An implementation of persistent memory for Claude using a local knowledge graph, allowing the AI to remember information about users across conversations with customizable storage location.
A production-ready reasoning engine that integrates Claude AI with specialized MCP tools for knowledge retrieval, schema validation, and domain-specific rubric evaluation. It enables structured RAG-based analysis across legal, health, and science domains via a RESTful API.
Persistent memory and handoff intelligence layer for MCP agents. Most memory servers retrieve text — Memory Nexus compounds operational context, learning from usage and progressively synthesizing observations into higher-order intelligence across sessions and tools.
Reduces token consumption for AI coding agents by 50-70% through intelligent code context filtering, Git delta tracking, and local SQLite/Tree-sitter indexing.
Transforms codebases into a knowledge graph for AI agents, enabling semantic search, impact analysis, and persistent session memory with up to 94% token savings.
Enables efficient AI workflow orchestration by chaining multi-step LLM operations while keeping intermediate results out of the context window, reducing token usage by 90%+ and supporting multiple AI providers.
Provides versioned, structured memory for AI agents, allowing them to store facts, detect conflicts, and track knowledge history via a hosted SaaS platform. It enables efficient hierarchical information retrieval and semantic search while keeping token usage constant as memory scales.
A vendor-agnostic cognitive persistence layer for AI agents. Eliminate the "repetition tax" by transporting your context, preferences, and history across sessions. Features an auto-adaptation engine that syncs global instructions to ensure operational cohesion and optimize token usage across any LLM or multi-agent workflow.
