NovaSky-AI/Sky-T1-32B-Preview
Text Generation
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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
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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Paper for those who wish to read it themselves https://arxiv.org/abs/2305.19860
Github from authors https://github.com/WLiK/LLM4Rec-Awesome-Papers
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Wow, I just added Langfuse tracing to the Deepseek Artifacts app and it's really nice π₯
It allows me to visualize and track more things along with the cfahlgren1/react-code-instructions dataset.
It was just added as a one click Docker Space template, so it's super easy to self host πͺ
It allows me to visualize and track more things along with the cfahlgren1/react-code-instructions dataset.
It was just added as a one click Docker Space template, so it's super easy to self host πͺ
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