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🤖 Code Reviewer Agent

An autonomous AI-powered code review agent that analyzes GitHub Pull Requests using advanced language models and provides comprehensive code quality insights through intelligent multi-agent workflows.

Python FastAPI GitHub Actions Workflow Status Codecov Code Style

🚀 Features

  • 🧠 AI-Powered Analysis: Multi-agent LangGraph workflow with intelligent decision-making
  • 🔄 Async Processing: Celery-based distributed task queue for scalable analysis
  • 📊 Comprehensive Reports: Security, performance, style, and maintainability insights
  • 🐙 GitHub Integration: Seamless GitHub API integration with rate limiting and caching
  • 🗄️ Type-Safe Storage: PostgreSQL with SQLModel ORM for robust data persistence
  • ⚡ Redis Infrastructure: High-performance caching and message brokering

🏗️ System Architecture

graph TB
    subgraph "Client Layer"
        Client[REST Client]
        Browser[Web Browser]
    end

    subgraph "API Gateway"
        FastAPI[FastAPI Server<br/>• Authentication<br/>• Validation<br/>• Documentation]
    end

    subgraph "Processing Layer"
        Celery[Celery Workers<br/>• Task Queue<br/>• Background Processing<br/>• Progress Tracking]

        subgraph "AI Agents"
            LangGraph[LangGraph Workflow<br/>• Decision Making<br/>• File Prioritization]
            LLM[LLM Service<br/>• Code Analysis<br/>• Issue Detection]
        end
    end

    subgraph "External Services"
        GitHub[GitHub API<br/>• PR Data<br/>• File Content<br/>• Metadata]
        OpenAI[OpenAI/Pollinations<br/>• GPT Models<br/>• Code Analysis]
    end

    subgraph "Data Layer"
        PostgreSQL[(PostgreSQL<br/>• Task Storage<br/>• Analysis Results<br/>• Audit Logs)]
        Redis[(Redis<br/>• Message Queue<br/>• Caching<br/>• Session Store)]
    end

    Client --> FastAPI
    Browser --> FastAPI
    FastAPI --> Celery
    Celery --> LangGraph
    LangGraph --> LLM
    LLM --> OpenAI
    Celery --> GitHub
    FastAPI --> PostgreSQL
    Celery --> PostgreSQL
    FastAPI --> Redis
    Celery --> Redis
    LangGraph --> Redis

    classDef api fill:#e1f5fe
    classDef processing fill:#f3e5f5
    classDef storage fill:#e8f5e8
    classDef external fill:#fff3e0

    class FastAPI api
    class Celery,LangGraph,LLM processing
    class PostgreSQL,Redis storage
    class GitHub,OpenAI external
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🎯 Why This Architecture?

The Code Reviewer Agent is designed to handle pull requests of varying sizes while maintaining responsiveness and scalability.

Key Benefits

  • Non-blocking Analysis: PR reviews run asynchronously through Celery workers, preventing long-running requests from impacting API responsiveness.
  • Scalable Processing: Multiple workers can analyze different pull requests concurrently.
  • Intelligent File Prioritization: The AI triage stage focuses attention on high-impact files first, reducing analysis costs and improving review quality.
  • Persistent Results: Analysis reports are stored in PostgreSQL, allowing users to retrieve results even after task completion.
  • Fault Tolerance: Redis-backed queues ensure tasks can be retried and recovered if a worker fails.
  • Provider Flexibility: The architecture supports multiple LLM providers, reducing vendor lock-in and improving reliability.
  • Extensible Workflow: New analysis tools, detectors, and review stages can be added to the LangGraph workflow with minimal changes to the existing system.

Typical Analysis Lifecycle

  1. User submits a Pull Request URL and PR number.
  2. FastAPI validates the request and queues a background task.
  3. Celery workers fetch PR metadata, changed files (+ diffs), and existing comments from GitHub.
  4. An orchestrator agent triages the changed files and decides what to review directly vs. delegate.
  5. Sub-agents review delegated files in parallel; the orchestrator reviews the rest — all via tools (diffs, search, file ranges) and aware of the PR's existing comments.
  6. Findings are deduplicated (against existing comments and each other) and aggregated into a final report.
  7. Results are stored, and inline comments are posted back to the PR.

🤖 AI Agent Workflow

The reviewer uses an orchestrator–worker agent architecture. A single orchestrator agent is seeded only with the PR's intent and a manifest of the changed files (names + line counts) — not the file contents. It then drives the whole review by calling tools: reviewing trivial files itself, and delegating non-trivial files to dedicated sub-agents that run in parallel, each with its own isolated context window. The model decides what to review and what to delegate; the orchestration code executes those decisions.

Everything is served from data already fetched via the GitHub API for the PR — no repository cloning and no extra per-tool API calls.

graph TD
    Start([PR Analysis Request]) --> Fetch[Fetch PR metadata, existing<br/>comments, changed files + diffs]
    Fetch --> Triage[Triage: keep reviewable<br/>code files by language]
    Triage --> Orch

    subgraph ORCH["🧭 Orchestrator Agent — one LLM tool-loop"]
        Orch[Seeded with PR intent +<br/>file manifest only]
        Orch --> Decide{Per file:<br/>trivial or non-trivial?}
        Decide -->|trivial| Self[Review itself via<br/>get_file_diff / search_code /<br/>read_file_range]
        Decide -->|non-trivial| Deleg[spawn_file_reviewer]
    end

    Deleg -->|parallel, semaphore-bounded| SUB

    subgraph SUB["🔧 Sub-agents — one LLM tool-loop per file, isolated context"]
        S1[Sub-agent reviews ONE file<br/>data tools only, cannot delegate]
    end

    S1 -->|findings + summary| Merge
    Self --> Merge
    Merge[Consolidate + Dedup:<br/>drop should_report=false,<br/>guard vs existing comments,<br/>drop within-run duplicates]
    Merge --> Synth[Synthesize: counts +<br/>severity / type breakdown]
    Synth --> Save[Save to DB +<br/>post inline PR comments]
    Save --> Done([Analysis Complete])

    classDef agent fill:#e3f2fd
    classDef data fill:#e8f5e8
    class Orch,Self,Deleg,S1 agent
    class Merge,Synth,Save data
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Orchestrator & Sub-agents

Orchestrator Sub-agent
Count one per review one per delegated file (parallel)
Context window shared, sees the manifest + what it pulls isolated, scoped to its single file
Tools all data tools + spawn_file_reviewer data tools only (no delegation → one level deep)
Output its own direct findings findings for its file (returned to the orchestrator as a summary)
Round cap max(12, n_files + 8) tool-loops MAX_TOOL_ITERATIONS (default 4)

Each agent is a tool-calling loop: it calls tools → receives results → calls more → and finally emits structured findings validated by a Pydantic schema. Sub-agent findings and the orchestrator's direct findings are merged, deduped, and grouped per file.

🧰 Tools

All tools answer from the PR data already in memory (diffs + file contents + pre-fetched comments). spawn_file_reviewer is special — it launches a sub-agent rather than returning stored data, so only the orchestrator has it.

Tool Available to Parameters What it does
get_existing_comments orchestrator + sub-agent — Returns the review + conversation comments already on the PR (from humans and other bots), so findings can be deduplicated.
list_changed_files orchestrator + sub-agent — Lists every changed file with its +added/-deleted line counts.
get_file_diff orchestrator + sub-agent file_path: str Returns the unified diff (patch) of a changed file.
search_code orchestrator + sub-agent query: str, file_path?: str Case-insensitive substring search across the changed files' content (optionally scoped to one file); returns path:line: text matches.
read_file_range orchestrator + sub-agent file_path: str, start_line: int, end_line: int Returns a line-numbered slice of a file's content (capped at 100 lines per call).
spawn_file_reviewer orchestrator only file_path: str Delegates a whole file to a dedicated sub-agent with its own context window. Runs in parallel; returns a short summary of what the sub-agent found.

🔁 Deduplication & the skip mechanism

Because the agent can read the PR's existing comments, every finding carries a should_report flag and a skip_reason, and duplicates are removed in two layers:

  1. Model judgment (prompt-driven): for each finding the model sets should_report = false with a skip_reason of duplicate, low_confidence, nitpick, or out_of_scope — e.g. if the same issue was already raised by a human or another bot (CodeRabbit / Copilot / Sourcery), or in a previous run.
  2. Deterministic guard (_consolidate): a code-level backstop drops any finding the model marked skip, plus anything that lands on (or within a few lines of) an existing comment, plus within-run duplicates. Every drop is logged with its reason.

Only the surviving findings are saved and posted — so re-running the review never re-posts comments that already exist.

🎛️ Caps (keep the model-driven loops bounded)

  • Orchestrator rounds: max(12, n_files + 8) — scales with PR size.
  • Sub-agent rounds: MAX_TOOL_ITERATIONS (default 4).
  • One-level delegation: sub-agents have no spawn_file_reviewer, so they cannot recurse.
  • Parallel sub-agents: bounded by max_concurrent_analyses (semaphore).
  • read_file_range: ≤ 100 lines per call. LLM requests: 90s timeout.

🛠️ Technology Stack

Backend Framework

  • FastAPI - Modern async web framework
  • SQLModel - Type-safe database ORM combining Pydantic and SQLAlchemy
  • Celery - Distributed task queue with Redis broker for async processing
  • Redis - High-performance caching and message broker
  • PostgreSQL - Robust relational database with JSON support
  • UV - Lightning-fast Python package manager and dependency resolver

AI & Analysis Engine

  • LangGraph - Advanced AI workflow orchestration with state management
  • OpenAI/Pollinations - Multiple LLM provider support for code analysis
  • PyGithub - Comprehensive GitHub API integration with rate limiting
  • Instructor - Structured LLM output validation with Pydantic models
  • Custom Analysis Tools - Specialized code analysis utilities and detectors

Development & Infrastructure

  • Docker Compose - Containerized development environment
  • Alembic - Database schema migrations with version control
  • Loguru - Advanced structured logging with rotation and filtering
  • Pytest - Comprehensive testing framework with async support
  • Ruff - High-performance Python linting and formatting

Frontend

  • React 19 - Modern UI library with hooks and concurrent features
  • TypeScript - Type-safe JavaScript with static analysis
  • Vite - Fast development server and optimized production builds
  • Tailwind CSS v4 - Utility-first CSS with CSS-native configuration
  • SWR - Stale-while-revalidate data fetching with polling support

🚀 Quick Start

Prerequisites

Ensure you have the following installed on your system:

  • Python 3.13+
  • Docker & Docker Compose (For infrastructure services)
  • UV Package Manager
  • Git (For version control)

For the optional web UI:

  • Node.js 20+
  • npm (comes with Node.js)

Optional

  • GitHub Personal Access Token (Create here) - For private repositories and higher rate limits
  • OpenAI API Key (Get yours here) - For using GPT instead of pollinations.ai

1. Clone & Environment Setup

# Clone the repository
git clone https://github.com/RJSIN147/Code-Review-Copilot
cd code-review-agent

# Install all dependencies with UV
uv sync

# Copy environment template and configure
cp .env.example .env

2. Environment Configuration

Edit your .env file with appropriate values:

# Database Configuration (Docker services)
DATABASE_URL=postgresql://postgres:postgres@localhost:5433/code_review
REDIS_URL=redis://localhost:6379/0

# Celery Task Queue
CELERY_BROKER_URL=redis://localhost:6379/0
CELERY_RESULT_BACKEND=redis://localhost:6379/0

# GitHub Integration (Optional but recommended)
GITHUB_TOKEN=ghp_your_github_token_here

# AI Analysis (Choose your provider)
OPENAI_API_KEY=sk-your_openai_api_key_here

# Security (Generate secure keys)
SECRET_KEY=your-secure-secret-key-here
API_KEY=your-api-authentication-key

# Environment
ENVIRONMENT=development  # development, staging, production

🔐 Security Key Generation

Generate secure keys for production:

# Generate SECRET_KEY
python -c "import secrets; print(f'SECRET_KEY={secrets.token_urlsafe(32)}')"

# Generate API_KEY
python -c "import secrets; print(f'API_KEY={secrets.token_urlsafe(16)}')"

3. Infrastructure Services

Start the required infrastructure services:

# Start whole infra
docker-compose up

# Verify services are running
docker-compose ps

Wait for services to be healthy, then run database migrations:

# Initialize database schema
uv run alembic upgrade head

Test the API

Submit PR Analysis

POST /api/v1/analyze-pr

Submit a GitHub Pull Request for comprehensive analysis.

Request Body:

{
  "repo_url": "https://github.com/owner/repository",
  "pr_number": 123,
  "github_token": "ghp_optional_token_for_private_repos"
}

Response (202 Accepted):

{
  "task_id": "550e8400-e29b-41d4-a716-446655440000",
  "status": "pending",
  "message": "Analysis task queued successfully",
  "estimated_duration": "2-5 minutes"
}

cURL Example:

curl -X POST "http://localhost:8000/api/v1/analyze-pr" \
  -H "Content-Type: application/json" \
  -d '{
    "repo_url": "https://github.com/owner/repo",
    "pr_number": 123,
    "github_token": "optional_token"
  }'

Check Analysis Status

GET /api/v1/status/{task_id}

Monitor the progress of an analysis task.

Response Examples:

{
  "task_id": "uuid-task-id",
  "status": "pending",
  "message": "Analysis task queued successfully"
}

CURL Example:

curl "http://localhost:8000/api/v1/status/uuid-task-id"

List Analysis Tasks

GET /api/v1/tasks

List recent analysis tasks with pagination and optional status filtering.

Query Parameters:

  • limit (int, default 20) - Maximum number of tasks to return
  • offset (int, default 0) - Number of tasks to skip
  • status_filter (string, optional) - Filter by status (pending, processing, completed, failed, cancelled)

Response (200 OK):

{
  "tasks": [
    {
      "task_id": "uuid-task-id",
      "status": "completed",
      "repo_url": "https://github.com/owner/repo",
      "pr_number": 123,
      "progress": 100.0,
      "created_at": "2025-09-17T12:01:17.308745"
    }
  ],
  "total_count": 1,
  "has_more": false
}

cURL Example:

curl "http://localhost:8000/api/v1/tasks?limit=10&offset=0&status_filter=completed"

Cancel Analysis Task

DELETE /api/v1/tasks/{task_id}

Cancel a running or pending analysis task.

Request Body (optional):

{
  "reason": "User requested cancellation"
}

Response:

{
  "task_id": "uuid-task-id",
  "status": "cancelled",
  "message": "Task cancelled successfully"
}

cURL Example:

curl -X DELETE "http://localhost:8000/api/v1/tasks/uuid-task-id" \
  -H "Content-Type: application/json" \
  -d '{"reason": "User requested cancellation"}'

Get Analysis Results

GET /api/v1/results/{task_id}

Response (200 OK):

{
  "task_id": "uuid-task-id",
  "status": "completed",
  "progress": 100.0,
  "files": [
    {
      "name": "main.py",
      "path": "src/main.py",
      "language": "python",
      "size": 2048,
      "issues": [
        {
          "type": "security",
          "severity": "high",
          "line": 42,
          "description": "Potential SQL injection vulnerability.",
          "suggestion": "Use parameterized queries.",
          "confidence": 0.95
        }
      ]
    }
  ],
    "summary": {
        "total_files": 1,
        "total_issues": 1,
        "critical_issues": 0,
        "high_issues": 1,
        "medium_issues": 0,
        "low_issues": 0,
        "style_issues": 0,
        "bug_issues": 0,
        "performance_issues": 0,
        "security_issues": 0,
        "maintainability_issues": 0,
        "best_practice_issues": 0,
        "code_quality_score": 0.0,
        "maintainability_score": 0.0
    },
    "created_at": "2025-09-17T12:01:17.308745",
    "started_at": "2025-09-17T12:01:17.940944",
    "completed_at": "2025-09-17T12:01:40.288990",
    "analysis_duration": 22.348046,
    "error_message": null
}

CURL Example:

curl "http://localhost:8000/api/v1/results/uuid-task-id"

🎨 Frontend Development

A React-based web UI is available in the frontend/ directory for interactively submitting PRs, monitoring tasks, and viewing analysis results.

Setup

# Install frontend dependencies
make frontend-install

# Or run directly in the frontend folder
cd frontend && npm install

Running the Dev Server

# Start the frontend on http://localhost:3000
make frontend-dev

The Vite dev server proxies /api/* requests to http://localhost:8000, so the backend must be running.

Building for Production

make frontend-build

Verifying the API Contract

A curl-based verification script covers the main API flows used by the frontend:

bash frontend/scripts/verify.sh

Expected output: all checks pass (health, validation error, submit, list, status, cancel).

Frontend Project Structure

frontend/
├── public/              # Static assets
├── scripts/
│   └── verify.sh        # API contract verification script
├── src/
│   ├── components/      # UI components (forms, lists, cards, panels)
│   ├── components/ui/   # Reusable primitives (Button, Input, Badge, etc.)
│   ├── hooks/           # SWR data-fetching hooks
│   ├── services/        # Typed API client
│   ├── types/           # TypeScript API types
│   ├── App.tsx          # Dashboard layout
│   └── main.tsx         # Application entry point
├── index.html
├── package.json
├── tsconfig.json
└── vite.config.ts

🌐 Live Testing

The Code Reviewer Agent is deployed and available for live testing at https://code-review.spoo.me.

Quick Start with Sample PR

Try the service using our sample PR from the URL shortener project:

1. Start Analysis Task

Submit a PR for analysis:

curl -X POST "https://code-review.spoo.me/api/v1/analyze-pr" \
  -H "Content-Type: application/json" \
  -d '{
    "repo_url": "https://github.com/spoo-me/url-shortener",
    "pr_number": 79,
    "github_token": "ghp_your_github_token_here"
  }'

💡 Tip: It's highly recommended to pass your own GitHub access token in the request. This provides more relaxed rate limits and ensures better service reliability, especially for private repositories or when the public rate limit is exhausted.

Expected Response:

{
  "task_id": "uuid-task-id",
  "status": "pending",
  "message": "Analysis task queued successfully",
  "estimated_duration": "2-5 minutes"
}

2. Check Analysis Status

Monitor your task progress using the returned Task ID:

curl "https://code-review.spoo.me/api/v1/status/uuid-task-id"

Response Examples:

// Initial status
{
  "task_id": "uuid-task-id",
  "status": "pending",
  "message": "Analysis task queued successfully"
}

// In progress
{
  "task_id": "uuid-task-id",
  "status": "in_progress",
  "progress": 45.0,
  "message": "Analyzing file: src/components/Dashboard.tsx"
}

// Completed
{
  "task_id": "uuid-task-id",
  "status": "completed", 
  "progress": 100.0,
  "message": "Analysis completed successfully"
}

3. Get Analysis Results

Retrieve the comprehensive analysis report using the Task ID:

curl "https://code-review.spoo.me/api/v1/results/uuid-task-id"

Example Response:

{
  "task_id": "uuid-task-id",
  "status": "completed",
  "progress": 100.0,
  "files": [
    {
      "name": "main.py",
      "path": "src/main.py",
      "language": "python",
      "size": 2048,
      "issues": [
        {
          "type": "security",
          "severity": "high",
          "line": 42,
          "description": "Potential SQL injection vulnerability.",
          "suggestion": "Use parameterized queries.",
          "confidence": 0.95
        }
      ]
    }
  ],
    "summary": {
        "total_files": 1,
        "total_issues": 1,
        "critical_issues": 0,
        "high_issues": 1,
        "medium_issues": 0,
        "low_issues": 0,
        "style_issues": 0,
        "bug_issues": 0,
        "performance_issues": 0,
        "security_issues": 0,
        "maintainability_issues": 0,
        "best_practice_issues": 0,
        "code_quality_score": 0.0,
        "maintainability_score": 0.0
    },
    "created_at": "2025-09-17T12:01:17.308745",
    "started_at": "2025-09-17T12:01:17.940944",
    "completed_at": "2025-09-17T12:01:40.288990",
    "analysis_duration": 22.348046,
    "error_message": null
}

Additional API Endpoints

Cancel Running Task

curl -X POST "https://code-review.spoo.me/api/v1/cancel/uuid-task-id"

Analyze Your Own PR

Replace the sample values with your repository details:

curl -X POST "https://code-review.spoo.me/api/v1/analyze-pr" \
  -H "Content-Type: application/json" \
  -d '{
    "repo_url": "https://github.com/your-username/your-repo",
    "pr_number": <your-pr-number>,
    "github_token": "ghp_your_github_token_here"
  }'

GitHub Token Setup

To get your GitHub personal access token:

  1. Go to GitHub Settings > Developer settings > Personal access tokens
  2. Click "Generate new token (classic)"
  3. Select scopes: repo (for private repos) or public_repo (for public repos only)
  4. Copy the generated token and use it in the github_token field

🧪 Development & Testing

Testing Infrastructure

The project maintains a comprehensive test suite with multiple test categories:

# Run full test suite
uv run pytest

# Run with coverage reporting
uv run pytest --cov=app --cov-report=html --cov-report=term-missing

Code Quality Tools

# Code formatting with Ruff
uvx ruff format

# Linting and style checks
uvx ruff check
uvx ruff check --fix      # Auto-fix issues

Database Testing

# Create migration
uv run alembic revision --autogenerate -m "description"

# Apply migrations
uv run alembic upgrade head

# Rollback migration
uv run alembic downgrade -1

🏗️ Design Decisions & Architecture

Core Technology Choices

UV vs. pip/poetry

  • Why UV: 10-100x faster dependency resolution and installation
  • Benefits: Unified tool for dependencies, virtual environments, and Python versions

SQLModel vs. SQLAlchemy

  • Why SQLModel: Type-safe ORM with Pydantic integration
  • Benefits: Automatic API serialization, unified data models
  • Trade-offs: Less mature than pure SQLAlchemy
  • Performance: Comparable to SQLAlchemy with better developer experience

Celery vs. ARQ/TaskIQ

  • Current: Celery for mature ecosystem and Redis integration
  • Benefits: Battle-tested, extensive monitoring, complex workflows, task cancellation support
  • Trade-offs: Heavier weight, not async-native
  • Future: Migration to ARQ planned (see Future Improvements)

LangGraph vs. LangChain

  • Why LangGraph: State-based AI workflows
  • Benefits: Visual workflow representation, cyclic graph support
  • Trade-offs: Newer, smaller ecosystem
  • Use Case: Perfect for multi-step code analysis workflows

🚀 Future Improvements

  • Fully async Task Queue using Arq/TaskIQ
  • Fully async redis client for github repo caching using async-redis
  • Better tools for more robust code analysis
  • Direct Github PR comment bot

📂 Project Structure

code_reviewer_agent/
├── app/                 # FastAPI backend
│   ├── agents/          # AI workflow logic
│   │   ├── ai_workflow.py
│   │   ├── analyzer.py
│   │   └── tools/       # Analysis tools
│   ├── api/             # FastAPI routes
│   │   └── v1/endpoints/
│   ├── config/          # Configuration management
│   ├── models/          # Database & API models
│   ├── services/        # Business logic
│   │   ├── github.py
│   │   └── llm_service.py
│   ├── tasks/           # Celery tasks
│   └── utils/           # Utilities
├── frontend/            # React + TypeScript web UI
│   ├── scripts/         # API verification scripts
│   ├── src/             # Components, hooks, types, API client
│   ├── index.html
│   └── package.json
├── tests/               # Test suite
│   ├── fixtures/        # Test fixtures
│   ├── integration/     # Integration tests
│   └── unit/           # Unit tests
├── migrations/          # Database migrations
├── Makefile            # Convenience commands (frontend-dev, etc.)
└── docs/               # Documentation

🔍 Analysis Features

Code Issues Detected

  • 🔒 Security vulnerabilities
  • 🐛 Potential bugs
  • ⚡ Performance problems
  • 🎨 Style violations
  • 🔧 Maintainability concerns

AI Capabilities

  • Context-aware analysis
  • Intelligent prioritization
  • Detailed explanations
  • Fix suggestions

🤝 Contributing

  1. Fork the repository
  2. Create a feature branch (git checkout -b feature/amazing-feature)
  3. Commit your changes (git commit -m 'Add amazing feature')
  4. Push to the branch (git push origin feature/amazing-feature)
  5. Open a Pull Request

🛠️ Development Guidelines

  • Write tests for new features
  • Follow existing code style
  • Update documentation
  • Ensure all tests pass

📊 Monitoring

Logs

  • Application logs: logs/app.log
  • Structured JSON logging with Loguru
  • Configurable log levels

🙏 Acknowledgments

  • FastAPI for the excellent async framework
  • LangGraph for AI workflow orchestration
  • OpenAI for language model capabilities
  • GitHub for comprehensive API access

© zingzy . 2025

All Rights Reserved

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