Universal LLM Gateway: One API, every LLM. OpenAI-compatible endpoints with multi-provider translation and intelligent load-balancing.

8 Open Issues Need Help Last updated: Feb 25, 2026

Open Issues Need Help

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AI Summary: This issue aims to enhance the project's README file as a 'quick win'. The improvements include adding common elements like status badges (e.g., build, license) and practical usage examples to make the project more accessible and appealing to new users.

Complexity: 2/5
documentation enhancement good first issue quick-wins kilo-triaged kilo-auto-fix

Universal LLM Gateway: One API, every LLM. OpenAI-compatible endpoints with multi-provider translation and intelligent load-balancing.

Python

AI Summary: This issue is a quick win task to add an `.editorconfig` file to the repository. The purpose is to establish and enforce consistent coding styles across different editors and IDEs for all contributors, improving code quality and maintainability.

Complexity: 1/5
enhancement good first issue quick-wins kilo-triaged kilo-auto-fix

Universal LLM Gateway: One API, every LLM. OpenAI-compatible endpoints with multi-provider translation and intelligent load-balancing.

Python

AI Summary: This issue addresses a bug where the system fails to correctly handle certain complex G4F model IDs. The task involves thoroughly testing these problematic IDs to identify the root cause and then implementing a fix to ensure proper handling and functionality.

Complexity: 3/5
help wanted bugs kilo-triaged

Universal LLM Gateway: One API, every LLM. OpenAI-compatible endpoints with multi-provider translation and intelligent load-balancing.

Python

AI Summary: This issue proposes adding pre-commit hooks to the project's development workflow. The goal is to automate code quality checks and formatting before code is committed, ensuring consistency and reducing errors. It's labeled as a "Quick Win," indicating it's considered a relatively straightforward task with immediate benefits.

Complexity: 2/5
enhancement good first issue quick-wins kilo-triaged kilo-auto-fix

Universal LLM Gateway: One API, every LLM. OpenAI-compatible endpoints with multi-provider translation and intelligent load-balancing.

Python

AI Summary: This issue requests adding descriptive comments to each variable within the `.env.example` file. The goal is to provide helpful explanations for what each environment variable is used for, making it easier for new contributors or developers to understand and configure the project. It's categorized as a 'Quick Win' task.

Complexity: 1/5
documentation enhancement good first issue quick-wins kilo-triaged kilo-auto-fix

Universal LLM Gateway: One API, every LLM. OpenAI-compatible endpoints with multi-provider translation and intelligent load-balancing.

Python

AI Summary: This issue proposes adding docstrings to all modules within the codebase. Categorized as a 'Quick Win,' the goal is to enhance code documentation and readability across the project by ensuring every module has a descriptive docstring.

Complexity: 2/5
documentation enhancement good first issue quick-wins kilo-triaged kilo-auto-fix

Universal LLM Gateway: One API, every LLM. OpenAI-compatible endpoints with multi-provider translation and intelligent load-balancing.

Python
enhancement good first issue quick-wins kilo-triaged kilo-auto-fix

Universal LLM Gateway: One API, every LLM. OpenAI-compatible endpoints with multi-provider translation and intelligent load-balancing.

Python
enhancement good first issue phase-1.1-testing kilo-triaged kilo-auto-fix

Universal LLM Gateway: One API, every LLM. OpenAI-compatible endpoints with multi-provider translation and intelligent load-balancing.

Python