Andrii Petrykovskyi

Jupeppa
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reacted to CultriX's post with ❀️ 5 days ago
# Space for Multi-Agent Workflows using AutoGen Hi all, I created this "AutoGen Multi-Agent Workflow" space that allows you to experiment with multi-agent workflows. By default, it allows code generation with built-in quality control and automatic documentation generation. It achieves this by leveraging multiple AI agents working together to produce high-quality code snippets, ensuring they meet the specified requirements. In addition to the default, the space allows users to set custom system messages for each assistant, potentially completely changing the workflow. # Workflow Steps 1. User Input: - The user defines a prompt, such as "Write a random password generator using python." - Outcome: A clear task for the primary assistant to accomplish. 2. Primary Assistant Work: - The primary assistant begins working on the provided prompt. It generates an initial code snippet based on the user's request. - Outcome: An initial proposal for the requested code. 3. Critic Feedback: - The critic reviews the generated code provides feedback or (if the output meets the criteria), broadcasts the APPROVED message. (This process repeats until the output is APPROVED or 10 messages have been exchanged). - Outcome: A revised Python function that incorporates the critic's feedback. 4. Documentation Generation: - Once the code is approved, it is passed to a documentation assistant. The documentation assistant generates a concise documentation for the final code. - Outcome: A short documentation including function description, parameters, and return values. Enjoy! https://huggingface.co/spaces/CultriX/AutoGen-MultiAgent-Example
liked a Space 23 days ago
DawnC/PawMatchAI
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reacted to CultriX's post with ❀️ 5 days ago
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# Space for Multi-Agent Workflows using AutoGen

Hi all, I created this "AutoGen Multi-Agent Workflow" space that allows you to experiment with multi-agent workflows.

By default, it allows code generation with built-in quality control and automatic documentation generation. It achieves this by leveraging multiple AI agents working together to produce high-quality code snippets, ensuring they meet the specified requirements.

In addition to the default, the space allows users to set custom system messages for each assistant, potentially completely changing the workflow.

# Workflow Steps
1. User Input:
- The user defines a prompt, such as "Write a random password generator using python."
- Outcome: A clear task for the primary assistant to accomplish.

2. Primary Assistant Work:
- The primary assistant begins working on the provided prompt.
It generates an initial code snippet based on the user's request.
- Outcome: An initial proposal for the requested code.

3. Critic Feedback:
- The critic reviews the generated code provides feedback or (if the output meets the criteria), broadcasts the APPROVED message.
(This process repeats until the output is APPROVED or 10 messages have been exchanged).
- Outcome: A revised Python function that incorporates the critic's feedback.

4. Documentation Generation:
- Once the code is approved, it is passed to a documentation assistant.
The documentation assistant generates a concise documentation for the final code.
- Outcome: A short documentation including function description, parameters, and return values.

Enjoy!
CultriX/AutoGen-MultiAgent-Example
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reacted to hexgrad's post with πŸ€— 22 days ago
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Tonight, Adam & Michael join the 82M Apache TTS model in hexgrad/Kokoro-82M
liked a Space about 1 year ago