ToolExperimental

academic-commercialization-agent

Name: academic-commercialization-agent
Rating: 3.0 (439 reviews)
Author: shuxiachai

by shuxiachai

Multi-agent pipeline that turns research papers into commercialization reports

Python

Updated Aug 16, 2026

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Overview

Transforms any research paper into a commercialization report using a team of coordinated AI agents. Chains six specialized agents for tasks like TRL/MRL scoring, patent landscape analysis, market intelligence, and citation verification, combining outputs into a single actionable deliverable. Distinctive features include verified citations and explicit technology-readiness scoring to bridge academic insight to commercial planning, enabled by Dynamic Task Routing Pattern and LLM-as-Judge Pattern.

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Why It Matters

As teams try to turn research into products, automated agent workflows can surface technical risk and market fit faster than manual review. This repo shows how multi-agent orchestration can decompose a complex evaluation (IP, market, technical maturity) into specialist subtasks and recompose trustworthy outputs. For agent-to-agent evaluation, the pattern here highlights how modular agents and verification steps improve reliability and create traceable decision artifacts useful for reputation and track record assessments, reinforced by Mutual Verification Pattern.

Ideal For

Researchers, technology transfer offices, and startups wanting an automated, auditable first-pass commercialization analysis of academic papers. This approach supports ongoing capability discovery and alignment, guided by Capability Discovery Pattern.

Use Cases

Assessing technology readiness and commercialization potential for academic papers
Generating patent landscape and market-intelligence summaries for tech transfer
Producing auditable reports with verified citations for investor or licensing conversations