CodeCompanion integrates with various language models (LLMs) through built-in and community-created adapters, allowing users to code efficiently. It supports major models like OpenAI, Anthropic, and more, while enabling custom adapter creation. Key features include inline transformations, code generation, asynchronous execution, and a built-in prompt library for common tasks, enhancing user productivity. It also accommodates multiple simultaneous chats and accepts visual inputs.
For installation and troubleshooting, extensive documentation is provided. Users can run a health check, enable debug logging for better diagnostics, and use a minimal configuration file to resolve issues independently. Community contributions are welcomed, but implementation is at the discretion of the project lead. Special thanks are given to contributors who shaped various aspects of CodeCompanion, demonstrating a collective effort in improving the tool.
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