Trading activity:
Reported 24-hour volume equals 0.03% of market capitalization. The local markets snapshot lists Aerodrome (Base) among venues with observed trading activity.
YearBull indicators:
YearBull Rank #7,652. Bull Score 47/100.
YB Market Risk Low. This relative market-volatility label is not an investment-safety assessment.
Cycle Early. Observed price change: 24h 1.27% · 7d -4.54% · 30d -9.16%.
Values are descriptive and should be read together rather than as a price forecast.
Read the YearBull methodology.
Snapshot date: 2026-10-27.Data history: 90 days available in the latest 90-day window.
Methodology responsibility:
YearBull’s analytical methodology and presentation rules are developed and maintained by
Alan Zelvin, Founder & Lead Crypto Researcher.
This note identifies responsibility for the methodology; it does not attribute authorship of this data snapshot.
What is GraphAI (GAI)?
Source description
“GraphAI: Delivering real-time blockchain intelligence to power on-chain AI agents integrating Real World Assets (RWAs). GraphAI enables the next generation of autonomous agents by providing high-fidelity, real-time data streams from on-chain and off-chain sources. Our infrastructure bridges the gap between decentralized networks and real-world asset ecosystems, empowering AI agents to reason, act, and transact with trustless precision. From tokenized real estate and commodities to carbon credits and financial instruments, GraphAI provides structured, actionable intelligence that allows AI agents to make informed decisions, manage risk, and execute transactions autonomously. Our platform transforms fragmented blockchain data into a unified, interoperable knowledge graph — the foundation for scalable, secure AI automation in decentralized finance (DeFi), supply chain, and asset management. Built for developers, enterprises, and protocol architects, GraphAI offers modular APIs, event-driven indexing, and semantic asset tagging — enabling the creation of truly intelligent, composable agents that can interact natively with Real World Assets in a decentralized environment. With GraphAI, we’re not just observing the blockchain — we’re teaching AI to understand and navigate it.”
This source-supplied description may contain old, promotional, or unverified claims and is not YearBull editorial analysis.
Official links and contract records appear in Key Facts. Project details can change, so verify current information with the project.
GraphAI (GAI) FAQ
How does GraphAI describe its role in supporting AI agents that work with blockchain data?
The project describes GraphAI as infrastructure that delivers real-time intelligence from on-chain and off-chain sources. It states that these data streams help autonomous AI agents reason, act, and transact with greater precision. Rather than presenting the system as a simple blockchain data viewer, GraphAI positions its platform as a way to give agents structured, actionable information for decisions and operations involving decentralized networks.
What does GraphAI mean by creating a unified knowledge graph for blockchain information?
GraphAI states that it transforms fragmented blockchain data into a unified, interoperable knowledge graph. According to the project, this structure is intended to connect information across decentralized finance, supply chains, and asset management. The stated purpose is to make data easier for AI agents and other applications to interpret and use, rather than leaving relevant information separated across different sources or formats.
Which Real World Asset categories does GraphAI say its platform can support?
The project specifically refers to tokenized real estate, commodities, carbon credits, and financial instruments. GraphAI presents its infrastructure as a bridge between decentralized networks and these real-world asset ecosystems. It states that structured intelligence about such assets can help AI agents assess information, manage risk, and execute transactions autonomously, although these are described platform capabilities rather than independently verified outcomes.
What developer features does GraphAI describe for building applications around its data?
GraphAI describes a set of modular APIs, event-driven indexing, and semantic asset tagging. Modular APIs are presented as building blocks for developers, while event-driven indexing is intended to support updates tied to blockchain or related events. Semantic tagging is described as a way to add meaningful labels and structure to assets, helping applications and AI agents work with information in a more composable form.
GraphAI metric comparison
This comparison is a stored snapshot generated 2026-10-23 06:30 UTC from 293 daily observations available from 2025-12-30 through 2026-10-23. It is separate from the latest analytical cards above. Percentiles compare the snapshot value with that day's analytical universe; a higher percentile means a larger observed value, not necessarily a better investment characteristic.
Metric
Snapshot
30d before
90d before
Change vs 30d
Universe percentile
Price
$0.00334924
$0.00380012
$0.00320793
-11.9%
n/a
Market cap
$156.5K
$151.2K
$136.2K
+3.5%
P41.1
YearBull Rank
#6,431
#7,337
#2,252
Improved 906
P24.7
Bull Score
42/100
26/100
55/100
+16.0 pts
P36.9
Turnover
0.11%
0.86%
0.89%
-0.7 pts
P32.1
YB Market Risk
Low
Low
Low
Unchanged
n/a
Cycle
Early
Early
Early
Unchanged
n/a
Median absolute daily movement 2.85%; distance from the highest local daily price -94.0%; circulating supply change -27.1%. These measurements are descriptive and do not predict future direction.
YearBull Rank context
Most recent YearBull Rank reading for graphai is #7652.
Rank timeline (last 365 days)
Rank change (reference points).
Reading rule: lower numbers mean higher placement.
7d window (2026-10-21): #6536 → #7652 (down by 1116).
30d window (2026-09-28): #7740 → #7652 (up by 88).
YearBull Rank is a comparative ordering used on YearBull to place a coin versus others using a consistent set of inputs. Smaller numbers mean the coin sits higher in the YearBull list.
Market depth: peer movement can shift relative placement even without news.
Venue context: improvement with higher churn can be a rotation phase.
Risk read: consistency often matters more than speed.
Cycle read: a single week rarely defines a phase on its own.
Editorial note:
This analysis was prepared by the YearBull research team under the direction of
Alan Zelvin,
Founder and Lead Crypto Researcher.
The assessment follows YearBull’s internal research methodology and editorial standards.
Methodology ·
Editorial Policy
Related Research
Continue with contextually connected profiles and category data:
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Swag analysis - nearby market scale in the current dataset.
Stored venue snapshot. Markets last checked: 2026-07-26. Updated on a rolling basis; no fixed refresh date is promised. Venue listings and volumes are stored snapshots, not live quotes.
