Top AI Repos — open-source AI, indexed and scored
Top AI Repos tracks AI repositories on GitHub and answers two different questions about each one: is it moving right now, and would you bet a product on it.
Top AI Repos tracks AI repositories on GitHub and answers two different questions about each one: is it moving right now, and would you bet a product on it.
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
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Today
— stars today
This week
+1 stars this week
This month
+23 stars this month
Momentum
116.0
growth rate 0.11%/day
# Retentioneering Retentioneering is an open-source Python library for **understanding user behavior from event logs**. Find where users get stuck, compare the paths different groups take, and discover patterns across clicks, sessions and repeat visits. Explore the data yourself or let an AI agent work with the same tools. [](https://pypi.org/project/retentioneering/) [](https://pypi.org/project/retentioneering/) [](https://pepy.tech/project/retentioneering) [](https://github.com/retentioneering/retentioneering-tools/blob/master/LICENSE) [](https://discord.com/invite/hBnuQABEV2) [](https://t.me/retentioneering_support) [](https://colab.research.google.com/github/retentioneering/retentioneering-tools/blob/master/notebooks/retentioneering_5_tour.ipynb) [Run a demo notebook in Google Colab](https://colab.research.google.com/github/retentioneering/retentioneering-tools/blob/master/notebooks/retentioneering_5_tour.ipynb) – no need to install the library locally, sample data included. [Quick start](#quick-start) · [Use cases](#common-use-cases) · [Use an AI agent](#work-with-an-ai-agent) · [Docs](https://retentioneering.com/docs/) <a href="https://retentioneering.com/docs/widgets/transition-graph"><img src="https://raw.githubusercontent.com/retentioneering/retentioneering-tools/master/.github/readme/transition-graph.gif" width="820" alt="A demo of the interactive features of the transition graph widget. You can highlight a route, compare path groups, focus on the step you want to investigate, and explore incoming and outgoing transition probabilities in the ego view."></a> *A demo of the interactive features of the [transition graph](https://retentioneering.com/docs/widgets/transition-graph) widget. You can highlight a route, compare path groups, focus on the step you want to investigate, and explore incoming and outgoing transition probabilities in the ego view.* ## Is it for you? Use Retentioneering when you have a question about *the sequence of actions* like these: - A metric changed. Where did journeys change and move the metric? - Users stop before reaching a goal. What do they do instead and how do successful paths differ? - You want to understand engagement. What behavior types are represented, and how does usage evolve across visits? - You need to investigate a flow. Where do checkout, onboarding, support or agent runs loop, branch or stall? Bring an event-level export from your analytics platform, warehouse or application logs. All you need is a **user, session or case ID; an event name; and a timestamp**. ## Quick start Python: 3.11 - 3.13. Environment: Jupyter (Jupyter Notebook, JupyterLab, JupyterLab Desktop), VS Code, Cursor, Google Colab. To install the library, run ```bash pip install retentioneering ``` Within a notebook, use `%pip install retentioneering` instead. See the [installation guide](https://retentioneering.com/docs/installation) for the details. To try out the library quickly, without [loading your own data](#bring-your-own-data), you can use a built-in e-commerce dataset: ```python import retentioneering as rete stream = rete.datasets.load_ecom() # bundled synthetic store stream.transition_graph() ``` ## Common use cases Below are a few examples of how you can apply retentioneering tools to approach common analytical problems on the [bundled e-commerce demo dataset](https://retentioneering.com/docs/eventstream#sample-dataset). The code chunks assume `stream = rete.datasets.load_ecom()`. <details> <summary>A key metric dropped. Where did
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Would you bet a product on this? Bounded 0–100 and slow moving.
matched fp:e1100ed7d7b392e3, desc:reproducible