AvantGraph - The next-generation graph analytics engine

AvantGraph will be released under an open license soon.

Until then, you can try out some of the features with the provided docker image.

Getting Started

A note on compatibility

AvantGraph currently requires the Linux-only io_uring API. We recommend running it on an x86-64 linux host with a kernel version 5.15 or newer.

Pull the docker container

Make sure you have installed docker and that the daemon is running.

AvantGraph is published on our GitHub container registry. You can fetch it using the following command:

docker pull ghcr.io/avantlab/avantgraph:release-2025-06-19

Start the docker container

Start an instance of the image you have just pulled:

docker run -it --rm \
    -p 127.0.0.1:7687:7687/tcp \
    --privileged \
    ghcr.io/avantlab/avantgraph:release-2025-06-19

This opens an interactive shell inside the AvantGraph container. The --privileged flag enables io_uring support on Linux hosts. Important: Keep this terminal open - you'll run all subsequent commands inside this container.

Load a graph

Start by creating a graph and loading vertex and edge data. Below we provide sample instructions for loading a small toy graph. Detailed instructions are available here

export GRAPH_DIR=/tmp/toots

# Initialize the graph directory
ag-schema create-graph "${GRAPH_DIR}"

# Define the graph schema
ag-schema create-vertex-table "${GRAPH_DIR}" --vertex-label=Person --vertex-property=id=CHAR_STRING --vertex-property=name=CHAR_STRING --vertex-property=age=S64
ag-schema create-vertex-table "${GRAPH_DIR}" --vertex-label=Person --vertex-label=Moderator --vertex-property=id=CHAR_STRING --vertex-property=name=CHAR_STRING --vertex-property=age=S64
ag-schema create-vertex-table "${GRAPH_DIR}" --vertex-label=Toot --vertex-property=id=CHAR_STRING --vertex-property=likes=S64 --vertex-property=retoots=S64 --vertex-property=content=CHAR_STRING

ag-schema create-edge-table "${GRAPH_DIR}" --edge-label=Follows --src-labels=Person --trg-labels=Person --edge-property=days=S64
ag-schema create-edge-table "${GRAPH_DIR}" --edge-label=Tooted --src-labels=Person --trg-labels=Toot --edge-property=days=S64
ag-schema create-edge-table "${GRAPH_DIR}" --edge-label=Likes --src-labels=Person --trg-labels=Toot --edge-property=days=S64
ag-schema create-edge-table "${GRAPH_DIR}" --edge-label=Retoots --src-labels=Person --trg-labels=Toot --edge-property=days=S64
ag-schema create-edge-table "${GRAPH_DIR}" --edge-label=Mentions --src-labels=Toot --trg-labels=Person
ag-schema create-edge-table "${GRAPH_DIR}" --edge-label=RepliesTo --src-labels=Toot --trg-labels=Toot
ag-schema create-edge-table "${GRAPH_DIR}" --edge-label=Removed --src-labels=Person --src-labels=Moderator --trg-labels=Toot --edge-property=reason=CHAR_STRING

# JSON in Neo4J dump format
cat <<EOF > /tmp/toots.json
{"type": "node", "id": "0", "labels": ["Person"], "properties": {"name": "Ana", "age": 50}}
{"type": "node", "id": "1", "labels": ["Person"], "properties": {"name": "Jim"}}
{"type": "node", "id": "2", "labels": ["Person"], "properties": {"name": "David", "age": 25}}
{"type": "node", "id": "3", "labels": ["Toot"], "properties": {"content": "Happy birthday @Jim!", "likes": 15, "retoots": 4}}
{"type": "node", "id": "4", "labels": ["Toot"], "properties": {"content": "It is not his birthday!", "likes": 10, "retoots": 2}}
{"type": "node", "id": "5", "labels": ["Person", "Moderator"], "properties": {"name": "Bruno"}}
{"type": "relationship", "id": "0", "start": {"id": "0", "labels": ["Person"]}, "end": {"id": "1", "labels": ["Person"]}, "label": "Follows", "properties": {"days": 50}}
{"type": "relationship", "id": "1", "start": {"id": "0", "labels": ["Person"]}, "end": {"id": "2", "labels": ["Person"]}, "label": "Follows", "properties": {"days": 80}}
{"type": "relationship", "id": "2", "start": {"id": "1", "labels": ["Person"]}, "end": {"id": "2", "labels": ["Person"]}, "label": "Follows", "properties": {"days": 20}}
{"type": "relationship", "id": "3", "start": {"id": "0", "labels": ["Person"]}, "end": {"id": "4", "labels": ["Toot"]}, "label": "Likes", "properties": {}}
{"type": "relationship", "id": "6", "start": {"id": "0", "labels": ["Person"]}, "end": {"id": "3", "labels": ["Toot"]}, "label": "Tooted", "properties": {}}
{"type": "relationship", "id": "4", "start": {"id": "1", "labels": ["Person"]}, "end": {"id": "4", "labels": ["Toot"]}, "label": "Tooted", "properties": {}}
{"type": "relationship", "id": "5", "start": {"id": "2", "labels": ["Person"]}, "end": {"id": "3", "labels": ["Toot"]}, "label": "Retoots", "properties": {"days": 200}}
{"type": "relationship", "id": "7", "start": {"id": "4", "labels": ["Toot"]}, "end": {"id": "3", "labels": ["Toot"]}, "label": "RepliesTo", "properties": {}}
{"type": "relationship", "id": "8", "start": {"id": "5", "labels": ["Person", "Moderator"]}, "end": {"id": "4", "labels": ["Toot"]}, "label": "Removed", "properties": {"reason": "Offensive content"}}
EOF

# Load JSON into AvantGraph
ag-load-graph --graph-format=json /tmp/toots.json "$GRAPH_DIR"

Run a query using the AvantGraph CLI

The avantgraph binary is the command line interface to AvantGraph. Define the query to run in a file, and use the avantgraph binary to execute it against a loaded graph:

cat <<EOF > /tmp/query.cypher
MATCH (a:Person)-[:Tooted]->(t:Toot)
RETURN a.name AS person, t.content AS message;
EOF

avantgraph /tmp/toots --query-type=cypher /tmp/query.cypher

The query returns two result tuples:

tuple:  %person=CHAR_STRING:"Ana" %message=CHAR_STRING:"Happy birthday @Jim!"
tuple:  %person=CHAR_STRING:"Jim" %message=CHAR_STRING:"It is not his birthday!"

Use AvantGraph in server mode

You can also start AvantGraph in server mode and interact with it through the Neo4J BOLT protocol. The tool to start the local server is ag-server:

ag-server --listen 0.0.0.0:7687 /tmp/toots

Now open a new terminal on your host machine (not inside the Docker container). Install the Neo4J client library for python:

pip3 install --user neo4j

Create a new file bolt_client.py. Paste in the following script:

#!/usr/bin/env python3

# AvantGraph is wire-compatible with Neo4J.
# We can use the standard neo4j python library to interact with it.
import neo4j
from neo4j import GraphDatabase, Result

# Connect to the AvantGraph server running in the docker container.
driver = GraphDatabase.driver("bolt://localhost:7687")

# The Cypher query to execute
query = """
MATCH (a:Person)-[:Tooted]->(t:Toot)
RETURN a.name AS person, t.content AS message;
"""

# Execute the query and read the results into a dataframe.
df = driver.execute_query(query, result_transformer_=Result.to_df)
print(df)

Run the script:

python3 bolt_client.py

We get an output identical to the command line version, but in a pandas dataframe:

  person                  message
0    Ana     Happy birthday @Jim!
1    Jim  It is not his birthday!

Run a graph algorithm

Graph Algorithm support is currently included as a preview, it is not stable yet. You may encounter performance issues with some algorithms as a result. Documentation on the algorithm language will come in a future release. In the meantime, if you would like to run custom algorithms in AvantGraph, contact us :)

AvantGraph includes support for user-provided graph algorithms written in the GraphAlg language. To run a custom algorithm, include it in a Cypher query using the WITH ALGORITHM clause:

WITH ALGORITHM "
func MyAlgorithm(graph: Matrix<s, s, bool>) -> Vector<s, real> {
    // Implementation goes here
    // ...
}
"
CALL MyAlgorithm()

An example script to load a graph and compute PageRank scores for the nodes is given below:

export GRAPH_DIR=/tmp/pr

# Initialize the graph directory
ag-schema create-graph "${GRAPH_DIR}"

# Define the graph schema
ag-schema create-vertex-table "${GRAPH_DIR}" --vertex-label=evlp_vertex --vertex-property=orig_id=U64
ag-schema create-edge-table "${GRAPH_DIR}" --edge-label=evlp_edge --src-labels=evlp_vertex --trg-labels=evlp_vertex

# JSON in Neo4J dump format
cat <<EOF > /tmp/pr.json
{"type": "node","id": "1","labels": ["evlp_vertex"],"properties": {"orig_id": 1}}
{"type": "node","id": "2","labels": ["evlp_vertex"],"properties": {"orig_id": 2}}
{"type": "node","id": "3","labels": ["evlp_vertex"],"properties": {"orig_id": 3}}
{"type": "node","id": "4","labels": ["evlp_vertex"],"properties": {"orig_id": 4}}
{"type": "node","id": "5","labels": ["evlp_vertex"],"properties": {"orig_id": 5}}
{"type": "node","id": "6","labels": ["evlp_vertex"],"properties": {"orig_id": 6}}
{"type": "node","id": "7","labels": ["evlp_vertex"],"properties": {"orig_id": 7}}
{"type": "node","id": "8","labels": ["evlp_vertex"],"properties": {"orig_id": 8}}
{"type": "node","id": "9","labels": ["evlp_vertex"],"properties": {"orig_id": 9}}
{"type": "node","id": "10","labels": ["evlp_vertex"],"properties": {"orig_id": 10}}
{"type": "node","id": "11","labels": ["evlp_vertex"],"properties": {"orig_id": 11}}
{"type": "node","id": "12","labels": ["evlp_vertex"],"properties": {"orig_id": 12}}
{"type": "node","id": "13","labels": ["evlp_vertex"],"properties": {"orig_id": 13}}
{"type": "node","id": "14","labels": ["evlp_vertex"],"properties": {"orig_id": 14}}
{"type": "node","id": "15","labels": ["evlp_vertex"],"properties": {"orig_id": 15}}
{"type": "node","id": "16","labels": ["evlp_vertex"],"properties": {"orig_id": 16}}
{"type": "node","id": "17","labels": ["evlp_vertex"],"properties": {"orig_id": 17}}
{"type": "node","id": "18","labels": ["evlp_vertex"],"properties": {"orig_id": 18}}
{"type": "node","id": "19","labels": ["evlp_vertex"],"properties": {"orig_id": 19}}
{"type": "node","id": "20","labels": ["evlp_vertex"],"properties": {"orig_id": 20}}
{"type": "node","id": "21","labels": ["evlp_vertex"],"properties": {"orig_id": 21}}
{"type": "node","id": "22","labels": ["evlp_vertex"],"properties": {"orig_id": 22}}
{"type": "node","id": "23","labels": ["evlp_vertex"],"properties": {"orig_id": 23}}
{"type": "node","id": "24","labels": ["evlp_vertex"],"properties": {"orig_id": 24}}
{"type": "node","id": "25","labels": ["evlp_vertex"],"properties": {"orig_id": 25}}
{"type": "node","id": "26","labels": ["evlp_vertex"],"properties": {"orig_id": 26}}
{"type": "node","id": "27","labels": ["evlp_vertex"],"properties": {"orig_id": 27}}
{"type": "node","id": "28","labels": ["evlp_vertex"],"properties": {"orig_id": 28}}
{"type": "node","id": "29","labels": ["evlp_vertex"],"properties": {"orig_id": 29}}
{"type": "node","id": "30","labels": ["evlp_vertex"],"properties": {"orig_id": 30}}
{"type": "node","id": "31","labels": ["evlp_vertex"],"properties": {"orig_id": 31}}
{"type": "node","id": "32","labels": ["evlp_vertex"],"properties": {"orig_id": 32}}
{"type": "node","id": "33","labels": ["evlp_vertex"],"properties": {"orig_id": 33}}
{"type": "node","id": "34","labels": ["evlp_vertex"],"properties": {"orig_id": 34}}
{"type": "node","id": "35","labels": ["evlp_vertex"],"properties": {"orig_id": 35}}
{"type": "node","id": "36","labels": ["evlp_vertex"],"properties": {"orig_id": 36}}
{"type": "node","id": "37","labels": ["evlp_vertex"],"properties": {"orig_id": 37}}
{"type": "node","id": "38","labels": ["evlp_vertex"],"properties": {"orig_id": 38}}
{"type": "node","id": "39","labels": ["evlp_vertex"],"properties": {"orig_id": 39}}
{"type": "node","id": "40","labels": ["evlp_vertex"],"properties": {"orig_id": 40}}
{"type": "node","id": "41","labels": ["evlp_vertex"],"properties": {"orig_id": 41}}
{"type": "node","id": "42","labels": ["evlp_vertex"],"properties": {"orig_id": 42}}
{"type": "node","id": "43","labels": ["evlp_vertex"],"properties": {"orig_id": 43}}
{"type": "node","id": "44","labels": ["evlp_vertex"],"properties": {"orig_id": 44}}
{"type": "node","id": "45","labels": ["evlp_vertex"],"properties": {"orig_id": 45}}
{"type": "node","id": "46","labels": ["evlp_vertex"],"properties": {"orig_id": 46}}
{"type": "node","id": "47","labels": ["evlp_vertex"],"properties": {"orig_id": 47}}
{"type": "node","id": "48","labels": ["evlp_vertex"],"properties": {"orig_id": 48}}
{"type": "node","id": "49","labels": ["evlp_vertex"],"properties": {"orig_id": 49}}
{"type": "node","id": "50","labels": ["evlp_vertex"],"properties": {"orig_id": 50}}
{"type":"relationship","id":"0","label":"evlp_edge","start":{"id":"1","labels":["evlp_vertex"]},"end":{"id":"19","labels":["evlp_vertex"]},"properties":{"weight":0}}
{"type":"relationship","id":"1","label":"evlp_edge","start":{"id":"1","labels":["evlp_vertex"]},"end":{"id":"21","labels":["evlp_vertex"]},"properties":{"weight":0}}
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{"type":"relationship","id":"223","label":"evlp_edge","start":{"id":"47","labels":["evlp_vertex"]},"end":{"id":"16","labels":["evlp_vertex"]},"properties":{"weight":0}}
{"type":"relationship","id":"224","label":"evlp_edge","start":{"id":"47","labels":["evlp_vertex"]},"end":{"id":"28","labels":["evlp_vertex"]},"properties":{"weight":0}}
{"type":"relationship","id":"225","label":"evlp_edge","start":{"id":"47","labels":["evlp_vertex"]},"end":{"id":"29","labels":["evlp_vertex"]},"properties":{"weight":0}}
{"type":"relationship","id":"226","label":"evlp_edge","start":{"id":"47","labels":["evlp_vertex"]},"end":{"id":"34","labels":["evlp_vertex"]},"properties":{"weight":0}}
{"type":"relationship","id":"227","label":"evlp_edge","start":{"id":"47","labels":["evlp_vertex"]},"end":{"id":"35","labels":["evlp_vertex"]},"properties":{"weight":0}}
{"type":"relationship","id":"228","label":"evlp_edge","start":{"id":"47","labels":["evlp_vertex"]},"end":{"id":"40","labels":["evlp_vertex"]},"properties":{"weight":0}}
{"type":"relationship","id":"229","label":"evlp_edge","start":{"id":"47","labels":["evlp_vertex"]},"end":{"id":"42","labels":["evlp_vertex"]},"properties":{"weight":0}}
{"type":"relationship","id":"230","label":"evlp_edge","start":{"id":"47","labels":["evlp_vertex"]},"end":{"id":"46","labels":["evlp_vertex"]},"properties":{"weight":0}}
{"type":"relationship","id":"231","label":"evlp_edge","start":{"id":"47","labels":["evlp_vertex"]},"end":{"id":"50","labels":["evlp_vertex"]},"properties":{"weight":0}}
{"type":"relationship","id":"232","label":"evlp_edge","start":{"id":"48","labels":["evlp_vertex"]},"end":{"id":"8","labels":["evlp_vertex"]},"properties":{"weight":0}}
{"type":"relationship","id":"233","label":"evlp_edge","start":{"id":"48","labels":["evlp_vertex"]},"end":{"id":"19","labels":["evlp_vertex"]},"properties":{"weight":0}}
{"type":"relationship","id":"234","label":"evlp_edge","start":{"id":"48","labels":["evlp_vertex"]},"end":{"id":"30","labels":["evlp_vertex"]},"properties":{"weight":0}}
{"type":"relationship","id":"235","label":"evlp_edge","start":{"id":"48","labels":["evlp_vertex"]},"end":{"id":"35","labels":["evlp_vertex"]},"properties":{"weight":0}}
{"type":"relationship","id":"236","label":"evlp_edge","start":{"id":"48","labels":["evlp_vertex"]},"end":{"id":"38","labels":["evlp_vertex"]},"properties":{"weight":0}}
{"type":"relationship","id":"237","label":"evlp_edge","start":{"id":"48","labels":["evlp_vertex"]},"end":{"id":"43","labels":["evlp_vertex"]},"properties":{"weight":0}}
{"type":"relationship","id":"238","label":"evlp_edge","start":{"id":"48","labels":["evlp_vertex"]},"end":{"id":"50","labels":["evlp_vertex"]},"properties":{"weight":0}}
{"type":"relationship","id":"239","label":"evlp_edge","start":{"id":"49","labels":["evlp_vertex"]},"end":{"id":"7","labels":["evlp_vertex"]},"properties":{"weight":0}}
{"type":"relationship","id":"240","label":"evlp_edge","start":{"id":"49","labels":["evlp_vertex"]},"end":{"id":"8","labels":["evlp_vertex"]},"properties":{"weight":0}}
{"type":"relationship","id":"241","label":"evlp_edge","start":{"id":"49","labels":["evlp_vertex"]},"end":{"id":"17","labels":["evlp_vertex"]},"properties":{"weight":0}}
{"type":"relationship","id":"242","label":"evlp_edge","start":{"id":"49","labels":["evlp_vertex"]},"end":{"id":"18","labels":["evlp_vertex"]},"properties":{"weight":0}}
{"type":"relationship","id":"243","label":"evlp_edge","start":{"id":"50","labels":["evlp_vertex"]},"end":{"id":"4","labels":["evlp_vertex"]},"properties":{"weight":0}}
{"type":"relationship","id":"244","label":"evlp_edge","start":{"id":"50","labels":["evlp_vertex"]},"end":{"id":"28","labels":["evlp_vertex"]},"properties":{"weight":0}}
{"type":"relationship","id":"245","label":"evlp_edge","start":{"id":"50","labels":["evlp_vertex"]},"end":{"id":"47","labels":["evlp_vertex"]},"properties":{"weight":0}}
EOF

# Load JSON into AvantGraph
ag-load-graph --graph-format=json /tmp/pr.json "$GRAPH_DIR"

# Write the query to a file
cat <<EOF > /tmp/pr.cypher
WITH ALGORITHM "
func withDamping(degree:int, damping:real) -> real {
    return cast<real>(degree) / damping;
}

func PageRank(
        graph: Matrix<s1, s1, bool>) -> Vector<s1, real> {
    damping = real(0.85);
    iterations = int(14);

    n = graph.nrows;
    teleport = (real(1.0) - damping) / cast<real>(n);
    rdiff = real(1.0);

    d_out = reduceRows(cast<int>(graph));

    d = apply(withDamping, d_out, damping);

    connected = reduceRows(graph);
    sinks = Vector<bool>(n);
    sinks<!connected>[:] = bool(true);

    pr = Vector<real>(n);
    pr[:] = real(1.0) / cast<real>(n);

    for i in int(0):iterations {
        sink_pr = Vector<real>(n);
        sink_pr<sinks> = pr;
        redist = (damping / cast<real>(n)) * reduce(sink_pr);

        w = pr (./) d;

        pr[:] = teleport + redist;
        pr += cast<real>(graph).T * w;
    }

    return pr;
}
"
CALL PageRank()
RETURN row.orig_id AS vid, val;

EOF

# Run the query with embedded algorithm.
avantgraph /tmp/pr --query-type=cypher /tmp/pr.cypher

GraphAlg is available anywhere Cypher is allowed, including in queries sent over the Neo4J BOLT protocol to an AvantGraph server.