How Nansen powers onchain intelligence across 500M+ wallets & 30+ chains. Nansen is an agentic trading platform that brings onchain analytics and execution, allowing investors to trade everything onchain with AI. Powered by 500 million labeled addresses with real-time data across major networks, Nansen helps investors, funds, and protocols turn complex onchain activity into actionable insights, providing visibility into market movements, wallet activity, and capital flows - right alongside trading. Its AI-powered features enable users to discover opportunities, execute trades, and manage portfolios through a single platform more efficiently. In 2026, Nansen expanded into active trading execution across Hyperliquid, Solana, and Base, while continuing to add support for emerging networks such as Robinhood Chain. CoinGecko API supports this expansion by giving Nansen consistent access to real-time and historical market data across both established and newly launched networks. Key metrics at a glance. * CoinGecko delivers price and market data across the 30+ blockchains Nansen tracks, covering assets traded on centralized exchanges and long-tail onchain tokens. * CoinGecko API's data powers key Nansen features, including Smart Money, Portfolio, and PnL, providing the pricing foundation users rely on to analyze opportunities and make trading decisions. * Nansen added support for Robinhood Chain within one day, enabled by CoinGecko's ready coverage of new networks, helping the team move quickly when new market demand emerges. The challenge: scaling onchain intelligence across multiple chains. Nansen needed timely and reliable market data across more than 30+ chains, covering established assets and long-tail tokens traded exclusively onchain. As the platform expanded from research into live execution, this data needed to support more than static dashboards. It had to provide actionable market context for token discovery, portfolio analysis, Smart Money intelligence, and real-time trading decisions. Nansen also required historical market data to help users examine performance over time and give Nansen AI deeper context when analyzing onchain activity. Why Nansen chose CoinGecko API. CoinGecko gives Nansen a unified market data layer across centralized and decentralized markets. Its coverage of major assets, long-tail onchain tokens, and newly launched networks allows Nansen to expand into new chains and product experiences without building separate market data integrations for each one. CoinGecko's batched API endpoints also allow Nansen to retrieve data for multiple assets within a single request. Combined with a predictable credit model where one API request consumes one call credit, this helps Nansen manage data retrieval and infrastructure costs efficiently as its asset and chain coverage grows. While Nansen combines multiple data sources to maintain data quality, CoinGecko remains an integral source for market prices across its products and experiences. What Nansen says about CoinGecko API. "Tracking Smart Money across dozens of chains only works if the underlying price data holds up. CoinGecko's coverage and uptime are what let us make that promise to our users. It also helps us respond quickly when new market demand emerges. We wouldn't have been able to integrate Robinhood Chain within a day if CoinGecko hadn't already had that network covered and readily available." - Hans Olav, Director of Engineering at Nansen How Nansen leverages CoinGecko API. CoinGecko market data is integrated across Nansen's core product experiences, powering token screeners, Smart Money intelligence, token pages, Nansen AI, and user portfolios. Smart Money and token screeners. Nansen's Token Screener combines market performance with proprietary signals such as Smart Money flows and trending onchain activity. This allows traders to compare what the market is doing with the wallets and entities driving that activity. The /coins/markets endpoint returns prices, market capitalization, fully diluted valuation, trading volume, rankings, and price changes for up to 250 assets in one request. For long-tail tokens traded onchain, Nansen uses /onchain/networks/{network}/tokens/multi/{addresses} to retrieve token and market data across multiple contract addresses efficiently. Together, these endpoints give Nansen a scalable market data layer for screening established coins and newly active onchain tokens alongside its Smart Money signals. Detailed token pages and charts. Nansen's detailed token pages bring market performance, onchain activity, and project information into a single view. For assets traded on both CEXs and DEXs, Nansen uses the /coins/{id} endpoint to obtain market data and metadata, while the /coins/{id}/market_chart endpoint provides historical prices, market capitalization, and trading volume for line charts. For assets exclusively traded onchain, Nansen uses the /onchain/networks/{network}/tokens/{token_address}/ohlcv/{timeframe} endpoint to display candlestick charts of a specific token. The /onchain/networks/{network}/tokens/{address}/info endpoint enriches token pages with names, symbols, logos, websites, descriptions, and social profiles. Nansen also uses /onchain/tokens/info_recently_updated to efficiently identify recently updated token information and subsequently retrieves new data to keep its product surfaces current. Onchain intelligence in Nansen AI. Nansen's agentic experience enables users to interact with onchain data through natural language, including generating live charts and intelligence views within a conversation. When Nansen AI delivers market insights or visualizations, it draws from the same market and onchain price endpoints used across the platform, ensuring consistent data across conversational experiences and core product surfaces. Portfolio and PnL valuations. Nansen's Portfolio and PnL experiences require current pricing across the assets held by each user, including long-tail tokens that trade exclusively onchain. For coins traded on CEXs and DEXs, the /simple/price endpoint provides lightweight price lookups across up to 500+ assets in a single API call. For onchain tokens, the /onchain/simple/networks/{network}/token_price/{addresses} endpoint retrieves real-time prices across multiple contract addresses in an efficient batched request. For historical performance analysis, Nansen uses /coins/{id}/market_chart to access time-series data including historical prices, market caps, and trading volumes, supporting realized and unrealized PnL calculations. Business outcomes: powering market context efficiently across Nansen. CoinGecko's market data complements Nansen's intelligence layer of more than 500M+ labeled addresses across over 30+ chains. This combination connects prices and market performance with wallet activity, Smart Money flows, known entities, and portfolio positions across Nansen's trading experiences, AI features, and developer tools. To efficiently scale market context across 30+ chains, Nansen uses an activity-based model that leverages its own onchain activity data to identify actively traded assets, then queries CoinGecko's endpoints for the assets that require updated pricing. By bringing timely market context and onchain intelligence together, Nansen gives traders a more complete view of what is moving, which wallets are involved, and how those movements affect their portfolios. What's next on Nansen's roadmap. Nansen is deepening its agentic trading infrastructure with AI-powered agents that can surface, analyze, and act on onchain signals. Its roadmap includes execution across more chains and assets, improved Smart Money tracking and trading strategies, and new API and CLI tools for builders. CoinGecko's price and market data will remain foundational as Nansen extends these capabilities across additional assets and product experiences. Get in touch. Ready to supercharge your crypto project? Fill in the form below to connect with our API Sales team for a custom CoinGecko API plan tailored to enterprise needs.

Labeling 500 million wallets to bridge the gap between onchain data and AI agents. July 18, 2026 nansen onchain-data ai-agents agentic-trading wallet-labels x402 crypto-apis Hürcan Polat watched the Turkish Lira slide faster than usual in 2020. He was a metallurgical and materials engineering graduate with little interest in crypto at the time, but the economic shift forced a change in focus. He spent months in Telegram groups and following traders on social media to understand the market before landing his first part-time role in the sector. By 2021, he had left his engineering career behind. After roles at the Lamden project, a data analytics firm, and a perpetual exchange, Polat joined Nansen to lead growth for developer products, where he has spent close to two years building the tools that connect raw blockchain activity to systematic users. Listen on your favorite platform. A city phone book without names. Blockchains are transparent by design, but the raw data they produce is often difficult to interpret for both humans and machines. A standard indexer can show that an address moved 500 ETH, but it cannot explain if that movement represents a significant market shift or a routine internal transfer. Nansen addresses this gap by adding labels, entities, and categories to more than 500 million wallets across more than 20 chains. Polat uses a specific analogy to describe the difference between raw data and Nansen's intelligence layer: You can think of raw data sources, either RPCs or indexers, as an entire city's phone book, but without any names, right? So, it's just phone numbers. You can technically call them or try to understand who they are, but it would take years of work to understand who's who. Nansen's labeled data is the same phone book, but with names, job titles, reputation scores, and credit scores attached. For an AI agent, this context changes the nature of onchain analysis. Instead of processing a string of hexadecimal characters, an agent can recognize that a tier-1 venture fund such as Paradigm or Wintermute is rotating assets. This labeling relies on a dedicated attribution team and internal AI tools that map interconnected webs of wallets. The labels carry behavioral context, identifying if a wallet belongs to a whale, a retail investor, or a smart money participant. This historical perspective includes PnL tracking over 30 to 90 days and a record of previous counterparties, providing the breadcrumbs an agent needs to reason about the intent behind a transaction. Laying the rails. The progress of AI agents has accelerated since the release of models like Claude and GPT-4, which made the distinction between a simple language model and an autonomous agent clear. Large institutions have started building the infrastructure for agentic commerce, with Coinbase pushing AI-native wallets and Stripe implementing virtual payment protocols. However, the adoption of these tools for financial execution still faces a barrier of trust. Many users remain hesitant to hand real capital to an autonomous system that might misinterpret intent or fail during a critical operation. Polat views the current phase as a period of building the necessary infrastructure before the next wave of adoption. He describes the state of the industry: I think DevNTell is still laying the rails right now. I mean, especially since OpenAI, Claude, and other well-known AI agents came to presence, DevNTell is seeing much faster progress because now people understand how different it is to use an agent versus using an LLM, and the capabilities they have. The goal is to move past the human-in-the-loop requirement where a person must verify every step. As models improve and hallucinations decrease, the infrastructure will be ready to handle more complex autonomous tasks. Polat compares the current AI development cycle to the early days of crypto, where new applications appeared daily and the pace of innovation was often faster than the market could absorb. Agents hate subscriptions. Traditional software access relies on a subscription model that is poorly suited for autonomous agents. A typical agent does not visit a website, fill out a form, verify an email address, or manage a recurring credit card payment. This friction has led Nansen to adopt more flexible access and payment methods, including the Model Context Protocol (MCP) and a dedicated command-line interface (CLI). The CLI allows an agent to run commands and receive structured responses directly in its operating environment, while the MCP server enables tools like Claude to query Nansen data during a conversation. To match this access, Nansen has integrated pay-per-request payment standards such as x402 and MPP. Polat has tracked the x402 protocol since its release, noting that while early volumes were likely driven by wash trading, the start of 2026 has shown a genuine increase in usage. The logic for this shift is straightforward: Agents hate subscriptions, right? They don't go to your website, fill in their stuff, approve their email, generate their API keys through a web app or whatever. So, you have to find a way to give them an easier way of access and an easier way for paying for things. These micropayments are typically settled in USDC on networks like Base or Solana, with costs starting as low as one cent per query. This model allows an agent to manage its own wallet and pay for only the data it consumes. While traditional REST API usage with API keys still represents the majority of Nansen's traffic, the growth in per-request payments signals a move toward a more open and agent-native economy. This competition between institutional payment routes and open-source standards like x402 will likely determine how agentic commerce scales over the next year. Single stop for alpha. The roadmap for Nansen moves from providing data to enabling execution. The company recently launched trading on Hyperliquid and plans to support spot trading on Solana and Base. The objective is to create a unified experience where a user or an agent can find a signal and act on it without moving funds between different platforms. Later in 2026, this will expand to include tools for creating and backtesting trading strategies against historical data, allowing users to verify their ideas before committing capital. Prediction markets represent another area where labeled wallet data provides a clear advantage. Nansen has already integrated Polymarket data into its API to summarize market results, but the next step involves applying smart money analysis to these markets. By identifying named wallets or likely insiders, Nansen aims to provide transparency in markets that are often moved by social media posts or privileged information. Polat points to recent examples of social platforms selling faster access to specific feeds, which can create opportunities for manipulation that are only visible when tracked onchain. Blockchain technology provides the verifiable and trustless rails that AI agents need to manage financial decisions. Stablecoins have emerged as a primary tool in this space because they are accepted across different smart contracts and are not tied to the banking system of a single country. For Nansen, the focus remains on refining the labeling and delivery of this data so that both human traders and autonomous agents can make informed decisions in a fast-moving market.