DappLooker

DappLooker is a multi-chain blockchain analytics and data infrastructure platform founded in 2021 and headquartered in Pune, India. Its stated mission is to make blockchain data analysis simple and affordable for teams ranging from community-run DeFi protocols to enterprise trading desks. The platform operates across two complementary product lines: a no-code analytics interface aimed at non-technical users, and an AI-ready intelligence API suite designed for developers building autonomous trading agents and quantitative systems.

The Problem It Solves

Blockchain data is fragmented across dozens of networks, indexing layers, and data vendors. Teams that want to track protocol metrics, token flows, or wallet behavior typically face a choice between hiring data engineers to build bespoke pipelines or paying for expensive institutional analytics subscriptions. DappLooker positions itself as a third path: a unified, low-cost platform that abstracts the data ingestion layer and exposes it through visual tooling for non-technical users and machine-readable APIs for developers.

Core Mechanism

DappLooker indexes data from multiple sources and normalizes it into queryable schemas. The primary data inputs are smart contracts (directly queried on supported chains), subgraphs (indexed via The Graph protocol), SubQuery indexers, and off-chain data such as CSV files and JSON APIs. Once ingested, this data is available through two interfaces.

The first is the Visual SQL editor: a drag-and-drop query builder that allows users to filter, join, and aggregate on-chain data without writing SQL. Results feed into a dashboard editor where users can create charts, tables, and time-series visualizations. Dashboards can be shared publicly with the community, forked, and embedded in external applications via the platform charting API.

The second interface is the DappLooker AI API suite: a set of RESTful endpoints returning structured JSON optimized for machine consumption rather than human dashboards. This layer emphasizes what the platform calls agent-first infrastructure, meaning the outputs are formatted for autonomous agents and bots to consume programmatically without additional parsing.

Key Products

DappLooker Analytics is the original no-code dashboard product. It supports smart contract queries, subgraph integrations, SubQuery-indexed data, and off-chain imports. The platform provides Python and JavaScript SDKs for embedding charts and accessing data programmatically. Dashboards can be subscribed to or accessed via API keys, with crypto payments accepted in USDC and USDT stablecoins.

DappLooker AI is the intelligence API layer, positioned as the intelligence layer for autonomous AI agents, next-gen DeFi, and trading. It delivers four data categories:

Trading Intelligence covers historical price, volume, and market cap data; technical analysis across multiple timeframes; and perpetual contract data with a focus on Hyperliquid.

Wallet Intelligence provides portfolio overview, position tracking, transaction history, P&L analysis, and smart-money trend identification across EVM-compatible chains and Solana.

Yield and Treasury covers validator APR data, staking reward tracking, lending and borrowing rates, and yield optimization routing across supported protocols.

Agent Infrastructure includes natural language market intelligence, action feeds for autonomous decision-making, and a CLI interface for direct agent access. The platform also integrates x402 payments, allowing agents to purchase data access without human-in-the-loop authorization.

HyprEarn is DappLooker's perpetual trading automation product, which packages trading intelligence into automated execution flows aimed at reducing research overhead for active traders.

Loky AI is a personalized signal engine for trading agents, converting live market data and on-chain signals into contextual outputs sized for agent context windows.

Solana Support

Solana is included within DappLooker's Wallet Intelligence API layer. The platform documentation explicitly references support for any EVM or Solana wallet, enabling portfolio tracking, transaction history, and smart-money analysis for Solana addresses alongside EVM chains. The broader analytics platform covers 200-plus networks in total, with 25-plus chains covered by real-time multi-chain indexing infrastructure. While Solana does not feature as prominently in DappLooker's marketing as chains like Arbitrum, Celo, or Polygon, the wallet intelligence coverage gives Solana users access to the platform's core portfolio and on-chain analysis capabilities.

Scale and Adoption

By the end of 2024, DappLooker reported indexing data from more than 1,000 protocols, completing over 10,000 analyses, and generating seven-figure revenue representing a three-times year-over-year increase. The platform lists more than 1,200 enterprise clients including trading firms such as Kosher Capital, DeFi protocols, and AI teams including Virtuals and Sentient. Client-reported outcomes cited by the platform include 60% cost savings on analytics infrastructure, 80% time savings on data workflows, and a 30x improvement in community analytics output.

Team and History

DappLooker was co-founded in 2021 by Abhay Singh, who serves as CEO, and Abhinav Singh, who serves as Chief Product Officer. The company is based in Pune, India and employs approximately 15 people as of mid-2026. No external funding rounds have been disclosed; the company appears to have grown on product revenue. Notable 2024 partnerships include an integration with Vara Network and a validator role with CrossFi Network, with dedicated explorer products including VaraSpy and CeloSpy built for specific chain communities.

Ecosystem Fit

Within the Solana ecosystem, DappLooker occupies a distinct niche from native Solana analytics tools. Where platforms like Solscan, Step Finance, and Hello Moon are purpose-built around Solana's account model and program architecture, DappLooker's strength is cross-chain breadth and no-code accessibility for teams that need consistent analytics tooling across multiple networks simultaneously. Its agent-ready API layer positions it for teams building AI-driven trading systems that require unified data inputs across Solana and EVM chains without maintaining separate integrations per chain. The platform's community-driven model, where dashboards are forked, shared, and published, also creates a public repository of analytics templates that Solana protocol teams can adapt without starting from scratch.