How to Build a Private AI Agent with Ollama and LlamaIndex
Running a local LLM is a no-cost alternative to using commercial APIs
Running an LLM locally with Ollama is not difficult and implementing an agent with LlamaIndex is also straightforward. But don’t expect the same performance you get from Anthropic, OpenAI or Google.
Industrial strength AI runs on powerful servers with lots of resources. Nothing that lives on your desk can compete with that sort of power, so while we will attempt to implement something interesting and, hopefully, useful that will run on your day-to-day laptop, don’t expect miracles.
We’ll use Ollama to run a local LLM. It’s not the only choice, for example, LM Studio and GPT4All are possible alternatives, but Ollama works nicely with LlamaIndex, so we’ll go with that.
Likewise, LlamaIndex is not the only way to implement an AI agent but it’s pretty easy. I’ve already explored other alternatives: AI Agents vs. AI Pipelines: A Practical Guide to Coding Your LLM Application, which is based on CrewAI, and How to Build a ReAct AI Agent with Claude 3.5 and Python, which is a DIY approach - now it’s someone else’s turn.