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Summary of ChangesHello @indurireddy-TF, I'm Gemini Code Assist1! I'm currently reviewing this pull request and will post my feedback shortly. In the meantime, here's a summary to help you and other reviewers quickly get up to speed! This pull request delivers a robust automation solution for managing GitHub issues by introducing a Go-based stale bot. The bot intelligently processes issues, identifying those requiring attention or closure based on predefined activity thresholds and maintainer input. It streamlines issue maintenance, ensuring that discussions remain active and unresolved items are handled efficiently, while also providing alerts for potentially overlooked changes. Highlights
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Code Review
This pull request introduces a new stale bot agent for GitHub repositories, implemented in Go using the ADK framework. The agent's core functionality is defined in agent.go, which includes fetching issue data via GraphQL, building a chronological event timeline, and replaying this history to determine an issue's current state (e.g., last actor, activity time, whether it's stale, or if a maintainer alert is needed for silent edits). It also provides tool functions for interacting with GitHub, such as adding/removing labels, posting comments, and closing issues. The PROMPT_INSTRUCTION.txt file outlines the detailed decision-making workflow for the AI agent, covering scenarios for active, stale, and pending issues based on user roles and activity. Configuration parameters like thresholds and API limits are managed in config.go, while main.go orchestrates the agent's execution, processing issues concurrently in chunks and logging performance metrics. Utility functions for robust HTTP requests with retries and API call counting are provided in util.go. Review comments primarily focus on improving error handling and consistency: specifically, using safer type assertions for GraphQL error responses, handling potential json.Marshal errors in HTTP requests, correcting a placeholder format in the prompt instruction, changing a log.Printf level from 'FATAL' to 'ERROR', suggesting a more efficient isMaintainer check using a map, and standardizing variable naming conventions for labels.
| - **IF (Question == YES) AND (Time == YES) AND (Internal Discussion Check == FALSE):** | ||
| - **Action**: Call `add_stale_label_and_comment`. | ||
| - **Check**: If '{REQUEST_CLARIFICATION_LABEL}' is not in `current_labels`, call `add_label_to_issue` with '{REQUEST_CLARIFICATION_LABEL}'. | ||
| - **Report**: "Analysis for Issue #[number]: STALE. Maintainer asked question [days_since_activity] days ago. Marking stale." |
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The placeholder [days_since_activity] in the report string is not a standard {}-style placeholder used elsewhere in the prompt. This might lead to the literal string [days_since_activity] being reported instead of the actual value. Please update it to {days_since_activity} for consistency and correct replacement.
- **Report**: "Analysis for Issue #[number]: STALE. Maintainer asked question {days_since_activity} days ago. Marking stale."
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Why is an agent and LLM needed for this? Why is a rule based system insufficient? It looks like the rules are well defined and I'm pretty sure this is what most people are doing when I have seen it over the years (since before LLMs) |
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What this PR does
Introduces a stale issue automation bot implemented in Go.
Applies a clear decision workflow:
Marks issues as stale when maintainers request clarification and no response is received within the threshold.
Removes the stale label when users respond.
Alerts maintainers on silent description edits (ghost edits).
Automatically closes issues that remain stale beyond the close threshold.
Supports concurrent issue processing with rate limiting.
Uses configurable time thresholds and labels via environment variables.