{
  "schema_version": "1.0.0",
  "verification_stage_contract": "bounded-workflow-v2",
  "generated_at": "2026-08-13T20:20:15Z",
  "build_id": "rr-20260813T202015Z-50afa6e387dd",
  "entity_id": "gh:intel/auto-round",
  "points": [
    {
      "id": "gh:intel/auto-round",
      "score": 8.0,
      "popularity": 1.56,
      "stars": 1565,
      "momentum": 9.0,
      "score_components": {
        "usefulness": 9.0,
        "novelty": 8.0,
        "momentum": 8.0,
        "maturity": 6.3,
        "open_source_build_quality": 8.4,
        "evidence": 7.2,
        "commercial_workflow_potential": 9.1,
        "setup_ease": 6.4
      },
      "tier": "Gold",
      "verdict": "try_now",
      "risk": "low",
      "verification_stage": "discovered",
      "pricing_fingerprint": null,
      "pricing_summary": null,
      "pricing_facts": [],
      "pricing_checked_at": null,
      "pricing_stale": null,
      "version": "v0.14.2",
      "last_release_at": "2026-07-13T05:27:21Z",
      "last_release_url": "https://github.com/intel/auto-round/releases/tag/v0.14.2",
      "maintenance_status": "active",
      "integration_fingerprint": "961f918f4b560abe",
      "integration_count": 1,
      "integrations": [
        "vLLM"
      ],
      "source_observed_at": "2026-08-13T19:31:03.163686Z",
      "observation_source_url": "https://api.github.com/graphql",
      "observation_extractor_version": "github-observer-v2",
      "observation_refs": {
        "stars": "obs_f3662c42595f4cf33242886a",
        "version": "obs_4ab7d809763d6efb6c3d84d3",
        "last_release_at": "obs_cc04ceabff92e976b7d8ae49",
        "last_release_url": "obs_879f25de3fc18c67c4532efd",
        "pushed_at": "obs_0a26e997d7993912f0b6da43",
        "archived": "obs_f915732705a0a4e0d2b5470a",
        "default_branch": "obs_506eafd3b799d87319abe45e",
        "default_branch_updated_at": "obs_bbff7ba9f9e5c9ef3af95612",
        "maintenance_status": "obs_ec5ef2ca30bdb75900d57ef4",
        "momentum": "obs_b76eb27120309bf7ac5000a3",
        "repository_topics": "obs_357f8cfc95516d195d52cbd4",
        "integrations": "obs_9d01848abbc1a06eaba77ea4",
        "integration_fingerprint": "obs_8999017d7f654ff1f40d0cef",
        "deployment_models": "obs_22cc1de1362d844b6ed63ba4",
        "extensibility": "obs_90f97fec10bc6d172890087c"
      },
      "observed_fields": [
        "integration_count",
        "integration_fingerprint",
        "integrations",
        "last_release_at",
        "maintenance_status",
        "momentum",
        "popularity",
        "pricing_checked_at",
        "pricing_facts",
        "pricing_fingerprint",
        "pricing_stale",
        "pricing_summary",
        "risk",
        "score",
        "score_components",
        "stars",
        "tier",
        "verdict",
        "verification_stage",
        "version"
      ],
      "date": "2026-08-13",
      "source_build_id": "rr-20260813T202015Z-badadd6c8c80"
    }
  ],
  "changes": [
    {
      "id": "evt_c18240527b58c9f64776",
      "entity_id": "gh:intel/auto-round",
      "item_url": "/item/intel-auto-round/",
      "title": "intel/auto-round",
      "category": "other",
      "kind": "version_changed",
      "occurred_at": "2026-08-13T19:31:03.163686Z",
      "before": null,
      "after": "v0.14.2",
      "explanation": "Version changed: not recorded → v0.14.2.",
      "explanation_scope": "catalog_snapshot_delta",
      "evidence_refs": {},
      "tier": "Gold",
      "risk": "low",
      "verdict": "try_now",
      "score": 8.0,
      "entity_kind": "open_source",
      "verification_stage": "discovered",
      "type": "Library",
      "section": "Inference & Serving",
      "local_ai": false,
      "local_model_compatible": false,
      "discovery_topic": "inference_runtime",
      "discovery_topic_status": "candidate",
      "setup_ease": 6.4,
      "best_for": [
        "AI engineering teams and platform teams who need to deploy LLMs / VLMs at 2-4 bit precision on CPU / XPU / CUDA",
        "inference-platform teams standardizing on vLLM / SGLang / Transformers who want a single quantization path that works"
      ],
      "risk_categories": [],
      "search_text": "intel/auto-round intel open_source other inference_runtime gold low try_now ai engineering teams and platform teams who need to deploy llms / vlms at 2-4 bit precision on cpu / xpu / cuda hardware without losing accuracy inference-platform teams standardizing on vllm / sglang / transformers who want a single quantization path that works across all three with",
      "material_mover": false
    },
    {
      "id": "evt_424d38b19b7497838928",
      "entity_id": "gh:intel/auto-round",
      "item_url": "/item/intel-auto-round/",
      "title": "intel/auto-round",
      "category": "other",
      "kind": "maintenance_changed",
      "occurred_at": "2026-08-13T19:31:03.163686Z",
      "before": "source_activity_not_yet_measured",
      "after": "active",
      "explanation": "Maintenance changed: source_activity_not_yet_measured → active.",
      "explanation_scope": "catalog_snapshot_delta",
      "evidence_refs": {},
      "tier": "Gold",
      "risk": "low",
      "verdict": "try_now",
      "score": 8.0,
      "entity_kind": "open_source",
      "verification_stage": "discovered",
      "type": "Library",
      "section": "Inference & Serving",
      "local_ai": false,
      "local_model_compatible": false,
      "discovery_topic": "inference_runtime",
      "discovery_topic_status": "candidate",
      "setup_ease": 6.4,
      "best_for": [
        "AI engineering teams and platform teams who need to deploy LLMs / VLMs at 2-4 bit precision on CPU / XPU / CUDA",
        "inference-platform teams standardizing on vLLM / SGLang / Transformers who want a single quantization path that works"
      ],
      "risk_categories": [],
      "search_text": "intel/auto-round intel open_source other inference_runtime gold low try_now ai engineering teams and platform teams who need to deploy llms / vlms at 2-4 bit precision on cpu / xpu / cuda hardware without losing accuracy inference-platform teams standardizing on vllm / sglang / transformers who want a single quantization path that works across all three with",
      "material_mover": false
    },
    {
      "id": "evt_d5d2fab443bb1f6871c5",
      "entity_id": "gh:intel/auto-round",
      "item_url": "/item/intel-auto-round/",
      "title": "intel/auto-round",
      "category": "other",
      "kind": "new_entity",
      "occurred_at": "2026-08-13T00:11:05Z",
      "before": null,
      "after": "try_now",
      "explanation": "Newly added to RepoRadar's decision catalog.",
      "explanation_scope": "catalog_snapshot_delta",
      "evidence_refs": {},
      "tier": "Gold",
      "risk": "low",
      "verdict": "try_now",
      "score": 8.0,
      "entity_kind": "open_source",
      "verification_stage": "discovered",
      "type": "Library",
      "section": "Inference & Serving",
      "local_ai": false,
      "local_model_compatible": false,
      "discovery_topic": "inference_runtime",
      "discovery_topic_status": "candidate",
      "setup_ease": 6.4,
      "best_for": [
        "AI engineering teams and platform teams who need to deploy LLMs / VLMs at 2-4 bit precision on CPU / XPU / CUDA",
        "inference-platform teams standardizing on vLLM / SGLang / Transformers who want a single quantization path that works"
      ],
      "risk_categories": [],
      "search_text": "intel/auto-round intel open_source other inference_runtime gold low try_now ai engineering teams and platform teams who need to deploy llms / vlms at 2-4 bit precision on cpu / xpu / cuda hardware without losing accuracy inference-platform teams standardizing on vllm / sglang / transformers who want a single quantization path that works across all three with",
      "material_mover": false
    }
  ]
}
