SmythOS Runtime Environment
The SmythOS Runtime Environment (SRE) is an open-source, cloud-native runtime for agentic AI. Secure, modular, and production-ready, it lets developers build, run, and manage intelligent agents across local, cloud, and edge environments.
What is SmythOS Runtime Environment?
SmythOS Runtime Environment is a Model Context Protocol (MCP) server that allows AI assistants like Claude, Cursor, and VS Code to smythos runtime environment (sre) is an open-source, cloud-native runtime for agentic ai. secure, modular, and production-ready, it lets developers build, run, and manage intelligent agents across loc...
The SmythOS Runtime Environment (SRE) is an open-source, cloud-native runtime for agentic AI. Secure, modular, and production-ready, it lets developers build, run, and manage intelligent agents across local, cloud, and edge environments.
This server falls under the Cloud Services category on MCPgee, the world's largest MCP server directory with 33,000+ servers.
Features
- The SmythOS Runtime Environment (SRE) is an open-source, clo
Use Cases
Maintainer
Works with
Installation
Manual Installation
npx sreConfiguration
Configuration Details
claude_desktop_config.json
Performance
Response Metrics
Resource Usage
How to Set Up and Use SmythOS Runtime Environment
The SmythOS Runtime Environment (SRE) is an open-source, cloud-native runtime for building, running, and managing production AI agents across local, cloud, and edge environments. It provides a modular SDK with 40+ built-in components covering LLM operations, web capabilities, data processing, storage management, and serverless code execution, with pluggable connectors for storage (S3, GCS, Azure), vector databases (Pinecone, Milvus), caching (Redis), credential vaults (HashiCorp, AWS Secrets Manager), and LLM providers (OpenAI, Anthropic, Google AI, AWS Bedrock, Groq). Platform engineers and AI developers use SRE to build secure, observable, production-ready agentic systems without managing low-level infrastructure.
Prerequisites
- Node.js 18+ and npm for the CLI and SDK
- API keys for at least one LLM provider: OpenAI, Anthropic, Google AI, AWS Bedrock, or Groq
- Optional: AWS, GCP, or Azure credentials for cloud storage and secrets management connectors
- Optional: Redis for production caching, or HashiCorp Vault for enterprise credential management
Install the SmythOS CLI globally
Install the @smythos/cli package globally via npm to access the sre command and project scaffolding tools.
npm i -g @smythos/cliCreate a new SRE project
Use the CLI to scaffold a new agent project with the standard SmythOS structure.
sre createInstall the SRE SDK in an existing project
To add SRE to an existing Node.js project, install the SDK package directly.
npm install @smythos/sdkConfigure production connectors
Initialize SRE with your production infrastructure connectors. For local development, SRE defaults to RAM cache and local storage with no extra configuration needed.
Build and run an agent
Define your agent using the SRE SDK's component model. Use built-in components for LLM calls, web search, data processing, and storage, then run the agent with the CLI or programmatically.
SmythOS Runtime Environment Examples
Client configuration
Claude Desktop MCP configuration for integrating with the SmythOS Runtime Environment.
{
"mcpServers": {
"sre": {
"command": "npx",
"args": ["sre"],
"env": {
"LOG_LEVEL": "info"
}
}
}
}Production SRE initialization (TypeScript)
Example of initializing SRE with production-grade connectors for vault, cache, and storage.
import { SRE } from '@smythos/sdk';
const sre = SRE.init({
Vault: {
Connector: 'Hashicorp',
Settings: { url: 'https://vault.company.com' }
},
Cache: {
Connector: 'Redis',
Settings: { url: 'redis://prod-cluster' }
},
Storage: {
Connector: 'S3',
Settings: { bucket: 'company-ai-agents' }
}
});Prompts to try
Example prompts for an AI assistant integrated with SmythOS SRE capabilities.
- "Build an agent that scrapes a news website, summarizes each article using an LLM, and stores the results in S3."
- "Create a multi-step agent that queries a vector database for similar documents and then calls an LLM to synthesize them."
- "Set up an agent workflow that handles retries and exponential backoff for API calls."
- "Deploy an agent to process incoming webhook events and store structured results in Redis cache."Troubleshooting SmythOS Runtime Environment
sre command not found after 'npm i -g @smythos/cli'
Check that npm's global bin directory is on your PATH. Run 'npm bin -g' to find the directory and add it to your PATH. On macOS with nvm, the global bin location changes per Node version — ensure you are using the same Node version where you installed the CLI.
LLM provider authentication fails at runtime
Set the LOG_LEVEL environment variable to 'debug' to get detailed error output. Ensure your API keys are available in the environment where the agent runs. For production, configure a Vault connector (HashiCorp or AWS Secrets Manager) to inject credentials securely rather than relying on plain environment variables.
Vector database connector errors with Pinecone or Milvus
Confirm the connector-specific settings (index name, namespace, host URL) match your actual Pinecone or Milvus configuration. For Milvus, ensure the gRPC port (19530) is reachable. For Pinecone, verify your API key and environment region match the index region.
Frequently Asked Questions about SmythOS Runtime Environment
What is SmythOS Runtime Environment?
SmythOS Runtime Environment is a Model Context Protocol (MCP) server that smythos runtime environment (sre) is an open-source, cloud-native runtime for agentic ai. secure, modular, and production-ready, it lets developers build, run, and manage intelligent agents across local, cloud, and edge environments. It connects AI assistants to external tools and data sources through a standardized interface.
How do I install SmythOS Runtime Environment?
Follow the installation instructions on the SmythOS Runtime Environment GitHub repository. Clone the repo, install dependencies, and add the server config to your AI client.
Which AI clients work with SmythOS Runtime Environment?
SmythOS Runtime Environment works with all major MCP-compatible AI clients including Claude Desktop, Claude Code, Cursor, VS Code (GitHub Copilot), Windsurf, and Cline.
Is SmythOS Runtime Environment free to use?
Yes, SmythOS Runtime Environment is open source and available under the MIT license. You can use it freely in both personal and commercial projects.
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Set Up SmythOS Runtime Environment in Your Editor
Choose your AI client for step-by-step setup instructions.
Quick Config Preview
Add this to your claude_desktop_config.json or .cursor/mcp.json
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