Overview
Features
CPI Adaptive Interrupts
Phase-aware prompts that challenge assumptions, providing alignment and robustness by interrupting risky reasoning steps at inflection points.
Multi-provider LLM
Supports Gemini, OpenAI, Anthropic, and OpenRouter, offering flexibility in provider choice and integration.
History Continuity
Summarizes prior advice when a sessionId is supplied to maintain context across interactions.
Optional vibe_learn
Logs mistakes and fixes for future reflection, enabling self-improvement over time.
Session Constitution (per-session rules)
API set to update, reset, and check session-specific rules to enforce CPI-guided behavior.
Transport & Client Plug-and-Play
Supports stdio and HTTP transports with quickstart npx commands and compatibility with common MCP clients (Claude Desktop, Cursor, Windsurf, VS Code).
Who Is This For?
- AI developers:Use as an external mentor layer to keep agents aligned, reflective, and safe via CPI.
- MCP integrators:Integrate across MCP transports and clients for oversight and minimal viable-agent guidance.
- Researchers:Study CPI-based mentorship and RLI mitigation as part of alignment research.




