funcy
Fancy and practical functional tools for Python — extends itertools with practical FP utilities for collections, functions, and flow control. funcy features: chunks/partition/flatten for sequence manipulation, merge/merge_with/walk for dict operations, memoize/cached_property for caching, retry/silent/ignore decorators for error handling, juxt/compose/partial for function composition, select/reject/group_by/count_by for filtering/grouping, rpartial/curry for partial application, and imap/ifilter lazy generators. Focused on practical everyday FP patterns without requiring monad/type system overhead.
Score Breakdown
⚙ Agent Friendliness
🔒 Security
Pure utility library with no network calls. No security concerns. @silent and @ignore suppress exceptions — ensure they don't hide security-relevant errors (auth failures, permission errors) in agent code.
⚡ Reliability
Best When
Practical functional programming in Python without type system overhead — funcy's collection utilities, decorators, and dict manipulation replace boilerplate loops with expressive one-liners.
Avoid When
You need type-safe FP (use returns), async operations, or performance-critical tight loops (use comprehensions).
Use Cases
- • Agent collection processing — from funcy import chunks, flatten, distinct; data = [1, 2, 2, 3, [4, 5]]; flat = flatten(data); unique = distinct(flat); batches = list(chunks(3, unique)) — process nested collections; agent batch processing splits large datasets into fixed-size chunks; distinct removes duplicates while preserving order
- • Agent dict manipulation — from funcy import merge, walk_values, select_values; configs = merge(defaults, overrides); numbers_only = select_values(lambda v: isinstance(v, int), config); doubled = walk_values(lambda v: v * 2, numbers_only) — dict transformation; agent config merging walks values without explicit loops; merge is shallow (use deep_merge for nested)
- • Agent retry decorator — from funcy import retry; @retry(3, errors=Exception); def call_api(url): return requests.get(url) — automatic retry with count; agent API calls retry on transient failures; retry(5, timeout=30) for time-bounded retry; combine with @silent to suppress final failure
- • Agent grouping and counting — from funcy import group_by, count_by; events = [{'type': 'click', 'user': 'a'}, ...]; by_type = group_by(lambda e: e['type'], events); by_user = count_by(lambda e: e['user'], events) — group and count collections; agent analytics groups events by category and counts per user; returns dicts of lists or counts
- • Agent memoized computation — from funcy import memoize; @memoize; def expensive_compute(key): return fetch_and_process(key) — decorator-based caching; agent repeated computation with same arguments returns cached result; memoize caches by all args; @memoize(key=lambda *a, **kw: a[0]) for custom cache keys
Not For
- • Type-safe FP — funcy has no type system or monadic error handling; for typed FP use returns library
- • Performance-critical loops — funcy convenience functions add overhead vs direct loops; for hot paths use list comprehensions or numpy
- • Async operations — funcy functions are synchronous; for async FP use toolz with asyncio or custom async utilities
Interface
Authentication
No auth — pure Python utility library.
Pricing
funcy is BSD licensed. Free for all use.
Agent Metadata
Known Gotchas
- ⚠ @retry retries on all exceptions by default — funcy.retry(3) catches all Exception types; agent code calling APIs where some errors are permanent (404 Not Found) should use: @retry(3, errors=requests.Timeout) to only retry timeout errors; retrying on 400 Bad Request wastes 3 API calls on invalid input
- ⚠ merge() is shallow not deep — funcy.merge({'a': {'x': 1}}, {'a': {'y': 2}}) produces {'a': {'y': 2}} overwriting nested dict; agent merging nested configs loses inner keys; use funcy.deep_merge() for recursive merging or dict.update() with custom deep merge logic
- ⚠ chunks() returns iterator not list — funcy.chunks(3, range(10)) returns generator; agent code accessing chunks multiple times must: batches = list(chunks(3, data)); generator is consumed on first iteration; same applies to imap, ifilter, and other lazy variants prefixed with 'i'
- ⚠ memoize is unbounded by default — @memoize caches all unique argument combinations forever; agent code with many unique inputs (URLs, user IDs) leaks memory; use @memoize(100) for LRU with max size or functools.lru_cache(maxsize=100) for stdlib alternative
- ⚠ group_by returns defaultdict(list) not dict — funcy.group_by() returns collections.defaultdict; agent code checking 'if key in result' works; but JSON serialization of defaultdict needs: dict(group_by(...)) to convert; also: missing keys return [] not KeyError
- ⚠ walk vs walk_keys vs walk_values — funcy.walk(f, d) applies f to each (k, v) pair and expects (new_k, new_v) return; walk_keys(f, d) transforms only keys; walk_values(f, d) transforms only values; agent code using walk() for value transformation gets TypeError expecting 2-tuple return from function
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Scores are editorial opinions as of 2026-03-06.