OpenInterviewer: Qualitative Interviews

Scale lets you hear voices that scheduling would have silenced.

OpenInterviewer is an open-source platform for qualitative research interviews. You design the study and share a link. Participants then engage with an AI interviewer that adapts based on their responses.

AI providers: Gemini (default) or Claude, with per-study model selection

Deploy: one-click Vercel deployment

Why it matters

• pilot studies — test interview protocols before committing to full human-conducted studies
• larger samples — collect interview data from 50+ participants without scheduling constraints
• exploratory research — gather perspectives on emerging topics
• cross-timezone studies — engage participants on their own schedule
• complementary data — pair with human interviews for methodological triangulation

How it works

For researchers

1. Create a study — Define research questions, participant profiles, and interview mode
2. Configure the interviewer — Choose structured, standard, or exploratory mode; select your AI model
3. Share the link — Participants access via a simple URL (with optional expiration)
4. Monitor and analyze — Real-time synthesis of themes, contradictions, and patterns
5. Generate follow-ups — Create new studies based on synthesis findings to dig deeper

For participants

1. Open the link — No account or app required
2. Consent — Standard consent flow
3. Conversation — Natural dialogue with an AI interviewer that adapts to responses
4. Demographics — Collected conversationally, not as a separate form

Tools

Interview modes

Model selection

Each study can use a different AI model. Select it from a dropdown in the study setup page. This lets you balance cost, speed, and quality per study.

This is the list the tool actually offers, not a recommendation. Per-token prices move, so we do not restate them: see Anthropic's pricing and Gemini API pricing.

Model priority: per-study UI selection takes precedence over environment variable defaults.

AI reasoning mode

For analytical operations like synthesis, the platform switches to its configured higher-capability model with extended thinking enabled. This produces richer thematic analysis without slowing down the interview itself.

Each study can override this behavior: Automatic (recommended default), Always enabled (slower interviews but deeper responses), or Always disabled (faster but less thorough synthesis). Disabling reasoning reduces thinking-token use; it does not change the configured synthesis model. Keep in mind that synthesis uses a higher-cost configured model. Monitor costs if you run many interviews.

Built-in analysis

• Per-interview synthesis — Automatic extraction of stated vs revealed preferences, themes, and contradictions
• Cross-interview analysis — Pattern identification across all participants
• Aggregate reporting — Themes, outliers, and convergence points
• Follow-up studies — New research questions generated from synthesis findings to iteratively deepen your inquiry

Link management

When generating participant links, you can set expiration windows (7 days, 30 days, 90 days, or never). You can also toggle link access on or off from the study detail page. Use this to close data collection on a schedule, pause a study, or revoke links if they've been shared beyond your intended sample.

Security

• API keys stay server-side, never exposed to participants
• Researcher dashboard is password-protected
• Participant tokens are JWT-signed
• Data stored in encrypted Vercel KV (Redis)

Host support

OpenInterviewer is a Next.js web platform, not a plugin or MCP server. It runs anywhere you can run a Node app.

If you're choosing between platforms: Vercel takes about two minutes; self-host if you need data residency control.

Install

The fastest path is the one-click Vercel deployment. Open the deployment link, set two environment variables, and the app is usually live in about two minutes.

Deploy OpenInterviewer on Vercel

For local development:

git clone https://github.com/linxule/openinterviewer.git
cd openinterviewer

bun install
cp .env.example .env.local
# Edit .env.local with your API keys
bun run dev

Environment variables

Vercel configures KV credentials (KV_REST_API_URL, KV_REST_API_TOKEN, etc.) automatically when you connect an Upstash Redis store through the Vercel dashboard.

Architecture

Built on Next.js with a clean separation between researcher and participant flows:

OPENINTERVIEWER
      |
      +-- Researcher dashboard
      |     +-- study management
      |     +-- live monitoring
      |     +-- cross-interview analysis
      |
      +-- Participant interface
      |     +-- consent flow
      |     +-- AI-conducted interview
      |     +-- demographic collection
      |
      +-- Backend
            +-- model abstraction
            +-- data persistence
            +-- participant authentication

Methodological considerations

AI-conducted interviews are a young method. When designing a study, disclose the AI interviewer, check institutional ethics requirements, and expect shorter, more structured conversations. AI interviewers offer consistent coverage; human interviewers offer unexpected depth. The two are strongest together.

Part of Research Memex

OpenInterviewer sits at the data-collection end of the stack. Most of the Toolkit helps you interpret material you already hold; this platform produces the material: consented, structured interview data that arrives as text, ready for the same interpretive work the rest of the stack supports.

• Interpretive Orchestration — the staged human-AI workflow for analyzing the transcripts this platform collects
• Memex Plugin — session memory that holds the analytic thread as a study accumulates interviews
• AI Model Reference Guide — background for the per-study model selection above

Carrel status: OpenInterviewer is outside Carrel's install scope. It is a standalone web platform, deployed on Vercel or self-hosted, not a tool added to a coding agent.