OpenAI Responses API

Status: Production Ready Last Updated: 2026-01-09 API Launch: March 2025 Dependencies: openai@6.15.0 (Node.js) or fetch API (Cloudflare Workers)

---

What Is the Responses API?

OpenAI's unified interface for agentic applications, launched March 2025. Provides stateful conversations with preserved reasoning state across turns.

Key Innovation: Unlike Chat Completions (reasoning discarded between turns), Responses preserves the model's reasoning notebook, improving performance by 5% on TAUBench and enabling better multi-turn interactions.

vs Chat Completions:

Feature	Chat Completions	Responses API
State	Manual history tracking	Automatic (conversation IDs)
Reasoning	Dropped between turns	Preserved across turns (+5% TAUBench)
Tools	Client-side round trips	Server-side hosted
Output	Single message	Polymorphic (8 types)
Cache	Baseline	40-80% better utilization
MCP	Manual	Built-in

---

Quick Start

npm install openai@6.15.0

import OpenAI from 'openai';

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

const response = await openai.responses.create({
  model: 'gpt-5',
  input: 'What are the 5 Ds of dodgeball?',
});

console.log(response.output_text);

Key differences from Chat Completions:

• Endpoint: /v1/responses (not /v1/chat/completions)
• Parameter: input (not messages)
• Role: developer (not system)
• Output: response.output_text (not choices[0].message.content)

---

When to Use Responses vs Chat Completions

Use Responses:

• Agentic applications (reasoning + actions)
• Multi-turn conversations (preserved reasoning = +5% TAUBench)
• Built-in tools (Code Interpreter, File Search, Web Search, MCP)
• Background processing (60s standard, 10min extended timeout)

Use Chat Completions:

• Simple one-off generation
• Fully stateless interactions
• Legacy integrations

---

Stateful Conversations

Automatic State Management using conversation IDs:

// Create conversation
const conv = await openai.conversations.create({
  metadata: { user_id: 'user_123' },
});

// First turn
const response1 = await openai.responses.create({
  model: 'gpt-5',
  conversation: conv.id,
  input: 'What are the 5 Ds of dodgeball?',
});

// Second turn - model remembers context + reasoning
const response2 = await openai.responses.create({
  model: 'gpt-5',
  conversation: conv.id,
  input: 'Tell me more about the first one',
});

Benefits: No manual history tracking, reasoning preserved, 40-80% better cache utilization

Conversation Limits: 90-day expiration

---

Built-in Tools (Server-Side)

Server-side hosted tools eliminate backend round trips:

Tool	Purpose	Notes
code_interpreter	Execute Python code	Sandboxed, 30s timeout (use background: true for longer)
file_search	RAG without vector stores	Max 512MB per file, supports PDF/Word/Markdown/HTML/code
web_search	Real-time web information	Automatic source citations
image_generation	DALL-E integration	DALL-E 3 default
mcp	Connect external tools	OAuth supported, tokens NOT stored

Usage:

const response = await openai.responses.create({
  model: 'gpt-5',
  input: 'Calculate mean of: 10, 20, 30, 40, 50',
  tools: [{ type: 'code_interpreter' }],
});

---

MCP Server Integration

Built-in support for Model Context Protocol (MCP) servers to connect external tools (Stripe, databases, custom APIs).

Basic MCP:

const response = await openai.responses.create({
  model: 'gpt-5',
  input: 'Roll 2d6 dice',
  tools: [{
    type: 'mcp',
    server_label: 'dice',
    server_url: 'https://example.com/mcp',
    authorization: process.env.TOKEN, // ⚠️ NOT stored, required each request
  }],
});

MCP Output Types:

• mcp_list_tools - Tools discovered on server
• mcp_call - Tool invocation + result
• message - Final response

---

Reasoning Preservation

Key Innovation: Model's internal reasoning state survives across turns (unlike Chat Completions which discards it).

Visual Analogy:

• Chat Completions: Model tears out scratchpad page before responding
• Responses API: Scratchpad stays open for next turn

Performance: +5% on TAUBench (GPT-5) purely from preserved reasoning

Reasoning Summaries (free):

response.output.forEach(item => {
  if (item.type === 'reasoning') console.log(item.summary[0].text);
  if (item.type === 'message') console.log(item.content[0].text);
});

---

Background Mode

For long-running tasks, use background: true:

const response = await openai.responses.create({
  model: 'gpt-5',
  input: 'Analyze 500-page document',
  background: true,
  tools: [{ type: 'file_search', file_ids: [fileId] }],
});

// Poll for completion (check every 5s)
const result = await openai.responses.retrieve(response.id);
if (result.status === 'completed') console.log(result.output_text);

Timeout Limits:

• Standard: 60 seconds
• Background: 10 minutes

---

Polymorphic Outputs

Returns 8 output types instead of single message:

Type	Example
message	Final answer, explanation
reasoning	Step-by-step thought process (free!)
code_interpreter_call	Python code + results
mcp_call	Tool name, args, output
mcp_list_tools	Tool definitions from MCP server
file_search_call	Matched chunks, citations
web_search_call	URLs, snippets
image_generation_call	Image URL

Processing:

response.output.forEach(item => {
  if (item.type === 'reasoning') console.log(item.summary[0].text);
  if (item.type === 'web_search_call') console.log(item.results);
  if (item.type === 'message') console.log(item.content[0].text);
});

// Or use helper for text-only
console.log(response.output_text);

---

Migration from Chat Completions

Breaking Changes:

Feature	Chat Completions	Responses API
Endpoint	/v1/chat/completions	/v1/responses
Parameter	messages	input
Role	system	developer
Output	choices[0].message.content	output_text
State	Manual array	Automatic (conversation ID)
Streaming	data: {"choices":[...]}	SSE with 8 item types

Example:

// Before
const response = await openai.chat.completions.create({
  model: 'gpt-5',
  messages: [
    { role: 'system', content: 'You are a helpful assistant.' },
    { role: 'user', content: 'Hello!' },
  ],
});
console.log(response.choices[0].message.content);

// After
const response = await openai.responses.create({
  model: 'gpt-5',
  input: [
    { role: 'developer', content: 'You are a helpful assistant.' },
    { role: 'user', content: 'Hello!' },
  ],
});
console.log(response.output_text);

---

Error Handling

8 Common Errors:

1. Session State Not Persisting

• Cause: Not using conversation IDs or using different IDs per turn
• Fix: Create conversation once (const conv = await openai.conversations.create()), reuse conv.id for all turns

2. MCP Server Connection Failed (mcp_connection_error)

• Causes: Invalid URL, missing/expired auth token, server down
• Fix: Verify URL is correct, test manually with fetch(), check token expiration

3. Code Interpreter Timeout (code_interpreter_timeout)

• Cause: Code runs longer than 30 seconds
• Fix: Use background: true for extended timeout (up to 10 min)

4. Image Generation Rate Limit (rate_limit_error)

• Cause: Too many DALL-E requests
• Fix: Implement exponential backoff retry (1s, 2s, 3s delays)

5. File Search Relevance Issues

• Cause: Vague queries return irrelevant results
• Fix: Use specific queries ("pricing in Q4 2024" not "find pricing"), filter by chunk.score > 0.7

6. Cost Tracking Confusion

• Cause: Responses bills for input + output + tools + stored conversations (vs Chat Completions: input + output only)
• Fix: Set store: false if not needed, monitor response.usage.tool_tokens

7. Conversation Not Found (invalid_request_error)

• Causes: ID typo, conversation deleted, or expired (90-day limit)
• Fix: Verify exists with openai.conversations.list() before using

8. Tool Output Parsing Failed

• Cause: Accessing wrong output structure
• Fix: Use response.output_text helper or iterate response.output.forEach(item => ...) checking item.type

---

Critical Patterns

✅ Always:

• Use conversation IDs for multi-turn (40-80% better cache)
• Handle all 8 output types in polymorphic responses
• Use background: true for tasks >30s
• Provide MCP authorization tokens (NOT stored, required each request)
• Monitor response.usage.total_tokens for cost control

❌ Never:

• Expose API keys in client-side code
• Assume single message output (use response.output_text helper)
• Reuse conversation IDs across users (security risk)
• Ignore error types (handle rate_limit_error, mcp_connection_error specifically)
• Poll faster than 1s for background tasks (use 5s intervals)

---

References

Official Docs:

• Responses API Guide: https://platform.openai.com/docs/guides/responses
• API Reference: https://platform.openai.com/docs/api-reference/responses
• MCP Integration: https://platform.openai.com/docs/guides/tools-connectors-mcp
• Blog Post: https://developers.openai.com/blog/responses-api/
• Starter App: https://github.com/openai/openai-responses-starter-app

Skill Resources: templates/, references/responses-vs-chat-completions.md, references/mcp-integration-guide.md, references/built-in-tools-guide.md, references/migration-guide.md, references/top-errors.md