Cypher

Neo4j uses the Cypher query language.

FalkorDB is a Redis-based graph database that supports the property graph model and the OpenCypher query language, includes a built-in graph algorithm module, and stores graph data as sparse matrices.

Cypher is a declarative query language designed by Neo4j for property graph databases, and FalkorDB is compatible with a subset of its syntax.

The solution employs agentic retrieval to give the retrieval process active reasoning capabilities, and uses the Cypher query language to perform structured queries on the graph.

The graph structure of a knowledge graph natively supports graph traversal algorithms, allowing queries such as 'find all places that call function A' to complete in milliseconds.

OpenCypher is the open-source specification of Cypher, the de facto standard declarative query language in the graph database field, using ASCII-art-style pattern matching to describe graph structures; MERGE is used for idempotent writes and MATCH only reads; parameterized queries avoid injection risks and reuse execution plans.

The CONTAINS operator in Cypher performs a case-sensitive substring containment check, not full-text retrieval or semantic search.

GraphGuard includes a built-in library of more than 20 parameterized Cypher queries, of which three, including TRACE_INCIDENT_PATH, form the pillars of the investigation engine.

Cypher is a graph query language proposed by Neo4j, and FalkorDB is compatible with its syntax.

Each of Pedigree's flags is essentially a Cypher query: traversing upward along the lineage up to six levels, looking for obligations carried by the parent license but missing from the child license.

For §4.2.1, the Cypher query returns the chain: email→thread→draft -24→-25→-26→-27→-28→RFC 8446→§4.2.1.

At data ingestion, the team built the CO_DISCUSSED projection with Cypher (an edge is created when two people speak in the same thread), then ran algo.pageRank and algo.betweenness on it.

Each answer card has a panel listing the Cypher actually executed by FalkorDB, the parameters, the row count, and the database's self-reported run_time_ms; for the §4.2.1 question, for example, there are 4 queries totaling under 1 millisecond on a laptop.

Ratified's answers come from graph traversal rather than model memory; no model writes Cypher, and a deterministic router picks one handwritten query for each question; if the traversal returns nothing, the card plainly says "the graph doesn't know."

CircuitDoctor uses Cypher's shortestPath query to search for a path between two pins over ON_NET and THROUGH relationships, and highlights it once found.

In the LLM tool-calling paradigm, the model only outputs structured parameters, which the host program validates against a predefined JSON Schema and binds to precompiled Cypher query templates, so user or alert text content is only ever passed in as parameter values, making it naturally immune to Cypher injection

NexusOps's core design principle is not letting the LLM hand-write raw Cypher, but calling through a schema-validated, parameterized graph tool layer, thereby guaranteeing zero query injection, no syntax errors, and deterministic evidence paths

In FalkorDB, permissions are determined by traversing up to 8 levels of nested groups along MEMBER_OF relationships via Cypher queries, with a median query latency of 0.45 milliseconds.

Ownership Lens's LLM never writes raw Cypher; instead, it chooses from six typed, parameterized, read-only MCP tools: resolve_entity, ownership_chain, shared_links, path_between, cluster_view, and recall_cases.

ClauseRipple wraps 10 graph queries as MCP tools, so the planner only needs to choose tool names and parameters without generating Cypher.