EDSL — Design and run AI-powered surveys and experiments with many AI agents and LLMs at once.
Analyzed by Sai Pavan Gopularam · AI · Research Tools · View on GitHub
- Stars: 511
- Forks: 89
- Commits last 30 days: 100
- Health: Active (100 commits this month)
- Language: Python
- License: MIT
What It Is
EDSL is a Python library that lets you design and run surveys and experiments, but instead of asking humans, you ask AI agents powered by large language models (LLMs). Think of it like a SurveyMonkey or Qualtrics for AI, where you can define questions, create different AI personas (agents), and then collect their "responses" at scale.
This matters because it slashes the cost and time of traditional market research or data labeling. You can rapidly test ideas, gather synthetic feedback, or generate labeled datasets without waiting for human input, allowing for much faster iteration on products and research.
License Verdict
MIT License — Build and Sell Freely — Commercial Use Approved • No Copyleft Restrictions
The MIT license allows you to use, modify, and distribute this software for any purpose, including commercial projects. You can integrate it into proprietary software and sell your product without needing to open-source your own code.
How to Use It
To get started, install the Python package. You'll need API keys for the language models you want to use, or you can use Expected Parrot's unified key. Creating an account with Expected Parrot is optional but provides remote caching and collaboration features.
Prerequisites:
- Python 3.9 - 3.13
- LLM API keys (OpenAI, Anthropic, etc.)
Estimated setup time: 10 minutes.
pip install edsl
# Then you'd typically write a Python script
# to define questions, agents, and run a survey.
# Example:
# python your_survey_script.pyWhat I'd Build With This
AI Persona Feedback Loop (micro-saas)
Build a service where users define a product idea or marketing copy, and EDSL simulates feedback from various AI personas (e.g., 'tech enthusiast', 'budget shopper', 'skeptic'). The service summarizes qualitative feedback and quantifies sentiment, helping early-stage founders refine their offerings before spending on real user testing. Users pay for each feedback report generated.
Effort: 3 Days Build Time · Target: Indie Hackers, Early-Stage Startups · Pricing: $29/report or $99/mo for 10 reports
Synthetic Market Research Studio (saas)
Develop a comprehensive web platform for market research agencies or product teams. Users can design complex surveys, create diverse AI agent populations reflecting target demographics, and run large-scale simulations for product validation, pricing sensitivity, or messaging effectiveness. The platform would offer advanced analytics, visualization dashboards, and comparison tools for different scenarios. Subscription tiers based on agent count and survey complexity.
Effort: 3 Months Build Time · Target: Marketing Agencies, Product Management Teams · Pricing: $299 - $999/mo
AI Policy Impact Simulator (enterprise)
Offer a specialized solution for large organizations in regulated industries (finance, healthcare). The platform would allow them to upload new internal policies or regulatory changes and simulate how different AI agents (representing employees, customers, or compliance officers) interpret and react to them. This helps identify potential misunderstandings, compliance risks, or operational bottlenecks before real-world implementation, saving millions in potential fines or rework.
Effort: 6 Months Build Time · Target: Enterprise Compliance & Legal Departments · Pricing: Custom Enterprise Contracts ($50k+/year)
Sai Pavan Gopularam's Take
This is a powerful toolkit for anyone looking to generate synthetic data or quickly test ideas without the overhead of human studies. I see a clear path to building a Micro-SaaS for rapid product feedback, charging $49/month for unlimited AI persona tests. It solves a real pain point for early-stage founders.
Watch Out For
- AI Responses Aren't Human: The README explicitly states that AI agent responses reflect statistical patterns, not actual human opinions. While useful for prototyping, these results should be validated with real human data for measuring actual attitudes or behaviors.
- LLM API Costs: Running large-scale simulations with many agents and LLMs can quickly accumulate API costs from providers like OpenAI or Anthropic. Budgeting and cost monitoring are essential, especially for commercial applications.
- Expected Parrot Lock-in: While EDSL is open-source, features like remote caching, unified API key management, and the "Coop" collaboration platform are tied to the Expected Parrot service. Relying heavily on these might create vendor lock-in.
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