PokeAPI/pokeapi

The Pokémon API

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Summary Information

Updated 16 minutes ago
Added to GitGenius on September 12th, 2026
Created on December 4th, 2014
Open Issues & Pull Requests: 146 (+0)
GitHub issues: Enabled
Number of forks: 1,164
Total Stargazers: 5,373 (+0)
Total Subscribers: 70 (+0)

Repository Insights (GitGenius)

Median issue/PR response: 16.6 hours
Mean response time: 77.6 days
90th percentile: 77.0 days
Tracked items: 254

Most active contributors

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How this project is maintained

Around half of the issues opened in the past year never receive a reply. 97% of open issues come from outside the core team, so the backlog reflects real-world use rather than internal planning. Only 8% of issues opened in the past year have been closed. Three people close 78% of everything that gets resolved.

Charts & Analytics

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Issue Activity (beta)

Open issues: 75
New in 7 days: 2
Closed in 7 days: 6
Avg open age: 488 days
Stale 30+ days: 53
Stale 90+ days: 38

Recent activity

Opened in 7 days: 2
Closed in 7 days: 4
Comments in 7 days: 0
Events in 7 days: 0

Top labels

  • help wanted (9)
  • wontfix (5)
  • bug (4)
  • data-missing/inaccurate (3)
  • enhancement (3)
  • question (3)
  • Beginner friendly (1)
  • cloudflare (1)

Detailed Description

PokéAPI is a RESTful API for Pokémon data that also offers beta GraphQL support.

The project solves the problem of accessing comprehensive Pokémon information programmatically. It provides a structured interface to query details about Pokémon, their attributes, moves, abilities, and related game data. The API is built on a relational database populated from CSV data files, with a REST interface serving the primary access method. GraphQL support is being rolled out through integration with Hasura Engine, allowing developers to query data using GraphQL alongside the traditional REST endpoints.

PokéAPI suits developers building Pokémon-related applications, educational projects, or tools that need reliable access to game data. The project is explicitly marked as beginner-friendly, making it accessible to developers new to API consumption. It supports multiple deployment approaches: local development with Python and uv, containerized deployment via Docker Compose for production-like environments, and Kubernetes for larger-scale deployments. The tool is useful for anyone needing a stable, well-structured data source rather than scraping game data manually.

Development activity shows consistent maintenance with attention to code quality standards. The project enforces pre-commit hooks for code consistency and is moving toward stricter linting and type-checking requirements. Database management is handled through a build system that regenerates tables from CSV sources, with migration support for schema changes. The codebase uses Python with modern tooling including uv for dependency management and a Makefile-based task system for common operations.