IssueSwarm

Developers spend significant amounts of time classifying, labeling, and triaging GitHub issues. On average, each issue takes 15–20 minutes to evaluate and resolve. This slows down development cycles, delays progress, and leaves projects vulnerable to risks such as operator error, integration gaps, and speed constraints. Our team saw an opportunity to streamline this process and reduce the repetitive manual work that keeps developers from focusing on building meaningful solutions. With the rise of AI tooling, we wanted to create something that would not just save time but also improve accuracy and consistency across issue management.

IssueSwarm is an AI-driven issue management system built with Google’s Agent Development Kit (ADK) and Gemini. It automates the classification and triage of GitHub issues by:

Scanning repositories to flag bugs, syntax errors, and anomalies.
Automatically tagging and labeling issues consistently with Gemini to minimize operator error.
Extracting and organizing key details such as issue number, repository, title, and description for better tracking and documentation.
Suggesting potential solutions, allowing developers to move from identification to resolution more quickly.

The result is a system that reduces average processing time from 15–20 minutes down to under 2 minutes, making the issue management process faster, more reliable, and scalable.

We leveraged Google’s ADK to design an AI agent capable of integrating directly with GitHub repositories. Gemini powers the agent’s reasoning, enabling it to classify issues accurately, detect anomalies in code, and suggest actionable fixes. The system was trained to extract structured information from GitHub issue metadata and documentation, which we organized into a pipeline for easier classification. By combining ADK’s flexibility with Gemini’s reasoning and natural language capabilities, we created a workflow that feels both intelligent and intuitive for developers.

One challenge was ensuring consistent labeling across issues. Developers often use slightly different terminology for similar problems, which can cause confusion when tracking or searching issues later. Training the model to recognize these nuances required fine-tuning and iteration. Another challenge was balancing automation with developer control while AI can suggest solutions, it’s important for teams to review and confirm changes before implementation. Integration with GitHub’s API also presented hurdles, particularly around rate limits and formatting extracted data for clear presentation.

We’re proud of creating a system that significantly reduces wasted time and makes developer workflows more efficient. Beyond speed, we’re excited that IssueSwarm improves consistency, reduces operator error, and scales easily across different repositories. Most importantly, we built a system that doesn’t just automate it empowers developers to focus on solving meaningful problems instead of repetitive tasks.

Through this project, we learned how to apply Google’s ADK and Gemini in a practical, developer-focused setting. We deepened our understanding of AI-driven automation, particularly in how reasoning models like Gemini can provide not only classification but also actionable solutions. We also gained experience in integrating AI systems with GitHub’s infrastructure and addressing the challenges of real-world workflows where accuracy and speed must work hand in hand.

Looking ahead, we plan to expand IssueSwarm’s capabilities by improving its solution-generation features, making its suggestions even more context-aware and actionable. We want to explore integration with other project management tools like Jira and Trello to create a unified system for tracking and resolving issues across platforms. Additionally, we’re interested in adding analytics features so teams can measure time saved, track recurring issues, and gain insights into their development process. Ultimately, our goal is for IssueSwarm to become an indispensable tool that helps development teams move faster, reduce risk, and focus on building better software.