FastCode Beats Claude Code: 3× Faster and 50% Cheaper Codebase Understanding

FastCode is an open‑source code‑base understanding framework from the Hong Kong University Data Science Lab. It does not generate code; instead, it enables large language models (LLMs) to read code repositories quickly and cheaply, supporting Python, JavaScript, TypeScript, Java, Go, Rust, C/C++, and C#.

The tool’s cost‑saving logic differs from Cursor’s approach. Cursor repeatedly loads entire files, searches, and reloads, burning many tokens on large codebases. FastCode first builds a hierarchical map of files, classes, functions, and documents, then creates three relationship graphs (call, dependency, inheritance) and a hybrid BM25‑vector index, allowing it to query only the most relevant paths and limit traversal to two call‑chain levels. A dynamic “cost awareness” mechanism decides whether further reading is worthwhile based on query complexity, repository size, and tokens already spent.

Benchmark results on the same test set show FastCode completing the task in 1 hour 33 minutes, while Cursor took 3 hours 21 minutes and Claude Code 5 hours 45 minutes. The monetary cost was $0.032 for FastCode, $0.071 for Cursor, and $0.057 for Claude Code, making FastCode three times faster and roughly half as expensive.

Key capabilities include:

Reading codebases in eight languages with AST parsing and per‑unit indexing.

Analyzing multiple repositories together to reveal cross‑repo dependencies, useful for micro‑service architectures.

Intelligent structural navigation that shows only headlines (function signatures, class definitions) before deep‑reading the implementation.

Running on local models such as qwen3‑coder‑30b via Ollama, eliminating cloud token costs.

Providing an MCP server so editors like Cursor, Claude Code, and Codex can call FastCode directly.

Web UI for visual exploration and a REST API (api.py) for workflow integration.

A Feishu bot that automatically clones, indexes, and answers questions from a shared channel.

Practical demo using Codex:

git clone https://github.com/HKUDS/FastCode.git
cd FastCode
pip install uv
uv venv --python=3.12
source .venv/bin/activate
uv pip install -r requirements.txt

After a two‑minute installation, the Web UI is started: python web_app.py --host 0.0.0.0 --port 5777 The user loads the macrozheng/mall Java e‑commerce project via the UI, which clones, parses, and indexes the repository in about two to three minutes.

First query – authentication flow – prompts FastCode to locate files such as UserDetailsService, JWT utilities, and security configuration, returning that the project uses stateless JWT authentication.

Second query – order module dependencies – triggers FastCode to traverse the dependency graph, identifying OmsOrderService, OmsPortalOrderService, OmsOrderDetail, and related payment callbacks, while reading only seven or eight relevant files instead of the entire module.

Third query – cross‑repo analysis – loads both the front‑end mall-admin-web and back‑end mall repositories. FastCode matches the front‑end login API path to the corresponding back‑end controller, presenting the full front‑back link.

FastCode also offers a CLI for direct queries:

python main.py query --repo-path ./repos/mall --query "用户权限是怎么设计的？"

The terminal output includes file references and token‑consumption statistics.

For editor integration, FastCode runs as an MCP server. Adding the following JSON to ~/.cursor/mcp.json registers it for Cursor:

{
  "mcpServers": {
    "fastcode": {
      "command": "/path/to/FastCode/.venv/bin/python",
      "args": ["/path/to/FastCode/mcp_server.py"],
      "env": {
        "MODEL": "gpt-4",
        "BASE_URL": "https://api.openai.com/v1",
        "OPENAI_API_KEY": "sk-..."
      }
    }
  }
}

After restarting Cursor, a query like “Use FastCode to analyze the authentication flow of the mall project” is handled by FastCode, returning results with file references and preserving session context for follow‑up questions.

Claude Code can be linked similarly with a single command.

In the author’s view, FastCode’s Web UI is less polished than Cursor’s, but its core functionality is robust. It processes the same question three times faster and reduces token usage by about 90 %, making it a valuable companion for reading large codebases while keeping the editor workflow intact.

The project is open‑source under the MIT license and can be cloned from:

https://github.com/HKUDS/FastCode