Maestro - A Framework for Claude Opus, GPT and local LLMs to Orchestrate Subagents

This Python script demonstrates an AI-assisted task breakdown and execution workflow using the Anthropic API. It utilizes two AI models, Opus and Haiku, to break down an objective into sub-tasks, execute each sub-task, and refine the results into a cohesive final output.

New: Run locally with LMStudio or Ollama

Lmstudio

First download the app here https://lmstudio.ai/

Then run the local server using your preferred method. I also recommend removing any system prompt for the app (leave your prompt field empty so it can take advantage of the script prompts).

Then

python maestro-lmstudio.py

Ollama

Mestro now runs locally thanks to the Ollama platform. Experience the power of Llama 3 locally!

Before running the script

Install Ollama client from here https://ollama.com/download

then

pip install ollama

And

ollama.pull('llama3:70b')

This will depend on the model you want to use it, you only need to do it once or if you want to update the model when a new version it's out. In the script I am using both versions but you can customize the model you want to use

ollama.pull('llama3:70b') ollama.pull('llama3:8b')

Then

python maestro-ollama.py

Highly requested features

GROQ SUPPORT Experience the power of maestro thanks to Groq super fast api responses.

pip install groq

Then

python maestro-groq.py

SEARCH 🔍

Now, when it's creating a task for its subagent, Claude Opus will perform a search and get the best answer to help the subagent solve that task even better.

Make sure you replace your Tavil API for search to work

Get one here https://tavily.com/

GPT4 SUPPORT

Add support for GPT-4 as an orchestrator in maestro-gpt.py Simply

python maestro-gpt.py

After you complete your installs.

Features

Breaks down an objective into manageable sub-tasks using the Opus model
Executes each sub-task using the Haiku model
Provides the Haiku model with memory of previous sub-tasks for context
Refines the sub-task results into a final output using the Opus model
Generates a detailed exchange log capturing the entire task breakdown and execution process
Saves the exchange log to a Markdown file for easy reference
Utilizes an improved prompt for the Opus model to better assess task completion
Creates code files and folders when working on code projects.

Prerequisites

To run this script, you need to have the following:

Python installed
Anthropic API key
Required Python packages: anthropic and rich

Installation

Clone the repository or download the script file.
Install the required Python packages by running the following command:

pip install -r requirements.txt

Replace the placeholder API key in the script with your actual Anthropic API key:

client = Anthropic(api_key="YOUR_API_KEY_HERE")

If using search, replace your Tavil API

tavily = TavilyClient(api_key="YOUR API KEY HERE")

Usage

Open a terminal or command prompt and navigate to the directory containing the script.
Run the script using the following command:

python maestro.py

Enter your objective when prompted:

Please enter your objective: Your objective here

The script will start the task breakdown and execution process. It will display the progress and results in the console using formatted panels.

Once the process is complete, the script will display the refined final output and save the full exchange log to a Markdown file with a filename based on the objective.

Code Structure

The script consists of the following main functions:

opus_orchestrator(objective, previous_results=None): Calls the Opus model to break down the objective into sub-tasks or provide the final output. It uses an improved prompt to assess task completion and includes the phrase "The task is complete:" when the objective is fully achieved.
haiku_sub_agent(prompt, previous_haiku_tasks=None): Calls the Haiku model to execute a sub-task prompt, providing it with the memory of previous sub-tasks.
opus_refine(objective, sub_task_results): Calls the Opus model to review and refine the sub-task results into a cohesive final output.

The script follows an iterative process, repeatedly calling the opus_orchestrator function to break down the objective into sub-tasks until the final output is provided. Each sub-task is then executed by the haiku_sub_agent function, and the results are stored in the task_exchanges and haiku_tasks lists.

The loop terminates when the Opus model includes the phrase "The task is complete:" in its response, indicating that the objective has been fully achieved.

Finally, the opus_refine function is called to review and refine the sub-task results into a final output. The entire exchange log, including the objective, task breakdown, and refined final output, is saved to a Markdown file.

Customization

You can customize the script according to your needs:

Adjust the max_tokens parameter in the client.messages.create() function calls to control the maximum number of tokens generated by the AI models.
Change the models to what you prefer, like replacing Haiku with Sonnet or Opus.
Modify the console output formatting by updating the rich library's Panel and Console configurations.
Customize the exchange log formatting and file extension by modifying the relevant code sections.

License

This script is released under the MIT License.

Acknowledgements

Anthropic for providing the AI models and API.
Rich for the beautiful console formatting.