memory-journal-mcp and enhanced-mcp-memory

memory-journal-mcp
50
Established
enhanced-mcp-memory
49
Emerging
Maintenance 13/25
Adoption 5/25
Maturity 18/25
Community 14/25
Maintenance 2/25
Adoption 12/25
Maturity 18/25
Community 17/25
Stars: 11
Forks: 3
Downloads: —
Commits (30d): 0
Language: TypeScript
License: MIT
Stars: 30
Forks: 8
Downloads: 208
Commits (30d): 0
Language: Python
License: MIT
No risk flags
Stale 6m

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 enhanced-mcp-memory

cbunting99/enhanced-mcp-memory

An enhanced MCP (Model Context Protocol) server for intelligent memory and task management, designed for AI assistants and development workflows. Features semantic search, automatic task extraction, knowledge graphs, and comprehensive project management.

Implements a 5-stage sequential thinking engine with token optimization (30-70% compression) and automatic context summarization for conversation continuity, built on FastMCP with SQLite persistence. Automatically detects project conventions (OS, package managers, build tools, runtime types) and learns command patterns to correct AI suggestions—particularly useful for cross-platform development. Exposes 20+ MCP tools including thinking chains, task decomposition, and knowledge graph relationships that link memories to code file paths.

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