KlavisAI
KlavisAI builds live Dockerized environments for training AI agents on coding and agentic tool-use tasks.
If your post-training pipeline needs stateful, verifiable environments rather than another pile of labeled prompts, Klavis is one of the few vendors actually shipping that. The Dec 2025 Progressive Discovery MCP Server and Sandbox-as-a-Service additions are the parts worth asking about on a call — they're what separate this from a dataset dump. The catch is contact-only pricing and a real DevOps bar to integrate Dockerized environments.
Verified 15d ago · liveness 69/100 · cite: rightaichoice.com/tools/klavisai
- AI teams training coding agents on long-horizon tasks with RL or SFT
- Teams needing agentic tool-use datasets with live SaaS app and MCP server interactions
- Enterprises requiring GDPR compliance, SOC 2 Type 2, and on-premises MCP deployment with RBAC
- Evaluating agent performance in deterministic sandbox environments without touching production data
- Simple question-answering or classification dataset generation
- Teams without DevOps capacity to run and debug Dockerized environments
- Buyers needing self-serve pricing or a free tier — pricing is contact-only
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Skip Klavis AI if you need simple Q&A data, lack DevOps capacity to integrate Dockerized environments, or require a flat-rate pricing model without contact-based sales.
Contact-based pricing means you'll need to engage with sales to get a quote, which can be a time sink for evaluation.
Klavis AI uses contact-based pricing, so the cost depends on your data volume and requirements. It's likely priced higher than simple data labeling services but aligns with the value of verifiable, stateful environments. Teams with serious post-training needs should compare against building in-house or using generic synthetic data generators.
In short
KlavisAI — KlavisAI builds live Dockerized environments for training AI agents on coding and agentic tool-use tasks. Best for AI teams training coding agents on long-horizon tasks with RL or SFT, Teams needing agentic tool-use datasets with live SaaS app and MCP server interactions, Enterprises requiring GDPR compliance, SOC 2 Type 2, and on-premises MCP deployment with RBAC. Contact Sales pricing.
What's new in KlavisAI
Checked 7 days agoAcross the latest 4 updates: 1 feature update, 1 launch and 2 news mentions.
Agent Context Windows Stay Smart with Progressive Discovery MCP Server
Launched Progressive Discovery MCP Server to fetch tool definitions on demand, helping agents manage context windows.
Introducing Klavis Sandbox-as-a-Service: Deterministic MCP Environments for AI Agent Training and Evaluation
Launched deterministic MCP environments for benchmarking agents and RL training without production data.
Deploying Enterprise MCP Infrastructure: Why On-Premises Architecture Matters for AI Applications
Detailed on-premises MCP deployments with RBAC for security and compliance.
Klavis AI Achieves Full GDPR Compliance: What It Means for Enterprise AI Development
Achieved GDPR compliance with EU infrastructure migration and SOC 2 Type 2 certification.
Viability Score
How well maintained and how widely used is KlavisAI? Built from what the vendor actually publishes (docs, changelog, tutorials, integrations, pricing), whether the site is live, and how much real users discuss it. How we calculate this
Last calculated: September 2026
How we score →Key Features
- Long-horizon coding tasks with code editing, test writing, and debugging
- Programmatic verification via deterministic tests
- Granular rewards: deterministic, rubric, and LLM-judge
- Dockerized environments for reproducible agent runs
- Agentic tool-use data across 600+ real tools and live SaaS apps
- State-mutating workflows with logically consistent state and noisy inputs
- Production MCP server support for tool-use training
- Progressive Discovery MCP Server fetches tool definitions on demand to manage context windows
- Sandbox-as-a-Service for deterministic MCP environments in benchmarking and RL
- On-premises MCP deployment with RBAC
- SOC 2 Type 2 certification and full GDPR compliance
- EU infrastructure migration for data residency
- Open-source repository with 5.8k GitHub stars
- Data for RL and SFT post-training pipelines
About KlavisAI
KlavisAI is a data infrastructure platform for AI teams that need verifiable, real-world training data for frontier agents rather than static Q&A pairs or demo-only tool calls. It delivers long-horizon coding tasks and agentic workflows executed in live, Dockerized environments, built for RL and SFT pipelines where programmatic verification and granular rewards matter more than raw volume. The target buyer is a developer or AI team doing agent post-training, from post-training labs to enterprise R&D groups. The offering splits into two tracks. Coding-agent data covers tasks requiring code editing, test writing, and debugging, with deterministic tests and Docker-packaged environments so runs are reproducible. Agentic tool-use data covers long-horizon, state-mutating workflows across 600+ real tools, live SaaS apps, and production MCP servers, with noisy inputs and logically consistent state to mirror production reality. Every task ships with verifiable rewards, whether deterministic, rubric-based, or LLM-judge scored, so teams can train reward models and fine-tune with confidence. Recent releases push the enterprise angle: the Progressive Discovery MCP Server (Dec 16, 2025) fetches tool definitions on demand to manage context windows, and Sandbox-as-a-Service (Dec 10, 2025) provides deterministic MCP environments for benchmarking and RL training without touching production data. On-premises MCP deployment with RBAC is documented, and the company reports SOC 2 Type 2 certification plus full GDPR compliance with EU infrastructure migration. Klavis is not a generic data labeler. It positions itself as the option for agent post-training where deterministic, rubric, and LLM-judge rewards are required. Backed by Y Combinator with an open-source repo at 5.8k stars, it's a credible alternative to building your own environment infrastructure or relying on static datasets that miss the noise of real agentic workflows. Pricing is contact-based, so budget for a sales
Behind the Verdict
Most "agent data" vendors sell you a static snapshot: prompts, traces, maybe a rubric. Klavis sells environments. That distinction matters the moment your agent has to touch real state, because a task the agent can't mutate is a task it can't really fail at — and failure signal is the whole point of RL and SFT. Pick this when you're post-training a coding or tool-use agent and you need deterministic tests and programmatic verification, not human review. The 600+ real tools and live SaaS coverage means your agent sees the messy version of the world, which is the version it will deploy into. Sandbox-as-a-Service is the piece we'd reach for first if benchmarking a tool-using model without risking production data. Pass if you need simple Q&A or classification datasets — this is overkill and priced for it. Also pass if you lack the DevOps capacity to run Dockerized environments, or if a contact-sales cycle doesn't fit your procurement. There's no visible free tier on the pricing page. The closest alternative is building your own environment harness in-house. That's viable if you have a strong infra team and only need one or two domains, but you're then maintaining tool integrations, state consistency, and reward plumbing yourself. The other alternative is a generic data labeler, which will be cheaper per example and won't give you verifiable rewards. On the enterprise side, the GDPR compliance and SOC 2 Type 2 certification, plus on-premises MCP deployment with RBAC, are the boxes security review will ask about. The EU infrastructure migration matters if data residency is a hard requirement. Worth confirming current scope on a call, since compliance language on vendor sites tends to be broad. One caveat we'd flag: pricing is entirely contact-based, so there's no way to
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Real-world workflow fit
Concrete scenarios for the personas KlavisAI actually fits — and what changes day-one when you adopt it.
You need long-horizon coding tasks to fine-tune a model with RL. Klavis provides Dockerized environments with deterministic tests, so you can generate trajectories and verify agent behavior before training.
Outcome: You integrate the environments into your RL loop, train on high-quality data, and see improved agent performance on multi-step coding tasks.
You want to train an agent to use your internal SaaS tools (Slack, Jira). Klavis offers datasets with state-mutating workflows across 600+ tools, including your stack.
Outcome: You fine-tune your agent to handle multi-step tasks like creating tickets and updating records, with verifiable rewards ensuring reliability.
You need deterministic environments to compare different agent models. Klavis Sandbox-as-a-Service provides isolated, reproducible environments for benchmarking.
Outcome: You run controlled experiments to measure performance across tools and tasks, publish results with confidence.
Use Cases
- Training AI agents on long-horizon coding tasks across browser, code, and SaaS tools
- Running RL evaluations on agentic workflows with deterministic environments
- Testing agents with real stateful dependencies and multi-step progression
- Generating synthetic agentic data in realistic, managed sandboxes
- Benchmarking agent performance with verifiable outcomes and state export
- Debugging AI logic without touching production data using isolated sandboxes
- Context-window optimization with on-demand tool definitions via Progressive Discovery MCP Server
Models Under the Hood
as of 2026-09-22
Limitations
- Klavis AI builds live Dockerized environments for training AI agents on long-horizon coding and agentic tool-use tasks, with programmatic verification, granular rewards, and production MCP server support across 600+ real tools and live SaaS apps.
- It offers Sandbox-as-a-Service for deterministic MCP environments used in benchmarking and RL, plus on-premises MCP deployment with RBAC.
- It is open source (5.8k GitHub stars) and reports SOC 2 Type 2 certification, full GDPR compliance, and EU infrastructure for data residency.
- Blog posts discuss frontier models (e.g.
- GPT-5.2, Claude Opus 4.5, Gemini 3 Pro), but Klavis itself does not name a single fixed underlying model for its product.
as of 2026-08-29
Verification history
We have re-verified KlavisAI 18 times since . Each pass re-reads the vendor's own pages and re-checks every listed field against that evidence; passes where nothing had changed are marked as such.
- — re-verified summary, description, our verdict, our analysis, pricing model, pricing tiers, features, integrations, who it suits, who should skip it
- — re-verified summary, description, our verdict, our analysis, pricing model, pricing tiers, features, integrations, who it suits, who should skip it
- — re-checked, vendor evidence unchanged
- — re-verified summary, description, our verdict, our analysis, pricing model, pricing tiers, features, integrations, who it suits, who should skip it
- — re-checked, vendor evidence unchanged
- — re-verified summary, description, our verdict, our analysis, pricing model, pricing tiers, features, integrations, who it suits, who should skip it
Showing the 6 most recent of 18 verification passes.
Free to cite with attribution — this page re-verifies continuously.
Where the pricing makes sense
The company stage and team size where KlavisAI's pricing actually pencils out — and where peers do it cheaper.
Klavis AI uses contact-based pricing, so the cost depends on your data volume and requirements. It's likely priced higher than simple data labeling services but aligns with the value of verifiable, stateful environments. Teams with serious post-training needs should compare against building in-house or using generic synthetic data generators.
Setup time & first value
How long it actually takes to get something useful out of KlavisAI — broken out by persona, not the marketing-page minute.
Integration time varies: the Dockerized environment is straightforward for teams with CI/CD experience (1-2 days); connecting to existing training pipelines may require 1-2 weeks. Sales onboarding adds time. On-premises MCP deployment can take weeks to provision and configure.
Switching to or from KlavisAI
How to bring data in from common predecessors and how to get it back out — written for the switcher, not the buyer.
- →From static dataset pipelines: Replace your static Q&A pairs with Klavis's live environment data generation to get verifiable, stateful trajectories.
- ↗To in-house environment infrastructure: If your team can build custom Docker environments, you can replicate some functionality but lose the 600+ pre-built tool integrations.
Integrations
Resources & Guides
- Documentationklavis.ai
Paving the road to AGI - Klavis AI
Full product docs from klavis.ai
- Resourceklavis.ai
Klavis AI provides live environments for training AI agents. | Klavis AI
Klavis AI provides live environments for training AI agents. Powering frontier AI labs with real world MCP environments and complex, long-horizon agentic tool-use data.
- Documentationklavis.ai
Overview - Klavis AI
Full product docs from klavis.ai
- Resourceklavis.ai
Building AI Agents with Model Context Protocol on Google Cloud: A Complete Developer Guide
Learn how to build production-ready AI agents using Google ADK and Gemini with MCP servers on Google Cloud Platform. Complete tutorial with code examples.
- Resourceklavis.ai
Deploying Enterprise MCP Infrastructure: Why On-Premises Architecture Matters for AI Applications
Learn how on-premises MCP deployments with role-based access control provide enterprises with security, compliance, and performance advantages for production AI applications.
Tutorials & Learning
YouTube returned 6 videos for “KlavisAI”, and we withheld 5: 5 could not be judged, because “KlavisAI” is a single word that other videos use for other things. Showing the 1 we can prove is about KlavisAI.
Official links
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