KI-Agenten
Unla
Unla MCP Gateway: turn existing APIs and MCP servers into MCP endpoints with zero code changes.
★ 2,2 Tsd. SterneMIT
Anleitung ansehen →metamcp ist ein Open-Source-Projekt aus der Kategorie KI-Agenten von metatool-ai (metatool-ai/metamcp auf GitHub). Es hat 2.698 Sterne auf GitHub und 436 Forks, wird unter der Lizenz MIT veröffentlicht und wurde zuletzt am 22. Juni 2026 auf GitHub aktualisiert.
MCP aggregator, orchestrator, middleware and gateway in one Docker — compose many servers into one endpoint
List the MCP servers you want behind one endpoint and which tools each audience should see.
Clone the repo and copy example.env to .env.
Start it with docker compose up -d, then add your MCP server configurations in the UI.
Create a namespace from your servers, assign it to an endpoint, and connect your client to that endpoint URL.
Combines many MCP servers into one unified MCP server at runtime — no client-side config sprawl.
Group servers into namespaces, enable/disable servers or individual tools, and switch a namespace on an endpoint in one click.
Publish SSE, Streamable HTTP or OpenAPI endpoints with API-key auth (header or query) or standard MCP OAuth.
Namespace-level middleware intercepts and transforms requests/responses; a built-in example filters inactive tools to save context.
Rename tools, edit titles/descriptions and attach custom annotations per namespace without losing upstream metadata.
Built-in inspector with saved server configs, plus multi-tenancy with private/public scopes and per-user API keys.
Clone MetaMCP and enter the directory.
git clone https://github.com/metatool-ai/metamcp.git
cd metamcpCopy the example env file and start the stack with Docker Compose.
cp example.env .env
docker compose up -dPoint an MCP client at your MetaMCP endpoint (example for Cursor in the README).
{
"mcpServers": {
"MetaMCP": { "url": "http://localhost:12008/metamcp/<endpoint>/sse" }
}
}metatool-ai maintains it; docs are at docs.metamcp.com, and the README also links a community-maintained fork.
Yes, it is MIT-licensed and self-hosted in one Docker Compose stack.
No. It issues its own API keys for endpoint access; the MCP servers you aggregate may have their own credentials, handled via env references.
Both are self-hosted MCP gateways. MetaMCP emphasizes dynamic namespaces, middleware and tool overrides; MCPHub emphasizes smart routing, per-user credentials and operations monitoring.
Clone, cp example.env .env, docker compose up -d, add a server in the UI, create a namespace and endpoint, and connect your client.
Unla MCP Gateway: turn existing APIs and MCP servers into MCP endpoints with zero code changes.
Self-hosted MCP gateway and control plane — connect, control and operate all your MCP servers from one place
The fast, Pythonic way to build MCP servers and clients.
An open-source auth gateway connecting 1,500+ SaaS providers to AI agents via SDK, CLI, MCP and HTTP.
Archestra: the enterprise AI platform — chat UI, LLM gateway, and MCP gateway with guardrails
Shrimp Task Manager: an MCP server that turns natural language into structured, dependent dev tasks.
Die anderen Projekte der Kategorie KI-Agenten mit den meisten Sternen im LibreAI-Katalog — die realistische Auswahl, falls metamcp nicht das Richtige ist.
A personal AI that really does things — on any OS, with your data staying yours.
An agentic skills framework & software development methodology that works.
Agent harness optimization system with skills, memory and security for Claude Code, Codex and more
The agent that grows with you — a Python framework for adaptive AI agents.
DeepSeek's plugin-based agent harness where everything is a plugin.
Verwandte Projekte der Kategorie KI-Agenten im Katalog, die metamcp bei den Sternen am nächsten stehen — zum Weiterstöbern auf beiden Seiten.
Multi-agent court inspired by ancient China: specialized agents debate and decide.
Official agent skills that teach AI coding tools to work with Supabase.
Sync your AI coding setup: skills, agents, rules, MCP, hooks, everywhere.
YAO: rigorous engineering, evaluation, and governance for reusable agent skills.
Persistent memory layer for AI agents, backed by Markdown and Milvus.
Free Chinese AIGC course: prompting, agents, image, video, voice, open models.
Kostenlos und offen — hol dir den Quellcode, gib dem Repository einen Stern und leg los.