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by agentic-mcp-tools • Uncategorized
A lightweight MCP server providing persistent semantic memory storage, knowledge graphs, and conversational recall for AI agents.
Persistent, hierarchical memory storage with semantic search capabilities.
Interactive knowledge graph visualization and timeline-based memory exploration.
AI-powered deduplication and retrieval-augmented chat with memory context.
Memora offers persistent storage of AI agent memories using SQLite or cloud databases with optional synchronization. It supports semantic search with multiple embedding backends, advanced queries, and AI-powered deduplication. The server includes interactive knowledge graph visualization, a chat interface with retrieval-augmented generation, and tools for memory automation and analytics, enhancing cross-session context and memory linking for AI agents.
Create a new memory entry. Args: content: The memory content text metadata: Optional metadata dictionary tags: Optional list of tags suggest_similar: If True, find similar memories and suggest consolidation (default: True) similarity_threshold: Minimum similarity score for suggestions (default: 0.2)
Create a new issue/bug memory. Args: content: Description of the issue status: Issue status - "open" (default) or "closed" closed_reason: If closed, the reason - "complete" or "not_planned" severity: Issue severity - "critical", "major", "minor" (default) component: Component/area affected (e.g., "graph", "storage", "api") category: Issue category (e.g., "bug", "enhancement", "performance") Returns: Created issue memory with auto-assigned tag "memora/issues"
Create a new TODO/task memory. Args: content: Description of the task status: Task status - "open" (default) or "closed" closed_reason: If closed, the reason - "complete" or "not_planned" priority: Task priority - "high", "medium" (default), "low" category: Task category (e.g., "cloud-backend", "graph-visualization", "docs") Returns: Created TODO memory with auto-assigned tag "memora/todos"
Create a new section/subsection header memory. Section memories are organizational placeholders that: - Are NOT visible in the graph visualization - Are NOT included in duplicate detection - Do NOT compute embeddings or cross-references Args: content: Title/description of the section section: Parent section name (e.g., "Architecture", "API") subsection: Subsection path (e.g., "endpoints/auth") Returns: Created section memory with auto-assigned tag "memora/sections"
List memories, optionally filtering by substring query or metadata. Args: query: Optional text search query metadata_filters: Optional metadata filters limit: Maximum number of results to return (default: unlimited) offset: Number of results to skip (default: 0) date_from: Optional date filter (ISO format or relative like "7d", "1m", "1y") date_to: Optional date filter (ISO format or relative like "7d", "1m", "1y") tags_any: Match memories with ANY of these tags (OR logic) tags_all: Match memories with ALL of these tags (AND logic) tags_none: Exclude memories with ANY of these tags (NOT logic) sort_by_importance: Sort results by importance score (default: False, sorts by date)
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