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Scan a repository for AI systems

Repository Intelligence reads a connected GitHub repository at one immutable commit, detects source-backed AI components, and recommends evaluation packs for uncovered risks. It never executes repository code.

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Scan from the web app

  1. Connect the repository through the EvalGate GitHub App.
  2. Open Repository intelligence at /repository-intelligence.
  3. Select the connected repository and start a scan. You can pin a 40-character commit SHA or scan the current default-branch head.
  4. Review detected providers, model routes, agents, prompts, tools and MCP, retrieval and memory, guardrails, evaluation frameworks, telemetry, domain signals, and release assets.
  5. Inspect every finding’s exact file and line evidence before installing a recommended pack.
A source detection is not runtime confirmation. The result records graph completeness and truncation reasons so a partial scan cannot be presented as complete evidence.

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Scan and ask from the CLI

Configure an EvalGate API key and connect GitHub before running this authenticated workflow:
The TypeScript package exposes the same workflow through client.repositoryIntelligence. The Python package exposes it through client.repository_intelligence. Bot answers are bounded to one persisted graph version. Answers cite source locators when evidence exists and return an explicit insufficient-evidence state when it does not.

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Turn a finding into coverage

Open a recommended pack, review why its detector evidence caused the recommendation, install it, and run the resulting active release gate. This connects the repository commit to source evidence, evaluation coverage, and the release decision. Continue with Install an evaluation pack after reviewing the recommendation and its source evidence.