About
Klavis AI builds live environments and high-quality coding and agentic data for frontier AI labs, alongside open-source and hosted MCP infrastructure that helps AI applications use external tools. It serves AI labs, developers, and enterprise teams, differentiating through realistic, long-horizon agentic environments and production-ready hosted integrations with authentication and multi-tenancy support.
Market
Klavis competes in the AI-agent infrastructure and MCP integration market, connecting LLM-based agents to SaaS and API tools through hosted or self-hosted MCP servers and deterministic training environments. It positions itself as enterprise-grade production infrastructure rather than a basic connector catalog, emphasizing OAuth, multi-tenancy, RBAC, guardrails, compliance, reliability, and broad API coverage. Its main differentiation is Strata's progressive tool discovery, which guides agents from services to categories, actions, and schemas instead of loading every tool at once, reducing context-window overload and improving agent tool selection.
Ideal customers are frontier AI labs and enterprise software or product teams building production AI agents and applications that need to use many external SaaS and API tools. The likely buyers are engineering, platform, or AI-infrastructure teams that need secure multi-tenant authentication, reliability, compliance, self-hosting, and support for complex long-horizon workflows.
At a Glance
Problem
Klavis AI addresses the reliability and integration bottleneck that prevents AI agents from being useful in production. Agents need to interact with external systems such as Jira, GitHub, Slack, CRM platforms, and cloud storage, but MCP implementations often lack native or user-level authentication, enterprise-grade stability, remote access, and convenient client support. Building those integrations internally forces teams to spend substantial engineering effort on OAuth, credential management, MCP clients, and maintenance, while exposing security and reliability risks.
The deeper problem is that agents also struggle when presented with too many tools and excessively large context windows: tool overload can make selection unreliable, while long tool descriptions increase token usage and cost. Klavis's principal use case is enabling frontier AI labs and agent developers to train, benchmark, and improve agents in realistic, long-horizon workflows across live SaaS applications, MCP servers, and other real tools rather than relying only on simplified or static benchmarks.
Product / Service
Klavis provides an open-source and hosted MCP infrastructure stack with three complementary parts: prebuilt MCP integrations, Strata, and MCP Sandbox. Its integrations provide more than 100 connectors with OAuth support, while MCP Sandbox supplies scalable environments for LLM training and reinforcement learning. Strata acts as a single MCP server that progressively reveals tools by category and action, reducing tool and context overload while allowing an agent to work across many services.
The product is delivered through cloud-hosted endpoints, APIs, SDKs, and open-source software that customers can self-host. Developers can create individual MCP server instances or Strata configurations for specific users, connect services such as Gmail and Slack, and let Klavis handle user authentication and OAuth flows. The benefit is faster deployment of production-ready agent capabilities, lower integration and maintenance effort, better context efficiency, and a path from experimentation to enterprise or private deployment.
Market
Klavis competes in the emerging AI-agent infrastructure and Model Context Protocol integration market, with an expanding position in agent training, evaluation, and agentic-data environments. Its adjacent competitors include Pipedream, Composio, Smithery, Glama, and Superface; these companies overlap around connecting agents to external tools, although Klavis emphasizes enterprise MCP infrastructure, progressive tool discovery, and realistic environments for training and evaluation.
The company is an active Y Combinator Spring 2025 startup, and the available company profile lists it as founded in 2025 with a three-person team. Its open-source repository shows meaningful developer traction, including 5,771 stars, 547 forks, and 1,328 commits, alongside the product's stated 100-plus integrations. Tracxn identifies a seed round associated with Y Combinator, but the available evidence does not establish verified revenue, ARR, or named customer contracts; Klavis is therefore best described as an active, funded early-stage company with visible product and community traction, rather than definitively pre-revenue or demonstrably scaled commercially.
Founders & Leadership
Funding History
Y Combinator, SAV (Scale Asia Ventures)
Recent News
A July 2026 company-profile update describes Klavis AI as an open-source MCP integration platform and reports $500,000 raised across two rounds, an estimated $1.7 million valuation, and approximately 40,000 users. It also identifies the company as a Y Combinator Spring 2025 participant.
A Y Combinator job listing says Klavis AI builds coding and agentic data for frontier-AI post-training and works with leading AI labs on production-grade datasets for reinforcement learning and supervised fine-tuning. The listing identifies Klavis as San Francisco-based and backed by Y Combinator.
Klavis AI’s comparison article positions the company as infrastructure for deploying and managing MCP servers, emphasizing its role in AI-agent tooling and integrations.
A Threads post describes Klavis as an open-source Model Context Protocol platform whose Strata module offers more than 100 prebuilt integrations, including Gmail, Slack, GitHub, and Shopify, with one-click OAuth connections.
Klavis’s documentation explains how developers can connect to hosted remote MCP servers and lists integrations such as Google Forms and Google Sheets.
Klavis’s comparison article highlights progressive tool discovery as a differentiator, claiming it reduces context usage by 60% while improving accuracy by 15.2%, alongside enterprise-grade MCP infrastructure.
Klavis presents its Google Sheets MCP integration as a production-ready option that handles OAuth and credentials, and says developers can connect Sheets to an AI platform in about 15 minutes. The article also demonstrates workflows combining Google Sheets with Slack, Gmail, CRM, and other services.
The Klavis AI GitHub repository describes an open-source MCP integration platform designed to let AI agents use tools reliably at scale, including a single MCP server capable of handling thousands of tools.
Klavis AI announced Strata on Hacker News as an open-source MCP server for helping AI agents use thousands of API tools. The product uses a staged discovery flow from servers to categories, actions, and action details to avoid overwhelming models.
Active Roles
1Business Model
Klavis AI offers custom-priced access to its Sandbox Environment and monetizes hosted MCP infrastructure and API access, while also maintaining open-source MCP servers and clients. Its custom-pricing model is tailored to customers’ specific needs, particularly AI labs and enterprise users.