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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","deleted","label",[178,159],"lib","permalink","https://www.val.town/x/paulkinlan/email-agent/code/lib/agent.ts","extension","actionData","errors"]