Trading activity:
Reported 24-hour volume equals 0.21% of market capitalization. The local markets snapshot lists Uniswap V2 (Ethereum) among venues with observed trading activity.
YearBull indicators:
YearBull Rank #928. Bull Score 78/100.
YB Market Risk Low. This relative market-volatility label is not an investment-safety assessment.
Cycle Early. Observed price change: 24h 0.00% · 7d 0.00% · 30d 0.00%.
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: 67 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 Praxis (PRXS)?
Source description
“Praxis is a decentralized mesh network for AI Agent-to-Agent communication. Built on Ethereum, it enables autonomous agents to self-organize, collaborate, and evolve without central servers or gatekeepers. Using self-sovereign identity, local execution, and zero-knowledge proofs, Praxis establishes a trustless, privacy-preserving foundation for AI systems to coordinate in the open on your terms, not theirs.”
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.
Praxis (PRXS) FAQ
How does Praxis describe its approach to AI agent-to-agent communication?
Praxis presents itself as a decentralized mesh network for communication between autonomous AI agents. The project says agents can organize themselves, collaborate, and evolve without relying on central servers or gatekeepers. This positions PRXS around direct coordination among software agents rather than a centrally managed service, although the project does not specify the network’s operational limits or supported agent frameworks.
What role does self-sovereign identity play in Praxis?
The project states that Praxis uses self-sovereign identity as part of its foundation for agent coordination. In practical terms, this suggests that participating agents can manage their own identities rather than depending entirely on a central authority. Praxis connects this approach with open, trust-minimized coordination, but it does not specify the identity standards, credential formats, or recovery procedures involved.
How does Praxis address privacy during agent coordination?
Praxis positions local execution and zero-knowledge proofs as privacy-preserving components of its network. Local execution is presented as a way for activity to take place closer to the participating agents, while zero-knowledge proofs can support verification without revealing every underlying detail. The project does not specify which computations use these methods, their performance characteristics, or the exact privacy guarantees.
What does Praxis mean by allowing AI agents to operate without central gatekeepers?
The project says its mesh-network model allows autonomous agents to coordinate without central servers or gatekeepers. This implies a design preference for open participation and peer-to-peer interaction, rather than mandatory approval through one controlling intermediary. Praxis describes the result as a trustless foundation for AI systems, but it does not clarify how disputes, malicious agents, access rules, or network-wide decisions are handled.
How does Praxis connect agent collaboration with user control?
Praxis describes its model as enabling AI systems to coordinate “on your terms,” linking autonomous collaboration with user control. The project’s stated mechanisms—self-sovereign identity, local execution, and zero-knowledge proofs—are presented as supporting that aim. However, the project does not specify the user controls available in practice, such as permission settings, agent oversight, data management, or methods for revoking access.
Praxis metric comparison
This comparison is a stored snapshot generated 2026-10-24 06:30 UTC from 261 daily observations available from 2025-12-30 through 2026-10-24. 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
$0.00035477
$0.00035477
$0.00039051
0.0%
n/a
Market cap
$35.5K
$35.5K
$39.1K
0.0%
P18.0
YearBull Rank
n/a
n/a
n/a
n/a
n/a
Bull Score
n/a
n/a
n/a
n/a
n/a
Turnover
0.21%
0.21%
0.12%
0.0 pts
P39.1
YB Market Risk
n/a
n/a
Low
n/a
n/a
Cycle
n/a
n/a
Early
n/a
n/a
Median absolute daily movement 1.55%; distance from the highest local daily price -96.0%; circulating supply change 0.0%. These measurements are descriptive and do not predict future direction.
YearBull Rank update
Latest available YearBull Rank for praxis: #928.
Rank timeline (last 365 days)
Rank movement (time windows).
Reading rule: rank #120 sits higher than rank #200.
7d window: no reference point available.
30d window: no reference point available.
YearBull Rank is a relative ranking on YearBull designed to compare coins on a common scale and time window. Smaller numbers mean the coin sits higher in the YearBull list. It is best read as relative context across time windows, not as a guarantee.
Liquidity note: If the line improves during quiet periods, it can be accumulation. rank can move when liquidity redistributes across the cohort.
Listing context: If the line is step-like, watch for discrete market changes. changes can follow how the coin is routed across markets.
Phase read: If the line stair-steps, the cycle may be driven by discrete inputs. cycle pressure can surface as slow bleed in rank.
Volatility posture: If the curve is step-like, it may be reacting to discrete inputs. ranking moves can reflect regime shifts rather than one-off events.
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:
Alloca analysis - nearby market scale in the current dataset.
Stored venue snapshot. Markets last checked: 2026-07-27. Updated on a rolling basis; no fixed refresh date is promised. Venue listings and volumes are stored snapshots, not live quotes.
