Runsight is an open-source, YAML-first workflow engine designed specifically for AI agents. It empowers developers and teams to design, commit, run, and evaluate AI agent workflows with unparalleled transparency and control.
This SaaS is ideal for developers, data scientists, and engineering teams who are building and deploying AI agents and require robust tools for orchestration, debugging, cost management, and collaborative development.
Furthermore, the built-in cost tracking and budget caps are invaluable for managing expenses associated with LLM usage. Teams can set a maximum budget for a workflow, and Runsight will automatically halt execution if the cap is reached, preventing unexpected bills. The evaluation framework allows for rigorous testing and quality assurance, ensuring agents perform as expected before deployment, moving beyond "looks good" as a testing methodology.
This SaaS is ideal for developers, data scientists, and engineering teams who are building and deploying AI agents and require robust tools for orchestration, debugging, cost management, and collaborative development.
Key Features:
- Git-Native YAML Workflows: Define agent workflows in human-readable YAML files, committing them directly to Git for version control and collaboration.
- Per-Run Cost Tracking & Budget Caps: Monitor the cost of every block and run to the cent, with hard budget caps to prevent overspending.
- Built-in Evaluation Framework: Utilize assertions on block outputs, transform hooks, and regression testing for reliable agent performance.
- Visual Canvas & YAML Editor: Experience the duality of a visual canvas for intuitive design and a Monaco editor for direct YAML editing, with real-time synchronization.
- Pause & Kill Mid-Run: Gain control over running agents by pausing execution to inspect state or killing them to save budget.
- Self-Hosted & Open Source: Runs on your machine, uses your API keys, ensuring data privacy and no vendor lock-in.
Use Cases:
Runsight significantly streamlines the development lifecycle of AI agents. For instance, a research team can design complex multi-step AI agent workflows in YAML, visually track their execution on the canvas, and commit changes to Git, ensuring every iteration is version-controlled. This eliminates the common problem of scattered Python files and inconsistent agent definitions across teams.Furthermore, the built-in cost tracking and budget caps are invaluable for managing expenses associated with LLM usage. Teams can set a maximum budget for a workflow, and Runsight will automatically halt execution if the cap is reached, preventing unexpected bills. The evaluation framework allows for rigorous testing and quality assurance, ensuring agents perform as expected before deployment, moving beyond "looks good" as a testing methodology.
Pricing Information:
Runsight is an open-source project, meaning the software itself is free to use and self-host. Users are responsible for their own API keys and associated costs from AI model providers. The platform provides detailed per-run cost tracking to help manage these external expenses effectively.User Experience and Support:
The user experience is designed for flexibility, offering both a visual canvas for drag-and-drop workflow design and a powerful Monaco editor for direct YAML manipulation. Both views are synchronized, providing a seamless editing experience. Support is available through its open-source community on GitHub, with comprehensive documentation and quickstart guides to help users get started quickly.Technical Details:
Runsight leverages YAML for workflow definition, integrating deeply with Git for version control. It provides a web-based interface accessible vialocalhost:8000 after a simple uvx runsight command. The platform is designed to be self-hosted, giving users full control over their environment and API keys.Pros and Cons:
- Pros:
- Open-source and self-hosted, ensuring data privacy and no vendor lock-in.
- Git-native workflows for robust version control and collaboration.
- Precise per-run cost tracking with hard budget caps.
- Integrated visual canvas and YAML editor for flexible design.
- Comprehensive evaluation framework with assertions and regression testing.
- Ability to pause and kill agents mid-run to save resources.
- Cons:
- Requires some familiarity with YAML for advanced workflow design.
- Initial setup involves self-hosting, which might require basic technical knowledge.
- No explicit mention of enterprise-level support plans (typical for open-source).
Conclusion:
Runsight offers a powerful, transparent, and cost-effective solution for developing and managing AI agent workflows. Its Git-native, YAML-first approach, combined with robust cost tracking and evaluation tools, makes it an essential platform for any team serious about building reliable and efficient AI agents. Explore Runsight today to bring structure and control to your AI agent development.Comments
Achievement
Publisher
Michael Rogov
Launch Date2026-07-08
Platformweb
Pricingfree
Tech Stack
#web
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