Klavis AI is an open-source MCP integration platform that lets AI agents reliably use external tools at production scale. The product line is organized around Strata (intelligent connectors that compress and route tool context), 100+ prebuilt MCP integrations with OAuth, and an MCP Sandbox for live agent training and evaluation. Target customers are AI agent companies, RL teams, and enterprises that need long-horizon multi-app environments with seeded state, resets, and verifiable outcomes, plus SOC 2 Type II and GDPR posture. SDKs are available for Python and TypeScript/JavaScript, integrations cover Claude, OpenAI, Gemini, Cohere, Mistral, LangChain/LangGraph, LlamaIndex, CrewAI, Mastra, Agno, Fireworks, Together, and Google ADK, and the project is Apache-2.0 on GitHub.
Klavis AI publishes 4 APIs on the APIs.io network, including MCP Servers API, Sandbox API, Tools API, and 1 more. Tagged areas include MCP, MCP Servers, MCP Hosting, Connectors, and Authentication.
Klavis AI’s developer surface includes authentication, documentation, engineering blog, pricing, and 12 more developer resources.
Open Source Surface applies to this provider. This product is open source and we
read its repository directly, so Open Source Surface carries
10 points of the composite. It is scored from what the repository actually
publishes — a security policy, a contribution guide, a release history, a code of conduct — read live from the
provider rather than inferred from our own catalog pointers.
This facet adds; nothing was taken away to make room for it. An open-source project is not excused from
the commercial facets, because exemption would strip it of the points it does earn.
If we have the wrong repository, or this product is not open source, say so on your
provider repo and we
will drop the facet rather than have you publish against it.
Create-or-Update Ergonomics applies to this provider. This API accepts writes, so it
carries 10 points of the composite. It is scored from the published contracts
themselves: whether a caller can create-or-update in one call, whether the write accepts a key the caller already
holds, and whether the response says which branch ran. Without that, every write needs a search-and-branch in
front of it, and the first time that check is skipped a duplicate record is created.
Scored against the observed mean rather than raw — a provider at the catalog average is unchanged by this facet,
not penalised by it.
The six quality facets above are damped to 80 points between them,
because the conditional facet above carries the other
20. That is why each facet's contribution is shown against a damped
maximum: raising a quality facet moves the composite by 80% of its nominal
weight, not 100%. The full arithmetic is at apis.io/rating/.
Improve this rating by publishing the missing artifacts — every area above can be raised, and the full rubric is at apis.io/rating/. Every facet and dimension name above is a link: it opens that measurement's own page — what it means, the exact checks that feed it, how the whole catalog distributes on it, and the providers at the top of it. This rating is computed from github.com/api-evangelist/klavis: open an issue to ask a question, or submit a pull request to add artifacts.
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The Klavis API manages hosted MCP servers, OAuth flows for 50+ integrated services, Strata multi-tool servers, and live sandbox environments. Endpoints cover MCP server CRUD and...
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