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.
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.
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.
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.
Provides an MCP interface to the ROBOT command-line tool for OWL ontology editing, enabling operations like merging, reasoning, and conversion via natural language.
A local, cross-editor MCP server that provides persistent memory for coding agents, capturing and recalling decisions, conventions, and fixes across sessions without API keys.
Provides semantic code search and code insights via a knowledge graph, enabling AI to understand, navigate, and modify complex projects with deep dependency and architecture analysis.
A high-performance code knowledge graph server implementing MCP, indexing codebases into a structured AST knowledge graph with semantic search, call graph traversal, and HTTP route tracing.
Gives AI coding agents persistent memory by storing observations, decisions, and learnings in a local SQLite database with vector search, full-text search, and a rules engine.
MCP server for semantic code search and dependency graph analysis. Indexes codebases into a knowledge graph with vector embeddings for AI-powered code understanding.
A persistent, tree-sitter-backed code knowledge cache MCP server that reduces token usage by storing parsed structure and enabling fast symbol lookup, inheritance graph, call graph, and semantic search.
MCP server that enables persistent, hybrid, local memory for LLM agents, with vector + BM25 search, knowledge graph, and policy-driven retention, providing token-budgeted context injection for AI assistants.
Automatically captures technical and product decisions from Claude Mac app sessions and writes them into project files, enabling seamless context transfer between strategy and coding sessions.