{"repo":"retentioneering/retentioneering-tools","free":true,"listed":false,"github":"https://github.com/retentioneering/retentioneering-tools","clone":"git clone https://github.com/retentioneering/retentioneering-tools.git","description":"Python toolkit, MCP server, and agent skills for reproducible, auditable clickstream and event log analytics. Helps AI agents, data scientists and analysts build, validate, and cross-check product analytics, quantitative UX, customer journeys, graph-based user flows, behavioral segmentation, A/B tests, process mining models, Markov chain simulation","language":"Python","stars":913,"topics":["product-analytics","user-trajectories-analysis","machine-learning","predictive-analytics","predictive-modeling","clickstream","segmentation","graph-visualizer","user-trajectories","web-analytics"],"license":"Apache-2.0","category":"analytics","readme_excerpt":"What is Retentioneering? Retentioneering is an open-source Python toolkit, MCP server, and collection of agent skills for reproducible product analytics on clickstream and event log data. Instead of relying on one-off scripts generated for a single question, analysts and AI agents can use tested analytical primitives and reusable workflows to inspect customer journeys, explore graph-based user flows, discover behavioral segments, evaluate experiments, and cross-check results through independent, auditable computations. By reusing domain-specific analytics components instead of generating every analysis from scratch, Retentioneering can reduce implementation effort, agent token usage, and the risk of subtle analytical errors. Use Retentioneering when you want to turn raw sequences of user and system events into answers to questions such as: Where do users get stuck? Which journeys lead to conversion or churn? What behavioral segments exist in the data? How do flows differ between cohorts or experiment groups? Load a clickstream, product event log, or other timestamped event data into a Retentioneering Eventstream object, then explore interactive user-flow graphs and detailed step-matrix visualizations, compare conversion paths, analyze behavioral segments, evaluate A/B tests, or ask an AI agent to run and cross-check the analysis through Retentioneering MCP and agent skills. The resulting analyses, visualizations, and simulated interventions can be exported into clear, shareab","default_branch":null,"files":null,"tree":[],"storefront":"/r/retentioneering","claimed":false,"request_supported":{"post":"https://gitbuyer.com/r/retentioneering/retentioneering-tools/request-supported","requests":0},"note":"indexed from public GitHub; nothing is for sale on this page. Clone it from GitHub. Paid listings live at /search."}