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[!NOTE] Disclosure: This project is developed using LLM-assisted engineering with nWave.
Knowledge graphs as a long-term memory for your AI assistants
Add the plugin and built-in skills to your OpenCode configuration:
{
"$schema": "https://opencode.ai/config.json",
"plugin": ["graphmind"],
"skills": {
"paths": ["~/.cache/opencode/node_modules/graphmind/skill"]
}
}
OpenCode will automatically install the plugin on startup. See the Skills section for available skills and alternative path configurations.
Query Wikidata for factual information about the world using the wikidata_query tool. This tool is optimized for querying the Wikidata knowledge base, which contains structured data about people, places, events, concepts, and more.
Key Features:
Use Cases:
Execute SPARQL queries over RDF data sources using the sparql_query tool. This tool uses Comunica under the hood to provide flexible querying capabilities.
Supported Query Types:
SELECT - Retrieve bindings for variablesCONSTRUCT - Generate RDF triplesDESCRIBE - Retrieve RDF descriptionsASK - Boolean queriesSupported Data Sources:
Who is Douglas Adams and when was he born?
OpenCode will construct a Wikidata query like:
{
"query": "SELECT ?personLabel ?birthDate WHERE { wd:Q42 wdt:P569 ?birthDate. SERVICE wikibase:label { bd:serviceParam wikibase:language 'en'. } }"
}
What are the 10 most populous countries in the world?
What major events happened in 1969?
The tool automatically adds all necessary Wikidata prefixes, so you can focus on writing the query logic.
Query DBpedia for the first 10 triples:
Can you query DBpedia for the first 10 triples?
OpenCode will use the tool with parameters like:
{
"query": "SELECT * WHERE { ?s ?p ?o } LIMIT 10",
"sources": ["https://fragments.dbpedia.org/2016-04/en"]
}
Query across multiple RDF sources simultaneously:
Query DBpedia and Wikidata for entities related to "artificial intelligence"
Generate RDF triples:
Use a CONSTRUCT query to get book information from DBpedia
Request specific output formats:
{
"query": "SELECT * WHERE { ?s ?p ?o } LIMIT 10",
"sources": ["https://fragments.dbpedia.org/2016-04/en"],
"outputFormat": "text/csv"
}
Available Output Formats:
application/json (default for SELECT)application/sparql-results+jsonapplication/sparql-results+xmltext/csvtext/tab-separated-valuestext/turtle (default for CONSTRUCT)application/trigapplication/n-triplesapplication/n-quadsapplication/ld+json{
"query": "SELECT * WHERE { ?s ?p ?o } LIMIT 10",
"sources": ["https://fragments.dbpedia.org/2016-04/en"],
"baseIRI": "http://example.org/",
"lenient": true,
"httpTimeout": 30000,
"logLevel": "debug"
}
wikidata_query| Parameter | Type | Required | Description |
|---|---|---|---|
query | string | Yes | SPARQL query for Wikidata (prefixes added automatically) |
outputFormat | string | No | Output format: json, csv, tsv, xml (default: json) |
httpTimeout | number | No | HTTP timeout in milliseconds (default: 30000) |
logLevel | string | No | Log level: error, warn, info, or debug |
Note: The tool automatically adds common Wikidata prefixes (wd:, wdt:, p:, ps:, pq:, wikibase:, bd:, rdfs:) if they're not already in your query.
sparql_query| Parameter | Type | Required | Description |
|---|---|---|---|
query | string | Yes | The SPARQL query string to execute |
sources | string[] | Yes | One or more URLs to query over |
outputFormat | string | No | Output format for results |
baseIRI | string | No | Base IRI for the query |
lenient | boolean | No | If true, log errors instead of throwing |
httpTimeout | number | No | HTTP request timeout in milliseconds |
logLevel | string | No | Log level: error, warn, info, or debug |
Graphmind ships domain-specific skills that give your AI agent knowledge about SPARQL query patterns and RDF modeling conventions. Skills are instruction files that improve how agents use the plugin's tools.
| Skill | Description |
|---|---|
graphmind-wikidata | Answer real-world factual questions by querying Wikidata, the world's largest open knowledge base |
graphmind-sparql-patterns | Common SPARQL query patterns for querying RDF knowledge graphs with Graphmind |
graphmind-rdf-modeling | RDF modeling patterns and ontology conventions for knowledge graph construction |
Add the skills path to your opencode.json:
{
"$schema": "https://opencode.ai/config.json",
"plugin": ["graphmind"],
"skills": {
"paths": ["node_modules/graphmind/skills"]
}
}
If OpenCode installed the plugin to its cache directory, use the cache path instead:
{
"skills": {
"paths": ["~/.cache/opencode/node_modules/graphmind/skills"]
}
}
For local plugin installations, point to the local path:
{
"skills": {
"paths": [".opencode/plugins/graphmind/skills"]
}
}
MIT
FAQs
Knowledge graphs as a long-term memory for your AI assistants
We found that graphmind demonstrated a healthy version release cadence and project activity because the last version was released less than a year ago. It has 1 open source maintainer collaborating on the project.
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