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Labelbox
Labelbox provides data and simulated environments for training AI systems and deploying agents across business applications.
About Labelbox
Labelbox is a web-based AI infrastructure and MLOps tool that provides training environments and expert data for frontier AI labs to develop AI systems. Its Recursion platform offers autonomous agents that handle recurring enterprise work, including software engineering, reliability and infrastructure, security engineering, ML research, and cross-functional tasks such as finance, sales, and procurement. Agents operate in the background, triggered by schedules, alerts, commits, or API calls, and every run is graded to improve future performance.
Key features
- Training environments for frontier AI labs
- Expert data for AI system training
- Autonomous agents for recurring enterprise work
- Agents triggered by schedules, alerts, commits, API calls
- Graded sessions that build reusable agent skills
- Cross-functional agent support for non-engineering teams
Best for: Frontier AI labs and enterprise teams seeking to automate recurring work and train AI systems.
Good for
- Trusted by frontier AI labs for training environments and expert data
- Supports a wide range of recurring enterprise tasks across multiple domains
- Agent performance improves over time via graded, skill-building sessions
Labelbox at a glance
- Website
- labelbox.com
- Type
- Online tool
- Pricing
- Not confirmed. Check the site
- Platforms
- Web
- Open source
- Not stated
- Last checked
- Oct 6, 2026 · Online when checked
- Listed
- Oct 5, 2026
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Labelbox FAQ
What types of work can Labelbox agents handle?
Recursion agents handle software engineering, reliability and infrastructure, security engineering, ML research, and cross-functional tasks including finance, sales, and procurement.
What triggers Labelbox agent sessions?
Sessions can be started by schedules, alerts, commits, and API calls.
How do Labelbox agent sessions improve over time?
Graded sessions leave memories, notes, and skills that subsequent sessions read before acting.
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