{"categories":[{"label":"Build Tools","url":"https://skillfed.io/packages/category/software-development-build-tools"}],"enrichment":{"capability":"Pycarlo is Monte Carlo's Python SDK for querying and mutating data via their GraphQL API, with convenience features for common operations like dbt integration, circuit breaking, and PII filtering.","skillfed_tags":["graphql-client","data-observability","api-sdk"],"use_cases":["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."],"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.\n\nThe 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\u2014you 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.","worth_installing":"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."},"id":"pycarlo","links":{"html":"https://skillfed.io/packages/pycarlo","md":"https://skillfed.io/packages/pycarlo.md","pypi":"https://pypi.org/project/pycarlo/"},"maintenance":{"status":"active"},"meta":{"latest_release":"2026-08-14","license_spdx":null,"license_treatment":"permissive","name":"pycarlo","python_support":"supports_current","summary":"Monte Carlo's Python SDK"},"popularity":{"monthly_downloads":5853163,"position":2023,"tier":"top_5000"},"security":{"n_vulnerabilities":0},"version":"0.15.182"}