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Why Teams Choose Kodus

• Model Agnostic: Use Claude, GPT-5, Gemini, Llama, GLM, Kimi or any OpenAI-compatible endpoint.
• Zero Markup on LLM Costs: You pay model providers directly. No hidden multipliers.
• Learns from Your Context: Kody adapts to your architecture, standards, and workflow.
• You Set the Rules: Define custom review rules in plain language.
• Privacy & Security: Source code is not used to train models, data is encrypted in transit and at rest, and self-hosted runners are supported. Self-hosted instances send one anonymous heartbeat per day (aggregated counters only — no code, names, or identifiers); opt out with KODUS_TELEMETRY_DISABLED=true. See Anonymous Telemetry.
• Native Git Workflow: Works directly in PRs with GitHub, GitLab, Bitbucket, and Azure Repos.
• CLI + CI/CD Ready: Run reviews locally and in pipelines.
• Operational Impact: Track technical debt and delivery metrics while keeping review quality high.

Product Highlights

🔑 Bring Your Own Key

Connect your own provider credentials and choose the models behind Kodus reviews: OpenAI, Anthropic, Google Gemini, Vertex AI, Novita, or any OpenAI-compatible endpoint. Keep billing and usage under your own provider account, without hidden LLM markups.

📈 Token Usage

Track token consumption across AI code reviews, understand cost drivers, and keep model spend predictable as adoption grows.

⚙️ Kody Rules

Kody Rules let teams define review instructions in plain language and apply them across organizations, repositories, paths, or specific review scopes. Kody uses those rules as context when reviewing pull requests, helping enforce architecture decisions, security expectations, testing practices, and repository-specific conventions without relying on reviewers to repeat the same feedback manually.

📊 Cockpit

Cockpit helps teams measure Kodus review effectiveness, Kody Rule health, repository health, and delivery metrics across the engineering workflow.

🧩 Kody Issues

Automatically track unimplemented suggestions from closed pull requests, manage them by status, severity, category, and repository, and let Kody resolve them when the fix appears in a future PR.

🔎 See Kody reviewing a real pull request

Kody does more than summarize diffs. It reviews code with context, flags risks by severity, and suggests concrete fixes directly in the pull request.

In this example, Kody catches a critical IDOR risk where an organizationId query parameter could bypass tenant protection when passed as an array, then suggests an explicit runtime validation before the code is merged.

Get Started

Choose the workflow that matches how you want to use Kodus.

Monorepo Structure

Kodus is a monorepo with multiple applications, shared domain libraries, and published packages.

kodus-ai/
├── apps/
│   ├── api/          # NestJS API
│   ├── web/          # Next.js dashboard
│   ├── worker/       # Review execution and queue consumers
│   └── webhooks/     # Git provider webhook ingestion
├── libs/             # Shared NestJS domain modules
├── packages/
│   ├── kodus-flow/   # AI agent orchestration SDK
│   └── kodus-common/ # LLM abstraction package
└── scripts/          # Dev, deploy, benchmark, and automation scripts

For full setup instructions, follow the Local Quickstart.

Open Source vs. Teams vs. Enterprise

Compare all plan →

Resources

Contributing

We welcome contributions of all sizes 🧡

Fix a typo, improve the docs, report a bug, suggest a feature, or open a pull request for something you think should exist.

Not sure where to start? Open an issue or join the community. We’re happy to help.