Tracks how many unique multisigs were active each day for all agents across all supported networks. This metric is useful to understand the operational footprint and engagement of specific agents over time. The active multisig count reflects the number of unique multisigs that performed at least one on-chain interaction attributed to a given agent within the UTC day window.
The following query is used to compute daily active agents:
query DailyActiveMultisigs($timestamp_gt: Int!, $timestamp_lt: Int!) {
dailyActiveMultisigs_collection(
where: {
and: [
{ dayTimestamp_gt: $timestamp_gt }
{ dayTimestamp_lt: $timestamp_lt }
]
}
orderBy: dayTimestamp
orderDirection: desc
) {
id
count
}
}
Tracks how many unique multisigs were active each day for selected agents on Mode and Optimism. This metric is useful to understand the operational footprint and engagement of specific agents over time. The active multisig count reflects the number of unique multisigs that performed at least one on-chain interaction attributed to a given agent within the UTC day window.
The following query is used to compute daily active agents:
query DailyPerformance($timestamp_gt: Int!, $timestamp_lt: Int!) {
dailyAgentPerformances(
where: {
and: [
{ agentId: 40 }
{ dayTimestamp_gt: $timestamp_gt }
{ dayTimestamp_lt: $timestamp_lt }
]
}
orderBy: dayTimestamp
orderDirection: desc
) {
id
activeMultisigCount
}
}
Tracks how many unique multisigs were active each day for selected agents on Gnosis and Base. This metric is useful to understand the operational footprint and engagement of specific agents over time. The active multisig count reflects the number of unique multisigs that performed at least one on-chain interaction attributed to a given agent within the UTC day window.
The following query is used to compute daily active agents:
query DailyPerformance($timestamp_gt: Int!, $timestamp_lt: Int!) {
dailyAgentPerformances(
where: {
and: [
{
or: [
{ agentId: 9 }
{ agentId: 26 }
{ agentId: 29 }
{ agentId: 36 }
{ agentId: 37 }
]
}
{ dayTimestamp_gt: $timestamp_gt }
{ dayTimestamp_lt: $timestamp_lt }
]
}
orderBy: dayTimestamp
orderDirection: desc
) {
id
activeMultisigCount
}
}
Tracks how many unique multisigs were active each day for selected Predict agents on Gnosis. The active multisig countreflects the number of unique multisigs that performed at least one on-chain interaction attributed to a given agent within the UTC day window.
The following query is used to compute daily active agents:
Subgraph links: 1
query DailyPredictPerformances(
$agentIds: [Int!]!
$timestamp_gt: Int!
$timestamp_lt: Int!
) {
dailyAgentPerformances(
where: {
and: [
{ agentId_in: $agentIds }
{ dayTimestamp_gt: $timestamp_gt }
{ dayTimestamp_lt: $timestamp_lt }
]
}
orderBy: dayTimestamp
orderDirection: asc
first: 1000
) {
dayTimestamp
activeMultisigCount
}
}
Tracks the total number of unique operators across all supported networks. This metric aggregates operator data from multiple blockchain sources to provide a comprehensive view of the autonomous agent operator ecosystem and network participation.
The following query aggregates operators from all chains:
Subgraph links: GnosisBaseModeOptimismCeloEthereumPolygonArbitrum
query OperatorGlobals {
globals {
id
totalOperators
}
}
Tracks the total number of transactions executed by all agents across all supported networks. This metric provides insight into the overall activity level and transaction volume generated by the autonomous agent ecosystem.
The following query is used to compute total daily transactions:
query RegistryGlobals {
global(id: "") {
id
txCount
}
}
Breaks down total Predict transactions by agent category using agent-level counters. Categories mirror the UI: Traders, Mechs (Prediction Brokers), and Market Creators/Closers.
Market maker agent IDs: 13
Valory trader agent IDs: 14, 25
Mech agent IDs: 9, 26, 29, 37, 36
The following query is used to fetch per-agent tx counts:
Subgraph link: Gnosis
query AgentTxs($agentIds: [Int!]!) {
agentPerformances(
where: { id_in: $agentIds }
orderBy: id
orderDirection: asc
) {
id
txCount
}
}
Tracks agent-to-agent transactions across Gnosis and Base networks from multiple subgraph sources. This metric aggregates transaction data from Mech Marketplace and Legacy Mech subgraphs to provide a comprehensive view of autonomous agent interaction volume and cross-chain activity.
The following query aggregates ATA transactions from all sources:
Subgraph links: Gnosis MMBase MMGnosis LM
query AtaTransactions {
globals(where: { id: "" }) {
id
totalAtaTransactions
}
}
Tracks the total fees collected from the Mech Marketplace across three different sources: new mech fees from Gnosis and Base networks, and legacy mech fees from Gnosis. New mech fees are already in USD, while legacy fees are converted from wei to XDAI (treated as 1 USD equivalent) before being summed together to provide the total mech marketplace turnover.
The following queries aggregate mech fees from all three sources:
query NewMechFees {
global(id: "") {
id
totalFeesInUSD
}
}
Subgraph link: Gnosis
Note: Legacy mech fees are returned in wei and converted to XDAI by dividing by 10^18 before being added to the total turnover.
query LegacyMechFees {
global(id: "") {
id
totalFeesIn
}
}
Tracks the total amount of OLAS tokens currently staked across all agents in the ecosystem. This metric provides insight into the overall economic security and commitment level across all supported networks.
The following query is used to compute total OLAS staked:
query StakingGlobals {
global(id: "") {
totalRewards
currentOlasStaked
}
}
Total ROI shows your agent's overall earnings, including profits from predictions and staking rewards, minus all related costs such as bet amounts, gas fees, and Mech request fees. Requests made for unresolved (open) markets are excluded to ensure accuracy.
The following queries are used to define the value:
query TotalMechRequests {
global(id: "") {
totalRequests
}
}
query MechRequests {
_page1: requests(
first: 1000
skip: 0
where: { blockTimestamp_gt: 1756684800 }
) {
id
questionTitle
blockTimestamp
}
_page2: requests(
first: 1000
skip: 1000
where: { blockTimestamp_gt: 1756684800 }
) {
id
questionTitle
blockTimestamp
}
_page3: requests(
first: 1000
skip: 2000
where: { blockTimestamp_gt: 1756684800 }
) {
id
questionTitle
blockTimestamp
}
_page4: requests(
first: 1000
skip: 3000
where: { blockTimestamp_gt: 1756684800 }
) {
id
questionTitle
blockTimestamp
}
_page5: requests(
first: 1000
skip: 4000
where: { blockTimestamp_gt: 1756684800 }
) {
id
questionTitle
blockTimestamp
}
_page6: requests(
first: 1000
skip: 5000
where: { blockTimestamp_gt: 1756684800 }
) {
id
questionTitle
blockTimestamp
}
_page7: requests(
first: 1000
skip: 6000
where: { blockTimestamp_gt: 1756684800 }
) {
id
questionTitle
blockTimestamp
}
_page8: requests(
first: 1000
skip: 7000
where: { blockTimestamp_gt: 1756684800 }
) {
id
questionTitle
blockTimestamp
}
_page9: requests(
first: 1000
skip: 8000
where: { blockTimestamp_gt: 1756684800 }
) {
id
questionTitle
blockTimestamp
}
_page10: requests(
first: 1000
skip: 9000
where: { blockTimestamp_gt: 1756684800 }
) {
id
questionTitle
blockTimestamp
}
}
query MarketsAndBets {
fixedProductMarketMakerCreations(
where: { blockTimestamp_gt: 1756684800 }
) {
id
question
}
global(id: "") {
totalFees
totalPayout
totalTraded
}
}
Used for getting cumulative staking rewards in OLAS
Subgraph link
query StakingGlobals {
global(id: "") {
totalRewards
currentOlasStaked
}
}
Success rate shows how often your agent's predictions were correct in resolved markets. Bets on unresolved markets or with invalid outcomes are excluded, and the rate is based on the latest 10,000 bets from closed markets to ensure performance remains relevant.
The following query is used:
Used to fetch all bets along with their outcome and the final answer of the associated market
Subgraph link
query ClosedMarketsBets {
_page1: bets(
first: 1000
skip: 0
where: { fixedProductMarketMaker_: { currentAnswer_not: null } }
orderBy: timestamp
orderDirection: desc
) {
outcomeIndex
fixedProductMarketMaker {
id
currentAnswer
}
}
_page2: bets(
first: 1000
skip: 1000
where: { fixedProductMarketMaker_: { currentAnswer_not: null } }
orderBy: timestamp
orderDirection: desc
) {
outcomeIndex
fixedProductMarketMaker {
id
currentAnswer
}
}
_page3: bets(
first: 1000
skip: 2000
where: { fixedProductMarketMaker_: { currentAnswer_not: null } }
orderBy: timestamp
orderDirection: desc
) {
outcomeIndex
fixedProductMarketMaker {
id
currentAnswer
}
}
_page4: bets(
first: 1000
skip: 3000
where: { fixedProductMarketMaker_: { currentAnswer_not: null } }
orderBy: timestamp
orderDirection: desc
) {
outcomeIndex
fixedProductMarketMaker {
id
currentAnswer
}
}
_page5: bets(
first: 1000
skip: 4000
where: { fixedProductMarketMaker_: { currentAnswer_not: null } }
orderBy: timestamp
orderDirection: desc
) {
outcomeIndex
fixedProductMarketMaker {
id
currentAnswer
}
}
_page6: bets(
first: 1000
skip: 5000
where: { fixedProductMarketMaker_: { currentAnswer_not: null } }
orderBy: timestamp
orderDirection: desc
) {
outcomeIndex
fixedProductMarketMaker {
id
currentAnswer
}
}
_page7: bets(
first: 1000
skip: 6000
where: { fixedProductMarketMaker_: { currentAnswer_not: null } }
orderBy: timestamp
orderDirection: desc
) {
outcomeIndex
fixedProductMarketMaker {
id
currentAnswer
}
}
_page8: bets(
first: 1000
skip: 7000
where: { fixedProductMarketMaker_: { currentAnswer_not: null } }
orderBy: timestamp
orderDirection: desc
) {
outcomeIndex
fixedProductMarketMaker {
id
currentAnswer
}
}
_page9: bets(
first: 1000
skip: 8000
where: { fixedProductMarketMaker_: { currentAnswer_not: null } }
orderBy: timestamp
orderDirection: desc
) {
outcomeIndex
fixedProductMarketMaker {
id
currentAnswer
}
}
_page10: bets(
first: 1000
skip: 9000
where: { fixedProductMarketMaker_: { currentAnswer_not: null } }
orderBy: timestamp
orderDirection: desc
) {
outcomeIndex
fixedProductMarketMaker {
id
currentAnswer
}
}
}