Academic Commercialization Assessment Agent

Turn a paper or research topic into a source-linked commercialization assessment draft: a scorecard for technology readiness, manufacturing, patents and market, with citations to registered sources and the risks spelled out.

Open the application · Read sample reports — no key needed · Run locally · 中文

Current interface, captured 2026-10-20, showing a real run from 2026-08-09. That run predates today's automated checks, which the panel states; it is not a current-model result.

Try it without keys: the sample reports page shows three finished assessments and makes no model or search request. The same reports are in examples/, for instance Solid-state batteries for electric vehicles (frozen 2026-07-19 version, 9673a83). Starting a new analysis needs an operator-issued access code or BYOK (your own LLM API key and model, plus a Tavily or Serper search key), and incurs provider usage. The screenshot and samples are different historical runs; neither represents current model accuracy or an up-to-date market assessment.

Built with Python, CrewAI, FastAPI and a build-free JavaScript client. Start here: documentation and repository map · Case study

Finding your way around: the application is src/academic_agent/, api/, web/ and ui/; tests are in tests/ and e2e/. The many Python files at the root are benchmark commands and frozen experiment runners whose protocols bind their paths, so they stay put; none is needed to run the app. See the repository map for every directory.

What it does

• Submit a research topic or attach a paper PDF; choose the report language and scoring profile.
• Deterministic code retrieves and validates sources first; six LLM stages then reason over that frozen evidence. Autonomy is limited on purpose.
• The scorecard covers TRL, MRL, patent strength, market accessibility and evidence confidence. The weighted total is computed by code, not the model.
• Follow progress, inspect citations and reliability warnings, export Markdown or PDF, and share a run link.
• In Sources, find a saved source by ID or keyword and read its saved text without a model call. Saved text is not the paper's full text or verified claim support; see the operating guide.
• Optionally add Decision Context: the report distinguishes exploratory orientation from a specific actor's decision, and whether a threshold was approved by the decision owner.
• Resume an interrupted run from its last validated checkpoint. The resumed run is a new linked run; the original is never overwritten.
• Paid requests get durable receipts: after a lost reply or a refresh, the accepted run, PDF or resumed run can be recovered for up to 24 hours without paying again.

A report supports research triage; it is not technical, legal, regulatory, investment or freedom-to-operate due diligence. A valid citation ID does not establish that the source supports the claim.

Architecture

Topic / PDF + optional Decision Context
                   │
Deterministic retrieval → validation → frozen source registry
                   │
         ┌─────────┼─────────┐
     Academic    Patent    Market
         └─────────┼─────────┘
                 Writer
                   │
                 Reviewer
                   │
                  Scorer → deterministic weighted total
                   │
        Shared run artifacts + final run status
                   │
       FastAPI / browser / CLI / recovery

The three evidence specialists run in parallel; Writer, Reviewer and Scorer follow in sequence. A stage is not a promise of exactly one model request. See the code map and contribution rules before changing orchestration.

Measured results

The frozen baseline contains 10 topics × 3 live repetitions:

These are different checks, not a combined accuracy score. Expected TRL ranges were adjusted after early observations, so this is not independent held-out validation. The uncited-numeric proxy does not measure all hallucinations. Seven of ten topics met their TRL range in all three runs.

Held-out check. On ten new topics with ranges fixed from external evidence before any run (qwen3.7-plus, 2026-10-18), 21/30 runs fell in range, the pre-registered "supported" threshold. Scores were compressed toward 7, though: no run scored above 7, so mature, deployed technologies were under-scored. See the held-out result.

A second held-out cohort ran on 2026-10-19 with qwen3.8-max and the revised market retrieval. 25/30 runs fell in range, and 11 runs scored 8–9 on proven technologies. Low-maturity topics were not inflated, though the two lowest ones were scored below their floor. The topics and the model also changed, so the improvement is not attributed to any single change. Treat TRL as a coarse signal. See the v2 result. The public deployment has since moved to the smaller qwen3.8-27b, which neither held-out cohort measured.

Other completed evidence:

• 90-cell topology ablation: the four-node arm used 54.89% fewer median tokens and 47.03% lower median cost than the six-node arm. Six nodes were not established as universally necessary.
• Five-reviewer utility study: 20 eligible judgments; the registered success rule failed despite a 6:4 full-workflow preference in each round.
• Two target-user pilot: both retained DEFER and answered MAYBE to reuse; neither checked external sources. This does not establish product adoption.
• Recovery: 30/30 offline fault-injection recoveries completed; one production recovery reused four committed nodes. This is not an exactly-once or general cost-saving guarantee.
• Runtime RTI02: one normal Qwen completion passed 12/12 primary final-status checks, with a disclosed minor observer-cadence deviation. Timeout/fallback paths and general report quality were not validated by that run.

Protocols, source artifacts and limitations are linked in the current evidence ledger.

Quick start

Use Python 3.11 or 3.12 for the CI-tested environment and uv. Dependency installation needs network access; the default test suite does not call providers.

git clone https://github.com/shuxiachai/academic-commercialization-agent.git
cd academic-commercialization-agent
uv sync

Copy .env.example to .env. For Qwen, set these values and replace the placeholders locally; do not commit or share keys:

LLM_PROVIDER=qwen
DASHSCOPE_API_KEY=your-key
QWEN_MODEL=qwen3.8-27b
QWEN_API_BASE=https://dashscope.aliyuncs.com/compatible-mode/v1
TAVILY_API_KEY=your-search-key

Choose the endpoint matching your operator account/region. This project's browser BYOK default uses the China-region endpoint. With multiple LLM keys present, set LLM_PROVIDER explicitly; otherwise auto-selection is DeepSeek → Qwen → Anthropic → OpenAI. Tavily takes precedence over Serper when both search keys are present. Remove unused placeholder keys.

uv run uvicorn api.main:app --reload
# Browser: http://localhost:8000  (sample reports: http://localhost:8000/samples)
# Alternative CLI (starts real provider work):
uv run academic_agent --topic "solid-state batteries for electric vehicles"

Real analysis incurs provider usage. Duration is provider-bound: observed Qwen completions include 306 and 885 seconds, not a promised three-minute SLA. See the operating guide for HTTP endpoints, output files, Docker, access codes, BYOK, tracing and recovery.

Security and deployment

Run URLs carry 128 bits of randomness and act as read capabilities: anyone with the full URL can read that run until retention removes it. Changing a code-owned run additionally requires its owner/admin code; ownerless BYOK runs have no second server-side identity. Do not publish private