A unified workspace that delegates projects to agents and lets users swap AI models without losing built data, skills, or artifacts. Users interact via a web app or pre-packaged desktop installer to automate multi-step workflows and publish outputs.
Project overview
The platform addresses model lock-in by preserving configurations and artifacts when models are changed, and it scopes credentials per connection so agents never see raw keys.
Project type
AI Coding
Deployment
Refer to project documentation
License
MIT
Best for
Creators looking to automate office workflows and SaaS prototyping by delegating projects to agents that interact with connected business systems.
Users who want to swap AI models without losing built artifacts or needing to rebuild data connections and workflows.
Key capabilities
Securely links external systems like BigQuery, Postgres, Gmail, Drive, etc., scoping credentials per connection so agents never see raw keys.
Turn agent output into documents, dashboards, apps, and code, and publish to a live URL.
Automate repetitive, multi-step work that involves reading and writing: reports, monitoring, recurring workflows, and scheduled operations.
Limitations and risks
Operation involves external services for data connections such as BigQuery, Postgres, Gmail, Drive, HubSpot, Notion, and Linear.
The platform supports external AI models such as Claude, GPT, Gemini, DeepSeek, Qwen, and Kimi.
Cost dependencies, telemetry, GPU requirements, and minimum hardware are not documented.
Getting started
Setup difficulty is rated as easy. Users can directly access the web app or download pre-packaged desktop installers without needing to build from source.
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