This weblog put up focuses on new options and enhancements. For a complete listing, together with bug fixes, please see the launch notes.
We’re rolling out two key options that change the way you construct AI utilizing Clarifai: help for AI brokers and the Mannequin Context Protocol (MCP).
AI Brokers: Constructing Smarter, Autonomous AI
AI brokers are an enormous step past single-task AI fashions. As an alternative of simply doing one factor, an agent can purpose, plan, and take a number of actions to realize a bigger purpose. Consider them as AI packages that may break down advanced issues and use totally different instruments or fashions to get the job carried out.
With this launch, we’re making it simpler to construct these brokers on Clarifai. This implies you may:
- Create goal-oriented AI: Design programs that work in direction of particular aims, not simply offering remoted solutions.
- Chain collectively AI capabilities: Mix a number of fashions and instruments on our platform (or exterior ones) in sequence to resolve extra advanced issues.
- Automate multi-step processes: Scale back guide effort by having AI deal with total workflows.
This opens up prospects for extra superior AI purposes that may make selections and adapt to conditions.
To point out you what and how one can construct AI Brokers, we have created an AI Weblog Writing Agent utilizing Clarifai and CrewAI!
On this video, we construct an AI-powered weblog writing agent that generates full weblog posts from scratch. We use:
- CrewAI to handle agent orchestration
- Gemini 2.5 Professional mannequin powered by Clarifai
- Streamlit to create a easy and interactive UI
MCP: Giving AI Brokers Actual-World Context
For AI brokers to be actually helpful, they want entry to real-time info from outdoors their inner information. The Mannequin Context Protocol (MCP) solves this by offering a standardized approach for AI fashions and brokers to work together with exterior information sources and APIs.
We have built-in MCP, permitting you to:
- Join brokers to your information: Bridge your AI brokers along with your firm’s databases, information lakes, and different inner programs.
- Entry dwell information: Give your brokers present info from exterior APIs, like monetary information, information, or sensor readings.
- Construct customized information bridges: Create your individual MCP servers to tailor how your AI brokers entry and use exterior context.
Combining AI brokers with MCP means your AI can’t solely suppose and plan but in addition actively fetch and use real-world info, making your AI purposes extra highly effective and related. Be taught extra right here.
Clarifai now presents an OpenAI-compatible API endpoint, permitting you to make use of your present OpenAI code and workflows to run inferences with Clarifai fashions, together with people who combine or wrap OpenAI fashions.
The compatibility layer mechanically interprets OpenAI-style requests into Clarifai API calls, so you may entry Clarifai’s broad mannequin library as customized instruments inside your OpenAI-based tasks.
This removes the necessity to rewrite your code for Clarifai’s native API, making integration quick and easy for groups already accustomed to OpenAI.
Beneath is an instance that makes use of the OpenAI Python consumer library to work together with a Clarifai mannequin by way of Clarifai’s OpenAI-compatible API endpoint. Learn extra right here
We’ve made quite a few enhancements to the Python SDK to boost stability, usability, and integration capabilities:
We’re excited in regards to the new Agentic and MCP help in Clarifai and are wanting ahead to seeing the sorts of purposes the group builds round it. Try our video tutorial on constructing an AI Weblog Writing Agent to see AI Brokers in motion. It’s also possible to discover extra examples right here.
Discover the documentation and begin constructing in the present day. We’ll even be including extra agent examples and templates quickly, so keep tuned.
When you have any questions, ship us a message on our Neighborhood Discord channel. Thanks for studying!
