deepdiff
Deep Difference and Search of any Python object/data. Recreate objects by adding adding deltas to each other.
Decision gist · record as of 2026-08-14
Yes. DeepDiff is a mature, widely-used library in the top 1000 on PyPI with low install friction, no known vulnerabilities, permissive licensing, and active maintenance. Install it if you need to compare nested data structures, audit changes, or work with deeply nested objects in testing, validation, or data pipeline contexts.AI-flagged interpretation of the facts on this page — verify before relying
Before you install
- Requires Python 3.10 or newer; PyPy3 is also supported.
- Low friction: pure Python wheel with only two lightweight runtime dependencies (cachebox and orderly-set).
- Actively maintained with a recent release and steady commit history.
License · maintenance · safety
permissive license (permissive) — MIT license (permissive): you can use, modify, and distribute DeepDiff freely in commercial and private projects with minimal restrictions.
last release 2026-05-15 (91 days) · last repo commit 2026-08-04 · 2,520 stars
0 known vulnerabilities (OSV.dev, 2026-08-14) · 82,205,561 downloads/mo, #408 on PyPI
Alternatives
Verify before relying
pip install deepdiff
from deepdiff import DeepDiff
obj1 = {'a': 1, 'b': [1, 2, 3]}
obj2 = {'a': 2, 'b': [1, 2, 4]}
diff = DeepDiff(obj1, obj2)
print(diff)- Actual performance improvement from multiprocessing support on typical workloads and dataset sizes
- Whether glob pattern matching for exclude_paths/include_paths handles all edge cases users expect
- Real-world impact of the security fixes to Delta dunder-attribute traversal
What it is and what it does
DeepDiff is a library for comparing Python objects at any depth—dictionaries, lists, strings, custom classes, and more—to identify what has changed between two versions. It reports differences in a structured format, making it easy to see exactly what was added, removed, modified, or moved. The library also includes companion tools: DeepSearch to find objects nested inside other objects, DeepHash to compute content-based hashes, Delta to store and apply differences, and Extract to retrieve values by path.
Version 9.1.0 adds multiprocessing support for parallel comparison on large datasets, glob pattern matching for path filtering, and several security and correctness fixes. It depends only on cachebox and orderly-set, keeping the install lightweight. The package is production-stable, actively maintained, and tested on Python 3.10 through 3.14 and PyPy3.
Use it for
- Compare API responses or configuration files to detect unexpected changes during testing or monitoring
- Audit data mutations in applications by tracking what fields changed between object states
- Validate that data transformation pipelines produce expected output by diffing before/after structures
- Extract specific nested values from complex JSON or nested dictionaries using path-based lookups
- Generate content-based hashes of Python objects for deduplication or integrity checking
Worth the install?
AI-flagged interpretation of the facts on this page. Verify before relying on it.
Yes.
DeepDiff is a mature, widely-used library in the top 1000 on PyPI with low install friction, no known vulnerabilities, permissive licensing, and active maintenance. Install it if you need to compare nested data structures, audit changes, or work with deeply nested objects in testing, validation, or data pipeline contexts.
Install
deepdiff on PyPI
Before you install
Low friction: pure Python wheel with only two lightweight runtime dependencies (cachebox and orderly-set). Actively maintained with a recent release and steady commit history.
Requires Python 3.10 or newer; PyPy3 is also supported.
License in practice
MIT license (permissive): you can use, modify, and distribute DeepDiff freely in commercial and private projects with minimal restrictions.
Quickstart
pip install deepdiff
from deepdiff import DeepDiff
obj1 = {'a': 1, 'b': [1, 2, 3]}
obj2 = {'a': 2, 'b': [1, 2, 4]}
diff = DeepDiff(obj1, obj2)
print(diff)Verify before relying
- Actual performance improvement from multiprocessing support on typical workloads and dataset sizes
- Whether glob pattern matching for exclude_paths/include_paths handles all edge cases users expect
- Real-world impact of the security fixes to Delta dunder-attribute traversal
Package facts
| License | permissive license permissive |
| Python support | Supports the current Python release >=3.10 |
| Install friction | Low. Pure-Python wheel |
| Runtime dependencies | 2 packagescacheboxorderly-set |
| Maintenance | Actively maintained 91 days since the last release |
| Last repo commit | |
| First released | |
| Downloads | 82,205,561 / month, #408 on PyPI 30-day window, as of 2026-08-14 |
| Known vulnerabilities | None known OSV.dev, checked 2026-08-14 |
| Classifiers | Development Status :: 5 - Production/StableIntended Audience :: DevelopersLicense :: OSI Approved :: MIT LicenseOperating System :: OS IndependentProgramming Language :: Python :: 3.10Programming Language :: Python :: 3.11Programming Language :: Python :: 3.12Programming Language :: Python :: 3.13Programming Language :: Python :: 3.14Programming Language :: Python :: Implementation :: PyPyTopic :: Software Development |
Evidence: deepdiff-9.1.0-py3-none-any.whl
Tags
Let your AI agent find packages like this
Example. Real query, live index.
You found this page by searching. An agent finds it by wishing: SkillFed indexes 14,416 PyPI packages by what they can do, searchable in plain language.
wish › “find differences in nested data”
- deepdiffDeepDiff compares dictionaries, iterables, strings, and arbitrary…
- deepdiff6DeepDiff recursively compares two Python objects—dictionaries, lists,…
- dictdifferDictdiffer computes and applies diffs and patches to Python…
Give your agent the search over MCP, or paste the wish link into any chat.
More Software Development packages
Provides backported and experimental type hints for Python 3.9+, allowing use of newer typing features on older Python versions and enabling early experimentation with type system PEPs before they enter the standard library.
NumPy provides an N-dimensional array object and a comprehensive suite of mathematical, linear algebra, Fourier transform, and random number functions for scientific computing in Python.
FastAPI is a Python web framework for building REST APIs using type hints, with automatic request validation, serialization, and interactive API documentation.
Provides a way to document function parameters, class attributes, return types, and variables inline using Python's `Annotated` type hint syntax instead of traditional docstrings.
Typer builds command-line applications from Python functions using type hints, automatically generating help text, argument parsing, and shell completion.
Install it if you are building CLIs in Python.
Distlib provides low-level packaging utilities for building, distributing, and managing Python software—including metadata handling, version specifiers, wheel support, script installation, and dependency resolution.
See also deepdiff6 · recursive-diff · pytest-deepassert · jsondiff · json-delta · csv-diff · objsize · cdifflib · collate-data-diff · pyjson