What is FalkorDB?
FalkorDB is developing a novel graph database that revolutionizes the graph databases and AI industries. Our graph database is based on novel but proven linear algebra algorithms on sparse matrices that deliver unprecedented performance up to two orders of magnitude greater than the leading graph databases. Our goal is to provide the missing piece in AI in general and LLM in particular, reducing hallucinations and enhancing accuracy and reliability. We accomplish this by providing a fast and interactive knowledge graph, which provides a superior solution to the common solutions today.
FalkorDB is a tool in the Graph Databases category of a tech stack.
Key Features
✓
Work with structured and unstructured data, ✓
Ontology auto-detection, ✓
Built-in agent orchestrator, ✓
Real-time applications, ✓
Rich context and memory
FalkorDB Pros & Cons
Pros of FalkorDB
No pros listed yet.
Cons of FalkorDB
No cons listed yet.
FalkorDB Alternatives & Comparisons
What are some alternatives to FalkorDB?
Neo4j
Neo4j stores data in nodes connected by directed, typed relationships with properties on both, also known as a Property Graph. It is a high performance graph store with all the features expected of a mature and robust database, like a friendly query language and ACID transactions.
Dgraph
Dgraph's goal is to provide Google production level scale and throughput, with low enough latency to be serving real time user queries, over terabytes of structured data. Dgraph supports GraphQL-like query syntax, and responds in JSON and Protocol Buffers over GRPC and HTTP.
Power Virtual Agents
It empowers teams to easily create powerful bots using a guided, no-code graphical interface without the need for data scientists or developers. It addresses many of the major issues with bot building in the industry today. It eliminates the gap between the subject matter experts and the development teams building the bots, and the long latency between teams recognizing an issue and updating the bot to address it. It removes the complexity of exposing teams to the nuances of conversational AI and the need to write complex code. And, it minimizes the IT effort required to deploy and maintain a custom conversational solution.
