Simulate your customers

Expected Parrot is an open-source framework and an interactive app to design, test, and simulate agents for surveys and research.

Work in code

Work interactively

Accelerating research at leading institutions

Transparency

Open-source, fully transparent

Our open-source tools give you full visibility into and control over how surveys, agents, and results are generated, making it easy to audit methods, reproduce findings, and adapt workflows.

Validation

Compare agents with human survey data

See exactly how AI responses align with real human feedback. Validate your agent's performance against genuine human survey responses.

Research

Build reproducible simulations for research & market insights

Create experiments that can be replicated and validated by others. Generate reliable market insights through scientifically sound agent simulations.

Testimonials

What Researchers Are Saying

Real Stories, Real Impact.

How Expected Parrot Is Transforming

Research Workflows

Real Stories, Real Impact.

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