Graph Memory

AgenticAI-DialogGen chains ChatPreprocessor, KnowledgeExtractor, TopicAnalyzer, KnowledgeGraphBuilder, PersonaGenerator, DuelingChat Agent, ConversationValidator, ConversationRefiner, QAGeneration, and PostProcessing to turn raw multi-session chats into topic-guided, persona-grounded conversations with explicit short- and long-term memories. On the TGC / KG memory QA benchmark, Mistral-7B fine-tuned within AgenticAI-DialogGen achieves 87.36 F1, compared to GPT-4’s 83.77 F1 in a zero-shot setting on the same task.

ByteRover organizes memory through an LLM-driven Agent Layer, a sequential Execution Layer, and a file-based Context Tree with an Adaptive Knowledge Lifecycle. On LoCoMo, ByteRover reaches 96.1% overall accuracy versus 89.9% for HonCho, and on LongMemEval-S ByteRover scores 92.8% overall.

Codebase-Memory parses repositories with a multi-pass pipeline using the Parse stage, Build stage, Serve stage, FunctionRegistry, Louvain communities, and MCP tool interface to build a persistent SQLite knowledge graph. On a 31-language benchmark, Codebase-Memory reaches 0.83 quality versus 0.92 for an Explorer Agent while using ten times fewer tokens and 2.1 times fewer tool calls.

GAM builds a Hierarchical Graph Memory Architecture with a global Topic Associative Network, local Event Progression Graphs, State-Based Memory Consolidation, and Graph-Guided Multi-Factor Retrieval to decouple encoding from consolidation. On LoCoMo with Qwen2.5-7B, GAM attains an Average F1 of 40.00 compared to Mem0’s 35.38, and on LongDialQA with Qwen2.5-7B, GAM reaches 12.55 F1 vs MemoryOS at 6.76.

HGMEM represents working memory as a hypergraph with Hypergraph-based Memory Storage, Adaptive Memory-based Evidence Retrieval, and Dynamic Memory Evolving to build high-order correlations across entities and facts. On Prelude long narrative understanding, HGMEM with GPT-4o achieves 73.81% accuracy compared to 72.22% for HippoRAG v2, while also reaching 69.74 comprehensiveness on Longbench generative sense-making QA.

Memoria augments LLM chats with structured conversation logging, dynamic user persona via KG, session level memory for real time context, and seamless retrieval for context aware responses to provide persistent, interpretable memory. On LongMemEvals single-session-user and knowledge-update subsets, Memoria reaches 87.1% and 80.8% accuracy respectively, surpassing A-Mem (OpenAI) while using much shorter prompts.

MemoriesDB stores each Memory Record, Edges and Relations, and the Temporal Semantic Stack inside PostgreSQL with pgvector, exposing unified temporal–semantic–relational queries. MemoriesDB’s main result is a working implementation that demonstrates scalable time-bounded recall and hybrid semantic–structural queries on commodity SQL infrastructure without specialized vector or graph engines.

SGMem organizes long conversations via SGMem Construction and Management, SGMem Usage, sentence level graphs, and multi hop retrieval over sessions, rounds, turns, summaries, facts, and insights. SGMem achieves 0.700 Accuracy (Top 5) on LongMemEval and 0.526 on LoCoMo, beating the RAG-SMFI baseline at 0.676 and 0.510 respectively.