イベントタイプ

{
  "workflow_id": "wf-550e8400-e29b-41d4-a716-446655440000",
  "type": "AGENT_THINKING",
  "agent_id": "agent-001",
  "message": "タスクの複雑さを分析中...",
  "timestamp": "2024-10-27T10:00:00Z",
  "seq": 42,
  "stream_id": "stream-abc123",
  "payload": {}
}

{
  "type": "WORKFLOW_STARTED",
  "message": "ワークフローが開始されました",
  "data": {
    "query": "Q4の売上データを分析",
    "mode": "STANDARD",
    "session_id": "sess-123",
    "estimated_complexity": 0.75
  }
}

{
  "type": "WORKFLOW_COMPLETED",
  "message": "Workflow completed successfully",
  "data": {
    "result": "Analysis complete. Key findings: ...",
    "duration_ms": 45000,
    "total_tokens": 5420,
    "total_cost_usd": 0.0814,
    "agents_used": 3,
    "tools_invoked": 7
  }
}

{
  "type": "AGENT_THINKING",
  "agent_id": "researcher",
  "message": "Analyzing data structure...",
  "timestamp": "2025-01-20T10:00:02Z"
}

{ "type": "TOOL_INVOKED", "message": "web_search: q=\"LLM caching\"" }
{ "type": "TOOL_OBSERVATION", "message": "web_search: 12 results" }

{
  "type": "LLM_OUTPUT",
  "message": "Final summary of findings..."
}

{
  "type": "ERROR_OCCURRED",
  "message": "Provider rate limit exceeded",
  "data": { "error": "RATE_LIMIT", "retry_after": 30 }
}

{
  "type": "APPROVAL_REQUESTED",
  "message": "Delete temporary files older than 30 days?",
  "data": { "approval_id": "appr-456" }
}

{
  "type": "AGENT_STARTED",
  "agent_id": "data-analyst",
  "message": "Agent started",
  "data": {
    "role": "data-analyst",
    "subtask": "Analyze revenue trends",
    "tools_available": ["csv_loader", "pandas", "matplotlib"]
  }
}

{
  "type": "AGENT_THINKING",
  "agent_id": "data-analyst",
  "message": "Analyzing data structure...",
  "data": {
    "thought": "I need to first load the CSV and examine column types",
    "next_action": "invoke_tool",
    "confidence": 0.85
  }
}

{
  "type": "AGENT_COMPLETED",
  "agent_id": "data-analyst",
  "message": "Agent completed successfully",
  "data": {
    "result": "Revenue increased 15% YoY",
    "tokens_used": 1200,
    "cost_usd": 0.018,
    "duration_ms": 8000
  }
}

{
  "type": "AGENT_FAILED",
  "agent_id": "data-analyst",
  "message": "Agent failed: Tool execution error",
  "data": {
    "error": "TOOL_EXECUTION_FAILED",
    "error_message": "CSV file not found",
    "recoverable": true
  }
}

{
  "type": "TOOL_INVOKED",
  "message": "Invoking tool: csv_loader",
  "data": {
    "tool_name": "csv_loader",
    "tool_args": {
      "file_path": "sales_q4.csv"
    },
    "timeout_seconds": 30
  }
}

{
  "type": "TOOL_OBSERVATION",
  "message": "Tool result: csv_loader",
  "data": {
    "tool_name": "csv_loader",
    "result": {
      "rows": 15000,
      "columns": 12,
      "sample": ["date", "product", "revenue", "..."]
    },
    "duration_ms": 450,
    "truncated": false
  }
}

{
  "type": "TEAM_RECRUITED",
  "message": "Team recruited: 3 agents",
  "data": {
    "team_size": 3,
    "agents": [
      {
        "agent_id": "data-analyst",
        "role": "analyst",
        "capabilities": ["data_loading", "analysis"]
      },
      {
        "agent_id": "visualizer",
        "role": "visualization",
        "capabilities": ["charts", "graphs"]
      },
      {
        "agent_id": "writer",
        "role": "report_generation",
        "capabilities": ["summarization", "writing"]
      }
    ]
  }
}

{
  "type": "TEAM_RETIRED",
  "message": "Team retired after task completion",
  "data": {
    "team_size": 3,
    "duration_ms": 45000,
    "total_tokens": 8000,
    "reason": "task_completed"
  }
}

{
  "type": "TEAM_STATUS",
  "message": "Team progress update",
  "data": {
    "active_agents": 2,
    "idle_agents": 1,
    "tasks_completed": 5,
    "tasks_remaining": 3,
    "coordination_mode": "parallel"
  }
}

{
  "type": "DEPENDENCY_SATISFIED",
  "message": "Dependencies satisfied for subtask-3",
  "data": {
    "subtask_id": "subtask-3",
    "satisfied_dependencies": ["subtask-1", "subtask-2"],
    "can_proceed": true
  }
}

{
  "type": "MESSAGE_SENT",
  "agent_id": "supervisor",
  "message": "Message sent to data-analyst",
  "data": {
    "to": "data-analyst",
    "content": "Please analyze revenue trends",
    "message_type": "DELEGATION"
  }
}

{
  "type": "MESSAGE_RECEIVED",
  "agent_id": "data-analyst",
  "message": "Message received from supervisor",
  "data": {
    "from": "supervisor",
    "content": "Please analyze revenue trends",
    "acknowledged": true
  }
}

{
  "type": "LLM_PROMPT",
  "message": "Sending prompt to LLM",
  "data": {
    "model": "gpt-5",
    "prompt_length": 1200,
    "max_tokens": 2000,
    "temperature": 0.7,
    "sanitized_prompt": "Analyze the provided data..."
  }
}

{
  "type": "LLM_PARTIAL",
  "message": "Received partial LLM output",
  "data": {
    "chunk": "Based on the analysis",
    "chunk_index": 5,
    "total_tokens_so_far": 50
  }
}

{
  "type": "LLM_OUTPUT",
  "message": "LLM output complete",
  "data": {
    "output": "Analysis complete. Revenue increased 15% YoY...",
    "model": "gpt-5",
    "provider": "openai",
    "usage": {
      "total_tokens": 350,
      "input_tokens": 200,
      "output_tokens": 150
    },
    "cost_usd": 0.0105,
    "duration_ms": 2000
  }
}

{
  "type": "TOOL_OBSERVATION",
  "message": "Agent observed tool result",
  "data": {
    "tool_name": "web_search",
    "observation": "Found 10 relevant results about AI trends",
    "relevance_score": 0.85
  }
}

{
  "type": "PROGRESS",
  "message": "Progress: 60% complete",
  "data": {
    "percentage": 60,
    "current_step": 3,
    "total_steps": 5,
    "current_task": "Generating visualizations"
  }
}

{
  "type": "DATA_PROCESSING",
  "message": "Processing sales data",
  "data": {
    "operation": "data_analysis",
    "records_processed": 15000,
    "total_records": 15000,
    "processing_stage": "aggregation"
  }
}

{
  "type": "WAITING",
  "message": "Waiting for dependencies",
  "data": {
    "waiting_for": "subtask-2",
    "wait_reason": "dependency_not_satisfied",
    "estimated_wait_seconds": 10
  }
}

{
  "type": "ERROR_OCCURRED",
  "message": "Database connection failed",
  "data": {
    "error_type": "DATABASE_ERROR",
    "error_message": "Connection timeout",
    "recoverable": true,
    "retry_count": 2,
    "max_retries": 3
  }
}

{
  "type": "ERROR_RECOVERY",
  "message": "データベース接続エラーから回復中",
  "data": {
    "error_type": "DATABASE_ERROR",
    "recovery_action": "retry_connection",
    "attempt": 2,
    "max_attempts": 3,
    "success": true
  }
}

{
  "type": "APPROVAL_REQUESTED",
  "message": "ファイルシステムアクセスの承認を要求",
  "data": {
    "approval_id": "appr-123",
    "tool_name": "file_system",
    "operation": "write",
    "risk_level": "HIGH",
    "timeout_seconds": 7200,
    "details": {
      "file_path": "/data/critical.db",
      "action": "delete"
    }
  }
}

{
  "type": "APPROVAL_DECISION",
  "message": "承認されました",
  "data": {
    "approval_id": "appr-123",
    "decision": "approved",
    "approved_by": "user-456",
    "timestamp": "2024-10-27T10:05:00Z",
    "comments": "アクションが必要であることを確認"
  }
}

{
  "type": "WORKSPACE_UPDATED",
  "message": "ワークスペースが更新されました",
  "data": {
    "key": "loaded_datasets",
    "value": ["sales_q4.csv"],
    "action": "ADD"
  }
}

{
  "type": "ROLE_ASSIGNED",
  "agent_id": "agent-002",
  "message": "役割が割り当てられました: data-analyst",
  "data": {
    "role": "data-analyst",
    "capabilities": ["data_loading", "analysis", "visualization"],
    "tools": ["csv_loader", "pandas", "matplotlib"]
  }
}

{
  "type": "DELEGATION",
  "agent_id": "supervisor",
  "message": "data-analystに委任されました",
  "data": {
    "to_agent": "data-analyst",
    "task": "収益トレンドの分析",
    "priority": "HIGH"
  }
}

{
  "type": "BUDGET_THRESHOLD",
  "message": "予算閾値に達しました: 80%",
  "data": {
    "threshold_percentage": 80,
    "tokens_used": 8000,
    "tokens_limit": 10000,
    "tokens_remaining": 2000,
    "cost_usd": 0.24,
    "estimated_cost_at_limit": 0.30
  }
}

{"seq": 1, "type": "WORKFLOW_STARTED"}
{"seq": 2, "type": "AGENT_STARTED"}
{"seq": 3, "type": "AGENT_THINKING"}
{"seq": 4, "type": "TOOL_INVOKED"}
{"seq": 5, "type": "LLM_OUTPUT"}
{"seq": 6, "type": "WORKFLOW_COMPLETED"}

1. WORKFLOW_STARTED
2. AGENT_STARTED
3. AGENT_THINKING
4. TOOL_INVOKED (オプション)
5. LLM_OUTPUT (最終回答)
6. WORKFLOW_COMPLETED

# タスクのすべてのイベントを取得
GET /api/v1/tasks/{task_id}/events?limit=1000

# 再接続し、最後のシーケンス番号から再開
last_seq = 42  # 最後に受信したイベント
for event in client.stream_events(workflow_id, from_seq=last_seq + 1):
    process_event(event)