


Chakra Labs Research Initiative
High-fidelity trajectories and
environments for frontier AI research
Developed in collaboration with leading research teams
Developed in collaboration with leading research teams
Fig 1: Instructional video engineered for emotional context
Environments
Deterministic, pixel-perfect reinforcement learning environments with frame-accurate state control
Chakra environments are built alongside active research with frame-accurate state control, temporal integrity preservation, precise input alignment, and deterministic system behavior designed to explore emergent behaviors and novel learnings.

Select clone
Notion
Figma
Canva
Gmail
Salesforce
Amazon
Slack
JD.com
XiaoHongShu
Environment clone
Notion
Environment ready
Access granted to environment clones for demonstration purposes only.
Notion Home Page Public Demo
Type: GUI, MCP
Status: Request Access
Active
Fig 2: Dojo — frame 42 is exactly where you left it
Environments
Deterministic, pixel-perfect reinforcement learning environments with frame-accurate state control
Chakra environments are built alongside active research with frame-accurate state control, temporal integrity preservation, precise input alignment, and deterministic system behavior designed to explore emergent behaviors and novel learnings.
Fig 2: Dojo — frame 42 is exactly where you left it

Select clone
Notion
Figma
Canva
Gmail
Salesforce
Amazon
Slack
JD.com
XiaoHongShu
Environment clone
Notion
Environment ready
Access granted to environment clones for demonstration purposes only.
Notion Home Page Public Demo
Type: GUI, MCP
Status: Request Access
Active
Environments
Deterministic, pixel-perfect reinforcement learning environments with frame-accurate state control
Chakra environments are built alongside active research with frame-accurate state control, temporal integrity preservation, precise input alignment, and deterministic system behavior designed to explore emergent behaviors and novel learnings.

Select clone
Notion
Figma
Canva
Gmail
Salesforce
Amazon
Slack
JD.com
XiaoHongShu
Environment clone
Notion
Environment ready
Access granted to environment clones for demonstration purposes only.
Notion Home Page Public Demo
Type: GUI, MCP
Status: Request Access
Active
Fig 2: Dojo — frame 42 is exactly where you left it
41.39%
Claude 4.5 Sonnet
29.51%
ChatGPT 5.2
9.43%
Gemini 2.5 Pro
3.69%
Llama 4 Maverick
Methodology
Built for Speed
and Scientific Rigor
Chakra environments demonstrate strong generalization across diverse tasks to OSWorld. We've built isolated evaluation protocols that prevent reward hacking during training.
Our infrastructure prioritizes execution speed to enable rapid experimental iteration. All environments are delivered via containerization, with support for both hosted deployments and on-premises installations through Harbor.
Fig 3: Model performance on dojo-bench-mini leveraging Chakra environments
Environments
Deterministic, pixel-perfect reinforcement learning environments with frame-accurate state control
Chakra environments are built alongside active research with frame-accurate state control, temporal integrity preservation, precise input alignment, and deterministic system behavior designed to explore emergent behaviors and novel learnings.

Select clone
Notion
Figma
Canva
Gmail
Salesforce
Amazon
Slack
JD.com
XiaoHongShu
Environment clone
Notion
Environment ready
Access granted to environment clones for demonstration purposes only.
Notion Home Page Public Demo
Type: GUI, MCP
Status: Request Access
Active
Fig 2: Dojo — frame 42 is exactly where you left it
Environments
Deterministic, pixel-perfect reinforcement learning environments with frame-accurate state control
Chakra environments are built alongside active research with frame-accurate state control, temporal integrity preservation, precise input alignment, and deterministic system behavior designed to explore emergent behaviors and novel learnings.
Fig 2: Dojo — frame 42 is exactly where you left it

Select clone
Notion
Figma
Canva
Gmail
Salesforce
Amazon
Slack
JD.com
XiaoHongShu
Environment clone
Notion
Environment ready
Access granted to environment clones for demonstration purposes only.
Notion Home Page Public Demo
Type: GUI, MCP
Status: Request Access
Active
Environments
Deterministic, pixel-perfect reinforcement learning environments with frame-accurate state control
Chakra environments are built alongside active research with frame-accurate state control, temporal integrity preservation, precise input alignment, and deterministic system behavior designed to explore emergent behaviors and novel learnings.

Select clone
Notion
Figma
Canva
Gmail
Salesforce
Amazon
Slack
JD.com
XiaoHongShu
Environment clone
Notion
Environment ready
Access granted to environment clones for demonstration purposes only.
Notion Home Page Public Demo
Type: GUI, MCP
Status: Request Access
Active
Fig 2: Dojo — frame 42 is exactly where you left it
41.39%
Claude 4.5 Sonnet
29.51%
ChatGPT 5.2
9.43%
Gemini 2.5 Pro
3.69%
Llama 4 Maverick
Methodology
Built for Speed
and Scientific Rigor
Chakra environments demonstrate strong generalization across diverse tasks to OSWorld. We've built isolated evaluation protocols that prevent reward hacking during training.
Our infrastructure prioritizes execution speed to enable rapid experimental iteration. All environments are delivered via containerization, with support for both hosted deployments and on-premises installations through Harbor.
Fig 3: Model performance on dojo-bench-mini leveraging Chakra environments
Environments
Deterministic, pixel-perfect reinforcement learning environments with frame-accurate state control
Chakra environments are built alongside active research with frame-accurate state control, temporal integrity preservation, precise input alignment, and deterministic system behavior designed to explore emergent behaviors and novel learnings.

Select clone
Notion
Figma
Canva
Gmail
Salesforce
Amazon
Slack
JD.com
XiaoHongShu
Environment clone
Notion
Environment ready
Access granted to environment clones for demonstration purposes only.
Notion Home Page Public Demo
Type: GUI, MCP
Status: Request Access
Active
Fig 2: Dojo — frame 42 is exactly where you left it
Environments
Deterministic, pixel-perfect reinforcement learning environments with frame-accurate state control
Chakra environments are built alongside active research with frame-accurate state control, temporal integrity preservation, precise input alignment, and deterministic system behavior designed to explore emergent behaviors and novel learnings.
Fig 2: Dojo — frame 42 is exactly where you left it

Select clone
Notion
Figma
Canva
Gmail
Salesforce
Amazon
Slack
JD.com
XiaoHongShu
Environment clone
Notion
Environment ready
Access granted to environment clones for demonstration purposes only.
Notion Home Page Public Demo
Type: GUI, MCP
Status: Request Access
Active
Environments
Deterministic, pixel-perfect reinforcement learning environments with frame-accurate state control
Chakra environments are built alongside active research with frame-accurate state control, temporal integrity preservation, precise input alignment, and deterministic system behavior designed to explore emergent behaviors and novel learnings.

Select clone
Notion
Figma
Canva
Gmail
Salesforce
Amazon
Slack
JD.com
XiaoHongShu
Environment clone
Notion
Environment ready
Access granted to environment clones for demonstration purposes only.
Notion Home Page Public Demo
Type: GUI, MCP
Status: Request Access
Active
Fig 2: Dojo — frame 42 is exactly where you left it
41.39%
Claude 4.5 Sonnet
29.51%
ChatGPT 5.2
9.43%
Gemini 2.5 Pro
3.69%
Llama 4 Maverick
Methodology
Built for Speed
and Scientific Rigor
Chakra environments demonstrate strong generalization across diverse tasks to OSWorld. We've built isolated evaluation protocols that prevent reward hacking during training.
Our infrastructure prioritizes execution speed to enable rapid experimental iteration. All environments are delivered via containerization, with support for both hosted deployments and on-premises installations through Harbor.
Fig 3: Model performance on dojo-bench-mini leveraging Chakra environments
Datasets
Trajectory Library
/
Publications
001
Human computer-use trajectories
Chakra trajectories are designed for frontier researchers pushing capability boundaries. Nuance, feedback, and temporal structure preserved across full decision trees and multi-step workflows, built with precision.

Fig 4: Sample dataset table
2,500+
Hours of trajectories
25 Billion
Segmented image tokens
10 Million
Screenshot / action pairs
Datasets
Trajectory Library
/
Publications
001
Human computer-use trajectories
Chakra trajectories are designed for frontier researchers pushing capability boundaries. Nuance, feedback, and temporal structure preserved across full decision trees and multi-step workflows, built with precision.
Fig 4: Sample dataset table
2,500+
Hours of trajectories
25 Billion
Segmented image tokens
10 Million
Screenshot / action pairs
View sample trajectory data on desktop
Datasets
Trajectory Library
/
Publications
001
Human computer-use trajectories
Chakra trajectories are designed for frontier researchers pushing capability boundaries. Nuance, feedback, and temporal structure preserved across full decision trees and multi-step workflows, built with precision.
Fig 4: Sample dataset table
2,500+
Hours of trajectories
25 Billion
Segmented image tokens
10 Million
Screenshot / action pairs
View sample trajectory data on desktop
Datasets
Trajectory Library
/
Publications
001
Human computer-use trajectories
Chakra trajectories are designed for frontier researchers pushing capability boundaries. Nuance, feedback, and temporal structure preserved across full decision trees and multi-step workflows, built with precision.
Fig 4: Sample dataset table
2,500+
Hours of trajectories
25 Billion
Segmented image tokens
10 Million
Screenshot / action pairs
View sample trajectory data on desktop
Opinionated
Design Principles
Automated task generation
Bespoke task generation delivered in 24 hours. Our environments generate novel challenges automatically, enabling experiments with extended autonomous runtime. Models learn to recover from errors and adapt to new situations without human intervention.
GUI + API environments
Environments supporting simultaneous GUI and API interaction within single tasks. Agents can switch between visual navigation and programmatic commands based on task context. This mixed-modality capability enables research on workflows where both interaction modes are available.
Publications Library
Selected works spanning research findings, company announcements, SOTA analyses, technical notes, organizational updates, market observations, and perspectives on the evolving frontier of computer use agents.
Join us
Chakra is an applied research team pushing the boundaries of agents
We provide research-grade infrastructure for frontier-scale experiments in proprietary regimes where emergent behaviors develop. We work on hard infrastructure problems: frame-accurate state capture, deterministic environment design, mixed-modality training systems.



Realistic interactivity applies to our digital environments only - office space pictured is hypothetical.
Request
Platform Access
Access research-grade infrastructure for agent development. Deterministic environments with frame-accurate state control, high-fidelity trajectory datasets, and mixed-modality training capability.
Frontier Data Laboratory
Contact
Developer Resources
Company Resources
Inquiries & Contact Channels
Legal Resources
Newsletter
Copyright ©2026 Chakra Labs. Unauthorized duplication or use of the content of this website is prohibited.
Request
Platform Access
Access research-grade infrastructure for agent development. Deterministic environments with frame-accurate state control, high-fidelity trajectory datasets, and mixed-modality training capability.
Frontier Data Laboratory
Contact
Developer Resources
Company Resources
Inquiries & Contact Channels
Legal Resources
Newsletter
Copyright ©2026 Chakra Labs. Unauthorized duplication or use of the content of this website is prohibited.
Request
Platform Access
Access research-grade infrastructure for agent development. Deterministic environments with frame-accurate state control, high-fidelity trajectory datasets, and mixed-modality training capability.
Frontier Data Laboratory
Contact
Developer Resources
Company Resources
Inquiries & Contact Channels
Legal Resources
Newsletter
Copyright ©2026 Chakra Labs. Unauthorized duplication or use of the content of this website is prohibited.



