crisp-t 2.2.0

Qualitative Research support tools in Python!

🔍 CRISP-T (Sense-making from Text and Numbers!)

✨ Have you been co-creating reality with adaptive AI vibes?

TL;DR CRISP-T is a qualitative research method and a toolkit to perform mixed data (text + numeric) analytics for computational triangulation and sense-making. More importantly, CRISP brings "vibe analytics" "SKILLS" for mixed data with an AI agent, adopting an adaptive epistomology.

👉 CLI Cheatsheet | Demo & Examples | Documentation

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📖 Introduction

Qualitative research focuses on collecting and analyzing textual data to explore complex phenomena. While qualitative and quantitative data are often used together, there is no standard method for combining them.

CRISP-T integrates textual data (interview transcripts, field notes) and numeric data (surveys, demographics) into a unified corpus. It allows researchers to:

• Perform INDUCTIVE analysis (finding patterns).
• Triangulate findings by linking text topics to numeric trends.
• Use Semantic Search to find relevant literature or code documents.
• Leverage GenAI through our MCP server for agentic analysis.

Key Features

• ✅ No Python required - Full CLI support.
• ✅ Interpretivist approach - Designed for sense-making, not just data science.
• ✅ GenAI Ready - Augments LLMs with agentic tools.
• ✅ Open Source - GPL-3.0 License.

The CRISP tools may also be useful for:

• ⭕ Automated interview coding dictionary generation.
• ⭕ Semantically filtering journal articles for literature review.
• ⭕ Generating visualizations for qualitative (e.g. word cloud) and quantitative (e.g. TDABM) data.
• ⭕ Many other ….

CRISP is ❌ NOT for:

• ❌ Multimodal prediction. Use this instead!
• ❌ Sequential mixed methods research, where qualitative and quantitative data are collected and analyzed in separate phases.
• ❌ Convergent parallel mixed method designs where qualitative and quantitative data are collected simultaneously but analyzed separately.

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💻 Installation

pip install crisp-t

Recommended (for Machine Learning features):

pip install crisp-t[ml]

Optional (for XGBoost):

pip install crisp-t[xg]
# Mac users: brew install libomp

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🚀 Usage

CRISP-T provides three main CLI tools. See the CLI Cheatsheet for a full list of commands.

1. crisp (Analysis)

The main tool for data import and analysis.

• Import Data: crisp --source ./data --out ./corpus
• Analyze Text: crisp --inp ./corpus --topics --sentiment
• Analyze Numbers: crisp --inp ./corpus --kmeans --regression

2. crispviz (Visualization)

Generates charts and graphs from your analysis.

• Word Cloud: crispviz --inp ./corpus --wordcloud --out ./viz
• Interactive Topics: crispviz --inp ./corpus --ldavis --out ./viz

3. crispt (Corpus Toolkit)

Manages corpus structure and detailed queries.

• Semantic Search: crispt --inp ./corpus --semantic "query" --num 5
• Metadata: crispt --inp ./corpus --meta "project=phase1"

👉 View the Step-by-Step Demo

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🤖 MCP Server (Agentic AI)

CRISP-T includes a Model Context Protocol (MCP) server, allowing AI agents (like Claude Desktop) to interact directly with your data.

Configuration (Claude Desktop)

MacOS: ~/Library/Application Support/Claude/claude_desktop_config.json Windows: %APPDATA%\Claude\claude_desktop_config.json

{
  "mcpServers": {
    "crisp-t": {
      "command": "<python-path>crisp-mcp"
    }
  }
}

The MCP server provides tools for semantic search, topic modeling, clustering, and more, allowing you to ask your AI assistant to "analyze the trends in this corpus" directly. Most of the CLI commands are available as MCP tools.

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Data model

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📚 Documentation & References

• CLI Cheatsheet
• Demo / Tutorial
• Full Documentation
• Mettler et al. (2025) - Methodological Reflection

🤝 Contribution & Contact

• License: GPL-3.0
• Author: Bell Eapen (UIS)| Contact
• Social:

First presented at ICIS 2025.