Open Kritt

Big repositories, small questions: Open Kritt is a self-hosted platform that splits vulnerability research into focused tasks for AI agents and returns ranked, de-duplicated findings. Build a workflow as a tree of prompt steps, where each step declares the structured output it must produce and the next level launches one fresh agent per result. Scan remote or local repositories along with their dependencies, using Codex, Claude Code, or Grok Build as the harness. Every workflow ends in one fixed finding schema covering file, line, vulnerability type, trigger flow, and a malicious input example. Post-scripts then validate each finding, build a proof of concept, or write the report. Markdown severity rankers tell the model how to prioritize for your bounty program, and repeated findings collapse automatically. Model access comes from a Codex login or OpenAI, Anthropic, DeepSeek, OpenRouter, or xAI keys, with an Active switch to choose which accounts take new work. A dashboard tracks blueprints, scans, and findings, a headless command-line tool imports workflows and launches scans on servers without a browser, and finished scans export as one ZIP with a share-safe manifest. Built-in workflows, including an entry-point-driven hunt, give you a starting point. Running on a dedicated VPS on RepoCloud with guaranteed CPU, RAM, and SSD, full root SSH access, and a browser serial console. AGPL-3.0 licensed.

Open Kritt
Open Kritt
Open Kritt
Open Kritt
Open Kritt

Benefits

  • Focused tasks beat whole-repo prompts
  • Each workflow step hands one small, well-defined question to a fresh agent, so analysis stays on a single function or entry point instead of drowning in an entire repository.
  • One consistent finding schema
  • The terminal step must emit explanation, file path, line, vulnerability type, trigger flow, and malicious input example, so every finding is comparable, de-duplicated, and ready for ranking.
  • Validation and proofs of concept
  • Post-scripts run after the workflow to validate each finding, build a proof of concept, and write a report, cutting false positives before a human reads anything.
  • Your models, prompts, and infrastructure
  • Use a Codex login or OpenAI, Anthropic, DeepSeek, OpenRouter, or xAI keys, edit every prompt, and run the whole engine on your own machines with your own credentials.

Features

  • Visual workflow builder
  • Chain prompt steps into depth levels with typed JSON outputs, multi-output fan-out, and consume-all batching; the editor blocks saving until the final step emits the finding schema.
  • Parallel scans with harnesses
  • Run a workflow against a remote or local repository plus its dependencies, choosing Codex, Claude Code, or Grok Build as the agent harness and repeating runs for coverage.
  • Custom severity rankers
  • Write markdown rules that tell the model how to prioritize findings for your program, combined with automatic de-duplication of repeated results.
  • Headless command-line tool
  • Import workflows, post-scripts, skills, and rankers as JSON, create scans, watch stages and failure reasons, edit runtime settings, and export bundles from a server shell.
  • ZIP export of results
  • Package canonical findings, structured data, reports, and proofs of concept into one archive with a share-safe manifest; stopped scans with findings produce clearly marked partial exports.
  • Built-in starter workflows
  • Start from preinstalled research patterns, including an entry-point-driven vulnerability hunt, then adapt the prompts and steps to your own strategy.

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