# qlustered/deepdiff

DeepDiff: Deep Difference and search of any Python object/data. DeepHash: Hash of any object based on its contents. Delta: Use deltas to reconstruct objects by adding deltas together.

Repository: https://github.com/qlustered/deepdiff
Canonical: https://ross.abutalabs.com/products/deepdiff
Homepage: https://zepworks.com/deepdiff
Language: Python
License: NOASSERTION
License Family: other
Topics: python, tree, deep-search, repetition, difference, comparison, report-repetition, nested, recursive, diff, delta, distance, distance-calculation, deepdiff, deephash, hash, hashing, reconstruction
Last push: 2026-08-04T06:37:44+00:00

## Health v2 (maintenance only)
Score: 86/100 (v2, computed 2026-09-02T17:46:02.011165+00:00)
- activity 96, release rhythm 65, longevity 100
- inputs: {"age_days": 4359, "days_push": 29, "days_rel": 156, "gap_med": 53.0, "n_releases_24m": 9}
- flags: no_license
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 2524, forks 276 (observed 2026-08-28T04:06:58.460799+00:00)

## What it is
DeepDiff is a Python library for deep comparison, search, and hashing of arbitrary Python objects, including nested dictionaries and iterables. It also provides Delta objects that capture differences and can be applied to reconstruct or update other objects, plus a command-line interface.

## Use cases
- compare two nested dictionaries or JSON structures in tests
- find where a value is located inside a deeply nested Python object
- hash unhashable Python objects based on their content
- store and apply diffs between data snapshots like git commits for structured data
- extract a value from a nested object by path
- track changes in API responses or configuration data
- calculate distance between Python objects

## When to choose
- you need to diff deeply nested or arbitrarily typed Python objects
- you want content-based hashing for objects Python considers unhashable
- you need to serialize and replay changes between object states
- you are writing tests that assert deep equality with readable reports

## When to avoid
- you only need simple flat equality checks built into Python
- you need to diff non-Python data formats like binary files or images
- you need a general-purpose version control system rather than object deltas

## Facets
- artifact type: library
- maturity: active
- function: serialization, developer-tools, testing, data-science
- domain: developer-tools, data-science, testing
- platform: python, cli, cross-platform
- tags: deep-diff, deep-hash, delta, object-comparison, nested-objects, deep-search, data-validation, change-tracking, automation

## Member repositories
- qlustered/deepdiff (main) score 86

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:06:58.460799+00:00.
- Health v2: computed from the inputs above; adoption is never an input.
- Inferred fields (summary, facets, guidance): AI-extracted, prompt v1, taxonomy v1, on 2026-08-30T02:25:33.312240+00:00, confidence not recorded.
  - readme: https://github.com/qlustered/deepdiff (fetched 2026-08-28T04:06:58.460799+00:00, sha f4ee0720b0d4)
  - homepage: https://zepworks.com/deepdiff (fetched 2026-08-29T10:08:10.069035+00:00, sha 236c63c2d0fe)
  - site_page: https://zepworks.com/about (fetched 2026-08-29T10:08:10.078042+00:00, sha 6e483fbf3504)
  - registry_pypi: https://pypi.org/pypi/deepdiff/json (fetched 2026-08-29T10:08:10.082131+00:00, sha f8f1488ee0dd)
  - site_page: https://zepworks.com/deepdiff/current (fetched 2026-08-29T10:08:10.079973+00:00, sha fd309c0d87b4)
- Data as of 2026-08-30T08:39:29.467469+00:00.