FalkorDB
Official@falkordb · Israel
The fastest way to your knowledge
Agent Skills by FalkorDB
Showing 34 vetted skills indexed across 2 GitHub repositories.
FalkorDBfalkordb-skills
Provide FalkorDB guidance for Cypher queries, Docker deployment, and data migration.
FalkorDBLoad a JavaScript UDF library
Registers custom JavaScript User-Defined Functions in FalkorDB via falkor.register for Cypher queries.
FalkorDBList UDF libraries (with code)
List loaded FalkorDB UDF libraries and retrieve their JavaScript source code.
FalkorDBDelete or flush UDF libraries
Delete or flush FalkorDB user-defined function libraries.
FalkorDBRespect UDF limitations
Enforce read-only pure JavaScript patterns for FalkorDB UDF implementations.
FalkorDBCall a UDF from Cypher
Execute registered FalkorDB JavaScript UDFs within Cypher queries.
FalkorDBMigrate Neptune to FalkorDB
Convert AWS Neptune CSV exports into FalkorDB-ready CSV files.
FalkorDBMigrate Neo4j to FalkorDB
Extracts Neo4j nodes and relationships into CSV files for FalkorDB import.
FalkorDBUse Bulk Loader from CSV
Load CSV files into FalkorDB graph databases using the bulk loader utility.
FalkorDBMigrate SQL to FalkorDB
Migrate relational SQL data from PostgreSQL, MySQL, and BigQuery into FalkorDB graph databases.
FalkorDBRun the browser separately against a server
Configure separate FalkorDB browser and server containers with environment variables.
FalkorDBRun FalkorDB with browser for local development
Run FalkorDB containers with browser UI for local development.
FalkorDBSet authentication using REDIS_ARGS
Configure Redis authentication via REDIS_ARGS for FalkorDB Docker deployments.
FalkorDBRun server-only for production-style usage
Deploy FalkorDB server-only containers without the browser interface.
FalkorDBSet module config using FALKORDB_ARGS
Configure FalkorDB module settings via FALKORDB_ARGS environment variables.
FalkorDBUse Docker Compose for repeatable local stacks
Define repeatable FalkorDB container environments with Docker Compose configuration files.
FalkorDBUse MERGE to avoid duplicate nodes
Apply MERGE upserts to prevent duplicate nodes in FalkorDB graphs.
FalkorDBApply FalkorDB Cypher limitations correctly
Review FalkorDB Cypher queries for indexing constraints and full-scan risks.
FalkorDBVerify index usage
Verify FalkorDB index usage by inspecting GRAPH.EXPLAIN execution plans.
FalkorDBRun safe read-only queries
Execute read-only GRAPH.RO_QUERY operations in FalkorDB to prevent graph modifications.
FalkorDBCreate range indexes for exact/range lookups
Create FalkorDB range indexes on node properties for exact and range lookups.
FalkorDBProfile query runtime behavior
Profile FalkorDB graph queries with GRAPH.PROFILE to expose runtime execution statistics.
FalkorDBTrack slow queries
Identify slow FalkorDB queries using slowlog commands for performance monitoring.
FalkorDBCreate and query vector indexes
Create and query HNSW vector indexes in FalkorDB graph databases.
Frequently Asked Questions About FalkorDB
FAQPage SchemaWhat specific data migration tasks does FalkorDB support?▼
FalkorDB enables migration from AWS Neptune, Neo4j, and relational systems like PostgreSQL, MySQL, and BigQuery. It utilizes CSV-based extraction and bulk loading processes to transform existing node and relationship data into the native graph format.
Which technical personas benefit from these capabilities?▼
Database administrators, data engineers, and backend developers working with complex relationship-heavy datasets benefit from these capabilities. The platform is designed for those requiring high-speed graph traversal, vector similarity search, and custom JavaScript-based logic within their data layer.
How are FalkorDB instances deployed and configured?▼
Deployment is managed via containerized environments using Docker and Docker Compose. Users configure server-only or browser-integrated stacks by setting environment variables such as REDIS_ARGS and FALKORDB_ARGS to manage authentication, module settings, and memory usage.
What are the prerequisites for implementing vector search?▼
Implementing vector search requires the creation of HNSW indexes on node properties. Users must ensure their graph schema supports these indexes and utilize Cypher-based query patterns to perform similarity lookups against the indexed vector data.