🚀 Build 5000x Faster AI Agents with Agno and Ollama! | Future-Proof Agentic Systems

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AI Summary

Video Summary

• The video is a tutorial on creating AI agents using local LMS with a service called Agno, previously known as Fidata.
• The presenter emphasizes the importance of having a model, memory, storage, and tools to create an effective AI agent.
• Various models from different providers like OpenAI, AWS, and Azure are mentioned.
• Knowledge bases can include PDFs, CSVs, text files, archive documents, URLs, and S3 buckets, with Wikipedia as an example.
• Embeddings from OpenAI, local embeddings, and others from providers like Fireworks and Quadrant are discussed.
• Storage options include PostgreSQL, DynamoDB, SQLite, SingleStore, and MongoDB.
• Tools are essential for different actions and features, with examples like Appify, Calculator, Discord, Airflow, and YouTube.
• The target is to create multi-model, model-agnostic, multi-agent systems with fast memory management and knowledge stores.
• Vector databases like Pinecone and PG Vector are recommended for local systems.
• The presenter provides examples of agents, including a web agent, finance agent, and YouTube agent, each with specific functions.
• Installation instructions for Agno and models like AMA’s DSE R1 are provided, including CLI commands for downloading and testing models.
• The presenter demonstrates how to use Agno to create agents, including a basic agent that tells horror stories, a tool-using agent for searching academic papers, and a book recommendation system.
• A coding example is shown where dependencies like FastAPI are installed, and a Tetris game is developed using a coding agent.
• The video concludes with a showcase of different agents interacting with each other and a promise of more examples in future videos.

Detailed Instructions and URLs

• Install Agno with the command: pip install agno
• Run an example with: uvicorn main:app --reload
• Download AMA models from ama.com for different operating systems.
• Install a specific model using the command provided in the AMA model section after selecting the appropriate model for your system.
• List installed models with the command: ama list
• Set environment variables for API keys, such as the EXA tools API key, by copying it from the EXA website after logging in.
• Run examples with commands like uvicorn main2:app --reload and uvicorn main3:app --reload.
• Serve a playground for agents with uvicorn playground:app --reload.

Notes

• The presenter’s GitHub profile is mentioned as a source for the code examples, but no specific URL is provided.
• The presenter encourages viewers to subscribe to the channel for future videos on creating AI agents with Agno.
• No self-promotion or off-topic content is included in the summary.