{
  "baseline_candidate_id": "openclaw_incumbent",
  "candidates": [
    {
      "beats_baseline_capability": true,
      "candidate_id": "openai_agents_sdk_coordinator",
      "capabilities": {
        "awoooi_integration_fit": 3,
        "code_remediation_fit": 2,
        "durable_execution": 2,
        "evaluation_harness": 3,
        "human_in_loop": 3,
        "local_private_deploy": 1,
        "mcp_tool_ecosystem": 3,
        "observability_tracing": 3,
        "tool_guardrails": 3
      },
      "display_name": "OpenAI Agents SDK Coordinator",
      "gaps": [
        "local_private_deploy"
      ],
      "official_sources": [
        {
          "evidence": "Built-in tracing covers agent runs, model generations, tool calls, handoffs, guardrails, and custom events.",
          "title": "OpenAI Agents SDK tracing",
          "url": "https://openai.github.io/openai-agents-python/tracing/"
        },
        {
          "evidence": "Tool guardrails can validate or block custom tool calls before and after execution.",
          "title": "OpenAI Agents SDK guardrails",
          "url": "https://openai.github.io/openai-agents-js/guides/guardrails"
        }
      ],
      "rank": 1,
      "replay_priority": "p0_replay",
      "risks": [
        "Cloud dependency and sensitive trace handling must pass AWOOOI privacy gates.",
        "Built-in hosted execution tools need separate guardrail validation."
      ],
      "strengths": [
        "human_in_loop",
        "tool_guardrails",
        "observability_tracing",
        "evaluation_harness",
        "mcp_tool_ecosystem",
        "awoooi_integration_fit"
      ],
      "total_score": 0.87
    },
    {
      "beats_baseline_capability": true,
      "candidate_id": "microsoft_agent_framework",
      "capabilities": {
        "awoooi_integration_fit": 2,
        "code_remediation_fit": 1,
        "durable_execution": 3,
        "evaluation_harness": 2,
        "human_in_loop": 3,
        "local_private_deploy": 2,
        "mcp_tool_ecosystem": 3,
        "observability_tracing": 3,
        "tool_guardrails": 2
      },
      "display_name": "Microsoft Agent Framework",
      "gaps": [
        "code_remediation_fit"
      ],
      "official_sources": [
        {
          "evidence": "Combines agents, graph workflows, session state, middleware, telemetry, MCP clients, checkpointing, and HITL.",
          "title": "Microsoft Agent Framework overview",
          "url": "https://learn.microsoft.com/en-us/agent-framework/overview/"
        }
      ],
      "rank": 2,
      "replay_priority": "p1_replay",
      "risks": [
        "Public preview status and Microsoft ecosystem fit must be assessed.",
        "Python/FastAPI/K8s integration cost is likely higher than LangGraph or NeMo."
      ],
      "strengths": [
        "durable_execution",
        "human_in_loop",
        "observability_tracing",
        "mcp_tool_ecosystem"
      ],
      "total_score": 0.81
    },
    {
      "beats_baseline_capability": true,
      "candidate_id": "nemo_nemotron_fabric",
      "capabilities": {
        "awoooi_integration_fit": 3,
        "code_remediation_fit": 1,
        "durable_execution": 2,
        "evaluation_harness": 3,
        "human_in_loop": 2,
        "local_private_deploy": 3,
        "mcp_tool_ecosystem": 3,
        "observability_tracing": 3,
        "tool_guardrails": 2
      },
      "display_name": "NVIDIA NeMo Agent Toolkit + Nemotron Fabric",
      "gaps": [
        "code_remediation_fit"
      ],
      "official_sources": [
        {
          "evidence": "Framework-agnostic agent toolkit with profiling, observability, evaluation, and MCP support.",
          "title": "NVIDIA NeMo Agent Toolkit overview",
          "url": "https://docs.nvidia.com/nemo/agent-toolkit/latest/index.html"
        },
        {
          "evidence": "nat eval produces workflow outputs, evaluator outputs, profiling metrics, and request traces.",
          "title": "NVIDIA NeMo Agent Toolkit evaluation",
          "url": "https://docs.nvidia.com/nemo/agent-toolkit/latest/workflows/evaluate.html"
        }
      ],
      "rank": 3,
      "replay_priority": "p0_replay",
      "risks": [
        "Needs AWOOOI-specific HITL and dangerous-action policy integration.",
        "GPU/NIM operating cost must be compared against current local inference."
      ],
      "strengths": [
        "observability_tracing",
        "evaluation_harness",
        "mcp_tool_ecosystem",
        "local_private_deploy",
        "awoooi_integration_fit"
      ],
      "total_score": 0.8033
    },
    {
      "beats_baseline_capability": true,
      "candidate_id": "langgraph_incident_kernel",
      "capabilities": {
        "awoooi_integration_fit": 3,
        "code_remediation_fit": 1,
        "durable_execution": 3,
        "evaluation_harness": 2,
        "human_in_loop": 3,
        "local_private_deploy": 3,
        "mcp_tool_ecosystem": 2,
        "observability_tracing": 2,
        "tool_guardrails": 2
      },
      "display_name": "LangGraph Incident Kernel",
      "gaps": [
        "code_remediation_fit"
      ],
      "official_sources": [
        {
          "evidence": "Checkpoint persistence supports human-in-the-loop, memory, time travel debugging, and fault-tolerant execution.",
          "title": "LangGraph persistence",
          "url": "https://docs.langchain.com/oss/python/langgraph/persistence"
        },
        {
          "evidence": "Interrupts pause graph execution and resume through persisted graph state.",
          "title": "LangGraph interrupts",
          "url": "https://docs.langchain.com/oss/python/langgraph/human-in-the-loop"
        }
      ],
      "rank": 4,
      "replay_priority": "p0_replay",
      "risks": [
        "It is a workflow kernel, not a smarter model by itself.",
        "Tool safety and evaluation metrics must be implemented by AWOOOI adapters."
      ],
      "strengths": [
        "durable_execution",
        "human_in_loop",
        "local_private_deploy",
        "awoooi_integration_fit"
      ],
      "total_score": 0.7867
    },
    {
      "beats_baseline_capability": true,
      "candidate_id": "claude_agent_sdk_remediator",
      "capabilities": {
        "awoooi_integration_fit": 2,
        "code_remediation_fit": 3,
        "durable_execution": 2,
        "evaluation_harness": 1,
        "human_in_loop": 3,
        "local_private_deploy": 1,
        "mcp_tool_ecosystem": 3,
        "observability_tracing": 2,
        "tool_guardrails": 3
      },
      "display_name": "Claude Agent SDK Remediator",
      "gaps": [
        "evaluation_harness",
        "local_private_deploy"
      ],
      "official_sources": [
        {
          "evidence": "Embeds Claude Code's autonomous agent loop with programmatic control over tools, permissions, cost limits, and output.",
          "title": "Claude Agent SDK loop",
          "url": "https://platform.claude.com/docs/en/agent-sdk/agent-loop"
        },
        {
          "evidence": "SDK exposes context management, file operations, code execution, MCP, permissions, sessions, and monitoring.",
          "title": "Claude Agent SDK overview",
          "url": "https://docs.claude.com/es/api/agent-sdk/overview"
        }
      ],
      "rank": 5,
      "replay_priority": "p0_replay",
      "risks": [
        "Best fit is code and DevOps remediation, not necessarily central incident arbitration.",
        "API cost, subscription separation, and vendor boundary must be validated."
      ],
      "strengths": [
        "human_in_loop",
        "tool_guardrails",
        "mcp_tool_ecosystem",
        "code_remediation_fit"
      ],
      "total_score": 0.7533
    },
    {
      "beats_baseline_capability": true,
      "candidate_id": "claude_managed_agents_sandbox",
      "capabilities": {
        "awoooi_integration_fit": 2,
        "code_remediation_fit": 3,
        "durable_execution": 3,
        "evaluation_harness": 1,
        "human_in_loop": 2,
        "local_private_deploy": 2,
        "mcp_tool_ecosystem": 2,
        "observability_tracing": 2,
        "tool_guardrails": 3
      },
      "display_name": "Claude Managed Agents Sandbox",
      "gaps": [
        "evaluation_harness"
      ],
      "official_sources": [
        {
          "evidence": "Defines agents, environments, sessions, events, and pre-built agent tools for autonomous sessions.",
          "title": "Claude Managed Agents quickstart",
          "url": "https://platform.claude.com/docs/en/managed-agents/quickstart"
        }
      ],
      "rank": 6,
      "replay_priority": "p1_replay",
      "risks": [
        "Managed service and beta header make it less suitable as the first AWOOOI core replacement.",
        "Sandbox placement, data retention, and cost must be reviewed before shadow mode."
      ],
      "strengths": [
        "durable_execution",
        "tool_guardrails",
        "code_remediation_fit"
      ],
      "total_score": 0.75
    },
    {
      "beats_baseline_capability": true,
      "candidate_id": "google_adk_stack",
      "capabilities": {
        "awoooi_integration_fit": 2,
        "code_remediation_fit": 1,
        "durable_execution": 3,
        "evaluation_harness": 3,
        "human_in_loop": 2,
        "local_private_deploy": 2,
        "mcp_tool_ecosystem": 2,
        "observability_tracing": 2,
        "tool_guardrails": 2
      },
      "display_name": "Google Agent Development Kit Stack",
      "gaps": [
        "code_remediation_fit"
      ],
      "official_sources": [
        {
          "evidence": "ADK includes session management, state, events, memory, artifacts, evaluation, and developer UI.",
          "title": "Google ADK technical overview",
          "url": "https://google.github.io/adk-docs/get-started/about/"
        },
        {
          "evidence": "Runner retrieves sessions and exposes state/events to agents.",
          "title": "Google ADK sessions",
          "url": "https://google.github.io/adk-docs/sessions/session/"
        }
      ],
      "rank": 7,
      "replay_priority": "p1_replay",
      "risks": [
        "Gemini/Vertex ecosystem dependency must be justified against current local-first policy.",
        "AIOps tool safety and rollback gates still need AWOOOI-specific implementation."
      ],
      "strengths": [
        "durable_execution",
        "evaluation_harness"
      ],
      "total_score": 0.73
    },
    {
      "beats_baseline_capability": null,
      "candidate_id": "openclaw_incumbent",
      "capabilities": {
        "awoooi_integration_fit": 3,
        "code_remediation_fit": 1,
        "durable_execution": 1,
        "evaluation_harness": 1,
        "human_in_loop": 3,
        "local_private_deploy": 3,
        "mcp_tool_ecosystem": 2,
        "observability_tracing": 2,
        "tool_guardrails": 2
      },
      "display_name": "OpenClaw incumbent",
      "gaps": [
        "durable_execution",
        "evaluation_harness",
        "code_remediation_fit"
      ],
      "official_sources": [
        {
          "evidence": "Current production baseline and local integration evidence.",
          "title": "AWOOOI incumbent baseline snapshot",
          "url": "docs/evaluations/openclaw_incumbent_baseline_2026-06-01.json"
        }
      ],
      "rank": 8,
      "replay_priority": "baseline",
      "risks": [
        "Current baseline failed the false repair hard gate.",
        "Evaluation harness and durable execution are weaker than several market frameworks."
      ],
      "strengths": [
        "human_in_loop",
        "local_private_deploy",
        "awoooi_integration_fit"
      ],
      "total_score": 0.6467
    },
    {
      "beats_baseline_capability": false,
      "candidate_id": "crewai_flows_crews",
      "capabilities": {
        "awoooi_integration_fit": 1,
        "code_remediation_fit": 1,
        "durable_execution": 2,
        "evaluation_harness": 1,
        "human_in_loop": 2,
        "local_private_deploy": 3,
        "mcp_tool_ecosystem": 2,
        "observability_tracing": 2,
        "tool_guardrails": 2
      },
      "display_name": "CrewAI Flows + Crews",
      "gaps": [
        "evaluation_harness",
        "code_remediation_fit",
        "awoooi_integration_fit"
      ],
      "official_sources": [
        {
          "evidence": "Docs describe agents, crews, flows, guardrails, memory, knowledge, and observability.",
          "title": "CrewAI documentation",
          "url": "https://docs.crewai.com/"
        },
        {
          "evidence": "Flows coordinate tasks and crews with structured, event-driven workflows and state management.",
          "title": "CrewAI Flows",
          "url": "https://www.crewai.com/crewai-flows"
        }
      ],
      "rank": 9,
      "replay_priority": "watch",
      "risks": [
        "Better for rapid automation teams than high-risk production AIOps core.",
        "Durability, strict audit, and permission boundary must be proven in replay."
      ],
      "strengths": [
        "local_private_deploy"
      ],
      "total_score": 0.6033
    }
  ],
  "candidates_above_baseline": [
    "openai_agents_sdk_coordinator",
    "microsoft_agent_framework",
    "nemo_nemotron_fabric",
    "langgraph_incident_kernel",
    "claude_agent_sdk_remediator",
    "claude_managed_agents_sandbox",
    "google_adk_stack"
  ],
  "dimensions": {
    "awoooi_integration_fit": 0.07,
    "code_remediation_fit": 0.08,
    "durable_execution": 0.15,
    "evaluation_harness": 0.12,
    "human_in_loop": 0.14,
    "local_private_deploy": 0.08,
    "mcp_tool_ecosystem": 0.1,
    "observability_tracing": 0.12,
    "tool_guardrails": 0.14
  },
  "schema_version": "agent_market_capability_scorecard_v1",
  "scoring_version": "market_capability_v1"
}
