CodeKang vs Octopus

CodeKang has no write-up on Specifiction yet; its figures are above.

Octopus indexes a codebase and uses that context to review pull requests automatically. When a pull request opens, it retrieves related indexed code through vector search and sends the changes and context to a configured language model. Findings receive severity ratings and appear as inline comments. The GitHub integration also reviews updates, posts check runs, and can create issues from findings. Octopus supports GitHub, GitLab, Bitbucket, and Forgejo, and includes source-cited codebase chat plus a CLI for reviews, context queries, and repository checks. Teams can use knowledge-base documents to guide reviews, with rules, repositories, and reviewer settings shared across an organization. AI options include providers such as Anthropic Claude, OpenAI, Google, xAI Grok, Alibaba Qwen, Cohere, and OpenRouter. It can be self-hosted with Docker Compose, PostgreSQL, and Qdrant, with optional local models and embeddings through Ollama. The cloud service stores review and indexing data; findings persist through the PR lifetime and 90 days after closure, while indexed embeddings remain as long as a repository is connected. Free self-hosted and cloud tiers are listed. Live team telemetry requires a paid organization.