PyPy Strikes Back Core Developers Deny Abandonment Rumors Despite Funding Struggles.
In January 2026, the documentation for uv, a prominent Python package manager, sparked a debate by suggesting that the PyPy project had ceased active development. However, the PyPy core team has recently stepped forward to clarify its status. While they admit to facing significant resource shortages, they confirmed that development continues, albeit with a focus on bug fixes and stability rather than chasing the latest Python feature releases.
The Legacy of Speed
PyPy is a high-performance implementation of the Python language, famously written in Python itself using a Just-In-Time (JIT) compiler. Its history traces back to the Psyco project in 2002, which evolved into the independent PyPy project in 2007. For nearly two decades, PyPy has been the go-to choice for developers requiring execution speeds several times faster than the standard CPython implementation.
The Struggle to Keep Up
Matti Picus, a key developer for PyPy, noted that the project is currently prioritizing support for Python 3.11. Due to a lack of sufficient contributors and funding, the team has found it increasingly difficult to implement the rapid-fire feature updates introduced in newer CPython versions.
Meanwhile, the main CPython project has undergone an "Efficiency Revolution" since version 3.11. While PyPy still maintains an average performance lead, the gap is narrowing as CPython introduces its own specialized optimizations.
A key turning point that put PyPy in a difficult position was the "Faster CPython" project led by Guido van Rossum (the creator of Python) and his team from Microsoft. They aimed to make Python 3.13 and 3.14 twice as fast by directly integrating some PyPy techniques (such as JIT) into CPython, reducing the need to switch to PyPy.
PyPy's biggest problem wasn't just speed, but C extensions. Popular packages like NumPy, Pandas, and Scikit-learn are written in C for speed, and making PyPy fully compatible with this code (C-API compatibility) is complex and resource-intensive.
Even though CPython caught up, PyPy still retained its place in the long-running processes category (such as servers or data processing pipelines) because PyPy's JIT system performs most efficiently after the program has run for a while and its AI learns its "hot path." The code is accurate.
Besides CPython and PyPy, there are now new competitors like Mojo or the use of Rust-based Python tools (such as the uv project mentioned) as alternatives for increasing speed, putting pressure on PyPy from all sides.
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Source: Hacker News
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