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CREW AI Examples

This is a set of scripts (and accompanying (Youtube Tutorial)[https://youtu.be/OUgb3hKSn9U]) that allow you to create AI agents to accomplish tasks such as conducting research and reporting on findings using the CrewAI Agent Framework and nearly any large language model (through services like Langchain), including locally using Mozilla's LLamafile or Ollama.

Basic Example

Setup:

Installation:

In terminal, in root folder, type:

  • pip install crewai
  • pip install 'crewai[tools]'
  • pip install dotenv

Configuration

In app.py:

  • Set desired model name (OPENAI_MODEL_NAME='gpt-4o')
  • Modify agents to do whatever task you'd like to see performed.

Run Workflow

  • In terminal, type: python app.py

Llamafile Example

Mozilla's Llamafile allows you to run LLM models locally, and includes a langchain integration. To use it, in addition to the installation steps for CrewAI, do the following:

  • Get a llamafile model. In this case, we'll get TinyLlama:

wget https://huggingface.co/jartine/TinyLlama-1.1B-Chat-v1.0-GGUF/resolve/main/TinyLlama-1.1B-Chat-v1.0.Q5_K_M.llamafile

  • Make sure you can execute the file by changing its permissions:

chmod +x TinyLlama-1.1B-Chat-v1.0.Q5_K_M.llamafile

  • Run the model:

./TinyLlama-1.1B-Chat-v1.0.Q5_K_M.llamafile --server --nobrowser

This will open an API endpoint at localhost:8080.

  • If you haven't already, install the langchain community module: pip install langchain_community

  • Run the crew agent script:

python3 llamafile-app.py

User-prompt created agents with Llamafile

The app-input.py file allows you to create a custom agent and research task via command line prompt:

Providing terminal input to the model to create an agent

Llamafile Agent from UI input

Uses gradio to allow users to specify agent parameters via web-based text fields

Multi-agent llamafile

For more complex tasks, you'll need a model with a larger context window. For this, I'll use Llama2:

wget https://huggingface.co/Mozilla/llava-v1.5-7b-llamafile/resolve/main/llava-v1.5-7b-q4.llamafile

  • chmod +x llava-v1.5-7b-q4.llamafile
  • ./llava-v1.5-7b-q4.llamafile --server --nobrowser

Once this model is running, run the multiagent app (this will take a bit of time, as it needs to go through a series of steps to research and then write about AI):

  • python3 llamafile-multiagent-app.py

Ollama Example

In addition to the installation steps above, do the following:

  • Download ollama
  • In terminal, run pip install langchain_openai
  • Get the llama2 model: ollama pull llama2
  • In the terminal from project root, run bash crewai-create-llamafile.sh (llama2 is about 3.8 GB)
  • Once this is done, from terminal, run python ollama-app.py and view the output in your terminal