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DATAGEN
by starpig1129
Multi-agent research assistant for hypothesis, analysis, and report automation
Python
Updated Feb 5, 2026
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What It Does
Automates end-to-end research workflows by coordinating multiple specialized agents to generate hypotheses, run analyses, and draft reports. Uses an agent orchestration layer to assign tasks to data, analysis, and writing agents, then aggregates outputs into reproducible artifacts. Includes Python SDK and pipelines optimized for LLM-driven data analysis and code generation, leveraging patterns like Dynamic Task Routing Pattern and Agent Service Mesh Pattern.
The Value Proposition
As teams use many specialized agents, having a reproducible way to delegate data tasks and consolidate findings matters for traceability and evaluation. DATAGEN makes agent delegation explicit and reproducible so you can inspect which agent produced what insight and iterate on failure modes. That visibility helps when building agent track records or integrating evaluation signals into development workflows, anchored by practices such as LLM-as-Judge Pattern.
Best For
Data science teams and researchers who want to automate hypothesis generation, run repeatable LLM-driven analyses, and produce reproducible reports from coordinated agents.
Applications
- Automating hypothesis generation and rapid iteration on research questions
- Scaling LLM-driven data analyses and producing reproducible analysis artifacts
- Generating draft reports and code from aggregated agent outputs for review
Works With
langchainlanggraphopenaihuggingface
Topics
agentaiai-data-analysisartificial-intelligencecode-generationdata-analysisdata-analyticsdata-sciencelangchainlanggraph+5 more
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Keywords
multi-agentdata-analysisagent-evaluation