openai-assistants（OpenAI assistants 搜索）

OpenAI Assistants API v2

Status: Production Ready (Deprecated H1 2026) Package: openai@6.7.0 Last Updated: 2025-10-25 v1 Deprecated: December 18, 2024 v2 Sunset: H1 2026 (migrate to Responses API)

⚠️ Important: Deprecation Notice

OpenAI announced that the Assistants API will be deprecated in favor of the Responses API.

Timeline:

• ✅ Dec 18, 2024: Assistants API v1 deprecated
• ⏳ H1 2026: Planned sunset of Assistants API v2
• ✅ Now: Responses API available (recommended for new projects)

Should you still use this skill?

• ✅ Yes, if: You have existing Assistants API code (12-18 month migration window)
• ✅ Yes, if: You need to maintain legacy applications
• ✅ Yes, if: Planning migration from Assistants → Responses
• ❌ No, if: Starting a new project (use openai-responses skill instead)

Migration Path: See references/migration-to-responses.md for complete migration guide.

Table of Contents

1. Quick Start
2. Core Concepts
3. Assistants
4. Threads
5. Messages
6. Runs
7. Streaming Runs
8. Tools

- Code Interpreter - File Search - Function Calling

1. Vector Stores
2. File Uploads
3. Thread Lifecycle Management
4. Error Handling
5. Production Best Practices
6. Relationship to Other Skills

Quick Start

Installation

npm install openai@6.7.0

Environment Setup

export OPENAI_API_KEY="sk-..."

Basic Assistant (Node.js SDK)

import OpenAI from 'openai';

const openai = new OpenAI({
  apiKey: process.env.OPENAI_API_KEY,
});

// 1. Create an assistant
const assistant = await openai.beta.assistants.create({
  name: "Math Tutor",
  instructions: "You are a personal math tutor. Write and run code to answer math questions.",
  tools: [{ type: "code_interpreter" }],
  model: "gpt-4o",
});

// 2. Create a thread
const thread = await openai.beta.threads.create();

// 3. Add a message to the thread
await openai.beta.threads.messages.create(thread.id, {
  role: "user",
  content: "I need to solve the equation `3x + 11 = 14`. Can you help me?",
});

// 4. Create a run
const run = await openai.beta.threads.runs.create(thread.id, {
  assistant_id: assistant.id,
});

// 5. Poll for completion
let runStatus = await openai.beta.threads.runs.retrieve(thread.id, run.id);

while (runStatus.status !== 'completed') {
  await new Promise(resolve => setTimeout(resolve, 1000));
  runStatus = await openai.beta.threads.runs.retrieve(thread.id, run.id);
}

// 6. Retrieve messages
const messages = await openai.beta.threads.messages.list(thread.id);
console.log(messages.data[0].content[0].text.value);

Basic Assistant (Fetch - Cloudflare Workers)

// 1. Create assistant
const assistant = await fetch('https://api.openai.com/v1/assistants', {
  method: 'POST',
  headers: {
    'Authorization': `Bearer ${env.OPENAI_API_KEY}`,
    'Content-Type': 'application/json',
    'OpenAI-Beta': 'assistants=v2',
  },
  body: JSON.stringify({
    name: "Math Tutor",
    instructions: "You are a helpful math tutor.",
    model: "gpt-4o",
  }),
});

const assistantData = await assistant.json();

// 2. Create thread
const thread = await fetch('https://api.openai.com/v1/threads', {
  method: 'POST',
  headers: {
    'Authorization': `Bearer ${env.OPENAI_API_KEY}`,
    'Content-Type': 'application/json',
    'OpenAI-Beta': 'assistants=v2',
  },
});

const threadData = await thread.json();

// 3. Add message and create run
const run = await fetch(`https://api.openai.com/v1/threads/${threadData.id}/runs`, {
  method: 'POST',
  headers: {
    'Authorization': `Bearer ${env.OPENAI_API_KEY}`,
    'Content-Type': 'application/json',
    'OpenAI-Beta': 'assistants=v2',
  },
  body: JSON.stringify({
    assistant_id: assistantData.id,
    additional_messages: [{
      role: "user",
      content: "What is 3x + 11 = 14?",
    }],
  }),
});

// Poll for completion...

Core Concepts

The Assistants API uses four main objects:

1. Assistants

Configured AI entities with:

• Instructions (system prompt, max 256k characters)
• Model (gpt-4o, gpt-5, etc.)
• Tools (Code Interpreter, File Search, Functions)
• File attachments
• Metadata

2. Threads

Conversation containers that:

• Store message history
• Persist across runs
• Can have metadata
• Support up to 100,000 messages

3. Messages

Individual messages in a thread:

• User messages (input)
• Assistant messages (output)
• Can include file attachments
• Support text and image content

4. Runs

Execution of an assistant on a thread:

• Asynchronous processing
• Multiple states (queued, in_progress, completed, failed, etc.)
• Can stream results
• Handle tool calls automatically

Assistants

Create an Assistant

const assistant = await openai.beta.assistants.create({
  name: "Data Analyst",
  instructions: "You are a data analyst. Use code interpreter to analyze data and create visualizations.",
  model: "gpt-4o",
  tools: [
    { type: "code_interpreter" },
    { type: "file_search" },
  ],
  tool_resources: {
    file_search: {
      vector_store_ids: ["vs_abc123"],
    },
  },
  metadata: {
    department: "analytics",
    version: "1.0",
  },
});

Parameters:

• model (required): Model ID (gpt-4o, gpt-5, gpt-4-turbo)
• instructions: System prompt (max 256k characters in v2, was 32k in v1)
• name: Assistant name (max 256 characters)
• description: Description (max 512 characters)
• tools: Array of tools (max 128 tools)
• tool_resources: Resources for tools (vector stores, files)
• temperature: 0-2 (default 1)
• top_p: 0-1 (default 1)
• response_format: "auto", "json_object", or JSON schema
• metadata: Key-value pairs (max 16 pairs)

Retrieve an Assistant

const assistant = await openai.beta.assistants.retrieve("asst_abc123");

Update an Assistant

const updatedAssistant = await openai.beta.assistants.update("asst_abc123", {
  instructions: "Updated instructions",
  tools: [{ type: "code_interpreter" }, { type: "file_search" }],
});

Delete an Assistant

await openai.beta.assistants.del("asst_abc123");

List Assistants

const assistants = await openai.beta.assistants.list({
  limit: 20,
  order: "desc",
});

Threads

Threads store conversation history and persist across runs.

Create a Thread

// Empty thread
const thread = await openai.beta.threads.create();

// Thread with initial messages
const thread = await openai.beta.threads.create({
  messages: [
    {
      role: "user",
      content: "Hello! I need help with Python.",
      metadata: { source: "web" },
    },
  ],
  metadata: {
    user_id: "user_123",
    session_id: "session_456",
  },
});

Retrieve a Thread

const thread = await openai.beta.threads.retrieve("thread_abc123");

Update Thread Metadata

const thread = await openai.beta.threads.update("thread_abc123", {
  metadata: {
    user_id: "user_123",
    last_active: new Date().toISOString(),
  },
});

Delete a Thread

await openai.beta.threads.del("thread_abc123");

⚠️ Warning: Deleting a thread also deletes all messages and runs. Cannot be undone.

Messages

Add a Message to a Thread

const message = await openai.beta.threads.messages.create("thread_abc123", {
  role: "user",
  content: "Can you analyze this data?",
  attachments: [
    {
      file_id: "file_abc123",
      tools: [{ type: "code_interpreter" }],
    },
  ],
  metadata: {
    timestamp: new Date().toISOString(),
  },
});

Parameters:

• role: "user" only (assistant messages created by runs)
• content: Text or array of content blocks
• attachments: Files with associated tools
• metadata: Key-value pairs

Retrieve a Message

const message = await openai.beta.threads.messages.retrieve(
  "thread_abc123",
  "msg_abc123"
);

List Messages

const messages = await openai.beta.threads.messages.list("thread_abc123", {
  limit: 20,
  order: "desc", // "asc" or "desc"
});

// Iterate through messages
for (const message of messages.data) {
  console.log(`${message.role}: ${message.content[0].text.value}`);
}

Update Message Metadata

const message = await openai.beta.threads.messages.update(
  "thread_abc123",
  "msg_abc123",
  {
    metadata: {
      edited: "true",
      edit_timestamp: new Date().toISOString(),
    },
  }
);

Delete a Message

await openai.beta.threads.messages.del("thread_abc123", "msg_abc123");

Runs

Runs execute an assistant on a thread.

Create a Run

const run = await openai.beta.threads.runs.create("thread_abc123", {
  assistant_id: "asst_abc123",
  instructions: "Please address the user as Jane Doe.",
  additional_messages: [
    {
      role: "user",
      content: "Can you help me with this?",
    },
  ],
});

Parameters:

• assistant_id (required): Assistant to use
• instructions: Override assistant instructions
• additional_messages: Add messages before running
• tools: Override assistant tools
• metadata: Key-value pairs
• temperature: Override temperature
• top_p: Override top_p
• max_prompt_tokens: Limit input tokens
• max_completion_tokens: Limit output tokens

Retrieve a Run

const run = await openai.beta.threads.runs.retrieve(
  "thread_abc123",
  "run_abc123"
);

console.log(run.status); // queued, in_progress, requires_action, completed, failed, etc.

Run States

Polling Pattern

async function pollRunCompletion(threadId: string, runId: string) {
  let run = await openai.beta.threads.runs.retrieve(threadId, runId);

  while (['queued', 'in_progress', 'cancelling'].includes(run.status)) {
    await new Promise(resolve => setTimeout(resolve, 1000)); // Wait 1 second
    run = await openai.beta.threads.runs.retrieve(threadId, runId);
  }

  if (run.status === 'failed') {
    throw new Error(`Run failed: ${run.last_error?.message}`);
  }

  if (run.status === 'requires_action') {
    // Handle function calling (see Function Calling section)
    return run;
  }

  return run; // completed
}

const run = await openai.beta.threads.runs.create(threadId, { assistant_id: assistantId });
const completedRun = await pollRunCompletion(threadId, run.id);

Cancel a Run

const run = await openai.beta.threads.runs.cancel("thread_abc123", "run_abc123");

⚠️ Important: Cancellation is asynchronous. Check status becomes cancelled.

List Runs

const runs = await openai.beta.threads.runs.list("thread_abc123", {
  limit: 10,
  order: "desc",
});

Streaming Runs

Stream run events in real-time using Server-Sent Events (SSE).

Basic Streaming

const stream = await openai.beta.threads.runs.stream("thread_abc123", {
  assistant_id: "asst_abc123",
});

for await (const event of stream) {
  if (event.event === 'thread.message.delta') {
    const delta = event.data.delta.content?.[0]?.text?.value;
    if (delta) {
      process.stdout.write(delta);
    }
  }
}

Stream Event Types

Complete Streaming Example

async function streamAssistantResponse(threadId: string, assistantId: string) {
  const stream = await openai.beta.threads.runs.stream(threadId, {
    assistant_id: assistantId,
  });

  for await (const event of stream) {
    switch (event.event) {
      case 'thread.run.created':
        console.log('\\nRun started...');
        break;

      case 'thread.message.delta':
        const delta = event.data.delta.content?.[0];
        if (delta?.type === 'text' && delta.text?.value) {
          process.stdout.write(delta.text.value);
        }
        break;

      case 'thread.run.step.delta':
        const toolCall = event.data.delta.step_details;
        if (toolCall?.type === 'tool_calls') {
          const codeInterpreter = toolCall.tool_calls?.[0]?.code_interpreter;
          if (codeInterpreter?.input) {
            console.log('\\nExecuting code:', codeInterpreter.input);
          }
        }
        break;

      case 'thread.run.completed':
        console.log('\\n\\nRun completed!');
        break;

      case 'thread.run.failed':
        console.error('\\nRun failed:', event.data.last_error);
        break;

      case 'thread.run.requires_action':
        // Handle function calling
        console.log('\\nFunction calling required');
        break;
    }
  }
}

Tools

Assistants API supports three types of tools:

Code Interpreter

Executes Python code in a sandboxed environment.

Capabilities:

• Run Python code
• Generate charts/graphs
• Process files (CSV, JSON, text, images, etc.)
• Return file outputs (images, data files)
• Install packages (limited set available)

Example:

const assistant = await openai.beta.assistants.create({
  name: "Data Analyst",
  instructions: "You are a data analyst. Use Python to analyze data and create visualizations.",
  model: "gpt-4o",
  tools: [{ type: "code_interpreter" }],
});

// Upload a file
const file = await openai.files.create({
  file: fs.createReadStream("sales_data.csv"),
  purpose: "assistants",
});

// Create thread with file
const thread = await openai.beta.threads.create({
  messages: [{
    role: "user",
    content: "Analyze this sales data and create a visualization.",
    attachments: [{
      file_id: file.id,
      tools: [{ type: "code_interpreter" }],
    }],
  }],
});

// Run
const run = await openai.beta.threads.runs.create(thread.id, {
  assistant_id: assistant.id,
});

// Poll for completion and retrieve outputs

Output Files:

Code Interpreter can generate files (images, CSVs, etc.). Access them via:

const messages = await openai.beta.threads.messages.list(thread.id);
const message = messages.data[0];

for (const content of message.content) {
  if (content.type === 'image_file') {
    const fileId = content.image_file.file_id;
    const fileContent = await openai.files.content(fileId);
    // Save or process file
  }
}

File Search

Semantic search over uploaded documents using vector stores.

Key Features:

• Up to 10,000 files per assistant (500x more than v1)
• Automatic chunking and embedding
• Vector + keyword search
• Parallel queries with multi-threading
• Advanced reranking

Pricing:

• $0.10/GB/day for vector storage
• First 1GB free

Example:

// 1. Create vector store
const vectorStore = await openai.beta.vectorStores.create({
  name: "Product Documentation",
  metadata: { category: "docs" },
});

// 2. Upload files to vector store
const file = await openai.files.create({
  file: fs.createReadStream("product_guide.pdf"),
  purpose: "assistants",
});

await openai.beta.vectorStores.files.create(vectorStore.id, {
  file_id: file.id,
});

// 3. Create assistant with file search
const assistant = await openai.beta.assistants.create({
  name: "Product Support",
  instructions: "Use file search to answer questions about our products.",
  model: "gpt-4o",
  tools: [{ type: "file_search" }],
  tool_resources: {
    file_search: {
      vector_store_ids: [vectorStore.id],
    },
  },
});

// 4. Create thread and run
const thread = await openai.beta.threads.create({
  messages: [{
    role: "user",
    content: "How do I install the product?",
  }],
});

const run = await openai.beta.threads.runs.create(thread.id, {
  assistant_id: assistant.id,
});

Best Practices:

• Wait for vector store status to be completed before using
• Use metadata for filtering (coming soon)
• Chunk large documents appropriately
• Monitor storage costs

Function Calling

Define custom functions for the assistant to call.

Example:

const assistant = await openai.beta.assistants.create({
  name: "Weather Assistant",
  instructions: "You help users get weather information.",
  model: "gpt-4o",
  tools: [{
    type: "function",
    function: {
      name: "get_weather",
      description: "Get the current weather for a location",
      parameters: {
        type: "object",
        properties: {
          location: {
            type: "string",
            description: "City name, e.g., 'San Francisco'",
          },
          unit: {
            type: "string",
            enum: ["celsius", "fahrenheit"],
            description: "Temperature unit",
          },
        },
        required: ["location"],
      },
    },
  }],
});

// Create thread and run
const thread = await openai.beta.threads.create({
  messages: [{
    role: "user",
    content: "What's the weather in San Francisco?",
  }],
});

let run = await openai.beta.threads.runs.create(thread.id, {
  assistant_id: assistant.id,
});

// Poll until requires_action
while (run.status === 'in_progress' || run.status === 'queued') {
  await new Promise(resolve => setTimeout(resolve, 1000));
  run = await openai.beta.threads.runs.retrieve(thread.id, run.id);
}

if (run.status === 'requires_action') {
  const toolCalls = run.required_action.submit_tool_outputs.tool_calls;

  const toolOutputs = [];
  for (const toolCall of toolCalls) {
    if (toolCall.function.name === 'get_weather') {
      const args = JSON.parse(toolCall.function.arguments);
      // Call your actual weather API
      const weather = await getWeatherAPI(args.location, args.unit);

      toolOutputs.push({
        tool_call_id: toolCall.id,
        output: JSON.stringify(weather),
      });
    }
  }

  // Submit tool outputs
  run = await openai.beta.threads.runs.submitToolOutputs(thread.id, run.id, {
    tool_outputs: toolOutputs,
  });

  // Continue polling...
}

Vector Stores

Vector stores enable efficient semantic search over large document collections.

Create a Vector Store

const vectorStore = await openai.beta.vectorStores.create({
  name: "Legal Documents",
  metadata: {
    department: "legal",
    category: "contracts",
  },
  expires_after: {
    anchor: "last_active_at",
    days: 7, // Auto-delete 7 days after last use
  },
});

Add Files to Vector Store

Single File:

const file = await openai.files.create({
  file: fs.createReadStream("contract.pdf"),
  purpose: "assistants",
});

await openai.beta.vectorStores.files.create(vectorStore.id, {
  file_id: file.id,
});

Batch Upload:

const fileBatch = await openai.beta.vectorStores.fileBatches.create(vectorStore.id, {
  file_ids: ["file_abc123", "file_def456", "file_ghi789"],
});

// Poll for batch completion
let batch = await openai.beta.vectorStores.fileBatches.retrieve(vectorStore.id, fileBatch.id);
while (batch.status === 'in_progress') {
  await new Promise(resolve => setTimeout(resolve, 1000));
  batch = await openai.beta.vectorStores.fileBatches.retrieve(vectorStore.id, fileBatch.id);
}

Check Vector Store Status

const vectorStore = await openai.beta.vectorStores.retrieve("vs_abc123");

console.log(vectorStore.status); // "in_progress", "completed", "failed"
console.log(vectorStore.file_counts); // { in_progress: 0, completed: 50, failed: 0 }

⚠️ Important: Wait for status: "completed" before using with file search.

List Vector Stores

const stores = await openai.beta.vectorStores.list({
  limit: 20,
  order: "desc",
});

Update Vector Store

const vectorStore = await openai.beta.vectorStores.update("vs_abc123", {
  name: "Updated Name",
  metadata: { updated: "true" },
});

Delete Vector Store

await openai.beta.vectorStores.del("vs_abc123");

File Uploads

Upload files for use with Code Interpreter or File Search.

Upload a File

import fs from 'fs';

const file = await openai.files.create({
  file: fs.createReadStream("document.pdf"),
  purpose: "assistants",
});

console.log(file.id); // file_abc123

Supported Formats:

• Code Interpreter:.c,.cpp,.csv,.docx,.html,.java,.json,.md,.pdf,.php,.pptx,.py,.rb,.tex,.txt,.css,.jpeg,.jpg,.js,.gif,.png,.tar,.ts,.xlsx,.xml,.zip
• File Search:.c,.cpp,.docx,.html,.java,.json,.md,.pdf,.php,.pptx,.py,.rb,.tex,.txt,.css,.js,.ts,.go

Size Limits:

• Code Interpreter: 512 MB per file
• File Search: 512 MB per file
• Vector Store: Up to 10,000 files

Retrieve File Info

const file = await openai.files.retrieve("file_abc123");

Download File Content

const content = await openai.files.content("file_abc123");
// Returns binary content

Delete a File

await openai.files.del("file_abc123");

List Files

const files = await openai.files.list({
  purpose: "assistants",
});

Thread Lifecycle Management

Proper thread lifecycle management prevents common errors.

Pattern 1: One Thread Per User

async function getOrCreateUserThread(userId: string): Promise<string> {
  // Check if thread exists in your database
  let threadId = await db.getThreadIdForUser(userId);

  if (!threadId) {
    // Create new thread
    const thread = await openai.beta.threads.create({
      metadata: { user_id: userId },
    });
    threadId = thread.id;
    await db.saveThreadIdForUser(userId, threadId);
  }

  return threadId;
}

Pattern 2: Active Run Check

async function ensureNoActiveRun(threadId: string) {
  const runs = await openai.beta.threads.runs.list(threadId, {
    limit: 1,
    order: "desc",
  });

  const latestRun = runs.data[0];
  if (latestRun && ['queued', 'in_progress', 'cancelling'].includes(latestRun.status)) {
    throw new Error('Thread already has an active run. Wait or cancel first.');
  }
}

// Before creating new run
await ensureNoActiveRun(threadId);
const run = await openai.beta.threads.runs.create(threadId, { assistant_id });

Pattern 3: Thread Cleanup

async function cleanupOldThreads(maxAgeHours = 24) {
  const threads = await openai.beta.threads.list({ limit: 100 });

  for (const thread of threads.data) {
    const createdAt = new Date(thread.created_at * 1000);
    const ageHours = (Date.now() - createdAt.getTime()) / (1000 * 60 * 60);

    if (ageHours > maxAgeHours) {
      await openai.beta.threads.del(thread.id);
    }
  }
}

Error Handling

Common Errors and Solutions

1. Thread Already Has Active Run

Error: 400 Can't add messages to thread_xxx while a run run_xxx is active.

Solution:

// Wait for run to complete or cancel it
const run = await openai.beta.threads.runs.retrieve(threadId, runId);
if (['queued', 'in_progress'].includes(run.status)) {
  await openai.beta.threads.runs.cancel(threadId, runId);
  // Wait for cancellation
  while (run.status !== 'cancelled') {
    await new Promise(resolve => setTimeout(resolve, 500));
    run = await openai.beta.threads.runs.retrieve(threadId, runId);
  }
}

2. Run Polling Timeout

Long-running tasks may exceed reasonable polling windows.

Solution:

async function pollWithTimeout(threadId: string, runId: string, maxSeconds = 300) {
  const startTime = Date.now();

  while (true) {
    const run = await openai.beta.threads.runs.retrieve(threadId, runId);

    if (!['queued', 'in_progress'].includes(run.status)) {
      return run;
    }

    const elapsed = (Date.now() - startTime) / 1000;
    if (elapsed > maxSeconds) {
      await openai.beta.threads.runs.cancel(threadId, runId);
      throw new Error('Run exceeded timeout');
    }

    await new Promise(resolve => setTimeout(resolve, 1000));
  }
}

3. Vector Store Not Ready

Using vector store before indexing completes.

Solution:

async function waitForVectorStore(vectorStoreId: string) {
  let store = await openai.beta.vectorStores.retrieve(vectorStoreId);

  while (store.status === 'in_progress') {
    await new Promise(resolve => setTimeout(resolve, 2000));
    store = await openai.beta.vectorStores.retrieve(vectorStoreId);
  }

  if (store.status === 'failed') {
    throw new Error('Vector store indexing failed');
  }

  return store;
}

4. File Upload Format Issues

Unsupported file formats cause errors.

Solution:

const SUPPORTED_FORMATS = {
  code_interpreter: ['.csv', '.json', '.pdf', '.txt', '.py', '.js', '.xlsx'],
  file_search: ['.pdf', '.docx', '.txt', '.md', '.html'],
};

function validateFile(filename: string, tool: string) {
  const ext = filename.substring(filename.lastIndexOf('.')).toLowerCase();
  if (!SUPPORTED_FORMATS[tool].includes(ext)) {
    throw new Error(`Unsupported file format for ${tool}: ${ext}`);
  }
}

See references/top-errors.md for complete error catalog.

Production Best Practices

1. Use Assistant IDs (Don't Recreate)

❌ Bad:

// Creates new assistant on every request!
const assistant = await openai.beta.assistants.create({ ... });

✅ Good:

// Create once, store ID, reuse
const ASSISTANT_ID = process.env.ASSISTANT_ID || await createAssistant();

async function createAssistant() {
  const assistant = await openai.beta.assistants.create({ ... });
  console.log('Save this ID:', assistant.id);
  return assistant.id;
}

2. Implement Proper Error Handling

async function createRunWithRetry(threadId: string, assistantId: string, maxRetries = 3) {
  for (let i = 0; i < maxRetries; i++) {
    try {
      return await openai.beta.threads.runs.create(threadId, {
        assistant_id: assistantId,
      });
    } catch (error) {
      if (error.status === 429) {
        // Rate limit - wait and retry
        await new Promise(resolve => setTimeout(resolve, 2000 * (i + 1)));
        continue;
      }

      if (error.message?.includes('active run')) {
        // Wait for active run to complete
        await new Promise(resolve => setTimeout(resolve, 5000));
        continue;
      }

      throw error; // Other errors
    }
  }

  throw new Error('Max retries exceeded');
}

3. Monitor Costs

// Track usage
const run = await openai.beta.threads.runs.retrieve(threadId, runId);
console.log('Tokens used:', run.usage);
// { prompt_tokens: 150, completion_tokens: 200, total_tokens: 350 }

// Set limits
const run = await openai.beta.threads.runs.create(threadId, {
  assistant_id: assistantId,
  max_prompt_tokens: 1000,
  max_completion_tokens: 500,
});

4. Clean Up Resources

// Delete old threads
async function cleanupUserThread(userId: string) {
  const threadId = await db.getThreadIdForUser(userId);
  if (threadId) {
    await openai.beta.threads.del(threadId);
    await db.deleteThreadIdForUser(userId);
  }
}

// Delete unused vector stores
async function cleanupVectorStores(keepDays = 30) {
  const stores = await openai.beta.vectorStores.list({ limit: 100 });

  for (const store of stores.data) {
    const ageSeconds = Date.now() / 1000 - store.created_at;
    const ageDays = ageSeconds / (60 * 60 * 24);

    if (ageDays > keepDays) {
      await openai.beta.vectorStores.del(store.id);
    }
  }
}

5. Use Streaming for Better UX

// Show progress in real-time
async function streamToUser(threadId: string, assistantId: string) {
  const stream = await openai.beta.threads.runs.stream(threadId, {
    assistant_id: assistantId,
  });

  for await (const event of stream) {
    if (event.event === 'thread.message.delta') {
      const delta = event.data.delta.content?.[0]?.text?.value;
      if (delta) {
        // Send to user immediately
        sendToClient(delta);
      }
    }
  }
}

Relationship to Other Skills

vs. openai-api Skill

openai-api (Chat Completions):

• Stateless requests
• Manual history management
• Direct responses
• Use for: Simple text generation, function calling

openai-assistants:

• Stateful conversations (threads)
• Automatic history management
• Built-in tools (Code Interpreter, File Search)
• Use for: Chatbots, data analysis, RAG

vs. openai-responses Skill

openai-responses (Responses API):

• ✅ Recommended for new projects
• Better reasoning preservation
• Modern MCP integration
• Active development

openai-assistants:

• ⚠️ Deprecated in H1 2026
• Use for legacy apps
• Migration path available

Migration: See references/migration-to-responses.md

Migration from v1 to v2

v1 deprecated: December 18, 2024

Key Changes:

1. Retrieval → File Search: retrieval tool replaced with file_search
2. Vector Stores: Files now organized in vector stores (10,000 file limit)
3. Instructions Limit: Increased from 32k to 256k characters
4. File Attachments: Now message-level instead of assistant-level

See references/migration-from-v1.md for complete guide.

Next Steps

Templates:

• templates/basic-assistant.ts - Simple math tutor
• templates/code-interpreter-assistant.ts - Data analysis
• templates/file-search-assistant.ts - RAG with vector stores
• templates/function-calling-assistant.ts - Custom tools
• templates/streaming-assistant.ts - Real-time streaming

References:

• references/top-errors.md - 12 common errors and solutions
• references/thread-lifecycle.md - Thread management patterns
• references/vector-stores.md - Vector store deep dive

Related Skills:

• openai-responses - Modern replacement (recommended)
• openai-api - Chat Completions (stateless)

Last Updated: 2025-10-25 Package Version: openai@6.7.0 Status: Production Ready (Deprecated H1 2026)