CAMEL AI with Ollama: local agent setup using Llama 3

This tutorial, uploaded on November 11, 2024, demonstrates CAMEL AI with Ollama and Llama 3. It covers an open source framework for building an AI agent around a local LLM. The transcript does not identify the CAMEL or Ollama release numbers, so the installation and API examples describe the setup shown at that time.

The presenter creates a Python 3.10 virtual environment, clones the CAMEL repository and installs its requirements in editable form. He also reports support for Python 3.11 and 3.12. Ollama is already installed; he briefly discusses installation on Linux, Windows and Mac before downloading a quantized Llama 3 model of roughly 4.7 GB. His demonstration uses an NVIDIA RTX A6000 with 48 GB of VRAM, which is the test hardware rather than a stated minimum requirement.

In the Python interpreter, he imports CAMEL components, selects Ollama as the model provider and creates a chat agent with a helpful-assistant role. He explains temperature and advises matching the token limit to the model's context window.

A second prompt assigns a real estate role and asks about Sydney properties under $1 million. The presenter disputes parts of the response, illustrating the need to check factual answers. API integration and multi-turn chat are discussed as possible follow-up work rather than demonstrated here. He reports about 6 GB of VRAM use during this run.