AllegroGraph is a horizontally distributed, multi-modal Graph (RDF), Vector, and Document (JSON, JSON-LD) Knowledge Graph platform that includes SPARQL, Geospatial, Temporal, Social Networking, Text Analytics, and Large Language Model (LLM) capabilities for building Neuro-Symbolic AI applications. AllegroGraph features a built-in no-code visualization tool - Gruff and is the most secure Knowledge Graph platform on the market.
AllegroGraph: Enterprise Knowledge Graphs for Neuro-Symbolic and Agentic AI
AllegroGraph is a distributed, multi-modal Graph, Vector, and Document database that provides the foundation for scalable Enterprise Knowledge Graphs, Neuro-Symbolic AI, and agentic AI applications. It combines RDF, SPARQL, vector search, document intelligence, rules, reasoning, geospatial, temporal, social network analytics, and enterprise-grade security in one ACID-compliant platform.
GraphTalker extends AllegroGraph beyond traditional database interaction and simple natural-language query generation. It is a deeply integrated natural-language interface for AllegroGraph that enables users to ask questions, explore relationships, and gain insight from enterprise Knowledge Graphs without writing SPARQL manually. Integrated in a manner similar to Gruff, GraphTalker can be launched directly from WebView and connected to a selected repository.
GraphTalker is designed for agentic exploration. Rather than translating a question into a single query, it can inspect repository structure, examine schema and ontology patterns, generate and test queries, observe results, refine its approach, and return grounded answers. This makes AllegroGraph more accessible to business users and more productive for data scientists, KG developers, and application teams.
GraphTalker can also be integrated directly into end-user applications through APIs, allowing organizations to embed natural-language KG interaction into dashboards, portals, workflows, analytics tools, and AI-powered systems.
Industry-Leading Security
AllegroGraph security is designed to protect sensitive data in complex graph, vector, and document environments. Its Triple Attribute Security model applies controls directly to data elements, including triples, annotations, embeddings, and text fragments. This makes AllegroGraph well suited for healthcare, financial services, policing, intelligence, and government. The same framework applies across Knowledge Graph, vector, document, and GraphTalker workflows, giving organizations granular control without sacrificing performance.
Retrieval-Augmented Generation for Trusted AI
AllegroGraph supports Retrieval-Augmented Generation (GraphRAG) by grounding LLM responses in trusted enterprise Knowledge Graphs. Instead of relying only on model memory or unstructured text retrieval, AllegroGraph provides semantic context, relationships, rules, provenance, and governed access to enterprise data.
Natural-Language Queries and Reasoning
GraphTalker enables users to ask questions in plain language while working with AllegroGraph to understand repository structure, determine the right query strategy, and return reliable results. This is valuable when users do not already know the schema, ontology, or available relationships.
Enterprise Document Deep Insight
AllegroGraph's VectorStore capabilities connect enterprise documents with Knowledge Graphs, allowing organizations to query documents, text fragments, and graph relationships together. This helps transform previously inaccessible dark data into governed enterprise knowledge.
Symbolic Rules and Explainable AI
AllegroGraph includes built-in rule-based capabilities for symbolic reasoning. Organizations can encode business logic, infer new relationships, support classification, and produce more explainable outcomes based on enterprise knowledge.
Ontology, Taxonomy, and Semantic Model Development
AllegroGraph streamlines the creation and refinement of ontologies, taxonomies, and semantic models. LLM-assisted workflows and GraphTalker's natural-language interaction help users explore concepts, relationships, hierarchies, and classifications more efficiently.
Enhanced Scalability and Performance
AllegroGraph supports large-scale enterprise Knowledge Graph deployments through FedShard and high-availability architecture. These capabilities help distribute workloads, manage large repositories, improve query performance, and scale KG applications.
Modern Web Interface and Visualization
AllegroGraph provides a modern WebView experience for managing repositories, launching tools, and interacting with the platform. GraphTalker is deeply integrated into this experience, while Gruff provides advanced Knowledge Graph visualization for exploring RDF graphs, relationships, annotations, provenance, temporal context, scores, weights, and semantic structures.
Summary
AllegroGraph is more than a graph database. It is a governed semantic platform for building explainable, trustworthy, and enterprise-ready AI applications. By combining Knowledge Graphs, vector search, document intelligence, symbolic reasoning, enterprise security, scalability, visualization, and GraphTalker's agentic natural-language interface, AllegroGraph provides a semantic foundation for Neuro-Symbolic and Agentic AI.
Highlights
Horizontally distributed Graph, Vector, and Document database for highly scalable Knowledge Graph and Neuro-Symbolic AI Solutions.
AllegroGraph is 100 percent ACID, supporting Transactions: Commit, Rollback, and Checkpointing along with Multi-Master Replication for high availability requirements.
AllegroGraph supports SHACL, SPARQL 1.1, RDFS++, OWL2-RL, and Prolog rules and reasoning from numerous client applications as well as visualizations from Graph industry's leading browser - Gruff.
AWS Marketplace now accepts line of credit payments through the PNC Vendor Finance program. This program is available to select AWS customers in the US, excluding NV, NC, ND, TN, & VT.
AllegroGraph Free Edition carries no software charge, so you pay only the AWS EC2 hourly rate for the instance type you choose. The dimensions listed are not pricing tiers or feature levels. Each one maps to a specific EC2 instance size, billed by the hour. They differ by compute, memory, and storage profile, letting you match the instance to your workload. General-purpose, compute-optimized, memory-optimized, and storage-optimized families are all available. Larger instances with more vCPUs or RAM cost more per hour. You scale cost by selecting a bigger or smaller instance, not by unlocking features.
Top-of-mind questions for buyers
What runs on each instance under the Free Edition, and what limits should I expect?
Each instance runs the AllegroGraph Free Edition graph database server on 64-bit Linux. It supports RDF, SPARQL, OWL reasoning, and vector storage. The Free Edition unlocks a subset of capabilities; enterprise features like Multi-Master Replication require a separate license key installed after setup. Check the download documentation for feature specifics.
Am I charged when an instance is stopped or paused?
Software charges apply per running instance-hour. A fully stopped instance stops accruing hourly software charges. Since the software itself is free, you pay only the AWS EC2 rate, which also stops when the instance is stopped. Underlying AWS storage fees for attached volumes may still apply while stopped.
How do I choose between the general-purpose, compute-optimized, and memory-optimized instance options?
Each dimension maps to an EC2 instance family with a different balance of vCPUs, RAM, and storage. Compute-optimized types suit query-heavy processing. Memory-optimized types suit large in-memory triple stores. Storage-optimized types suit big on-disk datasets. Match the family to whether your workload is CPU-bound, memory-bound, or data-bound.
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An AMI is a virtual image that provides the information required to launch an instance. Amazon EC2 (Elastic Compute Cloud) instances are virtual servers on which you can run your applications and workloads, offering varying combinations of CPU, memory, storage, and networking resources. You can launch as many instances from as many different AMIs as you need.
Once the instance is running, SSH into it using the username
'ec2-user' and provide your Amazon private key. Run 'cat README' to
find the autogenerated password for the AllegroGraph 'admin' account.
Visit http://<your-public-ip>:10035 in your browser to access
AllegroGraph WebView. Log in as 'admin', using the password you found
in the README file. AllegroGraph is now ready to use via browser,
command line (via agtool), or various client libraries.
Consult the AllegroGraph Quick Start guide at
https://franz.com/agraph/support/documentation/current/agraph-quick-start.html#tutorial-dir
for examples of how to create a repository and load it with sample
data.
AWS Support is a one-on-one, fast-response support channel that is staffed 24x7x365 with experienced and technical support engineers. The service helps customers of all sizes and technical abilities to successfully utilize the products and features provided by Amazon Web Services.
AllegroGraph is a horizontally distributed, multi-modal Graph (RDF), Vector, and Document (JSON, JSON-LD) Knowledge Graph platform that includes SPARQL, Geospatial, Temporal, Social Networking, Text Analytics, and Large Language Model (LLM) capabilities for building Neuro-Symbolic AI applications. AllegroGraph features a built-in no-code visualization tool - Gruff and is the most secure Knowledge Graph platform on the market.
Writes Resource Description Framework (RDF) Data into a multitude of Semantic Graphs, and CSV Nodes and Edges data into a wide range of Property Graphs.
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