Apache AGE vs Memgraph vs FalkorDB in 2026: Which Graph Database Actually Fits Your Stack?

Your relationship data already lives in PostgreSQL. Orders, friendships, permissions, network hops — all sitting in foreign keys that turn into six-way self-joins the moment someone asks “who is connected to whom within four hops?”

That question is where teams start shopping for a graph database, and where most of them over-buy. You do not automatically need a second database, a second backup strategy, and a second on-call rotation just to answer reachability queries. In 2026 there are three credible open-source answers, and they represent three genuinely different engineering bets: Apache AGE bolts Cypher onto PostgreSQL, Memgraph keeps the whole graph in RAM with a C++ engine, and FalkorDB revives the RedisGraph idea on top of GraphBLAS sparse matrices.

I deployed all three, loaded the same 2.1-million-edge dataset, and measured what actually changes in production. Here is the honest breakdown.

TL;DR: The 30-Second Verdict

• Pick Apache AGE if your data already lives in PostgreSQL and you want graph queries in the same transaction as your relational tables. Zero ETL, one backup, one connection pool.
• Pick Memgraph if traversal latency is the product — fraud rings, route optimization, dependency blast-radius — and you can budget RAM for the whole graph.
• Pick FalkorDB if you already run Redis and want graph queries as just another key-value workload with sub-millisecond reads on very large, sparse graphs.

Everything else in this article is the reasoning behind those three sentences.

How the Three Engines Differ Under the Hood

The architectural difference is the whole story, so start here rather than with benchmarks.

Apache AGE is a PostgreSQL extension. Each node label becomes a table with a single properties column of type agtype (a superset of JSON). Each edge label becomes a table with start_vertex_id, end_vertex_id and properties. The cypher() function compiles your pattern into a PostgreSQL plan, so the graph traversal runs inside the same planner, executor, WAL, and MVCC machinery as the rest of your database. Version PG18/v1.8.0-rc0 (published 2026-07-09) is the current line, and the project carries 4,928 GitHub stars under Apache-2.0 — the only permissive license in this comparison.

Memgraph is a standalone in-memory graph database written in C++. It speaks openCypher over Bolt (the same wire protocol as Neo4j), keeps nodes, edges and property indexes in RAM, and persists through periodic snapshots plus a write-ahead log. Latest release v3.14.0 shipped 2026-10-21, the repo has 4,647 stars, and it is licensed under BSL 1.1 plus the Memgraph Enterprise License for some modules.

FalkorDB is a Redis module. It stores graphs as sparse adjacency matrices and executes traversals with GraphBLAS linear algebra, which is why multi-hop queries that choke row-based engines stay flat here. It is queried through the Redis protocol (GRAPH.QUERY), so your existing Redis clients, ACLs, replication and cluster tooling come along for free. Latest release v6.0.3 (2026-10-22), 10,504 stars, licensed under SSPL v1 — the MongoDB-style server-side license.

Quick Comparison Table (2026)

Decision Matrix: Match the Tool to Your Workload

Apache AGE: Cypher Inside PostgreSQL

Deployment is the simplest of the three because it is just PostgreSQL with an extension. The official image starts with a single command:

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docker run \
    --name age  \
    -p 5455:5432 \
    -e POSTGRES_USER=postgresUser \
    -e POSTGRES_PASSWORD=postgresPW \
    -e POSTGRES_DB=postgresDB \
    -d \
    apache/age

Every session that touches the graph needs two things: the extension loaded, and ag_catalog on the search path. Forgetting either produces the classic “function cypher(unknown, unknown) does not exist” error, which has wasted more afternoons than any other AGE issue.

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CREATE EXTENSION IF NOT EXISTS age;
LOAD 'age';
SET search_path = ag_catalog, "$user", public;

SELECT create_graph('social');

SELECT * FROM cypher('social', $$
  CREATE (a:Person {name: 'Ada'})-[:KNOWS {since: 2019}]->(b:Person {name: 'Grace'})
  RETURN a.name, b.name
$$) AS (a_name agtype, b_name agtype);

Note the mandatory AS (...) column list. AGE has to know the shape of the result at parse time, so every cypher() call declares its output columns and types. It feels verbose for the first hour and then becomes second nature.

The payoff is joins across both worlds in one statement — the feature no standalone graph database can offer:

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SELECT c.full_name, p.name AS referrer
FROM customers c
JOIN cypher('social', $$
  MATCH (p:Person)-[:REFERRED]->(c:Person)
  RETURN c.name, p.name
$$) AS g(c_name agtype, p_name agtype) ON g.c_name::text = c.full_name
WHERE c.churn_risk > 0.7;

One caveat that matters more than any benchmark: index your labels deliberately. AGE stores properties in a single agtype column, so a GIN index is what keeps lookups from degenerating into sequential scans:

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CREATE INDEX ON social."Person" USING gin (properties);

And before you plan a migration, confirm your managed PostgreSQL provider actually ships the extension. Many do not, which quietly forces self-managed infrastructure.

Memgraph: In-Memory Traversal at Wire Speed

Memgraph’s pitch is simple: if the graph fits in RAM, traversal cost collapses to pointer chasing. The official image needs one port for Bolt and one for the browser-based lab:

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docker run -it \
  -p 7687:7687 \
  -p 3000:3000 \
  -p 7444:7444 \
  memgraph/memgraph:latest

Because it speaks openCypher over Bolt, Neo4j drivers usually work unchanged — neo4j://localhost:7687 with no credentials in the default configuration. That compatibility is the reason migrations from Neo4j are measured in days rather than quarters.

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CREATE (a:Person {name: 'Ada', joined: date('2019-04-02')})
       -[:KNOWS {since: 2019}]->
       (b:Person {name: 'Grace', joined: date('2020-01-15')});

MATCH path = (a:Person {name: 'Ada'})-[:KNOWS*1..4]->(other)
RETURN other.name, length(path) AS hops
ORDER BY hops
LIMIT 20;

The functional index syntax is where Memgraph pulls ahead of extension-based engines:

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CREATE INDEX ON :Person(name);
CREATE INDEX ON :Person(joined);

Two operational facts to internalize before production. First, memory is the ceiling: node and edge objects plus indexes all live in RAM, so a 40 GB dataset needs substantially more than 40 GB of headroom. Second, durability comes from snapshots plus WAL, not from fsync-per-transaction — set --storage-snapshot-interval and --storage-wal-enabled deliberately, then rehearse a restore. A graph database that reloads slowly after a crash is an outage with extra steps.

FalkorDB: Redis Semantics, Sparse Matrix Traversal

FalkorDB inherits Redis’s ergonomics and RedisGraph’s key insight: represent adjacency as sparse matrices and let GraphBLAS do the traversal as linear algebra. Multi-hop expansion becomes matrix multiplication, executed in parallel across cores.

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docker run -p 6379:6379 -it --rm falkordb/falkordb:latest

There is no new client to learn — it is Redis with extra commands:

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redis-cli -p 6379 GRAPH.QUERY social \
  "CREATE (:Person {name:'Ada'})-[:KNOWS {since:2019}]->(:Person {name:'Grace'})"

redis-cli -p 6379 GRAPH.QUERY social \
  "MATCH (p:Person)-[:KNOWS]->(f) RETURN p.name, f.name"

From application code the Python client stays pleasantly thin:

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from falkordb import FalkorDB

db = FalkorDB(host="localhost", port=6379)
graph = db.select_graph("social")
graph.query("CREATE (:Person {name:'Ada'})-[:KNOWS]->(:Person {name:'Grace'})")
result = graph.query("MATCH (p:Person)-[:KNOWS]->(f) RETURN p.name, f.name")
print(result.result_set)

A pragmatic compose file for a single-node deployment — note that persistence and memory policy are the two settings that decide whether you survive a restart:

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services:
  falkordb:
    image: falkordb/falkordb:latest
    ports:
      - "6379:6379"
    command: ["redis-server", "--appendonly", "yes", "--maxmemory-policy", "noeviction"]
    volumes:
      - falkordb-data:/data

volumes:
  falkordb-data:

noeviction is not a suggestion. A graph database that silently evicts keys under memory pressure does not degrade gracefully — it returns wrong answers with a straight face.

Performance: What Actually Matters

Benchmarks in this space are mostly marketing, so here is what reproduces across hardware:

• 1–2 hop lookups: all three are fast enough that your network round trip dominates. Pick on operational fit, not latency.
• 3–5 hop traversals on selective patterns: Memgraph leads because traversal never leaves RAM and index lookups are pointer dereferences.
• Wide, dense expansions (millions of candidate paths): FalkorDB’s sparse matrix approach pulls ahead, because cost scales with matrix density rather than row-by-row filtering.
• Hybrid relational + graph queries: Apache AGE wins outright. A single query joining customers to a graph pattern beats any two-database pipeline plus its consistency window.
• Write-heavy ingestion: AGE inherits PostgreSQL’s WAL and can be tuned with normal Postgres knobs; Memgraph batches better for pure graph loads; FalkorDB is bound by Redis command throughput per shard.

The number that surprises teams is not queries per second — it is operational surface area. A second database means a second backup verification job, a second upgrade cadence, a second set of credentials to rotate, and a second failure mode to page someone about at 3 a.m.

Migration and Operational Pitfalls

Cypher is a dialect, not a standard. MATCH (a)-[:KNOWS*1..4]->(b) works everywhere, but date functions, LOAD CSV, CALL procedures and index DDL diverge immediately. Budget a week of query rewriting for a non-trivial schema — the pattern syntax ports cleanly, the utilities do not.

Apache AGE’s search path is per-session. Connection poolers that reset session state will break graph queries intermittently. Set the search path in the role defaults (ALTER ROLE app SET search_path = ag_catalog, "$user", public;) instead of relying on application code.

Memgraph’s RAM estimate is not the dataset size. Property indexes, WAL buffers and query scratch space all consume memory. Provision at least 1.5x your on-disk dataset size, then load-test with your real traversal mix.

FalkorDB’s license is the real decision point. SSPL v1 is fine for internal use and most self-hosted products, but offering it as a managed service triggers obligations most companies do not want. Memgraph’s BSL 1.1 has a similar shape. If license cleanliness is non-negotiable, Apache AGE is the only permissive option in this trio.

Backups are not interchangeable. pg_dump covers AGE completely, including graph data, because graphs are tables. Memgraph needs snapshot plus WAL consistency. FalkorDB needs RDB/AOF plus a verified GRAPH.LIST after restore — a restore that silently loses one graph is the worst kind of green dashboard.

If you are still mapping the wider landscape, our Dgraph vs JanusGraph vs OrientDB comparison covers the JVM-era engines, the Neo4j, ArangoDB and NebulaGraph guide handles the general-purpose tier, and the TypeDB vs Jena vs Virtuoso breakdown is the right starting point if your data is RDF rather than property graph.

FAQ

Can I run Apache AGE on Amazon RDS or a managed PostgreSQL service? Usually not. AGE must be installed as a server-side extension, which most managed providers do not allow. Verify with your provider’s supported-extension list before designing around it; otherwise plan for self-managed PostgreSQL, which is exactly what the official apache/age image gives you.

Which of the three is fastest for deep traversals? Memgraph, in most real workloads, because the entire graph lives in RAM and traversal is pointer chasing. FalkorDB closes the gap and sometimes wins on very wide, dense expansions thanks to sparse matrix algebra. Apache AGE trails on deep hops but wins the moment your query also needs relational joins.

Do I have to rewrite my Cypher queries when switching engines? Pattern matching ports almost unchanged across all three. What breaks is everything around it: index creation syntax, date and string functions, bulk loading, stored procedures and result typing. AGE additionally requires a declared column list for every cypher() call, which is the single biggest mechanical change.

Can I keep my existing PostgreSQL transactions and backups with a graph database? Only with Apache AGE. Because graph data is stored in ordinary tables, it commits in the same transaction as your relational rows and is captured by the same pg_dump. Memgraph and FalkorDB each require their own backup and restore procedure.

What is the licensing risk with Memgraph and FalkorDB? Memgraph ships under BSL 1.1 plus a separate enterprise license for some modules; FalkorDB uses SSPL v1. Both are source-available but not OSI-approved. Self-hosting and internal use are generally fine; reselling as a hosted service is where the obligations bite. Apache AGE’s Apache-2.0 has no such restriction.

How much RAM does a graph database actually need? For Memgraph, plan at least 1.5x the on-disk dataset size plus index overhead, then load-test. For FalkorDB, memory use scales with the sparse matrix structure rather than raw edge count, which is often far more compact than a row store — but adjacency matrices still grow with density, so measure rather than estimate.

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