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Agent Zero

Agent Zero is a general purpose AI agent application. You can simply ask it to accomplish tasks on the command line. It is designed to be dynamic, organically growing, and learning as users use it. It leverages your computer as a tool to accomplish your tasks.

Prerequisites​

Follow this guide to run an open-source LLM locally. In this example, we will use Llama-3.1-8B as the chat model and nomic-text-embed as the embedding model.

curl -LO https://huggingface.co/second-state/Meta-Llama-3.1-8B-Instruct-GGUF/resolve/main/Meta-Llama-3.1-8B-Instruct-Q5_K_M.gguf
curl -LO https://huggingface.co/second-state/Nomic-embed-text-v1.5-Embedding-GGUF/resolve/main/nomic-embed-text-v1.5-Q5_K_M.gguf

Then start the LlamaEdge API server with the Llama-3.1-8B model Meta-Llama-3.1-8B-Instruct-Q5_K_M and nomic embed model nomic-embed-text-v1.5.f16.

wasmedge --dir .:. \
    --nn-preload default:GGML:AUTO:Meta-Llama-3.1-8B-Instruct-Q5_K_M.gguf \
    --nn-preload embedding:GGML:AUTO:nomic-embed-text-v1.5.f16.gguf \
    llama-api-server.wasm \
    --model-alias default,embedding \
    --model-name Meta-Llama-3.1-8B-Instruct-Q5_K_M,nomic-embed-text-v1.5.f16 \
    --prompt-template llama-3-chat,embedding \
    --batch-size 128,8192 \
    --ctx-size 32768,8192

You can also start a Gaia node for the Llama-3.1-8B model and the nomic embed model. You can then use the node's API URL endpoint and model name in this app.

You will also need to make sure your Docker engine is running. Because the Agent Zero framework will leverage Docker to execute the generated code.

Configure the agent​

First, we will need to get the source code of a Gaia-compatible version of Agent Zero.

git clone https://github.com/JYC0413/agent-zero-gaia.git
cd agent-zero-gaia

Then, let's install the required dependencies.

pip install -r requirements.txt

Next, let's configure the gaia node and other parameters.

cp example.env .env

You will need to configure the following items:

  • CHAT_MODEL_BASE_URL: URL for the LLM API base URL. E.g., http://localhost:8080/v1
  • CHAT_MODEL_NAME: Name of the chat model to be used. E.g., Meta-Llama-3.1-8B-Instruct-Q5_K_M
  • CHAT_API_KEY: An API key to access the LLM services. You can enter several random characters here. E.g., LlamaEdge
  • EMBEDDING_MODEL_BASE_URL: URL for the embedding model API base URL. E.g., http://localhost:8080/v1
  • EMBEDDING_MODEL_NAME: Name of the embedding model name. E.g., nomic-embed-text-v1.5.f16
  • EMBEDDING_API_KEY: An API key to access the embedding services. You can enter several random characters here. E.g., LlamaEdge

Run the agent​

Finally, let's run the Agent Zero application backed by the Llama 3.1 8b model.

python main.py

You can interact with Agent Zero via the command line. You will see the Agent Zero framework will call the related tools and execute some code to complete your task.

Example 1​

Get time in Berlin and Seattle

Save the above result in a file using node.js

Example 2​

Install mc. do not use -y flag

Example 3​

Run top, show the system load

Example 4​

Memorize my openai api key - 563434643464

Example 5​

Extract an mp3 audio track from a mp4 video file using ffmpeg. You will need to put an mp4 file (e.g., video.mp4) into the work_dir and tell the agent about it.