Complete AI Agent Tutorial with Ollama + AnythingLLM

Summary

The video explains how to run an AI agent locally using Ollama and AnythingLLM, then demonstrates how agent skills can extend a standard chatbot into a tool that performs tasks.

Key Concepts

• Local AI: Running a language model on a personal computer can offer more control over privacy, faster access, and potentially lower costs than cloud-based services. The video uses Ollama to run a local Llama 3.1 8B model.
• GPU requirements: Local language models perform substantial computation. The presenter recommends an NVIDIA RTX GPU, citing its Tensor Cores and describing a notable performance difference between a MacBook and an RTX-equipped PC.
• Chatbots versus agents: A chatbot primarily responds to prompts. An AI agent can use tools or “skills” to carry out actions, such as searching and scraping websites, storing information, or preparing an email draft.

AnythingLLM Features Shown

• A chat interface for connecting to a selected language model.
• Workspace and agent configuration.
• Built-in skills, including retrieval-augmented generation (RAG), long-term memory, and web scraping.
• A Community Hub for adding custom agent skills.
• Local storage for chats and documents by default, according to the presenter.

Tutorial and Demonstration

1. Download AnythingLLM and connect it to the locally running Ollama model.
2. Create a workspace and test the model with a basic question.
3. Compare a regular chat prompt with an agent request: without a suitable skill, the model cannot scrape a website.
4. Enable web search and scraping in the agent settings. The demonstration requires a Google API key for this skill.
5. Ask the agent to gather information from a website, then save it to its vector database for later use.
6. Use the stored information to draft a sample proposal for improving the site’s software.
7. Enable a Gmail-draft skill from the Community Hub and ask the agent to create a draft link. The presenter opens the link and reviews the prepared email, noting that a proposal should be edited before sending.

The example uses the presenter’s State of Health website to illustrate a possible freelancer workflow. The video also points viewers interested in developing custom skills to the AnythingLLM Community Discord and GitHub.

Main Speaker and Sources

• Kenny Gunderman — presenter and narrator.
• NVIDIA — video sponsor; the presenter discusses RTX GPUs.
• Ollama and AnythingLLM — the local model runner and agent platform featured in the tutorial.