PEP 810 – Explicit lazy imports
Author : Pablo Galindo , Germán Méndez Bravo , Thomas Wouters , Dino Viehland , Brittany Reynoso , Noah Kim , Tim Stumbaugh Discussions-To : Discourse thread Status : Draft Type : Standards Track Created : 02-Oct-2025 Python-Version : 3.15
Abstract This PEP introduces lazy imports as an explicit language feature. Currently, a module is eagerly loaded at the point of the import statement. Lazy imports defer the loading and execution of a module until the first time the imported name is used. By allowing developers to mark individual imports as lazy with explicit syntax, Python programs can reduce startup time, memory usage, and unnecessary work. This is particularly beneficial for command-line tools, test suites, and applications with large dependency graphs. The proposal preserves full backwards compatibility: normal import statements remain unchanged, and lazy imports are enabled only where explicitly requested.
Motivation A common convention in Python code is to place all imports at the module level, typically at the beginning of the file. This avoids repetition, makes dependencies clear and minimizes runtime overhead by only evaluating an import statement once per module. A major drawback with this approach is that importing the first module for an execution of Python (the “main” module) often triggers an immediate cascade of imports, and optimistically loads many dependencies that may never be used. The effect is especially costly for command-line tools with multiple subcommands, where even running the command with --help can load dozens of unnecessary modules and take several seconds. This basic example demonstrates what must be loaded just to get helpful feedback to the user on how to run the program at all. Inefficiently, the user incurs this overhead again when they figure out the command they want and invoke the program “for real.” A somewhat common way to delay imports is to move the imports into functions (inline imports), but this practice is very manual to implement and maintain. Additionally, it obfuscates the full set of dependencies for a module. Analysis of the Python standard library shows that approximately 17% of all imports outside tests (nearly 3500 total imports across 730 files) are already placed inside functions, classes, or methods specifically to defer their execution. This demonstrates that developers are already manually implementing lazy imports in performance-sensitive code, but doing so requires scattering imports throughout the codebase and makes the full dependency graph harder to understand at a glance. The standard library provides importlib.util.LazyLoader to solve some of these inefficiency problems. It permits imports at the module level to work mostly like inline imports do. Scientific Python libraries have adopted a similar pattern, formalized in SPEC 1. There’s also the third-party lazy_loader package. Imports used solely for static type checking are another source of potentially unneeded imports, and there are similarly disparate approaches to minimizing the overhead. All use cases are not covered by these approaches; however, these approaches add runtime overhead in unexpected places, in non-obvious ways, and without a clear standard. This proposal introduces lazy imports syntax with a design that is local, explicit, controlled, and granular. Each of these qualities is essential to making the feature predictable and safe to use in practice. The behavior is local: laziness applies only to the specific import marked with the lazy keyword, and it does not cascade recursively into other imports. This ensures that developers can reason about the effect of laziness by looking only at the line of code in front of them, without worrying about whether imported modules will themselves behave differently. A lazy import is an isolated decision in a single module, not a global shift in semantics. The semantics are explicit. When a name is imported lazily, the binding is created in the importing module immediately, but the target module is not loaded until the first time the name is accessed. After this point, the binding is indistinguishable from one created by a normal import. This clarity reduces surprises and makes the feature accessible to developers who may not be deeply familiar with Python’s import machinery. Lazy imports are controlled, in the sense that deferred loading is only triggered by the importing code itself. In the general case, a library will only experience lazy imports if its own authors choose to mark them as such. This avoids shifting responsibility onto downstream users and prevents accidental surprises in library behavior. Since library authors typically manage their own import subgraphs, they retain predictable control over when and how laziness is applied. The mechanism is also granular. It is introduced through explicit syntax on individual imports, rather than a global flag or implicit setting. This allows developers to adopt it incrementally, starting with the most performance-sensitive areas of a codebase. As this feature is introduced to the community, we want to make the experience of onboarding optional, progressive, and adaptable to the needs of each project. In addition to the new lazy import syntax, we also propose a way to control lazy imports at the application level: globally disabling or enabling, and selectively disabling. These are provided for debugging, testing and experimentation, and are not expected to be the common way to control lazy imports. The design of lazy imports provides several concrete advantages: Command-line tools are often invoked directly by a user, so latency — in particular startup latency — is quite noticeable. These programs are also typically short-lived processes (contrasted with, e.g., a web server). Most conventions would have a CLI with multiple subcommands import every dependency up front, even if the user only requests tool --help (or tool subcommand --help ). With lazy imports, only the code paths actually reached will import a module. This can reduce startup time by 50–70% in practice, providing a visceral improvement to a common user experience and improving Python’s competitiveness in domains where fast startup matters most.
(or ). With lazy imports, only the code paths actually reached will import a module. This can reduce startup time by 50–70% in practice, providing a visceral improvement to a common user experience and improving Python’s competitiveness in domains where fast startup matters most. Type annotations frequently require imports that are never used at runtime. The common workaround is to wrap them in if TYPE_CHECKING: blocks . With lazy imports, annotation-only imports impose no runtime penalty, eliminating the need for such guards and making annotated codebases cleaner.
blocks . With lazy imports, annotation-only imports impose no runtime penalty, eliminating the need for such guards and making annotated codebases cleaner. Large applications often import thousands of modules, and each module creates function and type objects, incurring memory costs. In long-lived processes, this noticeably raises baseline memory usage. Lazy imports defer these costs until a module is needed, keeping unused subsystems unloaded. Memory savings of 30–40% have been observed in real workloads.
Rationale The design of this proposal is centered on clarity, predictability, and ease of adoption. Each decision was made to ensure that lazy imports provide tangible benefits without introducing unnecessary complexity into the language or its runtime. It is also worth noting that while this PEP outlines one specific approach, we list alternate implementation strategies for some of the core aspects and semantics of the proposal. If the community expresses a strong preference for a different technical path that still preserves the same core semantics or there is fundamental disagreement over the specific option, we have included the brainstorming we have already completed in preparation for this proposal as reference. The choice to introduce a new lazy keyword reflects the need for explicit syntax. Import behavior is too fundamental to be left implicit or hidden behind global flags or environment variables. By marking laziness directly at the import site, the intent is immediately visible to both readers and tools. This avoids surprises, reduces the cognitive burden of reasoning about imports, and keeps the semantics in line with Python’s tradition of explicitness. Another important decision is to represent lazy imports with proxy objects in the module’s namespace, rather than by modifying dictionary lookup. Earlier approaches experimented with embedding laziness into dictionaries, but this blurred abstractions and risked affecting unrelated parts of the runtime. The dictionary is a fundamental data structure in Python—literally every object is built on top of dicts—and adding hooks to dictionaries would prevent critical optimizations and complicate the entire runtime. The proxy approach is simpler: it behaves like a placeholder until first use, at which point it resolves the import and rebinds the name. From then on, the binding is indistinguishable from a normal import. This makes the mechanism easy to explain and keeps the rest of the interpreter unchanged. Compatibility for library authors was also a key concern. Many maintainers need a migration path that allows them to support both new and old versions of Python at once. For this reason, the proposal includes the __lazy_modules__ global as a transitional mechanism. A module can declare which imports should be treated as lazy (by listing the module names as strings), and on Python 3.15 or later those imports will become lazy automatically. On earlier versions the declaration is ignored, leaving imports eager. This gives authors a practical bridge until they can rely on the keyword as the canonical syntax. Finally, the feature is designed to be adopted incrementally. Nothing changes unless a developer explicitly opts in, and adoption can begin with just a few imports in performance-sensitive areas. This mirrors the experience of gradual typing in Python: a mechanism that can be introduced progressively, without forcing projects to commit globally from day one. Notably, the adoption can also be done from the “outside in,” permitting CLI authors to introduce lazy imports and speed up user-facing tools, without requiring changes to every library the tool might use. By combining explicit syntax, a simple runtime model, a compatibility layer, and gradual adoption, this proposal balances performance improvements with the clarity and stability that Python users expect. Other design decisions The scope of laziness is deliberately local and non-recursive. A lazy import only affects the specific statement where it appears; it does not cascade into other modules or submodules. This choice is crucial for predictability. When developers read code, they can reason about import behavior line by line, without worrying about hidden laziness deeper in the dependency graph. The result is a feature that is powerful but still easy to understand in context.
In addition, it is useful to provide a mechanism to activate or deactivate lazy imports at a global level. While the primary design centers on explicit syntax, there are scenarios—such as large applications, testing environments, or frameworks—where enabling laziness consistently across many modules provides the most benefit. A global switch makes it easy to experiment with or enforce consistent behavior, while still working in combination with the filtering API to respect exclusions or tool-specific configuration. This ensures that global adoption can be practical without reducing flexibility or control.
Specification Grammar A new soft keyword lazy is added. A soft keyword is a context-sensitive keyword that only has special meaning in specific grammatical contexts; elsewhere it can be used as a regular identifier (e.g., as a variable name). The lazy keyword only has special meaning when it appears before import statements: import_name: | 'lazy'? 'import' dotted_as_names import_from: | 'lazy'? 'from' ('.' | '...')* dotted_name 'import' import_from_targets | 'lazy'? 'from' ('.' | '...')+ 'import' import_from_targets Syntax restrictions The soft keyword is only allowed at the global (module) level, not inside functions, class bodies, with try / with blocks, or import * . Import statements that use the soft keyword are potentially lazy. Imports that can’t be lazy are unaffected by the global lazy imports flag, and instead are always eager. Examples of syntax errors: # SyntaxError: lazy import not allowed inside functions def foo (): lazy import json # SyntaxError: lazy import not allowed inside classes class Bar : lazy import json # SyntaxError: lazy import not allowed inside try/except blocks try : lazy import json except ImportError : pass # SyntaxError: lazy import not allowed inside with blocks with suppress ( ImportError ): lazy import json # SyntaxError: lazy from ... import * is not allowed lazy from json import * Semantics When the lazy keyword is used, the import becomes potentially lazy. Unless lazy imports are disabled or suppressed (see below), the module is not loaded immediately at the import statement; instead, a lazy proxy object is created and bound to the name. The actual module is loaded on first use of that name. Example: import sys lazy import json print ( 'json' in sys . modules ) # False - module not loaded yet # First use triggers loading result = json . dumps ({ "hello" : "world" }) print ( 'json' in sys . modules ) # True - now loaded A module may contain a __lazy_modules__ attribute, which is a sequence of fully qualified module names (strings) to make potentially lazy (as if the lazy keyword was used). This attribute is checked on each import statement to determine whether the import should be made potentially lazy. When a module is made lazy this way, from-imports using that module are also lazy, but not necessarily imports of sub-modules. The normal (non-lazy) import statement will check the global lazy imports flag. If it is “enabled”, all imports are potentially lazy (except for imports that can’t be lazy, as mentioned above.) Example: __lazy_modules__ = [ "json" ] import json print ( 'json' in sys . modules ) # False result = json . dumps ({ "hello" : "world" }) print ( 'json' in sys . modules ) # True If the global lazy imports flag is set to “disabled”, no potentially lazy import is ever imported lazily, and the behavior is equivalent to a regular import statement: the import is eager (as if the lazy keyword was not used). For a potentially lazy import, the lazy imports filter (if set) is called with the name of the module doing the import, the name of the module being imported, and (if applicable) the fromlist. If the lazy import filter returns True , the potentially lazy import becomes a lazy import. Otherwise, the import is not lazy, and the normal (eager) import continues. Lazy import mechanism When an import is lazy, __lazy_import__ is called instead of __import__ . __lazy_import__ has the same function signature as __import__ . It adds the module name to sys.lazy_modules , a set of module names which have been lazily imported at some point (primarily for diagnostics and introspection), and returns a “lazy module object.” The implementation of from ... import (the IMPORT_FROM bytecode implementation) checks if the module it’s fetching from is a lazy module object, and if so, returns a lazy object for each name instead. The end result of this process is that lazy imports (regardless of how they are enabled) result in lazy objects being assigned to global variables. Lazy module objects do not appear in sys.modules , they’re just listed in the sys.lazy_modules set. Under normal operation lazy objects should only end up stored in global variables, and the common ways to access those variables (regular variable access, module attributes) will resolve lazy imports (“reify”) and replace them when they’re accessed. It is still possible to expose lazy objects through other means, like debuggers. This is not considered a problem. Reification When a lazy object is first used, it needs to be reified. This means resolving the import at that point in the program and replacing the lazy object with the concrete one. Reification imports the module in the same way as it would have been if it had been imported eagerly, barring intervening changes to the import system (e.g. to sys.path , sys.meta_path , sys.path_hooks or __import__ ). Reification still calls __import__ to resolve the import. When the module is first reified, it’s removed from sys.lazy_modules (even if there are still other unreified lazy references to it). When a package is reified and submodules in the package were also previously lazily imported, those submodules are not automatically reified but they are added to the reified package’s globals (unless the package already assigned something else to the name of the submodule). If reification fails (e.g., due to an ImportError ), the exception is enhanced with chaining to show both where the lazy import was defined and where it was first accessed (even though it propagates from the code that triggered reification). This provides clear debugging information: # app.py - has a typo in the import lazy from json import dumsp # Typo: should be 'dumps' print ( "App started successfully" ) print ( "Processing data..." ) # Error occurs here on first use result = dumsp ({ "key" : "value" }) The traceback shows both locations: App started successfully Processing data... Traceback (most recent call last): File "app.py", line 2, in lazy from json import dumsp ImportError: deferred import of 'json.dumsp' raised an exception during resolution The above exception was the direct cause of the following exception: Traceback (most recent call last): File "app.py", line 8, in result = dumsp({"key": "value"}) ^^^^^ ImportError: cannot import name 'dumsp' from 'json'. Did you mean: 'dump'? This exception chaining clearly shows: (1) where the lazy import was defined, (2) that it was deferred, and (3) where the actual access happened that triggered the error. Reification does not automatically occur when a module that was previously lazily imported is subsequently eagerly imported. Reification does not immediately resolve all lazy objects (e.g. lazy from statements) that referenced the module. It only resolves the lazy object being accessed. Accessing a lazy object (from a global variable or a module attribute) reifies the object. Accessing a module’s __dict__ reifies all lazy objects in that module. Operations that indirectly access __dict__ (such as dir() ) also trigger this behavior. Example using __dict__ from external code: # my_module.py import sys lazy import json print ( 'json' in sys . modules ) # False - still lazy # main.py import sys import my_module # Accessing __dict__ from external code DOES reify all lazy imports d = my_module . __dict__ print ( 'json' in sys . modules ) # True - reified by __dict__ access print ( type ( d [ 'json' ])) # However, calling globals() does not trigger reification — it returns the module’s dictionary, and accessing lazy objects through that dictionary still returns lazy proxy objects that need to be manually reified upon use. A lazy object can be resolved explicitly by calling the get method. Other, more indirect ways of accessing arbitrary globals (e.g. inspecting frame.f_globals ) also do not reify all the objects. Example using globals() : import sys lazy import json # Calling globals() does NOT trigger reification g = globals () print ( 'json' in sys . modules ) # False - still lazy print ( type ( g [ 'json' ])) # # Explicitly reify using the get() method resolved = g [ 'json' ] . get () print ( type ( resolved )) # print ( 'json' in sys . modules ) # True - now loaded
Implementation Bytecode and adaptive specialization Lazy imports are implemented through modifications to four bytecode instructions: IMPORT_NAME , IMPORT_FROM , LOAD_GLOBAL , and LOAD_NAME . The lazy syntax sets a flag in the IMPORT_NAME instruction’s oparg ( oparg & 0x01 ). The interpreter checks this flag and calls _PyEval_LazyImportName() instead of _PyEval_ImportName() , creating a lazy import object rather than executing the import immediately. The IMPORT_FROM instruction checks whether its source is a lazy import ( PyLazyImport_CheckExact() ) and creates a lazy object for the attribute rather than accessing it immediately. When a lazy object is accessed, it must be reified. The LOAD_GLOBAL instruction (used in function scopes) and LOAD_NAME instruction (used at module and class level) both check whether the object being loaded is a lazy import. If so, they call _PyImport_LoadLazyImportTstate() to perform the actual import and store the module in sys.modules . This check incurs a very small cost on each access. However, Python’s adaptive interpreter can specialize LOAD_GLOBAL after observing that a lazy import has been reified. After several executions, LOAD_GLOBAL becomes LOAD_GLOBAL_MODULE , which accesses the module dictionary directly without checking for lazy imports. Examples of the bytecode generated: lazy import json # IMPORT_NAME with flag set Generates: IMPORT_NAME 1 (json + lazy) lazy from json import dumps # IMPORT_NAME + IMPORT_FROM Generates: IMPORT_NAME 1 (json + lazy) IMPORT_FROM 1 (dumps) lazy import json x = json # Module-level access Generates: LOAD_NAME 0 (json) lazy import json def use_json (): return json . dumps ({}) # Function scope Before any calls: LOAD_GLOBAL 0 (json) LOAD_ATTR 2 (dumps) After several calls, LOAD_GLOBAL specializes to LOAD_GLOBAL_MODULE : LOAD_GLOBAL_MODULE 0 (json) LOAD_ATTR_MODULE 2 (dumps) Lazy imports filter This PEP adds two new functions to the sys module to manage the lazy imports filter: sys.set_lazy_imports_filter(func) - Sets the filter function. The func parameter must have the signature: func(importer: str, name: str, fromlist: tuple[str, ...] | None) -> bool
- Sets the filter function. The parameter must have the signature: sys.get_lazy_imports_filter() - Returns the currently installed filter function, or None if no filter is set. The filter function is called for every potentially lazy import, and must return True if the import should be lazy. This allows for fine-grained control over which imports should be lazy, useful for excluding modules with known side-effect dependencies or registration patterns. The filter mechanism serves as a foundation that tools, debuggers, linters, and other ecosystem utilities can leverage to provide better lazy import experiences. For example, static analysis tools could detect modules with side effects and automatically configure appropriate filters. In the future (out of scope for this PEP), this foundation may enable better ways to declaratively specify which modules are safe for lazy importing, such as package metadata, type stubs with lazy-safety annotations, or configuration files. The current filter API is designed to be flexible enough to accommodate such future enhancements without requiring changes to the core language specification. Example: import sys def exclude_side_effect_modules ( importer , name , fromlist ): """ Filter function to exclude modules with import-time side effects. Args: importer: Name of the module doing the import name: Name of the module being imported fromlist: Tuple of names being imported (for 'from' imports), or None Returns: True to allow lazy import, False to force eager import """ # Modules known to have important import-time side effects side_effect_modules = { 'legacy_plugin_system' , 'metrics_collector' } if name in side_effect_modules : return False # Force eager import return True # Allow lazy import # Install the filter sys . set_lazy_imports_filter ( exclude_side_effect_modules ) # These imports are checked by the filter lazy import data_processor # Filter returns True -> stays lazy lazy import legacy_plugin_system # Filter returns False -> imported eagerly print ( 'data_processor' in sys . modules ) # False - still lazy print ( 'legacy_plugin_system' in sys . modules ) # True - loaded eagerly # First use of data_processor triggers loading result = data_processor . transform ( data ) print ( 'data_processor' in sys . modules ) # True - now loaded Global lazy imports control The global lazy imports flag can be controlled through: The -X lazy_imports= command-line option
command-line option The PYTHON_LAZY_IMPORTS= environment variable
environment variable The sys.set_lazy_imports(mode) function (primarily for testing) Where can be: "default" (or unset): Only explicitly marked lazy imports are lazy
(or unset): Only explicitly marked lazy imports are lazy "enabled" : All module-level imports (except in try or with blocks and import * ) become potentially lazy
: All module-level imports (except in or blocks and ) become potentially lazy "disabled" : No imports are lazy, even those explicitly marked with lazy keyword When the global flag is set to "enabled" , all imports at the global level of all modules are potentially lazy except for those inside a try or with block or any wild card ( from ... import * ) import. If the global lazy imports flag is set to "disabled" , no potentially lazy import is ever imported lazily, the import filter is never called, and the behavior is equivalent to a regular import statement: the import is eager (as if the lazy keyword was not used).
Backwards Compatibility Lazy imports are opt-in. Existing programs continue to run unchanged unless a project explicitly enables laziness (via lazy syntax, __lazy_modules__ , or an interpreter-wide switch). Unchanged semantics Regular import and from ... import ... statements remain eager unless explicitly made potentially lazy by the local or global mechanisms provided.
and statements remain eager unless explicitly made potentially lazy by the local or global mechanisms provided. Dynamic import APIs remain eager and unchanged: __import__() and importlib.import_module() .
and . Import hooks and loaders continue to run under the standard import protocol when a lazy object is reified. Observable behavioral shifts (opt-in only) These changes are limited to bindings explicitly made lazy: Error timing. Exceptions that would have occurred during an eager import (for example ImportError or AttributeError for a missing member) now occur at the first use of the lazy name. # With eager import - error at import statement import broken_module # ImportError raised here # With lazy import - error deferred lazy import broken_module print ( "Import succeeded" ) broken_module . foo () # ImportError raised here on first use
Exceptions that would have occurred during an eager import (for example or for a missing member) now occur at the first use of the lazy name. Side-effect timing. Import-time side effects in lazily imported modules occur at first use of the binding, not at module import time.
Import-time side effects in lazily imported modules occur at first use of the binding, not at module import time. Import order. Because modules are imported on first use, the order in which modules are imported may differ from how they appear in code.
Because modules are imported on first use, the order in which modules are imported may differ from how they appear in code. Presence in ``sys.modules``. A lazily imported module does not appear in sys.modules until first use. After reification, it must appear in sys.modules . If some other code eagerly imports the same module before first use, the lazy binding resolves to that existing (lazy) module object when it is first used.
A lazily imported module does not appear in until first use. After reification, it must appear in . If some other code eagerly imports the same module before first use, the lazy binding resolves to that existing (lazy) module object when it is first used. Proxy visibility. Before first use, the bound name refers to a lazy proxy. Indirect introspection that touches the value may observe a proxy lazy object representation. After first use, the name is rebound to the real object and becomes indistinguishable from an eager import. Thread-safety and reification First use of a lazy binding follows the existing import-lock discipline. Exactly one thread performs the import and atomically rebinds the importing module’s global to the resolved object. Concurrent readers thereafter observe the real object. Lazy imports are thread-safe and have no special considerations for free-threading. A module that would normally be imported in the main thread may be imported in a different thread if that thread triggers the first access to the lazy import. This is not a problem: the import lock ensures thread safety regardless of which thread performs the import. Subinterpreters are supported. Each subinterpreter maintains its own sys.lazy_modules and import state, so lazy imports in one subinterpreter do not affect others.
Security Implications There are no known security vulnerabilities introduced by lazy imports.
How to Teach This The new lazy keyword will be documented as part of the language standard. As this feature is opt-in, new Python users should be able to continue using the language as they are used to. For experienced developers, we expect them to leverage lazy imports for the variety of benefits listed above (decreased latency, decreased memory usage, etc) on a case-by-case basis. Developers interested in the performance of their Python binary will likely leverage profiling to understand the import time overhead in their codebase and mark the necessary imports as lazy . In addition, developers can mark imports that will only be used for type annotations as lazy . Below is guidance on how to best take advantage of lazy imports and how to avoid incompatibilities: When adopting lazy imports, users should be aware that eliding an import until it is used will result in side effects not being executed. In turn, users should be wary of modules that rely on import time side effects. Perhaps the most common reliance on import side effects is the registry pattern, where population of some external registry happens implicitly during the importing of modules, often via decorators but sometimes implemented via metaclasses or __init_subclass__ . Instead, registries of objects should be constructed via explicit discovery processes (e.g. a well-known function to call). # Problematic: Plugin registers itself on import # my_plugin.py from plugin_registry import register_plugin @register_plugin ( "MyPlugin" ) class MyPlugin : pass # In main code: lazy import my_plugin # Plugin NOT registered yet - module not loaded! # Better: Explicit discovery # plugin_registry.py def discover_plugins (): from my_plugin import MyPlugin register_plugin ( MyPlugin ) # In main code: plugin_registry . discover_plugins () # Explicit loading
. Instead, registries of objects should be constructed via explicit discovery processes (e.g. a well-known function to call). Always import needed submodules explicitly. It is not enough to rely on a different import to ensure a module has its submodules as attributes. Plainly, unless there is an explicit from . import bar in foo/__init__.py , always use import foo.bar; foo.bar.Baz , not import foo; foo.bar.Baz . The latter only works (unreliably) because the attribute foo.bar is added as a side effect of foo.bar being imported somewhere else.
in , always use , not . The latter only works (unreliably) because the attribute is added as a side effect of being imported somewhere else. Users who are moving imports into functions to improve startup time, should instead consider keeping them where they are but adding the lazy keyword. This allows them to keep dependencies clear and avoid the overhead of repeatedly re-resolving the import but will still speed up the program. # Before: Inline import (repeated overhead) def process_data ( data ): import json # Re-resolved on every call return json . dumps ( data ) # After: Lazy import at module level lazy import json def process_data ( data ): return json . dumps ( data ) # Loaded once on first call
keyword. This allows them to keep dependencies clear and avoid the overhead of repeatedly re-resolving the import but will still speed up the program. Avoid using wild card (star) imports, as those are always eager.
FAQ Q: How does this differ from the rejected PEP 690? A: PEP 810 takes an explicit, opt-in approach instead of PEP 690’s implicit global approach. The key differences are: Explicit syntax : lazy import foo clearly marks which imports are lazy
: clearly marks which imports are lazy Local scope : Laziness only affects the specific import statement, not cascading to dependencies
: Laziness only affects the specific import statement, not cascading to dependencies Simpler implementation: Uses proxy objects instead of modifying core dictionary behavior Q: What happens when lazy imports encounter errors? A: Import errors ( ImportError , ModuleNotFoundError , syntax errors) are deferred until first use of the lazy name. This is similar to moving an import into a function. The error will occur with a clear traceback pointing to the first access of the lazy object. The implementation provides enhanced error reporting through exception chaining. When a lazy import fails during reification, the original exception is preserved and chained, showing both where the import was defined and where it was first used: Traceback ( most recent call last ): File "test.py" , line 1 , in < module > lazy import broken_module ImportError : deferred import of 'broken_module' raised an exception during resolution The above exception was the direct cause of the following exception : Traceback ( most recent call last ): File "test.py" , line 3 , in < module > broken_module . foo () ^^^^^^^^^^^^^ File "broken_module.py" , line 2 , in < module > 1 / 0 ZeroDivisionError : division by zero Q: How do lazy imports affect modules with import-time side effects? A: Side effects are deferred until first use. This is generally desirable for performance, but may require code changes for modules that rely on import-time registration patterns. We recommend: Use explicit initialization functions instead of import-time side effects
Call initialization functions explicitly when needed
Avoid relying on import order for side effects Q: Can I use lazy imports with from ... import ... statements? A: Yes, as long as you don’t use from ... import * . Both lazy import foo and lazy from foo import bar are supported. The bar name will be bound to a lazy object that resolves to foo.bar on first use. Q: Does lazy from module import Class load the entire module or just the class? A: It loads the entire module, not just the class. This is because Python’s import system always executes the complete module file—there’s no mechanism to execute only part of a .py file. When you first access Class , Python: Loads and executes the entire module.py file Extracts the Class attribute from the resulting module object Binds Class to the name in your namespace This is identical to eager from module import Class behavior. The only difference with lazy imports is that steps 1-3 happen on first use instead of at the import statement. # heavy_module.py print ( "Loading heavy_module" ) # This ALWAYS runs when module loads class MyClass : pass class UnusedClass : pass # Also gets defined, even though we don't import it # app.py lazy from heavy_module import MyClass print ( "Import statement done" ) # heavy_module not loaded yet obj = MyClass () # NOW "Loading heavy_module" prints # (and UnusedClass gets defined too) Key point: Lazy imports defer when a module loads, not what gets loaded. You cannot selectively load only parts of a module—Python’s import system doesn’t support partial module execution. Q: What about type annotations and TYPE_CHECKING imports? A: Lazy imports eliminate the common need for TYPE_CHECKING guards. You can write: lazy from collections.abc import Sequence , Mapping # No runtime cost def process ( items : Sequence [ str ]) -> Mapping [ str , int ]: ... Instead of: from typing import TYPE_CHECKING if TYPE_CHECKING : from collections.abc import Sequence , Mapping def process ( items : Sequence [ str ]) -> Mapping [ str , int ]: ... Q: What’s the performance overhead of lazy imports? A: The overhead is minimal: Zero overhead after first use thanks to the adaptive interpreter optimizing the slow path away.
Small one-time cost to create the proxy object.
Reification (first use) has the same cost as a regular import.
No ongoing performance penalty unlike importlib.util.LazyLoader . Benchmarking with the pyperformance suite shows the implementation is performance neutral when lazy imports are not used. Q: Can I mix lazy and eager imports of the same module? A: Yes. If module foo is imported both lazily and eagerly in the same program, the eager import takes precedence and both bindings resolve to the same module object. Q: How do I migrate existing code to use lazy imports? A: Migration is incremental: Identify slow-loading modules using profiling tools Add lazy keyword to imports that aren’t needed immediately Test that side-effect timing changes don’t break functionality Use __lazy_modules__ for compatibility with older Python versions Q: What about star imports ( from module import * )? A: Wild card (star) imports cannot be lazy - they remain eager. This is because the set of names being imported cannot be determined without loading the module. Using the lazy keyword with star imports will be a syntax error. If lazy imports are globally enabled, star imports will still be eager. Q: How do lazy imports interact with import hooks and custom loaders? A: Import hooks and loaders work normally. When a lazy object is first used, the standard import protocol runs, including any custom hooks or loaders that were in place at reification time. Q: What happens in multi-threaded environments? A: Lazy import reification is thread-safe. Only one thread will perform the actual import, and the binding is atomically updated. Other threads will see either the lazy proxy or the final resolved object. Q: Can I force reification of a lazy import without using it? A: Yes, accessing a module’s __dict__ will reify all lazy objects in that module. Individual lazy objects can be resolved by calling their get() method. Q: What’s the difference between globals() and mod.__dict__ for lazy imports? A: Calling globals() returns the module’s dictionary without reifying lazy imports — you’ll see lazy proxy objects when accessing them through the returned dictionary. However, accessing mod.__dict__ from external code reifies all lazy imports in that module first. This design ensures: # In your module: lazy import json g = globals () print ( type ( g [ 'json' ])) # - your problem # From external code: import sys mod = sys . modules [ 'your_module' ] d = mod . __dict__ print ( type ( d [ 'json' ])) # - reified for external access This distinction means adding lazy imports and calling globals() is your responsibility to manage, while external code accessing mod.__dict__ always sees fully loaded modules. Q: Why not use importlib.util.LazyLoader instead? A: LazyLoader has significant limitations: Requires verbose setup code for each lazy import
Has ongoing performance overhead on every attribute access
Doesn’t work well with from ... import statements
statements Less clear and standard than dedicated syntax Q: Will this break tools like isort or black ? A: Tools will need updates to recognize the lazy keyword, but the changes should be minimal since the import structure remains the same. The keyword appears at the beginning, making it easy to parse. Q: How do I know if a library is compatible with lazy imports? A: Most libraries should work fine with lazy imports. Libraries that might have issues: Those with essential import-time side effects (registration, monkey-patching)
Those that expect specific import ordering
Those that modify global state during import When in doubt, test lazy imports with your specific use cases. Q: What happens if I globally enable lazy imports mode and a library doesn’t work correctly? A: Note: This is an advanced feature. You can use the lazy imports filter to exclude specific modules that are known to have problematic side effects: import sys def my_filter ( importer , name , fromlist ): # Don't lazily import modules known to have side effects if name in ( 'problematic_module' , 'another_module' ): return False # Import eagerly return True # Allow lazy import sys . set_lazy_imports_filter ( my_filter ) The filter function receives the importer module name, the module being imported, and the fromlist (if using from ... import ). Returning False forces an eager import. Alternatively, set the global mode to "disabled" via -X lazy_imports=disabled to turn off all lazy imports for debugging. Q: Can I use lazy imports inside functions? A: No, the lazy keyword is only allowed at module level. For function-level lazy loading, use traditional inline imports or move the import to module level with lazy . Q: What about forwards compatibility with older Python versions? A: Use the __lazy_modules__ global for compatibility: # Works on Python 3.15+ as lazy, eager on older versions __lazy_modules__ = [ 'expensive_module' , 'expensive_module_2' ] import expensive_module from expensive_module_2 import MyClass The __lazy_modules__ attribute is a list of module name strings. When an import statement is executed, Python checks if the module name being imported appears in __lazy_modules__ . If it does, the import is treated as if it had the lazy keyword (becoming potentially lazy). On Python versions before 3.15 that don’t support lazy imports, the __lazy_modules__ attribute is simply ignored and imports proceed eagerly as normal. This provides a migration path until you can rely on the lazy keyword. For maximum predictability, it’s recommended to define __lazy_modules__ once, before any imports. But as it is checked on each import, it can be modified between import statements. Q: How do explicit lazy imports interact with PEP-649/PEP-749 A: If an annotation is not stringified, it is an expression that is evaluated at a later time. It will only be resolved if the annotation is accessed. In the example below, the fake_typing module is only loaded when the user inspects the __annotations__ dictionary. The fake_typing module would also be loaded if the user uses annotationlib.get_annotations() or getattr to access the annotations. lazy from fake_typing import MyFakeType def foo ( x : MyFakeType ): pass print ( foo . __annotations__ ) # Triggers loading the fake_typing module Q: How do lazy imports interact with dir() , getattr() , and module introspection? A: Accessing lazy imports through normal attribute access or getattr() will trigger reification. Calling dir() on a module will reify all lazy imports in that module to ensure the directory listing is complete. This is similar to accessing mod.__dict__ . lazy import json # Before any access # json not in sys.modules # Any of these trigger reification: dumps_func = json . dumps dumps_func = getattr ( json , 'dumps' ) dir ( json ) # Now json is in sys.modules Q: Do lazy imports work with circular imports? A: Lazy imports don’t automatically solve circular import problems. If two modules have a circular dependency, making the imports lazy might help only if the circular reference isn’t accessed during module initialization. However, if either module accesses the other during import time, you’ll still get an error. Example that works (deferred access in functions): # user_model.py lazy import post_model class User : def get_posts ( self ): # OK - post_model accessed inside function, not during import return post_model . Post . get_by_user ( self . name ) # post_model.py lazy import user_model class Post : @staticmethod def get_by_user ( username ): return f "Posts by { username } " This works because neither module accesses the other at module level—the access happens later when get_posts() is called. Example that fails (access during import): # module_a.py lazy import module_b result = module_b . get_value () # Error! Accessing during import def func (): return "A" # module_b.py lazy import module_a result = module_a . func () # Circular dependency error here def get_value (): return "B" This fails because module_a tries to access module_b at import time, which then tries to access module_a before it’s fully initialized. The best practice is still to avoid circular imports in your code design. Q: Will lazy imports affect the performance of my hot paths? A: After first use, lazy imports have zero overhead thanks to the adaptive interpreter. The interpreter specializes the bytecode (e.g., LOAD_GLOBAL becomes LOAD_GLOBAL_MODULE ) which eliminates the lazy check on subsequent accesses. This means once a lazy import is reified, accessing it is just as fast as a normal import. lazy import json def use_json (): return json . dumps ({ "test" : 1 }) # First call triggers reification use_json () # After 2-3 calls, bytecode is specialized use_json () use_json () You can observe the specialization using dis.dis(use_json, adaptive=True) : === Before specialization === LOAD_GLOBAL 0 (json) LOAD_ATTR 2 (dumps) === After 3 calls (specialized) === LOAD_GLOBAL_MODULE 0 (json) LOAD_ATTR_MODULE 2 (dumps) The specialized LOAD_GLOBAL_MODULE and LOAD_ATTR_MODULE instructions are optimized fast paths with no overhead for checking lazy imports. Q: What about sys.modules ? When does a lazy import appear there? A: A lazily imported module does not appear in sys.modules until it’s reified (first used). Once reified, it appears in sys.modules just like any eager import. import sys lazy import json print ( 'json' in sys . modules ) # False result = json . dumps ({ "key" : "value" }) # First use print ( 'json' in sys . modules ) # True
Reference Implementation A reference implementation is available at: https://github.com/LazyImportsCabal/cpython/tree/lazy
Alternate Implementation Ideas Here are some alternative design decisions that were considered during the development of this PEP. While the current proposal represents what we believe to be the best balance of simplicity, performance, and maintainability, these alternatives offer different trade-offs that may be valuable for implementers to consider or for future refinements. Leveraging a Subclass of Dict Instead of updating the internal dict object to directly add the fields needed to support lazy imports, we could create a subclass of the dict object to be used specifically for Lazy Import enablement. This would still be a leaky abstraction though - methods can be called directly such as dict.__getitem__ and it would impact the performance of globals lookup in the interpreter. Alternate Keyword Names For this PEP, we decided to propose lazy for the explicit keyword as it felt the most familar to those already focused on optimizing import overhead. We also considered a variety of other options to support explicit lazy imports. The most compelling alternates were defer and delay .
Rejected Ideas Modification of the Dict Object The initial PEP for lazy imports (PEP 690) relied heavily on the modification of the internal dict object to support lazy imports. We recognize that this data structure is highly tuned, heavily used across the codebase, and very performance sensitive. Because of the importance of this data structure and the desire to keep the implementation of lazy imports encapsulated from users who may have no interest in the feature, we’ve decided to invest in an alternate approach. The dictionary is the foundational data structure in Python. Every object’s attributes are stored in a dict, and dicts are used throughout the runtime for namespaces, keyword arguments, and more. Adding any kind of hook or special behavior to dicts to support lazy imports would: Prevent critical interpreter optimizations including future JIT compilation Add complexity to a data structure that must remain simple and fast Affect every part of Python, not just import behavior Violate separation of concerns—the hash table shouldn’t know about the import system Past decisions that violated this principle of keeping core abstractions clean have caused significant pain in the CPython ecosystem, making optimization difficult and introducing subtle bugs. Placing the lazy Keyword in the Middle of From Imports While we found from foo lazy import bar to be a really intuitive placement for the new explicit syntax, we quickly learned that placing the lazy keyword here is already syntactically allowed in Python. This is because from . lazy import bar is legal syntax (because whitespace does not matter.) Placing the lazy Keyword at the End of Import Statements We discussed appending lazy to the end of import statements like such import foo lazy or from foo import bar, baz lazy but ultimately decided that this approach provided less clarity. For example, if multiple modules are imported in a single statement, it is unclear if the lazy binding applies to all of the imported objects or just a subset of the items. Returning a Proxy Dict from globals() An alternative to reifying on globals() or exposing lazy objects would be to return a proxy dictionary that automatically reifies lazy objects when they’re accessed through the proxy. This would seemingly give the best of both worlds: globals() returns immediately without reification cost, but accessing items through the result would automatically resolve lazy imports. However, this approach is fundamentally incompatible with how globals() is used in practice. Many standard library functions and built-ins expect globals() to return a real dict object, not a proxy: exec(code, globals()) requires a real dict
requires a real dict eval(expr, globals()) requires a real dict
requires a real dict Functions that check type(globals()) is dict would break
would break Dictionary methods like .update() would need special handling
would need special handling Performance would suffer from the indirection on every access The proxy would need to be so transparent that it would be indistinguishable from a real dict in almost all cases, which is extremely difficult to achieve correctly. Any deviation from true dict behavior would be a source of subtle bugs. Reifying lazy imports when globals() is called Calling globals() returns the module’s namespace dictionary without triggering reification of lazy imports. Accessing lazy objects through the returned dictionary yields the lazy proxy objects themselves. This is an intentional design decision for several reasons: The key distinction: Adding a lazy import and calling globals() is the module author’s concern and under their control. However, accessing mod.__dict__ from external code is a different scenario — it crosses module boundaries and affects someone else’s code. Therefore, mod.__dict__ access reifies all lazy imports to ensure external code sees fully realized modules, while globals() preserves lazy objects for the module’s own introspection needs. Technical challenges: It is impossible to safely reify on-demand when globals() is called because we cannot return a proxy dictionary — this would break common usages like passing the result to exec() or other built-ins that expect a real dictionary. The only alternative would be to eagerly reify all lazy imports whenever globals() is called, but this behavior would be surprising and potentially expensive. Performance concerns: It is impractical to cache whether a reification scan has been performed with just the globals dictionary reference, whereas module attribute access (the primary use case) can efficiently cache reification state in the module object itself. Use case rationale: The chosen design makes sense precisely because of this distinction: adding a lazy import and calling globals() is your problem to manage, while having lazy imports visible in mod.__dict__ becomes someone else’s problem. By reifying on __dict__ access but not on globals() , we ensure external code always sees fully loaded modules while giving module authors control over their own introspection. Note that three options were considered: Calling globals() or mod.__dict__ traverses and resolves all lazy objects before returning Calling globals() or mod.__dict__ returns the dictionary with lazy objects present Calling globals() returns the dictionary with lazy objects, but mod.__dict__ reifies everything We chose the third option because it properly delineates responsibility: if you add lazy imports to your module and call globals() , you’re responsible for handling the lazy objects. But external code accessing your module’s __dict__ shouldn’t need to know about your lazy imports—it gets fully resolved modules.
Acknowledgements We would like to thank Paul Ganssle, Yury Selivanov, Łukasz Langa, Lysandros Nikolaou, Pradyun Gedam, Mark Shannon, Hana Joo and the Python Google team, the Python team(s) @ Meta, the Python @ HRT team, the Bloomberg Python team, the Scientific Python community, everyone who participated in the initial discussion of PEP 690, and many others who provided valuable feedback and insights that helped shape this PEP.