npm install openai@6.16.0

import OpenAI from 'openai';

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

// 1. Create assistant
const assistant = await openai.beta.assistants.create({
  name: "Math Tutor",
  instructions: "You are a math tutor. Use code interpreter for calculations.",
  tools: [{ type: "code_interpreter" }],
  model: "gpt-5",
});

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

// 3. Add message
await openai.beta.threads.messages.create(thread.id, {
  role: "user",
  content: "Solve: 3x + 11 = 14",
});

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

// 5. Poll for completion
let status = await openai.beta.threads.runs.retrieve(thread.id, run.id);
while (status.status !== 'completed') {
  await new Promise(r => setTimeout(r, 1000));
  status = await openai.beta.threads.runs.retrieve(thread.id, run.id);
}

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

const assistant = await openai.beta.assistants.create({
  model: "gpt-5",
  instructions: "System prompt (max 256k chars in v2)",
  tools: [{ type: "code_interpreter" }, { type: "file_search" }],
  tool_resources: { file_search: { vector_store_ids: ["vs_123"] } },
});

// Create thread with messages
const thread = await openai.beta.threads.create({
  messages: [{ role: "user", content: "Hello" }],
});

// Add message with attachments
await openai.beta.threads.messages.create(thread.id, {
  role: "user",
  content: "Analyze this",
  attachments: [{ file_id: "file_123", tools: [{ type: "code_interpreter" }] }],
});

// List messages
const msgs = await openai.beta.threads.messages.list(thread.id);

// Create run with optional overrides
const run = await openai.beta.threads.runs.create(thread.id, {
  assistant_id: "asst_123",
  additional_messages: [{ role: "user", content: "Question" }],
  max_prompt_tokens: 1000,
  max_completion_tokens: 500,
});

// Poll until complete
let status = await openai.beta.threads.runs.retrieve(thread.id, run.id);
while (['queued', 'in_progress'].includes(status.status)) {
  await new Promise(r => setTimeout(r, 1000));
  status = await openai.beta.threads.runs.retrieve(thread.id, run.id);
}

const stream = await openai.beta.threads.runs.stream(thread.id, { assistant_id });

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

// Attach file to message
attachments: [{ file_id: "file_123", tools: [{ type: "code_interpreter" }] }]

// Access generated files
for (const content of message.content) {
  if (content.type === 'image_file') {
    const fileContent = await openai.files.content(content.image_file.file_id);
  }
}

// Create vector store
const vs = await openai.beta.vectorStores.create({ name: "Docs" });
await openai.beta.vectorStores.files.create(vs.id, { file_id: "file_123" });

// Wait for indexing
let store = await openai.beta.vectorStores.retrieve(vs.id);
while (store.status === 'in_progress') {
  await new Promise(r => setTimeout(r, 2000));
  store = await openai.beta.vectorStores.retrieve(vs.id);
}

// Use in assistant
tool_resources: { file_search: { vector_store_ids: [vs.id] } }

if (run.status === 'requires_action') {
  const toolCalls = run.required_action.submit_tool_outputs.tool_calls;
  const outputs = toolCalls.map(tc => ({
    tool_call_id: tc.id,
    output: JSON.stringify(yourFunction(JSON.parse(tc.function.arguments))),
  }));

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

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

Error: OpenAIError: Final run has not been received

try {
  const stream = await openai.beta.threads.runs.stream(thread.id, { assistant_id });
  for await (const event of stream) {
    if (event.event === 'thread.message.delta') {
      process.stdout.write(event.data.delta.content?.[0]?.text?.value || '');
    }
  }
} catch (error) {
  if (error.message?.includes('Final run has not been received')) {
    // Run ended with 'incomplete' status - thread can continue
    const run = await openai.beta.threads.runs.retrieve(thread.id, runId);
    if (run.status === 'incomplete') {
      // Handle: prompt user to continue, reduce max_completion_tokens, etc.
    }
  }
}

Error: No 'files' provided to process

// ✅ Correct
await openai.beta.vectorStores.fileBatches.uploadAndPoll(vectorStoreId, {
  files: fileStreams
});

// ❌ Wrong (shown in official docs)
await openai.beta.vectorStores.fileBatches.uploadAndPoll(vectorStoreId, fileStreams);

Error: Unsupported parameter: 'temperature' is not supported with this model

await openai.beta.assistants.update(assistantId, {
  model: 'o3-mini',
  reasoning_effort: 'medium',
  temperature: null,  // ✅ Must explicitly clear
  top_p: null
});

Error: Invalid 'batch_id': 'vs_...'. Expected an ID that begins with 'vsfb_'.

// Option 1: Use createAndPoll after separate upload
const batch = await openai.vectorStores.fileBatches.createAndPoll(
  vectorStoreId,
  { file_ids: uploadedFileIds }
);

// Option 2: List batches to find correct ID
const batches = await openai.vectorStores.fileBatches.list(vectorStoreId);
const batchId = batches.data[0].id; // starts with 'vsfb_'

// ❌ This deletes file from VS_A, VS_B, AND VS_C
await openai.vectorStores.files.delete('VS_A', 'file-xxx');

async function createRunSafely(threadId: string, assistantId: string) {
  // Check for active runs first
  const runs = await openai.beta.threads.runs.list(threadId, { limit: 1 });
  const activeRun = runs.data.find(r =>
    ['queued', 'in_progress', 'requires_action'].includes(r.status)
  );

  if (activeRun) {
    try {
      await openai.beta.threads.runs.cancel(threadId, activeRun.id);

      // Wait for cancellation to complete
      let run = await openai.beta.threads.runs.retrieve(threadId, activeRun.id);
      while (run.status === 'cancelling') {
        await new Promise(r => setTimeout(r, 500));
        run = await openai.beta.threads.runs.retrieve(threadId, activeRun.id);
      }
    } catch (error) {
      // Ignore "already completed" errors - run finished naturally
      if (!error.message?.includes('completed')) throw error;
    }
  }

  return openai.beta.threads.runs.create(threadId, { assistant_id: assistantId });
}