
Overview

Product video
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.
Details
Introducing multi-product solutions
You can now purchase comprehensive solutions tailored to use cases and industries.
Features and programs
Financing for AWS Marketplace purchases
Pricing
Dimension | Cost/hour |
|---|---|
m5a.2xlarge Recommended | $4.40 |
c5a.24xlarge | $52.80 |
x1e.32xlarge | $70.40 |
x1e.xlarge | $2.20 |
c5.18xlarge | $35.20 |
m5a.8xlarge | $17.60 |
c5a.16xlarge | $35.20 |
c5.12xlarge | $26.40 |
c5.9xlarge | $17.60 |
c5.24xlarge | $52.80 |
Vendor refund policy
30 days
How can we make this page better?
Legal
Vendor terms and conditions
Content disclaimer
Delivery details
64-bit (x86) Amazon Machine Image (AMI)
Amazon Machine Image (AMI)
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.
Version release notes
Additional details
Usage instructions
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.
Resources
Vendor resources
Support
Vendor support
Please allow 24 hours
AWS infrastructure support
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.


FedRAMP
GDPR
HIPAA
ISO/IEC 27001
PCI DSS
SOC 2 Type 2
Standard contract
Customer reviews
Unified customer graphs have improved order insights and support better decision making
What is our primary use case?
My main use case for AllegroGraph is to build a customer graph with relationships to depict all the information related to orders.
An example of how I use AllegroGraph for customer graph relationships is that we utilized it to view all the information in one comprehensive graph.
What is most valuable?
The best feature AllegroGraph offers is the user interface.
I use the simple user interface to view all the information together in one large graph.
AllegroGraph has positively impacted my organization as we have made significant steps forward to consolidate all information into one comprehensive knowledge graph.
These steps help with decision making.
What needs improvement?
AllegroGraph is perfect and has room for improvement.
For how long have I used the solution?
I have been using AllegroGraph for one year.
What do I think about the stability of the solution?
AllegroGraph is stable.
What do I think about the scalability of the solution?
AllegroGraph's scalability is good.
How are customer service and support?
I did not interact with the customer support team.
How would you rate customer service and support?
Which solution did I use previously and why did I switch?
I did not previously use a different solution before AllegroGraph.
What's my experience with pricing, setup cost, and licensing?
My experience with pricing, setup cost, and licensing was straightforward with no problems.
Which other solutions did I evaluate?
Before choosing AllegroGraph, I did not evaluate other options.
What other advice do I have?
My advice for others looking into using AllegroGraph is that the best way to use the product is to experiment with all the features that the product offers immediately. I gave this product a rating of 10.