This blog post focuses on new features and improvements. For a comprehensive list, including bug fixes, please see the release notes.
We’re rolling out two key features that change how you build AI using Clarifai: support for AI agents and the Model Context Protocol (MCP).
AI Agents: Building Smarter, Autonomous AI
AI agents are a big step beyond single-task AI models. Instead of just doing one thing, an agent can reason, plan, and take multiple actions to achieve a larger goal. Think of them as AI programs that can break down complex problems and use different tools or models to get the job done.
With this release, we’re making it easier to build these agents on Clarifai. This means you can:
- Create goal-oriented AI: Design systems that work towards specific objectives, not just providing isolated answers.
- Chain together AI capabilities: Combine multiple models and tools on our platform (or external ones) in sequence to solve more complex problems.
- Automate multi-step processes: Reduce manual effort by having AI handle entire workflows.
This opens up possibilities for more advanced AI applications that can make decisions and adapt to situations.
To show you what and how you can build AI Agents, we’ve created an AI Blog Writing Agent using Clarifai and CrewAI!
In this video, we build an AI-powered blog writing agent that generates complete blog posts from scratch. We use:
- CrewAI to manage agent orchestration
- Gemini 2.5 Pro model powered by Clarifai
- Streamlit to create a simple and interactive UI
MCP: Giving AI Agents Real-World Context
For AI agents to be truly useful, they need access to real-time information from outside their internal data. The Model Context Protocol (MCP) solves this by providing a standardized way for AI models and agents to interact with external data sources and APIs.
We’ve integrated MCP, allowing you to:
- Connect agents to your data: Bridge your AI agents with your company’s databases, data lakes, and other internal systems.
- Access live data: Give your agents current information from external APIs, like financial data, news, or sensor readings.
- Build custom data bridges: Create your own MCP servers to tailor how your AI agents access and use external context.
Combining AI agents with MCP means your AI can not only think and plan but also actively fetch and use real-world information, making your AI applications more powerful and relevant. Learn more here.
Clarifai now offers an OpenAI-compatible API endpoint, allowing you to use your existing OpenAI code and workflows to run inferences with Clarifai models, including those that integrate or wrap OpenAI models.
The compatibility layer automatically translates OpenAI-style requests into Clarifai API calls, so you can access Clarifai’s broad model library as custom tools within your OpenAI-based projects.
This removes the need to rewrite your code for Clarifai’s native API, making integration fast and simple for teams already familiar with OpenAI.
Below is an example that uses the OpenAI Python client library to interact with a Clarifai model via Clarifai’s OpenAI-compatible API endpoint. Read more here
We have made numerous improvements to the Python SDK to enhance stability, usability, and integration capabilities:
We’re excited about the new Agentic and MCP support in Clarifai and are looking forward to seeing the kinds of applications the community builds around it. Check out our video tutorial on building an AI Blog Writing Agent to see AI Agents in action. You can also explore more examples here.
Explore the documentation and start building today. We’ll also be adding more agent examples and templates soon, so stay tuned.
If you have any questions, send us a message on our Community Discord channel. Thanks for reading!
