Graph RAG Systems

This project is a tool for transforming unstructured text into semantic knowledge graphs. It uses local language models to extract entities and their relationships, converting text corpora into a structured network of linked concepts. The system provides a web interface for interactive network visualization, allowing users to navigate the resulting nodes and edges. It includes a topology analysis tool that calculates node degrees and identifies community clusters to determine the visual size and color of graph elements. Beyond visualization, the project enables graph-based information retrieval. This allows for the location of specific data by traversing semantic connections rather than relying on keyword searches.

KAG is a graph-augmented retrieval augmented generation system and knowledge graph engine. It functions as a framework that integrates large language models with graph retrieval and numerical calculation to resolve natural language queries. The system creates unified knowledge representations by aligning unstructured data and expert rules through semantic mapping. It maintains mutual indexing between graph structures and original text blocks to ensure that reasoning processes remain linked to verifiable source data. The project provides capabilities for semantic information integration, graph-based data retrieval, and hybrid logical reasoning. It employs a pipeline that combines semantic graph search with numerical calculations and symbolic logic.

R2R is an agentic retrieval-augmented generation platform that uses reasoning agents to perform multi-step data fetching for context-aware answering. It functions as a multimodal vector database manager and knowledge graph engine designed to ground artificial intelligence responses in verified factual knowledge. The platform distinguishes itself by combining reasoning agents for complex research automation with a knowledge graph that maps entity relationships. This allows the system to perform structured data traversal alongside unstructured vector search to resolve complex questions from internal knowledge bases and the web. The system covers multimodal content ingestion for various file types, hybrid semantic-keyword search, and collection-based data isolation for multi-tenant access control. These capabilities are exposed through a programmable REST API gateway.

GraphRAG is a data processing pipeline and retrieval engine designed to transform unstructured text into interconnected knowledge graphs. By utilizing language models to extract entities and relationships, it builds structured representations of information that enable context-aware retrieval for downstream applications. The system distinguishes itself through hierarchical graph clustering and large-scale data synthesis, which organize massive document corpora into multi-level structures. This approach allows for both vector-based semantic searches and graph-based traversals, providing a comprehensive method for navigating complex datasets and identifying hidden connections between concepts. The platform includes a modular orchestration pipeline that manages the entire lifecycle of information, from initial ingestion and indexing to query execution. Users can refine the synthesis and retrieval processes by adjusting prompt templates and configuration arguments to align with specific data characteristics.

LightRAG is a graph-based retrieval framework designed to build retrieval-augmented generation pipelines. It structures unstructured text into knowledge graphs, enabling multi-hop reasoning and complex query synthesis across large document collections. By integrating dense vector embeddings with structured knowledge graphs, the system facilitates both similarity-based and relationship-aware information retrieval. The framework distinguishes itself through a dual-level retrieval strategy that combines low-level keyword matching with high-level semantic graph traversal to capture both specific facts and broad thematic context. It supports incremental knowledge management, allowing the underlying graph structure to be updated dynamically as new data arrives without requiring a full re-indexing of the dataset. Additionally, the system functions as a multimodal information extractor, processing both text and visual data to create unified, searchable knowledge bases. The platform provides modular, prompt-driven pipeline orchestration to coordinate document parsing, knowledge extraction, and language model generation. These automated workflows allow for the synthesis of information across interconnected documents to provide context-aware responses to nuanced, multi-step inquiries.

This project is a retrieval augmented generation framework designed to build pipelines that connect unstructured data and knowledge graphs with large language models. It functions as a vector database orchestrator for indexing text and multimodal content, as well as a system for translating natural language queries into structured database commands. The framework integrates a hybrid retrieval engine that combines dense vector search with sparse keyword matching to increase the precision of retrieved contexts. It further enhances reasoning and relationship mapping through a graph-augmented retrieval system. The system includes a toolkit for measuring the quality of retrieval and generation processes using standardized metrics. It also provides mechanisms to enforce predefined schemas and patterns on model responses to ensure consistent output for downstream applications. The project is implemented in Python.

This project is a multi-model database system designed to store and manage information as documents, graphs, and key-value pairs within a single engine. It functions as a graph database and knowledge graph platform, providing the infrastructure to build, query, and visualize structured data models. By integrating vector search capabilities, the system serves as a vector database that supports retrieval-augmented generation for artificial intelligence applications. The platform distinguishes itself through a unified query language that allows users to perform document lookups, graph traversals, and vector searches across diverse data models simultaneously. It includes a dedicated graph analytics engine capable of executing structural algorithms, such as pathfinding and centrality analysis, to identify patterns and influential nodes within complex networks. These features enable the construction of knowledge graphs that ground generative AI models in verified enterprise context, reducing hallucinations and improving response accuracy. Beyond its core storage and retrieval capabilities, the system supports predictive machine learning by leveraging stored relationship data to classify elements and forecast connections. It provides an interactive web interface for the visual exploration and navigation of graph structures, facilitating the analysis of complex information networks. The software is documented and distributed as a comprehensive environment for managing multi-model data and building intelligent, context-aware systems.

Quivr is a retrieval-augmented generation platform designed to transform raw documents into searchable knowledge bases. It functions as a centralized environment where users can ingest files, index them into vector databases, and interact with language models to receive contextually relevant, data-backed responses. The platform distinguishes itself through an agentic workflow orchestrator that sequences retrieval tasks, tool execution, and model interactions to resolve complex, multi-step queries. This engine is entirely configuration-driven, allowing users to define document ingestion, chunking parameters, and workflow node sequences through structured schemas. By maintaining a unified knowledge management interface, the system tracks chat history alongside file storage, ensuring that interactions remain context-aware across diverse local and remote backends. Beyond its core orchestration, the system provides a comprehensive pipeline for document processing, including parsing for various file formats and asynchronous task execution to maintain responsiveness during data ingestion. It supports the development of specialized chatbots, including voice-enabled interfaces, by integrating speech-to-text and text-to-speech capabilities with its underlying retrieval systems. The project utilizes strict base classes to enforce configuration integrity, ensuring consistent data processing across all application settings.

Supermemory is an artificial intelligence memory management platform designed to provide autonomous agents with persistent, long-term knowledge bases. It functions as a centralized repository that synchronizes multimodal data, enabling agents to maintain context and historical information across complex, multi-session workflows. By serving as a knowledge graph engine and vector database orchestrator, the platform ensures that information remains accessible and relevant for automated tasks. The system distinguishes itself through its hybrid indexing approach, which combines vector similarity search with structured graph traversal to retrieve both semantic context and explicit relational data. It decomposes unstructured documents into granular, standalone facts and utilizes composable retrieval pipelines to refine information before it is injected into agent prompts. This architecture supports the creation of automated user profiles and fact hierarchies, allowing the system to learn and update information in real-time while managing the lifecycle of stored data. Beyond individual agent support, the platform facilitates enterprise knowledge sharing by maintaining collective repositories of project decisions and patterns. It automates data ingestion from diverse sources, including cloud storage, productivity platforms, and web content, using event-driven synchronization to ensure information freshness. The platform is designed for self-hosted, containerized deployment, providing users with full control over their data infrastructure and sovereignty.

Planning with files is an enterprise knowledge graph platform designed to transform unstructured organizational data into a searchable, interconnected network. By utilizing a graph-based retrieval-augmented generation engine, the system grounds language model outputs in verified internal data, ensuring that responses are explainable, traceable, and free from hallucinations. The platform distinguishes itself through a focus on data sovereignty and secure, private infrastructure deployment. It enables organizations to maintain full control over sensitive information by processing data locally or within regional cloud environments, preventing the use of internal knowledge for external model training. The architecture supports granular security through attribute-based access control and allows for the isolation of knowledge into distinct, domain-specific workspaces while maintaining a unified semantic logic across the entire organization. Beyond core retrieval, the system provides a comprehensive suite of tools for managing the data lifecycle, including automated business workflow execution and audit-ready event logging. It facilitates collective intelligence by aggregating expert experience and project documentation into a centralized repository, which can be analyzed to identify infrastructure dependencies and optimize operational efficiency. The project is implemented in Python and is designed for deployment within customer-managed infrastructure to meet strict regulatory compliance and data governance requirements.

UltraRAG is an LLM RAG orchestration platform and AI agent research framework designed to coordinate complex retrieval-augmented generation workflows. It functions as a multimodal RAG engine capable of retrieving and generating responses using text, images, and diverse data types, while providing tools for vector database management and RAG performance evaluation. The platform features a visual RAG pipeline builder that uses a canvas interface to construct and debug data flows, synchronizing visual designs directly with underlying code. It distinguishes itself through an autonomous research system that employs state-machine logic to route tasks between information gathering, planning, and writing to produce long-form research reports. The system covers a broad range of capabilities, including multimodal knowledge base management, real-time reasoning chain visualization, and the execution of complex workflows with loops and conditional branching. It also supports the integration of decoupled atomic servers for extensibility and provides toolkits for benchmarking model output quality against standardized datasets. The software can be deployed using Docker containers for standardized environments or as local research agents for offline operation.

This project is a comprehensive retrieval-augmented generation platform designed for building, managing, and deploying knowledge-based AI applications. It provides a unified environment for organizing datasets, configuring conversational chat assistants, and developing autonomous agents that execute multi-step reasoning workflows. By integrating document intelligence with advanced retrieval pipelines, the platform enables the creation of grounded, verifiable responses supported by traceable citations. The platform distinguishes itself through deep document understanding and sophisticated knowledge orchestration. It supports complex document parsing, including the extraction of tables and images, and utilizes graph-based indexing to enhance reasoning over large document collections. Users can configure multiple recall strategies and fused re-ranking to optimize retrieval accuracy, while the system maintains context through multi-turn dialogue management and flexible tool-use frameworks. The architecture is built on a modular, containerized microservice foundation that supports both local inference engines and external language model APIs. It includes asynchronous task processing for document ingestion and indexing, ensuring system responsiveness during heavy workloads. The platform also provides a standardized interface for model abstraction, allowing for seamless integration with existing language model ecosystems. Developers can interact with the platform through a comprehensive suite of RESTful endpoints and Python client libraries, which cover the full lifecycle of agents, datasets, and knowledge graphs. The system is designed for flexible deployment, offering configurable environment settings and support for custom containerized environments to facilitate local development and infrastructure portability.

Neo4j is a native graph database management system designed to store and query highly connected data using a property-graph model. It provides an ACID-compliant transaction engine that ensures data integrity, supported by a distributed cluster architecture that maintains causal consistency across nodes. Users interact with the system through a declarative query language, which allows for complex pattern matching and path traversal without requiring manual traversal logic. The platform distinguishes itself through its hybrid approach to data retrieval, combining traditional graph-based queries with high-dimensional vector indexing. This integration enables simultaneous semantic similarity searches and relational data analysis within a single environment. By supporting both structured graph patterns and vector embeddings, the system facilitates advanced analytical tasks such as community detection, pathfinding, and centrality calculations. The project covers a broad capability surface, including comprehensive database administration, security controls, and performance optimization tools. It provides extensive support for AI-augmented workflows, enabling the integration of large language models for retrieval-augmented generation, natural language query translation, and autonomous agent memory management. These features are accessible through standardized language drivers, HTTP interfaces, and native schema enforcement mechanisms. The software is distributed as a database engine with support for both self-managed and cloud-hosted infrastructure, offering command-line tools for provisioning, monitoring, and lifecycle management.

Potpie is an LLM codebase analysis platform and multi-agent orchestration framework designed to act as an AI software engineer. It parses repositories into a structured code knowledge graph, enabling AI agents to perform multi-hop reasoning, dependency tracing, and grounded technical analysis across large codebases. The system distinguishes itself through a spec-driven development framework where agents generate detailed technical specifications and architecture plans before implementing multi-file code changes. It utilizes a durable execution engine to coordinate specialized AI personas for complex workflows, such as automated root-cause analysis for memory leaks and race conditions or the generation of pattern-aligned code that adheres to existing project conventions. The platform covers a broad range of capabilities including semantic indexing via abstract syntax trees, automated pull request creation, and transitive change impact mapping. It also provides integrations for external documentation retrieval and connectivity with tools like GitHub, Jira, and Linear to manage the end-to-end software development lifecycle. The project is implemented in Python and provides an agent interaction API with support for streaming responses.

LlamaIndex is a comprehensive development framework designed to connect private or external data sources to large language models. It functions as a data-centric toolkit that enables the construction of retrieval-augmented generation systems, allowing developers to build applications that provide context-aware answers based on specific organizational information. The project distinguishes itself through a robust agentic orchestration engine that supports the creation of autonomous agents capable of multi-step reasoning, memory management, and complex tool execution. Beyond simple retrieval, it provides a flexible, event-driven architecture for composing modular pipelines, enabling developers to chain data ingestion, transformation, and retrieval steps into sophisticated, multi-agent systems that can coordinate tasks and hand off control between individual agents. The platform covers the entire lifecycle of language model applications, including advanced document processing for parsing and structuring complex file formats, and a diagnostic layer for observability that tracks execution traces and performance metrics. It also includes a suite of evaluation tools for measuring retrieval effectiveness and response quality, alongside mechanisms for query routing and custom post-processing to ensure high-precision information delivery.

Memary is a memory-augmented agent framework that stores and retrieves contextual information from a knowledge graph to personalize responses and maintain long-term memory across interactions. It automatically captures all agent interactions and stores them as structured memories without requiring explicit instrumentation, then injects top-ranked user entities and themes into the active context window to tailor agent responses dynamically. The framework distinguishes itself through a multi-retriever memory search that combines COLBERT reranking with recursive graph queries across databases, enabling fine-tuned agent recall. It decomposes complex user queries into sub-questions to retrieve more targeted information from memory stores, and supports switching between locally downloaded LLMs via Ollama integration for flexible on-device inference without external API dependencies. Memary also provides a conversational memory interface that allows users to query and review specific agent memories, supporting debugging and understanding of past reasoning. Beyond core memory management, the system includes a multi-agent memory orchestrator that manages separate memory stores and knowledge graphs for multiple agents, enabling personalized context per user. It tracks entity frequency and recency to infer a user's depth of knowledge, and can inject custom data into memory by combining multiple parsers for advanced ingestion. The framework also supports registering user-defined Python functions as tools that agents can call during task execution, and provides memory benchmarking capabilities to test and compare different memory strategies.

FastGPT is a comprehensive platform for building, deploying, and managing context-aware artificial intelligence applications. It provides a unified environment that integrates custom data sources with language models, utilizing a retrieval-augmented generation engine to ground responses in accurate, domain-specific information. The system is designed for enterprise-scale use, featuring multi-tenant architecture, administrative controls, and secure authentication protocols including OAuth 2.0 and custom single sign-on integration. The platform distinguishes itself through a visual, node-based workflow orchestrator that allows users to design complex business logic and automated task sequences without manual coding. It offers sophisticated knowledge base management, supporting multi-vector data mapping, hybrid search fusion, and automated website content synchronization. To ensure high-quality outputs, the system includes tools for search query optimization, result reranking, and automated performance evaluation, allowing developers to score and analyze the accuracy of their applications across multiple iterations. Beyond its core generation and retrieval capabilities, the platform provides extensive utilities for data handling and organizational management. This includes intelligent parsing of complex document formats, flexible search modes, and granular access controls for team management. Users can also leverage secure, sandboxed rendering for rich content and export cited documents for offline review, ensuring a complete lifecycle for production-ready AI services.

Embedchain is an LLM memory management framework and RAG orchestration engine designed to provide AI agents with a persistent storage layer. It functions as a long-term memory pipeline that extracts facts from unstructured interactions and stores them as permanent knowledge base entries to retain user preferences and interaction history across sessions. The system employs a hybrid vector database interface that combines semantic embeddings with traditional keyword search. It utilizes an entity-linking knowledge graph to connect related information points and applies temporal ranking to distinguish current states from historical data. The framework covers multi-level state management across user, session, and agent tiers and implements multi-signal retrieval to surface relevant context. It includes a command line interface for administering stored data and interaction history.

Graphiti is a backend framework and memory server designed to provide artificial intelligence agents with persistent, time-aware knowledge graph storage. It functions as a memory layer that enables agents to maintain context across long-term interactions by recording and evolving structured data over time. The system distinguishes itself through a specialized temporal graph database that tracks how entities and relationships change using validity windows. By combining semantic vector similarity, keyword matching, and graph topology traversal, the engine performs hybrid retrieval to locate relevant information. It further refines these results by calculating graph distances from central entities, ensuring that retrieved context is prioritized based on its structural relevance to the query. The platform supports schema-driven entity modeling, allowing for the enforcement of domain-specific structures on incoming data. It manages the ingestion of raw inputs into structured graphs and performs incremental updates to maintain the knowledge base without requiring full batch recomputation. Through standardized interfaces and protocol support, the system integrates with various large language model providers to automate data extraction and reasoning.