In the fast-paced world of artificial intelligence and computational research, one persistent challenge remains: reproducing results from complex academic papers. Researchers often spend hours understanding codebases, setting up environments and running scripts sometimes with limited success.
Paper2Agent offers a groundbreaking solution. It is a multi-agent AI system that automatically transforms research papers into interactive AI agents. By integrating seamlessly with AI coding platforms like Claude Code and Google Gemini CLI, Paper2Agent allows researchers to execute tutorials, analyze data and explore scientific findings interactively with minimal human intervention.
This blog explores what Paper2Agent is, its core features, installation, use cases and why it is revolutionizing the way researchers interact with academic publications.
What is Paper2Agent?
Paper2Agent is an open-source framework designed to convert research papers into functional AI agents. The system analyzes a paper’s codebase, extracts relevant scripts and tutorials, and wraps them into a Model Control Protocol (MCP) server.
Once created, these AI agents can execute code, answer research-specific questions and assist in data analysis. This approach not only saves time but also ensures reproducibility, accuracy and efficiency in research workflows.
Whether you are a data scientist, a computational biologist or a student exploring AI, Paper2Agent simplifies complex research pipelines into actionable, interactive AI agents.
Key Features
Paper2Agent is packed with features that make it an indispensable tool for AI-driven research:
1. Automated Agent Generation
Paper2Agent automatically detects tutorials, extracts scripts and configures MCP servers. Users simply provide the GitHub URL and a project directory and the system handles the rest.
2. Multi-Agent Architecture
Each research paper can generate one or more specialized agents. For example:
- AlphaGenome Agent: Genomic data analysis
- TISSUE Agent: Spatial transcriptomics and single-cell analysis
- Scanpy Agent: Single-cell preprocessing and clustering
3. AI Coding Platform Integration
Paper2Agent works seamlessly with Claude Code and Google Gemini CLI, enabling automated code execution and interactive querying.
4. Flexible Deployment
Agents can run locally or remotely using Hugging Face MCP servers. This flexibility ensures researchers have full control over computing resources, privacy and project configurations.
5. Tutorial and Tool Extraction
The system parses all tutorials from a repository, extracting reusable tools and scripts. It also allows filtering by tutorial title or URL enabling targeted tutorial processing.
6. Reproducible Research
Automated agent creation guarantees reproducibility. Each AI agent can rerun experiments and produce consistent results without manual setup.
Installation and Setup
Getting started with Paper2Agent is simple:
- Clone the Repository
git clone https://github.com/jmiao24/Paper2Agent.git
cd Paper2Agent
- Install Dependencies
pip install fastmcp
npm install -g @anthropic-ai/claude-code
claude
- Launch an Agent
To create an AlphaGenome Agent:
bash Paper2Agent.sh \
--project_dir AlphaGenome_Agent \
--github_url https://github.com/google-deepmind/alphagenome \
--api <ALPHAGENOME_API_KEY>
After setup, the MCP server is ready for interactive querying using Claude Code.
How Paper2Agent Works
Paper2Agent follows a structured pipeline:
- Tutorial Detection – Automatically scans repositories for relevant tutorials.
- Tool Extraction – Converts scripts into modular, reusable tools.
- MCP Server Generation – Packages tools into a server that AI agents can interface with.
- Agent Launch – Opens the agent in Claude Code or Gemini CLI.
- Interactive Querying – Users can input questions, run analyses and explore research results dynamically.
This automation ensures researchers can focus on analysis rather than troubleshooting complex code environments.
Real-World Applications
Paper2Agent has multiple practical applications:
1. Genomic Research
The AlphaGenome Agent analyzes genomic datasets, identifying causal genes for specific variants. Researchers can explore genomic disorders efficiently with interactive AI assistance.
2. Spatial Transcriptomics
The TISSUE Agent supports spatial gene expression analysis, helping scientists interpret single-cell data with uncertainty-aware predictions.
3. Single-Cell Data Preprocessing
The Scanpy Agent automates preprocessing, clustering, and analysis of single-cell datasets reducing manual workload and increasing reproducibility.
4. Education and Training
Students and new researchers can interact with real scientific codebases, learning by executing tutorials and visualizing results.
Why Paper2Agent is Revolutionary ?
Paper2Agent eliminates traditional barriers in scientific research by automating the conversion of papers into interactive AI agents. Its combination of AI automation, multi-agent architecture and interactive MCP servers makes it a unique tool that enhances reproducibility, efficiency, and accessibility in scientific workflows.
Conclusion
Paper2Agent is transforming the way researchers interact with academic papers. By converting static research documents into intelligent, interactive AI agents, it ensures reproducibility, streamlines complex workflows, and accelerates scientific discovery.
Whether you are working with genomic datasets, spatial transcriptomics, or single-cell analysis, Paper2Agent offers a reliable, efficient, and innovative solution to explore, analyze, and learn from research papers interactively.
Explore the project and start building your AI-powered research agents today: GitHub Repository
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References
- GitHub Repository
- Research Paper on arXiv
- AlphaGenome Agent Demo
- Scanpy Agent Demo
- TISSUE Agent Demo
- ResearchGate Publication
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