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This SDK allows you to create chat completions, handle file uploads, and manage various resources with ease.","maintainers":[{"name":"prxg22","email":"prxg22@gmail.com"},{"name":"0xthierry","email":"thierrysantoos123+npm@gmail.com"},{"name":"lucassiqueira08","email":"lucassiqueira.dev@gmail.com"},{"name":"rjmunhoz","email":"roz@rjmunhoz.me"},{"name":"amorim33","email":"aluamorim@hotmail.com"},{"name":"henrykunh","email":"henrycunh@gmail.com"},{"name":"rodrigo_lg","email":"rodrigolegod@gmail.com"},{"name":"czaghi","email":"czoliv@gmail.com"},{"name":"ianfireman","email":"imlbfireman@gmail.com"},{"name":"matheusvellone","email":"matheusvellone@gmail.com"},{"name":"thullyocunha","email":"thullyo.cunha@meistrari.com"},{"name":"renanllm","email":"renanllm01@gmail.com"}],"readme":"# Tela SDK for JavaScript\n\nThe Tela SDK for JavaScript provides a simple and powerful way to interact with the Tela API. This SDK allows you to create chat completions, handle file uploads, and manage various resources with ease.\n\n## Table of Contents\n\n- [Installation](#installation)\n- [Usage](#usage)\n- [Examples](#examples)\n  - [Canvas API](#canvas-api)\n    - [Synchronous Execution](#synchronous-execution)\n    - [Asynchronous Execution with Polling](#asynchronous-execution-with-polling)\n    - [Streaming Execution](#streaming-execution)\n    - [Event-Driven Execution](#event-driven-execution)\n    - [Schema Validation](#schema-validation)\n    - [Multiple Files](#multiple-files)\n    - [Webhook Notifications](#webhook-notifications)\n    - [Execution Tags](#execution-tags)\n  - [File Handling](#file-handling)\n  - [Tasks API](#tasks-api)\n  - [Vault API](#vault-api)\n  - [Agents API](#agents-api)\n- [Migration Guide from v1.x to v2](#migration-guide-from-v1x-to-v2)\n  - [Breaking Changes](#breaking-changes)\n  - [Migration Checklist](#migration-checklist)\n  - [New Features in v2](#new-features-in-v2)\n\n## Installation\n\nYou can install the Tela SDK via npm, yarn, or pnpm:\n\n```bash\nnpm install @meistrari/tela-sdk-js\nyarn add @meistrari/tela-sdk-js\npnpm add @meistrari/tela-sdk-js\n```\n\n## Usage\n\nFirst, you need to import the SDK and initialize it with your API key:\n\n```typescript\nimport { TelaSDK } from '@meistrari/tela-sdk-js'\n\nconst tela = new TelaSDK({ apiKey: 'your-api-key' })\n```\n\n## Examples\n\n### Canvas API\n\nThe Canvas API provides a type-safe way to execute prompts with schema validation and multiple execution modes.\n\n#### Synchronous Execution\n\nExecute a canvas and wait for the complete result:\n\n```typescript\nimport { TelaSDK } from '@meistrari/tela-sdk-js'\n\nconst tela = new TelaSDK({\n    apiKey: 'your-api-key'\n})\n\n// Get canvas with input/output schemas\nconst canvas = await tela.canvas.get({\n    id: 'canvas-id',\n    input: schema => schema.object({\n        query: schema.string()\n    }),\n    output: schema => schema.object({\n        result: schema.string()\n    }),\n})\n\n// Execute synchronously - direct result access\nconst result = await canvas.execute({ query: 'What is the capital of France?' }).result\n\nconsole.log(result) // { result: \"Paris\" }\n\n// Alternative: get execution object for more control\nconst execution = await canvas.execute({ query: 'What is the capital of France?' })\nconst result2 = await execution.result\n```\n\n#### Asynchronous Execution with Polling\n\nFor long-running operations, use async mode with automatic polling:\n\n```typescript\nconst canvas = await tela.canvas.get({\n    id: 'canvas-id',\n    input: schema => schema.object({\n        document: schema.file(),\n        query: schema.string()\n    }),\n    output: schema => schema.object({\n        summary: schema.string()\n    }),\n})\n\n// Execute asynchronously with polling - direct result access\nconst result = await canvas.execute(\n    {\n        document: TelaFile.create(documentBlob, { name: 'report.pdf' }),\n        query: 'Summarize this document'\n    },\n    {\n        async: true,\n        pollingInterval: 1000, // Poll every 1 second\n        pollingTimeout: 60000, // Timeout after 60 seconds\n    }\n).result\n\nconsole.log(result.summary)\n```\n\n#### Streaming Execution\n\nStream results as they're generated:\n\n```typescript\nconst canvas = await tela.canvas.get({\n    id: 'canvas-id',\n    input: schema => schema.object({\n        prompt: schema.string()\n    }),\n    output: schema => schema.object({\n        response: schema.string()\n    }),\n})\n\n// Execute with streaming - direct iteration\nfor await (const chunk of canvas.execute(\n    { prompt: 'Write a story about a robot' },\n    { stream: true }\n).result) {\n    process.stdout.write(chunk.response || '')\n}\n```\n\n#### Event-Driven Execution\n\nMonitor execution lifecycle with events. **Events are only useful with async executions** - synchronous and streaming executions complete too quickly for event-driven monitoring to be practical.\n\n```typescript\nconst canvas = await tela.canvas.get({\n    id: 'canvas-id',\n    input: schema => schema.object({\n        query: schema.string()\n    }),\n    output: schema => schema.object({\n        result: schema.string()\n    }),\n})\n\n// Start async execution to attach event listeners\nconst execution = await canvas.execute(\n    { query: 'What is the capital of France?' },\n    {\n        async: true,\n        pollingInterval: 1000,\n        pollingTimeout: 60000,\n    }\n)\n\n// Access current status at any time\nconsole.log(execution.status) // 'created'\n\n// Listen for status changes\nexecution.on('statusChange', (status) => {\n    console.log(`Status: ${status}`) // created → running → succeeded\n})\n\n// Listen for polling updates\nexecution.on('poll', (pollResult) => {\n    console.log(`Poll - Status: ${pollResult.status}, Request ID: ${pollResult.requestId}`)\n})\n\n// Listen for successful completion\nexecution.on('success', (result) => {\n    console.log('Success!', result)\n    console.log('Request ID:', result.requestId)\n})\n\n// Listen for errors (prevents throwing)\nexecution.on('error', (error) => {\n    console.error('Failed:', error.message)\n    // With error listener: error event is emitted, promise resolves to undefined\n})\n\n// Start event-driven polling (non-blocking)\nexecution.poll()\n\n// Events will fire as polling progresses\n// The result will be available in the success event callback\n// Continue with other work while polling happens in the background...\n```\n\n**Why Async Only?**\n\nEvents are designed for tracking progress over time. Synchronous executions complete immediately (blocking until done), so by the time you can access the execution object, all events have already fired. Streaming executions provide their own iteration mechanism via async generators, making events redundant.\n\n**Status Property:**\n\nThe `status` property provides the current execution state:\n- **Async**: `created` → `running` → `succeeded` or `failed`\n\nStatus is set to `succeeded` only after successful validation. If validation fails, status will be `failed` even if the API request succeeded.\n\n**Available Events (Async Executions Only):**\n- `success` - Emitted when execution completes successfully (after validation)\n- `error` - Emitted on failure (if listeners exist, errors won't throw)\n- `statusChange` - Emitted on status transitions\n- `poll` - Emitted during polling (initial response + each poll)\n\n**Error Handling:**\n- **With error listener**: Errors emitted as events, promise resolves to `undefined`\n- **Without error listener**: Errors throw and reject the promise\n\n**Important:** You must call `execution.poll()` or `await execution.result` to start polling - events won't fire until polling begins.\n\nSee [Event Examples](./examples/events/) for detailed usage patterns.\n\n#### Accessing Request IDs\n\nEvery API request to Tela returns a unique `requestId` (from the `x-request-id` header) that can be used for debugging, tracking, and correlating with server logs. The SDK exposes request IDs in three ways:\n\n```typescript\nconst canvas = await tela.canvas.get({ id: 'canvas-id' })\n\n// Start an async execution\nconst execution = await canvas.execute(\n    { query: 'test' },\n    { async: true }\n)\n\n// Method 1: Access directly from execution object\n// This is the request ID from the initial request that created the execution\nconst requestId = await execution.requestId\nconsole.log('Request ID:', requestId)\n\n// Method 2: Access from poll events\n// Each poll event includes the request ID from that specific polling request\nexecution.on('poll', (pollResult) => {\n    console.log('Request ID from this poll:', pollResult.requestId)\n})\n\n// Method 3: Access from success event\n// The success event includes the request ID from the final successful polling request\nexecution.on('success', (result) => {\n    console.log('Request ID from final request:', result.requestId)\n})\n\n// Start polling\nexecution.poll()\n```\n\n**Important Notes:**\n- `execution.requestId` - Request ID from the execution creation request (POST /v2/chat/completions)\n- `pollResult.requestId` - Request ID from each individual polling request (GET /v2/chat/completions/:id)\n- `result.requestId` - Request ID from the final successful polling request\n\n**Use Cases:**\n- **Debugging**: Identify specific API requests when troubleshooting issues\n- **Logging**: Track individual requests across distributed systems\n- **Support**: Reference specific requests in support tickets\n- **Monitoring**: Correlate client requests with server logs and traces\n\nSee [Request ID Example](./examples/events/request-id.ts) for a complete demonstration.\n\n#### Schema Validation\n\nThe Canvas API validates your inputs and outputs against the server schema:\n\n```typescript\nconst canvas = await tela.canvas.get({\n    id: 'canvas-id',\n    input: schema => schema.object({\n        text: schema.string(),\n        file: schema.file(), // TelaFile validator\n        settings: schema.object({\n            temperature: schema.number(),\n            maxTokens: schema.number(),\n        }),\n    }),\n    output: schema => schema.object({\n        analysis: schema.string(),\n        confidence: schema.number(),\n    }),\n    skipSchemaValidation: false, // Enable schema validation warnings during canvas retrieval (default)\n})\n\n// TypeScript will ensure you provide the correct types\nconst result = await canvas.execute({\n    text: 'Analyze this document',\n    file: TelaFile.create(blob, { name: 'data.pdf' }),\n    settings: {\n        temperature: 0.7,\n        maxTokens: 1000,\n    }\n}).result\n\n// Skip result validation to trust API response without Zod validation\nconst resultWithoutValidation = await canvas.execute(\n    {\n        text: 'Analyze this document',\n        file: TelaFile.create(blob, { name: 'data.pdf' }),\n        settings: {\n            temperature: 0.7,\n            maxTokens: 1000,\n        }\n    },\n    {\n        skipResultValidation: true // Skip Zod validation on results, just typecast\n    }\n).result\n```\n\n#### Multiple Files\n\nProcess multiple files in a single canvas execution:\n\n```typescript\nconst canvas = await tela.canvas.get({\n    id: 'canvas-id',\n    input: schema => schema.object({\n        documents: schema.array(schema.file()),\n        instructions: schema.string()\n    }),\n    output: schema => schema.object({\n        comparison: schema.string()\n    }),\n})\n\nconst result = await canvas.execute({\n    documents: [\n        TelaFile.create('https://example.com/doc1.pdf', { range: [0, 5] }),\n        TelaFile.create('https://example.com/doc2.pdf', { range: [0, 5] }),\n        TelaFile.create(localFileBlob, { name: 'doc3.pdf' })\n    ],\n    instructions: 'Compare these documents and identify key differences'\n}).result\n\nconsole.log(result.comparison)\n```\n\n#### Webhook Notifications\n\nReceive completion notifications via webhook for async executions:\n\n```typescript\nconst canvas = await tela.canvas.get({\n    id: 'canvas-id',\n    input: schema => schema.object({\n        document: schema.file(),\n        query: schema.string()\n    }),\n    output: schema => schema.object({\n        analysis: schema.string()\n    }),\n})\n\n// Execute with webhook notification - you can still poll if needed\nconst result = await canvas.execute(\n    {\n        document: TelaFile.create('https://example.com/large-report.pdf'),\n        query: 'Analyze this report'\n    },\n    {\n        async: true,\n        webhookUrl: 'https://your-server.com/webhook',\n        pollingTimeout: 300000, // 5 minutes\n    }\n).result\n// OR wait for the webhook notification on your server instead of polling\n```\n\n#### Execution Tags\n\nAdd tags to your canvas executions for filtering, categorization, and analytics:\n\n```typescript\nconst canvas = await tela.canvas.get({\n    id: 'canvas-id',\n    input: schema => schema.object({\n        query: schema.string()\n    }),\n    output: schema => schema.object({\n        result: schema.string()\n    }),\n})\n\n// Execute with tags (works with all execution modes)\nconst result = await canvas.execute(\n    { query: 'Analyze customer feedback' },\n    {\n        tags: ['production', 'analytics', 'customer-feedback']\n    }\n).result\n\n// Tags work with async executions\nconst asyncResult = await canvas.execute(\n    { query: 'Process large dataset' },\n    {\n        async: true,\n        tags: ['batch-processing', 'analytics']\n    }\n).result\n\n// Tags work with streaming executions\nfor await (const chunk of canvas.execute(\n    { query: 'Generate report' },\n    {\n        stream: true,\n        tags: ['streaming', 'reports']\n    }\n).result) {\n    process.stdout.write(chunk.result || '')\n}\n```\n\n**Use Cases:**\n- **Environment tracking**: Tag executions by environment (`production`, `staging`, `development`)\n- **Feature categorization**: Organize executions by feature (`analytics`, `reporting`, `summarization`)\n- **User segmentation**: Track executions by user tier or department\n- **Cost allocation**: Attribute API usage to specific projects or teams\n- **Performance monitoring**: Filter and analyze execution metrics by tag\n\nTags are sent to the Tela API and can be used for filtering and analytics in your Tela dashboard.\n\n#### Fetching Existing Executions\n\nRetrieve and monitor async executions by their ID, useful for job queues, resuming workflows, or multi-user dashboards:\n\n```typescript\n// Start an async execution\nconst execution = await canvas.execute(\n    { query: 'Process this data' },\n    { async: true }\n)\n\n// Store the execution ID (e.g., in a database)\nconst executionId = execution.id\n\n// Later, fetch the execution by ID\nconst fetchedExecution = await canvas.getExecution(executionId, {\n    pollingInterval: 2000, // Optional: custom polling interval\n    pollingTimeout: 120000 // Optional: custom timeout\n})\n\nconsole.log(fetchedExecution.status) // 'running', 'succeeded', or 'failed'\n\n// Use event-driven polling for non-blocking progress tracking\n// Note: poll() works for ANY async execution, not just fetched ones!\nfetchedExecution.on('statusChange', (status) => {\n    console.log(`Status: ${status}`)\n})\n\nfetchedExecution.on('success', (result) => {\n    console.log('Completed!', result)\n})\n\nfetchedExecution.on('error', (error) => {\n    console.error('Failed:', error)\n})\n\n// Start polling without blocking (returns immediately)\nfetchedExecution.poll()\n\n// Continue with other work while polling runs in background...\nconsole.log('Polling in background, doing other work...')\n\n// Later, you can still await the result if needed\nconst result = await fetchedExecution.result\n```\n\n**Alternative: Use `poll()` on newly started executions**\n\nYou can also use event-driven polling immediately when starting an execution:\n\n```typescript\nconst execution = await canvas.execute(\n    { query: 'Process this data' },\n    { async: true }\n)\n\n// Set up event listeners\nexecution.on('statusChange', status => console.log('Status:', status))\nexecution.on('success', result => console.log('Done!', result))\nexecution.on('error', error => console.error('Failed:', error))\n\n// Start non-blocking polling\nexecution.poll()\n\n// Do other work...\nconsole.log('Execution polling in background')\n```\n\n**Key Features:**\n\n- **`canvas.getExecution(id, options?)`** - Fetch execution by UUID\n  - Only async executions have UUIDs and can be fetched\n  - Returns execution in current state (running, succeeded, or failed)\n  - Supports custom polling options\n\n- **`execution.poll()`** - Start event-driven polling\n  - Returns `void` (non-blocking)\n  - Emits events: `statusChange`, `poll`, `success`, `error`\n  - Perfect for UI updates, dashboards, and concurrent monitoring\n\n**Important Notes:**\n\n⚠️ **Events require polling or awaiting `.result`**: Events are only emitted when polling is active. You must either:\n- Call `execution.poll()` to start event-driven polling, OR\n- Await `execution.result` which starts polling automatically\n\n```typescript\n// ❌ Events won't fire - no polling started\nconst execution = await canvas.execute({ query: 'test' }, { async: true })\nexecution.on('success', result => console.log('Done')) // This will never fire!\n\n// ✅ Correct - poll() starts event-driven polling\nconst execution = await canvas.execute({ query: 'test' }, { async: true })\nexecution.on('success', result => console.log('Done'))\nexecution.poll() // Now events will fire\n\n// ✅ Also correct - awaiting result starts polling\nconst execution = await canvas.execute({ query: 'test' }, { async: true })\nexecution.on('success', result => console.log('Done'))\nawait execution.result // Polling starts, events fire\n```\n\n⚠️ **Already completed executions don't emit events**: If you fetch an execution that has already finished (succeeded or failed), the `success` and `error` events won't fire because there's no polling needed. Instead, check the status and access the result directly:\n\n```typescript\nconst execution = await canvas.getExecution(executionId)\n\nif (execution.status === 'succeeded') {\n    // Access result directly - success event won't fire\n    const result = await execution.result\n    console.log('Already completed:', result)\n}\nelse if (execution.status === 'failed') {\n    // Handle failure - error event won't fire\n    try {\n        await execution.result\n    }\n    catch (error) {\n        console.error('Already failed:', error)\n    }\n}\nelse {\n    // Still running - events will fire when you start polling\n    execution.on('success', result => console.log('Completed!', result))\n    execution.on('error', error => console.error('Failed:', error))\n    execution.poll()\n}\n```\n\n**Use Cases:**\n- Job queue management systems\n- Resuming monitoring after page refresh or server restart\n- Multi-user dashboards showing execution status\n- Background processing with real-time updates\n- Retry mechanisms with persistent execution IDs\n\nSee [Fetch and Poll Examples](./examples/events/) for detailed usage patterns.\n\n### File Handling\n\nThe `TelaFile` API provides flexible file handling with support for various input types:\n\n```typescript\nimport { TelaFile } from '@meistrari/tela-sdk-js'\n\n// From a Blob or File\nconst fileFromBlob = TelaFile.create(blob, { name: 'custom.pdf' })\n\n// From a URL\nconst fileFromUrl = TelaFile.create('https://example.com/document.pdf')\n\n// From bytes with explicit MIME type\nconst fileFromBytes = TelaFile.create(\n    new Uint8Array(['...']),\n    {\n        mimeType: 'application/pdf',\n        name: 'document.pdf'\n    }\n)\n\n// From a ReadableStream with explicit MIME type\nconst fileFromStream = TelaFile.create(\n    readableStream,\n    {\n        mimeType: 'image/jpeg',\n        name: 'photo.jpg'\n    }\n)\n\n// Vault reference (for files already uploaded)\nconst vaultFile = TelaFile.create('vault://file-id')\n\n// With page range for PDFs\nconst fileWithRange = TelaFile.create(\n    'https://example.com/document.pdf',\n    {\n        range: [0, 5], // Pages 0-5\n        parserType: 'pdf'\n    }\n)\n```\n\n### Tasks API\n\nThe Tasks API exposes the persistent task records that workstation (application) canvases create. Use it to fetch, list, update, approve, edit, re-run, or delete tasks without dropping down to raw HTTP.\n\n```typescript\nimport { TelaSDK, extractTaskOutput, normalizeTaskStatus } from '@meistrari/tela-sdk-js'\n\nconst tela = new TelaSDK({ apiKey: 'your-api-key' })\n\n// Fetch a task by id\nconst task = await tela.tasks.get('task-id')\n\n// Read the parsed output (handles canvas vs workflow shape automatically)\nconst output = await tela.tasks.getOutput<MyOutput>('task-id')\n// or, if you already have the task in hand:\nconst sameOutput = extractTaskOutput<MyOutput>(task)\n\n// List input files attached to the task (e.g. uploaded PDFs)\nconst files = await tela.tasks.getInputFiles('task-id')\n\n// List tasks with filters\nconst { data, meta } = await tela.tasks.list({\n    promptApplicationId: 'app-id',\n    status: ['validating', 'completed'],\n    tags: 'product:my-app',\n    createdAtSince: new Date('2024-01-01'),\n    excludeInputOutputColumns: true, // omit raw input/input content/original output\n    orderBy: ['createdAt'],\n    order: 'desc',\n    limit: 50,\n})\n\n// Lean list rows still include outputContent and flattened inputFiles.\n// Fetch a single task when you need raw input or original output payloads.\nconst fullTask = await tela.tasks.get(data[0].id)\n\n// Mutations\nawait tela.tasks.rename('task-id', 'New name')\nawait tela.tasks.approve('task-id') // sets status: 'completed'\nawait tela.tasks.setTags('task-id', ['reviewed', 'q2'])\n\n// ─── Updating output content ────────────────────────────────────\n// The server uses *deep-merge* semantics on outputContent.content.\n// Keys you do not provide are left untouched. There is no way to\n// delete a field through this API. Three methods cover different\n// use-cases:\n\n// 1. Top-level partial patch (single PATCH, no fetch)\nawait tela.tasks.patchOutputContent('task-id', { reviewedBy: 'me' })\n\n// 2. Single-field shortcut for nested *object* paths\n//    Do NOT use this for array indices — use updateOutputContent below.\nawait tela.tasks.patchOutputField('task-id', 'profile.email', 'b@x.com')\n\n// 3. Read-modify-write — safe for arrays, multiple edits, and any\n//    change that needs the previous value. SDK fetches current,\n//    runs your updater, then sends the full content via patchOutputContent.\nawait tela.tasks.updateOutputContent<MenuOutput>('task-id', (current) => {\n    current.menu.items[2].price = 9.99 // mutate in place\n    current.menu.items.push({ name: 'New', price: 5 }) // grow arrays\n})\n// Updaters can also return a new value:\nawait tela.tasks.updateOutputContent('task-id', current => ({ ...current, reviewed: true }))\n\n// Re-run a task (only allowed when status === 'failed')\nconst rerun = await tela.tasks.rerun('task-id')\nconsole.log(rerun.executionRunId)\n\n// Delete\nawait tela.tasks.delete('task-id') // → { success: true }\nawait tela.tasks.deleteBulk(['id1', 'id2']) // → { deleted: 2, deletedIds: [...] }\nawait tela.tasks.undoApprovalBulk(['id1', 'id2']) // → { undoApproval: 2, undoApprovalIds: [...] }\n\n// Map server statuses to coarse client-friendly buckets\nconst uiStatus = normalizeTaskStatus(task.status)\n// 'created' | 'running'   → 'running'\n// 'validating'            → 'pending'\n// 'completed'             → 'succeeded'\n// 'failed' | 'cancelled'  → 'failed'\n```\n\nThe full `Task`, `TaskListItem`, `TaskInputFile`, `TaskListQuery`, `TaskUpdatePayload`, and `TaskStatus` types are exported from the package root, so you don't need to type any task-related responses by hand.\n\n### Vault API\n\nThe Vault API resolves and downloads files stored in the Tela vault. It accepts both `vault://<id>` references and bare ids, and passes through plain `https://` URLs unchanged.\n\n```typescript\nconst tela = new TelaSDK({ apiKey: 'your-api-key' })\n\n// Get a temporary signed URL\nconst signedUrl = await tela.vault.getSignedUrl('vault://abc-123')\n\n// Resolve any reference (vault:// or https://) to a downloadable URL\nconst downloadUrl = await tela.vault.resolve(file.url)\n\n// Download as a Blob\nconst blob = await tela.vault.download('vault://abc-123')\n\n// Stream the file\nconst stream = await tela.vault.stream('vault://abc-123')\n```\n\nThis is particularly useful when reading task input files: `tela.tasks.getInputFiles(id)` returns the file references, and `tela.vault.download(file.url)` downloads them in one call.\n\n### Agents API\n\n`tela.agents` runs agents through the Tela gateway (`run` → `POST /agent/:id/run`, `runDirectStream` → `POST /agent/:id/run/direct-stream`). Wake, streaming, timeline, cancellation, and model updates are delegated to [`@meistrari/agent-sdk`](https://www.npmjs.com/package/@meistrari/agent-sdk). The SDK adds the Tela-specific bits: addressing agents by **`agentId`**, flattening a per-variable `inputs` map into the wire `inputSchema` array, uploading `TelaFile` inputs to the vault, and mapping your Tela credentials onto the agent-sdk auth strategy.\n\n> **Transport:** `run` posts to the Tela gateway and is authenticated by your SDK credentials. The delegated wake and session calls use an agent-sdk client against `/v4/agents` and `/v4/sessions`; it defaults to your `baseURL`, set `agentBaseURL` when the v4 agent service lives at a different host:\n>\n> ```typescript\n> const tela = new TelaSDK({ apiKey: 'your-api-key', agentBaseURL: 'https://agents.example.com' })\n> ```\n\n```typescript\nconst tela = new TelaSDK({ apiKey: 'your-api-key' })\n\n// Discover agents (Tela gateway)\nconst agents = await tela.agents.list()\nconst agentId = agents[0].id\n\n// Warm a new session before the user sends their first message.\nconst warmed = await tela.agents.wake({ agentId })\n\n// Start a session by agentId.\nconst started = await tela.agents.run({\n    agentId,\n    message: 'Summarize the latest tickets',\n})\nif (!started.success)\n    throw new Error(started.error)\n\nconst { sessionId } = started\n```\n\nUse `runDirectStream` to opt the execution into the Agent API's direct stream transport. It accepts the same parameters and returns the same response as `run`:\n\n```typescript\nconst started = await tela.agents.runDirectStream({\n    agentId,\n    message: 'Summarize the latest tickets',\n})\n```\n\nClaim a prepared wake by passing only the Tela `agentId` and returned `wakeSessionId`.\nTela SDK and Tela API encapsulate the organization/repository lookup:\n\n```typescript\nconst wake = await tela.agents.wake({ agentId })\nif (!wake.success)\n    throw new Error(wake.error)\n\nconst modelUpdateDeadline = Date.now() + 30_000\nlet configured = await tela.agents.updateSessionModel({\n    sessionId: wake.sessionId,\n    model: 'claude-opus-4-8',\n})\nwhile (configured.success && configured.status === 'waking' && Date.now() < modelUpdateDeadline) {\n    await new Promise(resolve => setTimeout(resolve, 250))\n    configured = await tela.agents.updateSessionModel({\n        sessionId: wake.sessionId,\n        model: 'claude-opus-4-8',\n    })\n}\nif (!configured.success)\n    throw new Error(configured.error)\nif (configured.status !== 'ready')\n    throw new Error('Timed out while preparing the wake session model')\n\nconst started = await tela.agents.runDirectStream({\n    agentId,\n    wakeSessionId: wake.sessionId,\n    message: 'Use the prepared session',\n})\n```\n\n`run` returns `{ success, sessionId }` — execution is async, so stream the session's events by id:\n\n```typescript\nfor await (const event of await tela.agents.streamSession(sessionId)) {\n    switch (event.kind) {\n        case 'status':\n            console.log('status:', event.status)\n            break\n        case 'steps':\n            console.log('steps:', event.steps)\n            break\n        case 'result':\n            console.log('result:', event.result)\n            break\n        case 'error':\n            throw new Error(event.error)\n    }\n}\n\n// Timeline metrics (duration, token/cost) and cancellation, by session id:\nconst timeline = await tela.agents.fetchTimeline(sessionId)\nawait tela.agents.cancelSession(sessionId)\n```\n\n**Inputs** — `inputs` is a map keyed by variable name. Each value is a single entry or an array of entries; an entry can be a `TelaFile` (uploaded to the vault automatically), a ready `vault://` reference, or a text entry — each optionally carrying `metadata`. The SDK flattens this into the wire `inputSchema` array, tagging every entry with its variable name.\n\n```typescript\nawait tela.agents.run({\n    agentId,\n    message: 'Review this contract against the references',\n    inputs: {\n        // a TelaFile — uploaded to the vault for you\n        contract: tela.createFile(buffer, { name: 'contract.pdf', mimeType: 'application/pdf' }),\n        // multiple entries for one variable; ready vault refs with metadata\n        references: [\n            { vaultRef: 'vault://...', filename: 'reference.txt', metadata: 'prior version' },\n        ],\n        // a text input\n        notes: { type: 'text', content: 'Focus on the indemnity clauses.' },\n    },\n    environmentVariables: { LOCALE: 'en-US' },\n})\n```\n\n**Multi-turn** — continue an existing session by passing its `sessionId` alongside the `agentId`:\n\n```typescript\nawait tela.agents.run({ agentId, sessionId, message: 'Now implement step 1' })\n```\n\nChange the model whenever the session is parked: after a warm session has been claimed but before its first message, or between later turns. For a paused session whose sandbox has expired, update the model first, re-wake that exact session, wait for `status: 'ready'` if necessary, and then continue it:\n\n```typescript\nawait tela.agents.updateSessionModel({ sessionId, model: 'claude-opus-4-8' })\nawait tela.agents.wake({\n    agentId,\n    sessionId,\n    environmentVariables: { API_KEY: refreshedCredential },\n})\nawait tela.agents.run({ agentId, sessionId, message: 'Continue with the new model' })\n```\n\n`updateSessionModel` returns `status: 'waking' | 'ready'`. Repeat the same update while it is `waking`; do not change the model during an actively processing turn, which the Agent API rejects with `409`.\n\n**Webhooks** — register session lifecycle callbacks by passing `webhooks` to `run`. The gateway forwards the webhook config to the agent execution service unchanged. Passing `webhooks` on a continuation replaces the session's registration; omitting it keeps the existing registration.\n\n```typescript\nawait tela.agents.run({\n    agentId,\n    message: 'Summarize the latest tickets',\n    webhooks: [{\n        url: 'https://example.com/hooks/agent-session',\n        events: ['session.completed', 'session.failed', 'session.cancelled'],\n        secret: 'whsec_a-long-random-string',\n    }],\n})\n```\n\nWebhook URLs must be HTTPS and publicly reachable. Up to five webhooks can be registered per session. Use `verifyWebhookSignature` to verify signed deliveries.\n\n**Model updates** — set an agent repository's default model by id:\n\n```typescript\nawait tela.agents.updateAgentModel({ agentId, model: 'claude-opus-4-8', commitMessage: 'bump model' })\n```\n\nThe contract types (`SessionStreamEvent`, `SessionTimelineResponse`, `AgentRunWebhooks`, `SessionWebhookConfig`, …), `verifyWebhookSignature`, and `parseSessionStream` are re-exported from `@meistrari/tela-sdk-js` — the agent-sdk is the single source of truth for the session/streaming/webhook contract.\n\n**Migration note** — replace `tela.agents.executeAgent(...)` with `tela.agents.run(...)`. `agentId` is now required, `inputs` is keyed by variable name instead of a flat array, and `webhooks` moves unchanged onto `run`. The Tela gateway run path does not support agent-sdk `recover` requests; start or continue a session with `message` instead.\n\n```typescript\nawait tela.agents.run({\n    agentId,\n    message,\n    inputs: {\n        document: tela.createFile(file, { name: 'document.pdf' }),\n    },\n    webhooks,\n})\n```\n\n## Migration Guide from v1.x to v2\n\nVersion 2.0 of the Tela SDK introduces significant improvements to type safety, schema validation, and API design. This guide will help you migrate your code from v1.x to v2.\n\n### Breaking Changes\n\n#### 1. API Surface Changed: `completions.create()` → `canvas.get()` + `execute()`\n\nThe v1 API used a single `completions.create()` method. V2 introduces a two-step pattern: first retrieve a canvas, then execute it.\n\n**v1.x:**\n```typescript\nconst tela = new TelaSDK({ apiKey: 'your-api-key' })\n\nconst completion = await tela.completions.create({\n    canvasId: 'canvas-id',\n    variables: {\n        query: 'What is the capital of France?'\n    }\n})\n\nconsole.log(completion.choices[0].message.content)\n```\n\n**v2:**\n```typescript\nconst tela = new TelaSDK({ apiKey: 'your-api-key' })\n\n// First, get the canvas\nconst canvas = await tela.canvas.get({\n    id: 'canvas-id'\n})\n\n// Then execute it\nconst result = await canvas.execute({\n    query: 'What is the capital of France?'\n}).result\n\nconsole.log(result)\n```\n\n#### 2. Schema Validation and Type Safety (Optional)\n\nV2 introduces **optional** runtime and compile-time type safety with Zod schema validation. Schemas validate data at runtime (catching invalid data) and provide TypeScript types at compile-time. You can use the SDK without schemas if you prefer.\n\n**v1.x (no schema validation):**\n```typescript\nconst completion = await tela.completions.create<\n    { query: string },\n    { result: string }\n>({\n    canvasId: 'canvas-id',\n    variables: {\n        query: 'Hello'\n    }\n})\n```\n\n**v2 (without schemas - works just like v1.x):**\n```typescript\nconst canvas = await tela.canvas.get({\n    id: 'canvas-id'\n})\n\nconst result = await canvas.execute({ query: 'Hello' }).result\n// result is typed as unknown, no runtime validation\n```\n\n**v2 (with optional schema validation for runtime + compile-time safety):**\n```typescript\nconst canvas = await tela.canvas.get({\n    id: 'canvas-id',\n    input: schema => schema.object({\n        query: schema.string()\n    }),\n    output: schema => schema.object({\n        result: schema.string()\n    })\n})\n\n// Runtime validation ensures data matches schema\n// TypeScript knows the exact types at compile-time\nconst result = await canvas.execute({ query: 'Hello' }).result\n// result.result is a string (validated and typed)\n```\n\n#### 3. File Handling Changes\n\nThe `TelaFile` constructor now requires `mimeType` for `Uint8Array` and `ReadableStream` inputs (for compatibility with storage solutions).\n\n**v1.x:**\n```typescript\nimport { TelaFile } from '@meistrari/tela-sdk-js'\n\nconst file = new TelaFile(blob, { range: [0, 5] })\nconst fileFromBytes = new TelaFile(new Uint8Array([/* ... */])) // mimeType optional\n```\n\n**v2:**\n```typescript\nimport { TelaFile } from '@meistrari/tela-sdk-js'\n\n// No change needed for Blob, File, or URL strings\nconst file = new TelaFile(blob, { range: [0, 5] })\n\n// For Uint8Array or ReadableStream, mimeType is now required:\nconst fileFromBytes = new TelaFile(\n    new Uint8Array([/* ... */]),\n    {\n        mimeType: 'application/pdf',\n        name: 'document.pdf'\n    }\n)\n\n// Alternative: use tela.createFile() helper\nconst fileFromStream = tela.createFile(readableStream, { mimeType: 'image/jpeg' })\n```\n\n#### 4. Streaming API Changes\n\nStreaming now returns an async generator directly via the `.result` property.\n\n**v1.x:**\n```typescript\nconst stream = await tela.completions.create({\n    canvasId: 'canvas-id',\n    stream: true,\n    variables: { query: 'Tell me a story' }\n})\n\nfor await (const chunk of stream) {\n    process.stdout.write(chunk.message.content || '')\n}\n```\n\n**v2:**\n```typescript\nconst canvas = await tela.canvas.get({\n    id: 'canvas-id',\n    input: schema => schema.object({\n        query: schema.string()\n    }),\n    output: schema => schema.object({\n        response: schema.string()\n    })\n})\n\nconst execution = canvas.execute(\n    { query: 'Tell me a story' },\n    { stream: true }\n)\n\nfor await (const chunk of execution.result) {\n    process.stdout.write(chunk.response || '')\n}\n```\n\n#### 5. Async Execution with Polling\n\nV2 introduces async execution with automatic polling. V1.x only supported synchronous execution (the request would block until completion).\n\n**v1.x:**\n```typescript\n// Synchronous only - blocks until the execution completes\nconst response = await tela.completions.create({\n    canvasId: 'canvas-id',\n    variables: { query: 'Analyze this' }\n})\n// This could take a very long time for long-running executions\nconsole.log(response.choices[0].message.content)\n```\n\n**v2:**\n```typescript\nconst canvas = await tela.canvas.get({\n    id: 'canvas-id',\n    input: schema => schema.object({ query: schema.string() }),\n    output: schema => schema.object({ result: schema.string() })\n})\n\n// Automatic polling\nconst execution = canvas.execute(\n    { query: 'Analyze this' },\n    {\n        async: true,\n        pollingInterval: 1000, // Poll every 1 second\n        pollingTimeout: 60000, // Timeout after 60 seconds\n        webhookUrl: 'https://your-server.com/webhook' // Optional\n    }\n)\n\nconst result = await execution.result\nconsole.log(result.result)\n```\n\n#### 6. Response Structure Changes\n\nThe response structure has been simplified.\n\n**v1.x:**\n```typescript\nconst completion = await tela.completions.create({\n    canvasId: 'canvas-id',\n    variables: { query: 'Hello' }\n})\n\n// Access via nested structure\nconsole.log(completion.choices[0].message.content)\n```\n\n**v2:**\n```typescript\nconst canvas = await tela.canvas.get({\n    id: 'canvas-id',\n    output: schema => schema.object({\n        answer: schema.string()\n    })\n})\n\nconst response = await canvas.execute({ query: 'Hello' }).result\n// Direct access to structured output\nconsole.log(response.answer)\n```\n\n### Migration Checklist\n\n- [ ] Replace `tela.completions.create()` with `tela.canvas.get()` + `canvas.execute()`\n- [ ] (Optional) Add input/output schemas using the `schema` builder function for type safety\n- [ ] Add `mimeType` parameter when creating `TelaFile` from `Uint8Array` or `ReadableStream`\n- [ ] Update streaming code to use `.result` property for iteration\n- [ ] Update async execution to use polling parameters instead of manual polling\n- [ ] Update response access patterns to match new structure\n- [ ] (If using schemas) Test schema validation warnings to ensure schemas match server configuration\n\n### New Features in v2\n\n#### Event-Driven Execution Monitoring\n\nV2 introduces a comprehensive event system for monitoring execution lifecycle across all execution modes (sync, async, streaming):\n\n```typescript\nconst execution = await canvas.execute(\n    { query: 'Analyze this' },\n    { async: true }\n)\n\n// Monitor progress with events\nexecution.on('statusChange', (status) => {\n    console.log(`Status: ${status}`)\n})\n\nexecution.on('poll', (pollResult) => {\n    console.log(`Polling - ${pollResult.status}`)\n})\n\nexecution.on('success', (result) => {\n    console.log('Completed!', result)\n})\n\n// Graceful error handling\nexecution.on('error', (error) => {\n    console.error('Failed:', error.message)\n    // With listener: no throw, promise resolves to undefined\n})\n\nconst result = await execution.result\n```\n\n**Available Events:**\n- `success` - Completion with validated result\n- `error` - Failure notification (prevents throwing if listener exists)\n- `statusChange` - Status transitions\n- `poll` - Polling updates (async only)\n\nSee [Event Examples](./examples/events/) for comprehensive patterns and use cases.\n\n#### Promise-like Execution API\n\nV2 introduces a flexible execution API that supports both direct result access and execution object retrieval:\n\n```typescript\n// Direct result access (recommended for simple cases)\nconst result = await canvas.execute({ query: 'Hello' }).result\n\n// Get execution object for more control\nconst execution = await canvas.execute({ query: 'Hello' })\nconst result2 = await execution.result\n```\n\n#### Schema Validation Warnings\n\nV2 validates your schemas against the server configuration and warns you about mismatches:\n\n```typescript\nconst canvas = await tela.canvas.get({\n    id: 'canvas-id',\n    input: schema => schema.object({\n        wrongField: schema.string() // Will warn if not in server schema\n    }),\n    skipSchemaValidation: false // Enable warnings (default)\n})\n```\n\n#### Vault File References\n\nV2 supports vault file references:\n\n```typescript\nconst vaultFile = new TelaFile('vault://file-id')\n```\n\n#### Skip Result Validation\n\nWhen you have schemas defined, you can skip runtime validation for better performance (you'll still get TypeScript types):\n\n```typescript\nconst canvas = await tela.canvas.get({\n    id: 'canvas-id',\n    input: schema => schema.object({\n        query: schema.string()\n    }),\n    output: schema => schema.object({\n        answer: schema.string()\n    })\n})\n\nconst execution = canvas.execute(\n    { query: 'Hello' },\n    { skipResultValidation: true } // Skip runtime validation, trust API response\n)\n\n// Still typed as { answer: string }, but no validation at runtime\nconst result = await execution.result\n```\n\n#### Access Raw API Responses\n\nV2 provides access to the raw, unprocessed API response alongside the parsed result:\n\n```typescript\nconst canvas = await tela.canvas.get({\n    id: 'canvas-id',\n    output: schema => schema.object({\n        answer: schema.string()\n    })\n})\n\nconst execution = await canvas.execute({ query: 'Hello' })\n\n// Get the parsed, validated result\nconst result = await execution.result\nconsole.log(result.answer) // Type-safe access\n\n// Get the raw API response\nconst rawResult = await execution.rawResult\nconsole.log(rawResult) // Complete API response with all metadata\n\n// You can await either one first - both will be available\nconst raw = await execution.rawResult // Triggers execution\nconst parsed = await execution.result // Reuses same execution\n```\n\nThis is useful when you need:\n- Full API response metadata (execution IDs, timestamps, etc.)\n- Debugging and logging complete responses\n- Access to data not in your schema\n\n#### Fetch Existing Executions and Event-Driven Polling\n\nV2 introduces the ability to fetch existing async executions by ID and monitor them with event-driven polling:\n\n```typescript\n// Start an async execution\nconst execution = await canvas.execute(\n    { query: 'Process this data' },\n    { async: true }\n)\n\n// Store the execution ID (e.g., in a database)\nconst executionId = execution.id\n\n// Later, fetch the execution by ID\nconst fetchedExecution = await canvas.getExecution(executionId, {\n    pollingInterval: 2000,\n    pollingTimeout: 120000\n})\n\n// Use event-driven polling (non-blocking)\nfetchedExecution.on('statusChange', (status) => {\n    console.log(`Status changed: ${status}`)\n})\n\nfetchedExecution.on('success', (result) => {\n    console.log('Completed!', result)\n})\n\nfetchedExecution.on('error', (error) => {\n    console.error('Failed:', error)\n})\n\n// Start polling without blocking\nfetchedExecution.poll()\n\n// Continue with other work...\nconsole.log('Polling in background')\n```\n\nThis enables powerful use cases:\n- **Job queue management**: Start jobs, store IDs, fetch later to check status\n- **Resumable workflows**: Continue monitoring after page refresh or server restart\n- **Multi-user dashboards**: Monitor executions started by different users\n- **Real-time UI updates**: Non-blocking polling with event listeners\n- **Background processing**: Track multiple executions concurrently\n\nSee [Fetch and Poll Examples](./examples/events/) for detailed patterns.\n\n#### Execution Tags\n\nV2 introduces support for tagging executions for filtering, categorization, and analytics:\n\n```typescript\n// Add tags to any execution mode\nconst result = await canvas.execute(\n    { query: 'Analyze customer feedback' },\n    {\n        tags: ['production', 'analytics', 'customer-feedback']\n    }\n).result\n\n// Tags work with async executions\nconst asyncResult = await canvas.execute(\n    { query: 'Process large dataset' },\n    {\n        async: true,\n        tags: ['batch-processing', 'analytics']\n    }\n).result\n\n// Tags work with streaming executions\nfor await (const chunk of canvas.execute(\n    { query: 'Generate report' },\n    {\n        stream: true,\n        tags: ['streaming', 'reports']\n    }\n).result) {\n    process.stdout.write(chunk.result || '')\n}\n```\n\n**Use Cases:**\n- **Environment tracking**: Tag executions by environment (`production`, `staging`, `development`)\n- **Feature categorization**: Organize executions by feature (`analytics`, `reporting`, `summarization`)\n- **User segmentation**: Track executions by user tier or department\n- **Cost allocation**: Attribute API usage to specific projects or teams\n- **Performance monitoring**: Filter and analyze execution metrics by tag\n\n#### Application Execution Labels\n\nV2 adds support for setting workspace task titles when using deployed applications via `applicationId`:\n\n```typescript\nconst canvas = await tela.canvas.get({\n    applicationId: 'app-id' // Using deployed application instead of canvas ID\n})\n\n// Set the title of the workspace task\nconst result = await canvas.execute(\n    { query: 'Process request' },\n    {\n        label: 'Customer Dashboard Query'\n    }\n).result\n```\n\nWhen you execute a canvas using `applicationId`, it creates a task in the application's workspace. The `label` field sets the title of that workspace task, making it easier to identify and track executions in your Tela workspace.\n\n**Note:** The `label` field is only applicable when using `applicationId`. A warning will be logged if you provide a label without an `applicationId`.\n\n**Use Cases:**\n- **Task identification**: Give meaningful titles to workspace tasks for easier tracking\n- **Execution categorization**: Organize tasks by feature or workflow in the workspace\n- **User-friendly naming**: Display clear task names instead of generic execution IDs\n- **Dashboard clarity**: Make workspace task lists more readable and searchable\n\n### Need Help?\n\nIf you encounter issues during migration, please:\n- Check the [examples](./examples/) directory for updated usage patterns\n- Review the [API documentation](https://sdk-js.tela.tools/)\n- Generate the API reference locally with `bun run docs` when checking changes\n- Open an issue at [GitHub Issues](https://github.com/meistrari/tela-sdk-js/issues)\n\n## Selecting an agent provider\n\nFor a fresh session, `agents.run` and `agents.runDirectStream` accept an optional\n`provider` (`anthropic`, `bedrock`, or `vertex`). Omit it to keep the workspace default.\nExisting and prepared sessions retain their provider; retry with a new session to\nswitch providers. Requires the Tela API provider-forwarding support to be deployed.\n\n```typescript\nawait tela.agents.runDirectStream({\n    agentId: 'your-agent-id',\n    message: 'Retry the failed stage',\n    provider: 'anthropic',\n})\n```\n","readmeFilename":"README.md"}