Trading activity:
Reported 24-hour volume equals 1.32% of market capitalization.
YearBull indicators:
Bull Score 0/100.
YB Market Risk —. This relative market-volatility label is not an investment-safety assessment.
Observed price change: 24h 13.41% · 7d -57.60% · 30d -92.06%.
Values are descriptive and should be read together rather than as a price forecast.
Read the YearBull methodology.
Snapshot date: 2026-02-25.Data history: 52 daily observations 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 LABS (LABS)?
Source description
“Nethara Labs is building the core economic infrastructure for AI agents that gather real-time data, act independently, and transact directly on-chain. While today’s AI models can generate content and analyze data, agents remain economically isolated: they can think, but they cannot transact. Nethara solves this by enabling agent-level autonomy; where agents earn, coordinate, and contribute value without human intervention. Our flagship product, Verus, allows anyone to deploy autonomous agents in under a minute; no servers, code, or maintenance required. These agents gather real-time intelligence and earn LABS, the native token that powers identity, staking, and transactions across the agent economy.”
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.
LABS (LABS) FAQ
How does Verus let users deploy autonomous agents?
The project says Verus enables anyone to deploy autonomous agents in under a minute, without servers, coding, or ongoing maintenance. These agents are presented as tools that can gather real-time intelligence and operate independently. The claim describes a simplified deployment process, although the project does not specify the exact setup steps, supported agent types, or operational limits.
What role does LABS play in Nethara’s agent economy?
Nethara Labs presents LABS as the native token for its proposed agent economy. According to the project, the token supports agent identity, staking, and transactions. The project also says agents can earn LABS by contributing value through activities such as gathering real-time intelligence. Specific staking rules, reward calculations, transaction fees, and identity mechanisms are not specified.
How are AI agents expected to operate without direct human intervention?
The project positions its platform around agent-level autonomy, where agents can earn, coordinate, and contribute value without continuous human involvement. It describes these agents as capable of gathering real-time data, acting independently, and transacting on-chain. These are project claims; the materials do not explain the precise decision-making safeguards, permissions, or conditions under which an agent may act.
What economic problem does Nethara Labs say it is addressing?
Nethara Labs says current AI models can generate content and analyze data but remain economically isolated because they cannot transact directly. Its proposed infrastructure aims to connect autonomous agents with earning, coordination, and on-chain transactions. The project frames this as a way for agents to participate economically, while the practical scope of supported transactions and participating services remains unspecified.
Why is LABS associated with the Base ecosystem in this project’s model?
LABS is associated with Base, and the project describes on-chain transactions as part of its agent economy. However, the project does not explain what specific role Base plays beyond that deployment context. Details such as contract architecture, supported protocols, transaction flows, and any Base-specific integrations are not specified, so the relationship should be understood narrowly as the stated network association.
LABS metric comparison
This comparison is a stored snapshot generated 2026-10-23 06:31 UTC from 52 daily observations available from 2025-12-30 through 2026-02-25. 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
$0.000558
$0.007029
n/a
-92.1%
n/a
Market cap
$55.6K
$701.3K
n/a
-92.1%
P8.9
YearBull Rank
#5,778
#3,053
n/a
Lower by 2,725
P4.6
Bull Score
17/100
43/100
n/a
-26.0 pts
P2.5
Turnover
1.32%
0.35%
n/a
+1.0 pts
P47.2
YB Market Risk
Low
Low
n/a
Unchanged
n/a
Cycle
Mid
Mid
n/a
Unchanged
n/a
Median absolute daily movement 5.64%; distance from the highest local daily price -94.1%; circulating supply change 0.0%. These measurements are descriptive and do not predict future direction.
YearBull Rank overview
Most recent YearBull Rank reading for labs is #5778.
Rank timeline (last 365 days)
Rank movement (time windows).
Reading rule: lower is better in this ranking.
7d window: no reference point available.
30d window: no reference point available.
Risk framing: a calm line with small steps can be healthier than spikes. If the last week is quiet, the current rank is usually easier to trust.
Liquidity read: a steadier line can indicate steadier access. If the curve improves but won’t hold, treat it as flow-driven.
Cycle placement: in rotations, improving rank can happen without price leadership. If 7d and 30d disagree, treat it as a transition window.
Market structure: one venue can dominate the profile in short windows. If rank can’t hold gains, it can be concentrated pressure.
YearBull Rank is an internal ordering on YearBull that positions a coin relative to the rest of the tracked universe. Smaller numbers mean the coin sits higher in the YearBull list.
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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zenZEC analysis - nearby market scale in the current dataset.
Stored venue snapshot. Markets last checked: 2026-04-20. Updated on a rolling basis; no fixed refresh date is promised. Venue listings and volumes are stored snapshots, not live quotes.
Current YearBull daily analytics are not available for this asset yet; market listings have separate freshness.
No exchange markets were returned by the public venue source when last checked on 2026-04-20.
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