feat(team): run parallel nodes with isolated loops

app-instance/backend/beaver/coordinator/execution/scheduler.py

@@ -18,8 +18,9 @@ if TYPE_CHECKING:
 class TeamGraphScheduler:
     """Execute sequence, parallel, and DAG team graphs."""
 
-    def __init__(self, runner: LocalAgentRunner) -> None:
+    def __init__(self, runner: LocalAgentRunner, *, max_parallel_team_nodes: int = 3) -> None:
         self.runner = runner
+        self.max_parallel_team_nodes = max(1, int(max_parallel_team_nodes))
 
     async def run(
         self,
@@ -96,7 +97,18 @@ class TeamGraphScheduler:
         nodes: list[ExecutionNode],
         **kwargs,
     ) -> list[NodeRunResult]:
-        return list(await asyncio.gather(*(self._run_node(node, dependency_outputs={}, **kwargs) for node in nodes)))
+        semaphore = asyncio.Semaphore(self.max_parallel_team_nodes)
+
+        async def run_one(node: ExecutionNode) -> NodeRunResult:
+            async with semaphore:
+                return await self._run_node(
+                    node,
+                    dependency_outputs={},
+                    execution_mode="isolated_loop",
+                    **kwargs,
+                )
+
+        return list(await asyncio.gather(*(run_one(node) for node in nodes)))
 
     async def _run_dag(
         self,
@@ -164,6 +176,7 @@ class TeamGraphScheduler:
         inherited_pinned_skill_contexts: list["SkillContext"],
         allow_candidate_generation: bool,
         dependency_outputs: dict[str, str],
+        execution_mode: str = "shared_loop",
     ) -> NodeRunResult:
         try:
             pinned = self._merge_pinned(inherited_pinned_skills, node.inherited_pinned_skills)
@@ -189,6 +202,7 @@ class TeamGraphScheduler:
                 envelope,
                 provider_bundle=node_provider_bundle,
                 allow_candidate_generation=allow_candidate_generation,
+                execution_mode=execution_mode,
             )
         except asyncio.CancelledError:
             raise

app-instance/backend/beaver/coordinator/local.py

@@ -23,6 +23,7 @@ class LocalAgentRunner:
         *,
         provider_bundle: ProviderBundle | None = None,
         allow_candidate_generation: bool = False,
+        execution_mode: str = "shared_loop",
     ) -> NodeRunResult:
         if provider_bundle is not None and (envelope.agent.model or envelope.agent.provider_name):
             raise ValueError(
@@ -30,7 +31,14 @@ class LocalAgentRunner:
                 "build a node-specific provider bundle instead."
             )
         child_session_id = self._child_session_id(envelope)
-        runner = self.loop.submit_direct if self.loop.is_running else self.loop.process_direct
+        target_loop = self.loop
+        if execution_mode == "isolated_loop":
+            target_loop = AgentLoop(profile=self.loop.profile, loader=self.loop.loader)
+        runner = (
+            target_loop.process_direct
+            if execution_mode == "isolated_loop"
+            else (self.loop.submit_direct if self.loop.is_running else self.loop.process_direct)
+        )
         result = await runner(
             envelope.task,
             session_id=child_session_id,
@@ -48,7 +56,7 @@ class LocalAgentRunner:
             pinned_skill_contexts=envelope.inherited_pinned_skill_contexts,
             allow_candidate_generation=allow_candidate_generation,
         )
-        loaded = self.loop.boot()
+        loaded = target_loop.boot()
         evidence = EvidenceBuilder(loaded.session_manager).build_run_evidence(
             result.session_id,
             result.run_id,

app-instance/backend/beaver/services/team_service.py

@@ -16,10 +16,10 @@ if TYPE_CHECKING:
 class TeamService:
     """Internal service for Beaver-native multi-agent execution."""
 
-    def __init__(self, loop: AgentLoop) -> None:
+    def __init__(self, loop: AgentLoop, *, max_parallel_team_nodes: int = 3) -> None:
         self.loop = loop
         self.runner = LocalAgentRunner(loop)
-        self.scheduler = TeamGraphScheduler(self.runner)
+        self.scheduler = TeamGraphScheduler(self.runner, max_parallel_team_nodes=max_parallel_team_nodes)
 
     async def run_team(
         self,