memory-journal-mcp and MARM-Systems
About memory-journal-mcp
neverinfamous/memory-journal-mcp
MCP Server for AI Context + Project Intelligence. Overcome Disconnected AI Sessions with Persistent Project Memory, Automatic Session Briefing & Summation, Triple Search, Knowledge Graphs, GitHub Integration (Actions, Insights, Issues, Kanban, Milestones, and PRs), Automated Scheduling, 42 Tools, Tool Filtering, and HTTP/SSE & stdio Transport.
Persists project context across AI sessions using SQLite with full-text and semantic vector search (HuggingFace transformers + sqlite-vec), enabling agents to auto-brief from history and hand off context via structured session summaries. Provides 61 MCP tools organized in 10 groups including GitHub Commander for automated issue triage, PR review, and audit workflows, plus dynamic multi-repo routing via PROJECT_REGISTRY for managing multiple projects with a single server instance. Architecture emphasizes structured error handling with classification codes and recovery hints for agent reliability, backed by 96.7% test coverage and Alpine Docker deployment.
About MARM-Systems
Lyellr88/MARM-Systems
Turn AI into a persistent, memory-powered collaborator. Universal MCP Server (supports HTTP, STDIO, and WebSocket) enabling cross-platform AI memory, multi-agent coordination, and context sharing. Built with MARM protocol for structured reasoning that evolves with your work.
# Technical Summary Implements semantic vector-based memory indexing with auto-classification of conversation content (code, decisions, configs) and enables cross-session recall via FastAPI-backed HTTP/STDIO transports that integrate natively with Claude, Gemini, and other MCP-compatible agents. The architecture uses SQLite with WAL mode for persistent storage and connection pooling, exposing 18 MCP tools for granular memory control—including structured session logs, reusable notebooks, and smart context fallbacks when vector similarity alone is insufficient. Designed for production workflows requiring reliable long-term context across multiple AI agents and deployment cycles, with Docker containerization and rate-limiting built-in.
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