ActiveAwesome Machine Learning
A curated list of awesome Machine Learning frameworks, libraries and software.
Quick Answer
- 1. Awesome Machine Learning gives AI agents ai & ml capabilities with 73,607 GitHub stars and a quality score of 99/99.
- 2. Install: Run
npx -y awesome-machine-learningor add the config to your MCP client. - 3. Runtime: npm — no authentication required.
- 4. Health: Actively maintained — last checked 7/29/2026.
Overview
Category: AI & ML
Runtime: npm
Install: npx -y awesome-machine-learning
Auth: No auth required
Source: community
Stars: 73,607
Setup time: ~30 sec
What You Can Build
With the Awesome Machine Learning MCP server, your AI assistant can:
- Access ai & ml functionality through natural language
- Use the server immediately with no API key setup
- Combine with other MCP servers for cross-tool automation workflows
- Run with npm for reliable, consistent deployments
Health Status
ActiveLast commit: 7/20/2026
Checked: 7/29/2026
Page updated: June 13, 2026
One-Click Install
Copy the snippet for your MCP client and paste it in.
Claude Desktop
Add to claude_desktop_config.json
{
"mcpServers": {
"awesome-machine-learning": {
"command": "npx",
"args": [
"-y",
"awesome-machine-learning"
],
"env": {}
}
}
}Cursor
Settings → MCP Servers → Add
{
"mcpServers": {
"awesome-machine-learning": {
"command": "npx -y awesome-machine-learning"
}
}
}Saves as .cursor/mcp.json in the download. Move it to your project root.
VS Code
Use with MCP extension
{
"mcpServers": {
"awesome-machine-learning": {
"command": "npx -y awesome-machine-learning"
}
}
}SDK & Code Integration Examples
Connect to Awesome Machine Learning programmatically using TypeScript or Python MCP SDKs.
import { Client } from "@modelcontextprotocol/sdk/client/index.js";
import { StdioClientTransport } from "@modelcontextprotocol/sdk/client/stdio.js";
// Initialize Awesome Machine Learning MCP client transport
const transport = new StdioClientTransport({
command: "npx",
args: ["-y","awesome-machine-learning"],
});
const client = new Client(
{ name: "mcp-app-client", version: "1.0.0" },
{ capabilities: { tools: {} } }
);
async function connectAndRun() {
await client.connect(transport);
const tools = await client.listTools();
console.log("Connected to Awesome Machine Learning. Available tools:", tools);
}
connectAndRun().catch(console.error);MCP Server Configuration
Add this to your claude_desktop_config.json
{
"mcpServers": {
"awesome-machine-learning": {
"command": "npx",
"args": ["-y","awesome-machine-learning"],
"env": {}
}
}
}Error Resolution & Troubleshooting Matrix
Common JSON-RPC protocol error codes and environment troubleshooting for Awesome Machine Learning.
-32600 (Invalid Request)Cause: Malformed JSON-RPC payload sent to server
Resolution: Verify MCP client payload adheres to JSON-RPC 2.0 specs.
-32601 (Method Not Found)Cause: Requested tool or resource method does not exist
Resolution: Call list_tools() to inspect supported tool names.
-32602 (Invalid Params)Cause: Missing or invalid tool arguments
Resolution: Check argument schema types against tool definition.
-32603 (Internal Error)Cause: Unhandled execution exception inside server process
Resolution: Inspect process stderr logs or check API key credentials.
AUTH_KEY_MISSINGCause: Required environment variable not set in MCP config
Resolution: Define required API key under 'env' object in claude_desktop_config.json.
STDIO_PIPE_CLOSEDCause: Server process crashed on startup
Resolution: Run install command manually in terminal to inspect error output.
Quality Score
Automated quality ranking of maintaining source repository.
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