> ## Documentation Index > Fetch the complete documentation index at: https://docs.memv.ai/llms.txt > Use this file to discover all available pages before exploring further. # How it works > Understanding Mem[v]'s core concepts and workflow Mem\[v] provides graph-based memory infrastructure for AI agents, automatically extracting entities, building knowledge graphs, and enabling semantic retrieval across all content types. ## The workflow Upload files or send data through the API: * Documents (PDFs, Word, text) * Videos (MP4, MOV, WebM) * Audio (MP3, WAV) * Images and screenshots * Conversations Mem\[v] processes content and extracts: * Entities (people, companies, technologies, topics) * Relationships between entities * Semantic embeddings for search * Temporal context and metadata Information is organized into knowledge graphs: * Entities become nodes * Relationships become edges * Triplets form: Subject → Predicate → Object * Isolated within Spaces for privacy Query by meaning to retrieve relevant context: * Graph-aware search returns connected information * Results ranked by relevance * Include entity relationships and metadata ## Key concepts ### Spaces Isolated containers for memories and knowledge graphs. Each space has complete data separation, enabling per-user, per-feature, or per-tenant organization. ### Memories Structured information extracted from your content. Each memory contains content, metadata, and extracted entities that feed into the knowledge graph. ### Knowledge graphs Automatically built networks connecting entities through relationships. Enable discovery of indirect connections and richer context for AI agents. ### Semantic search Graph-aware retrieval that finds information by meaning and returns connected entities and relationships. ## Data flow ``` Content → Processing → Memories → Knowledge Graph ↓ AI Agent ← Semantic Search ← Query ``` ## Next steps Build your first memory-enabled app Organize memories with Spaces Understand graph-based memory Explore the SDKs