GitBook Assistant Ask chevron-down ⛓️ Kite ChainGoldsky-Kite AI Index Kite AI with Goldsky for high-performance blockchain data infrastructure
Goldsky provides high-performance blockchain data infrastructure for Kite AI, making it easy to extract, transform, and serve on-chain data for both production apps and analytics workloads.
For Kite AI builders, Goldsky is the recommended indexing layer for:
Agent analytics and observability
Protocol monitoring and data pipelines
Real-time and historical on-chain queries
Goldsky supports two primary data access models:
Subgraphs – High-performance, GraphQL-based indexing
Mirror – Real-time replication pipelines into databases and warehouses
Documentation Index
Before diving deeper, you can fetch Goldsky's full documentation index here:
https://docs.goldsky.com/chains/kite-ai#indexing-kite-ai-with-goldsky
This index lists all available pages, guides, and references, and is useful for discovering advanced features before implementing a specific flow.
Getting Started
To index Kite AI with Goldsky, you'll need to:
Authenticate using an API key
Note - Using Goldsky from the UI is as simple as clicking "+ New subgraph" to get started:

Install the Goldsky CLI & Log In
For macOS/Linux:
For Windows:
Create an API Key
Go to Project Settings in the Goldsky dashboard
Generate a new API key for your project
Authenticate via CLI
Verify Installation
Indexing Kite AI with Subgraphs
Subgraphs are the most common way to index Kite AI contracts and events.
Goldsky supports two deployment paths, depending on how much control you need.
Option 1: Deploy a Custom Subgraph (Full Control)
Use this option if you want:
Complex mappings or transformations
Requirements:
Mapping files (AssemblyScript)
Deploy command:
Option 2: Instant Subgraphs (Quick Start)
Use instant subgraphs if you want:
Event-level indexing from an ABI
You only need:
Deploy command:
Goldsky automatically generates:
Kite AI Network Configuration
Use the following chain slugs when deploying subgraphs for Kite AI:
These slugs are required for:
Common Kite AI Use Cases
Builders commonly use Goldsky on Kite AI for:
Agent payment flows & settlement tracking
ERC-20 / native token analytics
Protocol-level metrics (TPS, volume, usage)
Explorer backends & dashboards
Agent reputation, activity, and lifecycle analytics
Goldsky pairs especially well with agent-native apps that require:
Deterministic historical queries
Scalable analytics without running infra
Getting Support
If you run into issues or need help with advanced setups:
Goldsky Support: [email protected]