A robust server for managing long-term agent memory using Mem0, providing efficient storage and retrieval of agent memories with a lightweight Python-based implementation.
With Memori's MCP server, your agent can retrieve relevant memories before answering and store durable facts after responding, keeping context across sessions without any SDK integration.
With MCP, it can:
Store stable user facts and preferences after answering using the advanced_augmentation tool
Recall relevant memories before answering using the recall tool
Maintain context across sessions us
Persistent knowledge memory layer for AI agents. Hybrid semantic + full-text search with pgvector, code dependency graph with blast-radius impact analysis, and incremental indexing for 7 languages. In-process ONNX embeddings, no external API required.
Universal AI memory layer that provides cross-client, cross-repo context management with semantic search, automatic code indexing, and session management. Enables persistent developer memory across projects with typed memories, graph-based relationships, and RAG-powered retrieval.
Persistent memory layer for AI agents with entity resolution, PII detection, AES-256-GCM encryption at rest, and hybrid search. Self-hosted. 100% on LoCoMo benchmark.
🧠High-performance persistent memory system for Model Context Protocol (MCP) powered by libSQL. Features vector search, semantic knowledge storage, and efficient relationship management - perfect for AI agents and knowledge graph applications.