AQDA

Augmented Qualitative Data Analysis — free, open-source, local-first QDA tool with AI augmentation.
Published

March 19, 2026

GitHub

AQDA — Augmented Qualitative Data Analysis

A free, open-source tool for qualitative researchers. AI-powered, local-first, privacy-respecting.

AQDA gives you a modern coding interface with local AI assistance — without cloud subscriptions, without your data ever leaving your machine. It runs as a local web app in your browser.

Python License

What Can AQDA Do?

Core Qualitative Coding

  • Text coding — Select text, apply codes, build your codebook. Click on any coded passage to see applied codes or remove them.
  • Hierarchical codes — Organize codes in parent-child trees with colors and descriptions.
  • Image & audio support — Import images (JPG, PNG, GIF, WebP) and audio files (MP3, WAV, M4A) with optional local transcription via Whisper.
  • Memos — Write analytical notes at the project, document, or code level.
  • Document variables — Add metadata (author, date, source) to documents. Auto-extract from filenames on import.
  • Segments browser — Browse all coded segments across documents. Click to jump to the passage in context, or delete directly from the list.
  • Export — REFI-QDA (.qdpx) for MAXQDA/ATLAS.ti/NVivo, codebook (.qdc), CSV, JSON.

AI-Powered Augmentation

AQDA uses Ollama to run AI models locally on your computer. No internet connection required, no data shared with anyone.

Feature What it does
Topic Search Find passages across your documents that match a topic or theme you describe
Code Suggest Given a code, find uncoded passages that might belong to it — based on its definition and existing coded examples
Consistency Check Flag coded segments that seem like outliers within a code — like inter-rater reliability with yourself over time
Hierarchy Suggest After inductive coding, get suggestions for grouping your codes into parent categories
Code Definition Generator Applied a code many times but haven’t written a definition yet? Generate one from the actual coded passages

When you click on an AI result, AQDA jumps to the passage in the document and highlights it, so you can immediately see the context and decide whether to code it.

These tools are designed as a methodological interlocutor — they interrogate your coding rather than generate it. The researcher always has the final word.

Two Types of AI Models

AQDA uses two types of models for different purposes:

Model type What it does Used by Recommended model
Embedding model Converts text into numerical representations so similar passages can be found Topic Search, Code Suggest, Consistency Check nomic-embed-text (fast, 274 MB)
LLM (language model) Reads text and generates structured output (definitions, groupings) Hierarchy Suggest, Define Code, Text Analysis qwen3.5:9b (6 GB)

You need one of each. They are configured in Settings.

Getting Started

What You Need

  • Python 3.10 or newer
  • pipx (installs Python apps in isolated environments)
  • Ollama (optional, for AI features) — ollama.com/download

Install

Open a terminal and run:

pipx install git+https://github.com/tseidl/aqda.git

Then start AQDA:

aqda

This opens your browser at http://127.0.0.1:8765. To stop, press Ctrl+C in the terminal.

Don’t have Python or pipx?

Mac:

# Install Homebrew (skip if you already have it)
/bin/bash -c "$(curl -fsSL https://raw.githubusercontent.com/Homebrew/install/HEAD/install.sh)"

After Homebrew installs, it prints commands to add it to your PATH — copy and run those lines, then:

brew install python pipx
pipx ensurepath

Close and reopen Terminal, then install AQDA.

Windows:

Download Python from python.org — check “Add python.exe to PATH” during installation. Then:

pip install pipx
pipx ensurepath
Close and reopen Command Prompt, then install AQDA.
Getting “command not found” after install? Run pipx ensurepath, then close and reopen your terminal. This adds pipx’s install directory to your PATH.

Setting Up AI Features (Optional)

  1. Download and install Ollama

  2. Open a terminal and pull the models:

    ollama pull nomic-embed-text   # for similarity search
ollama pull qwen3.5:9b         # for analysis and definitions

  3. In AQDA, go to Settings and select your models under “Embedding Model” and “LLM Model”

  4. Open the AI panel (sparkle icon in the left sidebar)

All AI processing happens on your machine. Nothing is sent to any server.

Audio Transcription (Optional)

To transcribe audio files locally using Whisper:

pipx inject aqda "aqda[audio]"

Then import an audio file (MP3, WAV, M4A) and click the transcribe button.

Auto-Extract Metadata from Filenames (Optional)

If your files follow a naming convention, AQDA can automatically extract variables on import. In Settings → Filename Variable Parsing, set a regex pattern with named groups.

For example, files like 2025-03-10_guardian_from-border-crackdown.txt:

(?P<date>\d{4}-\d{2}-\d{2})_(?P<source>[^_]+)_(?P<title>.+)

This extracts date, source, and title as document variables automatically when you import.

Updating

pipx install --force git+https://github.com/tseidl/aqda.git

Uninstalling

pipx uninstall aqda

This removes the app but keeps your data in ~/.aqda/. To remove everything, also delete that folder.

Your Data

All your data lives in a single file: ~/.aqda/aqda.db. This file contains all your projects. You can change the location in Settings.

  • Back it up by copying this file
  • Move between machines by copying it to another computer
  • Deleted projects go to a trash bin and can be restored

Sharing a Project

AQDA supports sharing individual projects via .aqda files — small, self-contained databases with everything in that project (documents, codes, codings, memos).

The workflow:

  1. In your project, click Export → Share Project (.aqda) — downloads a file like My_Project.aqda
  2. Send the file to your collaborator (email, Google Drive, USB stick — whatever works)
  3. They open AQDA, click Import DB on the project list, and select the .aqda file
  4. The project appears in their AQDA with all data intact
  5. When they’re done, they export and send it back

Each import creates a new project — it never overwrites existing work. This is turn-based collaboration: one person works at a time.

Not supported: merging two people’s independent changes made in parallel.

Export Formats

Format Use case
.aqda Share a project with another AQDA user — full round-trip import/export
.qdpx REFI-QDA standard — import into MAXQDA, ATLAS.ti, NVivo
.qdc Codebook XML — share code hierarchies between projects
.csv Coded segments as a table — for further analysis in R, Excel, etc.
.json Full project data — for custom processing or archival

License

MIT

Acknowledgments

Built with substantial assistance from Claude Code (Claude Opus 4.6 by Anthropic). Architecture, backend, frontend, and AI integration were developed collaboratively through human-AI pair programming.

Inspired by QualCoder and the qualitative research community’s need for modern, accessible, AI-augmented analysis tools.