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AutoGroq

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Basic Information

AutoGroq is an open-source Streamlit application and toolkit for automatically generating tailored teams of AI agents and workflows based on a user's natural language request. It is designed to construct a configured workflow, expert agents, and skillsets from the syntax of a user's need rather than requiring manual agent design. The repository includes instructions to install dependencies, run the Streamlit UI, and configure a local settings file. It integrates with Autogen and supports multiple LLM providers, enabling users to import agents, skills, and workflows into Autogen. The project is released under the MIT License and aims to let developers and non-programmers rapidly spin up multi-agent solutions, rephrase prompts for clarity, and extract code snippets via a dedicated whiteboard interface.

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App URL
https://github.com/jgravelle/AutoGroq
Github Repository
https://github.com/jgravelle/AutoGroq/blob/main/README.md

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App Details

Features
AutoGroq provides dynamic expert agent generation that creates specialized agents for different domains automatically. It generates dynamic workflows so users can get a custom team and working pipeline within minutes via a Streamlit interface. The app offers natural, context-aware conversations and advanced prompt rephrasing to improve clarity and response accuracy. A Whiteboard extracts and presents code snippets found during interactions for easy reference. Users can manage agents flexibly by adding, modifying, or removing experts and can bulk upload agents, skills, and workflows into Autogen. The project supports multiple LLM providers including Groq, ChatGPT, and Ollama and allows adding custom provider models. Skill integration is supported by dropping valid skill files into the skills folder.
Use Cases
AutoGroq reduces the time and expertise needed to assemble agent teams and workflows by automating configuration and agent creation from user requests. It benefits developers and non-technical users by removing manual setup, enabling rapid prototyping and experimentation with multiple LLMs and Autogen integration. The Whiteboard feature helps capture and reuse code snippets produced during agent conversations. Bulk import and skill file support streamline moving agents and capabilities into Autogen. The Streamlit front end and provided installation steps make it straightforward to run locally with Python and conda, and a config_local.py pattern supports safe local configuration. The project also offers demo and video tutorial resources to shorten onboarding.

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