---
title: "OpenAI APIs"
source: Panaversity Learn Agentic AI
source_url: https://github.com/panaversity/learn-agentic-ai
licence: Apache-2.0
domain: agentic-ai
subdomain: panaversity-agentic
date_added: 2026-04-25
---

# OpenAI APIs

OpenAI offers two primary APIs for integrating AI language capabilities into applications: the Chat Completions API and the Responses API.

## Chat Completions API

The Chat Completions API enables developers to generate AI-driven conversational responses based on a sequence of input messages. This API operates on a stateless model, meaning each request requires the full conversation history to provide context. Developers structure inputs as a list of messages, and the model generates a corresponding reply. This approach is particularly useful for applications requiring straightforward conversational AI without the need for complex state management.

Major AI companies like **Google (Gemini API)**, **Anthropic (Claude API)**, and **DeepSeek** have released APIs compatible with **OpenAI's Chat Completion API** format. This compatibility is intentional, driven by the widespread adoption and developer familiarity with OpenAI's API structure, which has effectively become a de facto standard in the generative AI industry.

### Why is OpenAI’s Chat Completion API becoming an industry standard?

1. **Developer Familiarity**  
   OpenAI’s Chat Completion API is simple, intuitive, and well-documented, making it easy for developers to integrate generative AI into various applications.

2. **Ecosystem and Tools**  
   A rich ecosystem of open-source libraries (e.g., LangChain, AutoGen, CrewAI, OpenAI Python Client) has developed around OpenAI’s APIs, further encouraging standardization.

3. **Ease of Switching Providers**  
   With compatible APIs, developers can seamlessly switch between AI providers like OpenAI, Anthropic, Google, and DeepSeek without significant code modifications, reducing vendor lock-in.

4. **Rapid Innovation**  
   Standardized interfaces accelerate the pace of innovation, allowing developers to test and adopt new models more quickly.

### Companies adopting OpenAI-Compatible APIs:

- **Google**:  
  Google's Gemini APIs now support OpenAI-compatible endpoints (`chat.completions`), making Gemini models plug-and-play for developers already using OpenAI’s format.

- **Anthropic**:  
  Anthropic explicitly adopted OpenAI-compatible API endpoints for their Claude 3 models, simplifying integration and encouraging adoption.

- **DeepSeek**:  
  DeepSeek models, aimed at enterprise and open-source communities, follow OpenAI’s API conventions, reinforcing compatibility.

- **Others**:  
  Cohere, Mistral, Groq, and several open-source LLM providers (such as LM Studio, Ollama, and Hugging Face endpoints) also support or closely align their APIs to OpenAI’s Chat Completion API structure.


### Implications for the AI Industry:

This widespread adoption has effectively transformed OpenAI’s Chat Completion API into a **de facto industry standard**, analogous to REST APIs in web development or Docker in containerization. This standardization brings several benefits:

- Easier adoption of generative AI by enterprises.
- Increased competition among AI providers, leading to faster improvements.
- Broader compatibility across infrastructure and tooling, facilitating greater innovation and ecosystem maturity.

---


## Responses API

Introduced as an evolution of OpenAI’s API offerings, the Responses API combines the simplicity of the Chat Completions API with advanced functionalities to support more dynamic and interactive AI applications. Key features include:
	•	Stateful Interactions: Unlike the stateless Chat Completions API, the Responses API maintains state across interactions, allowing for seamless continuation of conversations without resending the entire history.
	•	Built-in Tools: The API integrates tools such as web search, file search, and computer use, enabling AI agents to perform tasks like retrieving real-time information, accessing documents, and executing operations on a user’s behalf.  ￼
	•	Enhanced Flexibility: With a more flexible structure, the Responses API supports complex workflows and agentic behaviors, making it suitable for developing sophisticated AI agents capable of handling a variety of tasks.  ￼

In summary, while the Chat Completions API is ideal for straightforward conversational applications, the Responses API offers advanced features for building more interactive and capable AI agents.


## Key Enhancements in the Responses API Compared to Chat Completions

OpenAI's new Responses API represents a significant evolution in its API infrastructure, combining the straightforwardness of Chat Completions with the advanced capabilities of Assistants. The most notable enhancements include:

1. **State Management**

   - *Chat Completions*: Operated on a stateless model, requiring developers to resend entire conversation histories with each interaction.

   - *Responses API*: Introduces statefulness by automatically storing responses, allowing for seamless conversation continuity through the use of `previous_response_id`.

2. **Expanded Functionality**

   - *Chat Completions*: Functioned on a basic input-output model, processing a list of messages to generate a single message response.

   - *Responses API*: Introduces "Items," a versatile structure that represents various inputs and outputs, including messages, reasoning processes, function calls, and web searches. It now natively supports functionalities such as file search, web search, structured outputs, and hosted tools.

3. **Enhanced Streaming and Event Handling**

   - *Previous APIs*: Employed delta streaming, emitting JSON differences that were challenging to integrate and lacked type safety.

   - *Responses API*: Implements semantic events, providing clearer and more structured data streams, exemplified by `response.output_text.delta`.

4. **Integrated Tools and Vector Search**

   - Offers straightforward integration for web search, file search, and upcoming code execution capabilities.

   - Introduces a new Vector Stores Search API, enabling OpenAI's Retrieval-Augmented Generation (RAG) capabilities to be utilized with any model.

5. **Improved API Design and Usability**

   - Simplifies structure by transitioning from externally-tagged to internally-tagged polymorphism.

   - Flattens JSON response structures, enhancing ease of parsing and integration.

   - Supports form-encoded inputs, streamlining the integration process.

The Responses API is crafted for contemporary, multimodal, and agentic AI applications, addressing the limitations of Chat Completions while offering enhanced flexibility, efficiency, and user-friendliness. Nonetheless, Chat Completions remains available as a stable option for businesses.


## What are the Chances of Responses API Becoming the Industry Standard?

It is early days, and the OpenAI Responses API is still fresh out of the oven. But let's explore its potential to become an industry standard like its sibling, the Chat Completions API.

### First, what exactly is the OpenAI Responses API?
OpenAI's **Responses API** (released in early 2025) simplifies creating structured responses, allowing developers to define explicit schemas for outputs (like JSON) directly within API calls. It shifts the paradigm slightly, moving from free-form chat responses to structured data extraction and function calling baked directly into the API design.

---

### What made Chat Completion API an industry standard?
- **Simplicity & Flexibility:** Intuitive design, simple input-output structures.
- **Wide Developer Adoption:** Huge ecosystem support (tools, libraries, frameworks).
- **Vendor Adoption:** Quick compatibility adoption by major AI providers.

---

### Now, let's see if Responses API has similar potential:

### ✅ **Factors favoring Responses API becoming a standard:**
1. **Clear Use Cases:**
   - Structured data extraction.
   - Reliable agentic workflows and automation.
   - Enhanced API integration into enterprise systems.

2. **Simplified Integration for Developers:**
   - Removing the pain of post-processing text responses into structured data (less regex gymnastics—your developers will thank you).

3. **Alignment with Industry Needs:**
   - Enterprises and developers increasingly demand structured, reliable AI outputs for automated systems, workflows, and agentic use-cases.

---

### ⚠️ **Factors that might limit widespread adoption:**
1. **It's Still Early:**
   - API has just been released, adoption momentum hasn't yet formed.

2. **Dependence on OpenAI-specific Capabilities:**
   - If Responses API heavily relies on advanced proprietary techniques (like specific fine-tuning or capability features unique to GPT models), other vendors might struggle to replicate compatibility exactly.

3. **Industry Fragmentation Risk:**
   - Vendors might create their own proprietary structured response APIs if they believe differentiation outweighs compatibility.

---

### 📊 **Likelihood of Industry Adoption (our estimation):**
| Factor                          | Likelihood of Adoption |
|---------------------------------|------------------------|
| Developer friendliness          | ✅ High                |
| Enterprise utility              | ✅ Very High           |
| Competitor/Vendor Adoption Ease | ⚠️ Medium              |
| Proprietary Lock-in Concerns    | ⚠️ Medium              |
| Ecosystem & Community Adoption  | ✅ Potentially High    |

- **Overall Likelihood:** Moderate to High 🎯  
  It has solid potential to become an industry standard due to clear, practical use-cases and strong enterprise demand—but widespread adoption will significantly depend on **how quickly other providers (Google, Anthropic, DeepSeek, Cohere, etc.) follow suit**.

---

### 🌐 **Early Signals to Watch (Next 6–12 months):**
- Whether major AI competitors like **Anthropic (Claude), Google (Gemini), DeepSeek**, or emerging open-source leaders adopt API-compatible structured responses.
- Availability of community tools and libraries (LangChain, AutoGen, CrewAI, LangGraph) integrating the Responses API seamlessly.
- Developer enthusiasm and enterprise uptake—early signs from GitHub, developer communities, and product integrations.

---

### 🚀 **Our Prediction (with a playful twist!):**
There's a good chance OpenAI’s Responses API will set the bar again. But remember, the AI world is like a dance party—it's not enough just to show up; others have to join the dance floor too! Right now, OpenAI has started playing a catchy tune 🎶—let’s see who else jumps in to dance.

In short, there's a decent chance (I'd say about **70% probability**) that the Responses API could become a widely adopted standard within the next 12–18 months—assuming other major players get inspired by the music and join the party!

---

## Contents

The repository contains the following Google Colab notebooks, with both Chat API and Responses API examples:

1. [Introduction to basic prompting using both APIs](https://colab.research.google.com/drive/1jkZ4t8nkntiqwasUH972ThdCGgQq6Fof?usp=sharing)
2. [Text and Prompting (Part 1)](https://colab.research.google.com/drive/1kqx9JV-D9_FYJB_xNaKphNOPUEfDZ9Pg?usp=sharing)
3. [Text and Prompting (Part 2)](https://colab.research.google.com/drive/1IX7S60YJO7PR7FouaLzjsxOH8maK9j69?usp=sharing)
4. [Handling and comparing streaming outputs](https://colab.research.google.com/drive/1XcQ2-gNi8Bcb4wG1-phpd-y5FT5p0A5-?usp=sharing)
5. [Managing conversation state across both APIs](https://colab.research.google.com/drive/1-bamDkOawlNMCN-c_eKzS8LJ-Rks-R2A?usp=sharing)
6. [Working with image inputs and comparing their handling](https://colab.research.google.com/drive/1F6jwmyD2WUscPKM4Eymmx1nIw8tNA0-g?usp=sharing) 
7. [Generating structured outputs (Part 1)](https://colab.research.google.com/drive/1js7Jh0ga3uCkA1dqgMvKdFFL8Lpbmqg5?usp=sharing)
8. [Generating structured outputs (Part 2)](https://colab.research.google.com/drive/1lxTHPhuMbgmiktlJFCuwpF7spabD0LHQ?usp=sharing) 
9. [Implementing function calls (Part 1)](https://colab.research.google.com/drive/1g0AJiTzwPfANFdtEO-OWp2AA-lMDuidZ?usp=sharing)
10. [Implementing function calls (Part 2)](https://colab.research.google.com/drive/1cEWTzNWx0IPc8rBkEkVM6lQwMITHBML6?usp=sharing)
11. [Implementing function calls (Part 3)](https://colab.research.google.com/drive/1R7OlZZlJUYCHHuJ51m5EeJLGCwX6TUxX?usp=sharing)
12. [Implementing function calls (Part 4)](https://colab.research.google.com/drive/1JEm8c0U0V7lkHUIf8rvaEsKJLiralCuv?usp=sharing)
13. [Implementing function calls (Part 5)](https://colab.research.google.com/drive/1ZnngtHTvk8DxcqMZ2mQq6bT5fd8s62lH?usp=sharing)
14. [Implementing function calls (Part 6)](https://colab.research.google.com/drive/1Sdoz5oGHoYiVgQSQ-RQrV_n2_kLvR_uF?usp=sharing)
15. [Handling file inputs in both APIs](https://colab.research.google.com/drive/1Qw8o2vJuSHePwMK3NoY2wyu_uPu1Abi3?usp=sharing)
16. [Demonstrating reasoning capabilities and comparisons](https://colab.research.google.com/drive/1_2fGJD1rXMsMrRMpBwDY5MgWjAUZhJ9_?usp=sharing)
17. [Integrating web search functionalities (Part 1)](https://colab.research.google.com/drive/1DToTG6A9CfqM3QrwAxVFnZZ7meh4ROrV?usp=sharing)
18. [Integrating web search functionalities (Part 2)](https://colab.research.google.com/drive/1IOT5BvYAfguWttNgWp0OnJDQ_q2FZFDN?usp=sharing)
19. [Integrating web search functionalities (Part 3)](https://colab.research.google.com/drive/1YsFwti-nF_jJc-cnhXXjf-EFFoulCrkH?usp=sharing)
20. [Integrating web search functionalities (Part 4)](https://colab.research.google.com/drive/101kvTJxlKs4HiRtzk_uJSEDzIutStmIN?usp=sharing)
21. [Implementing file search capabilities](https://colab.research.google.com/drive/1Ml_Z-w-gptkUOJp-wUa0G5KZ-Rd6AJXy?usp=sharing)
