Autonomous social science
A research agent that designs, fields, and analyzes studies for you.
Start with a research goal, then design, field, analyze, and validate studies in one workflow. Run surveys, adaptive interviews, AI simulations, and human studies with outputs that are shareable, reproducible, and extensible in open-source code.
Accelerating research at leading institutions
Start with a research goal
Describe what you want to learn, upload source materials, or bring an existing survey draft. The Research Agent helps turn it into a study plan, survey, interview guide, audience, and analysis workflow.
Run AI or human studies
Pilot with AI personas, field with human respondents, or do both in the same workflow. Run surveys, adaptive interviews, data labeling tasks, and experiments without switching tools.
Analyze and validate results
Compare AI and human responses, test robustness across models and question wording, label open-ended data, and generate reports you can inspect, share, reproduce, and extend in code.
What Researchers Are Saying
How Expected Parrot is transforming research workflows
"Expected Parrot is a game-changer for AI-driven research. It's Python-friendly, makes it easy to set up surveys and experiments with AI agents (and more), and allows you to tap into multiple LLMs at once. It's also open-source, adaptable, and a must-have for anyone looking to streamline their workflow."
Sophia Kazinnik
Researcher, Stanford University
"I love how Expected Parrot uses surveys—a familiar tool for social scientists—to interact with AI models. I could quickly scale a detailed analysis of thousands of transcripts. The platform is so easy to use that I could focus on the analysis rather than getting bogged down with the technical implementation."
Matthew Olckers
Researcher, Stellenbosch University
"While working on my research, I came across the edsl Python package, and it has been a game-changer. It saved me a tremendous amount of time in running my study, streamlining processes that would have otherwise been much more complex."
Julian Ustiyanovych
Researcher, London School of Economics
"Expected Parrot's libraries allow me to not only conjure agents from existing data, but also interact with a wide variety of variants to optimize for characteristics that are best able to explore spaces."
Marisa Boston
CEO, simthetic.ai
"Expected Parrot has been invaluable for our exploration of silicon sampling approaches in experimental research. The cached results feature saves us significant time and computing costs, and ensures perfect reproducibility across experimental iterations."
Thomas Graeber
Assistant Prof., Harvard Business School
"Expected Parrot makes high-quality LLM-based data labeling surprisingly easy—even with just basic Python skills. It's intuitive, lightweight, and perfect for quickly building and iterating on custom labeling tasks."
Jesse Bryant
Doctoral Candidate, Yale University