roo-code-memory-bank

🧠 Roo Code Memory Bank - Now with new debug mode! (Thanks @TheRealAlexV) Solve the AI context challenge: maintain deep project understanding across sessions with our structured VS Code memory system. Never repeat project details again! ✨

Stars: 200

Visit

Roo Code Memory Bank is a tool designed for AI-assisted development to maintain project context across sessions. It provides a structured memory system integrated with VS Code, ensuring deep understanding of the project for the AI assistant. The tool includes key components such as Memory Bank for persistent storage, Mode Rules for behavior configuration, VS Code Integration for seamless development experience, and Real-time Updates for continuous context synchronization. Users can configure custom instructions, initialize the Memory Bank, and organize files within the project root directory. The Memory Bank structure includes files for tracking session state, technical decisions, project overview, progress tracking, and optional project brief and system patterns documentation. Features include persistent context, smart workflows for specialized tasks, knowledge management with structured documentation, and cross-referenced project knowledge. Pro tips include handling multiple projects, utilizing Debug mode for troubleshooting, and managing session updates for synchronization. The tool aims to enhance AI-assisted development by providing a comprehensive solution for maintaining project context and facilitating efficient workflows.

README:

🧠 Roo Code Memory Bank

Persistent Project Context for AI-Assisted Development

🎯 Overview

Roo Code Memory Bank solves a critical challenge in AI-assisted development: maintaining context across sessions. By providing a structured memory system integrated with VS Code, it ensures your AI assistant maintains a deep understanding of your project across sessions.

Key Components

graph LR
    A[Memory Bank] --> B[Core Files]
    A --> C[Mode Rules]
    A --> D[VS Code UI]
    B --> E[Project Context]
    B --> F[Decisions]
    B --> G[Progress]
    C --> H[Architect]
    C --> I[Code]
    C --> J[Ask]
    C --> K1[Debug]
    K[Real-time Updates] --> B
    K --> L[Continuous Sync]
    L --> M[Auto-save]
    L --> N[Event Tracking]

🧠 Memory Bank: Persistent storage for project knowledge
📋 Mode Rules: YAML-based behavior configuration
🔧 VS Code Integration: Seamless development experience
⚡ Real-time Updates: Continuous context synchronization

🚀 Quick Start

1. Configure Custom Instructions

a. Copy Rule and Mode Files

Download and copy these files to your project's root directory:

Mode	Rule File	Purpose
Code	`.clinerules-code`	Implementation and coding tasks
Architect	`.clinerules-architect`	System design and architecture
Ask	`.clinerules-ask`	Information and assistance
Debug	`.clinerules-debug`	Troubleshooting and problem-solving
Modes	`.roomodes`	Additional modes

b. Configure VS Code Settings

⚠️ Important: Leave the "Custom Instructions" text boxes empty in VS Code settings (Roo Code Prompts section)

📷 View Settings Screenshot

2. Initialize Memory Bank

Switch to Architect or Code mode in Roo Code chat
Send a message (e.g., "hello")
Roo will automatically:
- 🔍 Scan for memory-bank/ directory
- 📁 Create it if missing (with your approval)
- 📝 Initialize core files
- 🚦 Provide next steps

💡 Pro Tip: Project Brief

Create a projectBrief.md in your project root before initialization to give Roo immediate project context.

File Organization

project-root/
├── .clinerules-architect
├── .clinerules-code
├── .clinerules-ask
├── .clinerules-debug
├── .roomodes
├── memory-bank/
│   ├── activeContext.md
│   ├── productContext.md
│   ├── progress.md
│   └── decisionLog.md
└── projectBrief.md

📚 Memory Bank Structure

graph TD
    MB[memory-bank/] --> AC[activeContext.md]
    MB --> DL[decisionLog.md]
    MB --> PC[productContext.md]
    MB --> PR[progress.md]
    MB --> PB[projectBrief.md]
    MB --> SP[systemPatterns.md]
    
    subgraph Core Files
        AC[Current Session State]
        DL[Technical Decisions]
        PC[Project Overview]
        PR[Progress Tracking]
    end
    
    subgraph Optional
        PB[Project Brief]
        SP[System Patterns]
    end

📖 View File Descriptions

File	Purpose
`activeContext.md`	Tracks current goals, decisions, and session state
`decisionLog.md`	Records architectural choices and their rationale
`productContext.md`	Maintains high-level project context and knowledge
`progress.md`	Documents completed work and upcoming tasks
`projectBrief.md`	Contains initial project requirements (optional)
`systemPatterns.md`	Documents recurring patterns and standards

✨ Features

🧠 Persistent Context

Remembers project details across sessions
Maintains consistent understanding of your codebase
Tracks decisions and their rationale

🔄 Smart Workflows

graph LR
    A[Architect Mode] -->|Real-time Design Updates| B[Memory Bank]
    C[Code Mode] -->|Real-time Implementation| B
    D[Ask Mode] -->|Real-time Insights| B
    F[Debug Mode] -->|Real-time Analysis| B
    B -->|Instant Context| A
    B -->|Instant Context| C
    B -->|Instant Context| D
    B -->|Instant Context| F
    E[Event Monitor] -->|Continuous Sync| B

Mode-based operation for specialized tasks
Automatic context switching
Project-specific customization via rules

📊 Knowledge Management

Structured documentation with clear purposes
Technical decision tracking with rationale
Automated progress monitoring
Cross-referenced project knowledge

💡 Pro Tips

Multiple Projects

graph TD
    A[Workspace] --> B[Project 1]
    A --> C[Project 2]
    B --> D[memory-bank/]
    C --> E[memory-bank/]
    D --> F[Automatic Detection]
    E --> F

Roo automatically handles multiple Memory Banks in your workspace!

Debug Mode

Roo Code Memory Bank includes a powerful Debug mode for systematic troubleshooting and problem-solving. This mode operates with surgical precision, focusing on root cause analysis and evidence-based verification.

Key Capabilities

🔍 Non-destructive Investigation: Read-only access ensures system integrity during analysis
📊 Diagnostic Tools: Access to logging, tracing, and system analysis tools
🔬 Systematic Analysis: Methodical problem investigation and isolation
🎯 Root Cause Identification: Traces error propagation through all system layers
✅ Evidence-based Verification: Validates findings through multiple checkpoints

Real-time Update Triggers

Debug mode actively monitors and updates Memory Bank files based on:

🐛 Bug discoveries and error patterns
💾 Memory leaks and resource issues
🔄 Race conditions and deadlocks
📈 Performance bottlenecks
📝 Log analysis and trace outputs

Memory Bank Integration

graph TD
    A[Debug Mode] --> B[Analysis]
    B --> C[Findings]
    C --> D[Memory Bank Updates]
    D --> E[activeContext.md]
    D --> F[progress.md]
    D --> G[decisionLog.md]
    E --> H[Current Issues]
    F --> I[Debug Progress]
    G --> J[Solution Decisions]

Switch to Debug mode when you need to:

Investigate system behavior
Analyze failure patterns
Isolate root causes
Verify fixes
Document debugging insights

Session Management

⚡ Real-time Updates: Memory Bank automatically stays synchronized with your work
💾 Manual Updates: Use "UMB" or "update memory bank" as a fallback when:
- Ending a session unexpectedly
- Halting mid-task
- Recovering from connection issues
- Forcing a full synchronization

📖 Documentation

View on GitHub • Report Issues • Get Roo Code

License

For Tasks:

Click tags to check more tools for each tasks

track project progress configure custom instructions initialize memory bank manage session updates utilize debug mode

For Jobs:

software developer ai engineer technical architect systems analyst devops engineer

Alternative AI tools for roo-code-memory-bank

Similar Open Source Tools

roo-code-memory-bank

github

: 200

agentscope

AgentScope is a multi-agent platform designed to empower developers to build multi-agent applications with large-scale models. It features three high-level capabilities: Easy-to-Use, High Robustness, and Actor-Based Distribution. AgentScope provides a list of `ModelWrapper` to support both local model services and third-party model APIs, including OpenAI API, DashScope API, Gemini API, and ollama. It also enables developers to rapidly deploy local model services using libraries such as ollama (CPU inference), Flask + Transformers, Flask + ModelScope, FastChat, and vllm. AgentScope supports various services, including Web Search, Data Query, Retrieval, Code Execution, File Operation, and Text Processing. Example applications include Conversation, Game, and Distribution. AgentScope is released under Apache License 2.0 and welcomes contributions.

github

: 6.7k

open-health

OpenHealth is an AI health assistant that helps users manage their health data by leveraging AI and personal health information. It allows users to consolidate health data, parse it smartly, and engage in contextual conversations with GPT-powered AI. The tool supports various data sources like blood test results, health checkup data, personal physical information, family history, and symptoms. OpenHealth aims to empower users to take control of their health by combining data and intelligence for actionable health management.

github

: 3.2k

HuatuoGPT-o1

HuatuoGPT-o1 is a medical language model designed for advanced medical reasoning. It can identify mistakes, explore alternative strategies, and refine answers. The model leverages verifiable medical problems and a specialized medical verifier to guide complex reasoning trajectories and enhance reasoning through reinforcement learning. The repository provides access to models, data, and code for HuatuoGPT-o1, allowing users to deploy the model for medical reasoning tasks.

github

: 480

PraisonAI

Praison AI is a low-code, centralised framework that simplifies the creation and orchestration of multi-agent systems for various LLM applications. It emphasizes ease of use, customization, and human-agent interaction. The tool leverages AutoGen and CrewAI frameworks to facilitate the development of AI-generated scripts and movie concepts. Users can easily create, run, test, and deploy agents for scriptwriting and movie concept development. Praison AI also provides options for full automatic mode and integration with OpenAI models for enhanced AI capabilities.

github

: 3.7k

Starmoon

Starmoon is an affordable, compact AI-enabled device that can understand and respond to your emotions with empathy. It offers supportive conversations and personalized learning assistance. The device is cost-effective, voice-enabled, open-source, compact, and aims to reduce screen time. Users can assemble the device themselves using off-the-shelf components and deploy it locally for data privacy. Starmoon integrates various APIs for AI language models, speech-to-text, text-to-speech, and emotion intelligence. The hardware setup involves components like ESP32S3, microphone, amplifier, speaker, LED light, and button, along with software setup instructions for developers. The project also includes a web app, backend API, and background task dashboard for monitoring and management.

github

: 457

moling

github

: 125

RookieAI_yolov8

RookieAI_yolov8 is an open-source project designed for developers and users interested in utilizing YOLOv8 models for object detection tasks. The project provides instructions for setting up the required libraries and Pytorch, as well as guidance on using custom or official YOLOv8 models. Users can easily train their own models and integrate them with the software. The tool offers features for packaging the code, managing model files, and organizing the necessary resources for running the software. It also includes updates and optimizations for better performance and functionality, with a focus on FPS game aimbot functionalities. The project aims to provide a comprehensive solution for object detection tasks using YOLOv8 models.

github

: 340

simba

Simba is an open source, portable Knowledge Management System (KMS) designed to seamlessly integrate with any Retrieval-Augmented Generation (RAG) system. It features a modern UI and modular architecture, allowing developers to focus on building advanced AI solutions without the complexities of knowledge management. Simba offers a user-friendly interface to visualize and modify document chunks, supports various vector stores and embedding models, and simplifies knowledge management for developers. It is community-driven, extensible, and aims to enhance AI functionality by providing a seamless integration with RAG-based systems.

github

: 1.2k

evalchemy

Evalchemy is a unified and easy-to-use toolkit for evaluating language models, focusing on post-trained models. It integrates multiple existing benchmarks such as RepoBench, AlpacaEval, and ZeroEval. Key features include unified installation, parallel evaluation, simplified usage, and results management. Users can run various benchmarks with a consistent command-line interface and track results locally or integrate with a database for systematic tracking and leaderboard submission.

github

: 317

R2R

R2R (RAG to Riches) is a fast and efficient framework for serving high-quality Retrieval-Augmented Generation (RAG) to end users. The framework is designed with customizable pipelines and a feature-rich FastAPI implementation, enabling developers to quickly deploy and scale RAG-based applications. R2R was conceived to bridge the gap between local LLM experimentation and scalable production solutions. **R2R is to LangChain/LlamaIndex what NextJS is to React**. A JavaScript client for R2R deployments can be found here. ### Key Features * **🚀 Deploy** : Instantly launch production-ready RAG pipelines with streaming capabilities. * **🧩 Customize** : Tailor your pipeline with intuitive configuration files. * **🔌 Extend** : Enhance your pipeline with custom code integrations. * **⚖️ Autoscale** : Scale your pipeline effortlessly in the cloud using SciPhi. * **🤖 OSS** : Benefit from a framework developed by the open-source community, designed to simplify RAG deployment.

github

: 5.9k

airunner

AI Runner is a multi-modal AI interface that allows users to run open-source large language models and AI image generators on their own hardware. The tool provides features such as voice-based chatbot conversations, text-to-speech, speech-to-text, vision-to-text, text generation with large language models, image generation capabilities, image manipulation tools, utility functions, and more. It aims to provide a stable and user-friendly experience with security updates, a new UI, and a streamlined installation process. The application is designed to run offline on users' hardware without relying on a web server, offering a smooth and responsive user experience.

github

: 307

shards

Shards is a high-performance, multi-platform, type-safe programming language designed for visual development. It is a dataflow visual programming language that enables building full-fledged apps and games without traditional coding. Shards features automatic type checking, optimized shard implementations for high performance, and an intuitive visual workflow for beginners. The language allows seamless round-trip engineering between code and visual models, empowering users to create multi-platform apps easily. Shards also powers an upcoming AI-powered game creation system, enabling real-time collaboration and game development in a low to no-code environment.

github

: 76

cortex.cpp

Cortex is a C++ AI engine with a Docker-like command-line interface and client libraries. It supports running AI models using ONNX, TensorRT-LLM, and llama.cpp engines. Cortex can function as a standalone server or be integrated as a library. The tool provides support for various engines and models, allowing users to easily deploy and interact with AI models. It offers a range of CLI commands for managing models, embeddings, and engines, as well as a REST API for interacting with models. Cortex is designed to simplify the deployment and usage of AI models in C++ applications.

github

: 2.3k

zo2

github

: 72

easy-dataset

Easy Dataset is a specialized application designed to streamline the creation of fine-tuning datasets for Large Language Models (LLMs). It offers an intuitive interface for uploading domain-specific files, intelligently splitting content, generating questions, and producing high-quality training data for model fine-tuning. With Easy Dataset, users can transform domain knowledge into structured datasets compatible with all OpenAI-format compatible LLM APIs, making the fine-tuning process accessible and efficient.

github

: 4.2k

For similar tasks

roo-code-memory-bank

github

: 200

For similar jobs

sweep

Sweep is an AI junior developer that turns bugs and feature requests into code changes. It automatically handles developer experience improvements like adding type hints and improving test coverage.

github

: 7.1k

teams-ai

The Teams AI Library is a software development kit (SDK) that helps developers create bots that can interact with Teams and Microsoft 365 applications. It is built on top of the Bot Framework SDK and simplifies the process of developing bots that interact with Teams' artificial intelligence capabilities. The SDK is available for JavaScript/TypeScript, .NET, and Python.

github

: 502

ai-guide

This guide is dedicated to Large Language Models (LLMs) that you can run on your home computer. It assumes your PC is a lower-end, non-gaming setup.

github

: 159

classifai

Supercharge WordPress Content Workflows and Engagement with Artificial Intelligence. Tap into leading cloud-based services like OpenAI, Microsoft Azure AI, Google Gemini and IBM Watson to augment your WordPress-powered websites. Publish content faster while improving SEO performance and increasing audience engagement. ClassifAI integrates Artificial Intelligence and Machine Learning technologies to lighten your workload and eliminate tedious tasks, giving you more time to create original content that matters.

github

: 620

chatbot-ui

Chatbot UI is an open-source AI chat app that allows users to create and deploy their own AI chatbots. It is easy to use and can be customized to fit any need. Chatbot UI is perfect for businesses, developers, and anyone who wants to create a chatbot.

github

: 27.7k

BricksLLM

BricksLLM is a cloud native AI gateway written in Go. Currently, it provides native support for OpenAI, Anthropic, Azure OpenAI and vLLM. BricksLLM aims to provide enterprise level infrastructure that can power any LLM production use cases. Here are some use cases for BricksLLM: * Set LLM usage limits for users on different pricing tiers * Track LLM usage on a per user and per organization basis * Block or redact requests containing PIIs * Improve LLM reliability with failovers, retries and caching * Distribute API keys with rate limits and cost limits for internal development/production use cases * Distribute API keys with rate limits and cost limits for students

github

: 953

uAgents

uAgents is a Python library developed by Fetch.ai that allows for the creation of autonomous AI agents. These agents can perform various tasks on a schedule or take action on various events. uAgents are easy to create and manage, and they are connected to a fast-growing network of other uAgents. They are also secure, with cryptographically secured messages and wallets.

github

: 1.3k

griptape

Griptape is a modular Python framework for building AI-powered applications that securely connect to your enterprise data and APIs. It offers developers the ability to maintain control and flexibility at every step. Griptape's core components include Structures (Agents, Pipelines, and Workflows), Tasks, Tools, Memory (Conversation Memory, Task Memory, and Meta Memory), Drivers (Prompt and Embedding Drivers, Vector Store Drivers, Image Generation Drivers, Image Query Drivers, SQL Drivers, Web Scraper Drivers, and Conversation Memory Drivers), Engines (Query Engines, Extraction Engines, Summary Engines, Image Generation Engines, and Image Query Engines), and additional components (Rulesets, Loaders, Artifacts, Chunkers, and Tokenizers). Griptape enables developers to create AI-powered applications with ease and efficiency.

github

: 2.2k