# jgravelle/AutoGroq

AutoGroq is a groundbreaking tool that revolutionizes the way users interact with Autogen™ and other AI assistants. By dynamically generating tailored teams of AI agents based on your project requirements, AutoGroq eliminates the need for manual configuration and allows you to tackle any question, problem, or project with ease and efficiency.

Repository: https://github.com/jgravelle/AutoGroq
Canonical: https://ross.abutalabs.com/products/autogroq
Homepage: https://autogroq.streamlit.app/
Language: Python
License Family: other
Topics: agents, ai, artificial-intelligence, autogen, crewai, groq, llm
Last push: 2024-12-23T21:00:56+00:00

## Health v2 (maintenance only)
Score: 15/100 (v2, computed 2026-09-03T02:20:16.233290+00:00)
- activity 0, release rhythm 8, longevity 63
- inputs: {"age_days": 887, "days_push": 618, "days_rel": null, "gap_med": null, "n_releases_24m": 0}
- flags: no_license
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 1503, forks 446 (observed 2026-08-28T04:04:54.578881+00:00)

## What it is
AutoGroq is a Python application that dynamically generates tailored teams of AI agents for Autogen and similar frameworks based on user project requirements, without manual configuration. It provides a UI for natural conversation with expert agents, workflow generation, skill creation, and support for multiple LLM providers like Groq, ChatGPT, and Ollama.

## Use cases
- automatically build a team of AI agents for my project
- use autogen without manually configuring agents
- generate agent workflows from a natural language request
- connect groq or ollama models to autogen agents
- create custom skills for AI agents without coding
- orchestrate multiple LLM experts on a problem

## When to choose
- you want to use Autogen-style multi-agent workflows without writing configuration code
- you want dynamic agent team generation from plain-language project descriptions
- you want a GUI for managing agents, skills, and workflows across multiple LLM providers

## When to avoid
- you need a production-grade, licensed, or well-maintained library (no license is provided)
- you prefer manually defining and controlling agent configurations in code
- you need a lightweight headless tool rather than an interactive application

## Facets
- artifact type: application
- maturity: active
- function: agent-framework, llm-inference, chatbot, prompt-engineering
- domain: artificial-intelligence, large-language-models, developer-tools
- platform: python, cross-platform
- tags: autogen, groq, crewai, multi-agent, no-code, agent-orchestration, ai-agents

## Member repositories
- jgravelle/AutoGroq (main) score 15

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:04:54.578881+00:00.
- Health v2: computed from the inputs above; adoption is never an input.
- Inferred fields (summary, facets, guidance): AI-extracted, prompt v1, taxonomy v1, on 2026-08-30T04:32:49.339391+00:00, confidence not recorded.
  - readme: https://github.com/jgravelle/AutoGroq (fetched 2026-08-28T04:04:54.578881+00:00, sha 4bed9ea2828d)
- Data as of 2026-08-30T08:39:29.467469+00:00.