AllegroGraph’s main monitoring/observability endpoint is /metrics, which exposes various metrics in Prometheus format. This endpoint requires superuser permissions.

In order to reduce computational load on the system, the /metrics endpoints supports requesting metrics of a particular kind (category). Currently supported categories are:

* system: comprehensive system metrics including CPU usage, memory utilization, disk I/O, network activity, and AllegroGraph-specific connection counts (backend, sessions, etc);

* jobs: information about active SPARQL queries (jobs) including total count and age statistics (maximum, minimum, and average age in seconds);

* queries: information about queries, like the total number of queries executed per active triple store, cumulative time, number of running queries etc.

* indices: reports on repository index health including total indexed triples and optimization scores for each index by class; only returns metrics for the repositories currently in operation to avoid starting the dormant ones;

* replication: monitors Multi-Master Replication status including commits behind primary, ingest queue length, controlling status, and replication state for each repository; like indices, only returns metrics for repositories currently in operation.

Read More in the documentation:
https://franz.com/agraph/support/documentation/8.5.0/monitoring.html#overview

GitHub Examples:

https://github.com/franzinc/agraph-examples

The post Monitoring AllegroGraph with Prometheus and Grafana appeared first on AllegroGraph.

KMWorld notes:  Companies on this list are wonderful examples of how their products expand the power of KM in changing knowledge environments. They stand out in the KM field, and we applaud their accomplishments.

“Our annual list of 100 Companies to Watch in the knowledge management space is a testament to their agility to thrive in an environment of rapidly changing technologies, while not losing track of the importance of human expertise. We’re proud to celebrate these organizations that are redefining what it means to lead, add business value, and recognize that human ingenuity and artificial intelligence are increasingly inseparable.”

New AllegroGraph v8.5

Franz Inc. recently announced AllegroGraph v8.5, with an Enhanced AI-powered Natural Language Query interface. The new release helps enterprises build Agentic AI solutions by enabling more intuitive, human-like interaction between users and intelligent systems—critical for agents that need to reason, plan, and act autonomously.

AllegroGraph v8.5 combines knowledge graphs, vector embeddings, and neuro-symbolic reasoning to provide the semantic layer needed for AI agents to interpret data meaningfully and deliver more accurate, explainable results.

“With AllegroGraph 8.5, we’re making it easier for enterprises to build AI agents that can understand intent, reason over complex data, and deliver more explainable results,” said Dr. Jans Aasman, CEO of Franz Inc. “By combining natural language access with Neuro-Symbolic AI and knowledge graphs, AllegroGraph provides a stronger semantic foundation for trusted enterprise AI.”

New Capabilities in AllegroGraph v8.5 include:

 * Optimized Natural Language Query (NLQ): Faster, more token-efficient translation of natural language questions into graph queries, reducing LLM usage while improving response times.

 * Expanded MCP Support: Simplifies connecting models, tools, and enterprise knowledge graph workflows into agentic AI systems.

 * Faster Vector Processing: Accelerates vector creation and supports configurable vector sizes to optimize performance and cost.

 * Enhanced Observability: Enhanced integration with Prometheus and Grafana for improved monitoring and operational visibility.

 * Production-ready AI Semantic Graph Infrastructure: Strengthens AllegroGraph’s role as a production-ready platform for AI applications that combine knowledge graphs, vector search, and LLM reasoning.

Franz Inc. stands at the forefront of AI innovation, offering Neuro-Symbolic AI solutions that transform complex data into actionable and comprehensible insights.  Contact us today to learn more – [email protected]

The post Top 100 Company in KM – Franz Inc. appeared first on AllegroGraph.

A pioneer’s origin story.

Wallace traces his path into AI back to the early era when ELIZA was the widely known “AI application,” and uses it as a lens for understanding why people attribute intelligence to language—sometimes too easily.

That history becomes a useful contrast to the current moment: modern systems can sound astonishingly fluent, but the core question remains the same—what do they actually understand, and how do we validate it?

What made ALICE different—and why AIML scaled

The interview highlights the practical breakthrough of ALICE and the value of a structured, symbolic approach to dialogue authoring. Wallace explains why AIML (Artificial Intelligence Markup Language) mattered: it made it possible to build conversational behavior in a modular, inspectable way—an early lesson in “explainability by design.”

That thread continues throughout the conversation: systems that can be understood and steered tend to be the ones that can be trusted and operationalized.

The Turing Test, the Loebner Prize, and measuring the wrong thing

Wallace offers a nuanced critique of the Turing Test—grounding it in Alan Turing’s original “imitation game” framing—and discusses how the Loebner Prize operationalized a simplified version of that idea (and why the “silver medal” was never awarded).

The takeaway isn’t that benchmarks are useless—it’s that evaluation needs to match reality: reliability, grounding, and the ability to explain “why” matter more than pure imitation.

Why black-box intelligence hits a wall in real applications

The discussion moves into modern AI and the interpretability gap: even as models improve, their decision-making can remain hard to inspect.

Wallace connects this to a practical need in enterprise settings: systems must be auditable, controllable, and aligned to domain rules—not just persuasive.

The next chapter: neural + symbolic, together

One of the most forward-looking parts of the interview is the explicit call for combining approaches—using neural methods where they excel (pattern learning, similarity, generalization) while retaining symbolic structures for logic, constraints, and transparent reasoning.

Wallace closes by pointing to real-world healthcare examples where predictions and decisions benefit from combining statistical learning with domain-grounded reasoning—exactly the kind of hybrid, neuro-symbolic direction the industry is moving toward.

The post From ALICE to Neuro-Symbolic AI: A Conversation with Dr. Richard Wallace appeared first on AllegroGraph.

The core enabler is Neuro-Symbolic AI—LLMs paired with knowledge graphs, rules, and reasoning—acting like a “cognitive OS” that constrains what agents know and can do, embeds compliance into the reasoning process (not as an afterthought), and supports LLM ensembles where multiple models check facts, policy, and performance. As Aasman puts it, “The future will be about collaborative machine reasoning”—and the payoff is durable enterprise memory that counters “corporate amnesia” by turning everyday communications into structured intelligence.

For the full piece (and the full context behind these examples) – Read the full article at The AI Innovator.

The post Collaborative Machine Reasoning Marks AI’s Next Inflection Point appeared first on AllegroGraph.

Dr. Jans Aasman, CEO, was quote throughout the article:

“All the inputs and outputs of the agents, every decision, goes into the orchestrating knowledge graph.”

Why it matters: agent systems will need an authoritative “memory + audit layer” for governance, traceability, and accountability.

“You have three or five LLMs read a document… and then there’s a resolver system… so we ultimately get data that’s 99.9% correct. If you use one, it might be only 60% correct.”

Why it matters: accuracy will increasingly come from model collaboration + reconciliation, not just picking a single “best” model.

“It tells me what’s possible and I say what I really want, and it says ‘let’s try this’… ‘this is cool, please store this in a visualization,’ and it keeps going.”

Why it matters: the interaction pattern is shifting toward AI-as-coach/collaborator that iterates with users and produces durable artifacts (like visualizations/structures).

“We can’t sustain the current investments in AI… This is unsustainable. So, the only way to go forward is with small models.”

Why it matters: economics will push architectures toward smaller, specialized models—which increases the need for interoperability and orchestration across many components.

Visit The New Stack to read the full article.

The post Data in 2026: Interchangeable Models, Clouds, and Specialization appeared first on AllegroGraph.

What sets AllegroGraph apart:

• AllegroGraph is built for Neuro-Symbolic AI, a hybrid architecture combining the power of neural networks (for learning, pattern recognition) with symbolic AI (for structured reasoning).
• It includes a full suite of capabilities: large language model (LLM) integration, vector generation and storage, graph neural networks (GNNs), graph analytics, streaming graph pipelines, and more.
• It is purpose-designed for enterprise scale: security, governance, performance, and real-time analytics are all part of the platform.

A heartfelt thanks to all the KMWorld readers who voted and to every customer, partner, and team member that has contributed to the AllegroGraph journey. Together we’re elevating what knowledge means for enterprises — and what AI truly can accomplish when it’s built on a solid knowledge-graph foundation.

The post AllegroGraph Named “2025 Best Knowledge Graph” by KMWorld Readers’ Choice appeared first on AllegroGraph.

In the article, Jans cuts through the hype to ask an important question: what happens when autonomous AI systems start making decisions on their own — and no one can explain why?

The answer, he argues, is accountability — and that’s where Knowledge Graphs come in. By giving agents a shared, semantically rich context (and a memory of every action they take), Knowledge Graphs make it possible to track, audit, and govern AI behavior.

Instead of “black-box” autonomy, Jans envisions a world of Accountable AI Agents — systems that are powerful and explainable, collaborative and controlled.

It’s a quick, insightful read that captures the future of Neuro-Symbolic and Agentic AI better than anything else out there.

Update:  TechTarget has absorbed Data Science Central and article links are not currently available.

This is a pdf copy of the article.

If the Data Science Central links are restored this is the article on Data Science Central

The post The rise of accountable AI agents: How knowledge graphs solve the autonomy problem appeared first on AllegroGraph.

Jans Aasman, CEO of Franz Inc., covered how Knowledge Graphs are transforming Agentic AI into practical, mission-ready systems.

Dr. Aasman discussed all the ways a Knowledge Graph can deliver accountability in existing and future agent frameworks. Using AllegroGraph he demonstrated how an Orchestrating Knowledge Graph (OKG) coordinates specialized agents, enabling natural language queries, real-time visualization, and explainable insights at scale.

Attendees learned how this powerful combination reduces inefficiencies, scales across petabytes of data, and empowers analysts to make faster, more confident decisions.

Summary of Key Topics:

• Discover how an Orchestrating Knowledge Graph (OKG) unifies data agents, analyst assistants, and entity trackers into a seamless AI ecosystem.
• See how AllegroGraph delivers Neuro-Symbolic reasoning and explainable intelligence at petabyte scale.
• Learn how Tom Sawyer Software brings real-time and data streaming and visualization to life for multi-modal, event-driven analytics.
• Understand how the emerging Model Context Protocol (MCP) transforms orchestration by enabling agents, models, and tools to interoperate through a common standard—eliminating brittle custom integrations and unlocking plug-and-play extensibility.
• Explore a real-world scenario where orchestrated agents provide actionable insights and transform decision-making in complex environments.

Watch the Webinar Recording on our YouTube Channel.

The post Webinar – Building Accountable AI Agents with Knowledge Graphs appeared first on AllegroGraph.

RDF as the Natural Knowledge Layer for AI

“RDF is the natural knowledge layer for AI systems because it captures meaning explicitly.” – Bryon Jacob

Artificial Intelligence is most powerful when it can combine the statistical power of machine learning with the explicit semantics of a knowledge graph. AllegroGraph embodies this neuro-symbolic paradigm by providing a structured knowledge layer that AI systems can “think with,” not just “learn from.”

• Explainability: Machine learning alone often struggles with explainability. AllegroGraph’s RDF-based approach encodes relationships, hierarchies, and constraints, allowing AI models to justify outcomes.
• Contextual Intelligence: If you want AI to be more than pattern recognition, you need the relationships that knowledge graphs provide.
• Interoperability: AllegroGraph’s standards-based model ensures AI applications can seamlessly integrate external data and ontologies.

RDF Triples: The Smallest Atom of Meaning

“RDF triples are the smallest atom of meaning with the largest scope of use.” – Bryon Jacob

Every data point in AllegroGraph is a triple: subject-predicate-object. This simple yet powerful building block enables the representation of any fact with semantic clarity.

• Flexibility: Triples allow modeling of both structured and unstructured data, enabling enterprises to unify silos.
• Global Coherence: Each triple is inherently linkable to others, building a knowledge graph that scales without breaking.
• Atomic Updates: Changes in data are managed at the smallest unit of meaning, ensuring high granularity.

From Facts to Knowledge: RDFS and OWL

“The RDF(S) and OWL layer cake is how you move from isolated facts to true knowledge.” – Bryon Jacob

AllegroGraph doesn’t stop at storing facts—it infers new knowledge by layering RDF Schema (RDFS) and Web Ontology Language (OWL) on top of triples.

• Ontology-Driven Inference: By understanding class hierarchies and property characteristics, AllegroGraph automatically infers new relationships.
• Semantic Integrity: Ontologies help maintain data quality by enforcing logical constraints.
• Complex Reasoning: Enterprises can move beyond simple lookups to rich inferencing across interconnected data.

SPARQL: Querying with Graph Thinking

“SPARQL isn’t just SQL with different syntax—it’s graph thinking codified.” – Bryon Jacob

AllegroGraph empowers users with SPARQL, a flexible query language designed for graphs, not tables.

• Expressive Power: Users can query multi-hop relationships and nested structures without writing complex joins.
• Scalability: SPARQL queries scale naturally as the knowledge graph grows.
• Integration with AI: SPARQL’s ability to extract context-rich subsets of the graph makes it ideal for Retrieval-Augmented Generation (RAG) pipelines.

RDF vs. Property Graphs: Choosing the Right Model

“Property graphs are great for specific applications, but RDF is for ecosystems.” – Bryon Jacob

While property graphs excel at localized graph traversal, AllegroGraph’s RDF-based model is unmatched when it comes to enterprise knowledge management.

• Standards-Based: AllegroGraph adheres to W3C standards, ensuring long-term interoperability and extensibility.
• Reasoning-Ready: Property graphs lack native inferencing; AllegroGraph thrives on it.
• Data Federation: AllegroGraph’s RDF design makes it easy to connect internal and external data sources without sacrificing consistency.

The RDF Epiphany: You’ve Been Building It All Along

“The RDF epiphany is when you realize you’ve been building it all along.” – Bryon Jacob

Many enterprises unknowingly build fragmented knowledge graphs by piecing together APIs, databases, and ontologies. AllegroGraph provides the unified foundation you’ve always needed.

• Schema Evolution: Easily adapt your model as your business changes.
• Heterogeneous Data Support: Bring together relational data, JSON, documents, and streaming events.
• Enterprise-Ready: AllegroGraph is designed for mission-critical deployments, with advanced features like multi-master replication and high availability.

Why AllegroGraph

AllegroGraph is not just a graph database; it’s an enterprise-grade knowledge platform engineered for the AI age. Its ability to store, infer, and reason over RDF data—while seamlessly supporting vector search, Retrieval-Augmented Generation (RAG), and other modern AI techniques—makes it the ultimate tool for building intelligent, future-proof solutions.

As AI adoption accelerates, the winners will be those who recognize the power of connecting silos and the power of multiple AI approaches. AllegroGraph provides the Neuro-Symbolic foundation to ensure that your AI doesn’t just analyze data—it understands it.

Try AllegroGraph Today

Ready to experience the power of AllegroGraph for yourself?

• Download AllegroGraph for free and start building your knowledge graph locally.
• Or, explore our fully managed cloud-hosted version for rapid deployment with zero infrastructure overhead.

The post The Neuro-Symbolic Foundation for the AI-Driven Enterprise appeared first on AllegroGraph.

Implications for Organizations

What should AI practitioners, architects, and business leaders take away from this recognition?

• If you are planning or evolving your knowledge management infrastructure, consider platforms that offer both semantic reasoning and neural / embedding-based approaches. One without the other may limit what you can achieve in explainability, flexibility, or performance.
• Look for tools that offer agentic capabilities or support workflows/agents—not just passive query and retrieval. Automating routine tasks, validating information, or supporting decision workflows are increasingly expected.
• Assess how well a KM tool handles “dark data” — content that is stored but underused due to format or accessibility issues (e.g. documents, PDFs, semi-structured text). Bringing that into the knowledge graph + vector space can unlock value.
• Don’t discount user experience: query language complexity, visualization, rule interpretability, edge provenance, etc., all matter for adoption, trust, and governance.

AllegroGraph’s recognition as a 2025 Trend-Setting Product underscores where KM is going: deeper integration of symbolic and neural AI, an increasing need for transparency, and usability front and center. For organizations seeking to stay at the cutting edge of knowledge management, adopting tools that embrace these principles will likely be a differentiator.

The post AllegroGraph Named a 2025 Trend Setting Product by KMWorld appeared first on AllegroGraph.