An AI-powered integration skill that translates GraphQL schemas and queries into native graph database operations (Cypher, Gremlin, SPARQL) and maps results back to GraphQL formats.
Install & Download
1. ClawHub CLI
The fastest way to install a skill directly from the registry.
npx clawhub@latest install graphql-graph-mapping
2. Manual Installation
Copy the skill folder to one of these locations
~/.openclaw/skills/ <project>/skills/ Priority: Workspace > Local > Bundled
3. Prompt Installation
Copy this prompt to OpenClaw to install it automatically.
Help me install graphql-graph-mapping using Clawhub. If Clawhub is not installed, install it first (npm i -g clawhub).
Prefer to download?
Get the raw skill files in a ZIP archive.
What is GraphQL Graph Mapping?
The GraphQL Graph Mapping skill is a highly versatile developer tool designed to bridge the gap between flexible GraphQL APIs and underlying graph databases. By automating the translation of incoming GraphQL schemas and queries into highly optimized native graph query languages—such as Neo4j's Cypher, Apache TinkerPop's Gremlin, and W3C's SPARQL—this integration dramatically simplifies the construction of API layers over complex knowledge graphs.
Built to empower developers within the ecosystem of Openclaw Skills, this automated service resolves nesting, filters, pagination, and multi-hop relationships out of the box. Rather than manually writing complex, error-prone database resolvers, developers can deploy this skill to expose clean, declarative API schemas that interact seamlessly with high-performance graph database clusters, ensuring structured data mapping with minimal latency.
GraphQL Graph Mapping Use Cases
- Exposing Knowledge Graphs as APIs: Quickly wrap an existing RDF triple store or Neo4j database into an interactive GraphQL API using ready-to-use Openclaw Skills workflows.
- Translating Nested Queries: Automatically generate multi-hop graph traversals from heavily nested GraphQL queries without manual resolver mapping.
- Multi-Database Federation: Map unified schema types to multiple backend database environments (e.g., Cypher for Neo4j and Gremlin for JanusGraph) using a single, cohesive translation layer.
- API Performance Optimization: Safeguard graph databases against expensive recursive traversals by generating standardized query execution plans with strict depth and complexity restrictions.
- Rapid Prototyping for Frontends: Instantly expose nodes, properties, and edge relationships to frontend client applications requesting custom JSON responses.
How GraphQL Graph Mapping Works
- Schema Parsing and Taxonomy Mapping: The system analyzes the incoming GraphQL schema definitions to map object types to node labels, fields to node properties, and relationship fields to edge types.
- GraphQL Query AST Extraction: When an API client submits a query, the service parses the query into an Abstract Syntax Tree (AST) to extract arguments, requested fields, nested selections, and filters.
- Query Engine Translation: The core translator converts the AST nodes into specialized database execution statements based on target configurations (e.g., generating Cypher MATCH patterns or Gremlin step-traversals).
- Database-Side Optimization: Apply index hints, reorder traversal steps, and compile pagination structures (LIMIT/OFFSET) to ensure the translated query executes at maximum efficiency.
- Execution and Result Marshalling: Execute the compiled query against the graph database, capture the structured result arrays, and map the outputs back into a compliant, nested GraphQL JSON response schema.
GraphQL Graph Mapping Setup
Prerequisites and Installation
To leverage this schema translation capability within your Openclaw Skills workspace, install the recommended core libraries and set up your environment mapping configuration:
pip install graphql-core graphene neo4j pydantic
Configuration
Create a mapping configuration to define how your GraphQL schema maps to your graph database labels and properties. A typical configuration looks like this:
database_type: "neo4j"
uri: "bolt://localhost:7687"
auth:
username: "neo4j"
password: "secure_password"
schema_mappings:
Person: "Person"
Company: "Company"
knows: "KNOWS"
worksAt: "WORKS_AT"
GraphQL Graph Mapping Data Schema & Taxonomy
Structural Concepts Mapping
This mapping layer translates GraphQL types, relationships, and queries to target graph schemas using the following structural taxonomy:
| GraphQL Concept | Graph Database Concept | Native Mapping Example |
|---|---|---|
type Person |
Node Type / Label | (:Person) |
name: String |
Node Property | Person.name |
knows: [Person] |
Edge / Relationship | [:KNOWS] |
id: ID! |
Unique Identifier | Node ID Property / Index |
| Query Root | Entrypoint Node | g.V().has('Person', 'id', 'alice') |
| Nested Fields | Graph Edge Traversal | (p)-[:KNOWS]->(f) |
Generated Execution Plan Schema
The translation step generates an execution plan containing metadata, execution statistics, and the raw target query. The JSON envelope matches the following structural layout:
{
"query_id": "uuid-123456",
"target_language": "cypher",
"translated_query": "MATCH (p:Person {id: 'alice'})-[:KNOWS]->(f:Person) RETURN p.name, collect(f.name) AS friends",
"execution_metrics": {
"traversal_depth": 2,
"complexity_score": 5,
"estimated_cost": 0.12
}
}
GraphQL Graph Mapping Advanced Features
- Custom Query Complexity Scoring: Assign custom complexity scores to fields and block abusive, ultra-deep recursive traversals prior to database execution.
- Automatic Pagination Translation: Seamlessly translate standard GraphQL cursor-based pagination arguments into graph-specific pagination mechanisms (e.g., Cypher LIMIT/SKIP or Gremlin range steps).
- Native Aggregation Mapping: Translate GraphQL schema metrics (e.g., average, count, group-by) directly into native Cypher or SPARQL aggregate functions.
- Multi-Tenant Schema Federation: Dynamically select database target drivers and connection pooling variables based on query-level contextual factors or headers.
- Execution Cache Hooks: Integrate seamless Redis caching layers over common query patterns, accelerating execution times for static graph traversals mapped via Openclaw Skills.
SKILL.md
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