FastCode: Up to 4× Faster and 44% Cheaper Than Claude Code for Codebase Understanding

FastCode Overview

FastCode is an open‑source framework for code‑base understanding and analysis. It first constructs a semantic map and a structural relationship graph of a repository, then loads only the relevant code before invoking a large language model, providing precise answers with reduced token usage.

Core Highlights

Speed – Benchmarks show FastCode is about 3× faster than Cursor and about 4× faster than Claude Code.

Cost – Token consumption is roughly 55% lower than Cursor and 44% lower than Claude Code.

Token efficiency – In the best cases FastCode saves up to 10× tokens.

Accuracy – Leads on benchmarks such as SWE‑QA and LongCodeQA.

Scouting‑First Approach

The framework follows a “Scouting‑First” strategy: first explore the terrain (build indexes), then strike precisely.

1. Build indexes before loading

Question → Build semantic map → Structure navigation → Precisely load target code → Answer

FastCode creates layered AST indexes (file → class → function → documentation) and uses a hybrid search (semantic vectors + BM25) to locate relevant code, avoiding repeated full‑file loading.

2. Multi‑layer code understanding

Hierarchical index – Indexes are built from files, classes, functions, and documentation.

Hybrid retrieval – Combines semantic similarity with keyword matching (BM25).

Relation graphs – Generates call, dependency, and inheritance graphs to guide the AI.

3. Smart skimming instead of full reading

Only titles—function names, class definitions, signatures—are fed to the model, dramatically reducing token usage.

4. Budget‑aware decision making

The system evaluates query complexity, repository size, and confidence level to decide whether deeper exploration is needed, avoiding unnecessary work.

Supported Scenarios

Web UI – Run python web_app.py --host 0.0.0.0 --port 5000, open http://localhost:5000, load a repository, and ask natural‑language questions.

CLI & REST API – Suitable for automation scripts and CI pipelines; supports multi‑repo queries.

MCP service – Integrates with Cursor, Claude Code, Windsurf via the Model Context Protocol (MCP).

Feishu bot – Teams can @ a bot in Feishu groups to query code.

Quick Start

Requirements: Python 3.12+ and Git.

# 1. Clone the project
git clone https://github.com/HKUDS/FastCode.git
cd FastCode

# 2. Install dependencies
pip install -r requirements.txt

# 3. Configure API key
cp env.example .env   # edit .env to set OPENAI_API_KEY, MODEL, BASE_URL

# 4. Launch the web interface
python web_app.py --host 0.0.0.0 --port 5000

After starting, open http://localhost:5000 in a browser, load your codebase, and begin asking questions.

Integration Example for Cursor MCP

{
  "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-..."
      }
    }
  }
}

The project also works with OpenRouter, Ollama, and local models such as qwen3‑coder‑30b.

Project Repository

https://github.com/HKUDS/FastCode