Expected Parrot: Tools for AI-Powered Research

Expected Parrot delivers powerful tools for conducting research with human and artificial intelligences.

This page provides documentation for Expected Parrot Domain-Specific Language (EDSL), a Python package for performing research with AI agents and language models, and Coop, a platform for creating, storing and sharing AI-based research projects.

  • EDSL is available to download from PyPI (run pip install edsl). The source code is available at GitHub.

  • Create an account to post and share content, run surveys and store results at the Expected Parrot survey. Learn more about how it works and start exploring.

Key features

Simplified access to hundreds of models

A single API key lets you conduct research with many popular models at once. Learn more.

Collaboration features

Use Coop to create, store and share your research projects seamlessly.

Data integrations

Easily import, analyze and extend many types of data. Learn more.

Hybrid human-AI surveys

Collect and combine responses from humans and AI. Learn more.

Built-in analysis tools

Readily visualize, analyze and compare responses. Learn more.

Use cases

Our tools simplify survey creation, experiment execution and response analysis. Common use cases include:

Data labeling

Use AI to answer qualitative and quantitative questions about your data, and extract insights. See examples.

Market research

Gather insights on consumer preferences, behaviors and trends. Simulate customer personas with AI agents and analyze their responses. See examples.

User experience research

Assess user satisfaction, usability and engagement. Use AI agents to simulate user profiles and analyze their feedback on products or services. See examples.

Integrate human and AI data

Combine human responses with AI-generated responses to create richer datasets. See examples.

Analyze survey data

Analyze survey data with built-in methods. Simulate follow-up interviews with respondents. See examples.

Social science research

Explore hypotheses, gather qualitative or quantitative data and generate new data using AI.

For more on EDSL’s key features and use cases, visit the Overview section.

Getting started

To use EDSL, you need to install the package and choose how to access language models. Please see the links in the steps below for more details:

  1. Install EDSL

    Run the following command to install the package:

    pip show edsl

    See Installation instructions for more details.

  2. Create a Coop account

    Create an account to access the Expected Parrot server, free storage and special features and collaboration tools.

  3. Manage API keys for language models

    Your account comes with a key that allows you to run surveys with all available models at the Expected Parrot server. You can also use and share your own keys from service providers.

    See instructions on Managing Keys for details and options.

  4. Run a survey.

    Read the Starter Tutorial and download a notebook to create a survey and run it. See examples for many use cases and tips on using EDSL effectively in the documentation.

Join our Discord channel to ask questions and chat with other users!

Researchers

Are you using EDSL for a research project? Send an email to info@expectedparrot.com and we’ll give you credits to run your project!

Introduction

  • Overview: An overview of the purpose, concepts and goals of the EDSL package.

  • Whitepaper: A whitepaper about the EDSL package (in progress).

  • Citation: How to cite the package in your work.

  • Papers & Citations: Research papers and articles that use or cite EDSL.

Core Concepts

  • Questions: Learn about different question types and applications.

  • Scenarios: Explore how questions can be dynamically parameterized for tasks like data labeling.

  • Surveys: Construct surveys with rules and conditions.

  • Agents: Design AI agents with relevant traits to respond to surveys.

  • Language Models: Select language models to generate results.

Working with Results

Coop

Coop is a platform for creating, storing and sharing AI-based research. It is fully integrated with EDSL and provides access to special features for working with AI agents and language models, free storage and collaboration tools, including:

  • Survey Builder: A user-friendly no-code interface for creating surveys and gathering responses from humans and AI agents.

  • Remote Inference: Access all available language models and run surveys at the Expected Parrot server.

  • Remote Caching: Automatically store results and API calls at the Expected Parrot server.

  • Notebooks & Colab Notebooks: Easily post and share .ipynb and .py files to the Coop and access with Colab.

  • File Store: Store and share data files for use in EDSL projects.

Learn more about how it works and purchasing credits.

How-to Guides & Notebooks

Examples of special methods and use cases for EDSL, including:

  • Data labeling

  • Data cleaning

  • Analyzing survey results

  • Adding data to surveys from CSVs, PDFs, images and other sources

  • Conducting agent conversations

  • Converting surveys into EDSL

  • Cognitive testing

  • Research methods