# falkordb/falkordb

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3,437 stars · 268 forks · C · other

## Links

- GitHub: https://github.com/FalkorDB/FalkorDB
- Homepage: https://www.falkordb.com/
- awesome-repositories: https://awesome-repositories.com/repository/falkordb-falkordb.md

## Topics

`cloud-database` `database` `database-as-a-service` `developer-tools` `devtools` `graph-database` `graphrag` `knowledge-graph` `realtime-database`

## Description

FalkorDB is a high-performance graph database management system and vector graph database. It serves as a knowledge graph construction tool and a GraphRAG knowledge store, integrating structured property graphs with vector search to provide grounded context for large language models. The engine is designed as a multi-tenant graph engine, capable of hosting thousands of isolated datasets within a single instance.

The system distinguishes itself by using linear algebra for query execution, treating relationship tensors as matrix multiplications to achieve low-latency multi-hop traversals. It utilizes sparse-matrix graph storage and vectorized traversals to process thousands of relationships simultaneously. These capabilities are combined with hybrid vector-graph indexing to unify semantic similarity search with structural graph exploration.

The platform covers a broad range of capabilities, including GraphRAG orchestration, AI agent memory implementation, and advanced graph analytics such as community detection and centrality ranking. It supports OpenCypher query execution and provides connectivity via the Bolt and RESP protocols. Additional functionality includes automated ontology loading, temporal data tracking, and real-time binary replication for high availability.

The database supports migration from Neo4j and can be deployed as a distributed cluster or as an embedded graph engine.

## Tags

### Data & Databases

- [Graph Data Models](https://awesome-repositories.com/f/data-databases/graph-data-models.md) — Implements a property graph model representing information as nodes and edges with rich attributes. ([source](https://www.falkordb.com/blog/how-to-build-a-knowledge-graph/))
- [Graph Databases](https://awesome-repositories.com/f/data-databases/graph-databases.md) — A high-performance graph database management system using OpenCypher and the Bolt protocol.
- [Hybrid Vector-Graph Databases](https://awesome-repositories.com/f/data-databases/hybrid-vector-graph-databases.md) — Unifies vector embeddings and property graph structures to combine semantic similarity search with structural traversal.
- [Linear Algebra Query Execution](https://awesome-repositories.com/f/data-databases/linear-algebra-query-execution.md) — Executes graph queries using sparse matrices and linear algebra to achieve ultra-low latency multi-hop traversals. ([source](https://www.falkordb.com/))
- [Data Persistence](https://awesome-repositories.com/f/data-databases/data-persistence.md) — Saves data to disk in real-time and implements backups to ensure durability and prevent data loss. ([source](https://www.falkordb.com/plans/))
- [Database Transactions](https://awesome-repositories.com/f/data-databases/database-transactions.md) — Guarantees that each transaction sees a consistent database snapshot to maintain full data integrity. ([source](https://www.falkordb.com/blog/glossary/acid-transactions-isolation-data-consistency/))
- [Graph Schema Definition](https://awesome-repositories.com/f/data-databases/graph-data-models/graph-data-modifiers/graph-schema-definition.md) — Defines the structural layout of graphs through manual specifications or automated extraction. ([source](https://www.falkordb.com/blog/what-is-graphrag/))
- [Multi-Hop Traversals](https://awesome-repositories.com/f/data-databases/graph-data-models/path-generation/multi-hop-traversals.md) — Executes complex multi-hop traversals using linear algebra to derive insights from interconnected data. ([source](https://www.falkordb.com/news-updates/incremental-knowledge-graph-indexing/))
- [Graph Querying](https://awesome-repositories.com/f/data-databases/graph-querying.md) — Provides a high-performance driver for executing graph operations, creating nodes, and retrieving subgraphs. ([source](https://www.falkordb.com/news-updates/nfalkordb-v1-dotnet-release/))
- [Hybrid Row-Columnar Operations](https://awesome-repositories.com/f/data-databases/graph-relational-databases/hybrid-analytics/hybrid-row-columnar-operations.md) — Combines row-based precision for metadata access with columnar efficiency for high-performance analytics. ([source](https://www.falkordb.com/blog/hybrid-databases-architecture-oltp-olap-graphs/))
- [Multi-Edge Tensors](https://awesome-repositories.com/f/data-databases/graph-relationship-modeling/multi-edge-tensors.md) — Represents multiple relationships of the same type between two entities using high-performance tensors. ([source](https://www.falkordb.com/blog/edges-in-falkordb/))
- [Graph Traversal](https://awesome-repositories.com/f/data-databases/graph-traversal.md) — Implements high-performance graph traversal by processing thousands of relationships simultaneously using sparse adjacency matrices. ([source](https://www.falkordb.com/blog/hybrid-databases-architecture-oltp-olap-graphs/))
- [In-Memory Databases with Persistence](https://awesome-repositories.com/f/data-databases/in-memory-databases-with-persistence.md) — Combines high-speed memory operations for queries with real-time disk persistence to ensure data durability.
- [Knowledge Graph Construction Tools](https://awesome-repositories.com/f/data-databases/knowledge-graph-construction-tools.md) — Provides tools to build structured knowledge graphs from unstructured text using large language models. ([source](https://www.falkordb.com/blog/knowledge-graph-llm/))
- [Graph Property Indexing](https://awesome-repositories.com/f/data-databases/knowledge-graph-indexers/index-creation/graph-property-indexing.md) — Supports the creation of exact-match or range indexes on properties to accelerate lookups and avoid full scans. ([source](https://www.falkordb.com/blog/cypher-query-cheatsheet-for-developers/))
- [Multi-Tenant Resource Isolation](https://awesome-repositories.com/f/data-databases/multi-tenant-resource-isolation.md) — Maintains independent graph datasets within a single instance to ensure data privacy and resource separation.
- [Structural Data Validators](https://awesome-repositories.com/f/data-databases/structural-data-validators.md) — Maintains explicit relationships between entities to ensure data consistency and structural validity. ([source](https://www.falkordb.com/blog/vectorrag-vs-graphrag-technical-challenges-enterprise-ai-march25/))
- [Vector Search](https://awesome-repositories.com/f/data-databases/vector-search.md) — Executes high-precision queries by integrating vector similarity search within a structured graph system. ([source](https://www.falkordb.com/news-updates/falkordb-integrates-with-cognee-for-enhanced-ai-precision/))
- [Matrix-Native](https://awesome-repositories.com/f/data-databases/vector-search/embedding-generation/matrix-native.md) — Performs mathematical operations over matrix-native structures to generate embeddings without transforming data. ([source](https://www.falkordb.com/blog/hybrid-databases-architecture-oltp-olap-graphs/))
- [Vector Similarity Search](https://awesome-repositories.com/f/data-databases/vector-similarity-search.md) — Implements semantic search using vector indexes and cosine distance metrics. ([source](https://www.falkordb.com/blog/graphrag-workflow-falkordb-langchain/))
- [Graph Community Detection](https://awesome-repositories.com/f/data-databases/anomaly-detection/graph-community-detection.md) — Groups nodes into clusters based on structural proximity using community detection algorithms. ([source](https://www.falkordb.com/blog/finding-connections-with-graph-algorithms-in-complex-networks/))
- [LLM-Optimized Serializers](https://awesome-repositories.com/f/data-databases/data-processing-pipelines/data-serialization/json-serializers/markdown-serializers/llm-optimized-serializers.md) — Serializes graph results into JSON and applies text normalization to optimize token consumption for LLMs. ([source](https://www.falkordb.com/blog/falkordb-udfs-javascript-graph-extension/))
- [Binary Protocols](https://awesome-repositories.com/f/data-databases/data-serialization-formats/binary-serialization-protocols/binary-protocols.md) — Interfaces with clients using standardized binary protocols like RESP and Bolt to ensure driver compatibility.
- [Natural Language to Cypher Conversion](https://awesome-repositories.com/f/data-databases/data-visualization-charts/natural-language-querying/natural-language-to-cypher-conversion.md) — Transforms unstructured user prompts into optimized Cypher graph queries using a language model. ([source](https://www.falkordb.com/blog/building-ai-agents-with-memory-langchain/))
- [Database Protocol Compatibility](https://awesome-repositories.com/f/data-databases/database-protocol-compatibility.md) — Implements standard database wire protocols like RESP and Bolt to ensure compatibility with various graph drivers. ([source](https://docs.falkordb.com/clients.html))
- [Distributed Graph Hosting](https://awesome-repositories.com/f/data-databases/distributed-graph-hosting.md) — Splits datasets across multiple primary servers to balance write loads and storage capacity. ([source](https://www.falkordb.com/blog/scale-out/))
- [Flexible Metadata Stores](https://awesome-repositories.com/f/data-databases/flexible-metadata-stores.md) — Updates entity types and relationships iteratively without requiring rigid data migrations. ([source](https://www.falkordb.com/blog/what-is-hybrid-search-in-ai/))
- [Graph Traversal Strategies](https://awesome-repositories.com/f/data-databases/graph-computing-systems/graph-processing/graph-traversal-strategies.md) — Automatically selects the most efficient traversal strategy by analyzing graph schemas and statistics. ([source](https://www.falkordb.com/blog/graph-database-anti-patterns-ai-performance/))
- [Relational-to-Graph Importers](https://awesome-repositories.com/f/data-databases/graph-data-models/bulk-data-importers/relational-to-graph-importers.md) — Converts relational database schemas into graph structures where tables and columns become nodes. ([source](https://www.falkordb.com/blog/implementing-agentic-memory-graphiti/))
- [CSV Bulk Importers](https://awesome-repositories.com/f/data-databases/graph-data-models/graph-data-loaders/csv-bulk-importers.md) — Provides a high-performance loader for importing large datasets from CSV files into the graph. ([source](https://docs.falkordb.com/))
- [Precomputed Paths](https://awesome-repositories.com/f/data-databases/graph-path-metrics/precomputed-paths.md) — Optimizes query performance by precomputing distances between frequently queried node pairs. ([source](https://www.falkordb.com/blog/reduce-graphrag-indexing-costs/))
- [High Availability Configurations](https://awesome-repositories.com/f/data-databases/high-availability-configurations.md) — Implements multi-zone deployment and automatic failover to ensure database service continuity. ([source](https://www.falkordb.com/plans/))
- [Incremental Indexing Engines](https://awesome-repositories.com/f/data-databases/incremental-indexing-engines.md) — Updates the graph store with new data incrementally without requiring a full re-index of the database. ([source](https://www.falkordb.com/blog/graph-database-ai-agents/))
- [Ontology Configurations](https://awesome-repositories.com/f/data-databases/knowledge-graph-retrieval/ontology-configurations.md) — Establishes structural rules and schemas for the graph database through manual or automated processes. ([source](https://www.falkordb.com/news-updates/graphrag-sdk-release-simplifies-rag-with-graph-databases/))
- [Automated Ontology Extraction](https://awesome-repositories.com/f/data-databases/knowledge-graph-retrieval/ontology-configurations/automated-ontology-extraction.md) — Extracts schemas and relationships directly from existing graphs to eliminate manual definition. ([source](https://www.falkordb.com/news-updates/graphrag-sdk-0-5-knowledge-graph-integration/))
- [Read-Write Splitting](https://awesome-repositories.com/f/data-databases/multi-master-replication/read-write-splitting.md) — Separates read replicas from the primary write server to scale different workload types independently. ([source](https://www.falkordb.com/blog/redisgraph-eol-migration-guide/))
- [Multi-Modal Data Management](https://awesome-repositories.com/f/data-databases/multi-modal-data-management.md) — Integrates diverse media types including text, images, and video within a centralized graph structure. ([source](https://www.falkordb.com/case-studies/how-virtuous-ai-created-a-high-performance-multi-modal-data-store-for-ethical-ai-development/))
- [Multi-Tenant Data Management](https://awesome-repositories.com/f/data-databases/multi-tenant-data-management.md) — Hosts and isolates thousands of independent graph datasets within a single instance.
- [Embedded Graph Engines](https://awesome-repositories.com/f/data-databases/object-storage/graph-storage-engines/embedded-graph-engines.md) — Allows initializing the graph engine as an isolated sub-process to eliminate network latency. ([source](https://www.falkordb.com/blog/falkordblite-embedded-python-graph-database/))
- [Asynchronous Replication Management](https://awesome-repositories.com/f/data-databases/primary-replica-replication/asynchronous-replication-management.md) — Syncs data changes from primary servers to replicas using binary change sets for high availability.
- [Visual Data Explorers](https://awesome-repositories.com/f/data-databases/redis-clients/visual-data-explorers.md) — Provides a graphical interface for browsing and managing datasets to simplify discovery and navigation. ([source](https://www.falkordb.com/blog/falkordb-browser-v070-update/))
- [Subgraph Indexes](https://awesome-repositories.com/f/data-databases/search-indexing-technologies/search-indexing/search-and-indexing/subgraph-indexes.md) — Utilizes tree-decomposition and trie structures for rapid structural pattern matching and resolution. ([source](https://www.falkordb.com/blog/reduce-graphrag-indexing-costs/))
- [Graph Analytics](https://awesome-repositories.com/f/data-databases/search-indexing-technologies/search-indexing/search-information-retrieval/search-engine-platforms/search-and-analytics-engines/graph-analytics.md) — Provides a suite of algorithms for label propagation and centrality to detect communities and pivot points. ([source](https://www.falkordb.com/blog/security-graphs-cloud-entitlements-guide/))
- [B-Tree](https://awesome-repositories.com/f/data-databases/storage-engines/b-tree.md) — Uses B-Tree structures to provide logarithmic-time complexity for range and exact-match lookups on node properties.
- [Natural Language Subgraph Grounding](https://awesome-repositories.com/f/data-databases/subgraph-extractions/natural-language-subgraph-grounding.md) — Retrieves specific subgraphs based on natural language questions to provide exact structural context for LLM responses. ([source](https://www.falkordb.com/blog/code-graph-is-the-secret/))

### Artificial Intelligence & ML

- [Graph-Based Context Retrieval](https://awesome-repositories.com/f/artificial-intelligence-ml/graph-based-context-retrieval.md) — Combines relationship retrieval with textual data to provide precise structural context and reduce LLM hallucinations. ([source](https://www.falkordb.com/blog/graph-neural-networks-llm-integration/))
- [Graph Retrieval Augmented Generation](https://awesome-repositories.com/f/artificial-intelligence-ml/graph-retrieval-augmented-generation.md) — Combines structured graph exploration with vector search to provide high-fidelity context for retrieval-augmented generation. ([source](https://www.falkordb.com/blog/graph-database-deployment-guide/))
- [Agent Memory Stores](https://awesome-repositories.com/f/artificial-intelligence-ml/agent-memory-stores.md) — Implements persistent storage for agent interaction history and factual knowledge to enable long-term recall. ([source](https://www.falkordb.com/blog/graph-database-ai-agents/))
- [Context Maintenance](https://awesome-repositories.com/f/artificial-intelligence-ml/agent-task-orchestration/context-maintenance.md) — Maintains session state within graph structures to provide AI agents with immediate historical context. ([source](https://www.falkordb.com/blog/graphrag-small-llms-bank-of-america/))
- [AI Agent Orchestrators](https://awesome-repositories.com/f/artificial-intelligence-ml/agentic-systems-frameworks/agent-orchestration-multi-agent/coordination-and-routing/ai-agent-orchestrators.md) — Implements systems to coordinate specialized AI agents through a central orchestrator for complex, multi-domain tasks. ([source](https://www.falkordb.com/blog/introducing-graphrag-sdk-v0-2-expanding-ai-and-knowledge-graph-horizons/))
- [Graph-Aware Agent Frameworks](https://awesome-repositories.com/f/artificial-intelligence-ml/ai-agent-frameworks/graph-aware-agent-frameworks.md) — Provides a framework for building AI agents that leverage graph traversals and vector search for personalized, context-aware responses. ([source](https://www.falkordb.com/))
- [Model Context Protocol Integrations](https://awesome-repositories.com/f/artificial-intelligence-ml/ai-assistant-integrations/model-context-protocol-integrations.md) — Provides standardized interfaces using the Model Context Protocol to expose graph data and schemas to AI models. ([source](https://www.falkordb.com/blog/graph-database-ai-agents/))
- [AI Knowledge Management](https://awesome-repositories.com/f/artificial-intelligence-ml/ai-knowledge-management.md) — Supplies interconnected data and structured knowledge to improve the accuracy of generative AI responses. ([source](https://www.falkordb.com/blog/graph-database-deployment-guide/))
- [AI-to-Graph Query Translators](https://awesome-repositories.com/f/artificial-intelligence-ml/ai-to-graph-query-translators.md) — Translates standard model requests into graph queries, allowing AI systems to retrieve and manipulate interconnected data. ([source](https://www.falkordb.com/blog/mcp-integration-falkordb-graphrag/))
- [Hybrid Search Systems](https://awesome-repositories.com/f/artificial-intelligence-ml/hybrid-search-systems.md) — Provides multi-modal retrieval by combining vector, graph, and lexical search methods. ([source](https://www.falkordb.com/blog/graph-rag-vs-vector-rag-solving-gartner-challenges/))
- [Orchestration Store Integrations](https://awesome-repositories.com/f/artificial-intelligence-ml/llm-integration-frameworks/orchestration-store-integrations.md) — Provides adapters that allow the graph store to serve as a pluggable memory backend within LLM orchestration frameworks. ([source](https://www.falkordb.com/try-free/))
- [LLM Provider Integrations](https://awesome-repositories.com/f/artificial-intelligence-ml/llm-provider-integrations.md) — Connects multiple generative AI model providers to the system to enable flexibility across use cases. ([source](https://www.falkordb.com/blog/introducing-graphrag-sdk-v0-2-expanding-ai-and-knowledge-graph-horizons/))
- [Graph Pattern Matching](https://awesome-repositories.com/f/artificial-intelligence-ml/pattern-matching-engines/graph-pattern-matching.md) — Implements engines for matching complex structural shapes and relationship sequences within the graph. ([source](https://www.falkordb.com/blog/graph-database-guide/))
- [Retrieval Path Traceability](https://awesome-repositories.com/f/artificial-intelligence-ml/reasoning-models/reasoning-trace-retrievers/retrieval-path-traceability.md) — Provides traceable pathways for information retrieval to ensure AI-generated decisions are explainable and grounded. ([source](https://www.falkordb.com/blog/kpmg-ai-report-graphrag-ai-agents/))
- [Text-to-Graph Transformers](https://awesome-repositories.com/f/artificial-intelligence-ml/text-to-graph-transformers.md) — Transforms unstructured text into structured graph formats to ground AI models with factual data. ([source](https://www.falkordb.com/blog/best-database-for-knowledge-graphs-falkordb-neo4j/))
- [Vector Embeddings](https://awesome-repositories.com/f/artificial-intelligence-ml/vector-embeddings.md) — Stores and updates vector embeddings from AI algorithms to improve foundational model training. ([source](https://www.falkordb.com/case-studies/how-virtuous-ai-created-a-high-performance-multi-modal-data-store-for-ethical-ai-development/))

### Education & Learning Resources

- [GraphRAG Integrations](https://awesome-repositories.com/f/education-learning-resources/ai-development-curricula/graphrag-integrations.md) — Integrates knowledge graphs with vector search to provide structured context for LLM retrieval.
- [Graph-LLM Integration SDKs](https://awesome-repositories.com/f/education-learning-resources/ai-development-curricula/graphrag-integrations/graph-llm-integration-sdks.md) — Ships a specialized SDK for combining graph-based reasoning with generative AI tasks. ([source](https://docs.falkordb.com/))

### Networking & Communication

- [Bolt Protocol Support](https://awesome-repositories.com/f/networking-communication/bolt-protocol-support.md) — Uses the Bolt protocol for compatibility with industry-standard graph clients and tools. ([source](https://www.falkordb.com/blog/falkordb-4-0-beta-released-major-improvements-and-critical-bug-fixes/))

### Scientific & Mathematical Computing

- [Matrix-Based Graph Traversals](https://awesome-repositories.com/f/scientific-mathematical-computing/linear-algebra-routines/matrix-based-graph-traversals.md) — Performs multi-hop graph queries by treating relationship tensors as matrix multiplication for sub-millisecond latency.
- [Sparse Matrix Storage](https://awesome-repositories.com/f/scientific-mathematical-computing/sparse-matrix-storage.md) — Uses compressed sparse adjacency matrices to store relationships and execute traversals via linear algebra.
- [Shortest Path Algorithms](https://awesome-repositories.com/f/scientific-mathematical-computing/numerical-mathematical-foundations/algorithms-and-complexity/algorithms/graph-processing/shortest-path-algorithms.md) — Calculates the most efficient path and minimum number of hops between two entities. ([source](https://www.falkordb.com/blog/cypher-query-cheatsheet-for-developers/))

### Security & Cryptography

- [Multi-Tenant Isolation Layers](https://awesome-repositories.com/f/security-cryptography/multi-tenant-isolation-layers.md) — Maintains strict isolation between different graph instances to support multi-tenant architectures. ([source](https://www.falkordb.com/blog/cypher-query-language/))
- [Database-Level Isolation](https://awesome-repositories.com/f/security-cryptography/multi-tenant-isolation-layers/application-data-isolation/database-level-isolation.md) — Ensures data privacy by hosting multiple independent graph datasets within a single instance. ([source](https://www.falkordb.com/comparison/memgraph-vs-neo4j-performance-and-architecture-for-production-workloads/))
- [Graph-Based Fraud Detection](https://awesome-repositories.com/f/security-cryptography/graph-based-fraud-detection.md) — Analyzes relationships between data points instantly to identify fraudulent activities and rings. ([source](https://www.falkordb.com/use-cases/))
- [Network Encryption](https://awesome-repositories.com/f/security-cryptography/network-encryption.md) — Secures data in transit using TLS encryption for all network communications. ([source](https://www.falkordb.com/plans/))
- [Relationship Pattern Analysis](https://awesome-repositories.com/f/security-cryptography/relationship-pattern-analysis.md) — Identifies unusual connections between entities to flag suspicious behaviors like fraud rings. ([source](https://www.falkordb.com/blog/graph-database-deployment-guide/))

### Software Engineering & Architecture

- [Matrix-Based Traversals](https://awesome-repositories.com/f/software-engineering-architecture/entity-management/graph-traversal/matrix-based-traversals.md) — Processes directed and undirected relationships using separate matrix structures for ultra-low latency. ([source](https://www.falkordb.com/blog/graph-database-anti-patterns-ai-performance/))

### User Interface & Experience

- [OpenCypher Implementations](https://awesome-repositories.com/f/user-interface-experience/chart-components/query-bound-chart-renderers/data-query-execution-apis/opencypher-implementations.md) — Processes graph data using the OpenCypher query language, including support for advanced proprietary extensions. ([source](https://cdn.jsdelivr.net/gh/falkordb/falkordb@master/README.md))
- [Relationship Visualizations](https://awesome-repositories.com/f/user-interface-experience/relationship-visualizations.md) — Renders graph data as dynamic, interactive visualizations to make connections and patterns easier to understand. ([source](https://www.falkordb.com/blog/falkordb-browser-v070-update/))

### Part of an Awesome List

- [Knowledge Graph Embeddings](https://awesome-repositories.com/f/awesome-lists/ai/knowledge-graph-embeddings.md) — Transforms graph structures into vector spaces to enable semantic similarity search. ([source](https://www.falkordb.com/blog/reduce-graphrag-indexing-costs/))

### Development Tools & Productivity

- [Graph Resource Discovery Interfaces](https://awesome-repositories.com/f/development-tools-productivity/ai-assistant-integrations/graph-resource-discovery-interfaces.md) — Provides a standardized interface for AI tools to discover graph resources, inspect schemas, and execute queries. ([source](https://www.falkordb.com/blog/text-to-cypher-natural-language-graph-queries/))
- [Binary Change Set Synchronization](https://awesome-repositories.com/f/development-tools-productivity/change-tracking/row-level-change-logs/change-data-capture/binary-change-set-synchronization.md) — Transmits binary representations of data changes from primary servers to replicas for high availability. ([source](https://www.falkordb.com/blog/scale-out/))

### DevOps & Infrastructure

- [Cluster Scaling Orchestrators](https://awesome-repositories.com/f/devops-infrastructure/cluster-scaling-orchestrators.md) — Dynamically adjusts cluster capacity by adding new nodes and rebalancing data without downtime. ([source](https://www.falkordb.com/blog/nosql-database-modern-architecture-scalability/))
- [Read Throughput Scaling](https://awesome-repositories.com/f/devops-infrastructure/read-throughput-scaling.md) — Increases read operation volume by adding multiple replicas per master server. ([source](https://www.falkordb.com/news-updates/multigraph-topology-isolation-linear-scale/))

### Web Development

- [Graph-Based Fact Validation](https://awesome-repositories.com/f/web-development/client-side-input-validators/schema-based-response-validation/ai-output-validation/graph-based-fact-validation.md) — Cross-references generated text against structured graph data to detect and correct factual inconsistencies. ([source](https://www.falkordb.com/blog/knowledge-graph-llm/))