shuxiachai/academic-commercialization-agent

Turn papers and research topics into source-linked commercialization assessment drafts. Python, FastAPI and CrewAI; auditable scoring, checkpoint recovery and durable request receipts.

What it solves

It automates the process of evaluating whether a research paper or academic topic has commercial potential. Instead of manually researching market trends and technical readiness, users can generate a source-linked commercialization assessment draft that includes an auditable scorecard and risk notes to help with research triage.

How it works

The system uses a multi-stage agentic workflow built with CrewAI and FastAPI. It follows a deterministic process to ensure reliability:

  1. Retrieval: It performs deterministic retrieval of academic, patent, and market data, creating a frozen source registry.
  2. Analysis: Three specialized evidence agents (Academic, Patent, and Market) analyze the data in parallel.
  3. Synthesis: A Writer agent synthesizes the findings, a Reviewer agent checks the work, and a Scorer agent calculates a deterministic weighted total for the commercialization score.
  4. Delivery: The final report is delivered via a browser interface, CLI, or API, with support for PDF and Markdown exports.

Who it’s for

It is designed for researchers, technology transfer officers, and innovators who need to quickly triage research topics to determine their market viability and technical readiness level (TRL).

Highlights

  • Auditable Evidence: Every claim is linked to specific source IDs from a frozen registry to prevent hallucinations.
  • Deterministic Scoring: Uses a weighted formula to provide a consistent commercialization score rather than relying on LLM intuition.
  • Fault Tolerance: Features content-addressed checkpoints and immutable recovery children to resume interrupted runs.
  • Multi-LLM Support: Compatible with various providers including Qwen, DeepSeek, Anthropic, and OpenAI.
  • Rigorous Testing: Includes a benchmark of 10 diverse topics (from CAR-T therapy to quantum computing) to calibrate TRL and scoring accuracy.

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