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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. 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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). 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For recurring tasks: calculates and sets next run time\n *\n * The task runner is now a thin scheduling layer — the actual work\n * happens inside the sandbox where Claude has full Linux tools.\n */\n\nimport { email as sendEmail } from \"https://esm.town/v/std/email\";\nimport {\n initDatabaseV2,\n getScheduledTasksDue,\n getUserV2,\n updateScheduledTaskStatus,\n rescheduleTask,\n addScheduledTask,\n} from \"./db/schema-v2.ts\";\nimport { executeSandboxTask, type ScheduleRequest } from \"./lib/sandbox-orchestrator.ts\";\nimport { getNextRecurrence, parseSchedule } from \"./lib/scheduler.ts\";\n\nexport default async function() {\n console.log(`⏰ Task runner v2 started at ${new Date().toISOString()}`);\n\n await initDatabaseV2();\n\n // Get all tasks that are due\n const dueTasks = await getScheduledTasksDue();\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 console.log(`🔧 Processing task ${task.id}: \"${task.title}\"`);\n\n // Get user details (for volume info)\n const user = await getUserV2(task.user_id);\n if (!user) {\n console.error(`❌ User ${task.user_id} not found for task ${task.id}`);\n await updateScheduledTaskStatus(task.id, \"failed\");\n continue;\n }\n\n try {\n // Mark as running\n await updateScheduledTaskStatus(task.id, \"running\");\n\n // Execute in the user's sandbox\n const result = await executeSandboxTask(\n user,\n {\n title: task.title,\n description: task.description,\n },\n { type: \"scheduled_task\", id: String(task.id) },\n );\n\n console.log(\n `📊 Task ${task.id} result: success=${result.success}, turns=${result.turns}, elapsed=${result.elapsedMs}ms`,\n );\n\n // Mark completed\n await updateScheduledTaskStatus(task.id, \"completed\");\n\n // Process any new schedule requests from the agent\n if (result.schedules.length > 0) {\n for (const schedule of result.schedules) {\n try {\n const parsed = await parseSchedule(schedule.schedule);\n await addScheduledTask({\n userId: user.id,\n title: schedule.title,\n description: schedule.description,\n nextRunAt: parsed.nextRunAt,\n scheduleDescription: schedule.schedule,\n isRecurring: schedule.is_recurring,\n });\n console.log(`⏰ New schedule from agent: \"${schedule.title}\" → ${schedule.schedule}`);\n } catch (err) {\n console.error(`Failed to schedule \"${schedule.title}\":`, err);\n }\n }\n }\n\n // Email the user with results\n if (result.response) {\n try {\n const emailOpts: Record = {\n to: user.email,\n subject: `${task.title}`,\n text: result.response,\n replyTo: \"memory-do@valtown.email\",\n };\n\n await sendEmail(emailOpts as Parameters[0]);\n console.log(`📤 Result emailed to ${user.email}: \"${task.title}\"`);\n } catch (emailError) {\n console.error(\n `Failed to email result for task ${task.id}:`,\n emailError,\n );\n }\n }\n\n // If recurring, schedule the next run\n if (task.is_recurring && task.schedule_description) {\n const nextRunAt = await getNextRecurrence(task.schedule_description);\n await rescheduleTask(task.id, nextRunAt);\n console.log(`🔄 Recurring task ${task.id} rescheduled for ${nextRunAt}`);\n }\n\n console.log(`✅ Task ${task.id} completed`);\n } catch (error) {\n const errMsg = error instanceof Error ? error.message : String(error);\n await updateScheduledTaskStatus(task.id, \"failed\");\n console.error(`❌ Task ${task.id} failed:`, errMsg);\n\n // Notify user of failure\n try {\n await sendEmail({\n to: user.email,\n subject: `Task failed: ${task.title}`,\n replyTo: \"memory-do@valtown.email\",\n text: `I'm sorry, I encountered an error while working on \"${task.title}\":\\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 (task.is_recurring && task.schedule_description) {\n try {\n const nextRunAt = await getNextRecurrence(task.schedule_description);\n await rescheduleTask(task.id, nextRunAt);\n console.log(\n `🔄 Failed recurring task ${task.id} rescheduled for ${nextRunAt}`,\n );\n } catch {\n console.error(`Failed to reschedule recurring task ${task.id}`);\n }\n }\n }\n }\n\n console.log(\n `✅ Task runner completed. Processed ${dueTasks.length} tasks.`,\n );\n}\n","label",[140],"6ad14db6-1819-11f1-b5ab-42dde27851f2","main.ts","222d82f2-17f4-11f1-9791-42dde27851f2",30,["D",1772663286967],3,"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","memory-do",[152],"5e843086-18bf-11f1-867c-42dde27851f2","email-handler-v2.ts","5e843112-18bf-11f1-867c-42dde27851f2",["D",1772734562850],"email","/**\n * Email Handler v2 — Sandbox-based architecture.\n *\n * When an email arrives:\n * 1. Identify/create the user from the sender's email\n * 2. Write the email (and any attachments) to the user's sandbox inbox\n * 3. Boot a sandbox and run agent-loop.mjs\n * 4. Read the agent's response from the outbox\n * 5. Process any schedule requests the agent made\n * 6. Email the response (and any generated files) back to the user\n *\n * The classifier is gone — the agent-loop.mjs inside the sandbox handles\n * everything: classification, memory management, task execution, etc.\n * Claude reads CLAUDE.md on the volume and decides what to do.\n */\n\nimport { email as sendEmail } from \"https://esm.town/v/std/email\";\nimport { initDatabaseV2, getOrCreateUserV2, addScheduledTask } from \"./db/schema-v2.ts\";\nimport { executeSandboxTask, type ScheduleRequest } from \"./lib/sandbox-orchestrator.ts\";\nimport { parseSchedule } from \"./lib/scheduler.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 // Guard against self-send loops — ignore emails from Val Town addresses\n if (email.from.includes(\"@valtown.email\")) {\n console.log(\"🚫 Ignoring email from @valtown.email (self-send loop prevention)\");\n return;\n }\n\n await initDatabaseV2();\n\n // 1. Get or create user\n const senderEmail = email.from;\n const user = await getOrCreateUserV2(senderEmail);\n console.log(`👤 User: ${user.email} (id: ${user.id})`);\n\n // 2. Build the task description from the email\n const body = email.text || email.html || \"(empty email)\";\n const subject = email.subject || \"Your message\";\n\n // Include attachment info in the task description\n let taskDescription = `## Email from: ${senderEmail}\\n`;\n taskDescription += `**Subject**: ${subject}\\n\\n`;\n taskDescription += body;\n\n // Process text-readable attachments into the description\n if (email.attachments && email.attachments.length > 0) {\n console.log(`📎 Processing ${email.attachments.length} attachment(s)...`);\n taskDescription += \"\\n\\n## Attachments\\n\";\n\n for (const file of email.attachments) {\n try {\n // Try to get text content\n const textContent = await file.text();\n if (textContent && textContent.length > 0) {\n const truncated = textContent.length > 10000\n ? textContent.slice(0, 10000) + \"\\n\\n[...truncated...]\"\n : textContent;\n taskDescription += `\\n### ${file.name} (${file.type})\\n\\`\\`\\`\\n${truncated}\\n\\`\\`\\`\\n`;\n } else {\n taskDescription += `\\n### ${file.name} (${file.type}, ${file.size} bytes) — binary file, content not shown\\n`;\n }\n } catch {\n taskDescription += `\\n### ${file.name} (${file.type}, ${file.size} bytes) — could not read content\\n`;\n }\n }\n }\n\n // 3. Execute in the user's sandbox\n console.log(\"🚀 Launching sandbox for task execution...\");\n const result = await executeSandboxTask(\n user,\n {\n title: subject,\n description: taskDescription,\n },\n { type: \"email\" },\n );\n\n console.log(\n `📊 Sandbox result: success=${result.success}, turns=${result.turns}, elapsed=${result.elapsedMs}ms, cost=$${result.costUsd?.toFixed(4) ?? \"unknown\"}`,\n );\n\n // 4. Process schedule requests from the agent\n if (result.schedules.length > 0) {\n console.log(`⏰ Agent requested ${result.schedules.length} schedule(s)`);\n for (const schedule of result.schedules) {\n try {\n await processScheduleRequest(user.id, schedule);\n console.log(`⏰ Scheduled: \"${schedule.title}\" → ${schedule.schedule}`);\n } catch (err) {\n console.error(`Failed to schedule \"${schedule.title}\":`, err);\n }\n }\n }\n\n // 5. Send reply email\n if (result.response) {\n try {\n const emailOpts: Record = {\n to: user.email,\n subject: `Re: ${subject}`,\n text: result.response,\n replyTo: \"memory-do@valtown.email\",\n };\n\n // TODO: Read generated files from sandbox and attach to email\n // For now, file attachment info is included in the text response\n if (result.filesCreated.length > 0) {\n console.log(`📎 Agent created ${result.filesCreated.length} file(s) — attachment support coming soon`);\n }\n\n await sendEmail(emailOpts as Parameters[0]);\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\n/**\n * Process a schedule request from the sandbox agent.\n * Parses the natural-language schedule into a concrete next_run_at timestamp.\n */\nasync function processScheduleRequest(\n userId: number,\n schedule: ScheduleRequest,\n) {\n const parsed = await parseSchedule(schedule.schedule);\n\n await addScheduledTask({\n userId,\n title: schedule.title,\n description: schedule.description,\n nextRunAt: parsed.nextRunAt,\n scheduleDescription: schedule.schedule,\n isRecurring: schedule.is_recurring,\n });\n}\n",[162],"bb517f38-181c-11f1-a52e-42dde27851f2","README.md","bb517fd8-181c-11f1-a52e-42dde27851f2",55,["D",1772664710595],"# 📧 Email Agent\n\nAn email-based personal AI agent built on [Val Town](https://val.town). Interact entirely via email — send messages, tasks, ideas, and memories to **`memory-do@valtown.email`** and the agent classifies, stores, executes, and replies.\n\n## How It Works\n\nSend an email. The agent reads it, figures out what you mean, and acts on it.\n\n```\nYou → \"Remind me Thursday to call the dentist\"\n → \"I like chickens\"\n → \"What's the latest news on WebAssembly?\"\n → \"Every Monday, give me a summary of my active tasks\"\n```\n\nUnder the hood:\n\n1. **Classify** — Claude analyzes your email and extracts structured entries (memories, tasks, ideas, people, meetings, projects)\n2. **Store** — Everything goes into a unified SQLite database with full-text search and a relationship graph linking entries together\n3. **Execute** — Instant tasks run immediately through an agentic loop; future/recurring tasks get scheduled\n4. **Reply** — You get an email back with results, follow-up questions, and any generated file attachments\n\n### Voice-Friendly\n\nEmails sent via voice transcription work great. The classifier is tuned for misspellings, run-on sentences, and implicit intent — it interprets what you *mean*, not what you literally typed.\n\n## Agentic Execution\n\nThe agent doesn't just make a single LLM call and reply. It runs an **iterative tool-use loop** — Claude can call tools mid-task to gather information, search the web, query your stored knowledge, and create follow-up work.\n\n```\n┌─────────────────────────────────────────────┐\n│ 1. Load user context (memories, tasks, etc) │\n│ 2. Send task + context + tools to Claude │\n│ 3. Claude responds: │\n│ ├─ Final answer? → Done, send reply │\n│ └─ Tool call? → Execute tool, loop to 2 │\n│ 4. Safety: max 15 iterations │\n│ 5. Extract attachments, generate subject │\n└─────────────────────────────────────────────┘\n```\n\n### Available Tools\n\n| Tool | What It Does |\n|---|---|\n| `search_memories` | Full-text search across all your stored entries |\n| `get_entries` | Browse entries by type (all tasks, all people, all ideas, etc.) |\n| `get_linked_entries` | Traverse the relationship graph between entries |\n| `store_memory` | Save new facts discovered during task execution |\n| `create_task` | Create follow-up tasks — instant, future, or recurring |\n| `fetch_url` | Fetch live data from the web (news, APIs, weather, docs) |\n\nThis means the agent can do multi-step research: search your memories, fetch a web page, cross-reference with your stored projects, save what it learned, and compose a rich reply — all in one email exchange.\n\n## What Gets Stored\n\nEverything is an **entry** with a type. The system is extensible — new types need zero code changes.\n\n| Type | Example |\n|---|---|\n| `memory` | \"I work at Acme Corp\", \"I like chickens\" |\n| `task` | \"Remind me Thursday to call the dentist\" |\n| `idea` | \"I have an idea for a recipe app...\" |\n| `person` | \"My friend Sarah works at Google\" |\n| `meeting` | \"Lunch with Bob on Friday at noon\" |\n| `project` | \"I'm building a Chrome extension\" |\n| `interaction` | Every email sent/received (automatic) |\n\nEntries are linked to each other in a relationship graph — \"this idea became this project\", \"these entries came from the same email\", \"this person is connected to this meeting\".\n\n## Task Types\n\n| Type | Behavior | Example |\n|---|---|---|\n| **Instant** | Executes immediately, reply in same email | \"What's the weather in London?\" |\n| **Future** | Scheduled for a specific time | \"Remind me Thursday at 10am to call dentist\" |\n| **Recurring** | Repeats on a schedule | \"Every Monday, summarize my active tasks\" |\n\nFuture and recurring tasks are processed by a task runner that checks every 15 minutes.\n\n## Per-User Personalization\n\nEach user gets a persistent system prompt — a personal \"CLAUDE.md\" that shapes how the agent behaves for them.\n\nSet it by emailing instructions:\n- *\"Be more casual and use emojis\"*\n- *\"Call me Paul\"*\n- *\"Always use bullet points\"*\n- *\"Don't be so wordy\"*\n\nThese are extracted automatically and applied to every future interaction.\n\n## Attachments\n\nFull bidirectional support:\n\n- **Inbound** — Email attachments (CSV, JSON, text, code files, etc.) are stored and their content is fed to both the classifier and the agent as context\n- **Outbound** — The agent can generate files (reports, CSV exports, data dumps) and send them back as email attachments\n\n## Architecture\n\n```\nemail-agent/\n README.md ← You are here\n CLAUDE.md — Internal dev guide and conventions\n main.ts — HTTP admin/debug endpoint\n email-handler.ts — Email ingestion (memory-do@valtown.email)\n task-runner.ts — Scheduled task processor (runs every 15 min)\n lib/\n classifier.ts — Claude-powered email → structured entries\n agent.ts — Agentic task execution with tool-use loop\n scheduler.ts — Natural language → ISO datetime\n attachments.ts — Blob storage for inbound/outbound files\n db/\n schema.ts — SQLite schema (entries + FTS5 + links)\n queries.ts — CRUD, search, user context builder\n```\n\n### Tech Stack\n\n- **Runtime**: [Val Town](https://val.town) (Deno-based serverless)\n- **LLM**: Claude Sonnet via [Anthropic SDK](https://docs.anthropic.com/en/docs/build-with-claude/tool-use)\n- **Database**: Project-scoped SQLite with FTS5 full-text search\n- **Storage**: Val Town Blob API for attachments\n- **Email**: Val Town email sending/receiving\n\n### Data Flow\n\n```\n ┌──────────────┐\n │ Incoming │\n │ Email │\n └──────┬───────┘\n │\n ┌──────▼───────┐\n │ Classifier │ ← Claude: extract entries, instructions, follow-ups\n │ (1 LLM call) │\n └──────┬───────┘\n │\n ┌──────▼───────┐\n │ Store in DB │ ← Entries, FTS sync, cross-links\n └──────┬───────┘\n │\n ┌───────┴────────┐\n │ │\n ┌────▼─────┐ ┌─────▼──────┐\n │ Instant │ │ Future / │\n │ Tasks │ │ Recurring │\n │ │ │ Tasks │\n │ ┌───────┐ │ │ │\n │ │ Agent │ │ │ Queued for │\n │ │ Loop │ │ │ task-runner│\n │ │ ↻ │ │ └────────────┘\n │ └───┬───┘ │\n │ │ │\n └─────┼─────┘\n │\n ┌─────▼─────┐\n │ Reply │ ← Results + attachments + follow-up questions\n │ Email │\n └────────────┘\n```\n\n## Environment Variables\n\n| Variable | Required | Description |\n|---|---|---|\n| `ANTHROPIC_API_KEY` | Yes | Used by all Claude API calls (classifier, scheduler, agent) |\n\n## Admin / Debugging\n\nThe HTTP endpoint (`main.ts`) provides:\n\n- `GET /` or `GET /stats` — System-wide entry counts, entries by type, link counts\n- `GET /user/:email` — Full user profile with entries grouped by type\n\n## Limitations\n\n- **No conversation threading** — Each email is independent (though recent history is loaded as context)\n- **Plain text replies** — No HTML email rendering\n- **No admin auth** — HTTP endpoints are open\n- **No rate limiting** — No flood protection\n- **No memory deduplication** — Semantic similarity dedup not yet implemented\n- **No system prompt reset** — Users can add instructions but can't easily clear them yet\n\n## License\n\nBuilt on Val Town. Do what you want with it.\n",[169],"6ead9e00-181c-11f1-9e7d-42dde27851f2","CLAUDE.md","ee2ddb2c-1817-11f1-944b-42dde27851f2",54,["D",1772664581983],5,"# 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",[176],"5e83aa1c-18bf-11f1-867c-42dde27851f2","CLAUDE-v2.md","5e83aa9e-18bf-11f1-867c-42dde27851f2",["D",1772734562850],"# Email Agent System — v2 (Sandbox Architecture)\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 processes requests inside per-user Deno Sandboxes with full Linux tooling.\n\n## Architecture Overview\n\n```\n┌───────────────────────────────────────────────────────────────┐\n│ VAL TOWN (orchestrator) │\n│ │\n│ email-handler-v2.ts (email) → receives email │\n│ │ │\n│ ├─ Get/create user (SQLite: users table) │\n│ ├─ Write email to user's sandbox inbox │\n│ ├─ Boot sandbox → run agent-loop.mjs │\n│ ├─ Read response from sandbox outbox │\n│ ├─ Process schedule.json → scheduled_tasks table │\n│ └─ Send reply email │\n│ │\n│ task-runner-v2.ts (interval, every 15 min) │\n│ │ │\n│ ├─ Query scheduled_tasks WHERE next_run_at <= now │\n│ ├─ For each due task: │\n│ │ ├─ Write task to user's sandbox inbox │\n│ │ ├─ Boot sandbox → run agent-loop.mjs │\n│ │ ├─ Read response, email user │\n│ │ └─ Reschedule if recurring │\n│ └─ Done │\n│ │\n│ main.ts (http) → admin/status dashboard │\n└───────────────────┬───────────────────────────────────────────┘\n │\n ▼\n┌───────────────────────────────────────────────────────────────┐\n│ DENO SANDBOX (per-user microVM) │\n│ │\n│ Runs: node /opt/agent/agent-loop.mjs │\n│ Uses: Claude Agent SDK with Bash/Read/Write/Edit/Glob/Grep │\n│ Mounts: user's persistent volume at /home/user/ │\n│ │\n│ /home/user/ │\n│ ├── CLAUDE.md ← agent instructions (Claude edits it!) │\n│ ├── agents.md ← persona/identity │\n│ ├── TODO.md ← persistent task list │\n│ ├── memories/ ← knowledge as markdown files │\n│ ├── ideas/ ← captured ideas │\n│ ├── people/ ← people directory │\n│ ├── repos/ ← cloned git repositories │\n│ ├── workspace/ ← scratch area │\n│ ├── .ssh/ ← SSH keys │\n│ ├── .mail/ │\n│ │ ├── inbox/ ← tasks arrive here (written by VT) │\n│ │ └── outbox/ ← responses go here (written by agent) │\n│ └── .agent-results/ ← cycle metrics (turns, cost, timing) │\n└───────────────────────────────────────────────────────────────┘\n```\n\n## File Structure\n\n```\nemail-agent/\n CLAUDE.md — Original system docs (v1, still active on main)\n CLAUDE-v2.md — This file. Sandbox architecture docs.\n README.md — Project overview\n\n # V2 (sandbox architecture) — on sandbox-integration branch\n email-handler-v2.ts — Email ingestion → sandbox execution (type: email)\n task-runner-v2.ts — Scheduled task polling → sandbox execution (type: interval)\n main.ts — HTTP endpoint for status/admin (type: http)\n db/\n schema-v2.ts — Simplified: users + scheduled_tasks + sandbox_executions\n lib/\n sandbox-orchestrator.ts — Volume CRUD, sandbox boot, result reading\n sandbox-agent-loop.ts — Source code for agent-loop.mjs (runs in sandbox)\n sandbox-claude-md.ts — Builds CLAUDE.md and agents.md for user volumes\n scheduler.ts — Natural language → ISO datetime (unchanged)\n admin/\n build-snapshot.ts — Builds the toolchain sandbox snapshot\n\n # V1 (original architecture) — still on main branch\n email-handler.ts — Original email handler with classifier\n task-runner.ts — Original task runner\n lib/\n classifier.ts — Email classification (replaced by sandbox agent)\n agent.ts — Tool-use agent loop (replaced by sandbox agent)\n attachments.ts — Blob-based attachments (replaced by sandbox files)\n db/\n schema.ts — Original unified schema with entries/FTS/links\n queries.ts — Original queries\n```\n\n## Key Design Principles\n\n### 1. The Volume IS the User's Brain\nAll user state lives on their persistent sandbox volume as plain files:\n- **Memories** → `memories/*.md` (one file per topic, Claude organizes)\n- **Tasks** → `TODO.md` (Claude maintains it directly)\n- **Ideas** → `ideas/*.md` (one per idea)\n- **People** → `people/*.md` (one per person)\n- **Preferences** → embedded in `CLAUDE.md` (Claude edits it!)\n- **SSH keys** → `.ssh/` (standard Unix location)\n- **Git repos** → `repos/` (persistent across sessions)\n\nNo more SQL for content storage. No FTS tables. No entry_links graph.\nClaude manages files naturally because it has Bash/Read/Write/Edit tools.\n\n### 2. SQLite = Thin Routing Layer\nSQLite only stores what Val Town needs to operate:\n- **users**: email → user mapping + volume reference\n- **scheduled_tasks**: when to wake up the sandbox (task runner polls this)\n- **sandbox_executions**: execution log for debugging and cost tracking\n\n### 3. The Sandbox Agent Does Everything\nNo more classifier, no more custom tool definitions, no more agent.ts loop.\nThe agent-loop.mjs inside the sandbox uses the **Claude Agent SDK** with:\n- `Bash` — run any command (git, npm, python, curl, etc.)\n- `Read` — read any file\n- `Write` — write any file\n- `Edit` — edit files with diffs\n- `Glob` — find files by pattern\n- `Grep` — search file contents\n\nClaude reads CLAUDE.md on the volume, checks the inbox, decides what to do,\ndoes it, and writes results to the outbox. The orchestrator just reads the output.\n\n### 4. Inbox/Outbox File Convention\nCommunication between Val Town and the sandbox is via files:\n- **Orchestrator → Sandbox**: writes task files to `.mail/inbox/`\n- **Sandbox → Orchestrator**: writes response files to `.mail/outbox/`\n- **Schedule requests**: agent writes `.mail/outbox/schedule.json`\n- **File attachments**: agent writes `.mail/outbox/response-{id}.files.json`\n\n## Database (v2)\n\nUses **project-scoped SQLite** (`std/sqlite/main.ts`). Do NOT change to `std/sqlite` or `std/sqlite/global.ts`.\n\n### Tables\n\n#### users\n```sql\nusers (\n id, email, name, volume_slug, volume_id,\n volume_region, sandbox_snapshot, created_at, updated_at\n)\n```\n\n#### scheduled_tasks\n```sql\nscheduled_tasks (\n id, user_id, title, description, next_run_at,\n schedule_description, is_recurring, status, created_at, updated_at\n)\n```\n\n#### sandbox_executions\n```sql\nsandbox_executions (\n id, user_id, trigger_type, trigger_id,\n status, turns, cost_usd, elapsed_ms, error_message,\n created_at, completed_at\n)\n```\n\n## Sandbox Lifecycle\n\n### Email Flow\n1. Email arrives → `email-handler-v2.ts`\n2. Get/create user from sender email\n3. Call `executeSandboxTask()`:\n a. Get/create user's volume (provisions if first time)\n b. Write email content to `.mail/inbox/task-{id}-{slug}.md`\n c. Boot sandbox, mount user's volume\n d. Run `node /opt/agent/agent-loop.mjs`\n e. Agent reads CLAUDE.md, reads inbox, processes task\n f. Agent writes response to `.mail/outbox/response-{id}.md`\n g. Agent optionally writes `schedule.json`, `files.json`\n h. Orchestrator reads outbox, shuts down sandbox\n4. Process schedule requests → insert into `scheduled_tasks`\n5. Send response email to user\n\n### Scheduled Task Flow\n1. `task-runner-v2.ts` fires every 15 min\n2. Query `scheduled_tasks` for due tasks\n3. For each: `executeSandboxTask()` (same as above)\n4. Email results to user\n5. Reschedule recurring tasks\n\n### Volume Initialization\nFirst time a user needs a sandbox:\n1. Create a persistent volume via `Client.volumes.create()` (slug: `email-agent-user-{userId}`, region: `ord`, capacity: `1GB`)\n2. Record volume_slug in users table\n3. Boot sandbox with volume mounted at `/home/app` via `Sandbox.create({ volumes: { \"/home/app\": slug } })`\n4. Create directory structure: `.mail/inbox`, `.mail/outbox`, `memories/`, etc.\n5. Write initial CLAUDE.md, agents.md, TODO.md\n6. Install agent-loop.mjs and dependencies\n7. All subsequent runs mount the same volume — memories, files, and agent state persist\n\n## Environment Variables\n\n- **ANTHROPIC_API_KEY** — required. Passed into sandboxes for Claude API calls.\n\n## Dependencies\n\n- `jsr:@deno/sandbox` — Deno Sandbox SDK (Client for volumes, Sandbox for execution)\n- `npm:@anthropic-ai/sdk@0.39.0` — Claude API client (for scheduler.ts)\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**\n- Always use parameterized SQL queries (`args`)\n- Emoji prefixes in console.log: 📧 📝 🧠 📊 💾 💡 ⏰ 🔄 ⚡ ✅ ❌ 📤 🔧 📋 🚀\n\n### Error Handling\n- Every sandbox execution is wrapped in try/catch\n- Task failures still send a notification email\n- Recurring tasks get rescheduled even on failure\n- Sandbox boot failures return a user-friendly error message\n\n### Sandbox Conventions\n- User volumes are named `email-agent-user-{userId}`\n- Sandbox region: `us-east-1` (configurable in orchestrator)\n- Sandbox timeout: 5 min per execution\n- Agent cycle timeout: 3 min (inside the sandbox)\n- Agent max turns: 30\n\n## Migration from v1\n\nTo migrate from the original architecture:\n1. For each user in the `users` table:\n a. Provision a volume\n b. Export their entries as markdown files → write to volume\n c. Export their system_prompt → write to CLAUDE.md on volume\n2. Switch email-handler.ts to email-handler-v2.ts\n3. Switch task-runner.ts to task-runner-v2.ts\n4. Original DB tables remain as read-only backup\n\n## Known Limitations & Future Work\n\n- **Sandbox boot latency**: ~2-5 seconds per execution. Not ideal for trivial questions. Consider a fast-path that skips the sandbox for simple queries.\n- **Concurrency**: Deno Sandbox has a 5 concurrent limit during pre-release. With multiple users emailing simultaneously, may need queuing.\n- **Volume persistence model**: Each user gets a dedicated Deno Sandbox volume (slug: `email-agent-user-{userId}`) mounted at `/home/app`. Files persist across sandbox sessions. Volumes are created via `Client.volumes.create()` and mounted via `Sandbox.create({ volumes: { \"/home/app\": slug } })`.\n- **File attachments**: Outbound file attachment support (reading files from sandbox, converting to email attachments) is stubbed but not yet implemented.\n- **No auth on HTTP admin**: The endpoints are open.\n- **No rate limiting**: No protection against flooding.\n- **Cost tracking**: Sandbox compute time + Claude API calls need monitoring. The `sandbox_executions` table logs this but there's no alerting or per-user limits yet.\n- **SSH key management**: The infrastructure is there (`.ssh/` dir on volume, SSH config in snapshot) but no user-facing flow for generating/managing keys yet.\n- **Snapshot management**: Need a process for updating the toolchain (new Node.js version, new tools, updated agent-loop.mjs) and rolling out to existing users.\n",[184],"edffb3bc-180f-11f1-a610-42dde27851f2","lib","edffb48e-180f-11f1-a610-42dde27851f2",["D",1772659212112],"directory",[190],"ede42160-180f-11f1-9915-42dde27851f2","db","ede42228-180f-11f1-9915-42dde27851f2",["D",1772659211984],[196],"5e852464-18bf-11f1-867c-42dde27851f2","admin","019cbf36-c21e-7599-8efe-0c7acaaf99fc",["D",1772734562850],[201],"pulls","branches","forks","envVars",{"_212":61,"_214":146},"connections",{"_212":146,"_213":146},"count","unfilledCount","emptyCount",{"_44":200,"_32":201,"_30":202,"_142":-5,"_42":43,"_41":143,"_144":125,"_39":350,"_13":146,"_34":193,"_36":-5,"_24":25,"_149":-5,"_237":351,"_27":352},{"_44":343,"_32":344,"_30":345,"_142":202,"_42":43,"_41":143,"_144":125,"_39":346,"_13":146,"_34":35,"_36":347,"_24":25,"_149":-5,"_237":348,"_27":349},{"_44":336,"_32":337,"_30":338,"_142":202,"_42":43,"_41":143,"_144":25,"_39":339,"_13":146,"_34":35,"_36":340,"_24":25,"_149":-5,"_237":341,"_27":342},{"_44":195,"_32":196,"_30":197,"_142":-5,"_42":43,"_41":61,"_144":125,"_39":333,"_13":146,"_34":193,"_36":-5,"_24":25,"_149":-5,"_237":334,"_27":335},{"_44":325,"_32":326,"_30":327,"_142":197,"_42":43,"_41":328,"_144":25,"_39":329,"_13":156,"_34":35,"_36":330,"_24":25,"_149":-5,"_237":331,"_27":332},{"_44":318,"_32":319,"_30":320,"_142":197,"_42":43,"_41":143,"_144":25,"_39":321,"_13":146,"_34":35,"_36":322,"_24":25,"_149":-5,"_237":323,"_27":324},{"_44":310,"_32":311,"_30":312,"_142":197,"_42":43,"_41":313,"_144":25,"_39":314,"_13":61,"_34":35,"_36":315,"_24":25,"_149":-5,"_237":316,"_27":317},{"_44":189,"_32":190,"_30":191,"_142":-5,"_42":43,"_41":156,"_144":125,"_39":307,"_13":146,"_34":193,"_36":-5,"_24":25,"_149":-5,"_237":308,"_27":309},{"_44":298,"_32":299,"_30":300,"_142":191,"_42":43,"_41":301,"_144":25,"_39":302,"_13":303,"_34":35,"_36":304,"_24":25,"_149":-5,"_237":305,"_27":306},{"_44":290,"_32":291,"_30":292,"_142":191,"_42":43,"_41":293,"_144":25,"_39":294,"_13":264,"_34":35,"_36":295,"_24":25,"_149":-5,"_237":296,"_27":297},{"_44":282,"_32":283,"_30":284,"_142":191,"_42":43,"_41":285,"_144":25,"_39":286,"_13":61,"_34":35,"_36":287,"_24":25,"_149":-5,"_237":288,"_27":289},{"_44":275,"_32":276,"_30":277,"_142":191,"_42":43,"_41":143,"_144":25,"_39":278,"_13":146,"_34":35,"_36":279,"_24":25,"_149":-5,"_237":280,"_27":281},{"_44":268,"_32":269,"_30":270,"_142":191,"_42":43,"_41":143,"_144":125,"_39":271,"_13":146,"_34":35,"_36":272,"_24":25,"_149":-5,"_237":273,"_27":274},{"_44":259,"_32":260,"_30":261,"_142":191,"_42":43,"_41":262,"_144":25,"_39":263,"_13":264,"_34":35,"_36":265,"_24":25,"_149":-5,"_237":266,"_27":267},{"_44":255,"_32":33,"_30":31,"_142":191,"_42":43,"_41":14,"_144":125,"_39":256,"_13":61,"_34":35,"_36":38,"_24":25,"_149":-5,"_237":257,"_27":258},{"_44":183,"_32":184,"_30":185,"_142":-5,"_42":43,"_41":143,"_144":25,"_39":252,"_13":146,"_34":20,"_36":187,"_24":25,"_149":-5,"_237":253,"_27":254},{"_44":175,"_32":176,"_30":177,"_142":-5,"_42":43,"_41":178,"_144":25,"_39":249,"_13":180,"_34":20,"_36":181,"_24":25,"_149":-5,"_237":250,"_27":251},{"_44":168,"_32":169,"_30":170,"_142":-5,"_42":43,"_41":171,"_144":25,"_39":246,"_13":146,"_34":20,"_36":173,"_24":25,"_149":-5,"_237":247,"_27":248},{"_44":161,"_32":162,"_30":163,"_142":-5,"_42":43,"_41":143,"_144":25,"_39":243,"_13":146,"_34":165,"_36":166,"_24":25,"_149":159,"_237":244,"_27":245},{"_44":151,"_32":152,"_30":153,"_142":-5,"_42":43,"_41":154,"_144":25,"_39":240,"_13":156,"_34":157,"_36":158,"_24":25,"_149":159,"_237":241,"_27":242},{"_44":139,"_32":140,"_30":141,"_142":-5,"_42":43,"_41":143,"_144":25,"_39":236,"_13":146,"_34":147,"_36":148,"_24":25,"_149":-5,"_237":238,"_27":239},["D",1772734562850],"children",[],[140],["D",1772663286967],[],[152],["D",1772734562850],[],[162],["D",1772664710595],[],[169],["D",1772664581983],[],[176],["D",1772734562850],[],[184],"dd6bcbb0-18c5-11f1-90c8-42dde27851f2",["D",1772737352748],[],[190,33],"9a1f6e66-18c5-11f1-b5e7-42dde27851f2","sandbox-orchestrator.ts","5e84aab6-18bf-11f1-867c-42dde27851f2",59,["D",1772737239845],1,"/**\n * Sandbox Orchestrator\n *\n * Manages the lifecycle of per-user Deno Sandbox sessions:\n * 1. Ensures the user has a persistent volume (creates one if first time)\n * 2. Tries to RECONNECT to the user's existing running sandbox\n * 3. Only creates a new sandbox if none is running\n * 4. Writes task to the user's inbox on the volume\n * 5. Runs agent-loop.mjs (Claude Agent SDK with full Linux tools)\n * 6. Reads results from the outbox\n * 7. Disconnects (close) without killing — sandbox stays warm for next request\n *\n * IMPORTANT: Sandboxes use `lifetime: \"30m\"` so they persist between email\n * requests. We label each sandbox with `user-id` so we can find it later\n * via `Sandbox.list({ labels })`. The sandbox ID is also stored in the\n * users table for direct reconnection.\n *\n * This prevents the \"25 orphaned sandboxes\" problem — each user should\n * have at most ONE sandbox running at any time.\n */\n\nimport { Client, Sandbox } from \"jsr:@deno/sandbox\";\nimport {\n completeSandboxExecution,\n logSandboxExecution,\n updateUserSandboxId,\n updateUserVolume,\n type UserV2,\n} from \"../db/schema-v2.ts\";\nimport {\n buildAgentPersona,\n buildSandboxClaudeMd,\n} from \"./sandbox-claude-md.ts\";\nimport {\n AGENT_LOOP_PACKAGE_JSON,\n AGENT_LOOP_SOURCE,\n} from \"./sandbox-agent-loop.ts\";\n\n// ============ CONFIGURATION ============\n\nconst USER_HOME = \"/home/app\";\nconst AGENT_DIR = \"/home/app/agent\";\nconst VOLUME_REGION = \"ord\";\nconst VOLUME_CAPACITY = \"1GB\";\n\n// How long a sandbox stays alive after we disconnect.\n// This allows the next email from the same user to reuse the warm sandbox.\nconst SANDBOX_LIFETIME = \"30m\";\n\n// Directories to ensure exist on the user's workspace\nconst VOLUME_DIRS = [\n \".mail/inbox\",\n \".mail/outbox\",\n \".agent-results\",\n \"memories\",\n \"repos\",\n \"workspace\",\n \"ideas\",\n \"people\",\n \".ssh\",\n];\n\n// ============ TYPES ============\n\nexport interface SandboxTaskInput {\n title: string;\n description: string;\n context?: string; // serialized memories, recent interactions, etc.\n}\n\nexport interface SandboxTaskResult {\n success: boolean;\n response: string; // agent's final output to send back to user\n filesCreated: string[]; // list of files the agent created/modified\n schedules: ScheduleRequest[]; // future tasks the agent wants scheduled\n exitCode: number;\n turns: number;\n costUsd: number | null;\n elapsedMs: number;\n error?: string;\n}\n\nexport interface ScheduleRequest {\n title: string;\n description: string;\n schedule: string; // natural language: \"tomorrow at 9am\", \"every Monday at 8am\"\n is_recurring: boolean;\n}\n\n// ============ VOLUME MANAGEMENT ============\n\n/**\n * Ensure the user has a persistent volume.\n * If the user already has a volume_slug in the DB, verify it exists.\n * If not, create a new volume and record it in the DB.\n *\n * Returns the volume slug to mount.\n */\nasync function ensureUserVolume(client: Client, user: UserV2): Promise {\n const volumeSlug = `email-agent-user-${user.id}`;\n\n if (user.volume_slug) {\n // User already has a volume recorded — verify it still exists\n try {\n await client.volumes.get(user.volume_slug);\n console.log(`📦 Reusing existing volume: ${user.volume_slug}`);\n return user.volume_slug;\n } catch {\n // Volume was deleted or doesn't exist — fall through to create a new one\n console.log(`⚠️ Volume ${user.volume_slug} not found, will recreate`);\n }\n }\n\n // Create a new volume for this user\n try {\n const volume = await client.volumes.create({\n slug: volumeSlug,\n region: VOLUME_REGION,\n capacity: VOLUME_CAPACITY,\n });\n console.log(\n `📦 Created new volume: ${volume.slug} (${volume.capacity} bytes)`,\n );\n } catch (err) {\n // Volume slug might already exist (e.g. DB was reset but volume persists)\n const errMsg = err instanceof Error ? err.message : String(err);\n if (errMsg.includes(\"already exists\") || errMsg.includes(\"conflict\")) {\n console.log(`📦 Volume ${volumeSlug} already exists, reusing`);\n } else {\n throw err;\n }\n }\n\n // Record the volume in the DB\n await updateUserVolume(user.id, volumeSlug, volumeSlug, VOLUME_REGION);\n\n return volumeSlug;\n}\n\n// ============ SANDBOX LIFECYCLE ============\n\n/**\n * Get or create a sandbox for the given user.\n *\n * Strategy:\n * 1. If the user has a sandbox_id in the DB, try to connect to it directly\n * 2. If that fails, list sandboxes with the user's label to find any running one\n * 3. If no running sandbox found, create a new one with lifetime + labels\n * 4. Store the sandbox ID in the DB for future reconnection\n *\n * Returns a connected Sandbox instance. Caller MUST call sandbox.close()\n * (not sandbox.kill()) when done to keep the sandbox alive for reuse.\n */\nasync function getOrCreateSandbox(\n user: UserV2,\n volumeSlug: string,\n): Promise<{ sandbox: Sandbox; isNew: boolean }> {\n const labels = { \"user-id\": String(user.id) };\n\n // Strategy 1: Try direct reconnect via stored sandbox_id\n if (user.sandbox_id) {\n try {\n console.log(\n `🔌 Attempting to reconnect to sandbox ${user.sandbox_id} for user ${user.id}...`,\n );\n const sandbox = await Sandbox.connect({ id: user.sandbox_id });\n console.log(\n `♻️ Reconnected to existing sandbox ${sandbox.id} for user ${user.id}`,\n );\n\n // Extend the lifetime so it doesn't expire while we're working\n try {\n await sandbox.extendTimeout(\"30m\");\n } catch {\n // Extension may fail if near max — that's okay\n console.log(`⏱️ Could not extend sandbox timeout (may be near max)`);\n }\n\n return { sandbox, isNew: false };\n } catch (err) {\n const errMsg = err instanceof Error ? err.message : String(err);\n console.log(\n `⚠️ Could not reconnect to sandbox ${user.sandbox_id}: ${errMsg}`,\n );\n // Clear stale sandbox_id\n await updateUserSandboxId(user.id, null);\n }\n }\n\n // Strategy 2: List sandboxes by label to find any running one for this user\n try {\n const existing = await Sandbox.list({ labels });\n if (existing.length > 0) {\n // Connect to the first (most recent) one\n const meta = existing[0];\n console.log(\n `🔍 Found existing sandbox ${meta.id} via label lookup for user ${user.id}`,\n );\n try {\n const sandbox = await Sandbox.connect({ id: meta.id });\n console.log(\n `♻️ Reconnected to labeled sandbox ${sandbox.id} for user ${user.id}`,\n );\n\n // Store this ID for faster reconnect next time\n await updateUserSandboxId(user.id, sandbox.id);\n\n // Extend lifetime\n try {\n await sandbox.extendTimeout(\"30m\");\n } catch {\n console.log(`⏱️ Could not extend sandbox timeout (may be near max)`);\n }\n\n return { sandbox, isNew: false };\n } catch (connectErr) {\n const errMsg = connectErr instanceof Error\n ? connectErr.message\n : String(connectErr);\n console.log(\n `⚠️ Found sandbox ${meta.id} but could not connect: ${errMsg}`,\n );\n // Fall through to create new\n }\n }\n } catch (listErr) {\n const errMsg = listErr instanceof Error ? listErr.message : String(listErr);\n console.log(`⚠️ Could not list sandboxes: ${errMsg}`);\n // Fall through to create new\n }\n\n // Strategy 3: Create a new sandbox\n console.log(`🆕 Creating new sandbox for user ${user.id}...`);\n const sandbox = await Sandbox.create({\n region: VOLUME_REGION,\n lifetime: SANDBOX_LIFETIME,\n labels,\n env: {\n HOME: USER_HOME,\n ANTHROPIC_API_KEY: Deno.env.get(\"ANTHROPIC_API_KEY\") || \"\",\n AGENT_MODEL: \"claude-sonnet-4-6\",\n AGENT_MAX_TURNS: \"30\",\n },\n volumes: {\n [USER_HOME]: volumeSlug,\n },\n });\n\n console.log(\n `✨ Created new sandbox ${sandbox.id} for user ${user.id} (lifetime: ${SANDBOX_LIFETIME})`,\n );\n\n // Store the sandbox ID for future reconnection\n await updateUserSandboxId(user.id, sandbox.id);\n\n return { sandbox, isNew: true };\n}\n\n// ============ VOLUME INITIALIZATION ============\n\n/**\n * Initialize a fresh user workspace with the directory structure,\n * CLAUDE.md, agent-loop, and base files.\n */\nasync function initializeVolume(sandbox: Sandbox, user: UserV2) {\n // Create directory structure\n for (const dir of VOLUME_DIRS) {\n await sandbox.sh`mkdir -p ${USER_HOME}/${dir}`;\n }\n\n // Write CLAUDE.md\n const claudeMd = buildSandboxClaudeMd({\n userName: user.name || undefined,\n });\n await sandbox.fs.writeTextFile(`${USER_HOME}/CLAUDE.md`, claudeMd);\n\n // Write agents.md persona\n const persona = buildAgentPersona({ userName: user.name || undefined });\n await sandbox.fs.writeTextFile(`${USER_HOME}/agents.md`, persona);\n\n // Write initial TODO.md\n await sandbox.fs.writeTextFile(\n `${USER_HOME}/TODO.md`,\n \"# TODO\\n\\n## Active\\n_No tasks yet._\\n\\n## Completed\\n\\n## Someday\\n\",\n );\n\n // Set up SSH directory permissions\n await sandbox.sh`chmod 700 ${USER_HOME}/.ssh`;\n\n // Write and install agent-loop\n await sandbox.sh`mkdir -p ${AGENT_DIR}`;\n await sandbox.fs.writeTextFile(\n `${AGENT_DIR}/agent-loop.mjs`,\n AGENT_LOOP_SOURCE,\n );\n await sandbox.fs.writeTextFile(\n `${AGENT_DIR}/package.json`,\n AGENT_LOOP_PACKAGE_JSON,\n );\n await sandbox.sh`cd ${AGENT_DIR} && npm install 2>&1`;\n\n console.log(`📦 Initialized workspace for user ${user.id} (${user.email})`);\n}\n\n// ============ TASK EXECUTION ============\n\n/**\n * Execute a task in the user's sandbox.\n *\n * This is the main entry point. It:\n * 1. Ensures the user has a persistent volume\n * 2. Gets or reconnects to the user's sandbox (creates only if needed)\n * 3. Initializes the workspace if this is the first use\n * 4. Writes the task to the inbox\n * 5. Runs agent-loop.mjs via bash script (captures all output)\n * 6. Reads results from the outbox\n * 7. Disconnects (close) — sandbox stays alive for next request\n */\nexport async function executeSandboxTask(\n user: UserV2,\n task: SandboxTaskInput,\n trigger: { type: \"email\" | \"scheduled_task\" | \"admin\"; id?: string },\n): Promise {\n // Log the execution\n const executionId = await logSandboxExecution({\n userId: user.id,\n triggerType: trigger.type,\n triggerId: trigger.id,\n });\n\n const startTime = Date.now();\n let sandbox: Sandbox | null = null;\n\n try {\n // Ensure the user has a persistent volume\n const client = new Client();\n const volumeSlug = await ensureUserVolume(client, user);\n\n // Get or create the user's sandbox (reuses existing if running)\n const { sandbox: sb, isNew } = await getOrCreateSandbox(user, volumeSlug);\n sandbox = sb;\n\n // Check if this workspace needs initialization (no CLAUDE.md yet)\n let needsInit = false;\n try {\n await sandbox.sh`test -f ${USER_HOME}/CLAUDE.md`;\n } catch {\n needsInit = true;\n }\n\n if (needsInit) {\n await initializeVolume(sandbox, user);\n } else if (isNew) {\n // New sandbox but volume already has CLAUDE.md — just ensure agent-loop is up to date\n await sandbox.sh`mkdir -p ${AGENT_DIR}`;\n await sandbox.fs.writeTextFile(\n `${AGENT_DIR}/agent-loop.mjs`,\n AGENT_LOOP_SOURCE,\n );\n await sandbox.fs.writeTextFile(\n `${AGENT_DIR}/package.json`,\n AGENT_LOOP_PACKAGE_JSON,\n );\n // Only npm install if node_modules doesn't exist yet\n try {\n await sandbox.sh`test -d ${AGENT_DIR}/node_modules`;\n } catch {\n await sandbox.sh`cd ${AGENT_DIR} && npm install 2>&1`;\n }\n }\n\n // Write the task to the inbox\n const taskId = Date.now();\n const taskFilename = `task-${taskId}-${slugify(task.title)}.md`;\n const taskContent = buildTaskFile(taskId, task);\n await sandbox.fs.writeTextFile(\n `${USER_HOME}/.mail/inbox/${taskFilename}`,\n taskContent,\n );\n\n // Run the agent loop via bash script to capture all output\n console.log(`🚀 Running sandbox task for user ${user.id}: \"${task.title}\"`);\n\n await sandbox.fs.writeTextFile(\n \"/tmp/run-agent.sh\",\n `cd ${USER_HOME} && node ${AGENT_DIR}/agent-loop.mjs > /tmp/agent.log 2>&1; echo \"EXIT=\\\\$?\" >> /tmp/agent.log`,\n );\n await sandbox.sh`bash /tmp/run-agent.sh`;\n\n // Read the captured output\n let agentLog = \"\";\n let exitCode = 0;\n try {\n agentLog = await sandbox.fs.readTextFile(\"/tmp/agent.log\");\n // Parse exit code from last line\n const exitMatch = agentLog.match(/EXIT=(\\d+)/);\n exitCode = exitMatch ? parseInt(exitMatch[1], 10) : 0;\n console.log(`Sandbox output:\\n${agentLog}`);\n } catch {\n console.log(\"Could not read agent log\");\n }\n\n // Read results from outbox\n const response = await readOutboxResponse(sandbox, taskId);\n const filesCreated = await readOutboxFiles(sandbox, taskId);\n const schedules = await readOutboxSchedules(sandbox);\n const cycleResult = await readLatestCycleResult(sandbox);\n\n const elapsedMs = Date.now() - startTime;\n const turns = cycleResult?.turns || 0;\n const costUsd = cycleResult?.cost_usd ?? null;\n\n // Disconnect but keep sandbox alive for next request\n await sandbox.close();\n sandbox = null; // Prevent double-close in finally\n\n // Log completion\n await completeSandboxExecution(executionId, {\n status: exitCode === 0 ? \"success\" : \"failure\",\n turns,\n costUsd: costUsd ?? undefined,\n elapsedMs,\n errorMessage: exitCode !== 0 ? agentLog : undefined,\n });\n\n return {\n success: exitCode === 0,\n response: response || `Task \"${task.title}\" was processed.`,\n filesCreated,\n schedules,\n exitCode,\n turns,\n costUsd,\n elapsedMs,\n };\n } catch (error) {\n const elapsedMs = Date.now() - startTime;\n const errMsg = error instanceof Error ? error.message : String(error);\n\n // Try to close cleanly (not kill) so sandbox can be reused\n if (sandbox) {\n try {\n await sandbox.close();\n } catch {\n // Ignore close errors\n }\n }\n\n await completeSandboxExecution(executionId, {\n status: \"failure\",\n elapsedMs,\n errorMessage: errMsg,\n });\n\n return {\n success: false,\n response:\n `I encountered an error while processing your request: ${errMsg}`,\n filesCreated: [],\n schedules: [],\n exitCode: 1,\n turns: 0,\n costUsd: null,\n elapsedMs,\n error: errMsg,\n };\n }\n}\n\n// ============ READING RESULTS ============\n\n/**\n * Read the agent's response from the outbox.\n */\nasync function readOutboxResponse(\n sandbox: Sandbox,\n taskId: number,\n): Promise {\n const responsePath = `${USER_HOME}/.mail/outbox/response-${taskId}.md`;\n try {\n return await sandbox.fs.readTextFile(responsePath);\n } catch {\n // Agent might not have written to the exact expected path.\n // Try to find any markdown response file in the outbox.\n try {\n const fileList = await sandbox\n .sh`ls -t ${USER_HOME}/.mail/outbox/*.md 2>/dev/null || true`.text();\n const files = fileList.trim().split(\"\\n\").filter(Boolean);\n if (files.length > 0) {\n return await sandbox.fs.readTextFile(files[0]);\n }\n } catch {\n // Ignore\n }\n return null;\n }\n}\n\n/**\n * Read list of files the agent created for the user.\n */\nasync function readOutboxFiles(\n sandbox: Sandbox,\n taskId: number,\n): Promise {\n try {\n const json = await sandbox.fs.readTextFile(\n `${USER_HOME}/.mail/outbox/response-${taskId}.files.json`,\n );\n return JSON.parse(json);\n } catch {\n return [];\n }\n}\n\n/**\n * Read schedule requests from the agent.\n */\nasync function readOutboxSchedules(\n sandbox: Sandbox,\n): Promise {\n try {\n const json = await sandbox.fs.readTextFile(\n `${USER_HOME}/.mail/outbox/schedule.json`,\n );\n const schedules = JSON.parse(json);\n\n // Clean up the schedule file after reading\n await sandbox.sh`rm -f ${USER_HOME}/.mail/outbox/schedule.json`;\n\n return Array.isArray(schedules) ? schedules : [];\n } catch {\n return [];\n }\n}\n\n/**\n * Read the latest cycle result from .agent-results/\n */\nasync function readLatestCycleResult(\n sandbox: Sandbox,\n): Promise<{ turns: number; cost_usd: number | null; status: string } | null> {\n try {\n const latestFile = await sandbox\n .sh`ls -t ${USER_HOME}/.agent-results/ | head -1`.text();\n const filename = latestFile.trim();\n if (!filename) return null;\n\n const json = await sandbox.fs.readTextFile(\n `${USER_HOME}/.agent-results/${filename}`,\n );\n return JSON.parse(json);\n } catch {\n return null;\n }\n}\n\n// ============ HELPERS ============\n\n/**\n * Build the task markdown file that goes into the inbox.\n */\nfunction buildTaskFile(taskId: number, task: SandboxTaskInput): string {\n const parts: string[] = [\n `# ${task.title}`,\n \"\",\n task.description,\n \"\",\n ];\n\n if (task.context) {\n parts.push(\"## Additional Context\", \"\", task.context, \"\");\n }\n\n parts.push(\n \"## Instructions\",\n \"\",\n \"Complete this task. Write your response (what to email back to the user) to:\",\n `\\`${USER_HOME}/.mail/outbox/response-${taskId}.md\\``,\n \"\",\n \"If you create any files the user should receive as email attachments, list their absolute paths in:\",\n `\\`${USER_HOME}/.mail/outbox/response-${taskId}.files.json\\``,\n \"(JSON array of absolute paths)\",\n \"\",\n \"If you need to schedule future work, write to:\",\n `\\`${USER_HOME}/.mail/outbox/schedule.json\\``,\n \"(JSON array of objects with: title, description, schedule, is_recurring)\",\n );\n\n return parts.join(\"\\n\");\n}\n\n/**\n * Convert a string to a URL-safe slug.\n */\nfunction slugify(text: string): string {\n return text\n .toLowerCase()\n .replace(/[^a-z0-9]+/g, \"-\")\n .replace(/^-|-$/g, \"\")\n .slice(0, 50);\n}",[],[190,260],"5e8478de-18bf-11f1-867c-42dde27851f2","sandbox-claude-md.ts","5e84796a-18bf-11f1-867c-42dde27851f2",["D",1772734562850],"/**\n * Builds the CLAUDE.md file that lives on each user's persistent volume.\n *\n * This is the instruction set that the agent-loop.mjs reads on every boot.\n * It tells Claude how to operate in the user's workspace: where to find things,\n * how to process tasks, and how to communicate results back.\n *\n * The user's personal instructions (equivalent of the old system_prompt column)\n * are embedded in a section at the bottom. Claude can also edit this file\n * directly — if the user says \"be more casual\", Claude updates CLAUDE.md itself.\n */\n\nconst USER_HOME = \"/home/app\";\n\n/**\n * Build the full CLAUDE.md content for a user's volume.\n *\n * @param userPreferences - Optional user-specific instructions (migrated from\n * the old system_prompt, or accumulated by Claude over time). If empty,\n * Claude will build this section organically as the user gives instructions.\n */\nexport function buildSandboxClaudeMd(opts?: {\n userPreferences?: string;\n userName?: string;\n}): string {\n const prefs = opts?.userPreferences?.trim();\n const nameSection = opts?.userName\n ? `The user's name is **${opts.userName}**.`\n : \"\";\n\n return `# Agent Workspace\n\nYou are a personal AI agent. You communicate with your user via email.\nYou wake up periodically to process new messages and complete tasks.\n${nameSection}\n\n## Your Environment\n\n- **Home**: ${USER_HOME}\n- **Inbox** (new work arrives here): \\`${USER_HOME}/.mail/inbox/\\`\n- **Outbox** (write your responses here): \\`${USER_HOME}/.mail/outbox/\\`\n- **Memories & Knowledge**: \\`${USER_HOME}/memories/\\`\n- **Task List**: \\`${USER_HOME}/TODO.md\\`\n- **Ideas**: \\`${USER_HOME}/ideas/\\`\n- **People**: \\`${USER_HOME}/people/\\`\n- **Git Repos**: \\`${USER_HOME}/repos/\\`\n- **SSH Keys**: \\`${USER_HOME}/.ssh/\\`\n- **Workspace** (scratch area): \\`${USER_HOME}/workspace/\\`\n\n## Workflow\n\nWhen you wake up for a work cycle:\n\n1. **Check inbox**: Read any new files in \\`.mail/inbox/\\`. Each file is a task or message.\n2. **For each inbox item**:\n a. Read the task/message carefully\n b. Consult your \\`memories/\\` for relevant context about the user\n c. Check \\`TODO.md\\` for related pending work\n d. Do the work — run commands, edit files, fetch URLs, clone repos, whatever is needed\n e. Write your response to the path specified in the task file (usually \\`.mail/outbox/response-{id}.md\\`)\n f. If you created files the user should receive as email attachments, list them in \\`.mail/outbox/response-{id}.files.json\\` (JSON array of absolute paths)\n3. **Check TODO.md**: Process any pending items that are due or actionable now\n4. **Update your knowledge**: If you learned new facts about the user, update files in \\`memories/\\`\n5. **Schedule future work**: If something needs to happen later, write to \\`.mail/outbox/schedule.json\\`:\n \\`\\`\\`json\n [\n {\n \"title\": \"Weekly stock report\",\n \"description\": \"Analyze the user's portfolio and email a summary\",\n \"schedule\": \"every Monday at 9am\",\n \"is_recurring\": true\n }\n ]\n \\`\\`\\`\n6. **Exit** when all work is done. You will wake up again when needed.\n\n## Memory Organization\n\nStore knowledge about the user in \\`memories/\\` as markdown files:\n- One file per topic or category — you decide the organization\n- Examples: \\`memories/preferences.md\\`, \\`memories/work.md\\`, \\`memories/family.md\\`, \\`memories/projects.md\\`\n- Keep files concise and current — remove stale information\n- You can restructure/reorganize files as the knowledge grows\n- When you learn something new, update or create the appropriate file\n\n## TODO.md Format\n\nMaintain \\`TODO.md\\` as a simple task list:\n\\`\\`\\`markdown\n# TODO\n\n## Active\n- [ ] Task description here\n- [ ] Another task (due: 2026-03-10)\n\n## Completed\n- [x] Previously completed task (done: 2026-03-05)\n\n## Someday\n- [ ] Low-priority items\n\\`\\`\\`\n\nUpdate this file as you complete work or receive new tasks.\n\n## People Directory\n\nTrack people the user mentions in \\`people/\\`:\n- One file per person: \\`people/sarah-chen.md\\`\n- Include: relationship, contact info, key facts, how they connect to the user\n- Update as you learn more\n\n## Ideas\n\nStore the user's explicitly captured ideas in \\`ideas/\\`:\n- One file per idea: \\`ideas/2026-03-05-app-concept.md\\`\n- Only store ideas the user explicitly asks to save\n- Include context, related thoughts, and any follow-up actions\n\n## Git & SSH\n\n- Repos are cloned to \\`repos/\\` — they persist between sessions\n- SSH keys live in \\`.ssh/\\` — generate with \\`ssh-keygen -t ed25519\\`\n- Configure git identity: \\`git config --global user.name \"...\"\\` and \\`git config --global user.email \"...\"\\`\n- For SSH host verification: \\`ssh-keyscan github.com >> ~/.ssh/known_hosts\\`\n\n## Responding to Tasks\n\nWhen writing response files:\n- Be clear, readable, and appropriately detailed\n- Format as markdown — it will be emailed to the user\n- If you need to send files, create them and list paths in the \\`.files.json\\`\n- Be efficient — don't over-explain unless asked\n\n## Scheduling Future Work\n\nThe \\`schedule.json\\` file in your outbox is how you tell the system to wake you up later.\nEach entry needs:\n- \\`title\\`: Short name for the task\n- \\`description\\`: What to do when it fires\n- \\`schedule\\`: Natural language time — \"tomorrow at 9am\", \"every Monday at 8am\", \"in 2 hours\"\n- \\`is_recurring\\`: true for repeating tasks, false for one-time\n\n## Updating These Instructions\n\nIf the user gives you standing instructions (\"be more casual\", \"always use bullet points\",\n\"call me Paul\"), **edit this file directly** — add or update the \"User's Personal Instructions\"\nsection below. This is how preferences persist across sessions.\n\n---\n\n## User's Personal Instructions\n\n${prefs || \"_No custom instructions yet. The user can tell you their preferences and you'll save them here._\"}\n`;\n}\n\n/**\n * Build the agents.md persona file for the sandbox.\n * This is loaded by agent-loop.mjs as supplementary identity/persona info.\n */\nexport function buildAgentPersona(opts?: {\n userName?: string;\n}): string {\n return `## Persona\n\nYou are a helpful, efficient personal assistant. You communicate via email with your user.\n\n### Core traits:\n- **Thorough**: When given a task, do it completely. Don't leave loose ends.\n- **Context-aware**: Always check your memories before responding. Personalize.\n- **Proactive**: If you notice something useful while working, mention it.\n- **Honest**: If you can't do something or aren't sure, say so.\n- **Organized**: Keep your workspace tidy. Update memories, clean up TODO.md.\n\n### What you can do:\n- Search the web for current information\n- Clone and work with git repositories\n- Run code (Node.js, Python, shell scripts)\n- Read and write files\n- Install packages (npm, pip, apt)\n- Manage SSH keys\n- Create documents, reports, data exports\n- Analyze data and code\n\n### What you should NOT do:\n- Never expose SSH private keys in your responses\n- Never include API keys or secrets in response files\n- Don't modify files outside your home directory unless explicitly asked\n- Don't run destructive commands (rm -rf /) without clear user intent\n${opts?.userName ? `\\n### User\\nThe user's name is ${opts.userName}.` : \"\"}\n`;\n}\n",[],[190,269],"5e845084-18bf-11f1-867c-42dde27851f2","sandbox-agent-loop.ts","5e845106-18bf-11f1-867c-42dde27851f2",["D",1772734562850],"/**\n * Agent loop source code — runs INSIDE the Deno Sandbox.\n *\n * This file is NOT executed by Val Town directly. It exports the source code\n * as a string constant that gets written to the user's volume and executed\n * with Node.js inside the sandbox.\n *\n * Uses the Claude Agent SDK (@anthropic-ai/claude-agent-sdk) with\n * Bash/Read/Write/Edit/Glob/Grep tools in bypassPermissions mode.\n *\n * The SDK's query() returns an async generator of messages. We iterate\n * through them to completion, then read the result message for cost/turns.\n *\n * Ref: https://platform.claude.com/docs/en/agent-sdk/overview\n * Ref: https://platform.claude.com/docs/en/agent-sdk/typescript\n */\n\n/**\n * The agent-loop.mjs source code.\n *\n * Environment variables expected:\n * - ANTHROPIC_API_KEY: For Claude API calls\n * - HOME: /home/app\n * - AGENT_MODEL: Claude model to use (default: claude-sonnet-4-20250514)\n * - AGENT_MAX_TURNS: Max tool-use turns (default: 30)\n */\nexport const AGENT_LOOP_SOURCE = `#!/usr/bin/env node\n/**\n * agent-loop.mjs — Runs inside the Deno Sandbox\n *\n * Reads tasks from .mail/inbox/, processes them with Claude Agent SDK,\n * writes results to .mail/outbox/, then exits.\n */\n\nimport { query } from \"@anthropic-ai/claude-agent-sdk\";\nimport { readdir, readFile, writeFile, unlink, mkdir } from \"node:fs/promises\";\nimport { join } from \"node:path\";\n\nconst HOME = process.env.HOME || \"/home/app\";\nconst MODEL = process.env.AGENT_MODEL || \"claude-sonnet-4-20250514\";\nconst MAX_TURNS = parseInt(process.env.AGENT_MAX_TURNS || \"30\", 10);\n\nconst INBOX = join(HOME, \".mail\", \"inbox\");\nconst OUTBOX = join(HOME, \".mail\", \"outbox\");\nconst RESULTS_DIR = join(HOME, \".agent-results\");\n\n// Ensure directories exist\nasync function ensureDirs() {\n for (const dir of [INBOX, OUTBOX, RESULTS_DIR, join(HOME, \"memories\"), join(HOME, \"repos\"), join(HOME, \"workspace\"), join(HOME, \"ideas\"), join(HOME, \"people\"), join(HOME, \".ssh\")]) {\n await mkdir(dir, { recursive: true });\n }\n}\n\n// Read CLAUDE.md for system instructions\nasync function readSystemPrompt() {\n const claudeMdPath = join(HOME, \"CLAUDE.md\");\n try {\n return await readFile(claudeMdPath, \"utf-8\");\n } catch {\n return \"You are a helpful personal AI agent. Check .mail/inbox/ for tasks.\";\n }\n}\n\n// Get all pending tasks from inbox\nasync function getInboxTasks() {\n try {\n const files = await readdir(INBOX);\n const tasks = [];\n for (const file of files.sort()) {\n if (file.startsWith(\".\")) continue;\n const content = await readFile(join(INBOX, file), \"utf-8\");\n tasks.push({ filename: file, content, path: join(INBOX, file) });\n }\n return tasks;\n } catch {\n return [];\n }\n}\n\n// Read TODO.md if it exists\nasync function readTodo() {\n try {\n return await readFile(join(HOME, \"TODO.md\"), \"utf-8\");\n } catch {\n return null;\n }\n}\n\n// Write heartbeat for monitoring\nasync function writeHeartbeat(status) {\n const heartbeatPath = join(HOME, \".agent-heartbeat\");\n const data = JSON.stringify({\n status,\n timestamp: new Date().toISOString(),\n model: MODEL,\n });\n await writeFile(heartbeatPath, data, \"utf-8\");\n}\n\n// Save cycle result for the orchestrator to read\nasync function saveCycleResult(result) {\n const filename = \"cycle-\" + Date.now() + \".json\";\n await writeFile(join(RESULTS_DIR, filename), JSON.stringify(result, null, 2), \"utf-8\");\n}\n\n// ============ MAIN ============\n\nasync function main() {\n const startTime = Date.now();\n\n await ensureDirs();\n await writeHeartbeat(\"starting\");\n\n const systemPrompt = await readSystemPrompt();\n const inboxTasks = await getInboxTasks();\n const todo = await readTodo();\n\n if (inboxTasks.length === 0 && !todo) {\n console.log(\"No tasks in inbox and no TODO.md. Nothing to do.\");\n await writeHeartbeat(\"idle\");\n await saveCycleResult({\n status: \"idle\",\n turns: 0,\n cost_usd: 0,\n elapsed_ms: Date.now() - startTime,\n });\n return;\n }\n\n // Build the prompt for this cycle\n const parts = [];\n\n if (inboxTasks.length > 0) {\n parts.push(\"## New Tasks in Inbox\\\\n\");\n for (const task of inboxTasks) {\n parts.push(\"### \" + task.filename + \"\\\\n\\\\n\" + task.content + \"\\\\n\");\n }\n }\n\n if (todo) {\n parts.push(\"## Current TODO.md\\\\n\\\\n\" + todo + \"\\\\n\");\n }\n\n parts.push(\"---\");\n parts.push(\"Process the above tasks following the instructions in CLAUDE.md. When done, exit.\");\n\n const userPrompt = parts.join(\"\\\\n\\\\n\");\n\n console.log(\"Starting agent cycle: \" + inboxTasks.length + \" inbox task(s), model=\" + MODEL + \", maxTurns=\" + MAX_TURNS);\n await writeHeartbeat(\"running\");\n\n let turns = 0;\n let costUsd = null;\n let resultText = \"\";\n let errorMessage = null;\n\n try {\n // query() returns an async generator of SDKMessage\n const agentQuery = query({\n prompt: userPrompt,\n options: {\n model: MODEL,\n maxTurns: MAX_TURNS,\n allowedTools: [\"Bash\", \"Read\", \"Write\", \"Edit\", \"Glob\", \"Grep\"],\n permissionMode: \"bypassPermissions\",\n cwd: HOME,\n systemPrompt: systemPrompt,\n },\n });\n\n // Iterate through all messages\n for await (const message of agentQuery) {\n // Log assistant messages for debugging\n if (message.type === \"assistant\" && message.message?.content) {\n for (const block of message.message.content) {\n if (\"text\" in block && block.text) {\n console.log(\"[assistant]\", block.text.substring(0, 200));\n } else if (\"name\" in block) {\n console.log(\"[tool_use]\", block.name);\n }\n }\n }\n\n // Capture the final result\n if (message.type === \"result\") {\n resultText = message.result || \"\";\n turns = message.num_turns || 0;\n costUsd = message.total_cost_usd ?? null;\n console.log(\"Agent cycle completed: \" + turns + \" turns, cost=$\" + (costUsd?.toFixed(4) ?? \"unknown\"));\n\n if (message.is_error) {\n errorMessage = \"Agent ended with error: \" + message.subtype;\n }\n }\n }\n\n // Clean up processed inbox files\n for (const task of inboxTasks) {\n try {\n await unlink(task.path);\n console.log(\"Cleaned up inbox: \" + task.filename);\n } catch (e) {\n console.warn(\"Failed to clean up \" + task.filename + \": \" + e.message);\n }\n }\n\n await writeHeartbeat(\"completed\");\n } catch (err) {\n errorMessage = err.message || String(err);\n console.error(\"Agent cycle error: \" + errorMessage);\n await writeHeartbeat(\"error\");\n }\n\n const elapsed = Date.now() - startTime;\n\n await saveCycleResult({\n status: errorMessage ? \"error\" : \"success\",\n turns,\n cost_usd: costUsd,\n elapsed_ms: elapsed,\n error: errorMessage,\n result_text: resultText,\n tasks_processed: inboxTasks.length,\n });\n\n console.log(\"Cycle finished in \" + elapsed + \"ms\");\n}\n\nmain().catch((err) => {\n console.error(\"Fatal error in agent loop:\", err);\n process.exit(1);\n});\n`;\n\n/**\n * package.json for the agent loop.\n *\n * Uses @anthropic-ai/claude-agent-sdk which includes the full agent\n * runtime with built-in tools (Bash, Read, Write, Edit, Glob, Grep).\n */\nexport const AGENT_LOOP_PACKAGE_JSON = JSON.stringify(\n {\n name: \"agent-loop\",\n version: \"1.0.0\",\n type: \"module\",\n dependencies: {\n \"@anthropic-ai/claude-agent-sdk\": \"latest\",\n },\n },\n null,\n 2,\n);\n",[],[190,276],"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",[],[190,283],"e8186e2e-181b-11f1-b9c4-42dde27851f2","attachments.ts","cf077a02-181a-11f1-81e1-42dde27851f2",49,["D",1772664356194],"/**\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",[],[190,291],"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",[],[190,299],["D",1772659212112],[223,224,225,226,227,228,229],[190],"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",[],[196,311],"5e83fc2e-18bf-11f1-867c-42dde27851f2","schema-v2.ts","5e83fcba-18bf-11f1-867c-42dde27851f2",["D",1772734562850],"/**\n * Database schema v2 — Simplified for sandbox-based architecture.\n *\n * With user state living on persistent volumes (memories, todos, ideas as files),\n * SQLite becomes a thin routing/scheduling layer:\n *\n * - **users**: email → user mapping + volume tracking\n * - **scheduled_tasks**: lightweight polling table for the task runner interval\n *\n * The heavy lifting (entries, FTS, entry_links, attachments) is replaced by\n * the filesystem on each user's Deno Sandbox volume, managed by Claude directly.\n */\n\nimport { sqlite } from \"https://esm.town/v/std/sqlite/main.ts\";\n\nexport async function initDatabaseV2() {\n // Core tables\n await sqlite.batch([\n // Users — each unique sender email, with their sandbox volume info\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 volume_slug TEXT UNIQUE,\n volume_id TEXT,\n volume_region TEXT DEFAULT 'ord',\n sandbox_snapshot TEXT DEFAULT 'agent-toolchain-v1',\n sandbox_id TEXT,\n created_at TEXT DEFAULT (datetime('now')),\n updated_at TEXT DEFAULT (datetime('now'))\n )`,\n },\n\n // Scheduled tasks — just enough for the task runner to poll.\n {\n sql: `CREATE TABLE IF NOT EXISTS scheduled_tasks (\n id INTEGER PRIMARY KEY AUTOINCREMENT,\n user_id INTEGER NOT NULL,\n title TEXT NOT NULL,\n description TEXT NOT NULL,\n next_run_at TEXT NOT NULL,\n schedule_description TEXT,\n is_recurring INTEGER DEFAULT 0,\n status TEXT DEFAULT 'pending',\n created_at TEXT DEFAULT (datetime('now')),\n updated_at TEXT DEFAULT (datetime('now')),\n FOREIGN KEY (user_id) REFERENCES users(id)\n )`,\n },\n\n // Sandbox execution log — track every sandbox boot for cost/debugging\n {\n sql: `CREATE TABLE IF NOT EXISTS sandbox_executions (\n id INTEGER PRIMARY KEY AUTOINCREMENT,\n user_id INTEGER NOT NULL,\n trigger_type TEXT NOT NULL,\n trigger_id TEXT,\n status TEXT DEFAULT 'started',\n turns INTEGER,\n cost_usd REAL,\n elapsed_ms INTEGER,\n error_message TEXT,\n created_at TEXT DEFAULT (datetime('now')),\n completed_at TEXT,\n FOREIGN KEY (user_id) REFERENCES users(id)\n )`,\n },\n\n // Indexes\n {\n sql: `CREATE INDEX IF NOT EXISTS idx_users_email ON users(email)`,\n },\n {\n sql: `CREATE INDEX IF NOT EXISTS idx_scheduled_tasks_due\n ON scheduled_tasks(next_run_at)\n WHERE status = 'pending'`,\n },\n {\n sql: `CREATE INDEX IF NOT EXISTS idx_scheduled_tasks_user\n ON scheduled_tasks(user_id)`,\n },\n {\n sql: `CREATE INDEX IF NOT EXISTS idx_sandbox_executions_user\n ON sandbox_executions(user_id, created_at DESC)`,\n },\n ]);\n\n // Migrate existing users table: add v2 columns if they don't exist.\n // SQLite doesn't have ADD COLUMN IF NOT EXISTS, so we catch errors.\n const newColumns = [\n { name: \"volume_slug\", def: \"TEXT\" },\n { name: \"volume_id\", def: \"TEXT\" },\n { name: \"volume_region\", def: \"TEXT DEFAULT 'ord'\" },\n { name: \"sandbox_snapshot\", def: \"TEXT DEFAULT 'agent-toolchain-v1'\" },\n { name: \"sandbox_id\", def: \"TEXT\" },\n { name: \"updated_at\", def: \"TEXT DEFAULT (datetime('now'))\" },\n ];\n for (const col of newColumns) {\n try {\n await sqlite.execute({\n sql: `ALTER TABLE users ADD COLUMN ${col.name} ${col.def}`,\n args: [],\n });\n console.log(` ✅ Added column users.${col.name}`);\n } catch {\n // Column already exists — that's fine\n }\n }\n\n console.log(\"💾 Database schema v2 initialized\");\n}\n\n// ============ USER QUERIES ============\n\nexport interface UserV2 {\n id: number;\n email: string;\n name: string | null;\n volume_slug: string | null;\n volume_id: string | null;\n volume_region: string;\n sandbox_snapshot: string;\n sandbox_id: string | null;\n created_at: string;\n updated_at: string;\n}\n\nexport async function getOrCreateUserV2(\n email: string,\n name?: string,\n): Promise {\n const existing = await sqlite.execute({\n sql: `SELECT * 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 parseUserRow(row);\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 volume_slug: null,\n volume_id: null,\n volume_region: \"ord\",\n sandbox_snapshot: \"agent-toolchain-v1\",\n sandbox_id: null,\n created_at: new Date().toISOString(),\n updated_at: new Date().toISOString(),\n };\n}\n\nexport async function getUserV2(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 return parseUserRow(result.rows[0] as Record);\n}\n\nexport async function getUserByEmailV2(email: string): Promise {\n const result = await sqlite.execute({\n sql: `SELECT * FROM users WHERE email = ?`,\n args: [email.toLowerCase()],\n });\n if (result.rows.length === 0) return undefined;\n return parseUserRow(result.rows[0] as Record);\n}\n\nexport async function updateUserVolume(\n userId: number,\n volumeSlug: string,\n volumeId: string,\n region: string,\n) {\n await sqlite.execute({\n sql: `UPDATE users SET volume_slug = ?, volume_id = ?, volume_region = ?, updated_at = datetime('now') WHERE id = ?`,\n args: [volumeSlug, volumeId, region, userId],\n });\n}\n\nexport async function updateUserSandboxId(\n userId: number,\n sandboxId: string | null,\n) {\n await sqlite.execute({\n sql: `UPDATE users SET sandbox_id = ?, updated_at = datetime('now') WHERE id = ?`,\n args: [sandboxId, userId],\n });\n}\n\n// ============ SCHEDULED TASK QUERIES ============\n\nexport interface ScheduledTask {\n id: number;\n user_id: number;\n title: string;\n description: string;\n next_run_at: string;\n schedule_description: string | null;\n is_recurring: boolean;\n status: string;\n created_at: string;\n updated_at: string;\n}\n\nexport async function addScheduledTask(opts: {\n userId: number;\n title: string;\n description: string;\n nextRunAt: string;\n scheduleDescription?: string;\n isRecurring?: boolean;\n}): Promise {\n const result = await sqlite.execute({\n sql: `INSERT INTO scheduled_tasks (user_id, title, description, next_run_at, schedule_description, is_recurring)\n VALUES (?, ?, ?, ?, ?, ?)`,\n args: [\n opts.userId,\n opts.title,\n opts.description,\n opts.nextRunAt,\n opts.scheduleDescription || null,\n opts.isRecurring ? 1 : 0,\n ],\n });\n return Number(result.lastInsertRowid);\n}\n\nexport async function getScheduledTasksDue(): Promise<\n (ScheduledTask & { user_email: string; user_name: string | null; volume_slug: string | null })[\n ]\n> {\n const now = new Date().toISOString().replace(\"T\", \" \").replace(\"Z\", \"\");\n const result = await sqlite.execute({\n sql: `SELECT st.*, u.email as user_email, u.name as user_name, u.volume_slug\n FROM scheduled_tasks st\n JOIN users u ON st.user_id = u.id\n WHERE st.status = 'pending'\n AND REPLACE(REPLACE(st.next_run_at, 'T', ' '), 'Z', '') <= ?\n ORDER BY st.next_run_at ASC\n LIMIT 20`,\n args: [now],\n });\n return (result.rows as Record[]).map((row) => ({\n ...parseScheduledTaskRow(row),\n user_email: row.user_email as string,\n user_name: row.user_name as string | null,\n volume_slug: row.volume_slug as string | null,\n }));\n}\n\nexport async function updateScheduledTaskStatus(\n taskId: number,\n status: string,\n) {\n await sqlite.execute({\n sql: `UPDATE scheduled_tasks SET status = ?, updated_at = datetime('now') WHERE id = ?`,\n args: [status, taskId],\n });\n}\n\nexport async function rescheduleTask(taskId: number, nextRunAt: string) {\n await sqlite.execute({\n sql: `UPDATE scheduled_tasks SET status = 'pending', next_run_at = ?, updated_at = datetime('now') WHERE id = ?`,\n args: [nextRunAt, taskId],\n });\n}\n\n// ============ SANDBOX EXECUTION LOG ============\n\nexport async function logSandboxExecution(opts: {\n userId: number;\n triggerType: \"email\" | \"scheduled_task\" | \"admin\";\n triggerId?: string;\n}): Promise {\n const result = await sqlite.execute({\n sql: `INSERT INTO sandbox_executions (user_id, trigger_type, trigger_id)\n VALUES (?, ?, ?)`,\n args: [opts.userId, opts.triggerType, opts.triggerId || null],\n });\n return Number(result.lastInsertRowid);\n}\n\nexport async function completeSandboxExecution(\n executionId: number,\n result: {\n status: \"success\" | \"failure\" | \"timeout\";\n turns?: number;\n costUsd?: number;\n elapsedMs?: number;\n errorMessage?: string;\n },\n) {\n await sqlite.execute({\n sql: `UPDATE sandbox_executions\n SET status = ?, turns = ?, cost_usd = ?, elapsed_ms = ?, error_message = ?, completed_at = datetime('now')\n WHERE id = ?`,\n args: [\n result.status,\n result.turns || null,\n result.costUsd || null,\n result.elapsedMs || null,\n result.errorMessage || null,\n executionId,\n ],\n });\n}\n\n// ============ HELPERS ============\n\nfunction parseUserRow(row: Record): UserV2 {\n return {\n id: row.id as number,\n email: row.email as string,\n name: row.name as string | null,\n volume_slug: row.volume_slug as string | null,\n volume_id: row.volume_id as string | null,\n volume_region: (row.volume_region as string) || \"ord\",\n sandbox_snapshot: (row.sandbox_snapshot as string) || \"agent-toolchain-v1\",\n sandbox_id: (row.sandbox_id as string) || null,\n created_at: row.created_at as string,\n updated_at: row.updated_at as string,\n };\n}\n\nfunction parseScheduledTaskRow(row: Record): ScheduledTask {\n return {\n id: row.id as number,\n user_id: row.user_id as number,\n title: row.title as string,\n description: row.description as string,\n next_run_at: row.next_run_at as string,\n schedule_description: row.schedule_description as string | null,\n is_recurring: (row.is_recurring as number) === 1,\n status: row.status as string,\n created_at: row.created_at as string,\n updated_at: row.updated_at as string,\n };\n}\n",[],[196,319],"bc71a476-181a-11f1-845e-42dde27851f2","queries.ts","07d912a6-1810-11f1-8d14-42dde27851f2",35,["D",1772663853383],"/**\n * Database query helpers — unified entries model.\n *\n * Everything is an entry with a type. 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",[],[196,326],["D",1772659211984],[219,220,221],[196],"5e838e92-18bf-11f1-867c-42dde27851f2","test-orchestrator.ts","5e838f1e-18bf-11f1-867c-42dde27851f2",["D",1772734562850],"/**\n * Test: Sandbox with @anthropic-ai/claude-agent-sdk\n * Tests the corrected package and API.\n */\nimport { Sandbox } from \"jsr:@deno/sandbox\";\nimport { initDatabaseV2 } from \"../db/schema-v2.ts\";\nimport { AGENT_LOOP_SOURCE, AGENT_LOOP_PACKAGE_JSON } from \"../lib/sandbox-agent-loop.ts\";\n\nconst USER_HOME = \"/home/app\";\nconst AGENT_DIR = \"/home/app/agent\";\n\nconsole.log(\"🧪 Testing with @anthropic-ai/claude-agent-sdk\\n\");\n\nawait initDatabaseV2();\n\nconsole.log(\"📦 Creating sandbox...\");\nawait using sandbox = await Sandbox.create({\n env: {\n HOME: USER_HOME,\n ANTHROPIC_API_KEY: Deno.env.get(\"ANTHROPIC_API_KEY\") || \"\",\n AGENT_MODEL: \"claude-sonnet-4-20250514\",\n AGENT_MAX_TURNS: \"5\",\n AGENT_CYCLE_TIMEOUT_MS: \"60000\",\n },\n});\nconsole.log(\" ✅ Sandbox created\\n\");\n\n// Install\nconsole.log(\"📦 Installing agent-loop...\");\nawait sandbox.sh`mkdir -p ${AGENT_DIR}`;\nawait sandbox.fs.writeTextFile(`${AGENT_DIR}/agent-loop.mjs`, AGENT_LOOP_SOURCE);\nawait sandbox.fs.writeTextFile(`${AGENT_DIR}/package.json`, AGENT_LOOP_PACKAGE_JSON);\nawait sandbox.sh`cd ${AGENT_DIR} && npm install 2>&1`;\nconsole.log(\" ✅ Installed\\n\");\n\n// Check what got installed\nconsole.log(\"=== 1. What's in node_modules/@anthropic-ai/ ===\");\nconst aiPkgs = await sandbox.sh`ls ${AGENT_DIR}/node_modules/@anthropic-ai/ 2>/dev/null`.text();\nconsole.log(aiPkgs);\n\nconsole.log(\"\\n=== 2. claude-agent-sdk package.json ===\");\nconst rawPkg = await sandbox.sh`cat ${AGENT_DIR}/node_modules/@anthropic-ai/claude-agent-sdk/package.json`.text();\nconst pkgData = JSON.parse(rawPkg);\nconsole.log(\" name:\", pkgData.name);\nconsole.log(\" type:\", pkgData.type);\nconsole.log(\" main:\", pkgData.main);\nconsole.log(\" exports:\", JSON.stringify(pkgData.exports)?.substring(0, 500));\nconsole.log(\" bin:\", JSON.stringify(pkgData.bin)?.substring(0, 200));\n\nconsole.log(\"\\n=== 3. Package files ===\");\nconst pkgFiles = await sandbox.sh`ls ${AGENT_DIR}/node_modules/@anthropic-ai/claude-agent-sdk/`.text();\nconsole.log(pkgFiles);\n\n// Test the import\nconsole.log(\"\\n=== 4. Test ESM import ===\");\nawait sandbox.fs.writeTextFile(`${AGENT_DIR}/test-import.mjs`,\n 'try {\\n const pkg = await import(\"@anthropic-ai/claude-agent-sdk\");\\n console.log(\"keys:\", Object.keys(pkg).join(\", \"));\\n console.log(\"query:\", typeof pkg.query);\\n} catch(e) {\\n console.log(\"error:\", e.message);\\n console.log(\"stack:\", e.stack?.substring(0, 800));\\n}\\n'\n);\nawait sandbox.fs.writeTextFile(\"/tmp/test-import.sh\",\n `cd ${AGENT_DIR} && node test-import.mjs > /tmp/import.log 2>&1; echo \"EXIT=\\$?\" >> /tmp/import.log`\n);\nawait sandbox.sh`bash /tmp/test-import.sh`;\nconsole.log(await sandbox.fs.readTextFile(\"/tmp/import.log\"));\n\n// Run the full agent-loop\nconsole.log(\"\\n=== 5. Run agent-loop ===\");\nawait sandbox.sh`mkdir -p ${USER_HOME}/.mail/inbox ${USER_HOME}/.mail/outbox ${USER_HOME}/.agent-results ${USER_HOME}/memories`;\nawait sandbox.fs.writeTextFile(`${USER_HOME}/CLAUDE.md`, \"You are a test agent. Say hello and list what tools you have.\");\nawait sandbox.fs.writeTextFile(`${USER_HOME}/TODO.md`, \"\");\n\nconst taskId = Date.now();\nawait sandbox.fs.writeTextFile(\n `${USER_HOME}/.mail/inbox/task-${taskId}.md`,\n \"# Test\\nSay hello briefly.\\n\\n## Instructions\\nWrite response to: `/home/app/.mail/outbox/response-\" + taskId + \".md`\\n\",\n);\n\nawait sandbox.fs.writeTextFile(\"/tmp/run-agent.sh\",\n `cd ${USER_HOME} && node ${AGENT_DIR}/agent-loop.mjs > /tmp/agent.log 2>&1; echo \"EXIT=\\$?\" >> /tmp/agent.log`\n);\n\nconsole.log(\" Running... (may take 30s-2min)\");\nawait sandbox.sh`bash /tmp/run-agent.sh`;\nconst agentLog = await sandbox.fs.readTextFile(\"/tmp/agent.log\");\nconsole.log(agentLog);\n\n// Check results\nconsole.log(\"\\n=== 6. Post state ===\");\nconsole.log(\"Heartbeat:\", await sandbox.sh`cat ${USER_HOME}/.agent-heartbeat 2>/dev/null || echo \"(none)\"`.text());\nconsole.log(\"Outbox:\", await sandbox.sh`ls ${USER_HOME}/.mail/outbox/ 2>/dev/null || echo \"(empty)\"`.text());\nconsole.log(\"Results:\", await sandbox.sh`ls ${USER_HOME}/.agent-results/ 2>/dev/null || echo \"(empty)\"`.text());\n\n// Read response if it exists\ntry {\n const response = await sandbox.fs.readTextFile(`${USER_HOME}/.mail/outbox/response-${taskId}.md`);\n console.log(\"\\n📝 Response:\\n\" + response);\n} catch {\n console.log(\"\\n(No response file)\");\n}\n\n// Read cycle result\ntry {\n const latestResult = await sandbox.sh`ls -t ${USER_HOME}/.agent-results/ | head -1`.text();\n if (latestResult.trim()) {\n const result = await sandbox.fs.readTextFile(`${USER_HOME}/.agent-results/${latestResult.trim()}`);\n console.log(\"\\n📊 Cycle result:\\n\" + result);\n }\n} catch {\n console.log(\"(No cycle result)\");\n}\n\nconsole.log(\"\\n✅ Done\");\n",[],[201,337],"5e834680-18bf-11f1-867c-42dde27851f2","build-snapshot.ts","5e83473e-18bf-11f1-867c-42dde27851f2",["D",1772734562850],"/**\n * Admin: Build Toolchain Snapshot\n *\n * Sets up a sandbox with the agent-loop and verifies all tools.\n *\n * Base image already provides: git, ssh, curl, node, npm, npx, deno, python3, sudo\n * We just need to: install a few extras, set up agent-loop, configure ssh/git.\n *\n * Key facts about the Deno Sandbox base image:\n * - OS: Debian 13 (trixie), x86_64\n * - User: app (home: /home/app)\n * - sudo works for apt-get\n * - Pre-installed: git, ssh, curl, node, npm, npx, deno, python3\n */\n\nimport { Sandbox } from \"jsr:@deno/sandbox\";\nimport { AGENT_LOOP_SOURCE, AGENT_LOOP_PACKAGE_JSON } from \"../lib/sandbox-agent-loop.ts\";\n\nconst AGENT_DIR = \"/home/app/agent\";\n\nexport async function buildToolchainSnapshot() {\n console.log(\"🔧 Building toolchain snapshot...\");\n\n console.log(\"📦 Creating sandbox...\");\n await using sandbox = await Sandbox.create();\n\n // 1. Install a few extra system packages\n console.log(\"\\n📦 Installing extra packages...\");\n await sandbox.sh`sudo apt-get update -y`;\n await sandbox.sh`sudo apt-get install -y jq tree wget unzip`;\n console.log(\" ✅ Extra packages installed\");\n\n // 2. Verify pre-installed tools\n console.log(\"\\n🔍 Verifying tools:\");\n const checks = [\n { name: \"git\", cmd: \"git --version\" },\n { name: \"node\", cmd: \"node --version\" },\n { name: \"npm\", cmd: \"npm --version\" },\n { name: \"deno\", cmd: \"deno --version\" },\n { name: \"python3\", cmd: \"python3 --version\" },\n { name: \"ssh\", cmd: \"ssh -V\" },\n { name: \"curl\", cmd: \"curl --version\" },\n { name: \"jq\", cmd: \"jq --version\" },\n { name: \"tree\", cmd: \"tree --version\" },\n ];\n for (const { name, cmd } of checks) {\n try {\n const output = await sandbox.sh`bash -c ${cmd}`.text();\n console.log(` ✅ ${name}: ${output.trim().split(\"\\n\")[0]}`);\n } catch {\n console.log(` ❌ ${name}: not available`);\n }\n }\n\n // 3. Set up agent-loop in user's home (no sudo needed)\n console.log(`\\n📦 Setting up agent-loop in ${AGENT_DIR}...`);\n await sandbox.sh`mkdir -p ${AGENT_DIR}`;\n await sandbox.fs.writeTextFile(`${AGENT_DIR}/agent-loop.mjs`, AGENT_LOOP_SOURCE);\n await sandbox.fs.writeTextFile(`${AGENT_DIR}/package.json`, AGENT_LOOP_PACKAGE_JSON);\n\n console.log(\" Installing npm dependencies...\");\n const installOutput = await sandbox.sh`cd ${AGENT_DIR} && npm install 2>&1`.text();\n // Show last few lines of install output\n const lines = installOutput.trim().split(\"\\n\");\n for (const line of lines.slice(-5)) {\n console.log(` ${line}`);\n }\n console.log(\" ✅ npm install complete\");\n\n // 4. Set up SSH config\n console.log(\"\\n🔧 Setting up SSH config...\");\n await sandbox.sh`mkdir -p /home/app/.ssh`;\n await sandbox.sh`chmod 700 /home/app/.ssh`;\n await sandbox.fs.writeTextFile(\n \"/home/app/.ssh/config\",\n \"Host github.com\\n StrictHostKeyChecking accept-new\\n IdentityFile ~/.ssh/id_ed25519\\n\",\n );\n await sandbox.sh`chmod 600 /home/app/.ssh/config`;\n console.log(\" ✅ SSH config written\");\n\n // 5. Set up git config\n console.log(\"🔧 Setting up git config...\");\n await sandbox.fs.writeTextFile(\n \"/home/app/.gitconfig\",\n \"[init]\\n defaultBranch = main\\n[safe]\\n directory = *\\n\",\n );\n console.log(\" ✅ Git config written\");\n\n // 6. Final verification\n console.log(\"\\n🧪 Final verification:\");\n\n const agentFiles = await sandbox.sh`ls -la ${AGENT_DIR}/`.text();\n console.log(agentFiles);\n\n const nodeModules = await sandbox.sh`bash -c \"ls ${AGENT_DIR}/node_modules/ 2>/dev/null | head -20\"`.text();\n console.log(`node_modules: ${nodeModules}`);\n\n // Quick test: can node parse the agent-loop?\n try {\n const parseCheck = await sandbox.sh`node --check ${AGENT_DIR}/agent-loop.mjs 2>&1 || echo \"parse failed\"`.text();\n console.log(` ✅ agent-loop.mjs syntax check: ${parseCheck.trim() || \"OK\"}`);\n } catch (e) {\n console.log(` ⚠️ agent-loop.mjs parse check: ${e}`);\n }\n\n // Show home directory structure\n const homeTree = await sandbox.sh`tree -L 2 /home/app/ -a`.text();\n console.log(`\\n📂 Home directory:\\n${homeTree}`);\n\n console.log(`\\n✅ Toolchain setup complete!`);\n console.log(`\\nKey info for orchestrator:`);\n console.log(` User: app`);\n console.log(` Home: /home/app`);\n console.log(` Agent dir: ${AGENT_DIR}`);\n console.log(` Agent loop: ${AGENT_DIR}/agent-loop.mjs`);\n}\n\n// Run when Val Town executes this script\nawait buildToolchainSnapshot();\n",[],[201,344],["D",1772734562850],[216,217],[201],["D",1772647273772],["D",1789516910456],{"_98":97,"_106":102},{"_98":97,"_99":100,"_101":102,"_18":55,"_103":104,"_105":25},{"_13":87,"_32":88,"_44":43,"_89":45,"_90":-5,"_91":-5,"_92":-5,"_93":-5,"_94":-5,"_95":46,"_96":97},"ctx",{"_80":-5,"_127":391,"_392":393,"_394":395,"_396":25,"_397":25,"_398":25,"_399":125,"_400":-5,"_401":-7,"_402":-7,"_403":146,"_404":25,"_405":25,"_406":-5,"_407":25,"_408":25,"_409":25,"_410":-5},"ENV",{"_362":363,"_364":365,"_366":88,"_367":368,"_369":370,"_371":372,"_373":374,"_375":376,"_377":378,"_379":380,"_381":382,"_383":384,"_385":386,"_387":388,"_389":390},"REACT_APP_SENTRY_DSN","https://586020d86f05489db63a554690acf1e9@o1398359.ingest.sentry.io/6724499","RENDER_GIT_COMMIT","5015b8df46402d8bba4f485ecc37aec202b0e06b","RENDER_GIT_BRANCH","POSTHOG_API_TOKEN","phc_nmoyuAkStEZuUp69rYlg1DZgrIt19Gx0Y4Hwf8tGgJP","POSTHOG_API_HOST","https://wart.val.town","WEB_API_TEMPLATE","https://{val}.web.val.run","APP_URL","https://www.val.town","MODULE_URL","https://esm.town","VANITY_WEB_API_TEMPLATE","https://{label}.val.run/","DENOLS_URL","https://lsp.val.town","NODE_ENV","production","CLOUDFLARE_TURNSTILE_SITE_KEY","0x4AAAAAACtaIZJH7s9rBMZ6","GOOGLE_CLIENT_ID","875989696899-jtsqkhlbdv5jrlsk8631p7ipjg3c6o62.apps.googleusercontent.com","STRIPE_PUBLISHABLE_KEY","pk_live_51N2zvWEpZTTLipIkVEwXrUG9AT0yeOOEYxszarZT1jpsZV58Ly6wlg3SltZpHN26CUalOBmUSY7adQRQiSkCZz2500qSXNkf8Y","API_URL","https://api.val.town",[],"preferencesKeybindings","default","preferencesTheme","light","preferencesFixers","preferencesCopilot","preferencesSelectionAi","preferencesCloseBrackets","preferencesChatPanelState","preferencesDprint","preferencesDenofmt","notificationCount","flagDenols","isBusinessOrgMember","businessOrgHandle","isBusinessOrgAdmin","isHigherTownieTier","navCollapsed","currentOrgId"]

Diff · v60