Overview

PM4Py is a Python library for building process-mining workflows directly into code, rather than working through a standalone interface. It is best suited to developers, data scientists, and researchers who need to analyze event data in Python. Its broad mining capabilities are compelling, but the AGPL terms and Graphviz setup make it a poor fit for some commercial and less technical teams.

Key features

Discovery and conformance

PM4Py can derive directly-follows graphs, Petri nets, BPMN models, and process trees from event data. That range gives technical users several ways to represent a process, while replay and alignment-based measures help compare observed behavior with a model. Conformance checking and root-cause analysis are supported, but data refresh is manual, so recurring analysis needs a workflow that handles updates.

Event data and object-centric mining

The library imports and exports event logs and supports XES, as well as OCEL 1.0 and OCEL 2.0. Object-centric analysis can account for events linked to multiple kinds of objects, such as orders, items, and deliveries. This makes PM4Py a stronger fit for event data that does not map neatly to a single case-based view.

Python workflow and visualization

PM4Py can extract machine-learning features for use with pandas and scikit-learn, making it relevant to teams that want process analysis alongside data-science work. Installation is through pip, with Windows and Linux instructions provided. Visualization requires Graphviz to be installed and configured, an extra setup step that may matter to teams seeking a ready-made interface.

Pricing

The Community Version costs 0.00 USD per free. It is intended for teaching, research, and open-source projects, and covered work must be published under AGPL v3. The free version lists no support, so it suits teams able to manage their own implementation and maintenance.

The Commercial Version has custom pricing: request a quote at info@processintelligence.solutions. It permits closed-source commercial use and includes regular releases, early feature access, and dedicated professional support. Commercial evaluation without open-sourcing code is available through a specific evaluation license. The choice is not simply free versus paid: organizations distributing AGPL-covered software or making it available over a network must publish the covered work under AGPL unless they obtain a commercial license.

Platforms

PM4Py is available as an API on Linux, macOS, and Windows. It is self-hosted, so teams control where their code and event data run; there is no automatic refresh to remove the need for data-pipeline work.

Who it's for

PM4Py is a strong choice for developers, data scientists, and researchers who want process mining as part of a Python codebase, need object-centric analysis, or plan to connect process features with pandas and scikit-learn. It is less suited to teams that want a graphical, managed service, hands-off data refresh, or closed-source commercial use without licensing work.

Pros and cons

Pros

Multiple discovery outputs: directly-follows graphs, Petri nets, BPMN models, and process trees support different ways to inspect discovered processes.
Object-centric event analysis: support for multiple object types and OCEL 1.0 and 2.0 fits logs that span entities such as orders, items, and deliveries.
Python ecosystem fit: feature extraction for pandas and scikit-learn helps bring process data into machine-learning workflows.
Free community option: teaching, research, and open-source projects can use the Community Version at no charge.

Cons

AGPL obligations: covered work distributed or made available over a network must be published under AGPL unless using a commercial license, which can rule out the free version for proprietary products.
Extra visualization setup: Graphviz must be installed and configured before using visualization features.
Manual refresh and self-hosting: teams must run their own environment and update data themselves.
No community-version support: users need to handle implementation issues without dedicated professional support.

Alternatives

For a broader category comparison, see Process Mining Software.

Mindzie Process Mining is worth considering if a desktop product is preferable: its free desktop edition includes full functionality for up to 1,000 cases and has no time-limited trial.
Celonis Process Mining Platform is a paid alternative with a free plan; its listed platforms include API, web, and Windows.
ProcessMind may suit teams looking for a web-based option with a free plan and trial; its Free plan covers one member, unlimited models, and 20 AI credits per month, with no additional seats or free viewer seats.
ARIS is another paid option, available through API, self-hosted, web, and Windows platforms.
Cortado is a free alternative for Linux, macOS, and Windows.
Pega CRM is a paid web option with a free trial.
RuM is a free alternative for Linux, macOS, and Windows.
UiPath Maestro Case Management offers a free trial and is self-hosted.

Verdict

Choose PM4Py if your team can work in Python and wants flexible process discovery, conformance checking, and object-centric mining within its own workflows. Its strongest reason to choose it is the breadth of analysis and event-data support in a free community library. Look elsewhere if you need a managed interface or cannot accept AGPL obligations; commercial closed-source use requires a separate license.

	Fluxicon Disco	PM4Py
Free plan	No	Yes
Free trial	Yes	No
Paid from	—	Free
Open source	No	No
Platforms	macOS, Windows	api, Linux, macOS, Windows
Free plan	No	Yes
Data refresh	manual	manual
Deployment model	self_hosted	self_hosted
Conformance checking	—	Yes
Root-cause analysis	—	Yes
ERP connectors	—	Yes

Fluxicon Disco vs PM4Py

Fluxicon Disco

PM4Py