OpenLens AI
VerifiedAutonomous agent that turns datasets and ideas into complete research papers.
What is OpenLens AI?
OpenLens AI functions as a fully autonomous research assistant that manages the entire pipeline from initial idea to final LaTeX paper. It combines literature retrieval, code execution through OpenHands, visualization feedback, and automated document creation into one continuous process.
Users supply a dataset and a single research question. The agent then performs searches, designs and runs experiments, analyzes results, and produces formatted reports while maintaining context across steps via vector search.
The tool targets researchers in medicine, statistics, and machine learning who need to accelerate data-driven projects without manually coordinating multiple tools or writing extensive code.
What you can build with OpenLens AI
Medical data study
Upload clinical datasets and a brief hypothesis to receive automated literature summaries, statistical analysis, and a draft paper.
ML experiment pipeline
Provide model training data and a research goal so the agent can design baselines, run code, and generate results sections.
Multilingual report creation
Request Chinese-language figures and papers directly from English prompts for international collaboration or publication.
Install OpenLens AI
git clone https://github.com/jarrycyx/openlens-ai.git --recurse-submodulesgit clone https://github.com/jarrycyx/openlens-ai.git --recurse-submodules
cd openlens-ai- 1Clone the repository with git clone and submodules.
- 2Install Docker and pull the provided runtime image.
- 3Set API keys for the chosen LLM and Tavily search service.
- 4Launch the Streamlit interface to monitor the agent.
- 5Submit a dataset folder and one-line research prompt to begin.
OpenLens AI: pros & cons
Pros
- +Handles the full research cycle without manual steps
- +Integrates code execution, literature search, and LaTeX output
- +Supports Chinese paper generation and general domains
- +Open-source with MIT license and Docker-based runtime
Cons
- –Requires Docker and multiple external API keys to run
- –Still under active development with some planned features missing
- –Performance depends on the quality of the chosen LLM
Frequently asked questions
No, once a dataset and prompt are provided the agent runs the full workflow autonomously.
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