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Production-ready MCP integrations for AI applications

Production-ready MCP integrations for AI applications

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klavis.aiAI & Agents5.8kUpdated 21w ago
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About Production-ready MCP integrations for AI applications

Klavis AI provides MCP integration platforms that let AI agents use external tools reliably. It offers prebuilt MCP integrations, the Strata connector and an MCP Sandbox, available as cloud-hosted or self-hosted options.

Key features

  1. 01100+ prebuilt integrations with OAuth support
  2. 02Strata connector to optimize context window
  3. 03Open source Strata that can be self-hosted
  4. 04Python and TypeScript SDKs
  5. 05REST API for creating Strata servers
  6. 06MCP Sandbox for LLM training and RL environments

Use cases

  1. 01Connecting AI agents to services such as Gmail and Slack
  2. 02Running MCP servers locally with Docker or pipx
  3. 03Building scalable MCP environments for LLM training and reinforcement learning

MCP support

The project exposes third-party integrations as MCP servers for MCP clients, and Strata aggregates multiple MCP servers behind a single connection point.

#api#open-source#oauth2#ai#integration#discord#mcp#developer-tools#agents#ai-agents#llm#function-calling#mcp-server#mcp-client

Configuration

Remote servers need no local install. Pick your client, then copy the config or install in one click.

Endpointhttps://strata.klavis.ai/mcp/
Copy install command
Claude CodeTerminal
Cursor.cursor/mcp.json
VS Code.vscode/mcp.json
Claude Desktopclaude_desktop_config.json
Windsurf~/.codeium/windsurf/mcp_config.json

Run commands in a terminal; paste JSON into the client’s MCP config and restart. Servers that need sign-in will prompt on first connect.

Tools

Details

From the project README
README image

Documentation Website Discord

Strata - One MCP server for AI agents to handle thousands of tools | Product Hunt

🎯 Choose Your Solution

Strata

Intelligent connectors for your AI agent, optimize context window

MCP Integrations

100+ prebuilt integrations out-of-the-box, with OAuth support

MCP Sandbox

scalable MCP environments for LLM training and RL

Quick Start

Option 1: Cloud-hosted - klavis.ai

Quickstart guide →

Option 2: Self-host
# Run any MCP Integration
docker pull ghcr.io/klavis-ai/github-mcp-server:latest
docker run -p 5000:5000 ghcr.io/klavis-ai/github-mcp-server:latest

# Install Open Source Strata locally
pipx install strata-mcp
strata add --type stdio playwright npx @playwright/mcp@latest
Option 3: SDK
# Python SDK
from klavis import Klavis
from klavis.types import McpServerName

klavis = Klavis(api_key="your-key")

# Create Strata instance
strata = klavis_client.mcp_server.create_strata_server(
    user_id="user123",
    servers=[McpServerName.GMAIL, McpServerName.SLACK],
)

# Or use individual MCP servers
gmail = klavis.mcp_server.create_server_instance(
    server_name=McpServerName.GMAIL,
    user_id="user123",
)

// TypeScript SDK
import { KlavisClient, McpServerName } from 'klavis';

const klavis = new KlavisClient({ apiKey: 'your-api-key' });

// Create Strata instance
const strata = await klavis.mcpServer.createStrataServer({
    userId: "user123",
    servers: [Klavis.McpServerName.Gmail, Klavis.McpServerName.Slack],
});

// Or use individual MCP servers
const gmail = await klavis.mcpServer.createServerInstance({
    serverName: McpServerName.GMAIL,
    userId: "user123"
});
Option 4: REST API
# Create Strata server
curl -X POST "https://api.klavis.ai/v1/mcp-server/strata" \
  -H "Authorization: Bearer your-api-key" \
  -H "Content-Type: application/json" \
  -d '{
    "user_id": "user123",
    "servers": ["GMAIL", "SLACK"]
  }'

# Create individual MCP server
curl -X POST "https://api.klavis.ai/v1/mcp-server/instance" \
  -H "Authorization: Bearer your-api-key" \
  -H "Content-Type: application/json" \
  -d '{
    "server_name": "GMAIL",
    "user_id": "user123"
  }'

Resources

Made with ❤️ by the Klavis Team

Comments

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New to MCP?

From what the protocol is to installing your first server, these answers cover most first questions.

An open protocol that defines how AI applications connect to external tools and data in a uniform way. Clients talk to the model; MCP servers provide tools, resources and prompt templates. They exchange standard messages, so one server can be reused by many clients.