ChainAware was named in CB Insights’ AI Fraud Prevention Market Map alongside Chainalysis, Elliptic, and TRM Labs – and remains the only Web3 AI token across all 200+ companies in that list. The 32 sub-agents documented here are the operational engine behind that recognition: real, deployed tools that DeFi protocols, compliance teams, launchpads, DAOs, and AI agent developers use in production today. Every agent is open-source, MIT-licensed, and available at github.com/ChainAware/behavioral-prediction-mcp .

This article classifies all 32 agents into two functional categories – Fraud Tech and Growth Tech – and for each agent provides a precise description, concrete use case, and the specific trigger conditions that signal when a team needs it. Use this as your reference guide for selecting, combining, and deploying ChainAware’s agent suite.

In This Article

• Two Categories – Fraud Tech and Growth Tech
• The Complete Classification Table – All 32 Agents
• Fraud Tech Agents – 17 Agents, Complete Reference
• Growth Tech Agents – 15 Agents, Complete Reference
• How Agents Compose Into Pipelines
• Getting Started – Integration in Three Steps
• Frequently Asked Questions

Two Categories – Fraud Tech and Growth Tech

ChainAware’s 32 agents divide into two functional categories that reflect the platform’s core thesis: the same behavioral data that prevents fraud also drives growth. Both categories draw from the same underlying Prediction MCP tools and the same 20M+ wallet persona database. The distinction lies in what question each agent answers and what action it enables.

Fraud Tech agents answer: “Can we trust this wallet, contract, token, or transaction?” They protect protocols from losses, enforce AML compliance, prevent Sybil attacks, and screen counterparties before execution. Consequently, Fraud Tech agents operate primarily at the gate – before onboarding, before transactions, before token distributions, before listing decisions. Their outputs are verdicts: allow, block, flag, reject, or escalate.

Growth Tech agents answer: “Now that we know this wallet is legitimate, how do we convert it, retain it, and grow it?” They turn behavioral intelligence into personalized acquisition, onboarding, conversion, and retention decisions. Moreover, Growth Tech agents operate primarily post-gate – after a wallet passes initial screening, they determine how to engage it most effectively. Their outputs are recommendations: which product to surface, which message to send, which onboarding flow to show, which upsell to offer.

Furthermore, both categories share a fraud gate: every Growth Tech agent checks probabilityFraud before generating any recommendation and blocks output for high-risk wallets. This means the categories are not sequential stages but parallel layers – fraud protection runs continuously across every growth decision. For the foundational framework explaining why behavioral intelligence is essential for both fraud prevention and growth, see our Web3 Behavioral User Analytics guide .

The Complete Classification Table – All 32 Agents

The table below lists every agent with its category, primary MCP tool, supported networks, and core function. Agents are sorted by category, then by specificity – from broad-purpose agents to narrow specialists. Use this as your quick-reference lookup before reading the detailed descriptions that follow.

#	Agent	Category	Primary Tool	Networks	Core Function
1	fraud-detector	Fraud Tech	predictive_fraud	ETH BNB POLYGON TON BASE TRON HAQQ	Wallet fraud probability (98% accuracy) + 19 AML forensic flags
2	rug-pull-detector	Fraud Tech	predictive_rug_pull	ETH BNB BASE HAQQ	90.1% rug pull prediction – contract + deployer behavioral analysis
3	aml-scorer	Fraud Tech	predictive_fraud	ETH BNB POLYGON TON BASE TRON HAQQ	AML score (0-100) with full forensic flag breakdown
4	trust-scorer	Fraud Tech	predictive_fraud	ETH BNB POLYGON TON BASE TRON HAQQ	Trust score (0.00-1.00) = 1 − fraud probability. Composable building block
5	sybil-detector	Fraud Tech	predictive_behaviour + predictive_fraud	ETH BNB BASE HAQQ SOL + fallback	Batch Sybil detection – wallet farms, coordinated attacks, proxy voting fraud
6	governance-screener	Fraud Tech	predictive_behaviour + predictive_fraud	ETH BNB BASE HAQQ SOL + fallback	DAO voter tier (Core Contributor → Disqualified) + voting weight multiplier
7	counterparty-screener	Fraud Tech	predictive_behaviour + predictive_fraud	ETH BNB BASE HAQQ SOL + fallback	Pre-transaction Safe / Caution / Block verdict in a single API call
8	compliance-screener	Fraud Tech	Orchestrator	Multi-chain via sub-agents	MiCA-aligned PASS / EDD / REJECT with full documented evidence trail
9	transaction-monitor	Fraud Tech	predictive_behaviour + predictive_rug_pull	ETH BNB BASE HAQQ SOL + fallback	Real-time ALLOW / FLAG / HOLD / BLOCK for autonomous agent pipelines
10	token-launch-auditor	Fraud Tech	predictive_rug_pull + predictive_fraud	ETH BNB BASE HAQQ	Launchpad listing audit → APPROVED / CONDITIONAL / REJECTED + safety badge
11	airdrop-screener	Fraud Tech	predictive_behaviour + predictive_fraud	ETH BNB BASE HAQQ SOL + fallback	Batch airdrop eligibility – filters bots, ranks eligible wallets by reputation
12	rwa-investor-screener	Fraud Tech	predictive_behaviour + predictive_fraud	ETH BNB BASE HAQQ SOL + fallback	RWA investor suitability → QUALIFIED / CONDITIONAL / REFER_TO_KYC / DISQUALIFIED
13	gamefi-screener	Fraud Tech	predictive_behaviour + predictive_fraud	ETH BNB BASE HAQQ SOL + fallback	P2E bot farm and multi-account cheater detection + player tier classification
14	credit-scorer	Fraud Tech	credit_score	ETH	Crypto credit score (1-9) combining fraud probability + social graph analysis
15	lending-risk-assessor	Fraud Tech	predictive_behaviour + credit_score	ETH BNB BASE HAQQ SOL	Borrower risk grade (A-F) + recommended collateral ratio + interest rate tier
16	portfolio-risk-advisor	Fraud Tech	predictive_rug_pull + token_rank_single	ETH BNB BASE HAQQ	Portfolio rug pull scan → grade A-F + prioritized exit/reduce plan
17	agent-screener	Fraud Tech	predictive_fraud + predictive_behaviour + predictive_rug_pull	ETH BNB BASE HAQQ SOL + fallback	AI agent trust score (0-10) screening agent wallet + feeder wallet
18	wallet-auditor	Growth Tech	predictive_behaviour	ETH BNB BASE HAQQ SOL	Complete 22-dimension Web3 Persona – fraud + behavioral + personalization
19	reputation-scorer	Growth Tech	predictive_behaviour	ETH BNB BASE HAQQ SOL	Reputation score (0-1000) = experience × risk_capability × (1 − fraud)
20	wallet-ranker	Growth Tech	predictive_behaviour	ETH BNB BASE HAQQ SOL	Global wallet rank from experience, total points, age, transaction count
21	whale-detector	Growth Tech	predictive_behaviour	ETH BNB BASE HAQQ SOL	Whale tier (Mega / Whale / Emerging) + Active/Dormant status + domain
22	ltv-estimator	Growth Tech	predictive_behaviour + predictive_fraud	ETH BNB BASE HAQQ SOL + fallback	12-month revenue potential (USD range) from behavioral + risk signals
23	lead-scorer	Growth Tech	predictive_behaviour + predictive_fraud	ETH BNB BASE HAQQ SOL + fallback	Lead score (0-100) + Hot/Warm/Cold/Dead + recommended outreach angle
24	wallet-marketer	Growth Tech	predictive_behaviour	ETH BNB BASE HAQQ SOL	Hyper-personalized marketing message (max 20 words) from on-chain signals
25	platform-greeter	Growth Tech	predictive_behaviour + predictive_fraud	ETH BNB BASE HAQQ SOL + fallback	Platform-specific welcome message (max 35 words) – different per platform
26	onboarding-router	Growth Tech	predictive_behaviour	ETH BNB BASE HAQQ SOL	Onboarding flow decision – Beginner / Intermediate / Skip from real experience
27	defi-advisor	Growth Tech	predictive_behaviour	ETH BNB BASE HAQQ SOL	Personalized DeFi product recommendations (3 tiers) by experience + risk
28	upsell-advisor	Growth Tech	predictive_behaviour	ETH BNB BASE HAQQ SOL	Upgrade readiness (0-100) + next product + trigger event + conversion probability
29	cohort-analyzer	Growth Tech	predictive_behaviour + predictive_fraud	ETH BNB BASE HAQQ SOL + fallback	Batch behavioral cohort segmentation – 8 cohorts + per-cohort strategy
30	token-ranker	Growth Tech	token_rank_list	ETH BNB BASE SOL	Token discovery by community strength – AI / RWA / DeFi / DeFAI / DePIN
31	token-analyzer	Growth Tech	token_rank_single + predictive_fraud	ETH BNB BASE SOL	Single-token deep-dive: community rank + top holder profiles + fraud screening
32	marketing-director	Growth Tech	Orchestrator (7 specialist agents)	All networks via sub-agents	Full-cycle campaign orchestrator → complete Marketing Campaign Brief

Fraud Tech Agents – 17 Agents, Complete Reference

ChainAware’s Fraud Tech agents protect Web3 protocols from the full spectrum of on-chain threats: wallet fraud, rug pulls, money laundering, Sybil attacks, governance manipulation, P2E cheating, and fraudulent AI agents. Together, they cover every point in the protocol lifecycle where malicious actors attempt to extract value – from the moment a wallet first connects to the moment a transaction executes. According to FATF’s Virtual Assets Recommendations , the compliance requirements for crypto asset service providers now demand pre-execution risk assessment that legacy forensic tools were never designed to deliver. ChainAware’s Fraud Tech agents fill that gap with predictive behavioral intelligence rather than reactive forensic lookup.

Moreover, these agents share a critical structural advantage over traditional blockchain forensics: they analyze behavioral patterns across 20M+ wallet personas rather than matching against static blocklists. Professional fraud operators deliberately evade blocklist-based tools by using fresh wallets and clean contract code. They cannot, however, mask their behavioral fingerprint – the pattern of on-chain activity that identifies an operator regardless of which specific address they use today. This is why ChainAware achieves 98% fraud detection accuracy on ETH where forensic tools frequently miss sophisticated operators. For the complete technical comparison, see our Forensic vs AI-Powered Analytics guide .

1. chainaware-fraud-detector

The flagship fraud detection agent calls predictive_fraud on any wallet address and returns a fraud probability score, wallet status (Not Fraud / Fraud / New Address), OFAC sanctions check, and 19 AML forensic flags covering mixers, darknet transactions, phishing wallets, fake token creation, money laundering patterns, cybercrime associations, and more. Accuracy reaches 98% on ETH and 96% on BNB, backtested against CryptoScamDB – the largest publicly available database of documented crypto fraud incidents. Coverage spans 7 networks: ETH, BNB, POLYGON, TON, BASE, TRON, and HAQQ.

Use Case: A DeFi lending protocol screens every wallet requesting a loan before processing the application. The team integrates chainaware-fraud-detector into its onboarding API – each new wallet receives a fraud probability score and forensic flag check in under one second. Wallets scoring above 0.70 are automatically declined. Wallets between 0.40 and 0.70 route to enhanced due diligence. Wallets below 0.20 pass to the standard lending flow. The same agent works equally well for exchange KYC pre-screening, NFT allowlist vetting, and airdrop participant verification.

When Is It Required: Deploy chainaware-fraud-detector whenever a protocol accepts wallet connections from unknown participants – particularly before any value transfer, credit extension, or whitelist grant. It is specifically required when a protocol falls under MiCA, AML5D, or equivalent regulation that mandates pre-onboarding risk assessment. Additionally, it is required before running any Growth Tech agent on a wallet – the fraud gate in chainaware-wallet-marketer and chainaware-ltv-estimator calls this agent’s underlying tool before generating any recommendation. For the complete implementation methodology, see our Fraud Detector guide .

2. chainaware-rug-pull-detector

Analyzes smart contracts, liquidity pools, and token launches for rug pull risk before any capital is deployed. The agent runs predictive_rug_pull on the contract address and predictive_fraud on the deployer wallet, combining both into a unified verdict. Critically, the deployer fraud score can escalate the overall verdict by one tier – a contract scoring 0.35 (Medium risk) paired with a deployer scoring 0.72 (High risk) produces a combined High Risk verdict. This escalation catches the most dangerous category of rug pulls: professionally deployed clean contracts by operators with documented fraud histories on other wallets. Accuracy on the PancakeSwap V2 dataset reaches 90.1%, covering $569M in documented rug pull losses from weeks 1-20 of 2026. Networks supported: ETH, BNB, BASE, HAQQ.

Use Case: A DEX launchpad reviews 50 new token submissions per week. Without automated screening, each review requires a developer to manually inspect contract code and trace the deployer wallet – a process taking 30-60 minutes per token. With chainaware-rug-pull-detector, the launchpad runs all 50 contracts in batch mode and receives a ranked risk table in minutes. Contracts scoring above 0.80 are automatically rejected. Contracts between 0.50 and 0.80 require manual review with specific red flags already identified. Contracts below 0.20 proceed to standard listing.

When Is It Required: Use chainaware-rug-pull-detector before listing any token on a DEX, before depositing LP into any new pool, before investing in any IDO or pre-sale, and before any yield vault strategy deploys capital into a new protocol. It is specifically required for launchpad teams that need a standardized, reproducible audit process rather than ad hoc developer reviews. It pairs with chainaware-token-launch-auditor when a full public-facing audit report with a safety badge is needed. For the detailed comparison against GoPlus, Token Sniffer, and Honeypot.is, see our Rug Pull Detection Tools guide .

3. chainaware-aml-scorer

Calculates a structured AML score (0-100) using a two-branch logic that separates forensic compliance from probabilistic fraud risk. If any forensic flag is present – mixer usage, sanctioned entity association, stolen funds link, darknet transaction, ransomware wallet interaction – the AML score is 0 regardless of the fraud probability score. This hard-zero rule reflects regulatory reality: a forensic flag requires human review and escalation regardless of the overall risk probability. When forensics are clean, the AML score equals (1 − probabilityFraud) × 100, providing a continuous risk gradient for compliance tiering. The agent returns the complete forensic breakdown alongside the score, producing output that is audit-ready for regulatory review under MiCA and equivalent frameworks.

Use Case: A crypto exchange onboards 500 new wallets per day and must document AML screening decisions for regulatory reporting. Previously, the compliance team ran manual checks on wallets flagged by a basic blocklist – a process that missed sophisticated operators and created a documentation backlog. With chainaware-aml-scorer, every onboarding wallet receives an automated AML report in under one second. Wallets scoring 0 (forensic flag detected) escalate to the compliance team with the specific flags identified. Wallets scoring 71-100 receive automated approval documentation. Wallets in the 41-70 range trigger enhanced due diligence with a specific set of additional checks, creating a complete and auditable compliance trail for every onboarded wallet.

When Is It Required: Deploy chainaware-aml-scorer for any platform falling under AML/CFT regulatory requirements – exchanges, OTC desks, lending protocols, and any DeFi platform accepting significant TVL from institutional wallets. It is also required when chainaware-compliance-screener is the orchestrating agent, since compliance-screener calls aml-scorer as one component of its structured MiCA-aligned report. See our MiCA Compliance guide for the full regulatory compliance stack.

4. chainaware-trust-scorer

Returns a single trust score using one formula: Trust Score = 1 − fraud_probability. The output ranges from 0.00 (confirmed fraud) to 1.00 (zero fraud probability). Designed as a composable building block rather than a standalone product, trust-scorer feeds into other calculations across the agent suite: reputation score uses it as the base fraud penalty, AML score uses it as the clean-forensics branch, governance vote weighting multiplies by it, and marketing campaign gates use it as a minimum threshold before message generation. Covers 7 networks via predictive_fraud. Response time is sub-100ms by design, making it the fastest agent in the suite.

Use Case: A developer building a custom reputation system for their DeFi protocol needs a standardized trust signal to combine with their own on-chain activity metrics. Rather than building a fraud detection model from scratch, they integrate chainaware-trust-scorer as the fraud component and combine it with their own activity score. The resulting composite score inherits ChainAware’s 98% fraud accuracy while adding protocol-specific activity signals that ChainAware’s general model does not capture. The trust score’s mathematical cleanliness – it is simply the complement of fraud probability – makes it easy to incorporate into any scoring formula.

When Is It Required: Use chainaware-trust-scorer whenever a custom scoring formula needs a standardized, high-accuracy fraud component – governance vote weighting, airdrop allocation, lending collateral ratios, and marketing campaign eligibility gates all benefit from incorporating the trust score as a fraud signal. It is the recommended starting point for teams building composite scores rather than using a pre-built agent, since its output is mathematically clean and directly interpretable.

5. chainaware-sybil-detector

Batch-screens wallet lists for Sybil attacks, coordinated voting fraud, and wallet farm operations. Beyond individual wallet scoring, the agent applies four pattern detection rules across the full submitted set: a cluster flag triggers when 10%+ of wallets share experience scores within ±0.2 points and were created in the same approximate period – the signature of a coordinated wallet farm. A fraud concentration flag triggers when 20%+ of voters show fraud probability above 0.25. A new wallet surge flag triggers when 30%+ of wallets have experience below 1.5. A uniform risk profile flag triggers when 60%+ share identical behavioral categories, indicating coordination rather than organic community diversity. Each wallet is classified as ELIGIBLE, REVIEW, or EXCLUDE, and the cleaned voter list is ready for Snapshot or on-chain governance integration.

Use Case: A DAO preparing a governance vote on a $2M treasury allocation notices unusual activity: 400 new wallets registered in the 48 hours before the vote, all with minimal transaction history. Running chainaware-sybil-detector on the full voter list identifies 312 of those 400 wallets as part of a coordinated new-wallet cluster, disqualifying them from the vote. The attack is neutralized before it reaches quorum. The cleaned voter list shows genuine community support from 89 ELIGIBLE voters, and the vote proceeds with integrity intact.

When Is It Required: Run chainaware-sybil-detector before any governance vote controlling significant treasury funds, parameter changes, or upgrade authority. It is specifically required before Snapshot votes for DAOs with public token distribution, before on-chain governance proposals reaching quorum thresholds, and before any delegation validation process where vote weight can be amplified through coordinated proxy delegation. For the complete governance protection framework, see our Governance Screeners guide .

6. chainaware-governance-screener

DAO voter screening with four-tier classification and voting weight calculation. The agent assigns each wallet to one of five tiers: Core Contributor (experience ≥ 8, fraud ≤ 0.10, protocols ≥ 5 → 2.0× multiplier), Active Member (experience ≥ 5, fraud ≤ 0.25, protocols ≥ 2 → 1.5×), Participant (experience ≥ 2, fraud ≤ 0.40 → 1.0×), Observer (new address or experience < 2 with low fraud → 0.5×), and Disqualified (fraud gate fails → 0.0×). Within each tier, the multiplier adjusts downward for elevated fraud probability. Three governance models are supported: token-weighted, reputation-weighted (ChainAware reputation score as direct weight), and quadratic (multiplier applies to square root of token balance).

Use Case: A DeFi protocol wants to implement reputation-weighted governance to counteract plutocracy – the tendency of token-weighted systems to concentrate governance power in the largest holders regardless of protocol engagement. Using chainaware-governance-screener in reputation-weighted mode, every voter’s influence is determined by behavioral quality rather than token balance alone. A Core Contributor holding 1,000 tokens has more governance weight than a dormant whale holding 100,000 tokens but showing no protocol engagement. The result is governance that rewards genuine contributors rather than passive large holders.

When Is It Required: Deploy chainaware-governance-screener for any DAO that needs to validate voter quality before a proposal goes live. It is particularly required for protocols implementing reputation-weighted or quadratic voting models, for DAOs with public token distributions vulnerable to Sybil accumulation, and for any governance system where a single bad-faith actor could acquire enough voting power to pass a malicious proposal. It works alongside chainaware-sybil-detector – the Sybil detector identifies coordinated wallet farms, while governance-screener classifies remaining legitimate voters by quality tier.

7. chainaware-counterparty-screener

Pre-transaction safety agent optimized for minimum latency and maximum decisiveness. A single predictive_behaviour call retrieves both the fraud probability and the behavioral context needed for ambiguous cases – eliminating the two-call pattern that adds latency to pre-transaction flows. The verdict logic applies decisive rules first (confirmed fraud or forensic flag → immediate Block; fraud probability ≤ 0.15 → immediate Safe) and contextual rules only for the 0.16-0.70 range. Transaction-type context adjusts the risk assessment: approve actions receive a 1.3× risk multiplier, bridge and liquidity actions 1.2×, stake actions 0.9×. Compact mode returns a single line for autonomous agent pipelines.

Use Case: A DeFi aggregator routes user transactions across multiple protocols and counterparties. Before executing any multi-hop route, the aggregator’s AI agent calls chainaware-counterparty-screener on every intermediate counterparty address. A Block verdict causes the agent to find an alternative route avoiding the flagged address. A Caution verdict triggers additional monitoring for the transaction. A Safe verdict allows execution to proceed normally. The entire screening adds under 200ms to the routing decision – negligible for a user experience that already involves multi-second blockchain confirmation times.

When Is It Required: Use chainaware-counterparty-screener immediately before signing any transaction with an unknown counterparty – particularly token approvals (highest risk action type), LP deposits (contract risk), bridge transactions (irreversible cross-chain exposure), and high-value transfers. For autonomous AI agents executing transactions without human review, this agent provides the fraud gate that substitutes for human judgment. It pairs naturally with chainaware-transaction-monitor: counterparty-screener handles the pre-transaction check on specific addresses, while transaction-monitor handles real-time pipeline risk scoring across sender, receiver, and contract simultaneously.

8. chainaware-compliance-screener

The most comprehensive compliance agent in the suite – a MiCA-aligned orchestrator sequencing AML scoring, fraud detection, and transaction risk assessment into a single structured Compliance Report with a three-tier verdict: PASS, ENHANCED DUE DILIGENCE, or REJECT. Unlike the individual specialist agents, compliance-screener is specifically designed to produce documentation: every signal, every flag, every threshold applied is recorded in the output, creating an audit trail that compliance officers can present to regulators. The verdict structure mirrors MiCA’s layered compliance approach – PASS wallets proceed normally, EDD wallets receive additional checks before service, REJECT wallets are declined with specific reasons documented.

Use Case: A crypto asset service provider (CASP) operating under MiCA needs to document its compliance process for every customer onboarding. Manual KYC combined with blockchain forensics produces reports taking hours per customer and lacking standardization. With chainaware-compliance-screener, every onboarded wallet receives an automated, structured Compliance Report in under 5 seconds – covering sanctions screening, AML forensic flags, behavioral fraud risk, and transaction pattern analysis. The report format is consistent across all wallets, making regulatory reporting systematic rather than ad hoc. EDD cases are automatically flagged with the specific signals that triggered the enhanced review requirement.

When Is It Required: Deploy chainaware-compliance-screener for any platform regulated under MiCA, AML5D, FinCEN guidance, or equivalent frameworks requiring documented pre-onboarding risk assessment. It is specifically required when a compliance team needs to demonstrate to regulators that their screening process is systematic, documented, and applied consistently – not selectively or manually. The agent is also the right choice for institutional DeFi platforms serving accredited investors where documented compliance is a prerequisite for institutional capital access. For the complete regulatory compliance cost comparison, see our MiCA Compliance guide .

9. chainaware-transaction-monitor

Real-time transaction risk scoring designed for autonomous AI agent pipelines rather than human compliance review. The agent screens sender, receiver, and contract address simultaneously, computes a composite risk score (0-100) using weighted contributions from each address, applies action-type multipliers (approve 1.3×, bridge and liquidity 1.2×, stake 0.9×, unknown 1.1×), and returns a machine-actionable ALLOW / FLAG / HOLD / BLOCK signal. Override rules immediately produce a BLOCK regardless of composite score whenever sender or receiver carries confirmed fraud status or any AML forensic flag. Compact mode returns a single-line signal for mempool monitoring and high-frequency agent pipelines where sub-50ms response is required.

Use Case: A DeFi trading bot executes 200+ transactions per day across multiple protocols. Without transaction monitoring, the bot has no way to detect when it is being routed through a fraudulent intermediary or interacting with a compromised contract. With chainaware-transaction-monitor as a pre-execution hook, every transaction is screened in under 100ms before signing. BLOCK signals cause the bot to abort the transaction and find an alternative path. FLAG signals execute but generate a compliance log entry for review. Over a 30-day period, the monitoring prevents the bot from executing 14 transactions with BLOCK-level counterparties – including two interactions with wallet addresses later confirmed as hack-related by blockchain investigators.

When Is It Required: Deploy chainaware-transaction-monitor for any autonomous AI agent executing blockchain transactions without per-transaction human approval. This specifically includes DeFi trading bots, yield optimization agents, automated treasury management systems, and any AI agent operating under the emerging ERC-8004 standard for on-chain agent identity. It is also required for any protocol needing ongoing post-onboarding transaction screening – complementing chainaware-fraud-detector (which handles one-time onboarding checks) with continuous monitoring of user activity. For the complete integration guide, see our Transaction Monitoring guide .

10. chainaware-token-launch-auditor

Launchpad listing audit agent combining rug pull detection on the contract with full fraud and behavioral analysis on the deployer wallet. The output includes a composite Launch Safety Score, a public-facing safety badge suitable for embedding on listing pages, and specific conditions the launchpad should impose – mandatory LP lock periods, restricted admin key permissions, or vesting schedule requirements. The three-tier verdict (APPROVED, CONDITIONAL, REJECTED) gives launchpad teams a standardized decision framework they can communicate publicly to investors. CONDITIONAL listings include explicit conditions that, if met, convert the listing to APPROVED.

Use Case: An IDO launchpad receives a new project application for a DeFi token on BNB. The applying team has a polished website, a detailed whitepaper, and a professionally written smart contract that passes standard code review. However, chainaware-token-launch-auditor detects that the deployer wallet has previously deployed three tokens on ETH, all of which experienced LP withdrawal events within 72 hours of launch – a behavioral signature of serial rug pull operations. The contract score is 0.28 (Medium) but the deployer score is 0.81 (Critical), producing a REJECTED verdict. The launchpad declines the listing. Three weeks later, the same team launches the token on an unscreened DEX, where it rugs within 36 hours.

When Is It Required: Run chainaware-token-launch-auditor before approving any token listing on a launchpad or DEX maintaining listing standards. It is specifically required for platforms displaying a safety badge or endorsement alongside listed tokens – without auditor-backed evidence, any safety claim creates legal and reputational liability. The agent is also required for any accelerator or incubator program vetting projects before providing funding or platform access. It works as a pre-listing screening gate for token sale platforms where retail investors rely on the platform’s due diligence.

11. chainaware-airdrop-screener

Batch airdrop eligibility engine that filters fraud wallets, bots, and Sybil clusters from token distribution lists, then ranks eligible wallets by ChainAware’s reputation formula for merit-based allocation. Five disqualification rules apply in order: fraud probability above 0.70 → HIGH FRAUD disqualified; confirmed fraud status → CONFIRMED FRAUD disqualified; new address with fraud above 0.40 → SUSPICIOUS NEW disqualified; new address with zero experience and no categories → BOT/FRESH disqualified; any AML forensic flag → AML FLAG disqualified. Surviving wallets receive a reputation score calculated as (1000/110) × (experience + 1) × (risk_capability + 1) × (1 − fraud_probability) and are assigned allocation multipliers from 0.5× (Low Score) to 4× (Elite). When a token budget is provided, the agent calculates exact per-wallet token allocations ready to plug into a Merkle tree contract.

Use Case: A DeFi protocol distributes 10 million tokens across 5,000 wallet addresses collected through a six-week quest campaign. Without screening, ChainAware’s analysis of similar campaigns finds that approximately 84% of campaign participants are ghost wallets – addresses with zero real engagement that bot operators control mechanically. Running chainaware-airdrop-screener on the 5,000 addresses disqualifies 3,420 as bots, fraud, or suspicious new wallets. The remaining 1,580 eligible wallets are ranked by reputation score and receive allocations scaled from 0.5× to 4× of the base amount. The protocol distributes tokens to genuine community members, avoids immediate sell pressure from farming wallets, and creates a foundation of quality token holders.

When Is It Required: Run chainaware-airdrop-screener before every token distribution event – regardless of campaign size. It is specifically required for distributions above 100,000 USD equivalent where bot farming has high economic incentive, for any distribution including vesting where recipient quality affects long-term token price stability, and for governance token airdrops where recipient quality directly affects the quality of future governance participation. The agent pairs naturally with chainaware-sybil-detector (which identifies coordination patterns before disqualification) and chainaware-reputation-scorer (which provides the ranking formula for tiered allocations).

12. chainaware-rwa-investor-screener

Real World Asset investor suitability screening assessing three dimensions simultaneously: AML/fraud compliance (40% weight), investor sophistication via on-chain experience score (35%), and risk profile alignment against the RWA’s declared risk tier (25%). The composite Suitability Score (0-100) maps to four tiers: QUALIFIED (full access, standard caps), CONDITIONAL (reduced cap, enhanced monitoring), REFER_TO_KYC (on-chain profile insufficient, route to manual KYC), and DISQUALIFIED (fraud gate, AML flag, or confirmed fraud). Recommended investment caps are tied to experience level within each tier – a QUALIFIED Sophisticated investor has no cap, while a QUALIFIED Intermediate investor caps at $25,000. Three RWA risk tiers define minimum experience thresholds: conservative (≥ 2.0), moderate (≥ 4.0), aggressive (≥ 6.5).

Use Case: A tokenized real estate platform onboards investors for a $50M moderate-risk RWA offering. Traditional KYC takes 3-5 days per investor. The platform needs to process 2,000 investor applications in a two-week window before the offering closes. Chainaware-rwa-investor-screener processes all 2,000 wallets in batch mode in under 10 minutes, classifying 1,240 as QUALIFIED, 380 as CONDITIONAL, 210 as REFER_TO_KYC, and 170 as DISQUALIFIED. The 170 disqualified wallets are excluded immediately. The 1,620 QUALIFIED and CONDITIONAL wallets complete automated onboarding in minutes – dramatically reducing compliance cost and time-to-investment for legitimate investors.

When Is It Required: Deploy chainaware-rwa-investor-screener for any tokenized asset platform needing automated investor suitability assessment. It is specifically required when traditional KYC throughput is insufficient for the number of investors the platform needs to process, when the regulatory framework requires documented suitability assessment rather than just AML screening, and when the platform offers products across multiple risk tiers requiring different investor qualification standards. It complements chainaware-compliance-screener (which handles AML compliance) by adding the investor sophistication and product suitability dimensions that pure AML screening does not cover.

13. chainaware-gamefi-screener

Play-to-Earn bot farm and multi-account cheater detection for Web3 games. The agent screens wallets connecting to a P2E platform for bot signatures (coordinated transaction timing, uniform behavioral patterns, zero genuine game interaction history), multi-account cheating (same operator controlling multiple wallets extracting parallel rewards), and reward abuse patterns (wallets appearing across multiple P2E reward events in behavioral coordination). Legitimate players are classified into experience tiers for matchmaking and receive P2E reward eligibility scores scaling allocations by behavioral quality. The fraud gate disqualifies wallets above 0.70 fraud probability regardless of game-specific behavior.

Use Case: A P2E game launches a tournament with $100,000 in prize pool rewards. Within 48 hours, 40% of tournament participants are identified as bot farms – coordinated wallet clusters playing mechanically to extract rewards without genuine gameplay. Chainaware-gamefi-screener deployed at tournament registration identifies the bot wallets before they accumulate rewards. The disqualified wallets are excluded. Remaining players are classified into tiers from Beginner to Expert and receive reward multipliers (0.5× to 4×) scaled to their on-chain gaming experience. Prize pool distribution shifts from bot-dominated to skill-correlated, improving tournament integrity and the genuine player community’s experience.

When Is It Required: Run chainaware-gamefi-screener at every P2E tournament registration, every in-game reward event, and every NFT loot drop in a play-to-earn context. It is specifically required for any P2E game with real economic value at stake – when rewards are worth more than the cost of running bots, bot farms appear without exception. The agent is also required for scholarship programs in P2E games, where scholarship managers need to verify that scholar wallets are controlled by genuine individual players rather than farming operations controlling multiple scholarship slots simultaneously.

14. chainaware-credit-scorer

Returns a crypto credit score from 1 to 9 using ChainAware’s credit_score tool, combining fraud probability with social graph analysis of the wallet’s transaction network. Score 9 is Prime (highest creditworthiness, best lending terms). Score 1 is Very High Risk (decline lending). Currently supported on ETH only, where social graph data density is highest. The credit score is the simplest borrower signal in the suite – designed specifically as a composable building block that chainaware-lending-risk-assessor combines with experience score and risk appetite to produce a full Borrower Risk Grade.

Use Case: A DeFi lending protocol wants to offer differentiated interest rates based on borrower quality – lower rates for high-credit-score borrowers to attract and retain the best users, higher rates for lower-credit-score borrowers to compensate for elevated default risk. Chainaware-credit-scorer provides the credit signal driving the rate differentiation. Prime borrowers (score 9) receive the protocol’s best rate. High-Risk borrowers (score 1-2) are declined or required to over-collateralize at 200%+. The differentiation improves risk-adjusted revenue and creates a meaningful incentive for borrowers to maintain clean on-chain behavior over time.

When Is It Required: Use chainaware-credit-scorer as a component within chainaware-lending-risk-assessor for full borrower risk assessment, or standalone when a simple 1-9 credit rating is sufficient for the use case. It is specifically required for ETH-based lending protocols wanting a standardized credit signal compatible with the broader DeFi lending ecosystem. For multi-chain lending platforms, chainaware-lending-risk-assessor provides broader coverage by combining the credit score with behavioral signals from the full Prediction MCP toolset. See our Credit Score guide for the complete methodology.

15. chainaware-lending-risk-assessor

Full borrower risk assessment for DeFi lending protocols – combining fraud probability, on-chain experience score, risk appetite classification, and (on ETH) credit score into a Borrower Risk Grade from A to F with specific recommended collateral ratio and interest rate tier. Grade A borrowers (low fraud, high experience, appropriate risk profile) receive the best terms. Grade F borrowers are declined. The agent covers ETH, BNB, BASE, HAQQ, and SOLANA – enabling multi-chain lending platforms to apply consistent underwriting standards across all supported networks using behavioral signals rather than collateral value as the only risk proxy.

Use Case: A DeFi lending protocol currently applies a flat 150% collateralization ratio to every borrower regardless of on-chain history. This approach drives away high-quality borrowers who resent over-collateralization for loans they will clearly repay. With chainaware-lending-risk-assessor, the protocol offers Grade A borrowers 110% collateralization at the best rate, Grade B borrowers 130% at standard rates, and Grade C borrowers 160% at elevated rates. Grade D-F wallets are declined or required to provide significant over-collateral. Capital efficiency improves, quality borrower acquisition increases, and risk-adjusted returns improve across the loan book.

When Is It Required: Deploy chainaware-lending-risk-assessor for any DeFi lending or credit protocol wanting to move beyond collateral-only risk assessment. It is specifically required for undercollateralized or uncollateralized DeFi lending products, where behavioral risk signals are the primary protection against default. Additionally, it is required for any lending protocol seeking to compete on borrower experience by offering differentiated rates – flat-rate protocols cannot attract and retain the highest-quality borrowers who have better options elsewhere.

16. chainaware-portfolio-risk-advisor

Portfolio-level rug pull risk scan that evaluates every token in a submitted portfolio, aggregates risk into a Portfolio Risk Score (0-100) and grade (A-F), flags dangerous concentrations, and produces a prioritized exit/reduce rebalancing plan. The primary signal for each token is its rug pull probability from predictive_rug_pull. Supplementary community rank from token_rank_single enriches the risk assessment with holder quality data for the approximately 2,500-3,000 tokens covered by the pre-calculated index. Concentration flags alert when a single high-risk token represents more than 20% of portfolio value (Critical Concentration) or when multiple tokens share the same deployer (Cluster Risk).

Use Case: A DeFi investor holds 12 positions across ETH and BNB, total value $85,000. Three tokens have no community rank data and significant social media promotion – a combination warranting scrutiny. Running chainaware-portfolio-risk-advisor identifies two of those three tokens as High Risk (TRS 58 and 71), with deployer behavioral signatures consistent with previous rug pull operations. The agent produces a rebalancing plan: exit both High Risk positions immediately ($12,400 combined), reduce a Moderate Risk position to 5% of portfolio, and hold the remaining nine positions scoring Low Risk. The investor exits before the highest-risk position rugs two weeks later.

When Is It Required: Run chainaware-portfolio-risk-advisor before deploying significant new capital into any multi-token DeFi position, before any rebalancing decision in a portfolio containing tokens launched in the last 90 days, and as a regular monthly audit of any DeFi portfolio containing more than five positions. It is specifically required for protocols managing DAO treasuries or yield strategies on behalf of users, where portfolio risk is a fiduciary responsibility rather than a personal investment choice.

17. chainaware-agent-screener

The first dedicated AI agent trust scoring tool in the on-chain intelligence market. Screens two addresses simultaneously: the agent wallet (the address the autonomous agent uses to transact) and the feeder wallet (the address that funds the agent). The feeder wallet is typically the most revealing signal – a fraudulent feeder means the agent operates on behalf of a bad actor regardless of how clean the agent wallet appears. The output is a normalized Agent Trust Score from 0 to 10: 0 means confirmed or likely fraud, 1 means new address with insufficient data, and 2.0-10.0 is a normalized reputation score. When the agent wallet is a smart contract rather than an EOA, behavioral data is unavailable and the score is capped at 6.0 with a proxy calculation. This directly addresses the structural vulnerability in the ERC-8004 agent registry – 196,000+ registered agents with no behavioral trust signals attached to their on-chain identities.

Use Case: A DeFi protocol evaluating whether to accept automated interactions from third-party AI trading agents faces a core challenge: without agent trust scoring, the protocol cannot distinguish between a legitimate institutional trading bot and a fraudulent agent designed to manipulate protocol state. Running chainaware-agent-screener on each agent’s wallet and feeder wallet produces a trust score used as an access gate. Agents scoring 7.0+ receive full access. Agents scoring 4.0-6.9 receive limited access with lower transaction limits and no admin function access. Agents scoring below 4.0 or with Score 0 are blocked entirely. Score 1 (new feeder wallet) triggers a manual review before access is granted.

When Is It Required: Deploy chainaware-agent-screener whenever a protocol, DEX, lending platform, or DAO accepts or considers accepting automated interactions from third-party AI agents. As the agentic economy grows – with AI agents increasingly operating autonomously across DeFi, executing trades, managing positions, and participating in governance – the need for behavioral trust assessment of agents becomes as important as the need for behavioral trust assessment of human wallets. The agent is also required for ERC-8004 registry participants seeking to validate the trustworthiness of other registered agents before delegating tasks or sharing resources with them. For context on the growing agentic economy and its fraud implications, see our Web3 Agentic Economy guide .

Growth Tech Agents – 15 Agents, Complete Reference

ChainAware’s Growth Tech agents convert the same behavioral intelligence that prevents fraud into measurable protocol growth – higher conversion rates, better user retention, smarter acquisition spend, and more relevant product recommendations. The foundational insight driving this category is that 84% of wallets connecting to a typical DeFi protocol after a marketing campaign are ghost wallets – addresses with zero real engagement that farming bots and airdrop hunters control. Traditional Web3 growth tools cannot distinguish these ghost wallets from genuine users because they lack behavioral intelligence. Growth Tech agents solve this by treating each wallet’s on-chain history as a behavioral fingerprint that reveals its intentions, experience, risk appetite, and likely lifetime value – before the protocol spends a single dollar acquiring or engaging it.

Together, these 15 agents cover the complete user lifecycle: identifying high-value targets before acquisition (lead-scorer, ltv-estimator), personalizing the first moment of engagement (platform-greeter, onboarding-router), recommending the right products (defi-advisor, wallet-marketer), retaining users through their journey (upsell-advisor), and understanding the full user base through segmentation (cohort-analyzer, whale-detector). Furthermore, every Growth Tech agent runs a fraud gate internally – a wallet that fails the fraud check receives no marketing message, no personalized greeting, and no upsell recommendation. For the foundational framework on why behavioral intelligence outperforms demographic or web analytics approaches for Web3 growth, see our Web3 User Segmentation guide .

18. chainaware-wallet-auditor

The flagship intelligence agent delivers the complete 22-dimension Web3 Persona for any wallet address in under one second. A single predictive_behaviour call returns the full behavioral profile: fraud probability (98% accuracy), all 12 intention probabilities (Borrow, Lend, Trade, Gamble, NFT, Stake ETH, Stake Yield Farm, Leveraged Staking, Leveraged Staking ETH, Leveraged Lending, Leveraged Long ETH, Leveraged Long Game), experience score (0-10), risk capability (0-9), AML forensic flags, Wallet Rank, behavioral categories, protocol usage history, and ChainAware’s direct personalization recommendations. This is the broadest intelligence output in the suite – used when a protocol needs everything about a wallet rather than a specific signal. Coverage: ETH, BNB, BASE, HAQQ, SOLANA.

Use Case: A DeFi protocol’s product team wants to understand who is actually connecting to their platform before redesigning the UI. Using chainaware-wallet-auditor on a sample of 500 recent connecting wallets reveals that 62% have High Lend intention, 18% have High Trade intention, 11% are experienced DeFi power users with 8+ experience scores, and 9% are ghost wallets with zero meaningful history. This behavioral distribution tells the product team that their core user is a yield-seeking lender, not the active trader they assumed. The UI redesign prioritizes lending product visibility – a decision driven by behavioral data rather than assumption.

When Is It Required: Use chainaware-wallet-auditor when the use case requires the complete behavioral picture rather than a single signal – individual due diligence on high-value wallets, building a comprehensive user understanding before product decisions, and providing the full context that orchestrating agents like chainaware-marketing-director need to compose complete reports. The free Wallet Auditor at chainaware.ai/audit runs this agent for any address with no signup required – start there to understand the full output before integrating via API. See our Wallet Auditor guide for the complete usage guide.

19. chainaware-reputation-scorer

Calculates the deterministic ChainAware reputation score (0-1000) using the standard formula: (1000/110) × (experience + 1) × (risk_capability + 1) × (1 − fraud_probability). A score of 1,000 represents the theoretical maximum – experience 10, risk capability 9, fraud probability 0.00. In practice, scores above 750 represent Elite wallets: expert DeFi users with aggressive risk profiles and clean fraud histories. Scores below 125 indicate either ghost wallets with no history or high-fraud-probability addresses. The score is deterministic – given the same MCP inputs, the formula always produces the same output, making it auditable and reproducible for governance and allocation purposes. Coverage: ETH, BNB, BASE, HAQQ, SOLANA.

Use Case: A DAO wants to create a community leaderboard that ranks members by contribution quality rather than token holdings. Using chainaware-reputation-scorer on all community wallets produces a ranked list where active DeFi power users with long track records rise to the top, while passive token holders with minimal protocol engagement remain at the bottom. The leaderboard displays publicly on the DAO’s governance portal, creating a visible quality signal that incentivizes genuine participation over passive holding. Top-ranked wallets receive additional governance weight, early access to new protocol features, and community recognition – none of which require manual review to assign.

When Is It Required: Use chainaware-reputation-scorer when a standardized, comparable quality metric is needed across a large set of wallets – governance leaderboards, airdrop tier allocation (used internally by chainaware-airdrop-screener), lending collateral ratios, and marketing campaign quality gates all benefit from the single-number reputation score. It differs from chainaware-wallet-ranker (which ranks by total points and transaction count) in that the reputation formula explicitly penalizes fraud probability – a wallet with high activity but elevated fraud risk scores lower than a wallet with moderate activity and a clean history.

20. chainaware-wallet-ranker

Returns global wallet rank from experience score, total points, wallet age, and transaction count across the 20M+ wallet network. The rank provides a comparable quality metric across wallets from different blockchains through the unified behavioral scoring model – a wallet’s experience score on ETH is directly comparable to one on SOLANA. Batch mode produces a ranked leaderboard sorted by total points descending, identifying the highest-quality wallets in any submitted list. Unlike reputation-scorer (which uses a specific formula), wallet-ranker reflects ChainAware’s internal composite scoring of each wallet’s overall on-chain quality without the explicit fraud penalty component.

Use Case: A DeFi protocol wants to identify its top 50 users for a VIP program offering fee discounts and early feature access. Running chainaware-wallet-ranker on all 12,000 addresses that have ever interacted with the protocol produces a ranked leaderboard. The top 50 wallets by total points become VIP members. Because wallet rank reflects genuine on-chain quality rather than just protocol-specific activity, the VIP list includes wallets that are highly engaged across DeFi broadly – users most likely to promote the protocol within their wider DeFi networks and generate the most valuable word-of-mouth acquisition.

When Is It Required: Deploy chainaware-wallet-ranker for community leaderboards, VIP tier identification, governance weight calculation, and token holder quality assessment. It pairs naturally with chainaware-whale-detector – whale-detector identifies high-value wallets by behavioral depth, while wallet-ranker produces the specific numerical rank for ordering and comparison purposes. For the complete framework on wallet quality signals, see our Wallet Rank guide .

21. chainaware-whale-detector

Classifies wallets into four whale tiers – Mega Whale (experience ≥ 9, total points ≥ 5,000, active categories ≥ 3), Whale (experience ≥ 7.5 and total points ≥ 2,000, or experience ≥ 7 with high protocol diversity), Emerging Whale (experience ≥ 5 and total points ≥ 500, or experience ≥ 6 with high stake and trade intent), and Not a Whale. Each tier also receives an Active or Dormant classification based on forward-looking intent signals: Active whales have at least one High intent probability; Dormant whales have high experience but all-Low intent – they were once significant participants but are not currently engaged. Domain classification further identifies the wallet’s primary area: Trading Whale, DeFi Whale, NFT Whale, Multi-Chain Whale, Yield Whale, or Multi-Dimensional Whale. Fraud gate excludes wallets above 0.30 fraud probability from any whale classification.

Use Case: A DeFi protocol is launching a new advanced yield product designed for sophisticated users. The marketing team needs to identify which existing wallets in their user base qualify as genuine whales – and specifically which whales are currently active vs. dormant. Running chainaware-whale-detector on all 8,000 wallets that have interacted with the protocol in the last 90 days identifies 23 Mega Whales, 87 Whales, and 214 Emerging Whales. Within those groups, 68% are Active and 32% are Dormant. Active Mega Whales receive direct personal outreach for the new product launch. Dormant Whales receive a re-engagement campaign. Emerging Whales receive nurture content designed to accelerate their progression to the next tier.

When Is It Required: Run chainaware-whale-detector before any VIP program launch, before direct outreach campaigns targeting high-value users, before governance voting weight design (where whales warrant different treatment than retail participants), and as a regular audit of any protocol’s most valuable users to identify when whales go dormant and need re-engagement before they migrate to a competitor. The domain classification adds a targeting layer – a protocol launching an NFT-adjacent feature should specifically target NFT Whales, while a new yield vault should target Yield Whales and DeFi Whales.

22. chainaware-ltv-estimator

Estimates 12-month revenue potential for any wallet as a USD range using a seven-step model. Step one derives the annual transaction rate from experience level (Beginner → 5 tx/year, Expert → 700 tx/year). Step two applies an intent multiplier from forward-looking signals (3+ High intents → 1.25×, all Low → 0.65×). Step three calculates average transaction value from wallet balance × platform share (configurable, defaults to 15%). Step four applies the fee rate (configurable, defaults to 0.1%). Step five applies a category multiplier from activity breadth (1 category → 1.0×, 5+ categories → 1.75× cap). Step six applies a risk multiplier from risk profile (Conservative → 0.70×, Aggressive → 1.40×). Step seven applies a retention factor from fraud probability (0.00-0.09 → 0.95, 0.51-0.70 → 0.20). The final estimate applies ±25% to produce a range. Hard reject conditions return $0 with no range for confirmed fraud, fraud above 0.70, or any AML forensic flag.

Use Case: A DeFi protocol’s growth team plans a user acquisition campaign with a $200,000 budget. Before spending, they run chainaware-ltv-estimator on 10,000 target wallet addresses from a purchased marketing list. Results reveal that 6,200 wallets have estimated 12-month LTV below $10 (Dormant tier), 2,800 wallets have LTV in the $10-$100 range (Low tier), 800 wallets have LTV in the $100-$1,000 range (Medium tier), and 200 wallets have LTV above $1,000 (High tier). Rather than spending the $200,000 uniformly across all 10,000 addresses, the team concentrates 80% of the budget on the 1,000 Medium and High LTV wallets. Expected ROI improves dramatically compared to uniform distribution.

When Is It Required: Use chainaware-ltv-estimator before any acquisition campaign to prioritize high-value targets, before VIP tier assignment to identify which wallets generate the most protocol revenue, and before marketing budget allocation decisions where targeting the right wallets determines whether the campaign generates positive ROI. It works alongside chainaware-lead-scorer – lead-scorer measures conversion probability, while ltv-estimator measures revenue magnitude. Combining both gives a complete acquisition prioritization signal: high-lead-score × high-LTV wallets deserve the most aggressive outreach investment.

23. chainaware-lead-scorer

Sales lead qualification engine returning a lead score (0-100), tier (Hot/Warm/Cold/Dead), conversion probability, and recommended outreach angle for any wallet. The scoring model weights five components: experience (35%), intent strength (25%), activity breadth (20%), risk appetite (10%), and fraud penalty (up to −10). Product context doubles the weight of the matching intent signal – a staking product doubles Prob_Stake, a cross-chain bridge doubles Prob_Bridge – making the score product-specific rather than generic. Hot leads (75-100) warrant immediate personalized outreach. Dead leads (0 or fraud-disqualified) are excluded from all campaigns entirely, preventing budget waste on wallets that would never convert.

Use Case: A DeFi yield aggregator launching on BASE wants to identify which ETH-based DeFi users are most likely to bridge and adopt the new platform. The growth team runs chainaware-lead-scorer on 25,000 ETH wallet addresses that have interacted with competing yield products, with product context set to “cross-chain yield aggregator on BASE.” The scoring returns 340 Hot leads (score 75+, high Prob_Bridge and Prob_Stake intent), 2,800 Warm leads (score 50-74), 15,000 Cold leads (score 25-49), and 6,860 Dead leads (below 25 or fraud-disqualified). The team focuses personalized outreach on the 340 Hot leads and runs automated campaigns for the 2,800 Warm leads. Acquisition cost per converted user drops significantly compared to the previous campaign that treated all 25,000 addresses identically.

When Is It Required: Run chainaware-lead-scorer before any acquisition outreach campaign, before direct sales team prioritization, and before budget allocation across different wallet segments. It is specifically required when a protocol launches a new product or feature and wants to identify existing wallet holders most likely to adopt it based on behavioral signals – rather than guessing based on past protocol interactions alone. See our Behavioral Analytics guide for the complete acquisition framework.

24. chainaware-wallet-marketer

Generates a hyper-personalized marketing message of maximum 20 words for any wallet, derived directly from its on-chain behavioral signals – no generic crypto copy, no templated language. Signal priority determines the message angle: Prob_Stake High leads with staking yield opportunity; Prob_Trade High leads with trading execution quality; Prob_Bridge High leads with cross-chain capability; Prob_NFT_Buy High leads with NFT feature; DeFi Lender category leads with lending/yield rates; experience above 7.5 leads with advanced power user features; experience below 2.5 leads with simple beginner-friendly onboarding. The message mirrors what the wallet actually does on-chain, making it feel personal rather than promotional. Fraud gate blocks message generation entirely for high-fraud-probability wallets.

Use Case: A DEX wants to run a re-engagement campaign targeting 5,000 wallets that connected once but never executed a trade. Running chainaware-wallet-marketer in batch mode on all 5,000 addresses produces 5,000 distinct messages – each derived from that specific wallet’s behavioral signals. A wallet with High Prob_Stake and DeFi Lender category receives: “Your lending habits earn yield. Our single-click vault automates it. Start here.” A wallet with High Prob_Trade and Active Trader category receives: “You trade fast. Our zero-slippage routing finds better fills. Try one swap.” A beginner wallet with experience below 2 receives: “New to DeFi? Earn your first yield in under two minutes. Start here.” The personalized messages achieve 3-4× higher click-through rates than the generic campaign the DEX ran previously.

When Is It Required: Use chainaware-wallet-marketer for any outbound campaign where personalization improves conversion – which is essentially every outbound campaign. It is specifically required when a protocol has a segmented user base with significantly different behavioral profiles, when re-engaging dormant users where a generic message will be ignored, and when the campaign budget is large enough that even a 2× improvement in conversion rate generates meaningful additional revenue. For the complete personalization framework, see our Why Personalization Matters guide .

25. chainaware-platform-greeter

Contextual welcome message engine generating platform-specific in-app messages of maximum 35 words at wallet connection. The same wallet receives a completely different message on Aave than on 1inch or OpenSea – because what matters to a DeFi lender visiting a lending platform differs fundamentally from what matters when that same wallet visits a DEX or an NFT marketplace. Platform type detection maps the wallet’s dominant behavioral signals to the most relevant platform angle. Returning users with protocol history receive “welcome back” framing with specific references to their history. First-time visitors with strong intent alignment receive “you know X, here’s what we do for X” framing. Low-experience first-timers receive simplified educational framing. Tone is configurable across friendly, professional, and bold to match brand voice.

Use Case: A lending protocol integrates chainaware-platform-greeter into its wallet connection event. When a DeFi Lender wallet with experience 8 and existing Aave positions connects, it sees: “Your lending positions are working – ETH supply rate is up 0.4% since your last visit. Check your health factor before rates move.” When a High Prob_Trade wallet connects for the first time, it sees: “You trade – here you can also earn on idle assets between swaps.” When a low-experience wallet connects for the first time, it sees: “New here? Deposit any token and earn interest automatically. No minimums.” Three different wallets, three different messages, all generated automatically at connection with zero manual configuration per user segment.

When Is It Required: Deploy chainaware-platform-greeter for any DeFi platform with diverse user types – a protocol serving both experienced DeFi power users and first-time users needs different first-moment experiences for each segment. It is specifically required when conversion analytics show a significant percentage of connecting wallets leaving without taking any action – a sign that the current generic landing experience does not resonate with the behavioral diversity of the connecting wallet population. The agent adds under 200ms to the wallet connection flow, negligible for user experience purposes.

26. chainaware-onboarding-router

Routes each connecting wallet to the correct onboarding experience based on verifiable on-chain experience rather than self-reported surveys or assumed user segments. Experience 0-2.5 → Beginner Tutorial (full guided walkthrough – this wallet needs hand-holding through every step). Experience 2.6-6 → Intermediate Guide (condensed tips that skip the absolute basics while still orienting the user to platform-specific features). Experience 6.1-10 → Skip Onboarding (power user, straight to the product – tutorials waste their time and signal that the platform doesn’t understand them). Secondary signals refine the route: a wallet with experience 5.5 that already uses the platform’s specific protocol category can skip most tutorials even though its overall score is technically Intermediate. New Address always routes to Beginner regardless of other signals.

Use Case: A DeFi platform’s user research team discovers that 23% of users who complete the full onboarding tutorial are experienced DeFi power users who were frustrated by being forced through beginner content. These users have 3× higher churn rates in the first week compared to users correctly identified as power users who skipped onboarding. Integrating chainaware-onboarding-router eliminates the mis-routing: power users (experience 6.1+) go directly to the product, intermediate users see a condensed orientation, and genuine beginners receive the full tutorial. First-week churn drops 31% as power users stop abandoning the platform out of frustration with irrelevant onboarding content.

When Is It Required: Deploy chainaware-onboarding-router for any platform with a multi-step onboarding flow and a diverse user base that includes both experienced DeFi users and newcomers. It is specifically required when product analytics show high drop-off during onboarding – a symptom that the current fixed onboarding experience is poorly matched to the actual experience distribution of the connecting wallet population. The agent works best in combination with chainaware-platform-greeter (which personalizes the first moment before onboarding begins) and chainaware-defi-advisor (which provides product recommendations post-onboarding). For the complete onboarding conversion analysis, see our DeFi Onboarding guide .

27. chainaware-defi-advisor

Personalized DeFi product recommendation engine with three product tiers calibrated to wallet experience and risk appetite. Tier 1 Safe Harbor covers Beginner and Conservative wallets: simple staking, stablecoin lending, savings vaults, fixed-rate lending. Tier 2 Yield Builder covers Intermediate and Moderate wallets: liquid staking, blue-chip LP pools, variable rate lending, multi-asset vaults. Tier 3 Yield Maximizer covers Experienced and Aggressive wallets: leveraged yield farming, options vaults (DOVs), concentrated liquidity CLMM active management, cross-chain yield arbitrage, and veToken strategy stacking. Intent signals boost recommendations within the tier: Prob_Stake High prioritizes staking products first; Prob_Trade High prioritizes LP pools and active liquidity. Protocol history adds a further targeting layer: a wallet that already uses Aave receives Aave-compatible product recommendations over generic alternatives.

Use Case: A DeFi aggregator platform connects 500 different wallets per day across its product suite. Without personalization, every wallet sees the same “Featured Products” section – typically the highest-APY products, which are also the highest-risk. Conservative beginners see leveraged products they don’t understand, and aggressive experts see beginner staking options that bore them. Integrating chainaware-defi-advisor personalizes the product menu for each connecting wallet: beginners see stablecoin lending and simple staking; power users see advanced leveraged strategies and CLMM management tools. First-session product interaction rates increase 2.4× across all experience tiers because every user sees products calibrated to their level.

When Is It Required: Use chainaware-defi-advisor for any multi-product DeFi platform where the right product for one user is actively wrong for another. It is specifically required when conversion analytics show significant variance in product adoption rates by user experience level – a sign that current product placement is suboptimal for at least one segment. For platforms launching new products, the agent identifies which existing wallet segments are most aligned with the new product’s requirements before the launch, enabling targeted pre-launch outreach to the highest-probability adopters. See our DeFi Platform Use Cases guide .

28. chainaware-upsell-advisor

Identifies the optimal next product to offer an existing user and the precise moment to offer it. Upgrade readiness score (0-100) combines experience headroom toward the next product tier (40% weight), intent alignment with the target product (35%), and risk appetite fit (25%). Score 80-100 → offer now, conversion probability above 65%. Score 60-79 → offer at the next behavioral trigger event. Score 40-59 → nurture first, offer after 1-2 more sessions. Score below 40 → do not upsell yet – the risk is churn rather than conversion. Trigger events are behavioral rather than time-based: a wallet ready for a staking upgrade gets the offer the next time it stakes or claims rewards, not on a fixed weekly cadence. A “What NOT to do” recommendation identifies the single upsell approach most likely to cause churn for each specific wallet – for example, “Don’t pitch leveraged products – this is a Conservative wallet and the complexity will cause churn.”

Use Case: A DeFi lending platform has 3,000 active users on its basic lending tier. The product team wants to introduce an advanced leveraged yield farming product and identify which users are ready to upgrade now vs. which need nurturing first. Running chainaware-upsell-advisor on all 3,000 users with the new product as context identifies 180 users with readiness above 80 (offer now), 620 users at 60-79 (offer at next trigger), 1,400 users at 40-59 (nurture first), and 800 users below 40 (do not upsell). The 180 “offer now” users receive immediate personalized outreach with specific trigger messaging aligned to their dominant intent signal. Within four weeks, 67% of the “offer now” group has upgraded – without wasting outreach budget on the 800 users who were not ready and would have churned if pushed.

When Is It Required: Deploy chainaware-upsell-advisor whenever a protocol launches a new product tier and wants to maximize adoption among existing users. It is specifically required for protocols with a tiered product structure where pushing the wrong product too early causes churn, for platforms with subscription-based models where upgrade timing significantly affects revenue, and for any DeFi protocol where the most valuable users are those engaging with multiple product tiers simultaneously. The trigger event recommendation is especially valuable – it replaces time-based upsell campaigns (which push users at arbitrary moments) with behavior-triggered campaigns (which engage users at the exact moment their intent signals indicate readiness).

29. chainaware-cohort-analyzer

Batch behavioral cohort segmentation for Web3 analytics teams. Classifies every wallet in a submitted list into one of eight behavioral cohorts: Power DeFi User (experience ≥ 7, DeFi Lender or Active Trader dominant, protocols ≥ 5), NFT Collector (NFT Collector dominant, experience ≥ 3), Yield Farmer (Yield Farmer dominant or Prob_Stake High with experience ≥ 5), Multi-Chain Explorer (Bridge User dominant or bridge-heavy protocol history), Active Trader (Prob_Trade High with experience ≥ 4), Casual User (experience 2-4.9, no dominant pattern), Dormant/Inactive (experience ≥ 2 but all intent signals Low), and New/Fresh Wallet (new address with clean fraud signals). Fraud exclusions – bots, confirmed fraud, AML flags, suspicious new wallets – are separated from behavioral cohorts entirely. Each cohort receives a specific engagement strategy recommendation, and the full report includes audience quality score, per-cohort statistics, and a three-priority action plan.

Use Case: A DeFi protocol planning its Q3 marketing budget wants to allocate spend across different user segments rather than running one generic campaign. Chainaware-cohort-analyzer on their 15,000-wallet user base reveals: 890 Power DeFi Users (6%), 1,200 NFT Collectors (8%), 2,100 Yield Farmers (14%), 800 Multi-Chain Explorers (5%), 3,400 Casual Users (23%), 2,800 Dormant wallets (19%), 1,600 New wallets (11%), and 2,210 excluded bots and fraud (15%). The budget allocation becomes data-driven: 35% to Yield Farmer acquisition for the new vault product, 25% to Casual User conversion, 20% to Dormant re-engagement, and 20% to New wallet onboarding. Each cohort receives a distinct message strategy rather than a generic campaign blasted to all 15,000 addresses.

When Is It Required: Run chainaware-cohort-analyzer before any marketing budget planning cycle, before product launch targeting decisions, and as a quarterly audit of user base composition to detect shifts in behavioral distribution. It is specifically required before an airdrop (to ensure token distribution aligns with cohort quality rather than farming behavior), before a governance token launch (to understand which community members qualify for each allocation tier), and before any significant UI redesign (to ensure the redesign serves the actual behavioral distribution rather than an assumed user persona).

30. chainaware-token-ranker

Discovers and ranks tokens by the behavioral quality of their holder community across five categories – AI Token, RWA Token, DeFi Token, DeFAI Token, DePIN Token – on ETH, BNB, BASE, and SOLANA. Community rank scores the aggregate behavioral strength of all token holders: wallet age, transaction history, protocol diversity, and experience scores across the 20M+ wallet network. A token whose holders are predominantly experienced, long-tenured, multi-protocol DeFi users ranks higher than a token with the same market cap but predominantly fresh wallets with minimal history. This ranking reflects genuine community quality – not just trading volume or price momentum, which can be manufactured. Supports sort by community rank, normalized rank, or holder count; category filtering; pagination; and name-based token search.

Use Case: An institutional DeFi fund wants to allocate capital to the top three AI tokens by community quality rather than market cap. Running chainaware-token-ranker for AI Token category on ETH and BNB returns a ranked list showing which AI tokens have the strongest holder bases of experienced, legitimate DeFi participants – and which have significant proportions of fresh wallets and farming addresses in their holder distribution. The fund identifies two tokens where community quality is significantly stronger than their market cap rank suggests – potential value opportunities where genuine community strength has not yet been reflected in price. Both tokens are added to the portfolio after individual deep-dives using chainaware-token-analyzer.

When Is It Required: Use chainaware-token-ranker for token portfolio research and selection when community quality is a meaningful signal, for DEX teams curating featured token listings based on genuine community strength rather than trading volume alone, and for any platform wanting to surface high-quality tokens to users before market price discovery catches up to community quality. It works as the first step in a two-step research process: token-ranker identifies the best candidates from a category, then chainaware-token-analyzer deep-dives each candidate’s specific holder composition. See our Token Rank guide for detailed methodology.

31. chainaware-token-analyzer

Deep-dives into a single token’s community rank and top holder profiles – returning each top holder’s wallet age, total transaction count, total points, and global rank across the 20M+ wallet network. Optional fraud screening on the top holders via predictive_fraud identifies whether the token’s largest positions are held by legitimate experienced wallets or by coordinated fraud networks disguising their concentration. The holder quality assessment computes average wallet age, average transaction count, and average global rank across the top holders, producing a Verdict (2-3 sentences on whether these are genuine power users or manufactured holders). Token-ranker identifies which tokens have the strongest community quality in aggregate; token-analyzer validates whether specific tokens actually back that aggregate signal with genuine individual holders.

Use Case: A crypto exchange is evaluating whether to list a new DeFi token. The community rank from chainaware-token-ranker shows the token in the top 20% of its category – strong enough to consider. Chainaware-token-analyzer deep-dives the top 20 holders: 14 have average wallet age above 800 days, high transaction counts, and global ranks in the top 10% of the 20M+ wallet network. However, three of the top 20 holders share a funding source and show coordinated acquisition patterns – signals of artificial holder concentration. The fraud screening confirms two of those three have elevated fraud probability. The exchange requires the team to reduce concentration before listing. Six weeks later, the concentration issue is resolved, and the token lists and performs well due to its genuinely strong community foundation.

When Is It Required: Run chainaware-token-analyzer before listing any token on an exchange or DEX with listing standards, before making significant portfolio allocation to a token where holder quality affects the investment thesis, and before any governance vote giving token holders significant power – understanding whether those holders are genuine community members or coordinated operators directly affects the legitimacy of governance outcomes. It is also required as part of due diligence for institutional crypto fund investments where holder composition is a material factor in the investment case.

32. chainaware-marketing-director

The orchestrator agent – a senior marketing strategist that delegates to seven specialist agents and synthesizes their outputs into a complete Marketing Campaign Brief. In batch mode (multiple wallets), the agent runs six sequential phases: segmentation via chainaware-cohort-analyzer, lead scoring and whale detection on the highest-potential wallets, per-cohort message generation via chainaware-wallet-marketer, upsell opportunity identification via chainaware-upsell-advisor, onboarding routing for new wallets, and executive campaign brief synthesis. In single-wallet mode, it runs five specialist agents simultaneously and returns a complete Wallet Marketing Profile including fraud risk, whale tier, lead score, personalized outreach message, platform welcome message, upsell path, and recommended onboarding flow. The Marketing Director represents the highest-level abstraction in ChainAware’s agent architecture – demonstrating what coordinated multi-agent intelligence delivers that no single specialist agent can replicate independently. It requires a platform description as input, using that context to make every generated message feel native to the specific protocol.

Use Case: A DeFi lending protocol is planning a growth push targeting 200 existing wallets that have connected but never borrowed. The growth lead does not have time to run each specialist agent separately and synthesize results manually. Running chainaware-marketing-director with the 200 wallet addresses and the platform description as input produces a complete Campaign Brief in one pass: 23 Hot leads requiring immediate personal outreach; 8 Mega and Whale wallets identified for VIP treatment; per-cohort message templates for the 6 behavioral cohorts represented in the wallet list; 31 wallets with upgrade readiness above 80 ready for a borrowing product offer; 18 new wallets routed to beginner onboarding; and 14 excluded as fraud or bots. The entire brief – segmentation, prioritization, messages, execution sequence – is ready for the growth team to execute.

When Is It Required: Use chainaware-marketing-director when a campaign needs the output of multiple specialist agents and the team does not have the resources to run them separately and synthesize results. It is specifically the right choice for time-sensitive campaigns where speed matters, for small growth teams needing a complete brief rather than raw intelligence, and for any campaign spanning multiple wallet segments requiring different strategies simultaneously. The agent is also the best entry point for teams new to ChainAware’s agent suite – a single Marketing Director run demonstrates the full capability range of the underlying specialist agents in one unified output. For the complete campaign planning framework, see our Web3 Marketing guide .

How Agents Compose Into Pipelines

The most powerful applications of ChainAware’s 32 agents emerge not from individual deployment but from composing them into pipelines – where the output of one agent becomes the input of the next. Every agent’s documentation includes a composability section mapping its natural connections to adjacent agents. Three core pipelines demonstrate the composability principle and cover the most common production deployments.

The Compliance Pipeline

The compliance pipeline sequences four agents: trust-scorer → aml-scorer → compliance-screener → transaction-monitor. Trust-scorer provides the fast first gate at under 50ms – any wallet below 0.30 trust score is immediately routed to enhanced review. AML-scorer adds forensic verification for wallets that pass the trust gate, checking all 19 forensic flag categories and producing the documented AML score needed for regulatory reporting. Compliance-screener orchestrates both signals plus transaction pattern analysis into the final PASS / EDD / REJECT verdict with full documented evidence trail. Transaction-monitor handles ongoing screening post-onboarding, flagging any transaction that exceeds risk thresholds after a wallet has been onboarded and approved.

Together, the four agents cover the complete compliance lifecycle from pre-onboarding screening through ongoing monitoring – the full stack required for MiCA-compliant operation. According to FATF’s Virtual Assets Recommendations , this kind of continuous monitoring is increasingly required rather than optional for regulated crypto asset service providers. Furthermore, the documented output from each agent in the pipeline creates the audit trail that regulators require – not just a screening decision, but the specific signals and thresholds applied to produce it.

The Growth Pipeline

The growth pipeline sequences six agents: cohort-analyzer → lead-scorer → whale-detector → wallet-marketer → onboarding-router → upsell-advisor. Cohort-analyzer segments the full wallet list and identifies fraud exclusions, producing the audience map for the campaign. Lead-scorer then ranks the highest-conversion targets within the highest-value cohorts. Whale-detector surfaces the VIP wallets within those cohorts for personal outreach. Wallet-marketer generates per-wallet personalized messages for the identified hot leads and whale wallets. Onboarding-router assigns new wallets in the cohort analysis to the correct first-time experience. Upsell-advisor identifies existing users ready for product upgrades, completing the full lifecycle from acquisition through retention.

Notably, chainaware-marketing-director runs this exact pipeline automatically – making it the recommended entry point for teams deploying the growth pipeline for the first time. The Marketing Director adds the synthesis layer that converts six separate agent outputs into a single actionable Campaign Brief, eliminating the manual work of combining results across multiple specialist runs.

The Token Intelligence Pipeline

The token intelligence pipeline sequences three agents: token-ranker → token-analyzer → rug-pull-detector. Token-ranker identifies the strongest tokens in a target category by community quality across ETH, BNB, BASE, or SOLANA – producing a shortlist of high-potential candidates. Token-analyzer then deep-dives each shortlisted token’s specific holder composition, validating whether the aggregate community quality score reflects genuine individual holders or manufactured concentration. Rug-pull-detector screens the contract address and deployer wallet for the tokens that pass both previous stages – confirming that the project behind the strong community is not itself a fraud risk.

The three agents together provide the complete due diligence stack for token investment decisions, exchange listing evaluation, and governance token selection. Moreover, they address the three distinct questions that token evaluation requires: which tokens have the strongest communities (token-ranker), are those communities genuinely strong or manufactured (token-analyzer), and is the contract itself safe (rug-pull-detector). Each question requires a different tool, and combining all three produces a confidence level in a token that no single tool delivers alone. For the complete framework on how behavioral intelligence applies to token research, see our Token Rank guide .

The Agentic Economy Pipeline

The agentic economy pipeline sequences two Fraud Tech agents with the transaction-monitor: agent-screener → counterparty-screener → transaction-monitor. As AI agents increasingly operate autonomously across DeFi – executing trades, managing positions, and participating in governance on behalf of humans – the need for agent-specific trust assessment becomes as important as wallet trust assessment. Agent-screener validates the trust score of any third-party AI agent before it is granted access to a protocol or given permission to interact with user funds. Counterparty-screener validates each specific address the agent will interact with before execution. Transaction-monitor provides continuous real-time risk scoring for every transaction the agent executes once granted access.

This pipeline addresses the structural vulnerability in the current ERC-8004 ecosystem – 196,000+ registered agents with no behavioral trust signals. ChainAware’s agentic economy pipeline provides the trust infrastructure that the registry itself lacks, making it the foundational security layer for any protocol accepting autonomous AI agent interactions. For the complete analysis of how AI agents are reshaping Web3 operations, see our Web3 Agentic Economy guide .

Getting Started – Integration in Three Steps

All 32 agents are available as open-source Claude Code agent definitions at github.com/ChainAware/behavioral-prediction-mcp . Integration requires three steps and no blockchain expertise. According to Anthropic’s Model Context Protocol documentation , MCP is rapidly becoming the standard integration layer for AI agent tool access – making ChainAware’s MCP-native delivery compatible with any LLM infrastructure that supports the standard.

Step one – register the Prediction MCP server in your Claude Code environment:

claude mcp add --transport sse chainaware-behavioral-prediction \
  https://prediction.mcp.chainaware.ai/sse \
  --header "X-API-Key: YOUR_KEY"

Step two – clone the repository and copy all 32 agent definitions into your project:

git clone https://github.com/ChainAware/behavioral-prediction-mcp.git
cp -r behavioral-prediction-mcp/.claude/agents/ your-project/.claude/agents/

Step three – invoke any agent directly from Claude Code:

claude --agent chainaware-fraud-detector
# or trigger from within Claude Code:
@chainaware-wallet-auditor

API keys are available at chainaware.ai/pricing . The free Wallet Auditor at chainaware.ai/audit demonstrates the full behavioral intelligence output with no API key or signup required – start there to understand the complete output before building your integration. Additionally, the free Fraud Detector at chainaware.ai/fraud-detector and Rug Pull Detector at chainaware.ai/rug-pull-detector demonstrate the Fraud Tech agent outputs with no setup. For the complete developer integration guide covering Claude Desktop, Cursor, and custom MCP client setups, see our Prediction MCP guide .

Frequently Asked Questions

What is a ChainAware sub-agent?

A ChainAware sub-agent is a pre-built Claude Code agent definition – a markdown file containing a name, description, role definition, decision logic, output format specification, and MCP tool references. When placed in a Claude Code project’s .claude/agents/ directory, the agent becomes invocable by name from any Claude Code session in that project. The agent calls ChainAware’s Prediction MCP tools (predictive_fraud, predictive_behaviour, predictive_rug_pull, credit_score, token_rank_list, token_rank_single) with the appropriate parameters, interprets the response according to its decision logic, and returns a structured output in the format defined in the agent file. All 32 agents are open-source under the MIT license at github.com/ChainAware/behavioral-prediction-mcp .

How do the 32 agents relate to the ChainAware Prediction MCP?

The Prediction MCP is the intelligence layer – the SSE endpoint at prediction.mcp.chainaware.ai/sse that exposes ChainAware’s six prediction tools as MCP-callable functions. The 32 agents are the application layer – pre-built Claude Code agents that call those tools with the right parameters, apply decision logic to the results, and return structured outputs ready for human or automated action. Any developer can call the raw MCP tools directly via the REST API for custom integrations. The agents provide a head start – 32 production-ready agent definitions covering the most common use cases, tested and maintained by ChainAware’s team. For the complete MCP integration guide, see our Prediction MCP guide .

Which agent should I start with?

Start with chainaware-wallet-auditor for the broadest view of what ChainAware’s intelligence produces – it returns the complete 22-dimension Web3 Persona in one call, showing every signal that the specialist agents use individually. The free Wallet Auditor at chainaware.ai/audit runs this agent for any wallet address with no setup required. Once you understand the full output, select the specialist agent matching your primary use case: fraud prevention teams start with chainaware-fraud-detector; launchpad teams start with chainaware-rug-pull-detector; compliance teams start with chainaware-aml-scorer; growth teams start with chainaware-cohort-analyzer for their existing user base; teams evaluating AI agent trustworthiness start with chainaware-agent-screener.

Can I modify the agent definitions for my specific use case?

Yes – all 32 agent definition files are open-source under the MIT license. Fork the repository, modify any agent’s decision thresholds, output format, or tool selection, and deploy your customized version alongside the standard agents. Common customizations include adjusting fraud probability thresholds for specific risk tolerances, adding platform-specific context to message templates in chainaware-wallet-marketer and chainaware-platform-greeter, and modifying the governance tier classification thresholds in chainaware-governance-screener to match specific DAO requirements. The only component that is proprietary and cannot be modified is the underlying Prediction MCP server and its trained ML models – the intelligence that powers the tool calls. Agent definitions, decision logic, and output formats are all freely modifiable.

What is the difference between Fraud Tech and Growth Tech agents?

Fraud Tech agents answer whether a wallet, contract, or transaction can be trusted – they produce verdicts (block, flag, allow, reject, qualify). Growth Tech agents answer how to engage a wallet that has passed trust assessment – they produce recommendations (which product to surface, what message to send, which onboarding flow to show). Both categories draw from the same 20M+ wallet persona database and the same Prediction MCP tools. However, every Growth Tech agent runs a fraud gate before producing any recommendation – a wallet that fails the fraud check receives no marketing message, no personalized greeting, and no upsell recommendation. This means the categories are parallel layers rather than sequential stages: fraud protection runs continuously through every growth decision, ensuring that behavioral personalization never extends to wallets that ChainAware’s models identify as fraudulent operators.

How accurate are ChainAware’s fraud detection models?

ChainAware achieves 98% fraud detection accuracy on ETH and 96% on BNB, backtested against CryptoScamDB – the largest publicly available database of documented crypto fraud incidents. The rug pull detection model achieves 90.1% accuracy, backtested on the PancakeSwap V2 dataset covering $569M in documented rug pull losses from weeks 1-20 of 2026. These accuracy figures measure the model’s ability to correctly identify fraudulent wallets and contracts before they commit their recorded offense – not accuracy on post-incident classification. The distinction matters: ChainAware’s models are designed to predict fraud before it executes, which is structurally harder than forensic classification of known fraud incidents. For the complete accuracy methodology and comparison against forensic approaches, see our Forensic vs AI-Powered Analytics guide .

Are the agents available on all blockchains?

Coverage varies by agent and underlying MCP tool. The predictive_fraud tool – used by fraud-detector, aml-scorer, trust-scorer, and counterparty-screener – covers the broadest network set: ETH, BNB, POLYGON, TON, BASE, TRON, and HAQQ. The predictive_behaviour tool – used by wallet-auditor, reputation-scorer, whale-detector, and the growth agents – covers ETH, BNB, BASE, HAQQ, and SOLANA. The predictive_rug_pull tool covers ETH, BNB, BASE, and HAQQ. Agents supporting networks not covered by their primary tool include automatic fallback logic – for example, chainaware-airdrop-screener falls back to predictive_fraud for POLYGON, TON, and TRON wallets. The classification table in this article lists exact network coverage per agent for quick reference.

Why did ChainAware build on Claude specifically?

Claude’s tool use and structured output capabilities make it particularly well-suited for the deterministic decision logic that fraud detection and compliance agents require. An agent applying five disqualification rules in strict order – stopping at the first failure – needs a model that follows logical sequences reliably without hallucinating intermediate steps. Additionally, Claude Code’s native agent support (the .claude/agents/ directory standard) makes deployment frictionless for teams already using Claude Code. Agents requiring faster, cheaper inference (chainaware-trust-scorer, chainaware-wallet-ranker) use Claude Haiku 4.5. Agents requiring richer analytical reasoning (chainaware-wallet-auditor, chainaware-cohort-analyzer, chainaware-marketing-director) use Claude Sonnet 4.6. The model selection is specified in each agent’s frontmatter and can be changed by forking the agent definition file. ChainAware’s Prediction MCP tools are model-agnostic – GPT-4, Gemini, and any other MCP-compatible model can call them directly via the REST API.

How does ChainAware’s intelligence relate to the CB Insights market map?

ChainAware was named in CB Insights’ AI Fraud Prevention Market Map in June 2026 – placed in the On-Chain Intelligence subcategory alongside Chainalysis, Elliptic, TRM Labs, Crystal Intelligence, and Blockaid. CB Insights selected companies based on Mosaic health scores and equity funding recency, filtering out thousands of projects that did not meet the institutional bar. ChainAware’s position on the map validates the Fraud Tech agents (fraud-detector, aml-scorer, compliance-screener, rug-pull-detector, and transaction-monitor) specifically – these are the agents that deliver the on-chain intelligence capability CB Insights recognized. Beyond the map placement, ChainAware is the only company in the entire CB Insights list with a publicly traded token listed in CoinGecko’s AI category – a position that reflects the dual institutional and decentralized distribution model that the 32 agents are built to serve.

External Sources: ChainAware GitHub Repository · Anthropic Model Context Protocol · FATF Virtual Assets Recommendations · ERC-8004 Agent Registry · CB Insights AI Fraud Prevention Market Map

The post ChainAware.ai’s 32 Claude Sub-Agents – Fraud Tech and Growth Tech for the Agentic Economy first appeared on ChainAware.ai.

Web3 lost over $3.6 billion to fraud and exploits in the first three quarters of 2025 alone. Remarkably, 57.8% of those losses came not from smart contract bugs but from access-control failures – the humans and systems operating around the code, not the code itself. This pattern reveals the central challenge of Web3 trust in 2026: the attack surface is not one problem. It is five distinct problems, each requiring a fundamentally different solution.

Most teams pick one trust tool and assume they have coverage. They verify identity with KYC and assume that covers fraud risk. They run a smart contract audit and assume that covers rug pull risk. They check a Sybil score and assume that covers behavioral quality. Each assumption is wrong – because each of these tools addresses a different layer of the trust stack. This guide maps the complete five-category Web3 trust verification landscape, explains what each provider actually covers, and shows precisely where ChainAware addresses the attack surfaces that every other category leaves unprotected.

The Five Trust Problems in Web3

Trust in Web3 is not a single dimension – it is a layered stack of five distinct questions that no single provider answers completely. Conflating them leads teams to select the wrong tools, build false confidence in partial coverage, and leave entire attack surfaces unprotected.

• Identity Trust: Is this a real, unique human with verifiable identity?
• Behavioral Trust: Is this wallet genuinely active, non-Sybil, and behaviorally high-quality?
• Social Trust: Does the community vouch for this person’s credibility and track record?
• Token and Protocol Trust: Is this smart contract safe? Is this token’s community genuine, or a manufactured rug pull setup?
• Agent Verification: Is this AI agent wallet – and the wallet funding it – trustworthy before I allow autonomous interaction with my protocol?

Each question requires different data, different methodology, and different tools. Furthermore, passing one trust check says nothing about performance on the others. A wallet can pass KYC, hold a clean Sybil score, have positive Ethos vouches, and still carry a 0.87 fraud probability in ChainAware’s behavioral model – because each layer catches threats that the others are structurally blind to. For how behavioral intelligence layers into the broader Web3 intelligence stack, see our Web3 Wallet Auditing Providers guide.

Category 1: Identity Trust – KYC and Document Verification

Identity trust answers the most foundational question: is this a real, unique person with verifiable government-issued identity? KYC providers verify document authenticity, biometric liveness, sanctions and PEP exposure, and ongoing AML obligations. Their 2026 market data reveals the scale of the problem – Sumsub analyzed over 23,000 fraud attempts daily and found that 55% of crypto firms confirmed experiencing fraud at least once in 2025, while 15% were unsure whether it happened at all.

Sumsub – The Market Leader

Sumsub works with 8 out of 10 top global crypto exchanges and covers the complete verification lifecycle: document verification (14,000+ document types across 220+ countries), biometric face matching, liveness detection, AML/PEP screening, Travel Rule compliance, KYB for businesses, and ongoing transaction monitoring. Their April 2026 State of the Crypto Industry report found that 74% of crypto firms now prioritize verification accuracy over onboarding speed – a structural shift from the growth-at-all-costs approach that dominated 2021-2023. According to Sumsub’s 2026 research , crypto companies are entering a phase where operational discipline matters more than momentum.

Civic Pass – Blockchain-Native KYC

Civic provides blockchain-native KYC through Civic Pass – an on-chain credential issued after off-chain identity verification. Available in 190+ countries, Civic covers liveness checks, document KYC, watchlist and PEP screening, VPN detection, and email and phone verification. The key differentiator is portability: users verify once and reuse their Civic Pass across any integrated DApp without re-submitting documents. This verify-once model significantly reduces onboarding friction while maintaining compliance. Fractal ID offers a similar Web3-native multi-chain identity stack positioned as a lighter-weight alternative for DeFi-native teams.

The Structural Limitation of KYC

Every KYC provider shares one fundamental constraint: they require active user participation. Document uploads, face scans, and liveness checks create friction that reduces conversion and makes KYC unsuitable for fully permissionless DeFi protocols. More critically, KYC verification is a point-in-time snapshot – it confirms who a wallet belonged to at verification date but says nothing about that wallet’s subsequent behavioral risk. A wallet can pass KYC completely and still develop a 0.91 fraud probability the following month based on new behavioral patterns. This gap is precisely where ChainAware’s behavioral layer operates. For how KYC connects to the broader compliance picture, see our Predictive AI for KYC and AML guide and our MiCA Compliance guide.

Free – No Signup Required

Audit Any Wallet in 1 Second – Fraud Score, AML Status, Behavioral Profile

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Audit Any Wallet Free Wallet Auditor Guide

Category 2: Behavioral Trust – On-Chain Reputation and Sybil Resistance

Behavioral trust operates entirely on public on-chain data – no user action required, fully permissionless, privacy-preserving. Providers in this category analyze wallet transaction history to answer whether a wallet is a genuine, active participant or a bot, farmer, or coordinated Sybil attacker. Two distinct methodologies dominate this space.

Trusta Labs / TrustScan – AI/ML Graph Pattern Detection

Trusta Labs applies Graph Neural Networks (GCNs, GATs) and Recurrent Neural Networks (GRUs, LSTMs) to detect four specific Sybil attack signatures in wallet transaction graphs: star-like transfer patterns (hub-and-spoke funding), chain-like transfer patterns (sequential wallet funding), bulk operations (coordinated timing), and similar behavior sequences (identical transaction fingerprints across wallets). Founded by ex-Alipay AI leaders, Trusta has analyzed 570 million wallets and integrated into Gitcoin Passport (1.54 points per verified address) and Galxe. For the complete Sybil protection landscape comparison, see our Web3 Sybil Protection Systems guide.

Nomis, RubyScore, and ReputeX – Activity-Based Reputation

Nomis scores historical activity volume, protocol diversity, wallet age, and cross-chain engagement across 50+ chains – issuing output as a portable on-chain NFT attestation. RubyScore provides a simpler activity quality filter with faster integration, suitable for projects needing lightweight Sybil gating without deep analysis. ReputeX takes a fusion approach combining multiple behavioral paradigms, though production deployment evidence remains limited.

All behavioral trust providers share a critical structural limitation: they are reactive and binary. They describe past behavior and produce pass/fail gates. None predicts future behavior, none scores behavioral quality beyond activity volume, and none provides the downstream deployment layer that converts screened wallets into transacting users. ChainAware closes all three gaps simultaneously. For keeping airdrop and IDO distributions clean from Sybil wallets, see the Sybil-Resistant Token Distribution use case. For the full reputation score comparison including Nomis, Ethos, Cred Protocol, and UTU, see our Web3 Reputation Score Comparison.

Category 3: Social Trust – Community Vouching and Staked Endorsements

Social trust builds reputation through community mechanisms rather than on-chain transaction analysis. Where behavioral trust asks “what has this wallet done?”, social trust asks “what does the community say about this person?” These are orthogonal signals – a wallet can have strong behavioral scores and poor social reputation, or vice versa. Combining both provides significantly more robust trust assessment than either alone.

Ethos Network – Staked Social Proof-of-Trust

Ethos Network launched mainnet on Base in January 2025 and represents the most sophisticated social trust system in Web3. The core mechanism requires users to stake ETH when vouching for others – making trust claims financially consequential rather than costless clicks. Participants can also slash (penalize) others for proven bad behavior, reducing the voucher’s staked amount. Credibility scores derive from the platform’s most engaged and reputable members, creating a peer-weighted system rather than simple vote counting. Ethos.Markets launched alongside the main platform, allowing users to financially speculate on trust scores through an AMM using the LMSR algorithm. Additionally, a Chrome extension shows Ethos credibility scores directly on Twitter/X profiles – bringing social trust verification into ambient browsing. The project raised $1.75M pre-seed from 60 Web3 community angel investors.

The primary limitation of Ethos is coverage: it only scores wallets with established Ethos profiles. Anonymous wallets with no Ethos history return no signal – which describes the vast majority of wallets that connect to any DeFi protocol. Furthermore, Ethos measures social community trust among known participants, not the behavioral quality or fraud risk of a wallet. A highly vouched wallet can still carry significant fraud probability based on its transaction patterns.

Karma3 Labs / OpenRank – Algorithmic Trust Propagation

Karma3 Labs builds ranking and reputation infrastructure using the EigenTrust algorithm – originally designed to improve trust propagation in distributed systems and later applied to Google’s PageRank concept. Their $4.5M seed round came from Galaxy and IDEO CoLab. OpenRank enables developers to build personalized search, discovery, and recommendation systems on top of on-chain social graph data, with notable deployment for Farcaster social graph trust scoring. Where Ethos is community-driven (humans staking on humans), Karma3 is algorithm-driven (EigenTrust computing trust propagation through the social graph). According to Karma3 Labs’ documentation , the OpenRank protocol enables context-aware trust that adapts to different application requirements.

UTU Protocol – Relationship-Context Trust

UTU Protocol builds trust through a non-transferable reputation token (UTT) and staked endorsements, with emphasis on relationship context – a user’s trusted network’s opinions carry more weight than a stranger’s. The UTT cannot be traded, only earned through genuine trust endorsements that later prove correct. Africa DeFi focus and Internet Computer deployment distinguish UTU from the other social trust providers. All three social trust systems – Ethos, Karma3, and UTU – address a genuine trust dimension that on-chain behavioral analysis cannot capture: long-standing human relationships and community standing that extend beyond wallet transaction history.

Category 4: Token and Protocol Trust – Code Audits, Short and Long Rug Pulls

This category covers two entirely different trust problems that are commonly conflated. Smart contract code audits (CertiK, Hacken) verify whether the code is technically safe. Behavioral token trust tools (ChainAware) verify whether the operator behind the code and the community around the token are genuine. CertiK’s H1 2025 Hack3d report recorded $2.47 billion lost across 344 incidents – with wallet compromise the largest category and phishing the most frequent. This confirms that the most expensive 2026 threats live around the code, not inside it. Yet most teams invest entirely in code audits while ignoring behavioral token trust.

CertiK and Hacken – Smart Contract Code Audits

CertiK is the dominant smart contract audit and security monitoring platform with 5,000+ enterprise clients, $600B+ in assets secured, and 180,000+ vulnerabilities identified. Its Skynet platform delivers real-time on-chain incident monitoring and alerting. The Spoq formal verification engine uses AI-driven automation to mathematically prove system correctness – validated at peer-reviewed venues OSDI 2023 and ASPLOS 2026. According to CertiK’s platform documentation , Skynet Enterprise meets the transparency and risk visibility requirements of institutional participants and regulators. Hacken provides security audits and a TRUST Score framework evaluating protocols across transparency, security, code quality, and community metrics – their 2025 TRUST Report tracked $3.6B stolen, with 57.8% from access-control exploits.

Both CertiK and Hacken audit code at a specific point in time. Neither analyzes the behavioral history of the wallet that deployed the contract, the fraud profile of the wallets that provided liquidity, or the quality of the token’s holder community. These are not limitations of the audit providers – they are simply a different layer of the trust stack. The critical mistake is treating a clean CertiK audit as comprehensive protection when 95% of PancakeSwap pools end in rug pulls and 99% of Pump.fun tokens extract money from buyers – most of them with no code vulnerabilities whatsoever. For the complete rug pull detection landscape, see our Rug Pull Detection guide.

ChainAware Rug Pull Detector – Short Rug Pull Detection via Creator Chain Traversal

ChainAware’s Rug Pull Detector (see the complete Rug Pull Detector guide) addresses the behavioral layer that code audits structurally cannot reach. The core insight: experienced rug pullers deliberately pass code reviews. Their malicious intent is not in the contract – it is in the wallet that deployed it, the wallets that provided liquidity, and the behavioral history that accumulates before the exploit.

The methodology uses creator chain traversal – a recursive process that climbs the deployment chain until it finds the terminal human-controlled wallet:

Token Contract
  └── contractCreatorAddress
         ├── If human wallet → score with predictive_fraud (98% accuracy)
         └── If contract (factory / proxy / deployer)
                  └── creator of THAT contract
                         ├── If human wallet → score with predictive_fraud
                         └── If contract → continue traversal...
                                  └── ... until terminal human wallet found

Sophisticated rug pull operators use deployment layers – factory contracts, proxy deployers, script contracts – specifically to sever the visible link between their personal wallet history and the new token. A naive rug pull checker that looks only one level up the creator chain sees a clean contract address and reports Low Risk. ChainAware’s traversal climbs through every layer until it finds the human operator, then scores their full behavioral fraud history across 19 forensic categories.

The “New Wallet” Risk Signal

When traversal terminates at a wallet created days or weeks before the token deployment, this carries elevated risk even without active fraud indicators. Legitimate protocol developers operate from established wallets with meaningful DeFi history. A new wallet at the chain terminus scores “New Address” rather than “Not Fraud” – and that distinction matters because it means the operator deliberately created a fresh wallet to avoid being traced from prior exploits. No prior fraud record is itself the red flag when combined with brand-new wallet age and a token launch event.

Liquidity Provider Fraud Scoring – The Second Dimension

Beyond creator analysis, the Rug Pull Detector independently scores every liquidity event. The `liquidityEvent` array returns every add/remove liquidity transaction with the `from_address` scored for fraud probability. Consequently, this catches the pattern where a clean creator wallet deploys the token but mixer outputs or darknet-linked wallets provide the liquidity – making those wallets the actual economic actors who will drain the pool. Creator analysis and liquidity provider scoring together cover the behavioral attack surface that 20+ code-level risk indicators alone miss. The overall tool achieves 68% detection accuracy before pool collapse – a dynamic prediction that updates as new behavioral data arrives. For how this fits the complete token analysis workflow, see our Fake Token Identification guide.

ChainAware Token Rank – Long Rug Pull Detection via Community Quality Scoring

Short rug pulls drain liquidity and disappear quickly. Long rug pulls unfold differently – the team builds apparent traction over months or years through manufactured social followers, inflated trading volume, and partnership announcements, while the actual holder base consists predominantly of bots, farm wallets, low-quality airdrop farmers, and coordinated Sybil wallets. When the team exits, price collapses because genuine community never existed. The fraud was in the community quality, not the code – and therefore invisible to any audit.

Token Rank detects long rug pulls by computing the median Wallet Rank across every meaningful token holder. Lower median Wallet Rank means higher holder quality. A token with 50,000 holders but a median Wallet Rank dominated by near-zero scores – new, inactive, single-chain wallets – has a manufactured community. A token with 5,000 holders and a median Wallet Rank of 2-3 has a genuinely high-quality community of experienced DeFi participants who chose to hold. Token Rank covers 2,500+ tokens across Ethereum, BNB Smart Chain, and other networks, exposing `communityRank`, `normalizedRank`, `totalHolders`, and the `topHolders` list with individual wallet profiles. No code audit, no tokenomics review, and no social metric reveals this – because it requires behavioral analysis of every individual holder. Token Rank is therefore the only tool that catches long rug pulls before they execute. See the Wallet Rank learn guide for how the underlying scoring methodology works, and the complete methodology in our Wallet Rank guide.

68% Detection Accuracy Before Pool Collapse

ChainAware Rug Pull Detector + Token Rank – Catch What Code Audits Miss

Creator chain traversal to the terminal human wallet. Liquidity provider fraud scoring. Community quality analysis across all holders. Short rug pulls and long rug pulls – both detected before you lose capital. Free for individual checks. MCP-native for AI agents.

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Category 5: Agent Verification – Why Voting Fails and Creator Chain Works

AI agents now execute DeFi strategies, manage DAO treasuries, run compliance pipelines, and interact with protocols autonomously – with significant capital and without any human in the loop. Worldchain noted that by some estimates 80% of blockchain transactions are already automated. As the Web3 agentic economy scales from thousands to millions of autonomous agent wallets, verifying the trustworthiness of those agents before granting them protocol access has become a critical infrastructure requirement. Every other trust category was designed for human wallets. None addresses the specific challenge of agent wallet verification. For the broader context of how AI agents are reshaping Web3 operations, see the ChainAware For AI Agents overview, our Web3 Agentic Economy guide and our 12 Blockchain Capabilities for AI Agents guide.

Why ERC-8004 and Voting-Based Agent Trust Fails

ERC-8004 and similar proposals attempt to build agent trust through on-chain reputation voting – agents vouch for each other, accumulate endorsements, and build scores based on peer consensus. The mechanism borrows from social trust systems like Ethos Network. However, it fails structurally when applied to agents rather than humans.

The manipulation attack is trivial and undetectable. A malicious operator deploys 50 agent wallets at near-zero cost. Each one votes up every other wallet in the cluster. Within days, all 50 accumulate high trust scores with zero genuine behavioral history. They then simultaneously vote down legitimate competing agents to suppress rival scores. The entire trust signal is manufactured – there is no Sybil resistance at the voting layer, no requirement for prior behavioral history, and no economic cost sufficient to deter a well-funded operator.

The deeper structural problem: AI agents have no social friction. When Ethos Network requires staked ETH behind a vouch, a human who vouches fraudulently loses money and social standing. An AI agent operator who creates 50 voting wallets and cross-vouches loses nothing – the wallets are free, the stake can be minimal, and the cluster rotates after each manipulation cycle. Voting-based agent trust is therefore not just gameable; it is machine-speed gameable by the very entities it is supposed to screen.

The Correct Approach: Creator Chain Traversal + Feeder Wallet Analysis

Agent trust does not require voting. It requires exactly the same methodology as short rug pull detection – creator chain traversal to the terminal human wallet, combined with independent feeder wallet analysis. The logic is identical:

Agent Wallet
  └── Who deployed this agent's controlling contract?
         ├── If human wallet → score with predictive_fraud
         └── If contract (factory / multi-sig / deployer)
                  └── creator of THAT contract
                         ├── If human wallet → score with predictive_fraud
                         └── If contract → continue traversal...

Feeder Wallet (who funds this agent's operations)
  └── Score independently with predictive_fraud
  └── Check: mixer interactions, darkweb, money_laundering,
             phishing, stealing_attack, sanctioned, 14 other forensic categories

This approach is manipulation-proof for a fundamental reason: blockchain history is immutable. A malicious operator cannot retroactively clean their terminal human wallet’s record of honeypot deployments, mixer interactions, or fraud associations. They cannot make a 6-day-old feeder wallet appear to have 3 years of legitimate DeFi history. They cannot remove the `honeypot_related_address` flag from a wallet that previously funded exit scams. The historical record makes creator chain analysis structurally Sybil-resistant in a way that no voting mechanism – regardless of its design – can achieve.

The Feeder Wallet – The Most Important Agent Trust Signal

Feeder wallet analysis is particularly critical because it catches the attack pattern that creator chain analysis alone misses. A sophisticated operator creates a clean deployment wallet specifically for the agent – passing creator chain analysis – while funding operations from a compromised wallet that reveals their actual risk profile. Both checks are necessary. Together they close the attack surface that any single-wallet screening approach leaves open.

ChainAware chainaware-agent-screener – The Only Agent Verification Tool

The `chainaware-agent-screener` (see Security & Fraud Agents) is the only purpose-built AI agent trust verification tool in the Web3 market. It screens both the agent wallet and the feeder wallet simultaneously, producing an Agent Trust Score from 0 to 10 (0 = confirmed fraud, 1 = new/insufficient data, 2-10 = normalized reputation). The agent uses both `predictive_fraud` and `predictive_behaviour` MCP tools and deploys via git clone and an API key – no custom engineering required.

Example output for a high-risk agent (from live documentation):

AGENT SCREENING
Agent Wallet: 0xSuspectAgent... | Network: Base
Feeder Wallet: 0xFundingSource... | Network: Base

Agent Trust Score: 2.1 / 10 ⚠

Agent Wallet:
  Fraud verdict: Elevated risk (0.52)
  On-chain age: 6 days ⚠
  Behaviour: Unusual - rapid fund movement, no prior agent pattern

Feeder Wallet:
  Fraud verdict: HIGH RISK (0.81) 🛑
  AML flags: Mixer interaction (Tornado Cash equivalent)
  Connected to 2 confirmed exit scams

→ 🛑 Do not allow. Feeder wallet has confirmed fraud indicators.
  Block and report to your security team.

The agent handles natural language prompts: “Is this agent wallet safe? 0xAgent… on Ethereum”, “Screen these 5 AI agents before we allow them into our protocol: [list of agent+feeder pairs]”, or “Can I trust this agent? It wants to execute trades on my behalf.” The growing adoption of multi-agent frameworks including ElizaOS, Fetch.ai, and Coinbase AgentKit makes this verification capability increasingly critical – every protocol integrating third-party agent infrastructure now requires a trust layer to screen those agents before granting access. For the complete AI agent capability reference, see our AI Agents for Web3 roadmap and our Blockchain Data Providers guide.

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Screen AI agent wallets and feeder wallets before granting protocol access. Manipulation-proof via creator chain traversal – not gameable by voting clusters. Works with Claude, GPT, and any MCP-compatible LLM. No custom build required. See the full Ready-Made Agents catalogue.

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ChainAware’s Unique Position Across All Five Categories

Having mapped all five categories, ChainAware’s competitive position becomes precise. Across the five trust problems, ChainAware plays a distinct role in each – complementary in some, competing and extending in others, and uniquely positioned as sole provider in two.

Category 1 (Identity Trust) – Complementary

KYC providers verify identity at a point in time. ChainAware adds ongoing behavioral fraud prediction that operates continuously after verification – catching wallets whose risk profile changes after KYC completion. Additionally, ChainAware’s permissionless approach covers the DeFi protocols that KYC is unsuitable for entirely, providing behavioral trust coverage without requiring user participation. The two layers are additive: KYC for regulatory compliance, ChainAware for continuous behavioral risk monitoring.

Category 2 (Behavioral Trust) – Competing and Extending

ChainAware operates in the same on-chain, permissionless, privacy-preserving space as Trusta, Nomis, and RubyScore – but answers fundamentally richer questions. Trusta detects coordination graph patterns. Nomis scores activity volume. ChainAware adds 22-dimension behavioral profiles, 12 forward-looking intention probabilities, 19-category forensic fraud analysis, AML/OFAC screening, governance tier classification, and 32 deployable agents. Furthermore, ChainAware is the only provider with a growth deployment layer – converting screened traffic into transacting users rather than just producing eligibility scores. For the full behavioral intelligence comparison, see our Web3 Analytics Tools Comparison.

Category 3 (Social Trust) – Complementary

Ethos, Karma3, and UTU measure what the community says about known participants. ChainAware measures what blockchain history predicts about any wallet’s future behavior. These signals are orthogonal: a highly vouched wallet can have high fraud probability, and a wallet with zero Ethos profile can have excellent behavioral quality scores. Both signals together provide more robust trust assessment than either alone. The practical combination: Ethos credibility scores for known community participants with established social standing, ChainAware behavioral intelligence for every wallet regardless of social profile.

Category 4 (Token and Protocol Trust) – Partially Competing

CertiK and Hacken own the code audit layer – ChainAware does not compete with smart contract formal verification. However, ChainAware owns the behavioral token trust layer that code audits structurally cannot reach. Rug Pull Detector (creator chain traversal + liquidity provider fraud scoring = short rug pull detection) and Token Rank (median Wallet Rank across all holders = long rug pull detection) address attack surfaces where CertiK and Hacken have no tools. A complete protocol trust stack requires both: CertiK/Hacken for code safety and ChainAware for behavioral token trust.

Category 5 (Agent Verification) – Sole Provider

No other provider has built agent wallet trust verification. ERC-8004 and voting-based proposals are manipulable at machine speed. Creator chain traversal with feeder wallet analysis – the methodology ChainAware applies through `chainaware-agent-screener` – is the only manipulation-proof approach, and ChainAware is the only provider that has implemented it. As the agentic economy scales, this category will grow from a niche capability to foundational infrastructure – and ChainAware currently has no competition in it.

The Recommended Trust Stack for 2026

No single provider covers all five trust dimensions. Consequently, the most sophisticated protocols in 2026 layer multiple tools addressing different attack surfaces. The following combinations map to the most common protocol types.

Regulated VASPs and Centralized Exchanges

Sumsub for document KYC, Travel Rule, and KYB compliance (mandatory regulatory layer) + ChainAware for ongoing behavioral fraud prediction and transaction monitoring (continuous behavioral layer) + CertiK audit for any smart contracts in the stack (code layer). Together these cover all five trust dimensions except social trust, which becomes relevant for DAO-adjacent products.

Permissionless DeFi Protocols

CertiK or Hacken for pre-launch smart contract audit (code layer) + ChainAware Rug Pull Detector pre-launch screening of the deployer wallet and liquidity setup (behavioral token trust) + Trusta or Nomis for airdrop Sybil filtering (campaign gate) + ChainAware Wallet Rank and fraud probability at wallet connection (quality and safety gate) + ChainAware Growth Agents to convert screened wallets into transacting users (deployment layer). For the complete DeFi compliance framework, see our DeFi Compliance Tools guide.

DAOs with Treasury and Governance

ChainAware `chainaware-governance-screener` before every governance vote (behavioral Sybil detection + tier classification + voting weight multipliers – the only tool that does this) + Ethos credibility scores for known community members (social layer) + Hacken TRUST Score for ongoing protocol security assessment. Additionally, ChainAware Token Rank continuously monitors holder community quality – detecting whether a coordinated low-quality holder base is accumulating governance tokens for a long-term governance attack. For the governance attack surface in depth, see our Governance Screeners guide.

Protocols Integrating Third-Party AI Agents

ChainAware `chainaware-agent-screener` for every third-party agent requesting protocol access – screening both the agent wallet and feeder wallet before granting any permissions + `chainaware-transaction-monitor` for ongoing real-time scoring of every agent transaction (ALLOW / FLAG / HOLD / BLOCK pipeline action) + ChainAware fraud detector for the agent operator wallet if known. This creates a complete agent trust perimeter: pre-access screening, real-time transaction monitoring, and operator background verification. For how AI agents integrate with Web3 protocols at scale, see our Real AI Use Cases for Web3 guide.

Token Investors and Pre-Investment Due Diligence

ChainAware Rug Pull Detector on the token contract (creator chain traversal + LP fraud scoring = short rug pull risk) + ChainAware Token Rank on the token’s holder community (median Wallet Rank = long rug pull risk) + CertiK or Hacken audit status (code risk) together provide a three-dimensional token trust assessment that no single tool delivers alone. For how to identify fake tokens using these signals, see our Fake Token Identification guide.

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Frequently Asked Questions

What is the difference between KYC trust and behavioral trust?

KYC trust verifies that a wallet belongs to a real, identifiable person with verified government documents at a specific point in time. Behavioral trust analyzes what that wallet has done on-chain to predict future fraud risk and behavioral quality. Both are necessary because a wallet can pass KYC and subsequently develop high fraud probability, and a wallet can have strong behavioral quality scores without any KYC verification. The two layers address different attack surfaces: KYC for regulatory compliance and identity certainty, behavioral trust for ongoing fraud risk and quality assessment.

Can a smart contract audit replace rug pull detection?

No – and this is one of the most dangerous misconceptions in Web3 security. Smart contract audits verify code correctness at audit time. Rug pull detection verifies the behavioral risk of the human operator behind the code. Experienced rug pullers deliberately write clean, auditable code – their malicious intent is in their wallet’s history, not the contract. The creator chain traversal approach catches this by climbing through every deployment layer to find the terminal human wallet and score their full behavioral fraud history. A clean CertiK audit combined with a high-risk creator wallet is a warning sign, not a green light. Running both checks is the complete picture.

What is a long rug pull and how does Token Rank detect it?

A long rug pull unfolds over months or years. The team builds apparent community through manufactured holder counts, inflated trading volume, and partnership announcements – while the actual holder base consists of bots, farm wallets, and coordinated Sybil wallets with no genuine community intent. When they exit, the price collapses because no real community existed to support it. Token Rank detects this by computing the median Wallet Rank across all meaningful holders. A high holder count combined with near-zero median Wallet Rank scores – dominated by new, inactive, single-chain wallets – signals a manufactured community before the collapse. No code audit, tokenomics review, or social metric catches this because it requires behavioral analysis of the individual holder base, not the contract.

Why is ERC-8004 voting-based agent trust inadequate?

ERC-8004 and similar proposals are trivially manipulable because AI agents have no social friction or economic consequences for false vouching. A malicious operator deploys a cluster of 50 agent wallets at near-zero cost, cross-vouches them to inflate trust scores, and simultaneously downvotes legitimate competitors – all at machine speed. The manipulation cannot be distinguished from genuine vouching because agents produce no social record, no real-world identity damage, and no economic loss when participating in a trust manipulation scheme. Creator chain traversal with feeder wallet analysis solves this problem structurally – blockchain history is immutable, making it impossible to retroactively clean a terminal human wallet’s record of prior exploits, mixer usage, or fraud associations.

What does ChainAware provide that Ethos Network does not?

Ethos Network measures social community trust among known participants with established Ethos profiles. ChainAware measures behavioral intelligence for any wallet regardless of social profile. Practically, Ethos cannot screen anonymous wallets with no Ethos history – which describes most wallets connecting to any DeFi protocol. Furthermore, Ethos does not predict future behavior, does not provide AML/OFAC screening, does not detect token rug pull risk, and does not screen AI agent wallets. The two systems address orthogonal trust dimensions: Ethos for social standing among known community participants, ChainAware for behavioral risk assessment of any on-chain address.

How does ChainAware’s credit score relate to trust verification?

ChainAware’s credit score (1-9 trust score derived from AI analysis of on-chain inflows, outflows, fraud indicators, and social graph data) addresses financial trustworthiness specifically – answering whether a counterparty can be trusted to repay in undercollateralized lending contexts. This is a trust verification use case that no KYC provider, no Sybil detection tool, and no social trust platform addresses. KYC verifies identity but not creditworthiness. Behavioral reputation scores activity quality but not repayment reliability. ChainAware’s credit score is therefore a sixth trust dimension specifically relevant to DeFi lending protocols seeking to move beyond overcollateralized models. For the complete methodology, see our Web3 Credit Scoring guide.

What is the minimum setup to get meaningful trust coverage?

For most DeFi protocols, meaningful coverage starts with two free tools requiring zero engineering: the ChainAware Wallet Auditor for individual high-stakes wallet checks, and the Rug Pull Detector for any token or liquidity pool before depositing. Adding the free Web3 Behavioral Analytics pixel via Google Tag Manager provides population-level quality assessment of every wallet connecting to your DApp – revealing experience distribution, fraud rate, and intention profiles without any engineering sprint. For protocols needing automated coverage, the Prediction MCP connects any AI agent or LLM to all six intelligence dimensions in a single natural language tool call. For the complete integration reference, see our ChainAware Complete Product Guide.

External sources: Sumsub 2026 State of Crypto Industry Report · CertiK Platform Documentation · Karma3 Labs / OpenRank · Ethos Network · ChainAware Behavioral Prediction MCP – GitHub

The post Web3 Trust Verification Systems in 2026 – The Complete Five-Category Landscape first appeared on ChainAware.ai.

Sybil attacks cost Web3 protocols billions every year. Sybil addresses accounted for 40% of tokens deposited to exchanges in the Aptos airdrop alone. DAO treasuries now hold $21.4 billion in liquid assets – and governance attacks have already stolen hundreds of millions, including $181 million from Beanstalk in a single transaction. The problem is structural: wallets can be generated endlessly and anonymously at near-zero cost, making Sybil attacks fundamentally easier in Web3 than in any other digital context.

In 2026, a competitive market of on-chain Sybil protection systems has emerged to address this threat. However, these systems vary dramatically in methodology, depth, and what they actually protect against. Furthermore, the most important question in the Sybil landscape is one that most providers never answer: what happens after you filter the Sybils? This guide compares every major on-chain behavioral Sybil protection provider, explains the structural limits of each approach, and introduces ChainAware’s unique position as the only provider that connects Sybil protection to behavioral intelligence, governance design, and DApp conversion.

What Is a Sybil Attack in Web3?

A Sybil attack occurs when a single actor creates multiple fake wallet identities to game systems designed to reward unique participants. The attack targets any mechanism that treats each wallet as a distinct person: airdrop distributions, governance votes, quadratic funding rounds, community reward programs, and IDO allocations. Because wallet generation costs nothing and requires no identity verification, Sybil attacks scale effortlessly in Web3.

Consequently, the damage is concrete and measurable. Researchers found Sybil addresses claimed 40% of Aptos tokens that subsequently dumped. Governance attacks exploiting low voter turnout – the average DAO sees just 17% participation – have extracted hundreds of millions from protocol treasuries. The top ten voters already control between 45% and 58% of voting power in Uniswap and Compound, making governance capture significantly easier than most participants assume. For a detailed look at how governance attacks unfold and which screeners detect them, see our Web3 Governance Screeners guide.

Therefore, effective Sybil protection has become a prerequisite for any protocol distributing tokens, running governance, or building community programs. The question in 2026 is not whether to use Sybil protection – it is which approach to use, and what that approach actually covers.

The Two On-Chain Behavioral Approaches

The on-chain Sybil protection market divides into two methodologically distinct approaches. Both operate permissionlessly and without requiring user action – no biometric scans, no credential collection, no KYC friction. Both analyze public blockchain data only. However, they answer different questions and carry different structural strengths and limitations.

Approach A – AI/ML Transaction Graph Pattern Detection: Analyzes the relational structure of wallet transaction graphs to identify coordinated Sybil clusters. The key insight is that Sybil wallets, regardless of how they behave individually, must be funded from a common source – and that funding structure leaves detectable graph-level signatures. Trusta Labs / TrustScan is the primary representative of this approach.

Approach B – Activity-Based Reputation Scoring: Measures historical activity volume, protocol diversity, wallet age, and cross-chain engagement as proxy signals for genuine participation. The underlying assumption is that genuine Web3 users accumulate multi-dimensional activity history over time, while Sybil wallets tend to be newer, less active, and less diverse. Nomis, RubyScore, and ReputeX represent this approach.

Both approaches produce useful Sybil signals. Neither is sufficient on its own, and critically, neither answers the question that determines whether your protocol actually grows: who is this wallet, what will they do next, and how do you convert them into a transacting user? For the broader context of how Sybil protection fits into the full wallet intelligence stack, see our Web3 Wallet Auditing Providers guide.

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Trusta Labs / TrustScan – AI/ML Graph Pattern Detection

Trusta Labs is the most technically sophisticated pure on-chain Sybil detector available in 2026. Founded by ex-Alipay AI and security leaders, Trusta applies Graph Neural Networks (GCNs, GATs) and Recurrent Neural Networks (GRUs, LSTMs) to analyze wallet transaction graphs for four specific Sybil behavioral signatures.

The Four Sybil Attack Patterns TrustScan Detects

Star-like transfer graphs – one hub address funds many wallets in a spoke pattern, creating a distinctive radial topology in the transaction graph. Chain-like transfer graphs – sequential wallet funding where each wallet funds the next in a linear chain, a common pattern for automating multi-wallet creation. Bulk operations – coordinated timing patterns where multiple wallets execute the same transaction type within the same narrow time window. Similar behavior sequences – identical or near-identical transaction fingerprints across ostensibly separate wallets, revealing shared operational automation.

TrustScan produces a Sybil Score from 0 to 100 (higher equals more Sybil risk) plus a MEDIA Score across five dimensions: Monetary, Engagement, Diversity, Identity, and Age. The platform has analyzed 570 million wallets and integrated as a stamp in Gitcoin Passport (1.54 points per verified address) and as a credential in Galxe. Trusta ranks as the top Proof of Humanity provider on Linea and BSC, with 200K monthly active users.

TrustScan USP

The GNN approach models the relational structure between wallets – not just individual behavior but the network topology of how they were funded and operated. Consequently, this is genuinely difficult to fool at scale, because the attacker must maintain behavioral independence across thousands of wallets simultaneously. Battle-tested results across Celestia, Starknet, Manta, Plume, and major Gitcoin funding rounds demonstrate real-world effectiveness. Additionally, the permissionless approach means no user friction – any wallet can be scored without their knowledge or participation.

TrustScan Structural Limitations

First, the Sybil score is reactive – it detects patterns that have already formed. A brand-new wallet with no transaction history scores “Unknown,” not “Not Sybil,” which is precisely the profile of a Sybil wallet before it begins farming. Second, chain coverage is primarily EVM and TON, leaving significant gaps on Solana, Cosmos, and newer L1/L2 ecosystems. Third, output is a binary or scored gate – Trusta produces a risk score but no downstream deployment layer. The protocol team must build all governance tier logic, weight calculations, and conversion workflows themselves on top of the API. Finally, a determined Sybil operator spacing transactions carefully over time can reduce detection probability by avoiding the timing and graph signatures TrustScan targets. For how Sybil protection integrates with the broader governance security stack, see our Governance Screeners guide.

Nomis – Multi-Chain Activity Reputation

Nomis takes a different approach – measuring historical activity volume, protocol diversity, wallet age, and cross-chain engagement across 50+ chains using 30+ parameters. Rather than detecting coordination graph patterns, Nomis scores the richness and depth of a wallet’s on-chain history as a proxy for genuine participation. Output is a reputation score issued as an on-chain NFT attestation, making it portable across protocols and verifiable without re-querying the platform.

Nomis USP

Broadest chain coverage of any pure on-chain Sybil or reputation provider – 50+ chains versus Trusta’s EVM plus TON. The NFT attestation model gives portability: a wallet earning a high Nomis score on one protocol can present it to another without reverification. Moreover, Nomis works well for multi-chain campaigns where single-chain analysis would miss cross-chain behavioral context. According to Nomis’s platform documentation , the scoring model weighs recent activity more heavily than older history, reducing the effectiveness of pre-aged Sybil wallets.

Nomis Structural Limitations

Nomis measures quantity of activity rather than quality. A wallet making 500 low-value token swaps over three years earns a high Nomis score – but that history tells you nothing about whether the wallet will engage with your DeFi lending protocol. Furthermore, Nomis has no behavioral pattern detection capability. A Sybil operator spacing transactions across time and chains can accumulate a high Nomis score while still being a coordinated farm wallet. Additionally, the score reflects only the past – no forward-looking behavioral predictions or intention signals exist in the output. Finally, Nomis has no growth or conversion layer – their job ends at the eligibility gate. For a comprehensive comparison of Nomis against other Web3 reputation scoring platforms, see our Web3 Reputation Score Comparison.

RubyScore and ReputeX – Lightweight Reputation Filters

RubyScore provides activity quality scoring using transaction volume and diversity as proxy signals for genuine engagement – a simpler methodology than Nomis with fewer parameters and faster integration. As a result, it works well as an entry-level Sybil filter for projects that need a lightweight reputation gate without the analytical depth of Trusta or Nomis. Traffic quality improves noticeably over unfiltered campaigns, making RubyScore a practical starting point for smaller teams with limited engineering resources.

ReputeX takes a philosophically different stance – explicitly positioning around a “fusion approach” combining multiple behavioral paradigms rather than betting on a single methodology. The underlying thesis is sound: different Sybil attack patterns require different detection approaches, and a system combining multiple signals is more resilient against sophisticated operators than any single methodology. However, ReputeX remains early-stage with limited production deployment evidence. The fusion approach therefore promises more than it has currently demonstrated at scale.

Both RubyScore and ReputeX share all the structural limitations of the activity-based approach: they describe past behavior, produce binary gates, and provide no downstream intelligence about wallet quality, future intentions, or conversion probability. Neither has a governance-specific output, a growth layer, or an MCP integration for AI agents.

The Structural Limitation All Providers Share

Every provider above – Trusta, Nomis, RubyScore, ReputeX – answers a version of the same question: “Has this wallet demonstrated enough genuine on-chain history to be considered non-Sybil?” This is a necessary question. However, it is not a sufficient one, and it has two structural blind spots that no methodology improvement within this paradigm can resolve.

Blind Spot 1: The Timing Problem

Sybil attacks unfold in two phases: first the farm phase, where the attacker builds minimal on-chain history to pass screening thresholds, then the exploit phase, where they claim rewards and disappear. All current Sybil providers screen for wallets that look suspicious based on existing history. By the time a wallet has enough history to be definitively flagged, the exploit has often already occurred. A brand-new wallet with no history scores “Unknown” on Trusta, scores low on Nomis, and passes most eligibility thresholds – because it has no detectable Sybil fingerprint yet. Paradoxically, the very wallets most likely to be new Sybil wallets are the ones these systems find hardest to flag.

Blind Spot 2: The Quality Gap

Even a wallet passing every Sybil check – genuine, non-coordinated, with sufficient activity history – may still be a low-quality participant who will never transact meaningfully with your protocol. Sybil resistance proves uniqueness. It says nothing about intent, behavioral quality, or conversion probability. A non-Sybil wallet with Low Lend intention on a DeFi lending protocol will not convert regardless of how clean its history is. Yet no Sybil provider surfaces this signal – they confirm this wallet is probably one real person and leave everything else to you. For how on-chain behavioral intelligence closes this gap, see our Intention Analytics guide and our Web3 Reputation Score Comparison.

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ChainAware – Beyond Sybil Detection

ChainAware operates in the same purely on-chain, permissionless, privacy-preserving space as these providers – but answers fundamentally different questions. Rather than focusing narrowly on Sybil risk, ChainAware delivers a complete behavioral intelligence layer that starts where Sybil detection ends. Specifically, ChainAware answers five questions that no Sybil provider addresses:

1. Quality Beyond Uniqueness – Wallet Rank

Trusta confirms this wallet is probably not coordinating with fake wallets. Nomis confirms this wallet has accumulated activity. ChainAware’s Wallet Rank answers a completely different question: is this wallet a high-quality participant who is likely to engage genuinely with your protocol? A wallet can pass every Sybil check and still rank low on behavioral quality dimensions – shallow activity, concentrated in low-value interactions, no meaningful protocol engagement. Wallet Rank surfaces this distinction immediately. For the complete Wallet Rank methodology, see our Wallet Rank Complete Guide.

2. Forward-Looking Intent – 12 Intention Probabilities

Every Sybil provider describes the past. ChainAware predicts the future. Twelve intention probabilities – Borrow, Lend, Trade, Gamble, NFT, Stake ETH, Yield Farm, Leveraged Staking, Leveraged Staking ETH, Leveraged Long ETH, Leveraged Long Game – are ML predictions trained on 18M+ behavioral profiles. A wallet with High Lend intention is operationally more valuable to a lending protocol than one that merely passes the Sybil check, because a non-Sybil wallet with Low Lend intention will not convert regardless of how clean its history is. No competitor provides this signal. For how intention probabilities drive DApp conversion, see our DeFi Onboarding guide.

3. Fraud Prediction – Broader Than Sybil, Forward-Looking

ChainAware’s fraud prediction model achieves 98% accuracy against CryptoScamDB and covers a broader threat surface than pure Sybil detection. Sybil detection identifies wallets farming your airdrop. ChainAware’s fraud detection identifies wallets likely to commit financial crime – phishing operators, stolen fund recyclers, fake KYC actors, darknet-linked wallets, honeypot deployers, money launderers. Many high-risk wallets have clean transaction graphs that pass Trusta screening but exhibit fraud probability signals ChainAware catches through 19 forensic detail categories: cybercrime, money laundering, darkweb transactions, phishing activities, fake KYC, stealing attacks, mixer interactions, sanctioned addresses, malicious mining, fake tokens, and more. For the complete fraud detection methodology, see our Fraud Detector guide.

4. AML and OFAC Compliance – Absent From Every Sybil Provider

Trusta, Nomis, RubyScore, and ReputeX are all Sybil prevention tools. None screens for AML exposure, OFAC sanctions, or financial crime risk in the regulatory sense. ChainAware’s AML layer addresses the compliance requirement that MiCA and equivalent frameworks impose on DeFi protocols – screening every connecting wallet against sanctions lists and financial crime indicators automatically, without a compliance team in the loop. This covers a threat surface that Sybil providers entirely ignore. For the complete DeFi compliance use case including AML and MiCA requirements, see the DeFi Compliance use case guide. According to FATF’s Virtual Asset guidance , DeFi protocols with governance or token distribution mechanisms face specific AML obligations that pure Sybil screening cannot satisfy. For the full MiCA compliance framework, see our MiCA Compliance guide.

5. The Growth and Conversion Layer – Unique in the Market

Every Sybil provider’s output is a gate: pass or fail for campaign eligibility. ChainAware’s Growth & Marketing Agents take the behavioral intelligence – Wallet Rank, 12 intention probabilities, experience level, risk profile – and deploy it into DApp UI at wallet connection, personalizing content and CTAs in real time. Additionally, the Prediction MCP delivers behavioral predictions to any AI agent in a single natural language tool call. No Sybil provider has built any equivalent downstream capability – their job ends at the screening gate. For how ChainAware’s growth layer drives conversion from Sybil-filtered traffic, see our ChainAware Business Guide and our Web3 Analytics Tools Comparison.

ChainAware’s Sybil-Specific Ready-Made Agents

Here is the most significant competitive distinction that the comparison tables above understate: Trusta, Nomis, and RubyScore all ship API scores. ChainAware ships 32 ready-made open-source MIT-licensed agents that any team deploys via git clone and an API key – with no custom engineering required. The deployment gap between “score API” and “deployable agent” is the difference between a tool and a complete system. Three agents directly address Sybil protection use cases.

chainaware-sybil-detector

Standalone Sybil detection agent (see Security & Fraud Agents) for general use cases beyond governance – airdrop screening, campaign eligibility gating, counterparty vetting, and partnership due diligence. Rather than returning a raw score, the agent produces a structured Sybil assessment combining fraud probability from predictive_fraud with behavioral pattern analysis from predictive_behaviour. Output explicitly surfaces coordination signals – wallet age clustering, funding pattern similarity, behavioral fingerprint matching – with human-readable flag explanations rather than just a score number. This makes the output immediately actionable without requiring an analyst to interpret what a score of 73 means in context.

chainaware-reputation-scorer

Composite wallet reputation agent producing a structured assessment across five dimensions simultaneously: fraud probability, behavioral quality, experience level, AML status, and Wallet Rank. Designed specifically for use cases where a simple pass/fail Sybil gate is insufficient – undercollateralized lending protocols, DAO membership tiers, partnership vetting, KOL wallet verification, and counterparty due diligence. The agent combines what Nomis does (activity-based reputation) with what ChainAware’s fraud layer does (forward-looking fraud detection) into a single unified output – without requiring separate API calls to multiple providers. For how on-chain reputation scoring applies to DeFi credit decisions, see our Web3 Credit Scoring guide.

chainaware-airdrop-screener

Purpose-built for airdrop and IDO Sybil filtering at campaign level – screening wallet lists to identify bot farms, coordinated farm wallet clusters, and low-quality airdrop farmers before distribution. For the complete framework of Sybil-resistant token distribution design from first principles, including how to structure eligibility criteria that professional farm wallets cannot satisfy, see the Sybil-Resistant Token Distribution use case guide. The agent processes lists of addresses and returns a tiered eligibility assessment, identifying which wallets should receive full allocation, reduced allocation, or disqualification. Consequently, teams run the screener on their entire eligible wallet list before the distribution event rather than relying on post-distribution forensics. For how airdrop scam screening differs from Sybil filtering in airdrop campaigns, see our Airdrop Scam Screeners guide.

chainaware-governance-screener – The Most Advanced Governance Sybil Tool Available

The chainaware-governance-screener represents the most sophisticated governance-specific Sybil protection tool in the market – and nothing comparable exists from any competing provider. Running on claude-haiku-4-5-20251001 and using both predictive_fraud and predictive_behaviour MCP tools simultaneously, the agent does not merely flag suspected Sybils. Instead, it classifies every DAO member into a behavioral tier, calculates their voting weight multiplier, detects coordinated Sybil clusters, and produces a full governance health score – all from a single natural language prompt.

The Five Governance Tiers

Three Governance Models Supported

Token-weighted governance, reputation-weighted governance, and quadratic governance models are all natively supported. Specifying the governance model in the prompt adjusts how the agent calculates weight multipliers and flags concentration risks. Quadratic governance detection, for example, specifically surfaces scenarios where many low-quality wallets could collectively accumulate outsized influence – a Sybil attack vector unique to quadratic voting that standard token-weighted analysis misses entirely.

What the Output Looks Like

For a clean veteran wallet, the agent produces:

GOVERNANCE SCREENING - Wallet: 0xVoter... | Ethereum
Governance Model: Reputation-weighted

Tier: ✅ Core Contributor | Voting Weight: 2×
Sybil Risk: None detected

Experience: Veteran (3.6 years on-chain)
Fraud risk: Very Low (0.03) | AML: Clean
Governance history: 12 prior votes across 4 DAOs

→ Full voting rights. Eligible for governance committee nomination.

For a detected Sybil wallet, the output provides:

Tier: 🚫 DISQUALIFIED | Voting Weight: 0×
Sybil Risk: HIGH

- Wallet created 8 days ago ⚠
- 3 similar wallets with near-identical creation patterns detected ⚠
- Token balance acquired in single transaction (typical Sybil pattern) ⚠
- No prior governance participation

→ Block from voting. Flag the 3 related addresses for review.

For an entire DAO screened in one prompt, the governance health report surfaces:

GOVERNANCE HEALTH CHECK - 200 wallets | Ethereum

Core Contributors:  28 (14%) - 2× weight
Active Members:     61 (31%) - 1.5× weight
Participants:       74 (37%) - 1× weight
Observers:          22 (11%) - 0.5× weight
Disqualified:       15 (8%)  - 0× weight

Governance Health Score: 72/100 - Good
⚠ 4 address clusters detected (possible coordinated Sybil attack)
⚠ 15% of voting weight concentrated in 3 wallets (centralisation flag)
→ Recommend: minimum 90-day wallet age for new membership applications

Critically, no engineering work is required beyond cloning the agent from GitHub and configuring an API key. A DAO team can run this analysis before every governance vote using a natural language prompt – something that would require weeks of custom development to replicate using Trusta or Nomis APIs alone. For why DAO treasury governance security has become the most important Sybil protection use case in 2026, see our Governance Screeners guide and our Web3 Agentic Economy guide.

Deploy in Minutes – No Custom Build Required

32 Ready-Made Agents – Including Governance Screener, Sybil Detector, Airdrop Screener

Clone from GitHub, add your API key, and your agent has native Sybil detection, governance tier classification, airdrop screening, fraud detection, and AML compliance in natural language. MIT-licensed. Open source. No vendor lock-in. Works with Claude, GPT, and any MCP-compatible LLM. Full catalogue at the Prediction MCP documentation.

View on GitHub Agent Integration Guide

Full Provider Comparison Table

Capability	Trusta TrustScan	Nomis	RubyScore	ChainAware
Sybil detection method	GNN/RNN graph pattern analysis	Activity volume scoring	Activity quality scoring	Behavioral ML + 19-category forensic layer
Fraud probability (forward-looking)				98% accuracy
AML / OFAC screening				Full forensic detail layer
Intention prediction				12 intention probabilities
Behavioral quality score	Partial (MEDIA 5 dimensions)	Partial (activity volume)	Partial (activity quality)	Wallet Rank + 22 dimensions
Governance Sybil screening				chainaware-governance-screener
Governance tier classification				5 tiers (Core/Active/Participant/Observer/Disqualified)
Voting weight multipliers				2×/1.5×/1×/0.5×/0×
Quadratic governance support				Native model support
DAO health score (population)				Single prompt, full DAO
Airdrop Sybil screening agent	API only	API only	API only	chainaware-airdrop-screener
Standalone Sybil detection agent				chainaware-sybil-detector
Reputation scoring agent				chainaware-reputation-scorer
Ready-made deployable agents				32 MIT open-source agents
Custom engineering required	Significant	Significant	Moderate	git clone + API key
MCP / AI agent native				6 MCP tools
Growth / conversion layer				Growth Agents
Token holder quality				Token Rank
Chain coverage	EVM + TON	50+ chains	EVM-focused	ETH/BNB/BASE/POL/TON/TRON/HAQQ/SOL
Wallets analyzed / profiles	570M wallets scored	50+ chain coverage	EVM activity	18M+ behavioral profiles
Free individual lookup	Partial	Partial	Partial	Full Wallet Auditor free
Pricing	Freemium → API	Freemium → NFT	Freemium	Freemium → API tiers

The Recommended Stack for 2026

The right framing for ChainAware’s position against on-chain Sybil providers is not “a better Sybil detector” – it is “the layer that starts where Sybil detection ends.” Trusta and Nomis are useful campaign-gate tools. ChainAware is the behavioral intelligence, governance design, and conversion layer that follows. Together they provide complete coverage; separately, each leaves critical gaps.

For Airdrop and Token Distribution Campaigns

Run Trusta or Nomis at the campaign gate for population-level Sybil filtering – both are battle-tested specifically for this use case. Then apply ChainAware’s chainaware-airdrop-screener as a secondary quality layer, filtering eligible wallets by Wallet Rank and behavioral profile to ensure your distribution rewards genuine high-quality community members rather than simply non-Sybil wallets. Additionally, use ChainAware Fraud Detector to screen for AML exposure among eligible addresses – a compliance layer no Sybil provider covers. For how to design Sybil-resistant token distribution from first principles, see our Rug Pull Detection guide and our Wallet Rank guide.

For DAO Governance Protection

Deploy chainaware-governance-screener before every governance vote via a simple natural language prompt listing all voter addresses and specifying your governance model. The agent handles the complete workflow autonomously: Sybil detection, tier classification, weight calculation, cluster identification, health scoring, and specific recommendations. No engineering resources required after initial setup. Schedule it as a pre-vote automated check that runs 24 hours before any proposal closes. For the governance attack patterns this prevents and the real-world stakes involved, see our Governance Screeners guide.

For DApp Real-Time Wallet Screening

Use the Prediction MCP at wallet connection for sub-100ms Sybil and fraud screening of every connecting wallet before they interact with your protocol. The predictive_fraud tool returns fraud probability, forensic flags, and AML status. The predictive_behaviour tool returns the full Web3 Persona – experience level, intentions, risk profile, Wallet Rank. Together they give you both Sybil protection and the behavioral intelligence needed to personalize the DApp experience for every non-Sybil wallet that passes through. Combine with Growth Agents to automatically serve personalized content and CTAs based on the persona – turning Sybil-filtered traffic into transacting users. For the full AI agent integration architecture, see our 12 Blockchain Capabilities guide and our Web3 Agentic Economy guide.

ChainAware.ai – The Complete Sybil Protection Stack

Sybil Detection Tells You Who to Block. ChainAware Tells You Who to Trust – and Converts Them.

Free Wallet Auditor for individual lookups. 32 ready-made MIT agents for automated workflows. Prediction MCP for AI agent pipelines. Growth Agents for DApp conversion. One stack. No custom build required.

Free Wallet Audit Prediction MCP GitHub Agents

Frequently Asked Questions

What is the difference between Sybil detection and fraud detection?

Sybil detection identifies wallets that are likely controlled by the same actor – specifically targeting multi-wallet farming of airdrops, governance votes, and incentive programs. Fraud detection identifies wallets likely to commit financial crime – phishing operations, money laundering, stolen fund cycling, sanctioned addresses, darknet interactions. These threat surfaces overlap but are not identical. A sophisticated phishing operator typically uses unique, non-coordinated wallets that pass Sybil detection while scoring high on fraud probability. Conversely, an airdrop farmer might use obviously Sybil-pattern wallets that have no financial crime history. Comprehensive protection therefore requires both layers simultaneously – Sybil detection for campaign integrity and fraud detection for financial security. ChainAware’s chainaware-fraud-detector and chainaware-sybil-detector agents address both in a single deployable stack.

Can TrustScan detect all Sybil attacks?

Trusta’s GNN approach is genuinely effective at detecting the four coordination graph patterns it targets – star-like funding, chain-like funding, bulk operations, and similar behavior sequences. However, it has documented limitations. First, it cannot flag wallets with no prior transaction history, which includes all newly created Sybil wallets before the farming phase begins. Second, a sophisticated operator spacing transactions carefully over time and across chains can reduce their graph signature below detection thresholds. Third, Trusta’s coverage is primarily EVM and TON – projects on Solana, Cosmos, or newer chains face gaps. For the most robust protection, combining Trusta’s graph analysis with ChainAware’s behavioral fraud probability creates a more complete detection surface than either approach alone.

Is chainaware-governance-screener suitable for small DAOs?

Yes – the agent scales from individual wallet queries (“Should this wallet be allowed to vote?”) through batch processing of entire DAO member lists via a single prompt. Small DAOs with 20-50 members benefit immediately from the five-tier classification and voting weight recommendations without any custom engineering. Larger DAOs with hundreds or thousands of members can run the full governance health check before every major vote, receiving Sybil cluster detection, concentration flags, and specific recommendations in one output. The natural language interface means no technical expertise is required after the initial GitHub clone and API key configuration. For the governance attack patterns the screener prevents, see our Governance Screeners guide.

Why do Nomis and Trusta score the same wallet differently?

Nomis and Trusta measure fundamentally different things. Nomis scores how much activity a wallet has accumulated across its history – volume, diversity, age, and cross-chain engagement. Trusta scores how suspicious a wallet’s transaction graph topology looks – coordination patterns, similar behavior sequences, and bulk operations. A wallet can score high on Nomis (old, active, diverse) while scoring high on Trusta Sybil risk (because its funding pattern matches a hub-and-spoke Sybil cluster). Conversely, a wallet can score low on Nomis (young, limited activity) while having a clean Trusta score (because its transaction graph shows no coordination). These scores are complementary rather than redundant – using both reduces false positives while increasing detection coverage across different attack vectors.

How does ChainAware’s fraud probability differ from a Sybil score?

A Sybil score measures whether a wallet appears to be one of many controlled by the same actor – primarily a campaign integrity question. ChainAware’s fraud probability (98% accuracy, 0.00-1.00 scale) measures whether a wallet is likely to commit financial crime – a security and compliance question. The fraud model covers 19 forensic categories including phishing activities, money laundering, darkweb transactions, fake KYC, mixer interactions, sanctioned addresses, stealing attacks, malicious mining, fake tokens, and honeypot associations. Many high-risk fraud wallets have clean Sybil profiles because they operate as genuinely unique wallets – just wallets engaged in financial crime. ChainAware’s fraud layer catches this threat surface entirely separately from any Sybil signal.

Can the chainaware-governance-screener handle quadratic voting?

Yes – quadratic governance is a first-class supported model alongside token-weighted and reputation-weighted governance. Specifying “governance model: quadratic” in the prompt adjusts how the agent calculates weight multipliers and surfaces concentration risks. Specifically, quadratic governance introduces a Sybil attack vector unique to that model: many low-quality wallets can collectively accumulate outsized influence even without individually controlling large token positions. The governance screener flags this pattern explicitly – identifying when a significant number of Observer-tier wallets collectively represent a concentration risk under quadratic rules, even if none of them individually trigger Sybil flags. This is a governance design insight that no other tool in the market surfaces automatically. For how DAO governance attacks exploit structural weaknesses in voting mechanisms, see our Governance Screeners guide.

What does ChainAware cover that pure Sybil providers miss?

Five capabilities are entirely absent from Trusta, Nomis, and RubyScore. First, forward-looking behavioral predictions – 12 intention probabilities predicting what a wallet will do next (Borrow, Lend, Trade, Gamble, NFT, Stake ETH, Yield Farm, and six Leveraged variants). Second, AML and OFAC compliance screening across 19 forensic categories – a regulatory requirement that Sybil prevention tools don’t address. Third, governance tier classification with voting weight multipliers – turning Sybil screening into a governance design tool. Fourth, ready-made deployable agents – 32 MIT open-source agents deployable via git clone versus APIs requiring custom integration. Fifth, a growth and conversion layer – Growth Agents and the Prediction MCP that turn screened traffic into transacting users, not just filtered lists. For the complete product overview, see our ChainAware Complete Product Guide.

External sources: FATF Virtual Asset Recommendations · Nomis Platform Documentation · Trusta Labs / TrustScan · ChainAware Behavioral Prediction MCP – GitHub · Anthropic Model Context Protocol

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