Quickstart

This example shows the shortest meaningful path through design-research-agents.

Note

If you want a step-by-step editor workflow for creating a virtual environment, installing the published package, and running a first script, see VS Code Setup Guide.

1. Install

pip install design-research-agents

Or install from source:

git clone https://github.com/cmudrc/design-research-agents.git
cd design-research-agents
python -m venv .venv
source .venv/bin/activate
python -m pip install --upgrade pip
pip install -e .

2. Minimal Runnable Example

This snippet requires an OpenAI-compatible endpoint (local or remote).

from design_research_agents import DirectLLMCall, OpenAICompatibleHTTPLLMClient

with OpenAICompatibleHTTPLLMClient(
    base_url="http://127.0.0.1:8001/v1",
    default_model="qwen2-1.5b-q4",
) as llm_client:
    agent = DirectLLMCall(llm_client=llm_client)
    result = agent.run("List three interview themes about onboarding friction.")
    print(result.output)

3. What Happened

You instantiated a concrete participant (DirectLLMCall), executed one run through a configured backend client, and received structured output that can be traced and compared in later studies.

4. Where To Go Next

• Concepts

• Typical Workflow

• Examples Guide

• API

Ecosystem Note

In a typical study, design-research-agents provides executable participants, design-research-problems supplies the task, design-research-experiments defines the study structure, and design-research-analysis interprets the resulting records.