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Agentic AI Examples with Red Hat AI

Explore various Agentic AI frameworks and LLMs running on Red Hat AI platforms.

🚀 Framework Examples

Check out different Agentic AI frameworks:

🧠 LLMs Used in the Agentic AI Examples

These examples utilize the following Large Language Models (LLMs):

🔗 Function Calling

Many of these Agentic AI examples rely on Function Calling, a feature that enables LLMs to interact with external tools and APIs in a structured way. This allows models to:

  • Make API calls
  • Query databases
  • Execute code
  • Access external knowledge

To enable Function Calling in Red Hat AI, follow this guide.

🦆 Model Context Protocol (MCP)

MCP is an open protocol that standardizes how applications provide context to LLMs, enabling agent-based workflows and integrations. It offers:

  • Pre-built integrations for seamless LLM connectivity
  • Flexibility to switch between LLM providers and vendors
  • Security best practices for keeping data within your infrastructure

If you want to know more, follow this guide

🤝❤️ Contribute

Want to add a new example or missing framework? 🎉 Bring your own agentic example! PRs are always welcome (and much appreciated!😌).

🎓 Explore More: Links We Love

If you want to explore more and dig a bit more deeper, take a look to the following links: