Goast

The AI Assistant for Bug Fixing

Free Trial

Description

Goast is an AI-powered assistant designed to help engineering teams accelerate the bug-fixing process. It automatically analyzes errors and exceptions from error logs in both staging and production environments, aiming to resolve issues significantly faster. The tool integrates with various error monitoring platforms and version control systems, allowing it to identify root causes, generate context-aware code fixes, and submit pull requests for developer review.

By automating the more tedious aspects of bug resolution, Goast allows development teams to focus on other critical tasks. It can be prompted to perform root cause analyses, create step-by-step solution plans, and iterate on proposed fixes based on team feedback. Accessible via platforms like Slack, GitHub, or its web application, Goast supports a wide range of popular programming languages and frameworks to streamline workflows.

Key Features

Automated Bug Analysis & Resolution: Analyzes errors from logs and automatically generates fixes.
Real-Time Fixes: Sends bug fixes in real time as issues are detected.
Instant Root Cause Analysis: Pinpoints the root cause of an error in seconds.
Impact & Severity-Based Triage: Automates fix generation based on set impact and severity thresholds.
Iterative Pull Requests: Generates PRs and updates them based on team feedback, responding to comments.
Multi-Platform Access: Tag and interact with the AI from Slack, GitHub, or within your error monitoring platform.
Broad Integration Ecosystem: Connects with popular error trackers like Sentry, Datadog, BugSnag, Google Cloud, and git providers.
Wide Language & Framework Support: Works with major frameworks and languages including React, Flutter, TypeScript, Go, JavaScript, and Python.

Use Cases

Accelerating bug resolution in software development cycles.
Automating the analysis of error logs for faster issue identification.
Reducing manual effort in debugging and code correction for engineering teams.
Streamlining the pull request process for bug fixes.
Improving an engineering team's time-to-resolution for production and staging issues.
Automating responses to issues based on predefined impact and severity.