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$0Free plan available.
Goast.ai is an AI assistant designed to automatically analyze and resolve issues from error logs. It helps engineering teams fix bugs faster by providing real-time bug fixes, root cause analysis, step-by-step solution plans, and context-aware code generation. Goast integrates with popular error monitoring platforms and git providers to streamline the bug fixing process.
Connect Goast to your error monitoring platform and prompt it to analyze and resolve specific issues. Goast will then generate a solution plan, produce the necessary code, and submit a pull request for your review.
Goast supports most major frameworks and languages, including React, Flutter, TypeScript, Go, JavaScript, Python, and others.
Goast integrates with popular observability platforms like Sentry, Datadog, BugSnag, and Google Cloud, as well as git providers such as GitHub, GitLab, and Bitbucket. Additionally, you can interact with Goast via Slack.
Yes, Goast provides free and unlimited access for open source projects, subject to an application process.
Free plan available.
Use these comparison pages to understand the trade-offs between the models most relevant to Goast.ai.
Compare Gemini 1.0 Pro Deprecated and Gemini 1.5 Flash Vision Deprecated across pricing, context window, capabilities, benchmarks, and API access to choose the better fit for long-context workloads versus general-purpose AI workloads.
Compare Gemini 1.5 Pro Vision Deprecated and Gemini 1.0 Pro Deprecated across pricing, context window, capabilities, benchmarks, and API access to choose the better fit for general-purpose AI workloads versus long-context workloads.
Compare Gemini 1.0 Pro Vision Deprecated and Gemini 1.5 Flash Vision Deprecated across pricing, context window, capabilities, benchmarks, and API access to choose the better fit for general-purpose AI workloads versus general-purpose AI workloads.
Compare Gemini 1.5 Pro Vision Deprecated and Gemini 1.0 Pro Vision Deprecated across pricing, context window, capabilities, benchmarks, and API access to choose the better fit for general-purpose AI workloads versus general-purpose AI workloads.
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