🤖 AI-Agent

A professional code architect assistant built with Model Context Protocol (MCP) and LangGraph. It bridges Large Language Models (LLMs) with local file systems and remote services through standardized workflows.

🌟 Core Modes

This project provides two core interaction modes to adapt to different LLM capabilities:

1. 🚀 Standard Agent (sagent)

Best for: Modern models that support native tools parameters (e.g., GPT-4o, Claude 3.5, Qwen-Max).

• Native Function Calling: Leverages the model's built-in tool-calling capabilities for precise parameter parsing and low latency.
• Intuitive Interaction: A classic Chat loop suitable for instant Q&A, quick code lookups, or single-file refactoring tasks.

2. 🏗️ Graph Agent (sagent-graph)

Best for: Long-running automated audits or scenarios where the API does not support native tools.

• State Machine Architecture: Built on LangGraph to implement an automated "Think-Act-Observe-Summarize" loop.
• Audit Tracking: Built-in progress manager that automatically records analyzed files to prevent redundant analysis in complex projects.
• Auto-Settlement: Automatically summarizes and prints Total Tokens and execution duration upon task completion or manual exit.

🛠️ Built-in Toolset

The Agent comes pre-installed with a suite of tools specifically customized for code architecture analysis:

| Category | Tool Name | Description | | ---------------- | ----------------- | ------------------------------------------------------------------------- | | | list_directory | Lists files in a specified directory. | | | analyze_deps | Analyzes import dependencies of a specific file. | | | read_skeleton | Reads only code skeletons (class names/signatures) to save tokens. | | | edit_file | Precise Diff-based editing to avoid rewriting large files. | | | get_method_body | Precisely extracts the implementation of a specific method. | | | get_file_info | Gets file metadata like size, permissions, and timestamps. | | Architecture | get_repo_map | Core: Extracts export definitions and builds a module dependency map. | | General | search_files | Performs a global keyword search across the project. | | Navigation | directory_tree | Recursively gets the project structure (The entry point for analysis). | | Operations | read_text_file | Reads file content (supports pagination via head/tail). |

💻 CLI Guide

Installation & Build

yarn
yarn build

Usage

sudo npm i -g @saber2pr/ai-agent

• Start Standard Chat:

sagent

• Start Automated Audit:

sagent-graph

⚙️ Configuration

On the first run, the program will prompt you to configure ~/.saber2pr-agent.json. You can define your API keys and dynamically connect MCP Servers here:

{
  "baseURL": "https://api.example.com/v1",
  "apiKey": "sk-your-key",
  "model": "gpt-4o"
}

🔄 Professional Audit Workflow

Regardless of the mode, the Agent follows a standardized logic chain:

1. Phase 1: Panoramic Perception (Where): Uses directory_tree to identify project layout (e.g., Monorepo vs. traditional src structure).
2. Phase 2: Logic Mapping (What): Uses get_repo_map to establish logical relationships—understanding "who calls whom."
3. Phase 3: Source Diving (How): Locates critical implementations and performs detailed auditing via read_text_file or specific extraction tools.

📄 License

ISC