How to Use GPT‑Academic Code Analysis Features for Python, C, C++, and Java Projects

GPT‑Academic provides a plugin‑based code analysis engine that scans entire source trees using language‑specific glob patterns, processes files in parallel through an LLM, and synthesizes a markdown report with a Mermaid architecture diagram.

The binary-husky/gpt_academic repository ships with a sophisticated code analysis pipeline capable of parsing multi‑language projects automatically. By invoking the core driver 解析源代码新 and its language‑specific wrappers, you can generate comprehensive architectural summaries without manually inspecting each file.

How the Code Analysis Engine Works

The analysis pipeline centers on a generic driver implemented in crazy_functions/SourceCode_Analyse.py. According to the source code, the engine operates through four distinct phases:

1. Manifest Generation – Language wrappers collect files using glob.glob patterns (e.g., **/*.py for Python).
2. Parallel Processing – Each file is sent to the LLM via request_gpt_model_multi_threads_with_very_awesome_ui_and_high_efficiency in crazy_functions/crazy_utils.py (lines 187‑240), maintaining UI responsiveness during large scans.
3. Aggregation – Per‑file summaries are batched (default size = 16) for a second‑stage synthesis.
4. Report Generation – A final markdown document and Mermaid diagram are produced via make_diagram and returned to the chat interface.

Security is enforced by validate_path_safety in shared_utils/fastapi_server.py (lines 3‑12), which blocks path‑traversal attacks before directory access.

Analyzing Python Projects

Using the Python Project Plugin

To analyze a Python codebase, invoke the dedicated wrapper that targets .py files recursively.

插件: 解析一个Python项目
参数: /absolute/path/to/your/python/project

Internally, the wrapper function defined in crazy_functions/SourceCode_Analyse.py (lines 26‑42) validates the path and constructs a manifest of all **/*.py files. It then delegates to the core driver 解析源代码新 (lines 6‑106), which orchestrates the parallel analysis.

Analyzing C and C++ Projects

Full C/C++ Analysis

The C project plugin captures implementation files alongside headers, covering .c, .cpp, .h, and .hpp extensions.

插件: 解析一个C项目
参数: /absolute/path/to/your/c/project

As implemented in lines 84‑100 of crazy_functions/SourceCode_Analyse.py, this wrapper builds a comprehensive manifest suitable for mixed C/C++ codebases before invoking the generic analysis coroutine.

Header‑Only Analysis

For projects where logic resides entirely in header files (templates, inline libraries), use the header‑specific plugin.

插件: 解析一个C项目的头文件
参数: /absolute/path/to/your/header/collection

This wrapper (lines 64‑78) restricts the glob pattern to .h and .hpp files, ensuring the LLM focuses on interface definitions rather than implementation details.

Analyzing Java Projects

Java analysis covers source files alongside build configuration artifacts.

插件: 解析一个Java项目
参数: /absolute/path/to/your/java/project

According to lines 108‑122 in crazy_functions/SourceCode_Analyse.py, the Java wrapper collects .java, .jar, .xml, and .sh files, providing the LLM with context about both source code and project structure.

Custom File Patterns with 解析任意code项目

When built‑in plugins do not match your directory structure, use the flexible 解析任意code项目 plugin to define custom glob patterns.

插件: 解析任意code项目
参数: /path/to/project
高级参数 (advanced_arg): *.c, *.cpp, *.py

The implementation in lines 48‑76 parses the advanced_arg string into include/exclude regexes, constructs a filtered manifest, and passes it to the core driver. This approach supports niche languages or specific directory filtering without modifying source code.

Understanding the Multi‑Stage Pipeline

Stage 1: File‑Level Summarization

Each file in the manifest is processed individually. The driver sends a prompt requesting an overview (“请对下面的程序文件做一个概述 …”) to the configured LLM endpoint (OpenAI, Claude, or local models as defined in config.py).

Stage 2: Architectural Synthesis

After collecting individual summaries, the driver executes a second pass that groups results into batches. This aggregation step produces a coherent architectural description rather than a simple concatenation of file notes.

Stage 3: Visualization

The make_diagram helper (referenced in lines 108‑111 of the source) generates a Mermaid file‑tree diagram illustrating module relationships. The final output includes both this diagram and a downloadable HTML/MD report.

Summary

• GPT‑Academic provides dedicated plugins for Python, C/C++, and Java that automate codebase analysis through the 解析源代码新 driver in crazy_functions/SourceCode_Analyse.py.
• The system uses parallel threading via request_gpt_model_multi_threads_with_very_awesome_ui_and_high_efficiency to maintain performance on large projects.
• Security validation occurs through validate_path_safety in shared_utils/fastapi_server.py before any filesystem access.
• Custom patterns are supported via 解析任意code项目, allowing analysis of any language by specifying glob patterns in the advanced_arg parameter.
• Output includes per‑file summaries, an architectural overview, and a Mermaid diagram synthesizing the project structure.

Frequently Asked Questions

What file patterns does each language plugin use?

The Python plugin targets **/*.py. The C/C++ plugin includes .c, .cpp, .h, and .hpp. The C headers‑only variant restricts this to .h and .hpp. The Java plugin captures .java, .jar, .xml, and .sh. These patterns are hardcoded in their respective wrapper functions within crazy_functions/SourceCode_Analyse.py.

How does GPT‑Academic handle large codebases?

The engine processes files concurrently using request_gpt_model_multi_threads_with_very_awesome_ui_and_high_efficiency, which spawns multiple threads while keeping the UI responsive. During the aggregation phase, it batches files (default 16 per batch) to generate the final architectural summary without overwhelming the LLM context window.

Is it safe to analyze code from arbitrary directories?

Yes. Before accessing any path, the system invokes validate_path_safety from shared_utils/fastapi_server.py (lines 3‑12) to prevent path‑traversal attacks. This ensures the application cannot access files outside the intended directory scope.

Can I add support for other languages?

You can analyze any language immediately using the 解析任意code项目 plugin by specifying appropriate glob patterns (e.g., *.rs for Rust or *.go for Go) in the advanced_arg field. For a permanent plugin, you would create a new wrapper function similar to 解析一个Python项目 that calls the generic 解析源代码新 driver with your specific file pattern.

{
  "mcpServers": {
    "instagit": {
      "command": "npx",
      "args": ["-y", "instagit@latest"]
    }
  }
}