export const MOCKED_EVENTS = [
  {
    kind: 'calling_agents',
    data: {
      agents: [
        {
          agentId: 'PORTAL:agent:5HZiXIQD',
          agentName: 'Agent - food expert',
        },
        {
          agentId: 'PORTAL:agent:OwDRzCgI',
          agentName: 'Agent - agent for sql tool',
        },
        {
          agentId: 'ac_user_agent_1757341256904:agent:EYfTuxsw',
          agentName: 'Concept Coach',
        },
      ],
    },
  },
  {
    kind: 'agent_started',
    data: {
      agentId: 'PORTAL:agent:5HZiXIQD',
      agentName: 'Agent - food expert',
    },
  },
  {
    kind: 'agent_started',
    data: {
      agentId: 'ac_user_agent_1757341256904:agent:EYfTuxsw',
      agentName: 'Concept Coach',
    },
  },
  {
    kind: 'agent_started',
    data: {
      agentId: 'PORTAL:agent:OwDRzCgI',
      agentName: 'Agent - agent for sql tool',
    },
  },
  {
    kind: 'agent_event',
    data: {
      agentId: 'PORTAL:agent:OwDRzCgI',
      agentName: 'Agent - agent for sql tool',
      eventKind: 'AGENT_GETTING_READY',
      eventData: {},
    },
  },
  {
    kind: 'agent_event',
    data: {
      agentId: 'PORTAL:agent:5HZiXIQD',
      agentName: 'Agent - food expert',
      eventKind: 'AGENT_GETTING_READY',
      eventData: {},
    },
  },
  {
    kind: 'agent_event',
    data: {
      agentId: 'ac_user_agent_1757341256904:agent:EYfTuxsw',
      agentName: 'Concept Coach',
      eventKind: 'AGENT_GETTING_READY',
      eventData: {},
    },
  },
  {
    kind: 'agent_event',
    data: {
      agentId: 'ac_user_agent_1757341256904:agent:EYfTuxsw',
      agentName: 'Concept Coach',
      eventKind: 'AGENT_THINKING',
      eventData: {},
    },
  },
  {
    kind: 'agent_event',
    data: {
      agentId: 'PORTAL:agent:OwDRzCgI',
      agentName: 'Agent - agent for sql tool',
      eventKind: 'AGENT_THINKING',
      eventData: {},
    },
  },
  {
    kind: 'agent_chunk',
    data: {
      agentId: 'ac_user_agent_1757341256904:agent:EYfTuxsw',
      text: 'Hello',
    },
  },
  {
    kind: 'agent_chunk',
    data: {
      agentId: 'ac_user_agent_1757341256904:agent:EYfTuxsw',
      text: '!',
    },
  },
  {
    kind: 'agent_chunk',
    data: {
      agentId: 'ac_user_agent_1757341256904:agent:EYfTuxsw',
      text: ' How',
    },
  },
  {
    kind: 'agent_chunk',
    data: {
      agentId: 'ac_user_agent_1757341256904:agent:EYfTuxsw',
      text: ' can',
    },
  },
  {
    kind: 'agent_chunk',
    data: {
      agentId: 'ac_user_agent_1757341256904:agent:EYfTuxsw',
      text: ' I',
    },
  },
  {
    kind: 'agent_chunk',
    data: {
      agentId: 'ac_user_agent_1757341256904:agent:EYfTuxsw',
      text: ' assist',
    },
  },
  {
    kind: 'agent_chunk',
    data: {
      agentId: 'ac_user_agent_1757341256904:agent:EYfTuxsw',
      text: ' you',
    },
  },
  {
    kind: 'agent_chunk',
    data: {
      agentId: 'ac_user_agent_1757341256904:agent:EYfTuxsw',
      text: ' today',
    },
  },
  {
    kind: 'agent_chunk',
    data: {
      agentId: 'ac_user_agent_1757341256904:agent:EYfTuxsw',
      text: '?',
    },
  },
  {
    kind: 'agent_finished',
    data: {
      agentId: 'ac_user_agent_1757341256904:agent:EYfTuxsw',
      status: 'ok',
    },
  },
  {
    kind: 'agent_chunk',
    data: {
      agentId: 'PORTAL:agent:OwDRzCgI',
      text: 'Hi',
    },
  },
  {
    kind: 'agent_chunk',
    data: {
      agentId: 'PORTAL:agent:OwDRzCgI',
      text: ' there',
    },
  },
  {
    kind: 'agent_chunk',
    data: {
      agentId: 'PORTAL:agent:OwDRzCgI',
      text: '!',
    },
  },
  {
    kind: 'agent_chunk',
    data: {
      agentId: 'PORTAL:agent:OwDRzCgI',
      text: ' How',
    },
  },
  {
    kind: 'agent_chunk',
    data: {
      agentId: 'PORTAL:agent:OwDRzCgI',
      text: ' can',
    },
  },
  {
    kind: 'agent_chunk',
    data: {
      agentId: 'PORTAL:agent:OwDRzCgI',
      text: ' I',
    },
  },
  {
    kind: 'agent_chunk',
    data: {
      agentId: 'PORTAL:agent:OwDRzCgI',
      text: ' assist',
    },
  },
  {
    kind: 'agent_chunk',
    data: {
      agentId: 'PORTAL:agent:OwDRzCgI',
      text: ' you',
    },
  },
  {
    kind: 'agent_chunk',
    data: {
      agentId: 'PORTAL:agent:OwDRzCgI',
      text: ' today',
    },
  },
  {
    kind: 'agent_chunk',
    data: {
      agentId: 'PORTAL:agent:OwDRzCgI',
      text: '?',
    },
  },
  {
    kind: 'agent_finished',
    data: {
      agentId: 'PORTAL:agent:OwDRzCgI',
      status: 'ok',
    },
  },
  {
    kind: 'agent_event',
    data: {
      agentId: 'PORTAL:agent:5HZiXIQD',
      agentName: 'Agent - food expert',
      eventKind: 'AGENT_THINKING',
      eventData: {},
    },
  },
  {
    kind: 'agent_chunk',
    data: {
      agentId: 'PORTAL:agent:5HZiXIQD',
      text: 'Hello',
    },
  },
  {
    kind: 'agent_chunk',
    data: {
      agentId: 'PORTAL:agent:5HZiXIQD',
      text: '!',
    },
  },
  {
    kind: 'agent_chunk',
    data: {
      agentId: 'PORTAL:agent:5HZiXIQD',
      text: ' How',
    },
  },
  {
    kind: 'agent_chunk',
    data: {
      agentId: 'PORTAL:agent:5HZiXIQD',
      text: ' can',
    },
  },
  {
    kind: 'agent_chunk',
    data: {
      agentId: 'PORTAL:agent:5HZiXIQD',
      text: ' I',
    },
  },
  {
    kind: 'agent_chunk',
    data: {
      agentId: 'PORTAL:agent:5HZiXIQD',
      text: ' assist',
    },
  },
  {
    kind: 'agent_chunk',
    data: {
      agentId: 'PORTAL:agent:5HZiXIQD',
      text: ' you',
    },
  },
  {
    kind: 'agent_chunk',
    data: {
      agentId: 'PORTAL:agent:5HZiXIQD',
      text: ' today',
    },
  },
  {
    kind: 'agent_chunk',
    data: {
      agentId: 'PORTAL:agent:5HZiXIQD',
      text: '?',
    },
  },
  {
    kind: 'agent_finished',
    data: {
      agentId: 'PORTAL:agent:5HZiXIQD',
      status: 'ok',
    },
  },
  {
    kind: 'synthesizing_started',
    data: {
      agentCount: 3,
    },
  },
  {
    kind: 'synthesizing_chunk',
    data: {
      text: 'Hello',
    },
  },
  {
    kind: 'synthesizing_chunk',
    data: {
      text: '!',
    },
  },
  {
    kind: 'synthesizing_chunk',
    data: {
      text: ' How',
    },
  },
  {
    kind: 'synthesizing_chunk',
    data: {
      text: ' can',
    },
  },
  {
    kind: 'synthesizing_chunk',
    data: {
      text: ' I',
    },
  },
  {
    kind: 'synthesizing_chunk',
    data: {
      text: ' assist',
    },
  },
  {
    kind: 'synthesizing_chunk',
    data: {
      text: ' you',
    },
  },
  {
    kind: 'synthesizing_chunk',
    data: {
      text: ' today',
    },
  },
  {
    kind: 'synthesizing_chunk',
    data: {
      text: '?',
    },
  },
  {
    kind: 'TRACE',
    data: {
      trace: {
        type: 'span',
        begin: '2025-09-13T14:28:31.975Z',
        end: '2025-09-13T14:28:32.626Z',
        duration: 651,
        name: 'DKU_LLM_MESH_COMPLETION_QUERY_STREAMED',
        children: [
          {
            type: 'span',
            begin: '2025-09-13T14:28:31.982Z',
            end: '2025-09-13T14:28:31.982Z',
            duration: 0,
            name: 'DKU_LLM_MESH_QUERY_ENFORCEMENT',
            children: [],
            attributes: {},
          },
          {
            type: 'span',
            begin: '2025-09-13T14:28:31.982Z',
            end: '2025-09-13T14:28:32.626Z',
            duration: 644,
            usageMetadata: {
              promptTokens: 654,
              completionTokens: 9,
              totalTokens: 663,
              estimatedCost: 0.001725,
            },
            name: 'DKU_LLM_MESH_LLM_CALL_STREAMED',
            children: [
              {
                type: 'event',
                timestamp: '2025-09-13T14:28:32.563Z',
                name: 'DKU_LLM_MESH_LLM_CALL_STREAMED_FIRST_CHUNK',
                children: [],
                attributes: {},
              },
              {
                type: 'event',
                timestamp: '2025-09-13T14:28:32.625Z',
                name: 'DKU_LLM_MESH_LLM_CALL_STREAMED_STREAM_COMPLETE',
                children: [],
                attributes: {},
              },
            ],
            attributes: {
              llmId: 'openai:BSOpenAI:gpt-4o',
              llmProvider: 'OPENAI',
              llmModel: 'gpt-4o',
              completionQuery: {
                messages: [
                  {
                    role: 'system',
                    content:
                      '\n         \n        # Role and Guidelines\n        You are an assistant that synthesizes information from different agents to provide a final answer to the user query.\n        Your role is to read the initial user query and any answers generated by different agents to answer parts of the query or all and provide a final answer.\n\n        Your responsibilities:\n        - Read the user query and read answers from agents (based on sub-queries).\n        - Understand which agent provided which answer (via `agent_name` and `agent_id`).\n        - Each agent has its own scope and expertise. Use the answers provided by them as they are and rely on their answers to provide user with full answer. Do not make up your own.\n        - Synthesize the information from the answers provided by the different agents to provide a final answer to the user query.\n        - Do NOT alter the answers provided by external agents.\n        - If you need to provide a different answer, make an additional one and clearly mention it\'s your own.\n\n        Use the agent metadata to help you understand each agents\'s scope and its answers.\n        Given the initial user query, any possible generated answers, and the context of the conversation, provide a final answer to the initial user query.\n        Don\'t change the answer generated by the agents. If you need to provide a different answer, provide it in a new answer and mention that you are providing a different answer.\n        Here is additional metadata that might help you about the available external agents:\n        [\n  {\n    "id": "PORTAL:agent:5HZiXIQD",\n    "name": "Agent - food expert",\n    "description": "food expert"\n  },\n  {\n    "id": "PORTAL:agent:OwDRzCgI",\n    "name": "Agent - agent for sql tool",\n    "description": "agent that has access to dataset conversations"\n  },\n  {\n    "id": "ac_user_agent_1757341256904:agent:EYfTuxsw",\n    "name": "Concept Coach",\n    "description": null\n  }\n]\n        \n         Generated sub queries and answers by agents based on user query:\n         [\'{{"sub_query": "hello", "agent_answer": "Hello! How can I assist you today?", "agent_id": "ac_user_agent_1757341256904:agent:EYfTuxsw", "agent_name": "Concept Coach"}}\', \'{{"sub_query": "hello", "agent_answer": "Hi there! How can I assist you today?", "agent_id": "PORTAL:agent:OwDRzCgI", "agent_name": "Agent - agent for sql tool"}}\', \'{{"sub_query": "hello", "agent_answer": "Hello! How can I assist you today?", "agent_id": "PORTAL:agent:5HZiXIQD", "agent_name": "Agent - food expert"}}\']\n         ',
                  },
                  {
                    role: 'system',
                    content: 'Today is 2025-09-13. You are a helpful and friendly assistant.',
                  },
                  {
                    role: 'user',
                    content: 'Hello',
                  },
                  {
                    role: 'user',
                    content:
                      'Based on the system instructions and user query, provide the final answer to the initial user query.',
                  },
                ],
              },
              completionQuerySettings: {
                stopSequences: [],
              },
              completionResponse: {
                ok: false,
                text: 'Hello! How can I assist you today?',
                finishReason: 'unknown',
                toolCalls: [],
                predictedClassProbas: [],
                fromCache: false,
              },
            },
            inputs: {
              messages: [
                {
                  role: 'system',
                  text: '\n         \n        # Role and Guidelines\n        You are an assistant that synthesizes information from different agents to provide a final answer to the user query.\n        Your role is to read the initial user query and any answers generated by different agents to answer parts of the query or all and provide a final answer.\n\n        Your responsibilities:\n        - Read the user query and read answers from agents (based on sub-queries).\n        - Understand which agent provided which answer (via `agent_name` and `agent_id`).\n        - Each agent has its own scope and expertise. Use the answers provided by them as they are and rely on their answers to provide user with full answer. Do not make up your own.\n        - Synthesize the information from the answers provided by the different agents to provide a final answer to the user query.\n        - Do NOT alter the answers provided by external agents.\n        - If you need to provide a different answer, make an additional one and clearly mention it\'s your own.\n\n        Use the agent metadata to help you understand each agents\'s scope and its answers.\n        Given the initial user query, any possible generated answers, and the context of the conversation, provide a final answer to the initial user query.\n        Don\'t change the answer generated by the agents. If you need to provide a different answer, provide it in a new answer and mention that you are providing a different answer.\n        Here is additional metadata that might help you about the available external agents:\n        [\n  {\n    "id": "PORTAL:agent:5HZiXIQD",\n    "name": "Agent - food expert",\n    "description": "food expert"\n  },\n  {\n    "id": "PORTAL:agent:OwDRzCgI",\n    "name": "Agent - agent for sql tool",\n    "description": "agent that has access to dataset conversations"\n  },\n  {\n    "id": "ac_user_agent_1757341256904:agent:EYfTuxsw",\n    "name": "Concept Coach",\n    "description": null\n  }\n]\n        \n         Generated sub queries and answers by agents based on user query:\n         [\'{{"sub_query": "hello", "agent_answer": "Hello! How can I assist you today?", "agent_id": "ac_user_agent_1757341256904:agent:EYfTuxsw", "agent_name": "Concept Coach"}}\', \'{{"sub_query": "hello", "agent_answer": "Hi there! How can I assist you today?", "agent_id": "PORTAL:agent:OwDRzCgI", "agent_name": "Agent - agent for sql tool"}}\', \'{{"sub_query": "hello", "agent_answer": "Hello! How can I assist you today?", "agent_id": "PORTAL:agent:5HZiXIQD", "agent_name": "Agent - food expert"}}\']\n         ',
                },
                {
                  role: 'system',
                  text: 'Today is 2025-09-13. You are a helpful and friendly assistant.',
                },
                {
                  role: 'user',
                  text: 'Hello',
                },
                {
                  role: 'user',
                  text: 'Based on the system instructions and user query, provide the final answer to the initial user query.',
                },
              ],
            },
            outputs: {
              text: 'Hello! How can I assist you today?',
            },
          },
        ],
        attributes: {
          llmId: 'openai:BSOpenAI:gpt-4o',
          llmProvider: 'OPENAI',
          llmModel: 'gpt-4o',
          completionQuery: {
            messages: [
              {
                role: 'system',
                content:
                  '\n         \n        # Role and Guidelines\n        You are an assistant that synthesizes information from different agents to provide a final answer to the user query.\n        Your role is to read the initial user query and any answers generated by different agents to answer parts of the query or all and provide a final answer.\n\n        Your responsibilities:\n        - Read the user query and read answers from agents (based on sub-queries).\n        - Understand which agent provided which answer (via `agent_name` and `agent_id`).\n        - Each agent has its own scope and expertise. Use the answers provided by them as they are and rely on their answers to provide user with full answer. Do not make up your own.\n        - Synthesize the information from the answers provided by the different agents to provide a final answer to the user query.\n        - Do NOT alter the answers provided by external agents.\n        - If you need to provide a different answer, make an additional one and clearly mention it\'s your own.\n\n        Use the agent metadata to help you understand each agents\'s scope and its answers.\n        Given the initial user query, any possible generated answers, and the context of the conversation, provide a final answer to the initial user query.\n        Don\'t change the answer generated by the agents. If you need to provide a different answer, provide it in a new answer and mention that you are providing a different answer.\n        Here is additional metadata that might help you about the available external agents:\n        [\n  {\n    "id": "PORTAL:agent:5HZiXIQD",\n    "name": "Agent - food expert",\n    "description": "food expert"\n  },\n  {\n    "id": "PORTAL:agent:OwDRzCgI",\n    "name": "Agent - agent for sql tool",\n    "description": "agent that has access to dataset conversations"\n  },\n  {\n    "id": "ac_user_agent_1757341256904:agent:EYfTuxsw",\n    "name": "Concept Coach",\n    "description": null\n  }\n]\n        \n         Generated sub queries and answers by agents based on user query:\n         [\'{{"sub_query": "hello", "agent_answer": "Hello! How can I assist you today?", "agent_id": "ac_user_agent_1757341256904:agent:EYfTuxsw", "agent_name": "Concept Coach"}}\', \'{{"sub_query": "hello", "agent_answer": "Hi there! How can I assist you today?", "agent_id": "PORTAL:agent:OwDRzCgI", "agent_name": "Agent - agent for sql tool"}}\', \'{{"sub_query": "hello", "agent_answer": "Hello! How can I assist you today?", "agent_id": "PORTAL:agent:5HZiXIQD", "agent_name": "Agent - food expert"}}\']\n         ',
              },
              {
                role: 'system',
                content: 'Today is 2025-09-13. You are a helpful and friendly assistant.',
              },
              {
                role: 'user',
                content: 'Hello',
              },
              {
                role: 'user',
                content:
                  'Based on the system instructions and user query, provide the final answer to the initial user query.',
              },
            ],
          },
          completionQuerySettings: {
            stopSequences: [],
          },
          completionResponse: {
            ok: false,
            text: 'Hello! How can I assist you today?',
            finishReason: 'unknown',
            toolCalls: [],
            predictedClassProbas: [],
            fromCache: false,
          },
        },
        inputs: {
          messages: [
            {
              role: 'system',
              text: '\n         \n        # Role and Guidelines\n        You are an assistant that synthesizes information from different agents to provide a final answer to the user query.\n        Your role is to read the initial user query and any answers generated by different agents to answer parts of the query or all and provide a final answer.\n\n        Your responsibilities:\n        - Read the user query and read answers from agents (based on sub-queries).\n        - Understand which agent provided which answer (via `agent_name` and `agent_id`).\n        - Each agent has its own scope and expertise. Use the answers provided by them as they are and rely on their answers to provide user with full answer. Do not make up your own.\n        - Synthesize the information from the answers provided by the different agents to provide a final answer to the user query.\n        - Do NOT alter the answers provided by external agents.\n        - If you need to provide a different answer, make an additional one and clearly mention it\'s your own.\n\n        Use the agent metadata to help you understand each agents\'s scope and its answers.\n        Given the initial user query, any possible generated answers, and the context of the conversation, provide a final answer to the initial user query.\n        Don\'t change the answer generated by the agents. If you need to provide a different answer, provide it in a new answer and mention that you are providing a different answer.\n        Here is additional metadata that might help you about the available external agents:\n        [\n  {\n    "id": "PORTAL:agent:5HZiXIQD",\n    "name": "Agent - food expert",\n    "description": "food expert"\n  },\n  {\n    "id": "PORTAL:agent:OwDRzCgI",\n    "name": "Agent - agent for sql tool",\n    "description": "agent that has access to dataset conversations"\n  },\n  {\n    "id": "ac_user_agent_1757341256904:agent:EYfTuxsw",\n    "name": "Concept Coach",\n    "description": null\n  }\n]\n        \n         Generated sub queries and answers by agents based on user query:\n         [\'{{"sub_query": "hello", "agent_answer": "Hello! How can I assist you today?", "agent_id": "ac_user_agent_1757341256904:agent:EYfTuxsw", "agent_name": "Concept Coach"}}\', \'{{"sub_query": "hello", "agent_answer": "Hi there! How can I assist you today?", "agent_id": "PORTAL:agent:OwDRzCgI", "agent_name": "Agent - agent for sql tool"}}\', \'{{"sub_query": "hello", "agent_answer": "Hello! How can I assist you today?", "agent_id": "PORTAL:agent:5HZiXIQD", "agent_name": "Agent - food expert"}}\']\n         ',
            },
            {
              role: 'system',
              text: 'Today is 2025-09-13. You are a helpful and friendly assistant.',
            },
            {
              role: 'user',
              text: 'Hello',
            },
            {
              role: 'user',
              text: 'Based on the system instructions and user query, provide the final answer to the initial user query.',
            },
          ],
        },
        outputs: {
          text: 'Hello! How can I assist you today?',
        },
      },
    },
  },
  {
    kind: 'synthesizing_finished',
    data: {
      status: 'ok',
    },
  },
]
