--- title: "AI Code Review Agent: Automated Pull Request Analysis and Feedback" description: "Build an AI agent that automatically reviews pull requests, parses diffs, classifies issues by severity, and generates actionable feedback comments. A practical guide to augmenting code review workflows." canonical: https://callsphere.ai/blog/ai-code-review-agent-automated-pull-request-analysis-feedback category: "Learn Agentic AI" tags: ["Code Review", "AI Agents", "Python", "Pull Requests", "Developer Workflow"] author: "CallSphere Team" published: 2026-03-17T00:00:00.000Z updated: 2026-05-07T02:10:55.539Z --- # AI Code Review Agent: Automated Pull Request Analysis and Feedback > Build an AI agent that automatically reviews pull requests, parses diffs, classifies issues by severity, and generates actionable feedback comments. A practical guide to augmenting code review workflows. ## The Problem with Manual Code Review Code review is essential but slow. Reviewers get fatigued, miss subtle bugs, and spend time flagging style issues that a machine could catch. An AI code review agent automates the mechanical parts of review — spotting bugs, identifying security issues, enforcing conventions — so human reviewers can focus on architecture and design decisions. The goal is not to replace human reviewers. It is to give them a head start by surfacing the most important issues before they even open the PR. ## Designing the Review Agent The agent takes a unified diff as input, parses it into individual file changes, analyzes each change against a set of review criteria, and produces structured feedback with severity levels. ```mermaid flowchart LR INPUT(["User intent"]) PARSE["Parse plus classify"] PLAN["Plan and tool selection"] AGENT["Agent loop LLM plus tools"] GUARD{"Guardrails and policy"} EXEC["Execute and verify result"] OBS[("Trace and metrics")] OUT(["Outcome plus next action"]) INPUT --> PARSE --> PLAN --> AGENT --> GUARD GUARD -->|Pass| EXEC --> OUT GUARD -->|Fail| AGENT AGENT --> OBS style AGENT fill:#4f46e5,stroke:#4338ca,color:#fff style GUARD fill:#f59e0b,stroke:#d97706,color:#1f2937 style OBS fill:#ede9fe,stroke:#7c3aed,color:#1e1b4b style OUT fill:#059669,stroke:#047857,color:#fff ``` ```python from dataclasses import dataclass from enum import Enum from openai import OpenAI client = OpenAI() class Severity(Enum): CRITICAL = "critical" WARNING = "warning" SUGGESTION = "suggestion" NITPICK = "nitpick" @dataclass class ReviewComment: file: str line: int severity: Severity message: str suggestion: str | None = None class CodeReviewAgent: def __init__(self, model: str = "gpt-4o"): self.model = model self.review_criteria = [ "security_vulnerabilities", "bug_detection", "performance_issues", "error_handling", "code_style", ] ``` ## Parsing Unified Diffs Before the LLM can review code, you need to parse the diff into structured chunks that include file paths and line numbers. ```python import re def parse_diff(self, diff_text: str) -> list[dict]: files = [] current_file = None current_hunks = [] for line in diff_text.split("\n"): if line.startswith("diff --git"): if current_file: files.append({ "file": current_file, "hunks": current_hunks, }) match = re.search(r"b/(.+)$", line) current_file = match.group(1) if match else "unknown" current_hunks = [] elif line.startswith("@@"): match = re.search(r"\+(\d+)", line) start_line = int(match.group(1)) if match else 1 current_hunks.append({ "start_line": start_line, "lines": [], }) elif current_hunks and not line.startswith("---"): current_hunks[-1]["lines"].append(line) if current_file: files.append({"file": current_file, "hunks": current_hunks}) return files ``` This parser extracts each modified file and its hunks, preserving line numbers so feedback can reference exact locations. ## Analyzing Changes with the LLM Each file's changes are sent to the LLM with a structured prompt that forces output into a parseable format. ```python import json def review_file(self, file_info: dict) -> list[ReviewComment]: diff_content = "" for hunk in file_info["hunks"]: diff_content += f"Starting at line {hunk['start_line']}:\n" diff_content += "\n".join(hunk["lines"]) + "\n\n" system_prompt = """You are a senior code reviewer. Analyze the diff and return a JSON array of issues found. Each issue must have: - "line": integer line number - "severity": one of "critical", "warning", "suggestion", "nitpick" - "message": clear explanation of the issue - "suggestion": optional fix or improved code Focus on: security vulnerabilities, bugs, performance problems, missing error handling, and convention violations. Return [] if no issues found. Output ONLY valid JSON.""" response = client.chat.completions.create( model=self.model, messages=[ {"role": "system", "content": system_prompt}, {"role": "user", "content": ( f"File: {file_info['file']}\n\n{diff_content}" )}, ], temperature=0, response_format={"type": "json_object"}, ) raw = json.loads(response.choices[0].message.content) issues = raw if isinstance(raw, list) else raw.get("issues", []) return [ ReviewComment( file=file_info["file"], line=issue.get("line", 0), severity=Severity(issue.get("severity", "suggestion")), message=issue["message"], suggestion=issue.get("suggestion"), ) for issue in issues ] ``` ## Prioritizing and Formatting Output Raw review comments need to be sorted by severity so the developer sees critical issues first. ```python def review_pull_request(self, diff_text: str) -> list[ReviewComment]: files = self.parse_diff(diff_text) all_comments = [] for file_info in files: comments = self.review_file(file_info) all_comments.extend(comments) severity_order = { Severity.CRITICAL: 0, Severity.WARNING: 1, Severity.SUGGESTION: 2, Severity.NITPICK: 3, } all_comments.sort(key=lambda c: severity_order[c.severity]) return all_comments def format_review(self, comments: list[ReviewComment]) -> str: if not comments: return "No issues found. LGTM!" lines = [] for c in comments: icon = { Severity.CRITICAL: "[CRITICAL]", Severity.WARNING: "[WARNING]", Severity.SUGGESTION: "[SUGGESTION]", Severity.NITPICK: "[NITPICK]", }[c.severity] lines.append(f"{icon} {c.file}:{c.line} - {c.message}") if c.suggestion: lines.append(f" Suggested fix: {c.suggestion}") return "\n".join(lines) ``` ## FAQ ### How do I integrate this agent with GitHub or GitLab? Use the GitHub API (via the `PyGithub` library or `httpx`) to fetch PR diffs, then post review comments back using the PR review comments endpoint. For GitHub Actions, trigger the agent on `pull_request` events and use the `GITHUB_TOKEN` for authentication. ### Will the LLM produce false positives that annoy developers? Yes, false positives are the biggest usability risk. Mitigate this by setting the default severity threshold to `warning` or above when posting comments, and let developers configure ignored rules. Track feedback on comments to tune prompts over time. ### How do I handle large PRs that exceed the context window? Split the diff into per-file chunks and review each file independently. For very large files, split further by hunk. Maintain a summary context across files so the agent understands cross-file dependencies. --- #CodeReview #AIAgents #Python #PullRequests #DeveloperWorkflow #AgenticAI #LearnAI #AIEngineering --- Source: https://callsphere.ai/blog/ai-code-review-agent-automated-pull-request-analysis-feedback