Knowledge Graph on SoloSoft

Chat2Graph: Graph Native Agentic System for Multi-Agent Collaboration

Fri, 01 May 2026 00:00:00 +0000

The multi-agent AI paradigm has captured the imagination of developers and researchers alike. The vision is compelling: specialized agents working in concert, each contributing their unique capabilities to solve complex problems that no single agent could handle alone. But building such systems has proven difficult. Communication between agents, shared context, task decomposition, and reasoning traceability all present hard engineering challenges. Chat2Graph, developed by the TuGraph team, addresses these challenges through a novel approach: using graph databases as the native substrate for agent collaboration.

The core insight is that graph structures map naturally to the problems that multi-agent systems need to solve. Agent relationships form a graph. Knowledge and context form a graph. Task dependencies form a graph. Reasoning chains form a graph. By building the agent system directly on top of a graph database (TuGraph), Chat2Graph provides a native representation for all of these structures without the impedance mismatch of mapping graph concepts onto relational or document stores.

Code-Graph: Open-Source Tool for Analyzing Source Code as Queryable Knowledge Graphs

Fri, 01 May 2026 00:00:00 +0000

Understanding unfamiliar codebases is one of the hardest challenges in software development. Code-Graph by FalkorDB tackles this problem in a novel way: by transforming source code repositories into fully queryable knowledge graphs that you can interrogate in natural language.

Instead of reading through files linearly or relying on code search tools that treat code as flat text, Code-Graph analyzes your codebase at the Abstract Syntax Tree (AST) level, extracting every significant entity – classes, functions, methods, modules, arguments, variables – and mapping their relationships into a property graph stored in FalkorDB. The result is a structured, navigable representation of your entire codebase that supports natural language queries like “Show me all classes that depend on the DatabaseConnection class” or “Find unused utility functions in the auth module.”

FalkorDB: Ultra-Fast Open-Source Graph Database for Knowledge Graphs and GraphRAG

Fri, 01 May 2026 00:00:00 +0000

FalkorDB is an ultra-fast, open-source multi-tenant property graph database built specifically for LLM Knowledge Graphs and GraphRAG (Graph-based Retrieval-Augmented Generation). As the direct successor to RedisGraph – which was discontinued by Redis Inc. in 2023 – FalkorDB has been adopted by a growing community of AI practitioners who need graph database performance optimized for the age of large language models.

Under the hood, FalkorDB uses sparse matrix operations via the GraphBLAS standard to represent and query graph adjacency matrices. This approach is fundamentally different from the index-based traversal used by most graph databases, and it is the key to FalkorDB’s millisecond-latency query performance even on graphs with millions of nodes.

GNN-RAG: Graph Neural Network Enhanced Retrieval-Augmented Generation

Fri, 01 May 2026 00:00:00 +0000

Retrieval-Augmented Generation has become the standard approach for grounding LLM responses in factual knowledge. But standard RAG has a well-known limitation: it struggles with multi-hop questions that require connecting information across multiple documents or entities. When a question asks “What is the capital of the country where the inventor of the telephone was born?” the answer requires tracing a path through a knowledge graph – something flat text retrieval handles poorly. GNN-RAG addresses this gap by integrating graph neural networks into the RAG pipeline.

Developed by researcher cmavro, GNN-RAG represents a convergence of two powerful AI paradigms: the structured reasoning of graph neural networks and the generative fluency of large language models. The core insight is that many complex questions require relational reasoning that standard dense retrieval cannot capture. By modeling retrieved information as a graph and applying GNN message passing to propagate information across connected entities, GNN-RAG builds richer context representations before passing them to the LLM.

HippoRAG: Neurobiologically Inspired Long-Term Memory for LLMs (NeurIPS 2024)

Fri, 01 May 2026 00:00:00 +0000

Retrieval-Augmented Generation (RAG) has become the standard approach for grounding LLM outputs in external knowledge. But standard RAG has a fundamental limitation: it treats each query independently, with no memory of past retrievals or ability to connect information across documents. HippoRAG takes inspiration from the human brain’s hippocampus to overcome this, creating a long-term memory system that dramatically improves multi-hop question answering.

Published at NeurIPS 2024 and available at github.com/OSU-NLP-Group/HippoRAG, HippoRAG combines LLMs with knowledge graphs in a framework modeled on the hippocampal indexing theory of human memory. The result is a RAG system that builds a persistent knowledge structure from documents, enabling it to answer complex questions that require connecting information across multiple sources – achieving approximately 20% improvement over standard RAG on multi-hop QA benchmarks.

LightRAG: Simple and Fast Graph-Based Retrieval-Augmented Generation Framework

Fri, 01 May 2026 00:00:00 +0000

LightRAG is a research project from the University of Hong Kong (HKU) that reimagines retrieval-augmented generation (RAG) using knowledge graphs. Accepted at EMNLP 2025, it replaces the traditional flat vector store approach with a graph-based architecture that extracts entities and their relationships from documents, enabling dramatically better context understanding for LLM applications.

Where conventional RAG systems retrieve isolated document chunks by embedding similarity, LightRAG builds a structured knowledge graph from your documents – entities become nodes, relationships become edges. When a query arrives, it performs dual-level retrieval across this graph: low-level retrieval for specific factual answers, high-level retrieval for broader thematic summaries. The result is retrieval that understands not just what words appear together, but how concepts are actually connected.

LLM Graph Builder: Neo4j's RAG-to-Graph Pipeline

Fri, 01 May 2026 00:00:00 +0000

The limitations of traditional Retrieval-Augmented Generation (RAG) have become increasingly clear as organizations deploy AI systems in production. Vector search – the backbone of conventional RAG – does a reasonable job of finding semantically similar document chunks, but it fundamentally lacks structural understanding. It cannot express that “Apple acquired Beats in 2014” involves a relationship between two entities with a specific type and date. It cannot follow a chain of relationships across multiple documents. It treats the knowledge base as a flat bag of vectors rather than an interconnected web of facts.

Neo4j’s LLM Graph Builder addresses this limitation by bridging the gap between large language models and graph databases. It is an open-source tool that uses LLMs to automatically extract entities and relationships from unstructured documents, then populates a Neo4j knowledge graph with the resulting structured data. The output is a GraphRAG pipeline that combines the semantic understanding of LLMs with the structural precision of graph databases.

TerminusDB: Open-Source Knowledge Graph Database

Fri, 01 May 2026 00:00:00 +0000

Most databases treat data as a snapshot. TerminusDB treats data like a Git repository–every change is versioned, every update is tracked, and you can branch, merge, diff, and roll back any change. This makes it uniquely suited for knowledge graph applications where data provenance and collaboration are critical.

Developed by TerminusDB, this open-source knowledge graph database combines graph data modeling with document-oriented storage. Its WOQL (Web Ontology Query Language) query language enables expressive graph traversal, schema validation, and data transformation. The built-in version control makes it ideal for collaborative data projects, data pipeline management, and any application where data history matters.