This weblog submit focuses on new options and enhancements. For a complete record, 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 a substitute of simply doing one factor, an agent can purpose, plan, and take a number of actions to realize a bigger aim. Consider them as AI packages that may break down advanced issues and use totally different instruments or fashions to get the job performed.
With this launch, we’re making it simpler to construct these brokers on Clarifai. This implies you may:
- Create goal-oriented AI: Design methods that work in the direction of particular goals, 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 unravel extra advanced issues.
- Automate multi-step processes: Scale back handbook effort by having AI deal with complete 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 really helpful, they want entry to real-time data from outdoors their inside knowledge. The Mannequin Context Protocol (MCP) solves this by offering a standardized manner for AI fashions and brokers to work together with exterior knowledge sources and APIs.
We have built-in MCP, permitting you to:
- Join brokers to your knowledge: Bridge your AI brokers together with your firm’s databases, knowledge lakes, and different inside methods.
- Entry dwell knowledge: Give your brokers present data from exterior APIs, like monetary knowledge, information, or sensor readings.
- Construct customized knowledge 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 cannot solely assume and plan but in addition actively fetch and use real-world data, making your AI purposes extra highly effective and related. Be taught extra right here.
Clarifai now provides an OpenAI-compatible API endpoint, permitting you to make use of your current OpenAI code and workflows to run inferences with Clarifai fashions, together with those who combine or wrap OpenAI fashions.
The compatibility layer routinely 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.
Under is an instance that makes use of the OpenAI Python shopper library to work together with a Clarifai mannequin through Clarifai’s OpenAI-compatible API endpoint. Learn extra right here
We’ve got 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 trying ahead to seeing the sorts of purposes the neighborhood builds round it. Try our video tutorial on constructing an AI Weblog Writing Agent to see AI Brokers in motion. You too can discover extra examples right here.
Discover the documentation and begin constructing at the moment. We’ll even be including extra agent examples and templates quickly, so keep tuned.
When you’ve got any questions, ship us a message on our Group Discord channel. Thanks for studying!
