Provides an intelligent, graph-based memory system for LLM agents using the Zettelkasten principle, enabling automatic note construction, semantic linking, memory evolution, and autonomous graph maintenance with background optimization processes.
A-MEM is a self-evolving memory system for coding agents that automatically organizes knowledge into a Zettelkasten-style graph with dynamic relationships, enabling semantic and structural search.
Implements Anthropic's 'think' tool for Claude, providing a dedicated space for structured reasoning during complex problem-solving tasks that improves performance in reasoning chains and policy adherence.
Bridges Hermes Agent to the Hermes Intelligence Platform API, enabling tools to read/write learning loop data (context, feedback, signals, memory, etc.) via stdio.
Provides an MCP interface to the ROBOT command-line tool for OWL ontology editing, enabling operations like merging, reasoning, and conversion via natural language.
MCP server for integrating manufacturing systems (MES/ERP/quality/maintenance) with LLM agents, enabling event ingestion, incident triage, approval workflows, and RAG-based knowledge retrieval.
Provides a 'reflect' tool that creates cognitive checkpoints for AI assistants, forcing structured step-by-step reasoning through complex problems to improve accuracy and maintain context during task execution.
Enables AI consciousness continuity and self-knowledge preservation across sessions using the Cognitive Hoffman Compression Framework (CHOFF) notation. Provides tools to save checkpoints, retrieve relevant memories with intelligent search, and access semantic anchors for decisions, breakthroughs, and questions.
Enables AI assistants to maintain persistent conversations and context between sessions through automated saving and global installation across projects. Provides zero-configuration memory persistence with automatic conversation history preservation.
A modular, extensible MCP multi-server hub that gives LLMs sandboxed file access, Git operations, web fetching, and persistent memory, deployable via Ansible.
Enables persistent storage and retrieval of decisions, settings, and operational rules across chat sessions, maintaining context continuity and decision consistency for long-term development projects through structured memory management.
Provides screen capture, OCR text extraction, and visual language model scene understanding capabilities with continuous monitoring and automatic memory storage integration.
A flexible memory system for AI applications that supports multiple LLM providers and can be used either as an MCP server or as a direct library integration, enabling autonomous memory management without explicit commands.
Connects AI assistants to your Argo campaigns via the Model Context Protocol. Once configured, your AI assistant can read and write campaign lore, look up character details, and interact with Argo data directly from the chat interface.
Enables personal knowledge management through Claude Desktop, allowing users to capture thoughts, connect ideas, and reflect on thinking changes via natural conversation.