MADS
Overview
Legacy MIT multi-agent research pipeline that turns a CSV dataset and goal into supervised-ML analysis.
MADS, from AiFlowSolutions, is an MIT-licensed research pipeline that coordinates six AutoGen agents for supervised-machine-learning analysis. Users supply a CSV dataset and a goal, and the workflow produces analysis and reports.
The repository was last pushed in August 2024. Setup requires Python 3.11.7 or later, model keys and compatible dependencies. Groq and OpenAI are documented provider options; model calls and compute have separate costs.
Prompt optimization and forecasting extensions are roadmap items. The existing project is best suited to developers studying or adapting its research workflow.
Product overview
Capabilities, pricing and fit
MADS, from AiFlowSolutions, is an MIT-licensed research pipeline that coordinates six AutoGen agents for supervised-machine-learning analysis. Users supply a CSV dataset and a goal, and the workflow produces analysis and reports.
Information as of October 7, 2026
What it can do
- Core workflow
AiFlowSolutions/MADS uses a CSV dataset and a user goal to coordinate six AutoGen agents for a supervised-machine-learning pipeline and reports. It is not a reinforcement-learning drift simulator; prompt optimization and forecasting extensions remain roadmap items.[1]
- Availability
MIT source remains accessible and is not archived, with the last repository push August 8, 2024. Python 3.11.7+, model keys and legacy dependency/model compatibility require developer work[1]
- Connections
Groq and OpenAI are documented configured model providers. CSV input and local outputs provide a focused research path; AutoGen, Docker and Python dependencies do not establish a native business-app or public REST connector ecosystem.[1]
Where it fits best
- Researchers experimenting with supervised machine-learning pipelines and agent coordination.[1]
Pricing and availability
MIT source avoids a software subscription for a focused research workflow. Model calls, local compute, dependency maintenance and output review still cost money; accessible source does not mean free production operation.[1]
Platforms: MIT source remains accessible and is not archived, with the last repository push[1]
Sources (1)
- Product evidence: github.comOfficial site · checked 2026-10-07
Autonomy level
71/100
Our subjective rating of how independently the product carries out its documented tasks, considering the human setup, input, approval and review it needs. This is not a measured success rate.
Comparisons
Custom Comparisons
Some of the use cases of MADS:
- Researchers experimenting with supervised machine-learning pipelines and agent coordination.
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Popularity level: 33%
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