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klavis-ai/klavis

5,734PythonAudience · developerComplexity · 3/5Setup · moderate

TLDR

Klavis connects AI agents to 100+ external tools like Gmail, Slack, and GitHub using the Model Context Protocol (MCP). It handles authentication automatically and filters which tools are relevant at any moment, so your AI stays focused and reliable.

Mindmap

mindmap
  root((Klavis))
    Strata Connector
      Smart tool filtering
      Focused model context
      Agent orchestration
    MCP Integrations
      100 plus prebuilt tools
      Gmail Slack GitHub
      OAuth authentication
    Sandbox Training
      Model practice environment
      Scale tool usage
    Deployment Options
      Cloud hosted
      Docker self host
      Python TS SDKs
      REST API
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Things people build with this

USE CASE 1

Connect your AI chatbot or agent to Gmail, Slack, or GitHub without writing custom integrations from scratch

USE CASE 2

Build AI workflows that use dozens of external tools without overwhelming the model with too many options at once

USE CASE 3

Train AI models to use real-world tools in a safe sandbox environment before deploying them

USE CASE 4

Add OAuth-based authentication to your AI app so users can securely connect their own accounts

Tech stack

PythonTypeScriptDockerREST APIOAuthMCPFastAPI

Getting it running

Difficulty · moderate Time to first run · 30min

Available as hosted cloud, Docker self-host, Python/TypeScript SDK, or REST API. OAuth is handled by Klavis so no manual credential management needed.

Open source project maintained by the Klavis team. Exact license terms not specified in the explanation.

In plain English

Klavis is a platform that helps AI agents connect to and use external tools reliably. The core concept it builds around is called MCP, short for Model Context Protocol, which is a standard way for AI systems to call out to services like Gmail, Slack, GitHub, or hundreds of others. Rather than building and maintaining those connections yourself, Klavis provides them ready to use with built-in authentication support. The platform has three main pieces. The first is called Strata, which acts as a smart connector between an AI agent and a large collection of tools. When an agent has access to many tools at once, the list of available actions can become too long for the model to handle well. Strata is designed to manage that by surfacing only the relevant tools in a given moment, keeping the model's working memory focused. The second piece is a library of more than 100 prebuilt MCP integrations covering popular services, each with OAuth login support so users can authenticate without developers handling credentials manually. The third piece is a Sandbox environment intended for teams training AI models, providing a controlled space where language models can practice using tools at scale. You can use Klavis through their hosted cloud service, run it yourself with Docker, call it from Python or TypeScript code using their SDK, or use plain HTTP requests through a REST API. A short code example shows creating a connection to Gmail and Slack in a few lines of Python. The project is open source and maintained by the Klavis team. Documentation, a community Discord, and a link to report issues are provided on their website.

Copy-paste prompts

Prompt 1
Using the Klavis Python SDK, write code that creates an MCP connection to Gmail and Slack, then sends a message to a Slack channel summarizing the last 5 unread Gmail emails.
Prompt 2
I want to build an AI agent that can use tools from Klavis. Show me how to initialize the Klavis client, list available MCP integrations, and invoke the GitHub integration to list open pull requests for a repo.
Prompt 3
Using Klavis Strata, set up a smart tool router for an AI agent that has access to 50+ tools. Show how Strata filters to only surface relevant tools based on the current user request.
Prompt 4
Help me deploy Klavis using Docker for self-hosting. Walk me through the docker-compose setup, environment variables needed, and how to test that the MCP integrations are working correctly.
Prompt 5
I want to use Klavis to let users connect their own Gmail account via OAuth inside my Python app. Show the full flow from generating the OAuth link to making an authenticated API call on their behalf.
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