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:

• LangGraph
• AutoGen
• PhiData
• SMolAgents
• CrewAI (coming soon) ⏳
• Quarkus LangChain4j
• Bee (coming soon) 🐝
• Llama Stack (coming soon)

🧠 LLMs Used in the Agentic AI Examples

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

• Granite3
• Mistral
• DeepSeek (coming soon) 🔎

🔗 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:

• Links we Love