cb-tumblebug
Cloud-Barista Multi-Cloud Infra Management Framework
Stars: 72
CB-Tumblebug (CB-TB) is a system for managing multi-cloud infrastructure consisting of resources from multiple cloud service providers. It provides an overview, features, and architecture. The tool supports various cloud providers and resource types, with ongoing development and localization efforts. Users can deploy a multi-cloud infra with GPUs, enjoy multiple LLMs in parallel, and utilize LLM-related scripts. The tool requires Linux, Docker, Docker Compose, and Golang for building the source. Users can run CB-TB with Docker Compose or from the Makefile, set up prerequisites, contribute to the project, and view a list of contributors. The tool is licensed under an open-source license.
README:
CB-Tumblebug (CB-TB) is an advanced multi-cloud infrastructure management system that enables seamless provisioning, management, and orchestration of resources across multiple cloud service providers. Part of the Cloud-Barista project, CB-TB abstracts the complexity of multi-cloud environments into a unified, intuitive interface.
- đ Multi-Cloud Orchestration: Manage AWS, Azure, GCP, Alibaba Cloud, and more from a single platform
- ⥠Auto-provisioning: Intelligent resource recommendations and automated deployment
- đ Secure Operations: Encrypted credential management and hybrid encryption protocols
- đēī¸ Visual Infrastructure Map: Interactive GUI for infrastructure visualization and management
- đ¤ AI-Powered Management: NEW! Control infrastructure using natural language via our MCP Server
-
âī¸ Supported Cloud Providers & Resources
đ Note: Reference only - functionality not guaranteed. Regular updates are made.
Kubernetes support is currently WIP with limited features available.
đ Development Status & Contributing Notes
CB-TB has not reached version 1.0 yet. We welcome any new suggestions, issues, opinions, and contributors! Please note that the functionalities of Cloud-Barista are not yet stable or secure. Be cautious if you plan to use the current release in a production environment. If you encounter any difficulties using Cloud-Barista, please let us know by opening an issue or joining the Cloud-Barista Slack.
As an open-source project initiated by Korean members, we aim to encourage participation from Korean contributors during the initial stages of this project. Therefore, the CB-TB repository will accept the use of the Korean language in its early stages. However, we hope this project will thrive regardless of contributors' countries in the long run. To facilitate this, the maintainers recommend using English at least for the titles of Issues, Pull Requests, and Commits, while accommodating local languages in the contents.
đ¤ NEW: AI-Powered Multi-Cloud Management
- Control CB-Tumblebug through AI assistants like Claude and VS Code
- Natural language interface for infrastructure provisioning and management using MCP (Model Context Protocol)
- Streamable HTTP transport for modern MCP compatibility
- đ MCP Server Guide | đ Quick Start
đŽ GPU-Powered Multi-Cloud LLM Deployment
- Deploy GPU instances across multiple clouds for AI/ML workloads
- đ§ LLM Scripts & Examples
- ⥠Quick Start
- đ§ Prerequisites
- đ Installation & Setup
- đ How to Use
- đ ī¸ Development
- đ¤ Contributing
Get CB-Tumblebug running in under 5 minutes:
# 1. Automated setup (recommended for new users)
curl -sSL https://raw.githubusercontent.com/cloud-barista/cb-tumblebug/main/scripts/set-tb.sh | bash
# 2. Start all services
cd ~/go/src/github.com/cloud-barista/cb-tumblebug
make up
# 3. Configure credentials (see detailed setup below)
./init/genCredential.sh
# Edit ~/.cloud-barista/credentials.yaml with your cloud credentials
./init/encCredential.sh
make init
# 4. Access services
# - API: http://localhost:1323/tumblebug/api
# - MapUI: http://localhost:1324
# - MCP Server: http://localhost:8000/mcp (if enabled)đĄ New to CB-Tumblebug? Follow the detailed setup guide below for comprehensive instructions.
| Component | Minimum Specification | Recommended |
|---|---|---|
| OS | Linux (Ubuntu 22.04+) | Ubuntu 22.04 LTS |
| CPU | 4 cores | 8+ cores |
| Memory | 6 GiB | 16+ GiB |
| Storage | 20 GiB free space | 50+ GiB SSD |
| Example | AWS c5a.xlarge
|
AWS c5a.2xlarge
|
â ī¸ Performance Note: Lower specifications may cause initialization failures or performance degradation.
- Docker & Docker Compose (latest stable)
- Go 1.25.0+ (for building from source)
- Git (for cloning repository)
- đĻ View Dependencies
- đĄī¸ Software Bill of Materials (SBOM)
For new users on clean Linux systems:
# Download and run automated setup script
curl -sSL https://raw.githubusercontent.com/cloud-barista/cb-tumblebug/main/scripts/set-tb.sh | bashâšī¸ After the script finishes, you may need to log out and back in to activate Docker permissions and aliases. If you'd prefer to install dependencies and clone the repository manually, follow the steps below. đ
-
Clone the CB-Tumblebug repository:
git clone https://github.com/cloud-barista/cb-tumblebug.git $HOME/go/src/github.com/cloud-barista/cb-tumblebug cd ~/go/src/github.com/cloud-barista/cb-tumblebug
Optionally, you can register aliases for the CB-Tumblebug directory to simplify navigation:
echo "alias cdtb='cd $HOME/go/src/github.com/cloud-barista/cb-tumblebug'" >> ~/.bashrc echo "alias cdtbsrc='cd $HOME/go/src/github.com/cloud-barista/cb-tumblebug/src'" >> ~/.bashrc echo "alias cdtbtest='cd $HOME/go/src/github.com/cloud-barista/cb-tumblebug/src/testclient/scripts'" >> ~/.bashrc source ~/.bashrc
-
Check Docker Compose Installation:
Ensure that Docker Engine and Docker Compose are installed on your system. If not, you can use the following script to install them (note: this script is not intended for production environments):
# download and install docker with docker compose curl -sSL get.docker.com | sh # optional: add user to docker groupd sudo groupadd docker sudo usermod -aG docker ${USER} newgrp docker # test the docker works docker run hello-world
-
Start All Components Using Docker Compose:
To run all components, use the following command:
cd ~/go/src/github.com/cloud-barista/cb-tumblebug docker compose up
This command will start all components as defined in the preconfigured docker-compose.yaml file. For configuration customization, please refer to the guide.
The following components will be started:
- ETCD: CB-Tumblebug KeyValue DB
- CB-Spider: a Cloud API controller
- CB-MapUI: a simple Map-based GUI web server
- CB-Tumblebug: the system with API server
- CB-Tumblebug MCP Server: AI assistant interface (if enabled)
- PostgreSQL: Specs and Images storage
- Traefik: Reverse proxy for secure access
Container Architecture Overview:
graph TB subgraph "External Access" User[đ¤ User] AI[đ¤ AI Assistant<br/>Claude/VS Code] end subgraph "Docker Compose Environment" subgraph "Frontend & Interfaces" UI[CB-MapUI<br/>:1324] MCP[TB-MCP Server<br/>:8000] Proxy[Traefik Proxy<br/>:80/:443] end subgraph "Backend Services" TB[CB-Tumblebug<br/>:1323<br/>Multi-Cloud Management] Spider[CB-Spider<br/>:1024<br/>Cloud API Abstraction] ETCD[ETCD<br/>:2379<br/>Metadata Store] PG[PostgreSQL<br/>:5432<br/>Specs/Images DB] end end subgraph "Cloud Providers" AWS[AWS] Azure[Azure] GCP[GCP] Others[Others...] end %% User connections User -->|HTTP/HTTPS| Proxy User -->|HTTP| UI User -->|HTTP| TB AI -->|MCP HTTP| MCP %% Proxy routing Proxy -->|Route| UI %% Internal service connections UI -.->|API calls| TB MCP -->|REST API| TB TB -->|REST API| Spider TB -->|gRPC| ETCD TB -->|SQL| PG %% Cloud connections Spider -->|Cloud APIs| AWS Spider -->|Cloud APIs| Azure Spider -->|Cloud APIs| GCP Spider -->|Cloud APIs| Others %% Styling classDef frontend fill:#e3f2fd,stroke:#1976d2 classDef backend fill:#f3e5f5,stroke:#7b1fa2 classDef storage fill:#e8f5e8,stroke:#388e3c classDef cloud fill:#fff3e0,stroke:#f57c00 class UI,MCP,Proxy frontend class TB,Spider,ETCD,PG backend class AWS,Azure,GCP,Others cloud
After running the command, you should see output similar to the following:
Service Endpoints:
- CB-Tumblebug API: http://localhost:1323/tumblebug/api
- CB-MapUI: http://localhost:1324 (direct) or https://cb-mapui.localhost (via Traefik with SSL)
- MCP Server: http://localhost:8000/mcp (if enabled)
- Traefik Dashboard: http://localhost:8080 (reverse proxy monitoring)
Note: Before using CB-Tumblebug, you need to initialize it.
To provisioning multi-cloud infrastructures with CB-TB, it is necessary to register the connection information (credentials) for clouds, as well as commonly used images and specifications.
-
Create
credentials.yamlfile and input your cloud credentials-
Overview
-
credentials.yamlis a file that includes multiple credentials to use API of Clouds supported by CB-TB (AWS, GCP, AZURE, ALIBABA, etc.) - It should be located in the
~/.cloud-barista/directory and securely managed. - Refer to the
template.credentials.yamlfor the template
-
-
Create
credentials.yamlthe fileAutomatically generate the
credentials.yamlfile in the~/.cloud-barista/directory using the CB-TB scriptcd ~/go/src/github.com/cloud-barista/cb-tumblebug ./init/genCredential.sh
-
Input credential data
Put credential data to
~/.cloud-barista/credentials.yaml(Reference: How to obtain a credential for each CSP)### Cloud credentials for credential holders (default: admin) credentialholder: admin: alibaba: # ClientId(ClientId): client ID of the EIAM application # Example: app_mkv7rgt4d7i4u7zqtzev2mxxxx ClientId: # ClientSecret(ClientSecret): client secret of the EIAM application # Example: CSEHDcHcrUKHw1CuxkJEHPveWRXBGqVqRsxxxx ClientSecret: aws: # ClientId(aws_access_key_id) # ex: AKIASSSSSSSSSSS56DJH ClientId: # ClientSecret(aws_secret_access_key) # ex: jrcy9y0Psejjfeosifj3/yxYcgadklwihjdljMIQ0 ClientSecret: ...
-
-
Encrypt
credentials.yamlintocredentials.yaml.encTo protect sensitive information,
credentials.yamlis not used directly. Instead, it must be encrypted usingencCredential.sh. The encrypted filecredentials.yaml.encis then used byinit.py. This approach ensures that sensitive credentials are not stored in plain text.-
Encrypting Credentials
init/encCredential.sh
When executing the script, you have two options: 1) enter your password or 2) let the system generate a random passkey.
Option 1: Entering your password:
Option 2: Letting the system generate a random passkey, which MUST be securely stored in a safe location:
If you need to update your credentials, decrypt the encrypted file using
decCredential.sh, make the necessary changes tocredentials.yaml, and then re-encrypt it. -
-
(INIT) Register all multi-cloud connection information and common resources
-
How to register
Refer to README.md for init.py, and execute the
init.pyscript by theinit.sh. (enter 'y' for confirmation prompts)init.sh --helpwill show the available options.You can also simply use the
make initcommand as follows:cd ~/go/src/github.com/cloud-barista/cb-tumblebug make init
-
The credentials in
~/.cloud-barista/credentials.yaml.enc(encrypted file from thecredentials.yaml) will be automatically registered (all CSP and region information recorded incloudinfo.yamlwill be automatically registered in the system)- Note: You can check the latest regions and zones of CSP using
update-cloudinfo.pyand review the file for updates. (contributions to updates are welcome)
- Note: You can check the latest regions and zones of CSP using
-
Common images and specifications recorded in the
cloudimage.csvandcloudspec.csvfiles in theassetsdirectory will be automatically registered. -
init.pywill apply the hybrid encryption for secure transmission of credentials- Retrieve RSA Public Key: Use the
/credential/publicKeyAPI to get the public key. - Encrypt Credentials: Encrypt credentials with a randomly generated
AESkey, then encrypt theAESkey with theRSA public key. - Transmit Encrypted Data: Send
the encrypted credentialsandAES keyto the server. The server decrypts the AES key and uses it to decrypt the credentials.
This method ensures your credentials are securely transmitted and protected during registration. See init.py for a Python implementation. In detail, check out Secure Credential Registration Guide (How to use the credential APIs)
- Retrieve RSA Public Key: Use the
-
-
-
Shutting down CB-TB and related components
-
Stop all containers by
ctrl+cor type the commanddocker compose stop/docker compose down/make down(When a shutdown event occurs to CB-TB, the system will be shutting down gracefully: API requests that can be processed within 10 seconds will be completed) -
In case of cleanup is needed due to internal system errors
- Check and delete resources created through CB-TB
- Delete CB-TB & CB-Spider metadata using the provided script
cd ~/go/src/github.com/cloud-barista/cb-tumblebug make clean-db
-
-
Upgrading the CB-TB & CB-Spider versions
The following cleanup steps are unnecessary if you clearly understand the impact of the upgrade
- Check and delete resources created through CB-TB
- Delete CB-TB & CB-Spider metadata
cd ~/go/src/github.com/cloud-barista/cb-tumblebug make clean-db
- Restart with the upgraded version
- đ¤ Using CB-TB MCP Server (AI Assistant Interface) (NEW!)
- Using CB-TB MapUI (recommended)
- Using CB-TB REST API (recommended)
đ NEW: Control CB-Tumblebug with AI assistants like Claude!
The Model Context Protocol (MCP) Server enables natural language interaction with CB-Tumblebug through AI assistants:
- đ§ AI-Powered Infrastructure Management: Deploy and manage multi-cloud resources using natural language commands
- đ Seamless Integration: Works with Claude Desktop (via proxy), VS Code (direct), and other MCP-compatible clients
- đĄ Modern Protocol: Uses Streamable HTTP transport (current MCP standard)
-
⥠Quick Start: Enable with
make upand uncomment MCP service indocker-compose.yaml
# Enable MCP Server (Proof of Concept)
# 1. Uncomment cb-tumblebug-mcp-server in docker-compose.yaml
# 2. Launch with Docker Compose
make up
# Access MCP server at http://localhost:8000/mcpđ Complete MCP Server Guide â
Visual Infrastructure Management with Interactive Maps
CB-MapUI provides an intuitive, map-based interface for managing multi-cloud infrastructure:
- đēī¸ Geographic Visualization: See your infrastructure deployed across the globe
- đ Real-time Monitoring: Monitor resource status and performance
- đŽ Interactive Control: Create, manage, and control resources visually
- đ Multi-Cloud View: Unified view across all cloud providers
# Access CB-MapUI (auto-started with Docker Compose)
open http://localhost:1324
# Or run standalone MapUI container
./scripts/runMapUI.shFeatures:
- Drag-and-drop resource creation
- Real-time infrastructure mapping
- Cross-cloud resource relationships
- Performance metrics overlay
đ Learn More: CB-MapUI Repository
Programmatic Multi-Cloud Infrastructure Management
CB-Tumblebug provides a comprehensive REST API for automated infrastructure management:
đ API Dashboard & Documentation
- Interactive API Explorer: http://localhost:1323/tumblebug/api
-
Live Documentation:
đ Authentication CB-TB uses Basic Authentication (development phase - not production-ready):
# Include base64 encoded credentials in request headers
Authorization: Basic <base64(username:password)>đ Quick Infrastructure Creation Following the Quick MCI Creation Guide:
# 1. Create VM specification
curl -X POST "http://localhost:1323/tumblebug/ns/default/resources/spec" \
-H "Authorization: Basic <credentials>" \
-d '{"name": "web-spec", "connectionName": "aws-ap-northeast-2"}'
# 2. Create VM image
curl -X POST "http://localhost:1323/tumblebug/ns/default/resources/image" \
-H "Authorization: Basic <credentials>" \
-d '{"name": "ubuntu-image", "connectionName": "aws-ap-northeast-2"}'
# 3. Create Multi-Cloud Infrastructure
curl -X POST "http://localhost:1323/tumblebug/ns/default/mci" \
-H "Authorization: Basic <credentials>" \
-d @mci-config.jsonđ ī¸ Core API Categories
- Infrastructure Resources: VM specs, images, networks, security groups
- Multi-Cloud Infrastructure (MCI): Provision and manage distributed infrastructure
- Monitoring & Control: Performance metrics, scaling, lifecycle management
-
Credentials & Connections: Secure cloud provider configuration
- Create access key object
- Create, view, control, execute remote commands, shut down, and delete MCI using the MCI(multi-cloud infrastructure service) management APIs
- CB-TB optimal and dynamic provisioning
-
Setup required tools
-
Install: git, gcc, make
sudo apt update sudo apt install make gcc git
-
Install: Golang
-
Check https://golang.org/dl/ and setup Go
-
Download
wget https://go.dev/dl/go1.25.0.linux-amd64.tar.gz; sudo rm -rf /usr/local/go && sudo tar -C /usr/local -xzf go1.25.0.linux-amd64.tar.gz
-
Setup environment
echo 'export PATH=$PATH:/usr/local/go/bin:$HOME/go/bin' >> ~/.bashrc echo 'export GOPATH=$HOME/go' >> ~/.bashrc
source ~/.bashrc echo $GOPATH go env go version
-
-
-
-
Run Docker Compose with the build option
To build the current CB-Tumblebug source code into a container image and run it along with the other containers, use the following command:
cd ~/go/src/github.com/cloud-barista/cb-tumblebug sudo DOCKER_BUILDKIT=1 docker compose up --build
This command will automatically build the CB-Tumblebug from the local source code and start it within a Docker container, along with any other necessary services as defined in the
docker-compose.ymlfile.DOCKER_BUILDKIT=1setting is used to speed up the build by using the go build cache technique.
-
Build the Golang source code using the Makefile
cd ~/go/src/github.com/cloud-barista/cb-tumblebug/src make
All dependencies will be downloaded automatically by Go.
The initial build will take some time, but subsequent builds will be faster by the Go build cache.
Note To update the Swagger API documentation, run
make swag- API documentation file will be generated at
cb-tumblebug/src/interface/rest/docs/swagger.yaml - API documentation can be viewed in a web browser at http://localhost:1323/tumblebug/api (provided when CB-TB is running)
- Detailed information on how to update the API
- API documentation file will be generated at
-
Set environment variables required to run CB-TB (in another tab)
- Check and configure the contents of
cb-tumblebug/conf/setup.env(CB-TB environment variables, modify as needed)- Apply the environment variables to the system
cd ~/go/src/github.com/cloud-barista/cb-tumblebug source conf/setup.env
- (Optional) Automatically set the TB_SELF_ENDPOINT environment variable (an externally accessible address) using a script if needed
- This is necessary if you want to access and control the Swagger API Dashboard from outside when CB-TB is running
cd ~/go/src/github.com/cloud-barista/cb-tumblebug source ./scripts/setPublicIP.sh
- Apply the environment variables to the system
- Check and configure the contents of
-
Execute the built cb-tumblebug binary by using
make runcd ~/go/src/github.com/cloud-barista/cb-tumblebug/src make run
CB-TB welcomes improvements from both new and experienced contributors!
Check out CONTRIBUTING.
Thanks goes to these wonderful people (emoji key):
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This repository contains scripts for installing HPC and AI libraries and tools to build Azure HPC/AI images. It streamlines the process of provisioning compute-intensive workloads and crafting advanced AI models in the cloud, ensuring efficiency and reliability in deployments.
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cb-tumblebug
CB-Tumblebug (CB-TB) is a system for managing multi-cloud infrastructure consisting of resources from multiple cloud service providers. It provides an overview, features, and architecture. The tool supports various cloud providers and resource types, with ongoing development and localization efforts. Users can deploy a multi-cloud infra with GPUs, enjoy multiple LLMs in parallel, and utilize LLM-related scripts. The tool requires Linux, Docker, Docker Compose, and Golang for building the source. Users can run CB-TB with Docker Compose or from the Makefile, set up prerequisites, contribute to the project, and view a list of contributors. The tool is licensed under an open-source license.
awesome-devops-mcp-servers
A curated list of awesome Model Context Protocol (MCP) servers focused on DevOps tools and capabilities. MCP is an open protocol that enables AI models to securely interact with local and remote resources through standardized server implementations. This list focuses on DevOps-related MCP servers that extend AI capabilities through cloud infrastructure management, CLI operations, version control, security scanning, and other DevOps-related services.
robusta
Robusta is a tool designed to enhance Prometheus notifications for Kubernetes environments. It offers features such as smart grouping to reduce notification spam, AI investigation for alert analysis, alert enrichment with additional data like pod logs, self-healing capabilities for defining auto-remediation rules, advanced routing options, problem detection without PromQL, change-tracking for Kubernetes resources, auto-resolve functionality, and integration with various external systems like Slack, Teams, and Jira. Users can utilize Robusta with or without Prometheus, and it can be installed alongside existing Prometheus setups or as part of an all-in-one Kubernetes observability stack.
AI-CloudOps
AI+CloudOps is a cloud-native operations management platform designed for enterprises. It aims to integrate artificial intelligence technology with cloud-native practices to significantly improve the efficiency and level of operations work. The platform offers features such as AIOps for monitoring data analysis and alerts, multi-dimensional permission management, visual CMDB for resource management, efficient ticketing system, deep integration with Prometheus for real-time monitoring, and unified Kubernetes management for cluster optimization.
awesome-mcp-servers-devops
This repository, 'awesome-mcp-servers-devops', is a curated list of Model Context Protocol servers for DevOps workflows. It includes servers for various aspects of DevOps such as infrastructure, CI/CD, monitoring, security, and cloud operations. The repository provides information on different MCP servers available for tools like GitHub, GitLab, Azure DevOps, Gitea, Terraform, Vault, Pulumi, Kubernetes, Docker Hub, Portainer, Qovery, various command line and local operation tools, browser automation tools, code execution tools, coding agents, aggregators, CI/CD tools like Argo CD, Jenkins, GitHub Actions, Codemagic, DevOps visibility tools, build tools, cloud platforms like AWS, Azure, Cloudflare, Alibaba Cloud, observability tools like Grafana, Datadog, Prometheus, VictoriaMetrics, Alertmanager, APM & monitoring tools, security tools like Snyk, Semgrep, and community security servers, collaboration tools like Atlassian, Jira, project management tools, service desks, Notion, and more.
sre-agent
SRE Agent is an open-source AI agent designed to help Site Reliability Engineers (SREs) debug, maintain healthy Kubernetes systems, and simplify DevOps tasks. With a command-line interface (CLI), users can interact directly with the agent to diagnose issues, report diagnostics, and streamline operations. The agent supports root cause debugging, Kubernetes log querying, GitHub codebase search, and CLI-powered interactions. It is powered by the Model Context Protocol (MCP) for seamless connectivity. Users can configure AWS credentials, GitHub integration, and Anthropic API key to start monitoring deployments and diagnosing issues. The tool is structured with Python services and TypeScript MCP servers for development and maintenance.
serverless-pdf-chat
The serverless-pdf-chat repository contains a sample application that allows users to ask natural language questions of any PDF document they upload. It leverages serverless services like Amazon Bedrock, AWS Lambda, and Amazon DynamoDB to provide text generation and analysis capabilities. The application architecture involves uploading a PDF document to an S3 bucket, extracting metadata, converting text to vectors, and using a LangChain to search for information related to user prompts. The application is not intended for production use and serves as a demonstration and educational tool.
generative-bi-using-rag
Generative BI using RAG on AWS is a comprehensive framework designed to enable Generative BI capabilities on customized data sources hosted on AWS. It offers features such as Text-to-SQL functionality for querying data sources using natural language, user-friendly interface for managing data sources, performance enhancement through historical question-answer ranking, and entity recognition. It also allows customization of business information, handling complex attribution analysis problems, and provides an intuitive question-answering UI with a conversational approach for complex queries.