VegaMCP
Provides Docker sandbox v5.0 for isolating AI agent processes and running containerized tasks.
Provides integration with the GitHub API for repository management and rate limit improvements.
Provides self-hosted web search fallback for enhanced search capabilities.
Provides error tracking and monitoring for server errors and performance issues.
Provides persistent semantic memory and ultra-fast chat syncing via SQLite.
Click on "Install Server".
Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state.
In the chat, type
@followed by the MCP server name and your instructions, e.g., "@VegaMCPuse the agent swarm to analyze my codebase"
That's it! The server will respond to your query, and you can continue using it as needed.
Here is a step-by-step guide with screenshots.
VegaMCP is a production-grade MCP (Model Context Protocol) server providing an autonomous AI agent swarm, persistent semantic memory, browser automation, multi-model reasoning, security gateway, agent graphs, zero-trust identity, A2A protocol, Docker sandbox v5.0, AI-first testing suite (mobile, web, API, desktop, accessibility, security, visual), and 78+ tools — all accessible via any MCP-compatible client.
Version 7.2 (Sovereign Intelligence) introduces the production Claw Command Center, ultra-fast SQLite chat syncing, local vector Semantic Memory, LLM Output Evaluation, and 6 Unified Omni-Clusters.
📖 Complete Features
Read FEATURES.md for a comprehensive list of all 17 unified V7 capability clusters including Docker Sandbox v5.0.
Related MCP server: MCP Agent Memory
Quick Start
Prerequisites
Node.js 20+
npm 9+
Installation
# Clone the repository
git clone https://github.com/Pastarafian/VegaMCP.git
cd VegaMCP
# Install dependencies
npm install
# Copy environment config
cp .env.example .env
# Edit .env with your API keys
# Build
npm run buildConnect to VS Code (Gemini / Copilot)
Create .vscode/mcp.json in your workspace:
{
"servers": {
"REDACTED": {
"type": "stdio",
"command": "node",
"args": ["/path/to/VegaMCP/build/index.js"],
"cwd": "/path/to/VegaMCP"
}
}
}Note: API keys can be set in the
envblock ofmcp.jsonor in the.envfile (dotenvis loaded automatically).
Configuration
Copy .env.example to .env and configure:
# At least one reasoning model key required
OPENROUTER_API_KEY= # Supports ALL models via OpenRouter
DEEPSEEK_API_KEY= # Direct DeepSeek API (R1 + Chat)
KIMI_API_KEY= # Kimi K2.5 for coding
# Optional integrations
GITHUB_TOKEN= # GitHub API (60→5000 req/hr)
TAVILY_API_KEY= # AI-powered web search
SEARXNG_URL= # Self-hosted search fallback
SENTRY_AUTH_TOKEN= # Error tracking
SENTRY_ORG=
SENTRY_PROJECT=
# Budget controls
TOKEN_DAILY_BUDGET_USD=5.00
TOKEN_HOURLY_BUDGET_USD=1.00
# Tool profiles
VEGAMCP_TOOL_PROFILE=full # full | minimal | research | coding | opsProject Structure
VegaMCP/
├── src/
│ ├── index.ts # Server entry point + hub router
│ ├── mcp-extensions.ts # Sampling, logging, progress, roots
│ ├── mcp-protocol/ # v6.0 / v7.0 protocol modules
│ ├── db/ # SQLite + vector store
│ ├── swarm/ # Agent swarm (10 agents)
│ ├── tools/ # All tool implementations
│ ├── resources/ # MCP resource providers
│ ├── prompts/ # MCP prompt templates
│ └── security/ # Rate limiter, validator, guard
├── .env.example # Environment template
├── package.json
└── tsconfig.jsonLicense
MIT
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