Inspiration
Scientific research is built on the ability to reproduce and independently verify results. However, computational research often faces a reproducibility gap: experiments may depend on undocumented configurations, unavailable artifacts, inconsistent datasets, or assumptions that are difficult to validate independently.
We wanted to explore a different approach to AI-powered research. Instead of building another AI tool that simply summarizes papers or generates answers, we envisioned a system that questions scientific claims, examines their supporting evidence, and makes computational research more transparent and verifiable.
That idea became ReproLab.
What it does
ReproLab is an evidence-first research workbench designed to investigate computational scientific claims through structured, multi-perspective analysis.
At its core is a system of 12 specialised AI scientist roles, each representing a different research perspective. These include:
- Popper: Adversarial scientific review that challenges assumptions and claims.
- Fisher: Statistical analysis and examination of experimental results.
- Noether: Replication-oriented reasoning and reproducibility analysis.
- Curie: Data provenance and evidence traceability.
Rather than relying on a single AI response, ReproLab is designed around specialised research responsibilities, structured investigation workflows, and evidence-backed conclusions.
The broader goal is to move from AI-generated research answers to research that can be independently examined, challenged, and verified.
How we built it
We designed ReproLab with a modular architecture:
- Frontend: Next.js provides the research workbench and investigation interface.
- Backend: FastAPI and Pydantic handle application logic, validation, and structured workflows.
- Database: PostgreSQL with SQLAlchemy and Alembic provides the foundation for persistent research data and structured records.
- Artifact storage: Private local storage is designed to maintain research artifacts and supporting evidence.
We focused on separating the research interface, backend orchestration, data persistence, and artifact management so that the platform could evolve into a more comprehensive scientific verification environment.
Challenges we faced
One of the biggest challenges was designing a system that goes beyond conventional LLM-based research tools. Scientific verification requires more than generating plausible explanations: it requires traceability, structured evidence, reproducible procedures, and independent evaluation.
Another challenge was the development environment. We encountered infrastructure limitations while setting up PostgreSQL and container-based services, as Docker, Podman, and WSL were unavailable in our environment. This forced us to reconsider parts of our development workflow and distinguish between implemented application components and infrastructure-dependent functionality.
We also had to think carefully about how specialised scientist roles could contribute distinct perspectives without simply producing repetitive AI-generated responses.
What we learned
Building ReproLab reinforced an important lesson: a convincing AI-generated answer is not the same as a verified scientific result.
We explored how multi-agent architectures, structured workflows, data provenance, and evidence-first design can contribute to more trustworthy research systems.
What's next
Our vision is to evolve ReproLab into a platform where researchers can submit computational claims, define reproducible experimental protocols, execute independent verification workflows, and receive transparent evidence-backed assessments.
We want ReproLab to help make reproducibility a fundamental part of computational research rather than an afterthought.
Built With
- agentic
- ai
- alembic
- api
- data
- evidence-based
- fastapi
- language
- large
- models
- multi-agent
- next.js
- postgresql
- provenance
- pydantic
- python
- react
- reproducibility
- research
- rest
- scientific
- sqlalchemy
- systems
- tailwind
- typescript
