The 2026 Python Typing Survey received 1,794 responses. Type hint use changed little across three years, but checker selection changed sharply. Among respondents who reported which checkers they use, half selected more than one, and missing library type information was the most common difficulty.
Key Takeaways From the 2026 Python Typing Survey The 2026 Python Typing Survey highlights several ongoing opportunities for improvement. These include the need for broader and more accurate type information, especially to support users of data-science packages, which often present challenges due to dynamic frameworks. Tool builders should prioritize enhancing editor feedback and simplifying the initial setup for type checkers, along with providing clear error messages linked to explanations. Additionally, improving callable syntax is identified as a practical area for standards work. The survey also indicates that the interactions between AI tools and type checkers require further empirical study to determine which approaches are most beneficial for developers. AI Tools Now Write Types and Respond to Checker Errors The 2026 survey reveals a significant increase in the use of AI tools for Python typing. The percentage of respondents who believe AI-generated code becomes more accurate/reliable with type hints surged from 18.5% in 2025 to 38.4% in 2026. AI tools were used to write type hints by 42.5% of respondents and to improve AI-generated code by 32.6%. Overall, 61.0% of respondents utilized AI for at least one typing-related activity, including fixing or explaining type errors. Some developers noted that their AI coding tools invoke type checkers and subsequently fix the errors. However, there's considerable uncertainty regarding the actual usefulness of types for AI-assisted coding, with 29.8% unsure, highlighting a need for further research into effective AI-type checker interactions. Library Gaps Lead Overall; Occasional Users Struggle With Complexity The most common difficulty reported by developers was missing type information in third-party libraries (65.1%), followed by the type system's inability to describe all code behavior (44.6%). Dynamic constructs, decorators, metaprogramming, and framework behavior were frequently cited as problematic areas, with specific mention of libraries like Django, SQLAlchemy, NumPy, and pandas. The survey also found that the perceived complexity of the type system varied with usage frequency; 41.6% of rare/occasional users found it too complex, compared to 14.5% of frequent users. Developers new to typing, many of whom are experienced Python users, require guidance on adding types to existing repositories and user-friendly initial checks. Improved Callable syntax was identified as the most desired missing feature, selected by 45.7% of respondents. Newer Checkers Gained Ground Quickly While the overall rate of frequent type hint use remained stable (85.5% - 88.0%) across the 2024-2026 surveys, type checker selection showed a sharp shift. The availability of newer open-source checkers led to speed and performance becoming the top reasons for checker choice (56.5%). The proportion of respondents using two or more checkers increased from 46.0% in 2025 to 49.9% in 2026, indicating a diversifying landscape of tooling in the Python typing ecosystem. Developers Mix Checkers Across Their Workflows Developers are increasingly using multiple type checkers within their workflows. For instance, among Mypy users, a significant portion also used its editor plugin or other named checker integrations. Notably, 83.3% of Pyrefly respondents also used another checker. This multi-checker approach often stems from different team members using various IDEs or optimizing for performance, such as running a faster checker for initial checks and a more comprehensive one for critical paths. This trend, however, contributes to inconsistencies between checkers, which was reported as a difficulty by 33.3% of all respondents and 42.7% of those using multiple static checkers. IDE Support Remains Central as Typing Help Moves Away From Stack Overflow Readability and documentation are paramount motivators for using type hints, cited by 93.6% of respondents. Editor support, including features like auto-completion, navigation, hover information, and real-time error messages, is also highly valued, with 84.8% rating it highly. VS Code is the most popular IDE, with 59.3% of users. The shift in where developers seek help is also evident: Stack Overflow usage dropped significantly from 46.4% in 2024 to 22.9% in 2026, while reliance on large language model (LLM) chat increased from 26.0% to 41.7%. Python typing documentation and checker-specific documentation also saw increased usage. This trend emphasizes the importance of high-quality, easily accessible documentation and error messages that link to relevant explanations. Survey Results and Raw Data The complete results and raw data from the Python Typing Survey 2026 are available for download for further analysis and detailed insights. Acknowledgements The Python Typing Survey 2026 was conducted from August 5 to September 2, 2026, with collaboration from the Pyrefly team at Meta, PyCharm team at JetBrains, Python team at Microsoft, and ty team at Astral/OpenAI, alongside community contributors. The survey was disseminated across various platforms, and gratitude is extended to all participants, promoters, data reviewers, and contributors to the Python typing community.
Key Takeaways From the 2026 Python Typing Survey
The 2026 Python Typing Survey highlights several ongoing opportunities for improvement. These include the need for broader and more accurate type information, especially to support users of data-science packages, which often present challenges due to dynamic frameworks. Tool builders should prioritize enhancing editor feedback and simplifying the initial setup for type checkers, along with providing clear error messages linked to explanations. Additionally, improving callable syntax is identified as a practical area for standards work. The survey also indicates that the interactions between AI tools and type checkers require further empirical study to determine which approaches are most beneficial for developers.
AI Tools Now Write Types and Respond to Checker Errors
The 2026 survey reveals a significant increase in the use of AI tools for Python typing. The percentage of respondents who believe AI-generated code becomes more accurate/reliable with type hints surged from 18.5% in 2025 to 38.4% in 2026. AI tools were used to write type hints by 42.5% of respondents and to improve AI-generated code by 32.6%. Overall, 61.0% of respondents utilized AI for at least one typing-related activity, including fixing or explaining type errors. Some developers noted that their AI coding tools invoke type checkers and subsequently fix the errors. However, there's considerable uncertainty regarding the actual usefulness of types for AI-assisted coding, with 29.8% unsure, highlighting a need for further research into effective AI-type checker interactions.
Library Gaps Lead Overall; Occasional Users Struggle With Complexity
The most common difficulty reported by developers was missing type information in third-party libraries (65.1%), followed by the type system's inability to describe all code behavior (44.6%). Dynamic constructs, decorators, metaprogramming, and framework behavior were frequently cited as problematic areas, with specific mention of libraries like Django, SQLAlchemy, NumPy, and pandas. The survey also found that the perceived complexity of the type system varied with usage frequency; 41.6% of rare/occasional users found it too complex, compared to 14.5% of frequent users. Developers new to typing, many of whom are experienced Python users, require guidance on adding types to existing repositories and user-friendly initial checks. Improved Callable syntax was identified as the most desired missing feature, selected by 45.7% of respondents.
Newer Checkers Gained Ground Quickly
While the overall rate of frequent type hint use remained stable (85.5% - 88.0%) across the 2024-2026 surveys, type checker selection showed a sharp shift. The availability of newer open-source checkers led to speed and performance becoming the top reasons for checker choice (56.5%). The proportion of respondents using two or more checkers increased from 46.0% in 2025 to 49.9% in 2026, indicating a diversifying landscape of tooling in the Python typing ecosystem.
Developers Mix Checkers Across Their Workflows
Developers are increasingly using multiple type checkers within their workflows. For instance, among Mypy users, a significant portion also used its editor plugin or other named checker integrations. Notably, 83.3% of Pyrefly respondents also used another checker. This multi-checker approach often stems from different team members using various IDEs or optimizing for performance, such as running a faster checker for initial checks and a more comprehensive one for critical paths. This trend, however, contributes to inconsistencies between checkers, which was reported as a difficulty by 33.3% of all respondents and 42.7% of those using multiple static checkers.
IDE Support Remains Central as Typing Help Moves Away From Stack Overflow
Readability and documentation are paramount motivators for using type hints, cited by 93.6% of respondents. Editor support, including features like auto-completion, navigation, hover information, and real-time error messages, is also highly valued, with 84.8% rating it highly. VS Code is the most popular IDE, with 59.3% of users. The shift in where developers seek help is also evident: Stack Overflow usage dropped significantly from 46.4% in 2024 to 22.9% in 2026, while reliance on large language model (LLM) chat increased from 26.0% to 41.7%. Python typing documentation and checker-specific documentation also saw increased usage. This trend emphasizes the importance of high-quality, easily accessible documentation and error messages that link to relevant explanations.
Survey Results and Raw Data
The complete results and raw data from the Python Typing Survey 2026 are available for download for further analysis and detailed insights.
Acknowledgements
The Python Typing Survey 2026 was conducted from August 5 to September 2, 2026, with collaboration from the Pyrefly team at Meta, PyCharm team at JetBrains, Python team at Microsoft, and ty team at Astral/OpenAI, alongside community contributors. The survey was disseminated across various platforms, and gratitude is extended to all participants, promoters, data reviewers, and contributors to the Python typing community.
