pycarlo
Monte Carlo's Python SDK
Decision gist · record as of 2026-08-14
Yes. Pycarlo is actively maintained, has low install friction, carries no known vulnerabilities, and is licensed permissively. It's the official SDK for Monte Carlo's API, so it's the natural choice if you're already using or evaluating Monte Carlo for data observability. The main prerequisite is Python 3.10+ and valid Monte Carlo credentials.AI-flagged interpretation of the facts on this page — verify before relying
Before you install
- Requires Python 3.10 or greater.
- Credentials (API key or OAuth client credentials) must be configured via ~/.mcd/profiles.ini, environment variables, or passed explicitly to Session.
- Low install friction with a pure-Python wheel.
License · maintenance · safety
permissive license (permissive) — Licensed under Apache 2.0 (permissive), allowing commercial and private use with minimal restrictions—suitable for most production environments.
last release 2026-08-14 (0 days)
0 known vulnerabilities (OSV.dev, 2026-08-14) · 5,853,163 downloads/mo, #2,023 on PyPI
Alternatives
Verify before relying
pip install pycarlo
from pycarlo.core import Client, Query
client = Client()
query = Query()
query.get_user.__fields__('email')
print(client(query).get_user.email)- Exact scope and completeness of Monte Carlo API coverage—whether all queries and mutations are truly supported as claimed.
- Performance characteristics and rate-limiting behavior when executing large or complex queries.
- Whether the 'features' library (dbt, circuit breaking, PII filtering) is production-ready or still experimental.
What it is and what it does
Pycarlo wraps Monte Carlo's GraphQL API in a Python-native interface, letting you execute queries and mutations as first-class objects using sgqlc or as raw GraphQL strings. The core library handles all API operations supported by Monte Carlo, automatically converting between GraphQL's CamelCase and Python's snake_case for both requests and responses. Responses are returned as Box objects that support both dot notation and dictionary access, making them easy to work with in Python code.
The package includes a features library that adds convenience helpers for common tasks: dbt integration, circuit breaker logic for reliability, and PII filtering. Authentication is handled flexibly—you can use API keys, OAuth credentials, named profiles from a config file, or environment variables. It's designed for developers building data observability workflows, monitoring pipelines, or automating data quality checks against Monte Carlo's platform.
Use it for
- Query user details, tables, insights, or other Monte Carlo entities programmatically without writing raw GraphQL.
- Trigger and poll circuit breaker rules to detect and respond to data quality issues in your warehouse.
- Export insights or reports from Monte Carlo and upload them to S3 or other storage systems.
- Automate dbt integration tasks and metadata syncing with Monte Carlo via the features library.
- Build custom monitoring dashboards or alerting systems that pull data quality metrics from Monte Carlo.
Worth the install?
AI-flagged interpretation of the facts on this page. Verify before relying on it.
Yes.
Pycarlo is actively maintained, has low install friction, carries no known vulnerabilities, and is licensed permissively. It's the official SDK for Monte Carlo's API, so it's the natural choice if you're already using or evaluating Monte Carlo for data observability. The main prerequisite is Python 3.10+ and valid Monte Carlo credentials.
Install
pycarlo on PyPI
Before you install
Low install friction with a pure-Python wheel. Actively maintained with a recent release. Requires Python 3.10 or greater and five runtime dependencies (dataclasses_json, python-box, requests, responses, sgqlc), all standard libraries in the data/API ecosystem.
Requires Python 3.10 or greater. Credentials (API key or OAuth client credentials) must be configured via ~/.mcd/profiles.ini, environment variables, or passed explicitly to Session.
License in practice
Licensed under Apache 2.0 (permissive), allowing commercial and private use with minimal restrictions—suitable for most production environments.
Quickstart
pip install pycarlo
from pycarlo.core import Client, Query
client = Client()
query = Query()
query.get_user.__fields__('email')
print(client(query).get_user.email)Verify before relying
- Exact scope and completeness of Monte Carlo API coverage—whether all queries and mutations are truly supported as claimed.
- Performance characteristics and rate-limiting behavior when executing large or complex queries.
- Whether the 'features' library (dbt, circuit breaking, PII filtering) is production-ready or still experimental.
Package facts
| License | permissive license permissive |
| Python support | Supports the current Python release >=3.10 |
| Install friction | Low. Pure-Python wheel |
| Runtime dependencies | 5 packagesdataclasses_jsonpython-boxrequestsresponsessgqlc |
| Maintenance | Actively maintained 0 days since the last release |
| First released | |
| Downloads | 5,853,163 / month, #2,023 on PyPI 30-day window, as of 2026-08-14 |
| Known vulnerabilities | None known OSV.dev, checked 2026-08-14 |
| Classifiers | Development Status :: 3 - AlphaIntended Audience :: DevelopersLicense :: OSI Approved :: Apache Software LicenseNatural Language :: EnglishProgramming Language :: Python :: 3.10Programming Language :: Python :: 3.11Programming Language :: Python :: 3.12Topic :: Software Development :: Build Tools |
Evidence: pycarlo-0.15.182-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 › “monte carlo api python client”
- pycarloPycarlo is Monte Carlo's Python SDK for querying and mutating data…
- montecarlodataA command-line interface for interacting with Monte Carlo's data…
- airflow-mcdIntegrates Monte Carlo data observability into Apache Airflow…
Give your agent the search over MCP, or paste the wish link into any chat.
More Build Tools packages
Provides reusable utilities for Python packaging interoperability, including version handling, specifiers, markers, requirements, tags, and metadata parsing according to standards like PEP 440 and PEP 425.
Wraps any iterable to display a real-time progress bar in the terminal or Jupyter notebook, showing iteration count, elapsed time, and estimated time remaining.
pip is the standard installer for Python packages, enabling you to download and install packages from the Python Package Index and other indexes into your Python environment.
Hatchling is a standards-compliant Python build backend that handles packaging, metadata, and distribution of Python projects when configured in a project's pyproject.toml file.
Generates Python gRPC service stubs and message classes from Protocol Buffer definitions, enabling developers to build gRPC clients and servers.
pre-commit is a framework for installing and running git hooks written in any language before commits are made, automating code quality and validation checks across multi-language projects.
Install it if your team needs consistent, automated validation at commit time.
See also montecarlodata · sgqlc · airflow-mcd · gql · python-graphql-client · django-graphql-jwt · graphqlclient · pymc3 · graphene · graphene-pydantic