Agentifact assessment — independently scored, not sponsored. Last verified Apr 2, 2026.

Data & RetrievalNEEDS APPROVAL

Neo4j

Graph database with native vector index support (HNSW). Combines graph traversal with semantic similarity search for GraphRAG patterns. Entity relationships are stored alongside vector embeddings for multi-hop reasoning.

Visit Neo4jVerified · April 2, 2026

✓ Our Verdict

Viable option — review the tradeoffs

Use Case

You need to surface real-time personalized recommendations across millions of products and customer relationships without query latency killing user experience.

SolutionNeo4j's native graph traversal executes relationship queries in milliseconds rather than minutes, letting you model customer-product affinities and collaborative filtering as direct graph paths instead of expensive SQL joins.

SetupLoad customer interaction data and product metadata into Neo4j; define relationship types (purchased, viewed, rated); optionally add vector embeddings for content-based filtering. Typical deployment: managed cloud instance or self-hosted with replication for HA.

Walmart and eBay report 1000x+ faster queries than relational predecessors with 10-100x less code. Real-time serving at scale works, but you'll need to tune graph traversal depth and implement caching for hot paths. Cold starts on large graphs can still be slow.

Query latency and code simplicity are critical here.

Use Case

You're building fraud detection that must correlate events (logins, transactions, card registrations) across millions of entities in real time to catch schemes before they cause damage.

SolutionNeo4j lets you model fraud patterns as graph structures—linking accounts, devices, merchants, and transactions—then traverse these relationships instantly to detect anomalies and flag suspicious activity clusters that relational queries would struggle to express.

SetupIngest transaction and identity data; define entity nodes (accounts, cards, IPs, merchants) and relationship types (owns, used_at, transferred_to); implement trigger-based queries on login/order/registration events. Requires streaming pipeline for real-time event ingestion.

Credit card companies and money transfer agents use Neo4j for this. Detection latency is sub-second for most patterns. The tradeoff: you need to pre-model your fraud hypotheses as graph patterns; discovering entirely novel fraud types requires schema changes.

Real-time responsiveness and relationship expressiveness matter most.

Use Case

You need to manage complex hierarchical master data (employee org charts, product hierarchies, access control trees) where recursive queries in SQL become prohibitively slow and hard to maintain.

SolutionNeo4j natively represents hierarchies as parent-child relationships. Queries that would require recursive SQL JOINs become simple graph traversals, and the schema flexibly adapts as your hierarchy evolves without expensive migrations.

SetupModel entities as nodes and parent-child/reporting relationships as edges. For identity and access management (IAM), add permission and role nodes. Telenor reduced multi-minute IAM queries to milliseconds after migrating from relational.

Excellent for static or slowly-changing hierarchies. Performance scales well to tens of millions of nodes. Caveat: if your hierarchy is extremely wide (millions of direct children per node) or you need complex aggregations across levels, you may still need supplementary analytics.

Query speed and schema flexibility are the wins.

Use Case

You're building a knowledge graph or investigative tool that must link disparate entities (people, companies, transactions, documents) across silos to reveal hidden patterns and relationships.

SolutionNeo4j excels at unifying fragmented data into a single queryable graph. The Panama Papers investigation used Neo4j to link identities and activities across stolen records—a task practically impossible in relational databases. Vector embeddings (via HNSW) add semantic search on top of structural relationships.

SetupETL entity and relationship data from multiple sources into a unified schema; define node types and relationship semantics. Add vector embeddings for document/entity similarity if doing semantic search. Requires careful data governance to handle duplicates and entity resolution.

Powerful for exploratory queries and multi-hop reasoning. Builders report intuitive query language (Cypher) that non-technical stakeholders can understand. Performance depends heavily on graph density and query complexity; very dense graphs with deep traversals can slow down.

Relationship expressiveness and semantic search integration matter.

Limitation — major

ACID transactions limited to single-machine writes in most deployments

Neo4j's transactional guarantees are strong on a single instance but weaker in clustered setups. If you need strict ACID semantics across a distributed write workload, you'll hit consistency edge cases that relational databases handle more transparently.

Trust Breakdown

72

Trust scoreSolid

AGENT

Autonomous workflow delegation

TRUST

Transparency & verification

INTEROP

Protocol compatibility breadth

SECURITY

Security controls & audit trail

DOCS

Documentation completeness

How these scores are calculated →

What It Actually Does

In Plain English

Neo4j stores data as connected nodes and relationships, like a map of how things link together, instead of rigid tables. This makes it fast for finding patterns in complex networks, such as social connections or recommendations.[1][2][3]

Fit Assessment

Best for

✓database-query
✓knowledge-retrieval

72

Neo4j

Solid · 72/100

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Score Breakdown

AGENT

Autonomous workflow delegation

TRUST

Transparency & verification

INTEROP

Protocol compatibility breadth

SECURITY

Security controls & audit trail

DOCS

Documentation completeness

Protocol Support

MCP✓

A2A—

A2H—

REST API✓

Agent-callable✓

Capabilities

Transaction capable✓

ACP support—

Audit trace—

Governance

permission-scoping
api-key-auth

Pricing

Freemium

Free community edition, Enterprise from $XX/mo (contact sales)

Workflow Fit

database-queryknowledge-retrieval

Related Concepts

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