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Decision brief
edsl stands out for designing AI-powered surveys and experiments in social science and market research by letting users simulate large-scale studies involving multiple AI agents and LLMs.
Good fit when
- When conducting complex simulations of social science studies that require the use of multiple AI agents or interactions with LLMs.
- For market research scenarios where the design calls for analysis using a high volume of synthetic data generated by different levels of language models.
Avoid when
- If your project requires real human responses and feedback in experiments, edsl is a system designed around AI agents rather than living participants.
- In cases where the scope of the experiment does not involve social science or require significant simulation capabilities with large numbers of AI entities.
Observed Jul 15, 2026 · Source: enrich:decision_facts
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Maintenance and security
Full trust report- Maintenance
- Very active (1d since push)
- As of today
- Provenance
- Not a fork · Organization account
- As of today
- Security (OSV)
- No lockfile
- As of 1mo
Public GitHub metadata and optional OSV scans. Signals, not a guarantee. Trust methodology.
Install
pip install edslSimilar tools
Same-category neighbours. No typed graph edges are catalogued for this tool yet.
Evidence and technical details
Sourced facts, taxonomy, compatibility claims, README excerpt, and machine-readable endpoints.
Overview
expectedparrot/edsl is a Python-based platform to design, conduct, and analyze results of AI-powered surveys and experiments, particularly useful in social science and market research through simulation with large numbers of AI agents and LLMs.
Capability facts
- CLI
- CLI entrypoint
Source: pyproject.toml:[project.scripts] · Aug 25, 2026
- Languages
- python
Source: github.language+pyproject.toml · Aug 25, 2026
Categories
Compatibility
Sourced claims from the README excerpt - not unsourced marketing copy.
Tags
README
Getting started
-
Run
pip install edslto install the package. See instructions. -
Create an account to run surveys at the Expected Parrot server and access a universal remote cache of stored responses for reproducing results.
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Choose whether to use your own keys for language models or get an Expected Parrot key to access all available models at once. Securely manage keys, expenses and usage for your team from your account.
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Run the starter tutorial and explore other demo notebooks for a variety of use cases.
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Share workflows and survey results at Coop: a free platform for creating and sharing AI research.
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Join our Discord for updates and discussions!
Requirements
- Python 3.9 - 3.13
- API keys for language models. You can use your own keys or an Expected Parrot key that provides access to all available models. See instructions on managing keys and model pricing and performance information.
For agents
This page has a .md twin and JSON over the API.