🤖 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.

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🛠️ 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).

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💻 CLI Guide

Installation & Build

yarn
yarn build

Usage

sudo npm i -g @saber2pr/ai-agent

• Start Standard Chat:

sagent

• Start Automated Audit:

sagent-graph

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⚙️ 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"
}

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🔄 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.

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📄 License

ISC