Enterprise-scale Python transformation
Safely analyze, upgrade, and transform Python across thousands of repositories using governed semantic automation built for platform teams and coding agents.
Python at enterprise scale requires more than scripts and IDE refactors
Python is widely used across data pipelines, automation, integrations, and AI systems, yet most tools for working with Python are designed for single-repository changes and developer-scale refactors. As organizations adopt coding agents and automation-assisted development, the challenge shifts from editing files to coordinating safe, governed change across large, interconnected Python environments.
Moderne provides the execution infrastructure organizations need to move beyond one-off edits and enable portfolio-wide Python transformation with confidence.
Without semantic understanding and deterministic execution, large-scale Python change becomes:
- Risky to automate
- Difficult to validate
- Hard to coordinate across teams
- Prone to drift and inconsistency
Semantic understanding for Python with the Lossless Semantic Tree (LST)
With Python represented in the OpenRewrite Lossless Semantic Tree (LST), code is modeled structurally and semantically rather than treated as text. The LST preserves formatting and intent while resolving symbols, imports, types, and relationships across modules and repositories.
This enables both developers and coding agents to:
- Understand Python systems at a semantic level
- Analyze impact before changes are made
- Execute deterministic, reviewable transformations
- Coordinate change across repositories and languages
Instead of reconstructing meaning from source text, agents and developers operate from a shared, verified structural model of the codebase, making large-scale change safer, faster, and more predictable.
Governed execution for large-scale Python change
The LST models the code.
Recipes execute against that model.
Python participates in the same recipe-driven execution workflows already used to manage Java and JavaScript systems at scale. Teams can run coordinated Python change initiatives using deterministic transformations delivered as reviewable pull requests within existing CI/CD pipelines.
This allows organizations to:
- Apply consistent changes across many Python repositories
- Enforce organizational standards
- Review and validate changes before merge
- Re-run transformations safely over time
Coding agents can assist in identifying patterns and proposing changes. Recipes provide the governed execution mechanism that applies those changes consistently across repositories, environments, and time.
What you can do with Python in Moderne
Upgrade and modernize Python systems
- Upgrade Python runtimes and modernize deprecated APIs across many repositories
- Standardize language features and runtime behavior
Align dependencies and remediate vulnerabilities
- Add, change, or remove dependencies in pyproject.toml
- Upgrade direct and transitive dependencies across repositories
- Remediate Python CVEs at scale
Analyze and research Python portfolios
- Surface symbol usage and architectural patterns across Python systems
- Identify deprecated APIs and risky constructs
- Audit frameworks and library adoption across teams
Coordinate change across languages
- Run cross-language campaigns spanning Python, Java, and JavaScript/TypeScript
- Align dependencies across services and supporting Python automation
- Pair API changes in backend services with updates in dependent Python systems
Expanding the semantic surface for agents and platform teams
Python now sits alongside Java and JavaScript/TypeScript in the Lossless Semantic Tree, expanding the structural foundation available to both developers and coding agents. This extends safe, large-scale transformation into the data, automation, and AI layers of modern software systems.
More of your code is now structurally visible — and ready to evolve.
For platform teams, this means:
- More visibility across more of the stack
- Safer ways to evolve Python systems at scale
For agent-assisted workflows, it means:
- Less time reconstructing context
- More reliable execution of coordinated change
- Deterministic transformations across Python repositories
Coordinating Python change across the stack
Modern systems rarely evolve in isolation. Python frequently supports backend services, frontend tooling, data pipelines, and AI workflows. With Python participating in the same semantic and execution model, coordinated change initiatives can now span the full technology stack.
This enables organizations to:
- Align dependency upgrades across multiple languages
- Apply security remediations across runtimes in a single campaign
- Coordinate API changes between services and Python integrations
- Run portfolio-wide transformation initiatives across heterogeneous codebases
Rather than managing change separately by runtime, teams can orchestrate transformation across the stack using a shared execution model and governed workflow.
Frequently Asked Questions
