HKUDS/FastCode

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<div align="center"> <img src="assets/FastCode.svg" alt="FastCode Logo" width="200"/>  ### FastCode: Accelerating and Streamlining Your Code Understanding | **⚡ High Performance** | **💰 Cost Efficient** | **🚀 Fast & Scalable** | <a href="https://trendshift.io/repositories/21932?utm_source=trendshift-badge&utm_medium=badge&utm_campaign=badge-trendshift-21932" target="_blank" rel="noopener noreferrer"><img src="https://trendshift.io/api/badge/trendshift/repositories/21932/daily?language=Python" alt="HKUDS%2FFastCode | Trendshift" width="250" height="55"/></a> <a href="https://trendshift.io/repositories/21932?utm_source=trendshift-badge&utm_medium=badge&utm_campaign=badge-trendshift-21932" target="_blank" rel="noopener noreferrer"><img src="https://trendshift.io/api/badge/trendshift/repositories/21932/daily" alt="HKUDS%2FFastCode | Trendshift" width="250" height="55"/></a> [![Python 3.12+](https://img.shields.io/badge/python-3.12+-blue.svg)](https://www.python.org/downloads/) [![License](https://img.shields.io/badge/license-MIT-green.svg)](LICENSE) [![Code style: black](https://img.shields.io/badge/code%20style-black-000000.svg)](https://github.com/psf/black) <p> <a href="https://github.com/HKUDS/FastCode/issues/1"><img src="https://img.shields.io/badge/💬WeChat-Group-07c160?style=for-the-badge&logo=wechat&logoColor=white&labelColor=1a1a2e"></a> <a href="https://github.com/HKUDS/FastCode/issues/2"><img src="https://img.shields.io/badge/💬Feishu-Group-3370ff?style=for-the-badge&logo=bytedance&logoColor=white&labelColor=1a1a2e"></a> </p> [Features](#-why-fastcode) • [Quick Start](#-quick-start) • [Installation](#-installation) • [MCP Server](#mcp-server-use-in-cursor--claude-code--windsurf) • [Documentation](#-how-it-works) </div> --- ## 🎯 Why FastCode? FastCode is a token-efficient framework for comprehensive code understanding and analysis: delivering **superior speed**, **exceptional accuracy**, and **cost-effectiveness** for large-scale codebases and software architectures. 🚀 **Superior Speed Advantage** - Runs 3x faster than Cursor and 4x faster than Claude Code. 💰 **Significant Cost Savings** - Costs 55% less than Cursor and 44% less than Claude Code. ⚡ **Highest Accuracy** - Outperforms Cursor and Claude Code with the highest accuracy score. <div align="center"> <img src="assets/performance.png" alt="FastCode Performance vs Cost" width="850"/> </div> --- ## Key Features of FastCode ### 🎯 Core Performance Advantages - 2-4x Faster than competitors (Cursor/Claude Code) - 44-55% Cost Reduction compared to alternatives - Highest Accuracy Score across benchmarks - Up to 10x Token Savings through smart navigation ### 🛠️ Technical Capabilities - Large-Scale Repository Analysis - Handle massive codebases efficiently - Multi-Language Support - Python, JavaScript, TypeScript, Java, Go, C/C++, Rust, C# - Multi-Repository Reasoning - Cross-repo dependency analysis - Small Model Support - Local model compatibility (qwen3-coder-30b) ### 💻 User Experience - **MCP Server** - Use FastCode directly through MCP integration (e.g., Cursor, Claude Code) - Beautiful Web UI - Intuitive codebase exploration - Flexible API - Easy workflow integration - Smart Structural Navigation - Load only what you need --- ## 🎥 See FastCode in Action <div align="center"> [![Watch FastCode Demo](https://img.youtube.com/vi/NwexLWHPBOY/0.jpg)](https://youtu.be/NwexLWHPBOY) **Click to watch FastCode in action** - See how FastCode analyzes complex codebases with lightning speed. --- </div> ### Core Technologies Behind FastCode FastCode introduces a three-phase framework that transforms how LLMs understand and navigate codebases: <p align="center"> <img src="assets/framework.png" alt="FastCode Framework" width="100%"/> </p> ## 🏗️ Semantic-Structural Code Representation ### Multi-layered codebase understanding for comprehensive analysis - **🔍 Hierarchical Code Units** — Advanced multi-level indexing spanning files, classes, functions, and documentation using AST-based parsing across 8+ programming languages - **🔗 Hybrid Index** — Seamlessly combines semantic embeddings with keyword search (BM25) for robust and precise code retrieval - **📊 Multi-Layer Graph Modeling** — Three interconnected relationship graphs (Call Graph, Dependency Graph, Inheritance Graph) enabling structural navigation across the entire codebase ### 🧭 Lightning-Fast Codebase Navigation Finding the right code without opening every file - at lightning speed - **⚡ Two-Stage Smart Search** — Like having a research assistant that first finds potentially relevant code, then ranks and organizes the best matches for your specific question. - **📁 Safe File Browsing** — Explores your project structure securely, understanding folder organization and file patterns without compromising security. - **🌐 Following Code Connections** — Traces how code pieces connect (up to 2 steps away), like following a trail of breadcrumbs through your codebase. - **🎯 Code Skimming** — Instead of reading entire files, FastCode just looks at the "headlines" - function names, class definitions, and type hints. This is like reading a book's chapter titles instead of every page, saving massive amounts of processing power. ### 💰 Cost-Efficient Context Management Getting maximum insight while minimizing costs - automatically - **📈 Budget-Aware Decision Making** — Considers five key factors before processing: confidence level, query complexity, codebase size, resource cost, and iteration count. Like a cost-conscious financial advisor that weighs all options before making decisions. - **🔄 Resource-Optimized Learning** — Continuously adapts its approach in real-time, getting more efficient about what information to gather and when to stop. Think of it as an AI that maximizes value and becomes more cost-effective with every query. - **🎯 Value-First Selection** — Prioritizes high-impact, low-cost information first, like choosing the ripest fruit at the best price. This cost-optimized approach ensures you get maximum value for every token spent until the perfect stopping point is reached. --- ## 📊 Benchmark Performance FastCode has been rigorously tested on four major benchmarks representing real-world software engineering challenges: ### 🎯 Evaluation Datasets | Benchmark | Focus Area | What It Tests | |-----------|------------|---------------| | SWE-QA | Software Engineering Q&A | Complex technical question answering | | LongCodeQA | Extended Code Analysis | Long-context code understanding | | LOC-BENCH | Code Localization | Bug detection & feature requests | | GitTaskBench | Real-World Tasks | Production repository workflows | ### 🏆 Outstanding Results - ✅ **Superior Accuracy** — Consistently outperforms state-of-the-art baselines across all benchmarks - ✅ **10x Token Efficiency** — Achieves better results while using up to 90% fewer tokens - ✅ **Real-World Validation** — Proven performance on actual production codebases and workflows --- ## 🚀 Quick Start Get FastCode running in under 2 minutes: ```bash # 1. Clone the repository git clone https://github.com/HKUDS/FastCode.git cd FastCode # 2. Install dependencies pip install -r requirements.txt # 3. Configure your API keys cp env.example .env # Edit .env with your API keys # 4. Launch the Web UI python web_app.py --host 0.0.0.0 --port 5000 ``` Open http://localhost:5000 and start asking questions about your code! 🎉 --- ## 📦 Installation FastCode supports **Linux**, **macOS**, and **Windows**. Choose your platform below: > **💡 Recommendation:** We recommend using [uv](https://github.com/astral-sh/uv) for faster and more reliable dependency installation. <details> <summary><b>🐧 Linux Installation</b></summary> ### Prerequisites - Python 3.12 or higher - Git ### Step-by-Step Guide 1. **Clone FastCode** ```bash git clone https://github.com/HKUDS/FastCode.git cd FastCode ``` 2. **Install Dependencies** **Option A: Using uv (Recommended)** ```bash # Install uv pip install uv # create new env with uv uv venv --python=3.12 source .venv/bin/activate # Install dependencies with uv uv pip install -r requirements.txt ``` **Option B: Using pip** ```bash pip install -r requirements.txt ``` 3. **Configure Environment** ```bash cp env.example .env nano .env # or use your preferred editor ``` Add your API keys: ```env OPENAI_API_KEY=your_openai_api_key_here MODEL=gpt-5.2 BASE_URL=https://api.openai.com/v1 ``` 4. **Launch FastCode** ```bash # Web UI (Recommended) python web_app.py --host 0.0.0.0 --port 5000 # Or use the CLI python main.py query --repo-path /path/to/your/repo --query "Your question here" ``` </details> <details> <summary><b>🍎 macOS Installation</b></summary> ### Prerequisites - Python 3.12 or higher - Git ### Step-by-Step Guide 1. **Clone FastCode** ```bash git clone https://github.com/HKUDS/FastCode.git cd FastCode ``` 2. **Install Dependencies** **Option A: Using uv (Recommended)** ```bash # Install uv pip install uv # create new env with uv uv venv --python=3.12 source .venv/bin/activate # Install dependencies with uv uv pip install -r requirements.txt ``` **Option B: Using pip** ```bash pip install -r requirements.txt ``` 3. **Configure Environment** ```bash cp env.example .env nano .env # or use: open -e .env ``` Add your API keys: ```env OPENAI_API_KEY=your_openai_api_key_here MODEL=gemini-3-flash BASE_URL=https://... ``` 4. **Launch FastCode** ```bash # Web UI (Recommended) python web_app.py --host 0.0.0.0 --port 5000 # Or use the CLI python main.py query --repo-path /path/to/your/repo --query "Your question here" ``` **Note for Apple Silicon (M1/M2/M3):** All dependencies are compatible with ARM architecture. </details> <details> <summary><b>💻 Windows Installation</b></summary> ### Prerequisites - Python 3.12 or higher - Git ### Step-by-Step Guide 1. **Clone FastCode** ```cmd git clone https://github.com/HKUDS/FastCode.git cd FastCode ``` 2. **Install Dependencies** **Option A: Using uv (Recommended)** ```cmd # Install uv pip install uv # create new env with uv uv venv --python=3.12 .venv\Scripts\activate # Install dependencies with uv uv pip install -r requirements.txt ``` **Option B: Using pip** ```cmd pip install -r requirements.txt ``` 3. **Configure Environment** ```cmd copy env.example .env notepad .env ``` Add your API keys: ```env OPENAI_API_KEY=your_openai_api_key_here MODEL=qwen/qwen3-coder-30b-a3b-instruct BASE_URL=https://api.openai.com/v1 ``` 4. **Launch FastCode** ```cmd # Web UI (Recommended) python web_app.py --host 0.0.0.0 --port 5000 # Or use the CLI python main.py query --repo-path C:\path\to\your\repo --query "Your question here" ``` **Troubleshooting:** - If you get SSL errors: `pip install --upgrade certifi` - For permission errors, run Command Prompt as Administrator - If `faiss-cpu` fails to install: `pip install faiss-cpu --no-cache-dir` - If you run `./run_nanobot.sh` (bash script) on Windows, use **Git Bash** or **WSL**, and keep `.sh` files in **LF** line endings. This repo enforces LF for `*.sh` via `.gitattributes`, but older clones may need a one-time re-checkout. </details> --- ## 🎮 Usage ### Web Interface (Recommended) The Web UI provides the most intuitive experience: 1. **Launch the server:** ```bash python web_app.py --host 0.0.0.0 --port 5000 ``` 2. **Open your browser:** Navigate to http://localhost:5000 3. **Load a repository:** Use the sidebar to index your codebase 4. **Start asking questions:** - "Where is the authentication logic implemented?" - "How does the payment processing flow work?" - "What files would be affected if I change the User model?" - "Explain the dependency between module A and module B" ### Command Line Interface For automation and scripting: ```bash # Single repository query python main.py query --repo-path /path/to/repo --query "Your question" # Multi-repository query python main.py query --repos /path/to/repo1 /path/to/repo2 --query "Your question" # With custom model python main.py query --repo-path /path/to/repo --query "Your question" --model gpt-4-turbo ``` <details> <summary><b>REST API</b></summary> Integrate FastCode into your tools with the comprehensive REST API: ```bash # Start the API server python api.py --host 0.0.0.0 --port 8000 ``` The API provides all features available in the Web UI. Visit http://localhost:8000/docs for interactive API documentation. **Key API Endpoints:** <details> <summary><b>Repository Management</b></summary> ```bash # List available and loaded repositories GET /repositories # Load a repository from URL or local path POST /load { "source": "https://github.com/user/repo", "is_url": true } # Index the loaded repository POST /index?force=false # Load and index in one call POST /load-and-index { "source": "/path/to/repo", "is_url": false } # Load multiple existing indexed repositories POST /load-repositories { "repo_names": ["repo1", "repo2"] } # Index multiple repositories at once POST /index-multiple { "sources": [ {"source": "https://github.com/user/repo1", "is_url": true}, {"source": "/path/to/repo2", "is_url": false} ] } # Upload repository as ZIP file POST /upload-zip Content-Type: multipart/form-data file: <zip_file> # Upload and index in one call POST /upload-and-index?force=false Content-Type: multipart/form-data file: <zip_file> # Delete repositories and their indexes POST /delete-repos { "repo_names": ["repo1", "repo2"], "delete_source": true } # Get repository summary GET /summary ``` </details> <details> <summary><b>Query & Conversation</b></summary> ```bash # Query repository (single response) POST /query { "question": "How does authentication work?", "filters": null, "repo_filter": ["repo1"], "multi_turn": false, "session_id": null } # Query with streaming response (SSE) POST /query-stream { "question": "Explain the database schema", "multi_turn": true, "session_id": "abc123" } # Start a new conversation session POST /new-session?clear_session_id=old_session # List all conversation sessions GET /sessions # Get conversation history GET /session/{session_id} # Delete a conversation session DELETE /session/{session_id} ``` </details> <details> <summary><b>System & Status</b></summary> ```bash # Health check GET /health # Get system status GET /status?full_scan=false # Clear cache POST /clear-cache # Get cache statistics GET /cache-stats # Refresh index cache POST /refresh-index-cache # Unload current repository DELETE /repository ``` </details> **Example Usage:** ```python import requests # Start a session and query response = requests.post("http://localhost:8000/load-and-index", json={ "source": "https://github.com/user/repo", "is_url": True }) # Query the repository response = requests.post("http://localhost:8000/query", json={ "question": "Where is the main entry point?", "multi_turn": False }) result = response.json() print(result["answer"]) print(f"Tokens used: {result['total_tokens']}") ``` </details> <a id="mcp-server-use-in-cursor--claude-code--windsurf"></a> <details> <summary><b>MCP Server (Use in Cursor / Claude Code / Windsurf)</b></summary> FastCode can run as an [MCP (Model Context Protocol)](https://modelcontextprotocol.io/) server, allowing AI coding assistants like **Cursor**, **Claude Code**, and **Windsurf** to use FastCode's repo-level code understanding capabilities directly. #### Setup Add the following to your MCP configuration: Before configuring MCP, make sure FastCode dependencies are installed in the local uv virtual environment: ```bash git clone https://github.com/HKUDS/FastCode.git cd FastCode uv venv --python=3.12 source .venv/bin/activate uv pip install -r requirements.txt ``` The MCP server should be launched with `.venv/bin/python`, and it needs `OPENAI_API_KEY`, `MODEL`, and `BASE_URL`. **Cursor** (`~/.cursor/mcp.json`): ```json { "mcpServers": { "fastcode": { "command": "/path/to/FastCode/.venv/bin/python", "args": ["/path/to/FastCode/mcp_server.py"], "env": { "MODEL": "gpt-5.2", "BASE_URL": "https://api.openai.com/v1", "OPENAI_API_KEY": "sk-..." } } } } ``` **Claude Code** (`claude_desktop_config.json`): ```json { "mcpServers": { "fastcode": { "command": "/path/to/FastCode/.venv/bin/python", "args": ["/path/to/FastCode/mcp_server.py"], "env": { "MODEL": "gpt-5.2", "BASE_URL": "https://api.openai.com/v1", "OPENAI_API_KEY": "sk-..." } } } } ``` Or via `claude mcp add` (ensure the same env vars are available in your shell): ```bash claude mcp add fastcode -- /path/to/FastCode/.venv/bin/python /path/to/FastCode/mcp_server.py ``` **SSE transport** (for remote / shared deployments): ```bash OPENAI_API_KEY=sk-... MODEL=gpt-5.2 BASE_URL=https://api.openai.com/v1 \ /path/to/FastCode/.venv/bin/python /path/to/FastCode/mcp_server.py --transport sse --port 8080 ``` #### Available Tools | Tool | Description | |------|-------------| | `code_qa` | Core tool — ask questions about one or more code repositories. Automatically clones (if URL) and indexes repos that haven't been indexed yet. Supports multi-turn conversations. | | `list_indexed_repos` | List all repositories that have been indexed and are available for querying. | | `list_sessions` | List all existing conversation sessions with titles and turn counts. | | `get_session_history` | Retrieve the full Q&A history of a specific session. | | `delete_session` | Delete a conversation session and all its history. | | `delete_repo_metadata` | Delete indexed metadata for a repository (`.faiss`, `_metadata.pkl`, `_bm25.pkl`, `_graphs.pkl`) and remove its entry from `repo_overviews.pkl`, while keeping the repository source code. | #### `code_qa` Parameters | Parameter | Required | Default | Description | |-----------|----------|---------|-------------| | `question` | Yes | — | The question to ask about the code | | `repos` | Yes | — | List of repo sources (GitHub URLs or local paths). Multiple repos supported. | | `multi_turn` | No | `true` | Enable multi-turn conversation mode | | `session_id` | No | auto-generated | Session ID for continuing a conversation. Returned in the response of each call. | #### How It Works 1. **Auto-detection**: For each repo in `repos`, FastCode checks if it's already indexed. If yes, it skips indexing entirely. 2. **Auto-clone**: If a repo is a URL and not yet on disk, FastCode clones it to `./repos/` and indexes it. 3. **Multi-turn**: When `multi_turn` is enabled (default), previous Q&A context from the same `session_id` is used for query rewriting and answer generation. The `session_id` is returned in each response — pass it back in subsequent calls to continue the conversation. 4. **Multi-repo**: Pass multiple repos to `repos` and FastCode will search across all of them, using LLM-based repository selection to find the most relevant code. #### Usage Example In Cursor or Claude Code, simply ask: ``` Use FastCode to analyze what this repository at /path/to/repo_name is for. ``` or ``` Use FastCode to analyze the authentication flow in https://github.com/user/my-project ``` The AI assistant will call `code_qa` with the URL, FastCode will clone and index the repo (if needed), and return a detailed answer with source references. For follow-up questions, the assistant reuses the same `session_id` automatically: ``` Which files would be affected if I change the User model? ``` </details> --- ## 🔧 Configuration ### Supported LLM Providers FastCode works with multiple LLM providers: <details> <summary><b>OpenAI</b></summary> ```env OPENAI_API_KEY=sk-... MODEL=gpt-4 BASE_URL=https://api.openai.com/v1 ``` </details>  <details> <summary><b>OpenRouter (Multiple Models)</b></summary> ```env OPENAI_API_KEY=sk-or-... MODEL=google/gemini-flash-1.5 BASE_URL=https://openrouter.ai/api/v1 ``` </details> <details> <summary><b>Local Models (Ollama)</b></summary> ```env OPENAI_API_KEY=ollama MODEL=qwen3-coder-30b_fastcode BASE_URL=http://localhost:11434/v1 ``` </details> ### Supported Languages FastCode automatically detects and parses: - 🐍 Python - 📜 JavaScript / TypeScript - ☕ Java - 🦀 Rust - 🐹 Go - ⚙️ C / C++ - 💎 C# --- ## 🤖 Nanobot + Feishu (Lark) Integration (Docker Deployment) FastCode can be integrated with a Feishu (Lark) bot via [Nanobot](https://github.com/nanobot-ai/nanobot), enabling conversational code repository queries directly within Feishu. ### Architecture ``` Feishu User ←→ Feishu Open Platform ←→ Nanobot (WebSocket) ←→ FastCode API (port 18791) (port 8001) ``` - **FastCode Container**: Provides the code analysis REST API (port 8001) - **Nanobot Container**: Connects to Feishu via WebSocket and invokes FastCode tools (port 18791) - Both containers communicate over Docker's internal network — no public IP required ### Quick Start ```bash # One-command launch (automatically builds images and generates config on first run) ./run_nanobot.sh # Subsequent runs auto-detect the current state: # - Services running → show status + health check # - Services stopped → auto-restart # - Images not found → auto-build and start ``` <details> <summary><b>Configuration</b></summary> #### 1. Unified Configuration (`.env`) All API keys and model settings are centralized in the `.env` file, shared by both services: ```bash # ===== Shared API Configuration ===== OPENAI_API_KEY=sk-or-v1-xxx # Shared by both services BASE_URL=https://openrouter.ai/api/v1 # ===== FastCode Model (Code Analysis) ===== MODEL=google/gemini-3-flash-preview # ===== Nanobot Model (Agent Reasoning / Feishu Conversations) ===== NANOBOT_MODEL=minimax/minimax-m2.1 ``` The API key is automatically injected into the Nanobot container via `docker-compose.yml` environment variables — no need to duplicate it in `nanobot_config.json`. #### 2. FastCode Core Configuration - `config/config.yaml` — FastCode retrieval and indexing settings #### 3. Nanobot Configuration (`nanobot_config.json`) `run_nanobot.sh` auto-generates this file. You only need to configure **Feishu credentials and the systemPrompt** here; API keys and model settings are managed centrally via `.env`. ```json { "channels": { "feishu": { "enabled": true, "appId": "cli_xxxxxxxxx", "appSecret": "your_app_secret" } } } ``` </details> <details> <summary><b>Feishu App Setup</b></summary> Nanobot receives Feishu messages via a WebSocket long-lived connection — **no public IP or webhook required**. #### 1. Create an App Go to the [Feishu Open Platform](https://open.feishu.cn/app) → Create a **Custom Enterprise App** → Note the **App ID** (`cli_xxx`) and **App Secret**. #### 2. Enable Bot Capability Navigate to App Settings → **App Features** → Enable the "Bot" capability. #### 3. Add Permissions Add the following permissions under **Permission Management**: | Permission | Description | |------------|-------------| | `im:message` | Read messages | | `im:message:send_as_bot` | Send messages as the bot | | `im:resource` | Access resource files in messages | | `im:chat` | Retrieve group chat information | #### 4. Configure Event Subscriptions > ⚠️ **Note the order**: You must start Nanobot to establish the WebSocket connection first, then configure event subscriptions in the Feishu admin console. 1. Enter `appId` and `appSecret` in `nanobot_config.json`, then start the service (`./run_nanobot.sh`) 2. Verify the log shows `connected to wss://msg-frontier.feishu.cn`, confirming a successful connection 3. Go back to the Feishu admin console → **Events & Callbacks** → Select "**Receive events via long-lived connection**" → Save 4. Add event: `im.message.receive_v1` (Receive messages) #### 5. Publish the App Go to **Version Management** → Create a version → Submit for review (internal enterprise apps can be self-approved) → Once published, you can chat with the bot in Feishu. </details> <details> <summary><b>Docker Compose Structure</b></summary> ```yaml services: fastcode: # FastCode API (port 8001) build: . volumes: - ./.env:/app/.env:ro - ./config:/app/config:ro - ./data:/app/data - ./repos:/app/repos nanobot: # Nanobot Gateway (port 18791 → 18790) build: ./nanobot command: ["gateway"] volumes: - ./nanobot_config.json:/root/.nanobot/config.json:ro environment: - FASTCODE_API_URL=http://fastcode:8001 # API key and model injected from .env - NANOBOT_PROVIDERS__OPENROUTER__API_KEY=${OPENAI_API_KEY} - NANOBOT_AGENTS__DEFAULTS__MODEL=${NANOBOT_MODEL} depends_on: - fastcode ``` </details> <details> <summary><b>Management Commands</b></summary> | Command | Description | |---------|-------------| | `./run_nanobot.sh` | Smart launch (auto-detects state) | | `./run_nanobot.sh --build` | Force rebuild images | | `./run_nanobot.sh --fg` | Run in foreground (view live logs) | | `./run_nanobot.sh stop` | Stop all services | | `./run_nanobot.sh restart` | Restart all services | | `./run_nanobot.sh logs` | View live logs | | `./run_nanobot.sh status` | View service status + health check | | `./run_nanobot.sh config` | Re-check / regenerate Nanobot config | | `./run_nanobot.sh clean` | Stop and remove containers and images | </details> <details> <summary><b>Registered FastCode Tools in Nanobot</b></summary> On startup, Nanobot automatically registers the following tools via the `FASTCODE_API_URL` environment variable: | Tool Name | Function | |-----------|----------| | `fastcode_load_repo` | Load and index a code repository (GitHub URL or local path) | | `fastcode_query` | Query code using natural language, with multi-turn conversation support | | `fastcode_list_repos` | List loaded / available repositories | | `fastcode_status` | View FastCode system status | | `fastcode_session` | Manage conversation sessions (create / list / history / delete) | </details> ### Feishu Usage Example Chat with the bot in Feishu: ``` User: https://github.com/user/repo Bot: ✓ Repository loaded and indexed (152 files, 3847 code elements) User: How is the authentication logic implemented in this project? Bot: This project uses JWT for authentication... [detailed analysis] User: Which files would be affected by changes to the User model? Bot: The following 8 files would be affected... [impact analysis] ``` --- ## 🧠 How It Works FastCode uses a novel **scouting-first** approach that fundamentally differs from traditional code reasoning systems: ### Traditional Approach ❌ ``` Question → Load Files → Search → Load More Files → Search Again → ... → Answer 💸 High token cost from repeated file loading ``` ### FastCode Approach ✅ ``` Question → Build Semantic Map → Navigate Structure → Load Targets → Answer 💰 Minimal token cost with precise targeting ``` --- ## 📚 Examples ### Example 1: Understanding Authentication Flow **Query:** "How does user authentication work in this application?" **FastCode Process:** 1. 🗺️ Scouts for authentication-related structures 2. 🔍 Identifies `auth.py`, `middleware.py`, `user_model.py` 3. 📊 Traces dependencies between these files 4. 📖 Loads only relevant functions 5. ✅ Provides comprehensive answer ### Example 2: Impact Analysis **Query:** "What would break if I change the User model schema?" **FastCode Process:** 1. 🗺️ Locates User model definition 2. 🔗 Traces all imports and usages 3. 📊 Maps dependency graph 4. 📖 Loads affected code sections 5. ✅ Lists all impacted files and functions --- ## 🤝 Contributing We welcome contributions! FastCode is built for the community, by the community. ### Ways to Contribute - 🐛 **Report Bugs** - Found an issue? Let us know! - 💡 **Suggest Features** - Have ideas? We'd love to hear them! - 📝 **Improve Documentation** - Help others understand FastCode better - 🔧 **Submit Pull Requests** - Code contributions are always welcome  --- ## 📄 License FastCode is released under the MIT License. See [LICENSE](LICENSE) for details. --- ## 🌟 Star History <div align="center"> If FastCode saves you tokens and time, consider giving us a star! ⭐ **Built with ❤️ for developers who value efficiency** </div> <div align="center"> <a href="https://star-history.com/#HKUDS/FastCode&Date"> <picture> <source media="(prefers-color-scheme: dark)" srcset="https://api.star-history.com/svg?repos=HKUDS/FastCode&type=Date&theme=dark" /> <source media="(prefers-color-scheme: light)" srcset="https://api.star-history.com/svg?repos=HKUDS/FastCode&type=Date" /> <img alt="Star History Chart" src="https://api.star-history.com/svg?repos=HKUDS/FastCode&type=Date" style="border-radius: 15px; box-shadow: 0 0 30px rgba(0, 217, 255, 0.3);" /> </picture> </a> </div> <p align="center"> <em> Thanks for visiting ✨ FastCode!</em><br><br> <img src="https://visitor-badge.laobi.icu/badge?page_id=HKUDS.FastCode&style=for-the-badge&color=00d4ff" alt="Views"> </p>

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Softwareentwickler Sonstige Computerberufe

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cron

Softwareentwickler

Schedule reminders and recurring tasks.

Quellsprache: Englisch

2026-02-13

fastcode

Sonstige Computerberufe

Dedicated to calling the FastCode backend to index code repositories and answer questions about them.

Quellsprache: Englisch

2026-02-13

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