed (${result.iterationCount} iterations, ${result.toolCallCount} tool calls)`);\n } catch (error) {\n const errMsg = error instanceof Error ? error.message : String(error);\n await updateEntryStatus(taskId, \"failed\", errMsg);\n console.error(`❌ Task ${taskId} failed:`, errMsg);\n\n // Notify user of failure\n try {\n await sendEmail({\n to: userEmail,\n subject: `Task failed: ${taskTitle}`,\n replyTo: \"memory-do@valtown.email\",\n text: `I'm sorry, I encountered an error while working on \"${taskTitle}\":\\n\\n${errMsg}\\n\\nI'll try again if this is a recurring task.`,\n });\n } catch {\n // Silently fail on notification errors\n }\n\n // Still reschedule recurring tasks even on failure\n if (taskType === \"recurring\" && scheduleDescription) {\n try {\n const nextRunAt = await getNextRecurrence(scheduleDescription);\n await rescheduleEntry(taskId, nextRunAt);\n console.log(\n `🔄 Failed recurring task ${taskId} rescheduled for ${nextRunAt}`,\n );\n } catch {\n console.error(`Failed to reschedule recurring task ${taskId}`);\n }\n }\n }\n }\n\n console.log(`✅ Task runner completed. Processed ${dueTasks.length} tasks.`);\n}\n",[],[278],"files",[160,179,190,200,206,216,226,235,245,255,267,276],"counts",{"_291":33,"_292":222,"_293":33,"_294":295,"_298":299},"pulls","branches","forks","envVars",{"_296":222,"_297":33},"count","emptyCount","connections",{"_296":33,"_300":33},"unfilledCount","contents",[],{"_106":268,"_97":101,"_163":269,"_165":-5,"_166":148,"_167":270,"_169":170,"_108":304,"_102":185,"_172":272,"_174":273,"_175":22,"_176":264,"_100":305},["D",1772663286967],[101],"upstreamProject","upstreamBranch","emptyEnvVars","isNew","flags",[],"routes/_app.x.$handle.$name.branch.$branch.version.$version.code.$",{"_95":96,"_97":98,"_99":45,"_102":103,"_100":101,"_104":314,"_158":319,"_287":356,"_251":357,"_301":360,"_361":-5,"_362":-5,"_363":-5,"_364":-5,"_365":366,"_294":-5},{"_106":107,"_97":98,"_108":315,"_110":-5,"_111":22,"_112":113,"_114":-5,"_115":-5,"_14":116,"_117":-5,"_118":22,"_119":-5,"_120":-5,"_121":33,"_16":122,"_123":124,"_125":126,"_127":-5,"_128":129,"_130":129,"_131":132,"_133":132,"_134":132,"_135":129,"_136":129,"_23":316,"_140":317,"_145":318,"_157":22},["D",1772647273772],{"_95":96,"_138":139},{"_95":96,"_142":139,"_14":116,"_143":144},{"_102":147,"_97":45,"_106":148,"_149":107,"_150":-5,"_151":-5,"_152":-5,"_153":-5,"_154":-5,"_155":98,"_156":96},[320,329,344,347,350,353],{"_106":161,"_97":162,"_163":164,"_165":-5,"_166":148,"_167":168,"_169":170,"_108":171,"_102":33,"_172":173,"_174":-5,"_175":22,"_176":-5,"_177":321,"_100":328},[322,325],{"_106":180,"_97":181,"_163":182,"_165":164,"_166":148,"_167":183,"_169":170,"_108":184,"_102":185,"_172":186,"_174":187,"_175":22,"_176":-5,"_177":323,"_100":324},[],[162,181],{"_106":191,"_97":192,"_163":193,"_165":164,"_166":148,"_167":194,"_169":170,"_108":195,"_102":168,"_172":186,"_174":196,"_175":22,"_176":-5,"_177":326,"_100":327},[],[162,192],[162],{"_106":201,"_97":202,"_163":203,"_165":-5,"_166":148,"_167":185,"_169":170,"_108":204,"_102":33,"_172":173,"_174":-5,"_175":22,"_176":-5,"_177":330,"_100":343},[331,334,337,340],{"_106":207,"_97":208,"_163":209,"_165":203,"_166":148,"_167":210,"_169":170,"_108":211,"_102":212,"_172":186,"_174":213,"_175":22,"_176":-5,"_177":332,"_100":333},[],[202,208],{"_106":217,"_97":218,"_163":219,"_165":203,"_166":148,"_167":220,"_169":170,"_108":221,"_102":222,"_172":186,"_174":223,"_175":22,"_176":-5,"_177":335,"_100":336},[],[202,218],{"_106":227,"_97":228,"_163":229,"_165":203,"_166":148,"_167":230,"_169":170,"_108":231,"_102":168,"_172":186,"_174":232,"_175":22,"_176":-5,"_177":338,"_100":339},[],[202,228],{"_106":236,"_97":237,"_163":238,"_165":203,"_166":148,"_167":239,"_169":170,"_108":240,"_102":222,"_172":186,"_174":241,"_175":22,"_176":-5,"_177":341,"_100":342},[],[202,237],[202],{"_106":246,"_97":247,"_163":248,"_165":-5,"_166":148,"_167":103,"_169":170,"_108":249,"_102":250,"_172":251,"_174":252,"_175":22,"_176":-5,"_177":345,"_100":346},[],[247],{"_106":256,"_97":257,"_163":258,"_165":-5,"_166":148,"_167":259,"_169":170,"_108":260,"_102":261,"_172":262,"_174":263,"_175":22,"_176":264,"_177":348,"_100":349},[],[257],{"_106":268,"_97":101,"_163":269,"_165":-5,"_166":148,"_167":270,"_169":170,"_108":271,"_102":185,"_172":272,"_174":273,"_175":22,"_176":264,"_177":351,"_100":352},[],[101],{"_106":277,"_97":278,"_163":279,"_165":-5,"_166":148,"_167":280,"_169":170,"_108":281,"_102":282,"_172":283,"_174":284,"_175":22,"_176":-5,"_177":354,"_100":355},[],[278],[320,322,325,329,331,334,337,340,344,347,350,353],{"_106":268,"_166":148,"_163":269,"_97":101,"_174":273,"_172":272,"_102":185,"_165":-5,"_108":358,"_167":270,"_176":264,"_100":359},["D",1772663286967],[101],[],"upstream","pull","ahead","behind","templates",[],"actionData","errors"]