TinyScientist: A Lightweight Framework for Building Research Agents

Introduction

Tiny-Scientist is a lightweight, user-friendly framework for automating the entire lifecycle of scientific research—from ideation to implementation, writing, and review. Designed for flexibility, it integrates smoothly with your favorite LLMs and search tools.

Core Features

• 🧠 Think: Generate structured research ideas from an intent string.
• 💻 Code: Automatically generate and run experiments based on the idea.
• ✍️ Write: Convert your results and ideas into a conference-style paper.
• 📝 Review: Review any form of paper and output structured feedback in JSON.

Software Architecture

Our codebase is structured around three core components to support an extensible framework: core, tools, and formatters. The core module provides essential functionalities, tools enhance and extend these core capabilities, and formatters handle input/output tasks such as LaTeX template rendering.

Installation

Option 1: Install via pip (recommended)

pip install tiny-scientist

Option 2: Install from source

# create conda environment
conda create -n tiny-scientist python=3.10
conda activate tiny-scientist

# Install Poetry
curl -sSL https://install.python-poetry.org | python3
export PATH="$HOME/.local/bin:$PATH"

# Install dependencies
poetry install

Get started

Before running any code, set your API key:

export OPENAI_API_KEY=your-key-here
# or use DEEPSEEK_API_KEY, ANTHROPIC_API_KEY, or OPENROUTER_API_KEY

If you want to use local ollama models, set the API base:

export OLLAMA_API_BASE=http://192.168.23.11:11434

You can then specify ollama models like so: ollama/llama3.2:latest for example.

For LM Studio it is similar:

export LM_STUDIO_API_BASE=http://localhost:1234/v1

but you do need to specify an API key, even if it's a dummy value:

export LM_STUDIO_API_KEY=dummy-api-key

And the models are specified like so: lm_studio/qwen2.5-coder-32b-instruct-mlx

For other openAI compatible backend providers, set the following variables:

export OPENAI_API_BASE=http://192.168.9.14/v1
export OPENAI_API_KEY=your-key-here

and specify your model like so: openai/qwen3-30b-a3b

Now you can use Tiny-Scientist in Python with only a few lines of code:

from tiny_scientist import TinyScientist

scientist = TinyScientist(model="gpt-4o")

# Step 1: Generate a json-format research idea
idea = scientist.think(intent="Benchmarking adaptive step size strategies using a convex quadratic optimization function")

# Step 2: Run experiments (you can provide baseline_results if available)
status, experiment_dir = scientist.code(idea=idea)

# if the experiments run successfully
if status is True:
    # Step 3: Write a paper
    pdf_path = scientist.write(idea=idea, experiment_dir=experiment_dir)

    # Step 4: Review the paper
    review = scientist.review(pdf_path=pdf_path)

Managing API Keys (Optional)

You can configure keys using a .toml file for convenience beyond exporting.

Step 1: Copy the template

cp config.template.toml config.toml

Step 2: Fill in your API credentials

Edit config.toml to include your keys, such as:

[core]
llm_api_key = "xxxx"

No need to export environment variables manually—just set this once.

Developing

Develop Demo

To develop a demo (Both frontend and backend):

python backend/app.py

cd frontend
npm install
npm start

Q&A

If you face "cairo"-related errors, cario is a system-level dependency, please run conda install -c conda-forge cairo or brew install cairo.

If you face errors related to pdflatex, this is also a system-level dependency for latex rendering, please run brew install --cask mactex.

Contribution

We’re working on extending support for more tools, models, and paper formats. Contributions welcome!

Citation

@misc{tinyscientist,
author       = {Haofei Yu and Keyang Xuan and Fenghai Li and Zijie Lei and Jiaxuan You},
title        = {TinyScientist: A Lightweight Framework for Building Research Agents},
howpublished = {https://github.com/ulab-uiuc/tiny-scientist},
note         = {Accessed: 2025-04-14},
year         = {2025}
}