Klavis AI is infrastructure for developers and AI research teams working on agents that need to interact with external tools. Its current platform has two closely related sides: production MCP integrations that connect agents to real applications, and controlled environments for training and evaluating how well those agents use tools.

That distinction matters. Klavis isn't another visual agent builder or business chatbot. It is closer to the plumbing underneath an agent system, handling tool access, authentication, large tool catalogs, sandbox environments, and verifiable agent tasks.

One of Klavis AI's most useful components is Strata, a unified MCP server designed to expose tools progressively rather than showing an AI model every available function at once.

Large agents can run into a practical problem when dozens or hundreds of tools are placed in the context window. Tool descriptions consume tokens, and the model has more opportunities to choose the wrong action. Klavis describes Strata as a way to reduce tool overload and context overload while still supporting large tool collections.

Instead of configuring a separate MCP connection for every service, developers can create one Strata server and connect services such as Gmail and Slack behind it. Klavis supports access through its dashboard, API, SDK, and an open-source local version.

Klavis also maintains a large collection of MCP integrations. Its open-source repository describes more than 100 prebuilt integrations, while its current site says its agentic tool-use environments span more than 600 real tools and SaaS apps. Those figures describe different layers of the platform, but together they show the emphasis on broad tool coverage.

Supported services include categories such as email, calendars, CRM, project management, databases, storage, finance, developer tools, and collaboration software.

Authentication is handled through OAuth 2.0 for many major services. Klavis documents OAuth support for Gmail, GitHub, Google Drive, Salesforce, Slack, Notion, Jira, HubSpot, Supabase, QuickBooks, and others.

This is important for multi-user agent products. Developers still need to think carefully about permissions, but they don't have to create every third-party authorization flow from scratch.

Klavis becomes especially interesting for teams training or evaluating agents.

Its MCP Sandbox creates isolated environments where models can interact with realistic applications without operating on live production accounts. A developer can create a sandbox, initialize it with a known data state, let an agent perform tasks through MCP, inspect the resulting state, verify whether the goal was achieved, and then reset the environment for another run.

That repeatability is useful for reinforcement learning and agent benchmarks. Testing whether an agent can "manage a CRM" is hard if every run starts with different customer records. A deterministic sandbox gives researchers a known starting point and a result they can check programmatically.

Klavis currently documents sandbox environments for services including Gmail, GitHub, Google Calendar, HubSpot, Jira, Linear, Notion, Salesforce, Shopify, Slack, Snowflake, Supabase, WordPress, and others.

The current Klavis homepage puts more emphasis on coding and agentic data than its earlier MCP-focused positioning.

For coding agents, Klavis highlights long-horizon tasks involving code editing, test writing, debugging, deterministic tests, programmatic verification, and Dockerized environments. For tool-using agents, it focuses on longer workflows across live SaaS-style applications with state changes and verifiable rewards.

This makes Klavis relevant beyond developers who just need a connector. AI labs and model teams can use the underlying infrastructure to create training and evaluation data where success can be measured instead of judged only by how convincing an answer sounds.

A meaningful part of Klavis remains open source. Its GitHub repository includes MCP servers, Strata, examples, OAuth components, and self-hosting instructions. Individual integrations can be run through Docker, while Strata can also be installed locally. For engineering teams, that makes the platform easier to inspect and extend than a completely closed integration service.

AI agent developers can use Klavis to give agents access to many external tools without maintaining every MCP integration themselves.

Model training teams are a particularly strong fit for the sandbox infrastructure, where tasks can be initialized, run, verified, and reset repeatedly.

Agent evaluation teams can build tests around real state changes rather than relying only on subjective LLM judging.

Research teams working on tool use can use Strata to study how agents behave when the available tool catalog becomes large and complex.

Klavis is technical infrastructure. It won't replace an agent framework, workflow designer, or application interface. Teams still need to supply the model, agent logic, instructions, error handling, and user experience.

MCP also introduces security considerations. A connection that can send email, modify records, or access files needs careful permission management. Klavis supports OAuth and HTTPS-based APIs, but developers remain responsible for deciding what their agents should be allowed to do.

The platform's evolving positioning is another point to understand. The current homepage centers on training data and realistic agent environments, while much of the documentation still emphasizes MCP integrations and Strata. Both remain relevant, but prospective users should identify which layer they need.