Scope
Open-source platform for evaluating how AI coding agents handle your product using reusable criteria and repeatable experiments.
At a Glance
About Scope
Scope is an open-source agentic experience evaluation platform from Microsoft, labeled a research preview on GitHub. It lets teams give coding agents the same task, define success criteria, and inspect the evidence behind each result through a browser Portal, CLI, or REST API. The homepage lists GitHub Copilot and Claude Code as supported today, with OpenAI Codex and OpenCode planned.
What It Is
Scope evaluates how AI agents use a product and approach real tasks. A user writes a task prompt, defines criteria, and runs agents against it repeatedly to compare outcomes rather than relying on a generic leaderboard.
How evaluations work
Criteria are organized into ordered gates, such as requirements, build, and test. Inside each gate, criteria form a dependency graph, so if a prerequisite check fails, dependent checks are skipped. Agent configuration (model, skills, tools) is saved in versioned profiles so experiments can be rerun from a known setup. Runs can be followed through live logs, with iterations and criterion feedback available for inspection, and results cover gates passed, quality, tokens, and duration.
Deployment model
Scope runs on Kubernetes, using containerized agent workers, KEDA queue-driven scaling, and Flux GitOps deployments. Self-hosting guides cover Helm deployment. The source is released under the MIT License.
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Pricing
Open Source (MIT)
Scope core platform is open source under the MIT License and can be self-hosted on Kubernetes.
- MIT License
- Self-hosting on Kubernetes
- Portal and CLI
- Research preview
Capabilities
Key Features
- Reusable criteria library with dependency graphs
- Ordered evaluation gates
- Versioned agent profiles
- Repeatable experiments with the same task across agents
- Live run logs and criterion feedback
- Evidence on quality, tokens, and duration
- Portal, CLI, and REST API access
- Kubernetes self-hosting with KEDA scaling
