Stop Writing Integrations, Start Building Agents

You know the drill. You want to build an AI agent that can send emails, create GitHub issues, update spreadsheets, or manage your CRM. Then you spend the next three weeks fighting OAuth flows, parsing API docs, and handling rate limits instead of actually building your agent.

Klavis AI cuts through this integration mess with pre-built MCP servers for 100+ popular services. No more authentication headaches, no more API wrapper code, no more "it works in development but breaks in production" moments.

The Real Problem with AI Agent Development

Building agents that can actually do things requires connecting to external services. That means:

• Wrestling with OAuth2 flows for Google Workspace, GitHub, Slack
• Managing API keys across dozens of services
• Writing custom wrappers for each service's quirks
• Handling rate limits, retries, and error states
• Maintaining all this integration code as APIs change

You end up spending 80% of your time on plumbing and 20% on the actual AI logic. Klavis flips this ratio.

How Klavis Actually Works

Instead of building integrations from scratch, you get production-ready MCP servers with everything handled:

from klavis import Klavis
from openai import OpenAI

# Create a Gmail server instance - OAuth handled automatically
klavis = Klavis(api_key="your-key")
gmail_server = klavis.mcp_server.create_server_instance(
    server_name="gmail",
    user_id="user123",
    platform_name="MyApp"
)

# Get tools for your LLM - properly formatted, ready to use
tools = klavis.mcp_server.list_tools(
    server_url=gmail_server.server_url,
    format="openai"
)

# Your agent can now send emails, search messages, manage labels
openai_client = OpenAI()
response = openai_client.chat.completions.create(
    model="gpt-4o",
    messages=[{"role": "user", "content": "Send a follow-up email to [email protected]"}],
    tools=tools.tools
)

The OAuth flow is handled through gmail_server.oauth_url. The tool schemas are automatically generated. Error handling and retries are built-in. You focus on your agent logic, not API integration details.

Real Productivity Gains

Gmail + Calendar + Slack workflow in 15 minutes:

# Create multiple servers
gmail = klavis.mcp_server.create_server_instance(server_name="gmail", ...)
calendar = klavis.mcp_server.create_server_instance(server_name="google_calendar", ...)
slack = klavis.mcp_server.create_server_instance(server_name="slack", ...)

# Combine all tools
all_tools = []
all_tools.extend(klavis.mcp_server.list_tools(gmail.server_url).tools)
all_tools.extend(klavis.mcp_server.list_tools(calendar.server_url).tools)
all_tools.extend(klavis.mcp_server.list_tools(slack.server_url).tools)

# Agent can now coordinate across all three services
messages = [{"role": "user", "content": "Schedule a meeting with the team and send a Slack reminder"}]

Compare this to building the same functionality from scratch:

• Traditional approach: 2-3 weeks of OAuth setup, API integration, error handling
• With Klavis: 15 minutes to working prototype, production-ready from day one

The Integration Catalog That Actually Matters

Klavis covers the services developers actually use in production:

Developer Tools: GitHub, Jira, Linear, Notion, Confluence, Figma Business Apps: Salesforce, HubSpot, Asana, ClickUp, Motion Communication: Gmail, Slack, Discord, Microsoft Teams Data & Analytics: PostgreSQL, Supabase, Google Sheets Content: YouTube, WordPress, document conversion tools

Each server handles the specific authentication method (OAuth2, API keys, etc.) and provides a consistent interface for your agents.

Works With Your Existing Stack

Klavis integrates with whatever you're already using:

• LLM Providers: OpenAI, Anthropic, Together AI, any provider that supports function calling
• Agent Frameworks: LangChain, LlamaIndex, CrewAI, AutoGen, or custom implementations
• Languages: Python and TypeScript SDKs, plus REST API for everything else

You don't need to change your architecture or migrate to a new framework.

Hosted vs Self-Hosted: Your Choice

Start with the hosted API for rapid development:

pip install klavis
# Get API key from klavis.ai
# Start building immediately

Move to self-hosted when you need full control:

git clone https://github.com/klavis-ai/klavis.git
cd klavis/mcp_servers/github
docker build -t klavis-github .
docker run -p 8000:8000 klavis-github

All servers are open-source MIT licensed. You can inspect the code, modify it, or contribute improvements back to the community.

Production-Ready from Day One

Klavis handles the operational concerns you don't want to think about:

• Authentication: OAuth flows, token refresh, secure storage
• Rate Limiting: Built-in respect for service limits with automatic backoff
• Error Handling: Graceful degradation and meaningful error messages
• Scaling: Hosted infrastructure that handles millions of requests
• Security: Proper secret management and secure token handling

Getting Started

1. Get your API key at klavis.ai
2. Install the SDK: pip install klavis or npm install klavis
3. Pick your first integration from the 100+ available servers
4. Build your agent using familiar tools like OpenAI's function calling

The documentation at docs.klavis.ai includes working examples for every integration, OAuth setup guides, and multi-tool workflow patterns.

Your next AI agent project doesn't need to start with weeks of integration work. Start building the intelligence, not the plumbing.