Token tracking y cálculo de costes por mensaje

- Config: COST_PER_1M_INPUT y COST_PER_1M_OUTPUT configurables via .env
- OpenAI adapter: stream_options include_usage para capturar tokens reales
- base.py: acumula input/output tokens de cada iteración del agente
- planner.py: devuelve usage junto con el plan
- engine.py: suma tokens de planner + steps + review, calcula coste USD
- Response incluye usage{input_tokens, output_tokens} y total_cost_usd

Formato compatible con el bridge de Claude Code CLI para integración
con el frontend y reporting a Acai webservice.

Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>

src/orchestrator/engine.py

@@ -115,9 +115,10 @@ class OrchestratorEngine:
 
         # 2. Plan
         task.status = TaskStatus.PLANNING
+        planner_usage: dict[str, int] = {"input_tokens": 0, "output_tokens": 0}
         try:
             planner = self._create_agent(AgentRole.PLANNER)
-            plan_steps = await planner.plan(session)
+            plan_steps, planner_usage = await planner.plan(session)
             task.plan = plan_steps
             task.status = TaskStatus.EXECUTING
         except Exception as e:
@@ -234,6 +235,21 @@ class OrchestratorEngine:
             session_id=session.session_id,
         )
 
+        # Accumulate token usage: planner + all steps + review
+        total_input = planner_usage.get("input_tokens", 0)
+        total_output = planner_usage.get("output_tokens", 0)
+        for r in results:
+            total_input += r.get("usage", {}).get("input_tokens", 0)
+            total_output += r.get("usage", {}).get("output_tokens", 0)
+        # Add review usage if any
+        total_input += review_result.get("usage", {}).get("input_tokens", 0)
+        total_output += review_result.get("usage", {}).get("output_tokens", 0)
+        # Calculate cost
+        cost_usd = (
+            (total_input / 1_000_000) * settings.cost_per_1m_input
+            + (total_output / 1_000_000) * settings.cost_per_1m_output
+        )
+
         return {
             "session_id": session.session_id,
             "task_id": task.task_id,
@@ -245,6 +261,11 @@ class OrchestratorEngine:
             ),
             "review": review_result.get("content", ""),
             "status": status,
+            "usage": {
+                "input_tokens": total_input,
+                "output_tokens": total_output,
+            },
+            "total_cost_usd": round(cost_usd, 6),
         }
 
     def _error_result(self, session: SessionState, error: str) -> dict[str, Any]: