toolz
Functional programming utilities for Python — provides currying, function composition, itertools extensions, and dictionary utilities. toolz features: curry for partial application, compose/compose_left for function pipelines, pipe(data, f1, f2, f3) for data transformation, memoize for function caching, juxt for applying multiple functions, complement for boolean negation, thread_last/thread_first for Clojure-style threading, itertoolz (partition, sliding_window, frequencies, groupby, accumulate, topk), dicttoolz (assoc, dissoc, merge, valmap, keymap, keyfilter), and cytoolz C extension for 2-5x speedup.
Score Breakdown
⚙ Agent Friendliness
🔒 Security
Pure functional library with no network calls. memoize stores function arguments as cache keys — avoid memoizing functions that receive secrets as arguments; cache keys may be logged or inspected. No other security concerns.
⚡ Reliability
Best When
Writing functional Python code with data transformation pipelines — toolz provides the functional primitives (pipe, compose, curry, memoize) that make pipeline code readable without external dependencies.
Avoid When
You need async composition, vectorized array operations (use numpy/pandas), or bounded production caching (use cachetools).
Use Cases
- • Agent data pipeline — from toolz import pipe, curry; process = pipe(raw_data, parse_json, validate_schema, extract_fields, normalize_values) — pipe(data, *functions) threads data through transformation chain; agent data processing pipeline composes pure functions; each step receives previous output; readable left-to-right data flow
- • Agent function composition — from toolz import compose, curry; @curry; def filter_by(key, value, items): return [i for i in items if i[key] == value; active_users = filter_by('status', 'active') — curry creates partial application; compose(get_emails, active_users) creates pipeline function; agent builds reusable transformation components from simple functions
- • Agent dictionary operations — from toolz import dicttoolz as dt; config = dt.merge(defaults, user_config); updated = dt.assoc(config, 'retries', 5); cleaned = dt.dissoc(config, 'debug_key'); mapped = dt.valmap(str.upper, tags) — immutable dict operations; agent config management without mutation; assoc/dissoc return new dicts
- • Agent memoization — from toolz import memoize; @memoize; def expensive_embedding(text): return model.encode(text) — memoize caches by argument; agent repeated embedding calls use cache; memoize uses dict by default (unlimited); add cache parameter: @memoize(cache=LRU(1000)) for bounded cache
- • Agent frequency analysis — from toolz.itertoolz import frequencies, topk; word_counts = frequencies(tokens); top_words = topk(10, word_counts, key=word_counts.get) — frequencies() counts occurrences; topk() gets top N; agent text analysis counts token frequencies efficiently; no pandas required for simple counting
Not For
- • Async/concurrent code — toolz functions are synchronous; for async function composition use anyio or custom async pipeline
- • Large dataset processing — toolz is Python-level iteration; for DataFrames and arrays use pandas/numpy which have vectorized C operations
- • Production caching — toolz memoize is unbounded by default; for production caching use aiocache or cachetools with proper TTL and bounds
Interface
Authentication
No auth — pure Python utility library.
Pricing
toolz is BSD licensed. cytoolz is BSD licensed. Free for all use.
Agent Metadata
Known Gotchas
- ⚠ curry returns partial not error on insufficient args — @curry; def f(a, b): return a + b; f(1) returns curried function not error; agent code calling curried function with wrong arity gets a function back not an error; f(1)(2) returns 3; f(1, 2) returns 3; accidentally passing curried function where value expected causes downstream TypeError
- ⚠ memoize is unbounded by default — @memoize without cache parameter stores all unique inputs forever; agent function memoizing API responses with unique URLs fills memory over time; use: @memoize(cache={}) to start with empty dict or import LRU from functools and: @memoize(cache=dict()) with manual eviction; or use cachetools.LRUCache(maxsize=1000)
- ⚠ pipe() vs compose() argument order — pipe(data, f1, f2, f3) applies f1 first; compose(f3, f2, f1)(data) applies f1 first (rightmost first); compose_left(f1, f2, f3)(data) is equivalent to pipe; agent code mixing pipe and compose must remember compose applies right-to-left like mathematical composition
- ⚠ itertoolz functions return generators not lists — toolz.itertoolz.partition(3, range(10)) returns generator; agent code expecting list must convert: list(partition(3, range(10))); generators are lazy — calling len() raises TypeError; calling functions that consume iterables twice on generator gives empty second iteration
- ⚠ dicttoolz.merge uses last-wins for conflicts — dt.merge({'a': 1}, {'a': 2}) returns {'a': 2}; last dict wins for duplicate keys; agent config merge: dt.merge(defaults, overrides) — overrides win; dt.merge(overrides, defaults) — defaults win; order matters and is easy to get backwards
- ⚠ cytoolz is optional C extension — from cytoolz import pipe is 2-5x faster but requires compilation; from toolz import pipe is pure Python fallback; agent requirements should list toolz not cytoolz for portability; add cytoolz as optional: pip install toolz cytoolz; code can try: from cytoolz import pipe except ImportError: from toolz import pipe
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Scores are editorial opinions as of 2026-03-06.