NEW OpenAI Open Source Update 🤯

OpenAI just dropped new OpenAI open-source update and this is huge. They made it possible to swap AI providers without changing a single line of code. You can now run clawed GPT or even local models through the exact same interface. This is a gamecher for anyone building AI agents. Let me show you why this matters and how to use it right now. All right, let's talk about what

just happened. On January 14th, 2026, the open-source community announced something called open responses. This is a massive deal and if you're building anything with AI, you need to know about this. So, what is Open Responses? It's an open- source specification that extends OpenAI's responses API. Now, OpenAI launched their responses API back in March 2025. It was designed specifically for building AI agents, not just chat bots, real agents that can use tools, make decisions, and handle complex workflows. Open Responses takes that concept and makes it work across any AI provider, OpenAI, Anthropic, Google, local models, whatever, one interface to rule them all. Here's why

this matters. Right now, if you build an AI agent using OpenAI's API, you're locked in. If you want to switch to Claude or Gemini, you have to rewrite your code. Different API formats, different streaming methods, different tool implementations. It's a nightmare. Open Responses solves this by creating a unified standard that every provider can follow. You write your code once and it works everywhere. Let me give you a real example. Say you're running the AI profit boardroom and you want to build an agent that automatically responds to member questions in your community. You start with GPT4, but then you realize Claude Sonet gives better answers for technical questions. With open responses, you literally just change the model name in your config. Everything else stays the same. The streaming works, the tools work. Your entire workflow stays intact. That's the power here. And it gets better. You can

self-host everything. If you're worried about privacy or data security, you can run this entire setup on your own servers. Use local models like Olmer or DeepSeek. Your data never leaves your infrastructure. This is huge for businesses that handle sensitive information. For the AI profit boardroom, this means you could process member data and automate workflows without ever sending anything to external APIs. Now, let's talk about the

technical side, but I'm going to keep it simple. Open responses uses something called semantic event streaming. Instead of raw deltas, what does that mean? Instead of getting random chunks of text that you have to piece together, you get clean structured events. The agent is thinking, the agent is using a tool, the agent has an answer. It's way easier to work with. You can build better user experiences because you know exactly what's happening at each step. The spec also handles tool calls natively. So, if your agent needs to search the web, run code, or call your own custom tools, it's all built in. You can even set limits like max tool calls to prevent your agent from going into infinite loops. This is critical for production systems. You don't want an agent that just keeps calling tools forever and burning through your API budget. Here's another killer feature. Open responses is stateless by default. That means each request is independent. You don't have to manage conversation history or session state. This makes it way easier to scale. You can route requests to different servers, use load balancers, all that good stuff. And if you need stateful conversations, you can still build that on top. But the foundation is clean and simple. Let me show you how

easy this is to set up. You go to GitHub and find the Open Responses repository. It's got over 80 stars already, and it just launched. You run MPX Open Responses in it in your terminal. That's it. It sets up a self-hosted server that's compatible with the OpenAI SDK. Then you take your existing code and change one line. Instead of pointing to apiopenai. com, you point to localhost. Done. Now you're running through the open responses interface. And here's where it gets really practical for automation. Let's say you want to build a content generation system for the AI profit boardroom. You need to create social media posts, email newsletters, and a video script. With open responses, you can set up a routing system. Use GPT4 for creative writing. Use clawed for technical content. Use a local model for simple rewrites all through the same codebase. You test which model works best for each task and optimize your costs and quality at the same time. The GitHub repo has examples in Python and JavaScript. You can literally copy and paste the code and start testing. They support models like GPT40 Mini and Deep CR1 right out of the box. And because it follows the OpenAI SDK format, if you're already using the agents SDK, it just works. No migration needed. Now, I want to tell you about something important. If you're watching this and thinking about how to actually implement AI automation in your business, you need to check out the AI profit boardroom. This is exactly the kind of tool we teach inside. How to take cutting edge AI tech like open responses and turn it into real business value. How to automate your workflows, save hours every day, and scale your operations without hiring a massive team. We've got step-by-step guides, templates, and a community of people who are doing this right now. No fluff, just practical automation that works. Link is in the description. All right, back to open responses. Let's talk about why this announcement matters in the bigger picture. We're in 2026 now and the AI landscape is getting crowded. Open AAI, Anthropic, Google, Meta, Mistral, all these companies are competing and they all have different APIs. This creates fragmentation. As a developer or business owner, you're forced to pick a side. But what if that company raises prices? What if their model gets worse? What if they have downtime? You're stuck. Open responses breaks that lock in. It's like how HTML standardize the web. Now you can write code once and deploy it anywhere. This is going to accelerate innovation because developers aren't wasting time rewriting the same thing for different providers. They can focus on building better agents and better applications. And here's something interesting. This fits perfectly with OpenAI's 2026 roadmap. They've been talking about GPTO OSS models, open- source models that integrate with their ecosystem. Open responses makes that integration seamless. It's not just about open AI anymore. It's about the entire open ecosystem working together. For businesses, this means faster routing and evaluations. You can test multiple models side by side and see which one performs best for your specific use case. Maybe GPT4 is great for customer support, but Claude is better for code generation. Now you can switch between them in seconds. You can even run them in parallel and compare results in real time. The extensibility is another big deal. The spec supports text, images, JSON, and even video. So if you're building a video generation agent or a multimodal system, Open Responses has you covered. And because it's open source, the community can extend it. Need support for a new model? Add it. Need a custom tool? build it. The spec is flexible enough to grow with the technology. Let's talk about the self-hosting setup in more detail. You run the npx command and it spins up a docker container. Inside that container is a server that translates open responses requests into provider specific API calls. So when you say use claude sonnet, it knows how to call anthropics API. When you say use GPT4, it calls open AI. You don't have to know the differences. The server handles it. And here's the cool part. You can customize this server. Add authentication, add logging, add rate limiting. It's your infrastructure. If you're running a SAS business or an agency like the AI profit boardroom, you can white label this entire system. Your clients get a unified AI interface and you control everything behind the scenes. The HuggingFace blog has a detailed overview and even a proxy endpoint you can test right now. You don't even need to self-host to try it out. Just point your code to the HuggingFace proxy and start making requests. It's that easy. This lowers

the barrier to entry massively. Anyone can experiment with this technology today. Now, let's talk about practical use cases. Imagine you're running an AI automation agency. You've got clients in different industries, e-commerce, SAS, content creation, whatever. Each client has different needs, some need fast responses, some need accuracy, some need privacy. With Open Responses, you can offer custom AI solutions without building custom code for each client. You set up one system and configure it per client. This is how you scale an agency without drowning in technical debt. Or let's say you're building an AI assistant for the AI profit boardroom members. You want it to answer questions about AI automation, suggest tools, and even generate implementation plans. You start with one model, but as new models come out, you want to upgrade. With open responses, upgrading is trivial. You add the new model to your config to test it and deploy. No code changes, no downtime. Your members get better answers automatically. The community forum on OpenAI's site already has a thread about open responses. People are sharing use cases, asking questions, and contributing ideas. This is the early days, and the momentum is building fast. If you get in now, you're ahead of the curve. You understand the technology before it becomes mainstream. Here's another advantage. Open responses makes it easier to do cost optimization. Different models have different pricing. GPT4 is expensive. GPT40 Mini is cheaper. Local models are free. With open responses, you can route simple tasks to cheap models and complex tasks to expensive models all automatically. You set up rules based on the request type and the system handles the rest. And speaking of scale, the stateless design means you can handle massive traffic. Each request is independent, so you can distribute load across multiple servers. If one server goes down, the others keep running. This is production grade architecture. If you're building something serious, this matters. The spec also includes controls like max tool calls. This prevents runaway agents. If your agent gets stuck in a loop, it stops automatically. This protects your API budget and prevents unexpected behavior. It's these little details that make open responses production ready, not just a toy. Let me wrap this up with the big picture. AI is moving fast. New models are coming out every month. New capabilities are being added constantly. If your code is tightly coupled to one provider, you're going to fall behind. You're going to spend all your time migrating instead of innovating. Open responses futurep proofs your AI systems. You build once and you benefit from every advancement across every provider. This is the kind of technology that changes how we build AI applications. It's not flashy. It's not a new model with crazy capabilities, but it's infrastructure. And infrastructure is what makes everything else possible. Open responses is the rails that let AI agents run anywhere.

So here's what you should do right now. Go to the GitHub repo at github. com. Open responses. open responses, star it, read the readme, try the examples, set up the self-hosted server, and test it with a simple agent. Maybe build something for your own business. Automate a task that's eating up your time. And if you want the full process, SLPs, and 100 plus AI use cases like this one. Join the AI Success Lab. It's our free AI community. Links in the comments and description. You'll get all the video notes from there, plus access to our community of 40,000 members who are crushing it with AI. And let me be clear, this isn't about jumping on every new trend. This is about recognizing when something fundamental changes. Open responses is that change. It's making AI more accessible, more flexible, and more powerful. If you're building with AI, you need to know about this. Comment below and tell me what you're going to build with open responses. What's the first agent you're going to create? What problem are you going to solve? I want to hear from you. That's it for today. Thanks for watching.