Forge Cascade MCP

Adaptive MCP memory system for AI applications. Learns which retrieval strategies work for your data, scores results using cognitive science models, builds a knowledge graph automatically, and validates every parameter change against real query history before adopting it. Patent pending.

Highly efficient context management for agentic AI: MCP code search, evidence packs, graph context, and memory for large projects.

Self-hosted Rust-based MCP server for AI agent memory — persistent, queryable memory with hybrid search, knowledge graphs, built-in embeddings, and 14 core tools (expandable to 86+ with profile-based tiering).

Self-improving MCP memory server for AI agents. One command, no cloud, no config. Hybrid search, feedback loop quality system, dashboard UI, auto-linking knowledge graph. Gets better the more you use it.

Persistent memory for AI agents using a semantic knowledge graph. Store, retrieve, and connect memories with semantic search — so your AI remembers context across sessions.

Persistent memory for AI agents. Single SQLite file, 192 MCP tools. FTS5 search, knowledge graph, session handoffs, write gate. No server, no API keys, no LLM calls.

MCP memory server with Hebbian learning — concept connections strengthen through co-activation and weaken through disuse.

A knowledge graph server that provides persistent, multi-context memory for AI models.

Provides persistent memory for AI models using a local knowledge graph.

Local-first RLHF feedback loop for AI agents — capture preference signals, promote memories, block repeated mistakes, export DPO/KTO training pairs