Klavis AI
Live, deterministic environments for training AI agents on coding and tool-use tasks.
Klavis AI fills a critical gap for teams training agents on real tool-use tasks, offering live, deterministic environments with granular rewards. Its Dockerized setup and recent enterprise certifications (GDPR, SOC 2) make it enterprise-ready, but contact-only pricing limits accessibility. Recommended for serious RL/SFT teams.
Verified 17d ago · liveness 78/100 · cite: rightaichoice.com/tools/klavis-ai
- AI researchers training frontier coding models with RL and programmatic verification
- Teams developing agents for real-world tool use across 600+ SaaS apps
- Developers needing granular rewards for fine-tuning on coding or agentic tasks
- Enterprise AI teams requiring deterministic, compliant training environments
- Beginners seeking simple toy examples or static datasets
- Users needing free or low-cost data generation without enterprise pricing
- Projects requiring instant setup without Docker or cloud infrastructure
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Skip Klavis AI if you need a free or self-service dataset generation tool without Docker and cloud infrastructure, or if your work is limited to natural language tasks without coding or tool use.
Pricing is contact-only, so you must schedule a sales call to get a quote—no up-front cost visibility.
Klavis AI targets enterprise AI teams and is priced accordingly (contact-based). It is likely more expensive than static dataset providers like Scale AI, but offers live, deterministic environments that reduce wasted training effort. For teams doing frontier RL, the cost may be justified by the verifiability and reproducibility.
In short
Klavis AI — Live, deterministic environments for training AI agents on coding and tool-use tasks. Best for AI researchers training frontier coding models with RL and programmatic verification, Teams developing agents for real-world tool use across 600+ SaaS apps, Developers needing granular rewards for fine-tuning on coding or agentic tasks. Contact Sales pricing.
What's new in Klavis AI
Checked 16 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 help manage agent context windows efficiently.
Introducing Klavis Sandbox-as-a-Service: Deterministic MCP Environments for AI Agent Training and Evaluation
Launched Sandbox-as-a-Service for deterministic MCP environments for benchmarking and RL training without production data.
Claude Opus 4.5 vs Gemini 3 Pro vs GPT-5: The Ultimate Agentic AI Showdown for Developers
Benchmarked three frontier LLMs (Claude Opus 4.5, Gemini 3 Pro, GPT-5) for tool calling and agentic tasks.
Klavis AI Achieves Full GDPR Compliance: What It Means for Enterprise AI Development
Achieved full GDPR compliance with EU infrastructure migration and SOC 2 Type 2 certification.
Viability Score
How likely is Klavis AI to still be operational in 12 months? Based on 4 signals — momentum (how recently it shipped), wrapper dependency, revenue model, and web presence.
Last calculated: July 2026
How we score →Key Features
- Live deterministic environments for AI agent training
- Long-horizon coding tasks: code editing, test writing, debugging
- Programmatic verification with deterministic tests
- Granular reward functions: rubric, deterministic, LLM-judge
- Docker-packaged environments for reproducible training
- 600+ real tools and SaaS apps for agentic workflows
- Production MCP servers for live agent interactions
- State-mutating workflows with verifiable rewards
- Sandbox-as-a-Service for deterministic MCP environments (Dec 2025)
- Progressive Discovery MCP Server for context window management (Dec 2025)
- Supports RL and SFT training paradigms
- GDPR compliance and SOC 2 Type 2 certification (Nov 2025)
- Open-source codebase (5.8k GitHub stars)
- API-first design for integration
- Benchmarking across 300+ MCP-connected services
About Klavis AI
Klavis AI provides live, deterministic environments purpose-built for training AI agents on long-horizon coding and agentic tool-use tasks, targeting frontier post-training of large language models. The platform supports reinforcement learning (RL) and supervised fine-tuning (SFT) with programmatic verification, granular rewards, and real-world workflows. Backed by Y Combinator, it offers Docker-packaged environments for coding tasks like code editing, test writing, and debugging, with deterministic tests for reproducibility. For agentic tasks, Klavis integrates 600+ real tools and SaaS apps, production MCP servers, and state-mutating workflows that yield verifiable rewards. Recent launches include Sandbox-as-a-Service (December 2025) for deterministic MCP environments and Progressive Discovery MCP Server for efficient context window management. Klavis AI has achieved GDPR compliance and SOC 2 Type 2 certification (November 2025), solidifying enterprise readiness. Its open-source codebase (5.8k GitHub stars) and API-first design appeal to AI researchers and teams building sophisticated agents. Compared to static dataset providers or simulation-only platforms, Klavis AI delivers real, verifiable environments with granular control, making it a strong choice for advanced RL training.
Behind the Verdict
Klavis AI is a niche but powerful platform for training agents that need to interact with real tools and APIs. Its deterministic environments and granular reward functions give researchers precise control over agent behavior, which is rare. We'd reach for this when building a frontier coding model or an agent that must use dozens of SaaS tools reliably in production. However, the contact-only pricing and need for Docker/cloud infrastructure raise the barrier to entry. Compared to Scale AI or static dataset generators, Klavis offers live, state-mutating workflows and reproducible testing, but lacks a self-serve free tier. In practice, the 600+ real tools and production MCP server support are a huge advantage for benchmarking, but setup time is non-trivial. Where it bites: if you just want static training examples or a toy demo, look elsewhere. For serious agentic training teams, it's worth the evaluation.
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Real-world workflow fit
Concrete scenarios for the personas Klavis AI actually fits — and what changes day-one when you adopt it.
You need to fine-tune a model on long-horizon coding tasks with programmatic verification.
Outcome: Use Klavis's Docker-packaged coding environments to generate thousands of trajectories with deterministic tests, then apply RL with granular rewards.
You want to train an agent to interact with Jira, Slack, and Salesforce in a realistic workflow.
Outcome: Leverage Klavis's 600+ real tools and production MCP servers to create state-mutating scenarios with verifiable rewards.
You need to benchmark agent performance across diverse MCP services without production data.
Outcome: Use Sandbox-as-a-Service to run deterministic evaluations on 300+ services in isolated environments.
Use Cases
- Train reinforcement learning agents on realistic, long-horizon SaaS workflows.
- Evaluate agent performance across 300+ MCP-connected services in deterministic sandboxes.
- Debug AI agent logic with state export and verification capabilities.
- Run parallel agent training sessions with isolated environments.
- Integrate MCP servers into existing AI agent stacks like CrewAI and LlamaIndex.
- Generate high-quality coding data for frontier model fine-tuning.
- Benchmark agents in deterministic environments without production data.
Models Under the Hood
as of 2026-07-14
Limitations
- Pricing is not publicly listed on the website, requiring a sales call.
- The platform is clearly aimed at advanced users and teams, not individual developers or hobbyists.
- There are no free tiers or self-service signup options visible, which may limit accessibility.
- Docker and cloud infrastructure are prerequisites, adding setup overhead.
as of 2026-07-02
Where the pricing makes sense
The company stage and team size where Klavis AI's pricing actually pencils out — and where peers do it cheaper.
Klavis AI targets enterprise AI teams and is priced accordingly (contact-based). It is likely more expensive than static dataset providers like Scale AI, but offers live, deterministic environments that reduce wasted training effort. For teams doing frontier RL, the cost may be justified by the verifiability and reproducibility.
Setup time & first value
How long it actually takes to get something useful out of Klavis AI — broken out by persona, not the marketing-page minute.
For teams already using Docker and cloud infrastructure, initial setup (signing up, integrating SDK, launching first sandbox) can be done in a few hours. Full integration with custom reward functions and MCP servers may take a few days. For new teams, provisioning cloud resources and learning the platform may take 1-2 weeks.
Switching to or from Klavis AI
How to bring data in from common predecessors and how to get it back out — written for the switcher, not the buyer.
- →From Scale AI: Replace static dataset pipelines with Klavis's live environments for more realistic RL training.
- →From custom simulation: Use Klavis's Docker-packaged sandboxes to reduce engineering overhead in maintaining environments.
- ↗To Scale AI: For teams needing simpler, static datasets without Docker infrastructure.
- ↗To open-source frameworks (e.g., Gym, MDP): For teams preferring full control over environment design.
Integrations
Resources & Guides
- Resourceklavis.ai
Klavis AI provides live environments for training AI agents.
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
Paving the road to AGI
Full product docs from klavis.ai
- Documentationklavis.ai
Overview
Full product docs from klavis.ai
Official links
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