System Architecture

Core Components

Key Architectural Decisions:

1. Clear command line usage - Easily fit into workflows and agents as a command line program
2. Contextual Safety - File isolation during patching
3. Pluggable LLMs - Model-agnostic through AI Agent Toolbox
4. Easy API - A simple API to use in agent loops

1. Prompt Engine

Integrates with AI Agent Toolbox for reliable parsing

# Uses Toolbox for structured output
toolbox = create_toolbox()
toolbox.add_tool("diff", diff_handler)

2. Diff Generator

• Whole-codebase awareness by default
• Context-aware change proposals
• Git-compatible output format

Generation Process: 1. Environment snapshot creation 2. LLM makes diff of environment 3. Diff is applied 4. Validation is run

3. SmartApply System

graph LR
    A[Raw Diff] --> B(Per-File Split)
    B --> C{File Exists?}
    C -->|Yes| D[Patch Application]
    C -->|No| E[New File Creation]
    D --> F{Apply Success?}
    F -->|Yes| G[Commit Change]
    F -->|No| H[LLM Reconciliation]
    H --> I[Contextual Merge]
    I --> G

Conflict Resolution Logic: - Preserves surrounding code context - Maintains line ending/encoding consistency - Atomic file operations

AI Agent Toolbox Integration

Key Components: - FlatXMLParser: Ensures structured diff output - Toolbox: Manages diff generation and validation

Key Integration Points

1. Structured Output Parsing python parser = FlatXMLParser("diff") formatter = FlatXMLPromptFormatter(tag="diff")
2. Tool Definition python toolbox.add_tool(name="diff", fn=diff_handler, ...)
3. Error-Resistant Parsing - Handles malformed LLM responses