KodHauCapture undocumented wisdom so your AI codes like your senior engineers

KodHau MCP — The Governance Layer for Your AI Agents

KodHau MCP captures the undocumented tribal knowledge from your team's PR history and injects it directly into your AI agents' context. Before your agent writes a single line of code, it understands your architecture decisions, constraints, and why certain approaches were rejected—just like having your senior engineer whispering in its ear.

Product Highlights

• Tribal Knowledge Extraction: Automatically scans closed PRs for architecture decisions, code review comments, rejected approaches, and diff context that your team never documented
• MCP-Powered Context Injection: Single tool kodhau_get_wisdom delivers constraints, architecture decisions, and team knowledge for exact files and functions before any code generation
• Local-First Privacy: Runs entirely on your machine as an MCP server—your code and PR data never leave your local environment
• Universal Compatibility: Works seamlessly with Cursor, Claude Code, and any client supporting the Model Context Protocol
• Real-World Validation: Proven to prevent costly mistakes—like the dotnet/runtime case where Cursor alone suggested removing critical constraints, but KodHau surfaced three related PRs explaining why that would break the API contract

Use Cases

• Legacy Code Refactoring: AI agents understand why code exists before modifying it, preventing regression bugs from forgotten constraints
• Onboarding New Developers: Junior team members get instant access to years of undocumented architectural wisdom through their AI assistant
• Cross-Team Collaboration: Distributed teams preserve and share institutional knowledge that previously lived only in senior engineers' heads
• Safe Autonomous Coding: Enable AI agents to make complex changes with confidence, backed by your team's historical decision-making

Target Audience

KodHau serves solo developers seeking smarter AI assistance, daily-shipping individual developers, and enterprise teams running on institutional knowledge—anyone who wants their AI agents to reason with the same context as their best engineers.