Alternatives to TopBraid
Compare TopBraid alternatives for your business or organization using the curated list below. SourceForge ranks the best alternatives to TopBraid in 2026. Compare features, ratings, user reviews, pricing, and more from TopBraid competitors and alternatives in order to make an informed decision for your business.
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DataHub
DataHub
DataHub Cloud is an event-driven AI & Data Context Platform that uses active metadata for real-time visibility across your entire data ecosystem. Unlike traditional data catalogs that provide outdated snapshots, DataHub Cloud instantly propagates changes, automatically enforces policies, and connects every data source across platforms with 100+ pre-built connectors. Built on an open source foundation with a thriving community of 13,000+ members, DataHub gives you unmatched flexibility to customize and extend without vendor lock-in. DataHub Cloud is a modern metadata platform with REST and GraphQL APIs that optimize performance for complex queries, essential for AI-ready data management and ML lifecycle support. -
2
DataBuck
FirstEigen
DataBuck is an AI-powered data validation platform that automates risk detection across dynamic, high-volume, and evolving data environments. DataBuck empowers your teams to: ✅ Enhance trust in analytics and reports, ensuring they are built on accurate and reliable data. ✅ Reduce maintenance costs by minimizing manual intervention. ✅ Scale operations 10x faster compared to traditional tools, enabling seamless adaptability in ever-changing data ecosystems. By proactively addressing system risks and improving data accuracy, DataBuck ensures your decision-making is driven by dependable insights. Proudly recognized in Gartner’s 2024 Market Guide for #DataObservability, DataBuck goes beyond traditional observability practices with its AI/ML innovations to deliver autonomous Data Trustability—empowering you to lead with confidence in today’s data-driven world. -
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eccenca Corporate Memory
eccenca
eccenca Corporate Memory provides a multi-disciplinary integrative platform for managing rules, constraints, capabilities, configurations, and data in a single application. Overcoming the limitations of traditional, application-centric (meta) data management models, its semantic knowledge graph is both highly extensible, integrative as well as interpretable both by machines and business users. The enterprise knowledge graph platform re-establishes global data transparency in enterprises as well as line-of-business ownership in a complex and dynamic data environment. It enables you to drive agility, autonomy, and automation without disrupting existing IT infrastructures. Corporate Memory integrates and links data from any source in a central knowledge graph. Use user-friendly SPARQL and JSON-LD frames to explore your global data landscape. The data management in the enterprise knowledge graph platform is implemented by HTTP identifiers and metadata. -
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Timbr.ai
Timbr.ai
Timbr is the ontology-based semantic layer used by leading enterprises to make faster, better decisions with ontologies that transform structured data into AI-ready knowledge. By unifying enterprise data into a SQL-queryable knowledge graph, Timbr makes relationships, metrics, and context explicit, enabling both humans and AI to reason over data with accuracy and speed. Its open, modular architecture connects directly to existing data sources, virtualizing and governing them without replication. The result is a dynamic, easily accessible model that powers analytics, automation, and LLMs through SQL, APIs, SDKs, and natural language. Timbr lets organizations operationalize AI on their data - securely, transparently, and without dependence on proprietary stacks - maximizing data ROI and enabling teams to focus on solving problems instead of managing complexity.Starting Price: $599/month -
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HyperGraphDB
Kobrix Software
HyperGraphDB is a general purpose, open-source data storage mechanism based on a powerful knowledge management formalism known as directed hypergraphs. While a persistent memory model designed mostly for knowledge management, AI and semantic web projects, it can also be used as an embedded object-oriented database for Java projects of all sizes. Or a graph database, or a (non-SQL) relational database. HyperGraphDB is a storage framework based on generalized hypergraphs as its underlying data model. The unit of storage is a tuple made up of 0 or more other tuples. Each such tuple is called an atom. One could think of the data model as relational where higher-order, n-ary relationships are allowed or as graph-oriented where edges can point to an arbitrary set of nodes and other edges. Each atom has an arbitrary, strongly-typed value associated with it. The type system managing those values is embedded as a hypergraph and customizable from the ground up. -
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Reltio
Reltio
The digital economy requires organizations to be responsive and have a master data management platform that is highly scalable and supports hyper-personalization and real-time operations. Reltio Connected Data Platform is the only cloud-native data management platform that supports billions of customer profiles, enriched with thousands of attributes, relationships, transactions, and interactions from hundreds of data sources. Reltio powers enterprise-class mission-critical applications to operate 24/7 with thousands of internal and external users. Reltio Connected Data Platform scales seamlessly to deliver elastic performance and supports the throughput that enterprises need for any operational or analytical use case. Innovative polyglot data storage technology provides an unprecedented agility to add or remove data sources or attributes without any downtime. The Reltio platform is built on the foundation of master data management (MDM) and enriched with graph technology. -
7
ent
ent
An entity framework for Go. Simple, yet powerful ORM for modeling and querying data. Simple API for modeling any database schema as Go objects. Run queries, and aggregations and traverse any graph structure easily. 100% statically typed and explicit API using code generation. The latest version of Ent now includes a type-safe API enabling ordering by fields and edges. This API will soon be available in our GraphQL integration too. You can now visualize your Ent schema as an ERD with one command. The API enables you to easily integrate features such as logging, tracing, caching, and even implementing soft deletion with 20 lines of code! The Ent framework supports GraphQL using the 99designs/gqlgen library and provides various integrations. Generating a GraphQL schema for nodes and edges defined in an Ent schema. Efficient field collection to overcome the N+1 problem without requiring data loaders.Starting Price: Free -
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KgBase
KgBase
KgBase, or Knowledge Graph Base, is a collaborative, robust database with versioning, analytics & visualizations. With KgBase, any community or individual can create knowledge graphs to build insights about their data. Import your CSVs and spreadsheets, or use our API to work on data together. Build no-code knowledge graphs with KgBase, our easy-to-use UI lets you traverse the graph, show the results as tables and charts, and much more. Play with your graph data. Build your query and see results update in real time. It's like writing query code in Cypher or Gremlin, except easier. And fast. Your graph can be viewed as a table, allowing you to browse all results - no matter the size. KgBase works great with large graphs (millions of nodes), as well as simple projects. In the cloud, or self-hosted, with wide database support. Introduce graphs into your organization by seeding graph from a template. Results of any query can be easily turned into a chart visualization.Starting Price: $19 per month -
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Flow-Like
TM9657 GmbH
Flow-Like is an open-source, typed, local-first workflow automation engine for building and executing automation and AI workflows in self-hosted or offline environments. It combines visual, graph-based workflows with strong typing and deterministic execution, making complex systems easier to understand, validate, and maintain. Unlike many workflow tools that rely on untyped JSON, cloud-only backends, or opaque runtime behavior, Flow-Like makes data flow and execution explicit and inspectable. Workflows can run locally, on private servers, in containers, or in Kubernetes without changing semantics. The core runtime is written in Rust for performance, safety, and portability. Flow-Like supports event-driven automation, data processing, document ingestion, and AI pipelines, including typed agent and RAG workflows using local or hosted models. It is designed for developers and organizations that need reliable automation with full control over infrastructure and data.Starting Price: $9.99/month -
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GraphDB
Ontotext
*GraphDB allows you to link diverse data, index it for semantic search and enrich it via text analysis to build big knowledge graphs.* GraphDB is a highly efficient and robust graph database with RDF and SPARQL support. The GraphDB database supports a highly available replication cluster, which has been proven in a number of enterprise use cases that required resilience in data loading and query answering. If you need a quick overview of GraphDB or a download link to its latest releases, please visit the GraphDB product section. GraphDB uses RDF4J as a library, utilizing its APIs for storage and querying, as well as the support for a wide variety of query languages (e.g., SPARQL and SeRQL) and RDF syntaxes (e.g., RDF/XML, N3, Turtle). -
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Stardog
Stardog Union
With ready access to the richest flexible semantic layer, explainable AI, and reusable data modeling, data engineers and scientists can be 95% more productive — create and expand semantic data models, understand any data interrelationship, and run federated queries to speed time to insight. Stardog offers the most advanced graph data virtualization and high-performance graph database — up to 57x better price/performance — to connect any data lakehouse, warehouse or enterprise data source without moving or copying data. Scale use cases and users at lower infrastructure cost. Stardog’s inference engine intelligently applies expert knowledge dynamically at query time to uncover hidden patterns or unexpected insights in relationships that enable better data-informed decisions and business outcomes.Starting Price: $0 -
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Synaptica Graphite
Synaptica
Synaptica’s Graphite is a powerful tool for quickly designing, building, and managing Knowledge Organization Systems (KOS) using an intuitive graphical user interface. Graphite is based on Linked Data and Semantic Web standards and utilizes native RDF concept modeling. Powered by a graph database, Graphite offers speed and flexibility in the creation and management of various types of controlled vocabularies including taxonomies and ontologies. Quickly design, build, and manage enterprise Knowledge Organization Systems using an intuitive drag-and-drop graphical user interface and workflow. Centralize metadata KOS for rapid delivery to siloed information systems. Reuse schema templates to build standards-compliant KOS and EKGs in minutes. Reduce project costs and fast-track deliverables with libraries of public domain vocabularies. -
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Dgraph
Hypermode
Dgraph is an open source, low-latency, high throughput, native and distributed graph database. Designed to easily scale to meet the needs of small startups as well as large companies with massive amounts of data, DGraph can handle terabytes of structured data running on commodity hardware with low latency for real time user queries. It addresses business needs and uses cases involving diverse social and knowledge graphs, real-time recommendation engines, semantic search, pattern matching and fraud detection, serving relationship data, and serving web apps. -
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Fluree
Fluree
Fluree is an immutable RDF graph database written in Clojure and adhering to W3C standards, supporting JSON and JSON-LD while accommodating various RDF ontologies; it boasts a scalable, cloud-native architecture utilizing a lightweight Java runtime, with individually scalable ledger and graph database components, embodying a "Data-Centric" ideology that treats data as a reusable asset independent of singular applications, underpinned by an immutable ledger that secures transactions with cryptographic integrity, alongside a rich RDF graph database capable of various queries, and employs SmartFunctions for enforcing data management rules, including identity and access management and data quality. -
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Microsoft Graph Data Connect
Microsoft
Microsoft Graph is your organization's gateway to Microsoft 365 data for productivity, identity, and security. Microsoft Graph Data Connect enables developers to copy select Microsoft 365 datasets into Azure data stores in a secure and scalable way. It's ideal for training machine learning and AI models that uncover rich organizational insights and deliver new value to analytics solutions. Copy data at scale from a Microsoft 365 tenant and move it directly into Azure Data Factory without writing code. Get the data you need, delivered to your application on a repeatable schedule, in just a few simple steps. Control how your organization's data is accessed with the Microsoft Graph Data Connect granular consent model. It requires that developers specify exactly what types of data or filter content their application will access. Likewise, administrators must give explicit approval to access Microsoft 365 data before access is granted.Starting Price: $0.75 per 1K objects extracted -
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TIBCO Graph Database
TIBCO
To unveil the true value of constantly evolving business data, you need to understand the relationships in data in a much more profound way. Unlike other databases, a graph database puts relationships at the forefront, using Graph theory and Linear Algebra to traverse and show how complex data webs, data sources, and data points relate. TIBCO® Graph Database allows you to discover, store, and convert complex dynamic data into meaningful insights. Enable users to rapidly build data and computational models that establish dynamic relationships among organizational silos. These knowledge graphs deliver value by connecting your organization’s vast array of data and revealing relationships that let you accelerate optimization of assets and processes. Combined OLTP and OLAP features in a single enterprise-grade database. Optimistic ACID level transaction properties with native storage and access. -
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Oracle Spatial and Graph
Oracle
Graph databases, part of Oracle’s converged database offering, eliminate the need to set up a separate database and move data. Analysts and developers can perform fraud detection in banking, find connections and link to data, and improve traceability in smart manufacturing, all while gaining enterprise-grade security, ease of data ingestion, and strong support for data workloads. Oracle Autonomous Database includes Graph Studio, with one-click provisioning, integrated tooling, and security. Graph Studio automates graph data management and simplifies modeling, analysis, and visualization across the graph analytics lifecycle. Oracle provides support for both property and RDF knowledge graphs, and simplifies the process of modeling relational data as graph structures. Interactive graph queries can run directly on graph data or in a high-performance in-memory graph server. -
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PoolParty
Semantic Web Company
Integrate an award-winning Semantic AI platform to build smart applications and systems. Use PoolParty to automate metadata creation and make information readily available to be used, shared and analyzed. PoolParty links unstructured and structured data, and connects data which is scattered across databases. Benefit from the next generation of graph-based data and content analytics with state-of-the-art machine learning techniques. Benefit from your data. PoolParty increases the quality of data, which leads to more precise results from AI applications and improved decision-making. Understand why the world’s biggest companies are using Knowledge Graphs, and why yours should be too. Engage with experts, partners, and customers’ presentations to unlock the full potential of semantic technologies and 360-degree views. We have helped over 180 enterprise-level customers master the challenges of information management. -
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RDFox
Oxford Semantic Technologies
The world's most performant knowledge graph and semantic reasoning engine. Founded by three professors at the University of Oxford, Oxford Semantic Technologies emerged as a result of extensive research into Knowledge Representation and Reasoning (KRR), out of which came the most powerful knowledge graph and semantic reasoning engine on the market today, RDFox. As an AI reasoning engine, RDFox mirrors human reasoning principles. With unrivaled reasoning capabilities, relying on accuracy, truth, and explainability, it empowers the next generation of AI applications. By inferring new knowledge exclusively from factual data, RDFox ensures results are firmly grounded in reality. RDFox’s incremental reasoning capabilities cause the consequences of the rules-based AI to be applied to the database in real-time as data is added, changed, or removed, all without needing a restart. Only the relevant information is updated without needing to reanalyze the entire data set.Starting Price: Free -
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ArangoDB
ArangoDB
Natively store data for graph, document and search needs. Utilize feature-rich access with one query language. Map data natively to the database and access it with the best patterns for the job – traversals, joins, search, ranking, geospatial, aggregations – you name it. Polyglot persistence without the costs. Easily design, scale and adapt your architectures to changing needs and with much less effort. Combine the flexibility of JSON with semantic search and graph technology for next generation feature extraction even for large datasets. -
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GraphBase
FactNexus
GraphBase is a Graph Database Management System (Graph DBMS) engineered to simplify the creation and maintenance of complex data graphs. Complex and highly-connected structures are a challenge for the Relational Database Management System (RDBMS). A graph database provides much better modelling utility, performance and scalability. The current crop of graph database products - the triplestores and property graphs - have been around for nearly two decades. They're powerful tools, they have many uses, but they're still not suited to the management of complex data structures. With GraphBase, our goal was to simplify the management of complex data structures, so that your data could become something more. It could become Knowledge. We achieved this by redefining how graph data should be managed. In GraphBase, the graph is a first-class citizen. You get a graph equivalent of the "rows and tables" paradigm that makes a Relational Database so easy to use. -
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AllegroGraph
Franz Inc.
AllegroGraph is a breakthrough solution that allows infinite data integration through a patented approach unifying all data and siloed knowledge into an Entity-Event Knowledge Graph solution that can support massive big data analytics. AllegroGraph utilizes unique federated sharding capabilities that drive 360-degree insights and enable complex reasoning across a distributed Knowledge Graph. AllegroGraph provides users with an integrated version of Gruff, a unique browser-based graph visualization software tool for exploring and discovering connections within enterprise Knowledge Graphs. Franz’s Knowledge Graph Solution includes both technology and services for building industrial strength Entity-Event Knowledge Graphs based on best-of-class tools, products, knowledge, skills and experience. -
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Golden
Golden
The world is lacking a decentralized graph of canonical knowledge that is open, free, and permissionless, and incentivizes agents to enter data into the graph. Our vision is to create a protocol that maps the 10 billion entities that exist and the public knowledge that surrounds them. Triples, also known as fact triples or SPO triples, are the elemental building blocks of facts that link entities together forming a graph. They are the atoms that build the universe of knowledge as we know it. The protocol supports a rich set of triples types, qualifiers, and associated evidence. The triple graph can be used to power Dapps and services that require fundamental knowledge. Each agent can submit triples to be validated, and, if accepted, will be rewarded tokens. Validators and predictions from the knowledge graph itself decide if triples are accepted. In essence, the protocol incentivizes the knowledge graph construction while defending against gaming attacks. -
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Anzo
Cambridge Semantics
Anzo is a modern data discovery and integration platform that lets anyone find, connect and blend any enterprise data into analytics-ready datasets. Anzo’s unique use of semantics and graph data models makes it practical for the first time for virtually anyone in your organization – from skilled data scientists to novice business users – to drive the data discovery and integration process and build their own analytics-ready datasets. Anzo’s graph data models provide business users with a visual map of enterprise data that is easy to understand and navigate, even when your data is vast, siloed and complex. Semantics add business content to data, allowing users to harmonize data based on shared definitions and build blended, business-ready data on demand. -
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Maana Knowledge Platform
Maana
Build your Knowledge Layer with a highly-intuitive visual experience. Create and query the knowledge graph. Hydrate domain concepts in the knowledge graph with data. Trigger bots to enrich the knowledge graph with dynamic links. Create and compose services with function composition features. Add and orchestrate services on the knowledge graph. Provides interactive and scripted access to convenient system actions. Schema management, data loading, querying and administration. The command line interface is easily extensible with custom plug-ins. Allows extendibility with custom plugins making it easy for developers to add functionality. Knowledge applications are use cases developed by customers on Maana platform. They provide AI-driven recommendations into operational decisions. A knowledge application is made up by decision models that perform real-time calculations. A customer does not have access to knowledge applications developed by other customers. -
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AnzoGraph DB
Cambridge Semantics
With a huge collection of analytical features, AnzoGraph DB can enhance your analytical framework. Watch this video to learn how AnzoGraph DB is a Massively Parallel Processing (MPP) native graph database that is built for data harmonization and analytics. Horizontally scalable graph database built for online analytics and data harmonization. Take on data harmonization and linked data challenges with AnzoGraph DB, a market-leading analytical graph database. AnzoGraph DB provides industrialized online performance for enterprise-scale graph applications. AnzoGraph DB uses familiar SPARQL*/OWL for semantic graphs but also supports Labeled Property Graphs (LPGs). Access to many analytical, machine learning and data science capabilities help you achieve new insights, delivered at unparalleled speed and scale. Use context and relationships between data as first-class citizens in your analysis. Ultra-fast data loading and analytical queries. -
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Emotibot
Emotibot
Use AI to subvert traditional business cognition, precise industry insights, high efficiency and low operation and maintenance, and promote the digital transformation of enterprises. Possess the ability to mine knowledge, construct knowledge graphs and Ontology based on unsupervised learning. By mining and analyzing large-scale data, and applying accumulated industry knowledge and NLP capabilities, the traditional human-based knowledge engineering process can be automated, which greatly improves the efficiency of map construction and reduces the threshold of map construction. The fully self-developed ASR/TTS model, combined with self-collected training data and SOTA speech recognition algorithm, and the industry's best industry NLU capability language model, optimizes model performance for different business scenarios. With a complete training platform, it can support completely customized training for vertical fields. -
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InfiniteGraph
Objectivity
InfiniteGraph is a massively scalable graph database specifically designed to excel at high-speed ingest of massive volumes of data (billions of nodes and edges per hour) while supporting complex queries. InfiniteGraph can seamlessly distribute connected graph data across a global enterprise. InfiniteGraph is a schema-based graph database that supports highly complex data models. It also has an advanced schema evolution capability that allows you to modify and evolve the schema of an existing database. InfiniteGraph’s Placement Management Capability allows you to optimize the placement of data items resulting in tremendous performance improvements in both query and ingest. InfiniteGraph has client-side caching which caches frequently used node and edges. InfiniteGraph's DO query language enables complex "beyond graph" queries not supported by other query languages. -
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Establish a cohesive and harmonized master data management strategy across your domains to simplify enterprise data management, increase data accuracy, and reduce total cost of ownership. Kick-start your corporate master data management initiative in the cloud with a minimal barrier for entry and an option to build additional master data governance scenarios at your pace. Create a single source of truth by uniting SAP and third-party data sources and mass processing additional bulk updates on large volumes of data. Define, validate, and monitor established business rules to confirm master data readiness and analyze master data management performance. Enable collaborative workflow routing and notification to allow various teams to own unique master data attributes and enforce validated values for specific data points.
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GraphAware
GraphAware
GraphAware offers Hume, a connected data analytics and intelligence analysis platform powered by graph technology that transforms siloed structured and unstructured data into an interconnected network for deeper insight and decision-making. At its core, Hume uses knowledge graph and graph database principles to ingest, unify, and represent data as networks of nodes and relationships, enabling analysts and data scientists to intuitively navigate, query, and visualize multi-hop connections and hidden patterns without needing to learn complex query languages. It delivers a single view of truth across disparate data sources, accelerates discovery of hidden relationships and behavior patterns, and supports advanced graph data science, including node influence analysis, link prediction, community detection, and automated alerting through integrated machine learning and large language model (LLM) features. -
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RelationalAI
RelationalAI
RelationalAI is a next-generation database system for intelligent data applications based on relational knowledge graphs. Data-centric application design brings data and logic together into composable models. Intelligent data applications understand and make use of each relation that exists in a model. relational provides a knowledge graph system to express knowledge as executable models. These models can be easily extended through declarative, human-readable programs. RelationalAI’s expressive, declarative language leads to a 10-100x reduction in code. Applications are developed faster, with superior quality by bringing non-technical domain experts into the creation process and by automating away complex programming tasks. Take advantage of the extensible graph data model as the foundation of data-centric architecture. Integrate models to discover new relationships and break down barriers between applications. -
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QuerySurge
RTTS
QuerySurge leverages AI to automate the data validation and ETL testing of Big Data, Data Warehouses, Business Intelligence Reports and Enterprise Apps/ERPs with full DevOps functionality for continuous testing. Use Cases - Data Warehouse & ETL Testing - Hadoop & NoSQL Testing - DevOps for Data / Continuous Testing - Data Migration Testing - BI Report Testing - Enterprise App/ERP Testing QuerySurge Features - Projects: Multi-project support - AI: automatically create datas validation tests based on data mappings - Smart Query Wizards: Create tests visually, without writing SQL - Data Quality at Speed: Automate the launch, execution, comparison & see results quickly - Test across 200+ platforms: Data Warehouses, Hadoop & NoSQL lakes, databases, flat files, XML, JSON, BI Reports - DevOps for Data & Continuous Testing: RESTful API with 60+ calls & integration with all mainstream solutions - Data Analytics & Data Intelligence: Analytics dashboard & reports -
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←INTELLI•GRAPHS→
←INTELLI•GRAPHS→
←INTELLI•GRAPHS→ is a semantic wiki designed to unify disparate data into interconnected knowledge graphs that humans, AI assistants, and autonomous agents can co-edit and act upon in real time; it functions as a personal information manager, family tree/genealogy system, project management hub, digital publishing platform, CRM, document management system, GIS, biomedical/research database, electronic health record layer, digital twin engine, and e-governance tracker, all built on a next-gen progressive web app that is offline-first, peer-to-peer, and zero-knowledge end-to-end encrypted with locally generated keys. Users get live, conflict-free collaboration, schema library with validation, full import/export of encrypted graph files (including attachments), and AI/agent readiness via APIs and tooling like IntelliAgents, which provide identity, task orchestration, workflow planning with human-in-the-loop breakpoints, adaptive inference meshes, and continuous memory enhancement.Starting Price: Free -
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Alteryx
Alteryx
Step into a new era of analytics with the Alteryx AI Platform. Empower your organization with automated data preparation, AI-powered analytics, and approachable machine learning — all with embedded governance and security. Welcome to the future of data-driven decisions for every user, every team, every step of the way. Empower your teams with an easy, intuitive user experience allowing everyone to create analytic solutions that improve productivity, efficiency, and the bottom line. Build an analytics culture with an end-to-end cloud analytics platform and transform data into insights with self-service data prep, machine learning, and AI-generated insights. Reduce risk and ensure your data is fully protected with the latest security standards and certifications. Connect to your data and applications with open API standards. -
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Mastech InfoTrellis
Mastech Infotrellis
Mastech InfoTrellis is a specialist in Digital Transformation solutions and enables enterprises to discover business-relevant insights through Enterprise Knowledge Graphs. With tools and techniques like Ontologies, Machine Intelligence, we help enterprises bring data to life and absorb intricate business objects in an easily comprehensible arrangement. -
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SplineCloud
SplineCloud
SplineCloud is an open knowledge management platform designed to facilitate the discovery, formalization, and exchange of structured and reusable knowledge in science and engineering. It enables users to organize data into structured repositories, making it findable and accessible. The platform offers tools such as an online plot digitizer for extracting data from graphs and an interactive curve fitting tool that allows users to define functional relationships in datasets using smooth spline functions. Users can also reuse datasets and relations in their models and calculations by accessing them directly through the SplineCloud API or by utilizing open source client libraries for Python and MATLAB. The platform supports the development of reusable engineering and analytical applications, aiming to reduce redundancy in design processes, preserve expert knowledge, and facilitate better decision-making. -
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Apache TinkerPop
Apache Software Foundation
Apache TinkerPop™ is a graph computing framework for both graph databases (OLTP) and graph analytic systems (OLAP). Gremlin is the graph traversal language of Apache TinkerPop. Gremlin is a functional, data-flow language that enables users to succinctly express complex traversals on (or queries of) their application's property graph. Every Gremlin traversal is composed of a sequence of (potentially nested) steps. A graph is a structure composed of vertices and edges. Both vertices and edges can have an arbitrary number of key/value pairs called properties. Vertices denote discrete objects such as a person, a place, or an event. Edges denote relationships between vertices. For instance, a person may know another person, have been involved in an event, and/or have recently been at a particular place. If a user's domain is composed of a heterogeneous set of objects (vertices) that can be related to one another in a multitude of ways (edges).Starting Price: Free -
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txtai
NeuML
txtai is an all-in-one open source embeddings database designed for semantic search, large language model orchestration, and language model workflows. It unifies vector indexes (both sparse and dense), graph networks, and relational databases, providing a robust foundation for vector search and serving as a powerful knowledge source for LLM applications. With txtai, users can build autonomous agents, implement retrieval augmented generation processes, and develop multi-modal workflows. Key features include vector search with SQL support, object storage integration, topic modeling, graph analysis, and multimodal indexing capabilities. It supports the creation of embeddings for various data types, including text, documents, audio, images, and video. Additionally, txtai offers pipelines powered by language models that handle tasks such as LLM prompting, question-answering, labeling, transcription, translation, and summarization.Starting Price: Free -
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GraphQL
The GraphQL Foundation
GraphQL is a query language for APIs and a runtime for fulfilling those queries with your existing data. GraphQL provides a complete and understandable description of the data in your API, gives clients the power to ask for exactly what they need and nothing more, makes it easier to evolve APIs over time, and enables powerful developer tools. Send a GraphQL query to your API and get exactly what you need, nothing more and nothing less. GraphQL queries always return predictable results. Apps using GraphQL are fast and stable because they control the data they get, not the server. GraphQL queries access not just the properties of one resource but also smoothly follow references between them. While typical REST APIs require loading from multiple URLs, GraphQL APIs get all the data your app needs in a single request. Apps using GraphQL can be quick even on slow mobile network connections. -
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Amazon Neptune
Amazon
Amazon Neptune is a fast, reliable, fully managed graph database service that makes it easy to build and run applications that work with highly connected datasets. The core of Amazon Neptune is a purpose-built, high-performance graph database engine optimized for storing billions of relationships and querying the graph with milliseconds latency. Amazon Neptune supports popular graph models Property Graph and W3C's RDF, and their respective query languages Apache TinkerPop Gremlin and SPARQL, allowing you to easily build queries that efficiently navigate highly connected datasets. Neptune powers graph use cases such as recommendation engines, fraud detection, knowledge graphs, drug discovery, and network security. Proactively detect and investigate IT infrastructure using a layered security approach. Visualize all infrastructure to plan, predict and mitigate risk. Build graph queries for near-real-time identity fraud pattern detection in financial and purchase transactions. -
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Wikimedia Enterprise
Wikimedia Enterprise
Retrieve data from Wikimedia projects in any language, access metadata packaged exclusively for Wikimedia Enterprise, and detect vandalism or important updates at the article level. Unleash the potential within your own organization. Use Wikimedia Enterprise to build knowledge graphs, voice assistants or bots, training models, massive enriched datasets, and so much more. Access one of the largest public data sources on the internet with a single unified structure and guaranteed availability. Great for voice assistants, populating search engines, training machine learning models, augmenting private data sets, and much more. Enable your entire organization with a knowledge graph that can be consumed across teams.Starting Price: $.01 per request -
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PuppyGraph
PuppyGraph
PuppyGraph empowers you to seamlessly query one or multiple data stores as a unified graph model. Graph databases are expensive, take months to set up, and need a dedicated team. Traditional graph databases can take hours to run multi-hop queries and struggle beyond 100GB of data. A separate graph database complicates your architecture with brittle ETLs and inflates your total cost of ownership (TCO). Connect to any data source anywhere. Cross-cloud and cross-region graph analytics. No complex ETLs or data replication is required. PuppyGraph enables you to query your data as a graph by directly connecting to your data warehouses and lakes. This eliminates the need to build and maintain time-consuming ETL pipelines needed with a traditional graph database setup. No more waiting for data and failed ETL processes. PuppyGraph eradicates graph scalability issues by separating computation and storage.Starting Price: Free -
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Neo4j
Neo4j
Neo4j’s graph data platform is purpose-built to leverage not only data but also data relationships. Using Neo4j, developers build intelligent applications that traverse today's large, interconnected datasets in real time. Powered by a native graph storage and processing engine, Neo4j’s graph database delivers an intuitive, flexible and secure database for unique, actionable insights. -
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JanusGraph
JanusGraph
JanusGraph is a scalable graph database optimized for storing and querying graphs containing hundreds of billions of vertices and edges distributed across a multi-machine cluster. JanusGraph is a project under The Linux Foundation, and includes participants from Expero, Google, GRAKN.AI, Hortonworks, IBM and Amazon. Elastic and linear scalability for a growing data and user base. Data distribution and replication for performance and fault tolerance. Multi-datacenter high availability and hot backups. All functionality is totally free. No need to buy commercial licenses. JanusGraph is fully open source under the Apache 2 license. JanusGraph is a transactional database that can support thousands of concurrent users executing complex graph traversals in real time. Support for ACID and eventual consistency. In addition to online transactional processing (OLTP), JanusGraph supports global graph analytics (OLAP) with its Apache Spark integration. -
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Aiimi
Aiimi
Aiimi’s Workplace AI platform is an enterprise-scale AI and data management solution that connects all structured and unstructured data across an organization through a single Virtual Data Layer, enabling secure, scalable AI-powered search, analysis, automation, and actionable insights. It uses AI, machine learning, and Retrieval Augmented Generation (RAG) to discover, classify, enrich, and govern data at scale, turning fragmented information into trusted, “AI-ready” datasets that support natural language search, contextual chat and assistant features, advanced Q&A, and visualizations like knowledge graphs and timelines. It automates complex processes such as data governance, compliance monitoring, data quality improvement, DSAR/disclosure handling, and cloud/legacy system migration, while preserving access controls, permissions, and audit trails. -
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Memgraph
Memgraph
Memgraph offers a light and powerful graph platform comprising the Memgraph Graph Database, MAGE Library, and Memgraph Lab Visualization. Memgraph is a dynamic, lightweight graph database optimized for analyzing data, relationships, and dependencies quickly and efficiently. It comes with a rich suite of pre-built deep path traversal algorithms and a library of traditional, dynamic, and ML algorithms tailored for advanced graph analysis, making Memgraph an excellent choice in critical decision-making scenarios such as risk assessment (fraud detection, cybersecurity threat analysis, and criminal risk assessment), 360-degree data and network exploration (Identity and Access Management (IAM), Master Data Management (MDM), Bill of Materials (BOM)), and logistics and network optimization. -
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HugeGraph
HugeGraph
HugeGraph is a fast-speed and highly-scalable graph database. Billions of vertices and edges can be easily stored into and queried from HugeGraph due to its excellent OLTP ability. As compliance to Apache TinkerPop 3 framework, various complicated graph queries can be accomplished through Gremlin (a powerful graph traversal language). Among its features, it provides compliance to Apache TinkerPop 3, supporting Gremlin. Schema Metadata Management, including VertexLabel, EdgeLabel, PropertyKey and IndexLabel. Multi-type Indexes, supporting exact query, range query and complex conditions combination query. Plug-in Backend Store Driver Framework, supporting RocksDB, Cassandra, ScyllaDB, HBase and MySQL now and easy to add other backend store driver if needed. Integration with Hadoop/Spark. HugeGraph relies on the TinkerPop framework, we refer to the storage structure of Titan and the schema definition of DataStax. -
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RecallGraph
RecallGraph
RecallGraph is a versioned-graph data store - it retains all changes that its data (vertices and edges) have gone through to reach their current state. It supports point-in-time graph traversals, letting the user query any past state of the graph just as easily as the present. RecallGraph is a potential fit for scenarios where data is best represented as a network of vertices and edges (i.e., a graph) having the following characteristics: 1. Both vertices and edges can hold properties in the form of attribute/value pairs (equivalent to JSON objects). 2. Documents (vertices/edges) mutate within their lifespan (both in their individual attributes/values and in their relations with each other). 3. Past states of documents are as important as their present, necessitating retention and queryability of their change history. Also see this blog post for an intro - https://blog.recallgraph.tech/never-lose-your-old-data-again. -
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Cognee
Cognee
Cognee is an open source AI memory engine that transforms raw data into structured knowledge graphs, enhancing the accuracy and contextual understanding of AI agents. It supports various data types, including unstructured text, media files, PDFs, and tables, and integrates seamlessly with several data sources. Cognee employs modular ECL pipelines to process and organize data, enabling AI agents to retrieve relevant information efficiently. It is compatible with vector and graph databases and supports LLM frameworks like OpenAI, LlamaIndex, and LangChain. Key features include customizable storage options, RDF-based ontologies for smart data structuring, and the ability to run on-premises, ensuring data privacy and compliance. Cognee's distributed system is scalable, capable of handling large volumes of data, and is designed to reduce AI hallucinations by providing AI agents with a coherent and interconnected data landscape.Starting Price: $25 per month -
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LibrePhotos
LibrePhotos
A self-hosted Google Photos clone, with a slight focus on cool graphs. LibrePhotos is a fork of Ownphotos. A self-hosted open-source photo management service, with a slight focus on cool graphs. Django backend and React frontend. Step-by-step installation instructions are available in our documentation. Support for all types of photos including raw photos, support for videos, timeline view, scans pictures on the file system, with multiuser support. Generate albums based on events like "Thursday in Berlin", face recognition, face classification, reverse geocoding, object/scene detection, semantic image search, and search by metadata. You need a x86 processor, and it is recommended to have 8GBs of RAM. You will need at least 10 GB of HDD Space for the Docker images. It needs that space because of the machine learning models. Librephotos will also create a database and thumbnails which will need additional space. LibrePhotos comes with separate backend and frontend servers.
