Free
$0Free plan available.
Chatcody is a GPT-4 powered GitHub app that automates small tasks and reviews pull requests directly within your repository. It streamlines your workflow by handling coding chores and ensuring high-quality code reviews. Chatcody aims to increase productivity by embedding an AI engineer directly into your GitHub workflow.
Install the Chatcody GitHub app, create an issue or pull request, apply the 'chatcody' label, and the AI will process the task or review.
Simply add the 'ChatCody' label to your pull requests or issues. Once labeled, ChatCody will activate to assist with tasks and provide feedback.
Yes. Due to the context window limits of the underlying AI, extremely large issues or pull requests may be difficult to process. We recommend breaking extensive tasks into smaller, manageable segments for optimal results.
Clarity is essential. When creating issues, provide precise, well-defined instructions and expected outcomes. Clear requirements help the AI understand the context and assist you more effectively.
An 'event' is a user-triggered action that activates the AI assistant, such as creating a new issue, submitting a pull request, or posting a comment on an existing pull request. Each action prompts ChatCody to provide assistance, facilitating a more efficient workflow.
Free plan available.
Use these comparison pages to understand the trade-offs between the models most relevant to Chatcody.
Compare GPT 5.4 and GPT 5.4 Pro across pricing, context window, capabilities, benchmarks, and API access to choose the better fit for long-context workloads versus long-context workloads.
Compare GPT 5.5 and GPT 5.4 across pricing, context window, capabilities, benchmarks, and API access to choose the better fit for long-context workloads versus long-context workloads.
Compare GPT 5.5 and Claude 4.6 Opus across pricing, context window, capabilities, benchmarks, and API access to choose the better fit for long-context workloads versus long-context workloads.
Compare GPT 5.5 and Claude 4.6 Sonnet across pricing, context window, capabilities, benchmarks, and API access to choose the better fit for long-context workloads versus long-context workloads.
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