Cae
FastAPI backend with MCTS-based engine to optimize LLM conversation responses
A production-ready FastAPI backend and MCP server that applies Monte Carlo Tree Search (MCTS) to generate, simulate, and score multiple conversation branches to select optimal LLM responses. Includes async processing, Redis caching, PostgreSQL/PGVector storage, Prometheus metrics, and Docker Compose deployment. Suited for building advanced LLM applications that require conversation analysis, response optimization, and MCP-compatible integrations.
Features
MCTS Conversation Analysis
Multi-Branch Response Generation
Conversation Simulation
Goal-Oriented Scoring
Semantic Caching
Prometheus Metrics
Pricing
Free plan available
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