--- slug: "codemasterpro-mcp" source_type: "readme" source_url: "https://cdn.jsdelivr.net/gh/StefanKumarasinghe/CodeMasterPro@main/README.md" repo: "https://github.com/StefanKumarasinghe/CodeMasterPro" source_file: "README.md" branch: "main" --- # Your GPT for coding. Contextualize your codebase with a powerfully chained AI [![Docker Pulls](https://img.shields.io/docker/pulls/stefankumarasinghe/codemasterpro)](https://hub.docker.com/r/stefankumarasinghe/codemasterpro) [![License](https://img.shields.io/badge/License-MIT-yellow.svg)](https://opensource.org/licenses/MIT) CodeMasterPro is an advanced AI-powered coding assistant designed to elevate the software engineering experience. ## Features - **Understanding User** Understands user intentions - could be further enhanced - **AI-Powered Debugging:** Identifies potential errors and suggests fixes, streamlining the debugging process. - **Comprehensive Documentation:** Provides instant access to relevant documentation and examples, enhancing understanding and productivity. - **Customizable Settings:** Tailor CodeMasterPro to your specific coding style and preferences. - **Multi-Language Support:** Supports a wide range of programming languages, ensuring versatility across projects. - **Mutli-Model Support** - Requires you to have a together API to use the Reasoner pro for a diversified answer - **Runs Python and HTML** - Allows you to directly run the codes in a safe environment - **Memory, Internet and other tools** - Has support for many tools including internal docuemntation, web scraping of websites for effective RAG - **Previous Chats and Snippets** - You can now choose to save chats and snippets for later use and CodeMasterPro will always save your last unsaved chat - **Highly Customized** - You can customize the prompt and the preferences as you like that suits you - **Project Context** - We allow you to upload your projects either via Zip, folder or a github repo to be index and use it as context when asking questions - **Github Search** - Use GitHub to get tools online that will provide examples for the llm to use and make better responses ## Getting Started ### Prerequisites - [Docker](https://www.docker.com/get-started): Ensure Docker is installed and running on your system. - **Gemini API Key:** Required for core AI functionality. Obtain a free API key from [Google Gemini](https://ai.google.dev/). - **Brave API Key (Optional):** Enhances search capabilities. Obtain a free API key from [Brave Search API](https://brave.com/search/api/). - **Together API key (Optional, required for Reasoner Pro):** Enhances the answer way more, but way slower, useful for advance coding questions. Obtain a free API key from [Together API](https://api.together.ai/) ### Available Tools - **WEB** – Allows access to the internet. Prompt your queries with natural instructions like: *“Search online how to create an MCP server.”* - **STACK** – Searches Stack Overflow for developer-relevant answers and code examples. Great for resolving language-specific or library-related issues. - **INTERNAL** – Searches internal documentation or knowledge bases, such as team wikis or private datasets. Use when your answer likely exists within internal sources. - **PYTHON** – Executes Python code in an isolated environment. Useful for testing logic, calculations, or snippets. - **COMPUTE** – Handles complex mathematical computations or factorial operations. Example: *“What is 756!?”* - **VISUALIZE** – Transforms raw log data into charts or graphs. Example: *“Provide logs and give me a visualization of CPU usage over time.”* - **SAST** – Performs static application security testing (SAST) on Python code. Detects vulnerabilities, bad patterns, and unsafe practices. - **LIGHTNING** – Delivers very fast answers to lightweight or fact-based questions. Optimized for speed over deep reasoning. - **DEEP ANALYSIS** - Analyses the large codes in detail, mostly 50000+ character codes Can you give me a deep anaysis of the code - **GITHUB** - Uses GitHub API to fetch examples and learn from them more effectively - **CONTEXT** - Allows you to upload your codebase and provide context to your queries ## Main Chain of Thought ![image](https://github.com/user-attachments/assets/597b550e-105d-4cd0-9ee1-feac3db32f2e) ### ✅ Overview The workflow follows a structured decision-making and response generation pipeline, enhanced by reinforcement learning and multi-model reasoning. --- ### ⚙️ 1. Tool Selection & Execution * Checks for an appropriate tool. * Executes it immediately if available for a fast, actionable response. --- ### 🧠 2. Sentiment & Behavior Analysis * Evaluates user sentiment and past interactions. * Skipped if no behavioral history exists. --- ### 🌐 3. Resource Retrieval * Fetches additional resources (e.g., web data, Stack Overflow, internal KBs) when needed. --- ### 🧬 4. Reinforcement Learning Feedback * Uses sentiment data to reward or penalize the RL agent. * Influences future decisions and model behaviors. --- ### 🧾 5. Response Generation * Combines sentiment, context, and history to produce an initial response. --- ### 🪞 6. Response Refinement * Enhances clarity, quality, and coherence of the response. --- ### 🧠 7. Multi-Model Reasoning * Invokes models like Gemini and others for deeper, more diverse insights based on reasoning level. --- ### ⚡ 8. Fast Path Return * Skips validation when speed is critical and returns the response immediately. --- ### 🔁 9. Quick Reasoning Path * Uses a lightweight reasoner. * Validates up to 3 times using the best prior answer. * Iterates without external model input. --- ### 🧩 10. Pro-Level Reasoning * Engages advanced models and third-party outputs. * Ensures broader context and selects the best response. --- ### 🧠 11. RL Agent Optimization * Continuously tunes the RL agent via rewards/penalties. * Chooses the optimal result after multiple iterations. --- ### 🎯 12. Greedy Optimization Strategy * Prioritizes the best possible outcome. * May settle early if minimum quality is met. ## Code Analyst Chain ![image](https://github.com/user-attachments/assets/46dbc438-cb0f-4deb-9412-344c3cf74f08) Designed for deep analysis of large codebases, **CodeAnalystPro** excels at code explanation and modification. Below is the step-by-step workflow: ### 1. **Code Chunking** * Splits the source code into manageable segments of **2,000–5,000 characters**. ### 2. **Chunk Analysis with Retry Logic** * Each chunk is analyzed. * Up to **5 retry attempts** are allowed to improve accuracy. ### 3. **Context-Aware Modification** * Modifications are made with awareness of: * The **current chunk**. * **Previously generated outputs**. * Ensures output consistency across chunks. ### 4. **Fallback Mechanism** * If a chunk **fails validation**: * The **best previous version** is reused. * The **context window is reduced** to limit hallucinations. ### 5. **Validation Threshold** * Chunks must meet a **90% validation score** to proceed. * If validation passes, the system advances to the next chunk. ### 6. **Iterative Refinement** * Each chunk is either **corrected or explained** before moving on. ### 7. **Final Output Reconstruction** * All chunks are reassembled into a final version. * A **lightweight Gemini model** performs final polishing and cleanup. ## Python Chain ![image](https://github.com/user-attachments/assets/d7a8a3a8-a970-4e23-bc17-24ab9b718c67) ### Installation 1. **Pull the Docker image:** ```shell docker pull stefankumarasinghe/codemasterpro ``` Please use the relevant tags, for best performance, there is amd64, arm64 and latest which is compatible for both (but heavy) 2. **Run the Docker container:** ```shell docker run -e GOOGLE_API_KEY=YOUR_GEMINI_API_KEY -p 8000:8000 stefankumarasinghe/codemasterpro ``` While you can also have the BRAVE_API_KEY and TOGETHER_AI_API_KEY using -e and other Integrations, CodeMasterPro allows you to set via the UI - Replace `YOUR_GEMINI_API_KEY` with your actual Gemini API key. - Replace `YOUR_BRAVE_API_KEY` with your Brave API key (if you have one). - Replace `TOGETHER_AI_API_KEY` with your Together key - Make sure to create an .env, if you are doing it via the terminal (root-level) - Make sure to have the port 8000 ready as well - New update no longer requires you to put these variables as it can be set via the UI 3. **Access CodeMasterPro:** Open your web browser and navigate to `[CodeMasterPro](https://dwr4zchmi6x24.cloudfront.net/)`. You need to run the docker container, you can stop when you don't need it and start it without entering the token again ### Environment Variables - `GOOGLE_API_KEY`: Your Gemini API key. This is **required**. - Other tokens and intergrations can be added when you load the app ### Customization CodeMasterPro can be further customized through the UI - **Model Selection:** Choose between different AI models for code completion and debugging. - **Language Preferences:** Specify your preferred programming languages. - **UI Themes:** Customize the look and feel of the CodeMasterPro interface. - **So much more** ... ## Usage 1. **Code Editor Integration:** Integrate CodeMasterPro with your favorite code editor (e.g., VS Code, Sublime Text, Atom) using the provided plugins or extensions. 2. **Large and Accurate Code Generations** Can produce large outputs and also take in large inputs, thanks to Gemini's 1million token window 3. **Real-Time Assistance:** As you type, CodeMasterPro will provide real-time code suggestions, error detection, and documentation snippets. 4. **Debugging Tools:** Utilize the AI-powered debugging tools to identify and resolve issues quickly. 5. **Documentation Lookup:** Access comprehensive documentation for various programming languages and libraries directly within the CodeMasterPro interface. ## Example Prompt Example 1: Can you correct and improve this code Example 2: Can you check for vunerabilties in the python code Example 3: Visualize my logs please Example 4: Give me a full working chess game in HTML with all the logic and rules Example 5: Run this python code ## Contributing We welcome contributions to CodeMasterPro! Please follow these guidelines: 1. Fork the repository. 2. Create a new branch for your feature or bug fix. 3. Submit a pull request with a clear description of your changes. 4. If you were to fork this REPO, while this project is licensed under Non-commercial, you must credit me ## License This project is licensed under the Non Commercial License - see the [LICENSE](https://github.com/StefanKumarasinghe/CodeMasterPro/tree/HEAD/LICENSE) file for details. However, you must credit me, if you are using the app ## Support For questions, bug reports, or feature requests, please raise an issue or PR ## Acknowledgements - Powered by Google Gemini and Brave Search API and Together AI and CodeMasterPro's Chain of Thought