Skip to main content

controlvar (Python)

Beta. This is a 0.x release: pin the exact version you test against.

Synchronous, dependency-free Python client for the Controlvar public API. Experiment assignment and feature-flag evaluation are decided by the server by default (sticky per user + experiment); the SDK caches the answers per user + key. Server-side code can opt in to local evaluation (evaluation="local", beta): flags are answered in-process from the server's ruleset, with the server's answers, and anything that cannot be answered exactly goes to the server. See docs/sdk/local-evaluation.md.

Requires Python 3.9+. HTTP goes through the standard library (urllib), so there is nothing else to install.

Install

If you installed experimently, the name this package had before the project was renamed, uninstall it and install controlvar.

The controlvar package is on PyPI. 0.1.1 is a beta release. It installs and imports as controlvar.

pip install controlvar==0.1.1

To work on the SDK itself, install it from the root of a clone of this repository instead:

pip install -e sdk/python

Quick start

Set CONTROLVAR_API_KEY to an API key, then run the block as it is; the two print calls stand for your own code.

import os
from controlvar import ExperimentationClient, ExperimentationError

client = ExperimentationClient(
    api_url=os.environ.get("CONTROLVAR_API_URL", "http://localhost:8000"),  # origin only
    api_key=os.environ["CONTROLVAR_API_KEY"],
)

# Experiments — sticky assignment made by the server (POST /api/v1/tracking/assign)
variant = client.get_variant("checkout_flow", user_id="user-123", user_attributes={"plan": "pro"})
if variant == "treatment":
    print("show the new checkout")   # your own code

assignment = client.get_assignment("checkout_flow", "user-123")   # raises on failure
assignment.variant_name, assignment.is_control, assignment.configuration

# Feature flags (GET /api/v1/feature-flags/evaluate/{key}?user_id=…&context=<url-encoded attributes>)
if client.is_feature_enabled("new_search", "user-123", user_attributes={"plan": "pro"}):
    print("use the new search")      # your own code
flag = client.get_feature_flag("new_search", "user-123")          # FlagEvaluation(key, enabled, config, reason)
flags = client.get_all_flags("user-123", {"plan": "pro"})          # {"new_search": True, ...}

# Tracking (never raises)
client.track("user-123", "purchase", event_value=49.99, experiment_key="checkout_flow")
client.track("user-123", "page_view", properties={"page": "/"})   # no key: fanned out, see below

Constructor: ExperimentationClient(api_url, api_key, timeout_seconds=5.0, cache_ttl_seconds=300, default_variant="control", transport=None). Instances are thread-safe.

API

Method Returns Failure behaviour
get_assignment(experiment_key, user_id, user_attributes=None) Assignment(experiment_key, user_id, variant_id, variant_name, is_control, configuration, assigned, reason) raises ExperimentationError (status == 404 when the experiment is not ACTIVE)
get_variant(experiment_key, user_id, user_attributes=None) str variant name returns default_variant
get_feature_flag(flag_key, user_id, user_attributes=None) FlagEvaluation(key, enabled, config, reason) raises ExperimentationError (status == 404 when the flag key is unknown; a flag that is not ACTIVE returns enabled=False, reason="inactive")
is_feature_enabled(flag_key, user_id, user_attributes=None) bool returns False
get_all_flags(user_id, user_attributes=None) dict[str, bool] (not cached) raises ExperimentationError
track(user_id, event_name, event_value=None, properties=None, experiment_key=None, feature_flag_key=None, event_type=None, timestamp=None) bool — True when accepted never raises; False on failure or when nothing was sent
track_batch(events) BatchResult(success_count, failure_count, errors); .ok never raises; a failed chunk counts all its events as failures
get_assignments(user_id, active_only=True) list[dict] from the server raises ExperimentationError
cached_assignments(user_id) / cached_flags(user_id) cached, unexpired entries —
clear_cache() — —
ready(timeout_seconds=None), status(), close() / with local mode: readiness, ruleset state, shutdown (sends evaluation counts) never raise
consistent_hash(user_id, flag_key) / md5_hex(user_id, flag_key) cross-SDK MD5 bucket in [0, 1) / hex digest pure functions; not the flag bucketing function

ExperimentationError carries .status (HTTP status, None for network errors/timeouts) and .body (raw response text). Failures are never cached, so the next call retries. A 429 is retried once after Retry-After seconds (capped at 5 s).

user_attributes is sent as context on assignment and on flag evaluation (&context=<url-encoded JSON>, omitted when empty), so flag targeting rules evaluate against it (country also matches user.country; nested dicts flatten to dotted keys such as app.version). FlagEvaluation.reason (targeting_rule / rollout / inactive / error, or None) says why the server decided. Caches are keyed by user + key only — call clear_cache() after changing a user's attributes.

Enrolment: assigned and reason

An Assignment also carries assigned and reason. assigned is False means the server did not enrol the user (reason is "holdout", "mutual_exclusion" or "targeting") and returned the control variant so you render the default experience; no exposure was recorded. When you export exposures to a warehouse, exclude assignments with assigned is False: the user was not enrolled, and reason says why. A server that predates the fields sends neither, so assigned is None (never False). Decide deliberately how to treat those rows rather than dropping them with an assigned is True filter.

Tracking fan-out rule

  • with experiment_key and/or feature_flag_key → one POST /api/v1/tracking/track;
  • without a key → one POST /api/v1/tracking/batch (chunks of 100) containing one entry per experiment the user has been assigned to in this client (experiment_key) plus one per flag evaluated for the user (feature_flag_key), taken from the cache (successful, unexpired only);
  • nothing cached for the user → nothing is sent and track returns False.

event_type defaults to event_name; properties is sent as metadata; timestamp may be a datetime (naive values are treated as UTC) or an ISO-8601 string.

Backend endpoints used

Every request carries X-API-Key: <key>, Content-Type: application/json and Accept: application/json.

SDK call Method and path Body / query Response used
get_assignment, get_variant POST /api/v1/tracking/assign {experiment_key, user_id, context?} {experiment_key, user_id, variant_id, variant_name, is_control, configuration}; 404 when not ACTIVE
get_feature_flag, is_feature_enabled GET /api/v1/feature-flags/evaluate/{flag_key}?user_id=…&context=<url-encoded JSON> context = user_attributes (omitted when empty) {key, enabled, config, reason}; off with reason: "inactive" when the flag exists but is not ACTIVE; 404 only for an unknown key
get_all_flags GET /api/v1/feature-flags/user/{user_id}?context=<url-encoded JSON> context = user_attributes (omitted when empty) {"<flag_key>": bool, …}
track with a key POST /api/v1/tracking/track {event_type, event_name, user_id, experiment_key?, feature_flag_key?, value?, metadata?, timestamp?} ignored
track without keys, track_batch POST /api/v1/tracking/batch {events: [<track body>, …]} (≤ 100 per request) {success_count, failure_count, errors}
get_assignments GET /api/v1/tracking/assignments/{user_id}?active_only=true — list of dicts

Contract smoke

CONTROLVAR_API_KEY=<key> python sdk/python/examples/contract_smoke.py

It prints:

{"sdk":"python","assign":{"variant_name":"treatment","is_control":false,"sticky":true},"flag":{"enabled":true},"track":{"ok":true},"fanout":{"ok":true}}

Env: CONTROLVAR_API_URL (default http://localhost:8000), CONTROLVAR_API_KEY (required), CONTRACT_EXPERIMENT_KEY (default sdk_contract_ab), CONTRACT_FLAG_KEY (default sdk_contract_flag), CONTRACT_USER_ID (default random smoke-<uuid>).

CI runs this SDK's contract smoke against a real API: python is in the sdks: list of the SDK Live Contract job in pr-qa-gate.yml. Its fixtures, sdk_contract_ab and sdk_contract_flag, are seeded with backend/scripts/seed_sdk_contract.py; run it with python tests/sdk-contract/live/run_live_contract.py --sdk python --strict.

Testing your own code

controlvar.testing.FakeTransport records requests and serves canned responses:

from controlvar import ExperimentationClient
from controlvar.testing import FakeTransport

transport = FakeTransport().route("GET", "/api/v1/feature-flags/evaluate/new_search",
                                  json={"key": "new_search", "enabled": True, "config": None})
client = ExperimentationClient("http://test", "key", transport=transport)
assert client.is_feature_enabled("new_search", "user-1")
assert transport.last.query == {"user_id": "user-1"}

Development

source venv/bin/activate
python -m pytest sdk/python/tests -q -o addopts="" -p no:cacheprovider
  • python -m pytest sdk/python/tests -q -o addopts="" -p no:cacheprovider: HTTP is faked; CI runs them on Python 3.11

Metadata

Release files for controlvar 0.1.1

For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.

Source distribution (sdist)

Source distribution for controlvar 0.1.1
File Size Uploaded Provenance Actions
controlvar-0.1.1.tar.gz 54.8 kB With provenance Details for controlvar-0.1.1.tar.gz

Built distribution (wheel)

Table of built distributions (wheels) for controlvar 0.1.1
File Interpreter ABI Platform Actions
controlvar-0.1.1-py3-none-any.whl Python 3 none any Details for controlvar-0.1.1-py3-none-any.whl

Total release size: 87.6 kB

Release files / controlvar-0.1.1.tar.gz

Download URL controlvar-0.1.1.tar.gz
Size 54.8 kB
Tags Source
SHA-256 checksum
How to use checksums
387019ad7040adeaef75793c8112b619f90633aa5343a9db9192197d9771381f
BLAKE2b-256 checksum
How to use checksums
0007c538ab523116e532ecdd44fedb95b9ee3c37bc8939f14b5bb1f353451d6d
Upload date
Uploaded using Trusted Publishing?
What is trusted publishing?
Yes
Uploaded via twine/7.0.0 CPython/3.13.14

Provenance

Provenance describes where a file came from. On PyPI, provenance is shared via attestations, which provide a verifiable record of the build or publishing details. View details, limitations and caveats.

PyPI Publish Attestation

PyPI verified that this artifact, at this checksum, originated from the publisher listed below.

Signed by GitHub Actions, verified by PyPI on Oct 17, 2026.

Transparency log

Release files / controlvar-0.1.1-py3-none-any.whl

Download URL controlvar-0.1.1-py3-none-any.whl
Size 32.8 kB
Tags Python 3
SHA-256 checksum
How to use checksums
f7891f0231118bea7f3841920d1f5b7b8beb74c5f2944a8126d67a85b21128ac
BLAKE2b-256 checksum
How to use checksums
2df262ad19ed80a532be028ecccead9b69c00ccd6de6af4674624db6b86c554d
Upload date
Uploaded using Trusted Publishing?
What is trusted publishing?
Yes
Uploaded via twine/7.0.0 CPython/3.13.14

Provenance

Provenance describes where a file came from. On PyPI, provenance is shared via attestations, which provide a verifiable record of the build or publishing details. View details, limitations and caveats.

PyPI Publish Attestation

PyPI verified that this artifact, at this checksum, originated from the publisher listed below.

Signed by GitHub Actions, verified by PyPI on Oct 17, 2026.

Transparency log

Security for controlvar 0.1.1

Provenance

Provenance describes where a file came from. On PyPI, provenance is shared via attestations, which provide a verifiable record of the build or publishing details. View details, limitations and caveats.

Full provenance

Both files in this release have been uploaded with provenance metadata.

Select from 2 files

Attestation for controlvar-0.1.1.tar.gz

PyPI Publish Attestation

PyPI verified that this artifact, at this checksum, originated from the publisher listed below.

Signed by GitHub Actions, verified by PyPI on Oct 17, 2026.

Transparency log

A note on package security

PyPI is a repository for distributing packages, not a service for auditing them. While third-party security researchers and our staff do their best to detect and remove malicious packages, PyPI cannot guarantee the quality, security or suitability of any given package.

To learn more about protecting yourself from malicious actors, visit our package security documentation.

Anthropic, PBC Visionary sponsor Bloomberg Visionary sponsor Hudson River Trading Visionary sponsor Meta Visionary sponsor NVIDIA Visionary sponsor Microsoft Sustainability sponsor Depot Continuous Integration AWS Cloud computing and Security Sponsor Datadog Monitoring Fastly CDN Google Download Analytics Sentry Error logging StatusPage Status page