ArcadeDB Integrations

pip install langchain-arcadedb

# ArcadeDB with the Bolt plugin enabled
docker run --rm -p 2480:2480 -p 7687:7687 \
  -e JAVA_OPTS="-Darcadedb.server.rootPassword=playwithdata -Darcadedb.server.plugins=Bolt:com.arcadedb.bolt.BoltProtocolPlugin" \
  arcadedata/arcadedb:26.10.1

# create a database
curl -X POST http://localhost:2480/api/v1/server \
  -d '{"command":"create database mydb"}' \
  -u root:playwithdata -H "Content-Type: application/json"

from langchain_arcadedb import ArcadeDBGraph

graph = ArcadeDBGraph(
    url="bolt://localhost:7687",
    username="root",
    password="playwithdata",
    database="mydb",
)

# Schema is auto-detected from existing data
print(graph.get_schema)

# Run Cypher queries
result = graph.query("MATCH (n:Person) RETURN n.name AS name")

pip install arcadedb-haystack

docker run -d -p 2480:2480 \
  -e JAVA_OPTS="-Darcadedb.server.rootPassword=arcadedb" \
  arcadedata/arcadedb:26.10.1

export ARCADEDB_USERNAME=root
export ARCADEDB_PASSWORD=arcadedb

from haystack import Pipeline
from haystack_integrations.components.retrievers.arcadedb import ArcadeDBEmbeddingRetriever
from haystack_integrations.document_stores.arcadedb import ArcadeDBDocumentStore

store = ArcadeDBDocumentStore(database="myproject", embedding_dimension=768)

pipeline = Pipeline()
pipeline.add_component("retriever", ArcadeDBEmbeddingRetriever(document_store=store, top_k=10))

result = pipeline.run({"retriever": {"query_embedding": [0.1] * 768}})
documents = result["retriever"]["documents"]

pip install llama-index-graph-stores-arcadedb

from llama_index.core import PropertyGraphIndex
from llama_index.graph_stores.arcadedb import ArcadeDBPropertyGraphStore

graph_store = ArcadeDBPropertyGraphStore(
    host="localhost",
    port=2480,
    username="root",
    password="playwithdata",
    database="knowledge_graph",
    embedding_dimension=1536,  # match your embedding model
)

# documents: a list of LlamaIndex Document objects
index = PropertyGraphIndex.from_documents(
    documents,
    property_graph_store=graph_store,
    show_progress=True,
)

response = index.query("What are the main topics discussed?")

pip install cognee-community-hybrid-adapter-arcadedb

# Optional: Bolt support for faster graph operations
pip install "cognee-community-hybrid-adapter-arcadedb[bolt]"

import asyncio
import os
from cognee import config, add, cognify, search, SearchType

# Import the register module to enable ArcadeDB support
from cognee_community_hybrid_adapter_arcadedb import register


async def main():
    config.set_relational_db_config({"db_provider": "sqlite"})

    # Configure ArcadeDB as both vector and graph database
    config.set_vector_db_config({
        "vector_db_provider": "arcadedb",
        "vector_db_url": os.getenv("ARCADEDB_URL", "localhost"),
        "vector_db_port": int(os.getenv("ARCADEDB_HTTP_PORT", "2480")),
        "vector_db_username": os.getenv("ARCADEDB_USERNAME", "root"),
        "vector_db_password": os.getenv("ARCADEDB_PASSWORD", ""),
    })
    config.set_graph_db_config({
        "graph_database_provider": "arcadedb",
        "graph_database_url": os.getenv("ARCADEDB_URL", "localhost"),
        "graph_database_port": int(os.getenv("ARCADEDB_HTTP_PORT", "2480")),
        "graph_database_username": os.getenv("ARCADEDB_USERNAME", "root"),
        "graph_database_password": os.getenv("ARCADEDB_PASSWORD", ""),
    })

    await add("Natural language processing (NLP) is an interdisciplinary "
              "subfield of computer science and information retrieval.")
    await cognify()

    results = await search(query_type=SearchType.GRAPH_COMPLETION,
                           query_text="Tell me about NLP")
    for result in results:
        print(result)


asyncio.run(main())

/plugin marketplace add altugsogutoglu/arcadedb-claude
/plugin install arcadedb-claude-skills@arcadedb-claude

# inside Claude Code
/arcadedb-config set password changeme

# from a shell (npm install -g arcadedb-claude-skills)
arcadedb-skills search "why did we drop the test api default"
arcadedb-skills decisions list --all

curl -X POST -u root:playwithdata \
  -H "Content-Type: application/json" \
  -d '{"enabled": true, "allowReads": true}' \
  http://localhost:2480/api/v1/mcp/config

claude mcp add --transport http arcadedb http://localhost:2480/api/v1/mcp \
  --header "Authorization: Basic cm9vdDpwbGF5d2l0aGRhdGE="

export PASSWORD=playwithdata
npx @toolbox-sdk/server --config tools.yaml

kind: source
name: my-arcadedb-source
type: arcadedb
uri: bolt://localhost:7687
user: root
password: ${PASSWORD}
database: "mydb"
---
kind: tool
name: query_arcadedb
type: arcadedb-execute-cypher
source: my-arcadedb-source
readOnly: true
description: Execute read-only Cypher against ArcadeDB.

docker run --rm -d --name grafana -p 3000:3000 \
  -e GF_PLUGINS_ALLOW_LOADING_UNSIGNED_PLUGINS=arcadedb-arcadedb-datasource \
  -e GF_INSTALL_PLUGINS="https://github.com/ArcadeData/arcadedb-grafana-datasource/releases/download/v1.0.0-beta.2/arcadedb-arcadedb-datasource-1.0.0-beta.2.zip;arcadedb-arcadedb-datasource" \
  grafana/grafana:latest

SELECT ts, temperature FROM weather
WHERE $__timeFilter(ts)
ORDER BY ts

docker run --rm -p 2480:2480 \
  -e JAVA_OPTS="-Darcadedb.server.rootPassword=arcadedb -Darcadedb.server.plugins=Prometheus:com.arcadedb.metrics.prometheus.PrometheusMetricsPlugin" \
  arcadedata/arcadedb:26.10.1

curl -u root:arcadedb http://localhost:2480/prometheus

scrape_configs:
  - job_name: 'arcadedb'
    scrape_interval: 15s
    metrics_path: /prometheus
    static_configs:
      - targets: ['localhost:2480']
    basic_auth:
      username: root
      password: arcadedb

docker run --rm -p 2480:2480 \
  -e JAVA_OPTS="-Darcadedb.server.rootPassword=arcadedb -Darcadedb.serverMetrics.otlp.enabled=true -Darcadedb.serverMetrics.otlp.endpoint=http://otel-collector:4318" \
  arcadedata/arcadedb:26.10.1

-- JDBC URL: jdbc:postgresql://localhost:5432/mydb
{cypher} MATCH (m:Movie)<-[a:ACTED_IN]-(p:Person) RETURN m.title, count(p)