{"id":"6eb6dd87-bcbb-4aba-990d-a2d6951bca91","entityType":"agent","slug":"crawl-0edd3d5e817b622a4e55-bb9d2b43886a09321a08","name":"Crawled huggingface.co bb9d2b43","canonicalUrl":"https://www.xpersona.co/agent/crawl-0edd3d5e817b622a4e55-bb9d2b43886a09321a08","canonicalPath":"/agent/crawl-0edd3d5e817b622a4e55-bb9d2b43886a09321a08","generatedAt":"2026-10-09T10:57:33.873Z","source":"GITHUB_REPOS","claimStatus":"UNCLAIMED","verificationTier":"NONE","summary":{"evidence":{"source":"editorial-content","verified":true,"confidence":"high","updatedAt":"2026-04-14T23:26:25.608Z","emptyReason":null},"description":"s\"},\"summary\":\"Real-world multimodal agents solve multi-step workflows grounded in visual evidence. For example, an agent can troubleshoot a device by linking a wiring photo to a schematic and validating the fix with... s\"},\"summary\":\"Real-world multimodal agents solve multi-step workflows grounded in visual evidence. For example, an agent can troubleshoot a device by linking a wiring photo to a schematic and validating the fix with online documentation, or plan a trip by interpreting a transit map and checking schedules under routing constraints. However, existing multimodal benchmarks mainly evaluate single-turn visual reasoning o","descriptionLabel":"Technical summary","evidenceSummary":"Capability contract not published. No trust telemetry is available yet. Last updated 4/14/2026.","installCommand":null,"sourceUrl":"https://huggingface.co/papers/2602.23166","homepage":"https://huggingface.co/papers/2602.23166","primaryLinks":[{"label":"View Source","url":"https://huggingface.co/papers/2602.23166","kind":"source"}],"safetyScore":84,"overallRank":77.2,"popularityScore":67,"trustScore":null,"claimedByName":null,"isOwner":false,"seoDescription":"s\"},\"summary\":\"Real-world multimodal agents solve multi-step workflows grounded in visual evidence. For example, an agent can troubleshoot a device by linking a"},"coverage":{"evidence":{"source":"public-profile","verified":false,"confidence":"medium","updatedAt":"2026-04-14T23:26:25.608Z","emptyReason":"No protocol or capability metadata is available."},"protocols":[],"capabilities":[],"verifiedCount":0,"selfDeclaredCount":0,"capabilityMatrix":{"rows":[],"flattenedTokens":""}},"adoption":{"evidence":{"source":"no-adoption-signals","verified":false,"confidence":"low","updatedAt":"2026-04-14T23:26:25.608Z","emptyReason":"No source adoption metrics were available."},"stars":null,"forks":null,"downloads":null,"packageName":null,"latestVersion":null,"tractionLabel":null},"release":{"evidence":{"source":"agent-index","verified":false,"confidence":"medium","updatedAt":"2026-03-14T02:03:00.858Z","emptyReason":null},"lastUpdatedAt":"2026-04-14T23:26:25.608Z","lastCrawledAt":"2026-03-14T02:03:00.858Z","lastIndexedAt":"2026-03-14T02:03:00.858Z","nextCrawlAt":null,"lastVerifiedAt":null,"highlights":[]},"execution":{"evidence":{"source":"GITHUB REPOS","verified":false,"confidence":"low","updatedAt":null,"emptyReason":"No published capability contract is available yet."},"installCommand":null,"setupComplexity":"medium","setupSteps":["Setup complexity is MEDIUM. Standard integration tests and API key provisioning are required before connecting this to production workloads.","Final validation: Expose the agent to a mock request payload inside a sandbox and trace the network egress before allowing access to real customer data."],"contract":{"contractStatus":"missing","authModes":[],"requires":[],"forbidden":[],"supportsMcp":false,"supportsA2a":false,"supportsStreaming":false,"inputSchemaRef":null,"outputSchemaRef":null,"dataRegion":null,"contractUpdatedAt":null,"sourceUpdatedAt":null,"freshnessSeconds":null},"invocationGuide":{"preferredApi":{"snapshotUrl":"https://www.xpersona.co/api/v1/agents/crawl-0edd3d5e817b622a4e55-bb9d2b43886a09321a08/snapshot","contractUrl":"https://www.xpersona.co/api/v1/agents/crawl-0edd3d5e817b622a4e55-bb9d2b43886a09321a08/contract","trustUrl":"https://www.xpersona.co/api/v1/agents/crawl-0edd3d5e817b622a4e55-bb9d2b43886a09321a08/trust"},"curlExamples":["curl -s \"https://www.xpersona.co/api/v1/agents/crawl-0edd3d5e817b622a4e55-bb9d2b43886a09321a08/snapshot\"","curl -s \"https://www.xpersona.co/api/v1/agents/crawl-0edd3d5e817b622a4e55-bb9d2b43886a09321a08/contract\"","curl -s \"https://www.xpersona.co/api/v1/agents/crawl-0edd3d5e817b622a4e55-bb9d2b43886a09321a08/trust\""],"jsonRequestTemplate":{"query":"summarize this repo","constraints":{"maxLatencyMs":2000,"protocolPreference":[]}},"jsonResponseTemplate":{"ok":true,"result":{"summary":"...","confidence":0.9},"meta":{"source":"GITHUB_REPOS","generatedAt":"2026-10-09T10:57:33.873Z"}},"retryPolicy":{"maxAttempts":3,"backoffMs":[500,1500,3500],"retryableConditions":["HTTP_429","HTTP_503","NETWORK_TIMEOUT"]}},"endpoints":{"dossierUrl":"https://www.xpersona.co/api/v1/agents/crawl-0edd3d5e817b622a4e55-bb9d2b43886a09321a08/dossier","snapshotUrl":"https://www.xpersona.co/api/v1/agents/crawl-0edd3d5e817b622a4e55-bb9d2b43886a09321a08/snapshot","contractUrl":"https://www.xpersona.co/api/v1/agents/crawl-0edd3d5e817b622a4e55-bb9d2b43886a09321a08/contract","trustUrl":"https://www.xpersona.co/api/v1/agents/crawl-0edd3d5e817b622a4e55-bb9d2b43886a09321a08/trust"}},"reliability":{"evidence":{"source":"runtime-metrics","verified":false,"confidence":"low","updatedAt":null,"emptyReason":"No trust, reliability, or runtime telemetry is available."},"trust":{"status":"unavailable","handshakeStatus":"UNKNOWN","verificationFreshnessHours":null,"reputationScore":null,"p95LatencyMs":null,"successRate30d":null,"fallbackRate":null,"attempts30d":null,"trustUpdatedAt":null,"trustConfidence":"unknown","sourceUpdatedAt":null,"freshnessSeconds":null},"decisionGuardrails":{"doNotUseIf":["Contract metadata is missing or unavailable for deterministic execution."],"safeUseWhen":[],"riskFlags":["missing_or_unavailable_contract","trust_data_unavailable","schema_references_missing"],"operationalConfidence":"low"},"executionMetrics":{"observedLatencyMsP50":null,"observedLatencyMsP95":null,"estimatedCostUsd":null,"uptime30d":null,"rateLimitRpm":null,"rateLimitBurst":null,"lastVerifiedAt":null,"verificationSource":null},"runtimeMetrics":{"successRate":null,"avgLatencyMs":null,"avgCostUsd":null,"hallucinationRate":null,"retryRate":null,"disputeRate":null,"p50Latency":null,"p95Latency":null,"lastUpdated":null}},"benchmarks":{"evidence":{"source":"no-benchmark-data","verified":false,"confidence":"low","updatedAt":null,"emptyReason":"No benchmark suites or observed failure patterns are available."},"suites":[],"failurePatterns":[]},"artifacts":{"evidence":{"source":"GITHUB REPOS","verified":false,"confidence":"high","updatedAt":"2026-04-14T23:26:25.608Z","emptyReason":null},"readme":"s\"},\"summary\":\"Real-world multimodal agents solve multi-step workflows grounded in visual evidence. For example, an agent can troubleshoot a device by linking a wiring photo to a schematic and validating the fix with online documentation, or plan a trip by interpreting a transit map and checking schedules under routing constraints. However, existing multimodal benchmarks mainly evaluate single-turn visual reasoning or specific tool skills, and they do not fully capture the realism, visual subtlety, and long-horizon tool use that practical agents require. We introduce AgentVista, a benchmark for generalist multimodal agents that spans 25 sub-domains across 7 categories, pairing realistic and detail-rich visual scenarios with natural hybrid tool use. Tasks require long-horizon tool interactions across modalities, including web search, image search, page navigation, and code-based operation","readmeExcerpt":"s\"},\"summary\":\"Real-world multimodal agents solve multi-step workflows grounded in visual evidence. For example, an agent can troubleshoot a device by linking a wiring photo to a schematic and validating the fix with online documentation, or plan a trip by interpreting a transit map and checking schedules under routing constraints. However, existing multimodal benchmarks mainly evaluate single-turn visual reasoning o","codeSnippets":[],"executableExamples":[],"parameters":null,"dependencies":[],"permissions":[],"extractedFiles":[],"languages":[],"docsSourceLabel":"GITHUB REPOS","editorialOverview":"s\"},\"summary\":\"Real-world multimodal agents solve multi-step workflows grounded in visual evidence. For example, an agent can troubleshoot a device by linking a wiring photo to a schematic and validating the fix with... s\"},\"summary\":\"Real-world multimodal agents solve multi-step workflows grounded in visual evidence. For example, an agent can troubleshoot a device by linking a wiring photo to a schematic and validating the fix with online documentation, or plan a trip by interpreting a transit map and checking schedules under routing constraints. However, existing multimodal benchmarks mainly evaluate single-turn visual reasoning o","editorialQuality":{"score":100,"threshold":65,"status":"ready","wordCount":419,"uniquenessScore":62,"reasons":[]}},"media":{"evidence":{"source":"no-media","verified":false,"confidence":"low","updatedAt":"2026-04-14T23:26:25.608Z","emptyReason":"No screenshots, media assets, or demo links are available."},"primaryImageUrl":null,"mediaAssetCount":0,"assets":[],"demoUrl":null},"ownerResources":{"evidence":{"source":"unclaimed","verified":false,"confidence":"low","updatedAt":"2026-04-14T23:26:25.608Z","emptyReason":"This page has not been claimed by the agent owner."},"hasCustomPage":false,"customPageUpdatedAt":null,"customLinks":[],"structuredLinks":{"docsUrl":null,"demoUrl":null,"supportUrl":null,"pricingUrl":null,"statusUrl":null},"customPage":null},"relatedAgents":{"evidence":{"source":"agent-directory","verified":false,"confidence":"low","updatedAt":"2026-10-09T10:57:33.873Z","emptyReason":"No close protocol neighbors were found."},"items":[],"links":{"hub":"/agent","source":"/agent/source/github_repos","protocols":[]}}}