feat(experts):重写 TeamOrchestrator 为流水线模式 + TeamStatus.PLANNING

src/agentkit/experts/orchestrator.py

@@ -1,27 +1,26 @@
-"""TeamOrchestrator - hub-and-spoke 专家团队执行引擎
+"""TeamOrchestrator - 流水线模式专家团队执行引擎
 
-驱动 ExpertTeam 在 hub-and-spoke 模式下执行任务：
+驱动 ExpertTeam 在流水线模式下执行任务：
 
-1. Lead Expert 接收任务，自主分解为子任务
+1. Lead Expert 接收任务，分解为阶段（PlanPhase），阶段间有依赖关系（depends_on）
-2. 并行 spawn Task（每个 Task 是独立 Agent 执行实例，深度=1）
+2. 按依赖拓扑排序，同层无依赖阶段并行（asyncio.gather），层间串行
-3. 等待所有 Task 完成
+3. 每个阶段创建独立 ConfigDrivenAgent 实例（上下文隔离，KTD3）
-4. Lead Expert 汇总结果（BEST 策略）
+4. 阶段间数据通过 SharedWorkspace 传递（{task_id}/phase/{phase_id}/output）
-5. 返回最终结果
+5. Lead Expert 汇总所有阶段结果（BEST 策略）
+6. 返回最终结果
 
-约束：
+生命周期：FORMING → PLANNING → EXECUTING → SYNTHESIZING → COMPLETED
-- Task 深度=1（Task 不能再 spawn Task）
-- Task 之间无通信
-- Lead Expert 持有所有状态
 
 设计依据：
-- Claude Code: Task 工具深度=1，子 Agent 不能再生子 Agent
+- KTD2: Lead 分解为阶段而非子任务，支持流水线串行阶段
-- Codex: spawn_agent 层级式，结果返回父 Agent
+- KTD3: 上下文隔离，独立 ConfigDrivenAgent 实例
-- 去中心化协作的通信复杂度 O(N²)，hub-and-spoke 为 O(N)
+- KTD6: PLANNING 状态在分解阶段设置
 """
 
 from __future__ import annotations
 
 import asyncio
+import copy
 import json
 import logging
 import re
@@ -31,40 +30,47 @@ from typing import Any
 from agentkit.core.protocol import TaskMessage, TaskResult, TaskStatus
 
 from .expert import Expert
-from .plan import PlanStatus, SubTask, SubTaskStatus, TeamPlan
+from .plan import PhaseStatus, PlanPhase, PlanStatus, TeamPlan
 from .team import ExpertTeam, TeamStatus
 
 logger = logging.getLogger(__name__)
 
 
 class TeamOrchestrator:
-    """Hub-and-spoke orchestration engine.
+    """Pipeline orchestration engine.
 
-    Lead Expert acts as the hub: it decomposes the task, dispatches subtasks
+    Lead Expert decomposes the task into phases with dependencies (depends_on).
-    to member experts (spokes) in parallel, and synthesizes the final result.
+    Phases are executed in topological order: same-layer phases run in parallel
+    (asyncio.gather), layers run sequentially. Each phase gets an independent
+    ConfigDrivenAgent instance for context isolation (KTD3).
     """
 
-    MAX_SUBTASKS = 10  # Maximum subtasks Lead Expert can decompose
+    MAX_PHASES = 10  # Maximum phases Lead Expert can decompose
-    MAX_RETRIES = 1  # Retry once on subtask failure before marking failed
+    MAX_RETRIES = 1  # Retry once on phase failure before marking failed
 
     def __init__(self, team: ExpertTeam) -> None:
         self._team = team
+        # Track temporary agent names created for context isolation (KTD3)
+        # Maps phase_id -> temp_agent_name for cleanup
+        self._temp_agents: dict[str, str] = {}
 
     async def execute(self, task: str) -> dict[str, Any]:
-        """Execute a task in hub-and-spoke mode.
+        """Execute a task in pipeline mode.
 
         Flow:
         1. Emit team_formed event
-        2. Lead Expert decomposes task into subtasks
+        2. Set PLANNING status, Lead Expert decomposes task into phases
-        3. Spawn parallel subtasks (each independent Agent execution)
+        3. Emit plan_update with phase list
-        4. Wait for all subtasks to complete
+        4. Set EXECUTING status, topological sort, execute layers:
-        5. Lead Expert synthesizes results (BEST strategy)
+           - Same-layer phases parallel (asyncio.gather)
-        6. Emit team_synthesis and team_dissolved events
+           - Layer-by-layer sequential
+        5. Set SYNTHESIZING status, Lead synthesizes results (BEST strategy)
+        6. Set COMPLETED status, emit team_synthesis event
 
         Returns a dict with:
         - "status": "completed" | "failed" | "fallback"
         - "result": final synthesized result
-        - "subtask_results": dict of subtask_id -> result
+        - "phase_results": dict of phase_id -> result
         - "plan": TeamPlan instance
         """
         lead = self._team.lead_expert
@@ -74,7 +80,7 @@ class TeamOrchestrator:
                 return {
                     "status": "failed",
                     "result": None,
-                    "subtask_results": {},
+                    "phase_results": {},
                     "error": "No active expert available",
                 }
             lead = active[0]
@@ -85,7 +91,6 @@ class TeamOrchestrator:
             lead_expert=lead.config.name,
             status=PlanStatus.EXECUTING,
         )
-        self._team.set_status(TeamStatus.EXECUTING)
 
         # 1. Emit team_formed event
         await self._broadcast_event(
@@ -98,53 +103,68 @@ class TeamOrchestrator:
             },
         )
 
-        try:
+        # 2. Set PLANNING status, Lead decomposes task into phases
-            # 2. Lead Expert decomposes task into subtasks
+        self._team.set_status(TeamStatus.PLANNING)
-            subtasks = await self._decompose_task(lead, task)
+        phases = await self._decompose_task(lead, task)
-            if not subtasks:
+        if not phases:
-                # If decomposition fails, treat the whole task as a single subtask
+            logger.warning("Task decomposition returned no phases, executing as single phase")
-                logger.warning("Task decomposition returned no subtasks, executing as single task")
+            phases = [PlanPhase(name="执行", assigned_expert=lead.config.name, task_description=task)]
-                subtasks = [SubTask(description=task, assigned_expert=lead.config.name)]
 
-            plan.subtasks = subtasks[: self.MAX_SUBTASKS]
+        plan.phases = phases[: self.MAX_PHASES]
 
-            # 3. Emit plan_update with subtask list
+        # 3. Emit plan_update with phase list
         await self._broadcast_event(
             "plan_update",
             {
                 "plan_id": plan.id,
-                    "subtasks": [st.to_dict() for st in plan.subtasks],
+                "phases": [ph.to_dict() for ph in plan.phases],
             },
         )
 
-            # 4. Spawn parallel subtasks
+        # 4. Set EXECUTING status, execute phases
-            subtask_results: dict[str, dict[str, Any]] = {}
+        self._team.set_status(TeamStatus.EXECUTING)
+        phase_results: dict[str, dict[str, Any]] = {}
+
+        try:
+            # Topological sort phases into execution layers
+            layers = plan.topological_sort()
+
+            # Execute layers sequentially, phases within layer in parallel
+            for layer in layers:
+                # Filter out already-failed phases (from dependency failures)
+                ready = [ph for ph in layer if ph.status == PhaseStatus.PENDING]
+                if not ready:
+                    continue
+
+                # Execute all phases in this layer in parallel
                 results = await asyncio.gather(
-                *[self._execute_subtask(st) for st in plan.subtasks],
+                    *[self._execute_phase(ph, plan) for ph in ready],
                     return_exceptions=True,
                 )
 
-            for subtask, result in zip(plan.subtasks, results):
+                for ph, result in zip(ready, results):
                     if isinstance(result, Exception):
-                    logger.error(f"Subtask {subtask.id} failed: {result}")
+                        logger.error(f"Phase {ph.id} ({ph.name}) failed: {result}")
-                    plan.update_subtask_status(
+                        plan.update_phase_status(
-                        subtask.id, SubTaskStatus.FAILED, {"error": str(result)}
+                            ph.id, PhaseStatus.FAILED, {"error": str(result)}
                         )
-                    subtask_results[subtask.id] = {"error": str(result)}
+                        phase_results[ph.id] = {"error": str(result)}
+                        # Mark dependent phases as failed
+                        self._mark_dependents_failed(ph.id, plan, phase_results)
                     else:
-                    subtask_results[subtask.id] = result
+                        phase_results[ph.id] = result
 
-            # 5. Check if all subtasks failed
+            # 5. Check if all phases failed
-            completed = plan.completed_subtasks
+            completed = plan.completed_phases
             if not completed:
-                logger.warning("All subtasks failed, falling back to single agent")
+                logger.warning("All phases failed, falling back to single agent")
-                return await self._fallback_to_single_agent(task, plan, subtask_results)
+                return await self._fallback_to_single_agent(task, plan, phase_results)
 
             # 6. Lead Expert synthesizes results (BEST strategy)
             self._team.set_status(TeamStatus.SYNTHESIZING)
             plan.status = PlanStatus.COMPLETED
 
-            final_result = await self._synthesize_results(lead, task, [st for st in completed])
+            final_result = await self._synthesize_results(lead, task, completed)
 
             self._team.set_status(TeamStatus.COMPLETED)
 
@@ -153,33 +173,38 @@ class TeamOrchestrator:
                 "team_synthesis",
                 {
                     "content": final_result.get("content", ""),
-                    "subtasks_completed": len(completed),
+                    "phases_completed": len(completed),
-                    "subtasks_total": len(plan.subtasks),
+                    "phases_total": len(plan.phases),
                 },
             )
 
             return {
                 "status": "completed",
                 "result": final_result,
-                "subtask_results": subtask_results,
+                "phase_results": phase_results,
                 "plan": plan,
             }
 
-        except Exception as e:
+        except ValueError as e:
-            logger.error(f"Hub-and-spoke execution failed: {e}")
+            # Circular dependency or invalid reference from topological_sort
+            logger.error(f"Pipeline execution failed (invalid plan): {e}")
             plan.status = PlanStatus.FAILED
-            return await self._fallback_to_single_agent(task, plan, subtask_results)
+            return await self._fallback_to_single_agent(task, plan, phase_results)
+        except Exception as e:
+            logger.error(f"Pipeline execution failed: {e}")
+            plan.status = PlanStatus.FAILED
+            return await self._fallback_to_single_agent(task, plan, phase_results)
 
-    async def _decompose_task(self, lead: Expert, task: str) -> list[SubTask]:
+    async def _decompose_task(self, lead: Expert, task: str) -> list[PlanPhase]:
-        """Lead Expert decomposes task into subtasks using LLM.
+        """Lead Expert decomposes task into phases using LLM.
 
-        Returns a list of SubTask instances. If LLM decomposition fails,
+        Returns a list of PlanPhase instances. If LLM decomposition fails,
-        returns a single subtask with the original task.
+        returns a single phase with the original task.
         """
         gateway = self._get_llm_gateway(lead)
         if not gateway:
-            logger.warning("No LLM gateway available, treating task as single subtask")
+            logger.warning("No LLM gateway available, treating task as single phase")
-            return [SubTask(description=task, assigned_expert=lead.config.name)]
+            return [PlanPhase(name="执行", assigned_expert=lead.config.name, task_description=task)]
 
         member_names = [
             e.config.name for e in self._team.active_experts if e.config.name != lead.config.name
@@ -187,38 +212,47 @@ class TeamOrchestrator:
         available_experts = member_names if member_names else [lead.config.name]
 
         prompt = (
-            f"You are the Lead Expert in a team. Decompose the following task into "
+            f"You are the Lead Expert in a pipeline team. Decompose the following task into "
-            f"at most {self.MAX_SUBTASKS} independent subtasks that can be executed in parallel.\n\n"
+            f"at most {self.MAX_PHASES} phases with dependencies.\n\n"
             f"Task: {task}\n\n"
             f"Available experts: {', '.join(available_experts)}\n\n"
-            f"Return a JSON array of objects, each with:\n"
+            f"Return a JSON array of phase objects, each with:\n"
-            f'- "description": clear subtask description\n'
+            f'- "name": phase name (e.g., "规划", "前端", "后端", "QA", "评审")\n'
-            f'- "assigned_expert": name of the expert to assign (must be one of: {", ".join(available_experts)})\n\n'
+            f'- "assigned_expert": name of the expert to assign '
+            f'(must be one of: {", ".join(available_experts)})\n'
+            f'- "task_description": clear phase task description\n'
+            f'- "depends_on": array of phase names this phase depends on (empty array if none)\n\n'
+            f"Example:\n"
+            f'[{{"name":"规划","assigned_expert":"tech_lead",'
+            f'"task_description":"设计架构","depends_on":[]}},'
+            f'{{"name":"前端","assigned_expert":"frontend",'
+            f'"task_description":"实现UI","depends_on":["规划"]}}]\n\n'
             f"Return ONLY the JSON array, no other text."
         )
 
         try:
             response = await gateway.chat(
                 messages=[{"role": "user", "content": prompt}],
-                model="default",
+                model=self._get_model(lead),
             )
-            subtasks = self._parse_subtasks(response.content, available_experts, lead.config.name)
+            phases = self._parse_phases(response.content, available_experts, lead.config.name)
-            if subtasks:
+            if phases:
-                return subtasks
+                return phases
-            logger.warning("LLM decomposition returned no valid subtasks")
+            logger.warning("LLM decomposition returned no valid phases")
         except Exception as e:
             logger.warning(f"LLM task decomposition failed: {e}")
 
-        return [SubTask(description=task, assigned_expert=lead.config.name)]
+        return [PlanPhase(name="执行", assigned_expert=lead.config.name, task_description=task)]
 
     @staticmethod
-    def _parse_subtasks(
+    def _parse_phases(
         content: str, available_experts: list[str], lead_name: str
-    ) -> list[SubTask]:
+    ) -> list[PlanPhase]:
-        """Parse LLM response into SubTask list.
+        """Parse LLM response into PlanPhase list.
 
-        Extracts JSON array from the response content and creates SubTask instances.
+        Extracts JSON array from the response content and creates PlanPhase instances.
-        Validates assigned_expert against available_experts list.
+        Resolves depends_on from phase names to phase IDs. Validates assigned_expert
+        against available_experts list.
         """
         # Try to extract JSON array from the response
         json_match = re.search(r"\[.*\]", content, re.DOTALL)
@@ -233,46 +267,97 @@ class TeamOrchestrator:
         if not isinstance(items, list):
             return []
 
-        subtasks: list[SubTask] = []
+        # First pass: create phases with IDs, build name->id mapping
+        name_to_id: dict[str, str] = {}
+        raw_phases: list[dict[str, Any]] = []
+
         for item in items:
             if not isinstance(item, dict):
                 continue
-            description = item.get("description", "").strip()
+            name = item.get("name", "").strip()
-            if not description:
+            if not name:
                 continue
             assigned = item.get("assigned_expert", "").strip()
             # Validate assigned expert; fall back to lead if invalid
             if assigned not in available_experts:
                 assigned = lead_name
-            subtasks.append(SubTask(description=description, assigned_expert=assigned))
+            task_desc = item.get("task_description", "").strip() or name
-        return subtasks
+            depends_on_names = item.get("depends_on", [])
+            if not isinstance(depends_on_names, list):
+                depends_on_names = []
 
-    async def _execute_subtask(self, subtask: SubTask) -> dict[str, Any]:
+            phase = PlanPhase(
-        """Execute a single subtask using the assigned expert.
+                name=name,
+                assigned_expert=assigned,
+                task_description=task_desc,
+                depends_on=[],  # Will resolve to IDs in second pass
+            )
+            raw_phases.append({"phase": phase, "depends_on_names": depends_on_names})
+            name_to_id[name] = phase.id
 
-        Each subtask is an independent Agent execution (Task depth=1).
+        # Second pass: resolve depends_on from names to IDs
-        Subtasks cannot spawn further subtasks.
+        phases: list[PlanPhase] = []
+        for entry in raw_phases:
+            phase = entry["phase"]
+            for dep_name in entry["depends_on_names"]:
+                dep_id = name_to_id.get(dep_name)
+                if dep_id:
+                    phase.depends_on.append(dep_id)
+                else:
+                    logger.warning(
+                        f"Phase '{phase.name}' depends on unknown phase '{dep_name}', ignoring"
+                    )
+            phases.append(phase)
+
+        return phases
+
+    async def _execute_phase(self, phase: PlanPhase, plan: TeamPlan) -> dict[str, Any]:
+        """Execute a single phase using the assigned expert.
+
+        Creates an independent ConfigDrivenAgent instance for context isolation (KTD3).
+        Reads dependency outputs from SharedWorkspace, executes the phase task,
+        writes the phase output to SharedWorkspace.
         """
         # Resolve the assigned expert
-        expert = self._team.get_expert(subtask.assigned_expert)
+        expert = self._team.get_expert(phase.assigned_expert)
         if not expert or not expert.is_active:
-            # Fallback to lead expert or first active expert
             expert = self._team.lead_expert
             if not expert or not expert.is_active:
                 active = self._team.active_experts
                 if not active:
                     raise RuntimeError(
-                        f"Expert '{subtask.assigned_expert}' not available and no active fallback"
+                        f"Expert '{phase.assigned_expert}' not available and no active fallback"
                     )
                 expert = active[0]
             logger.warning(
-                f"Expert '{subtask.assigned_expert}' not available, "
+                f"Expert '{phase.assigned_expert}' not available, "
                 f"falling back to '{expert.config.name}'"
             )
+            phase.assigned_expert = expert.config.name
 
-        # Update subtask status
+        # Update phase status
-        subtask.status = SubTaskStatus.RUNNING
+        phase.status = PhaseStatus.RUNNING
-        subtask.assigned_expert = expert.config.name
+
+        # Emit phase_started event
+        await self._broadcast_event(
+            "phase_started",
+            {
+                "phase_id": phase.id,
+                "phase_name": phase.name,
+                "assigned_expert": phase.assigned_expert,
+                "depends_on": list(phase.depends_on),
+            },
+        )
+
+        # Read dependency outputs from SharedWorkspace
+        dependency_outputs: dict[str, Any] = {}
+        for dep_id in phase.depends_on:
+            dep_phase = plan.get_phase(dep_id)
+            if dep_phase and dep_phase.status == PhaseStatus.COMPLETED:
+                key = f"{plan.id}/phase/{dep_id}/output"
+                data = await self._team.workspace.read(key)
+                if data:
+                    dependency_outputs[dep_phase.name] = data.get("value", "")
 
         # Emit expert_step event
         await self._broadcast_event(
@@ -281,43 +366,72 @@ class TeamOrchestrator:
                 "expert_id": expert.config.name,
                 "expert_name": expert.config.name,
                 "expert_color": expert.config.color,
-                "content": subtask.description,
+                "content": phase.task_description,
-                "step": subtask.id,
+                "step": phase.id,
+                "phase_id": phase.id,
+                "phase_name": phase.name,
             },
         )
 
-        # Build TaskMessage for execution
+        # Build TaskMessage for execution with context isolation
-        task_msg = TaskMessage(
+        # Context includes: task description + persona + dependency outputs
-            task_id=subtask.id,
+        input_data: dict[str, Any] = {
-            agent_name=expert.config.name,
+            "task": phase.task_description,
-            task_type="team_subtask",
-            priority=0,
-            input_data={
-                "task": subtask.description,
             "team_id": self._team.team_id,
-                "is_subtask": True,  # Marker: depth=1, cannot spawn further subtasks
+            "phase_id": phase.id,
-            },
+            "phase_name": phase.name,
+            "is_phase": True,
+            "dependency_outputs": dependency_outputs,
+        }
+        if dependency_outputs:
+            input_data["context"] = (
+                "前置阶段输出:\n"
+                + "\n---\n".join(
+                    f"[{name}]:\n{output[:500] if isinstance(output, str) else str(output)[:500]}"
+                    for name, output in dependency_outputs.items()
+                )
+            )
+
+        task_msg = TaskMessage(
+            task_id=phase.id,
+            agent_name=expert.config.name,
+            task_type="team_phase",
+            priority=0,
+            input_data=input_data,
             callback_url=None,
             created_at=datetime.now(timezone.utc),
         )
 
-        # Execute with retry
+        # Execute with context isolation: try creating independent agent via pool
+        agent = await self._get_isolated_agent(expert, phase)
         last_error: str | None = None
+        result: dict[str, Any] | None = None
+
+        try:
             for attempt in range(self.MAX_RETRIES + 1):
                 try:
-                task_result: TaskResult = await expert.agent.execute(task_msg)
+                    task_result: TaskResult = await agent.execute(task_msg)
 
                     if task_result.status != TaskStatus.COMPLETED.value:
                         last_error = task_result.error_message or "unknown error"
                         if attempt < self.MAX_RETRIES:
-                        logger.info(f"Retrying subtask {subtask.id} (attempt {attempt + 1})")
+                            logger.info(f"Retrying phase {phase.id} (attempt {attempt + 1})")
                             continue
                         raise RuntimeError(f"Agent execution failed: {last_error}")
 
                     result = task_result.output_data or {"content": ""}
 
-                subtask.status = SubTaskStatus.COMPLETED
+                    # Update phase status
-                subtask.result = result
+                    phase.status = PhaseStatus.COMPLETED
+                    phase.result = result
+
+                    # Write phase output to SharedWorkspace
+                    output_key = f"{plan.id}/phase/{phase.id}/output"
+                    await self._team.workspace.write(
+                        output_key,
+                        result.get("content", str(result)),
+                        expert.config.name,
+                    )
 
                     # Emit expert_result event
                     await self._broadcast_event(
@@ -327,7 +441,20 @@ class TeamOrchestrator:
                             "expert_name": expert.config.name,
                             "expert_color": expert.config.color,
                             "content": result.get("content", str(result)),
-                        "subtask_id": subtask.id,
+                            "phase_id": phase.id,
+                        },
+                    )
+
+                    # Emit phase_completed event
+                    result_summary = result.get("content", str(result))
+                    if isinstance(result_summary, str) and len(result_summary) > 200:
+                        result_summary = result_summary[:200] + "..."
+                    await self._broadcast_event(
+                        "phase_completed",
+                        {
+                            "phase_id": phase.id,
+                            "phase_name": phase.name,
+                            "result_summary": result_summary,
                         },
                     )
 
@@ -336,24 +463,83 @@ class TeamOrchestrator:
                 except Exception as e:
                     last_error = str(e)
                     if attempt < self.MAX_RETRIES:
-                    logger.info(f"Retrying subtask {subtask.id} (attempt {attempt + 1})")
+                        logger.info(f"Retrying phase {phase.id} (attempt {attempt + 1})")
                         continue
                     raise
 
-        # Should not reach here, but just in case
+        finally:
-        subtask.status = SubTaskStatus.FAILED
+            # Clean up isolated agent if we created one
-        raise RuntimeError(f"Subtask {subtask.id} failed: {last_error}")
+            await self._cleanup_isolated_agent(phase)
+
+        # Should not reach here
+        phase.status = PhaseStatus.FAILED
+        raise RuntimeError(f"Phase {phase.id} ({phase.name}) failed: {last_error}")
+
+    async def _get_isolated_agent(self, expert: Expert, phase: PlanPhase) -> Any:
+        """Get an isolated ConfigDrivenAgent instance for the phase.
+
+        If AgentPool is available, creates a temporary agent with a unique name
+        for context isolation (KTD3). Otherwise, falls back to the expert's
+        existing agent.
+        """
+        pool = self._team.pool
+        if pool is None:
+            # No pool available (e.g., in tests), use expert's existing agent
+            return expert.agent
+
+        # Create a temporary config with unique name for this phase
+        temp_config = copy.deepcopy(expert.config)
+        temp_config.name = f"{expert.config.name}__phase_{phase.id[:8]}"
+
+        try:
+            agent = await pool.create_agent(temp_config)
+            # Track for cleanup
+            self._temp_agents[phase.id] = temp_config.name
+            return agent
+        except Exception as e:
+            logger.warning(
+                f"Failed to create isolated agent for phase {phase.id}, "
+                f"using expert's existing agent: {e}"
+            )
+            return expert.agent
+
+    async def _cleanup_isolated_agent(self, phase: PlanPhase) -> None:
+        """Clean up the temporary isolated agent if one was created."""
+        pool = self._team.pool
+        if pool is None:
+            return
+
+        temp_name = self._temp_agents.pop(phase.id, None)
+        if temp_name:
+            try:
+                await pool.remove_agent(temp_name)
+            except Exception as e:
+                logger.warning(f"Failed to clean up isolated agent '{temp_name}': {e}")
+
+    def _mark_dependents_failed(
+        self, failed_phase_id: str, plan: TeamPlan, phase_results: dict[str, dict[str, Any]]
+    ) -> None:
+        """Mark all phases that depend on the failed phase as FAILED."""
+        for ph in plan.phases:
+            if ph.status != PhaseStatus.PENDING:
+                continue
+            if failed_phase_id in ph.depends_on:
+                ph.status = PhaseStatus.FAILED
+                ph.result = {"error": f"Dependency phase '{failed_phase_id}' failed"}
+                phase_results[ph.id] = {"error": f"Dependency '{failed_phase_id}' failed"}
+                # Recursively mark their dependents
+                self._mark_dependents_failed(ph.id, plan, phase_results)
 
     async def _synthesize_results(
-        self, lead: Expert, task: str, completed_subtasks: list[SubTask]
+        self, lead: Expert, task: str, completed_phases: list[PlanPhase]
     ) -> dict[str, Any]:
         """Lead Expert synthesizes results using BEST strategy.
 
-        The Lead Expert evaluates all completed subtask results and produces
+        The Lead Expert evaluates all completed phase results and produces
         a final synthesized result. Uses LLM when available, otherwise
         concatenates results.
         """
-        results = [st.result or {} for st in completed_subtasks]
+        results = [ph.result or {} for ph in completed_phases]
         if not results:
             return {"content": ""}
 
@@ -363,7 +549,7 @@ class TeamOrchestrator:
             return {
                 "content": content,
                 "strategy": "best",
-                "subtasks_completed": 1,
+                "phases_completed": 1,
             }
 
         gateway = self._get_llm_gateway(lead)
@@ -375,22 +561,22 @@ class TeamOrchestrator:
             return {
                 "content": combined,
                 "strategy": "best",
-                "subtasks_completed": len(results),
+                "phases_completed": len(results),
             }
 
         # Build result summaries for LLM evaluation
         summaries = []
-        for i, st in enumerate(completed_subtasks):
+        for i, ph in enumerate(completed_phases):
-            r = st.result or {}
+            r = ph.result or {}
             content = r.get("content", str(r)) if isinstance(r, dict) else str(r)
             summaries.append(
-                f"Subtask {i + 1} (by {st.assigned_expert}, task: {st.description[:100]}):\n"
+                f"Phase {i + 1}: {ph.name} (by {ph.assigned_expert}, task: {ph.task_description[:100]}):\n"
                 f"{content[:500]}"
             )
 
         prompt = (
             f"Original task: {task}\n\n"
-            f"Below are {len(results)} subtask results from your team members. "
+            f"Below are {len(results)} phase results from your team members. "
             f"Synthesize them into a single comprehensive final result that "
             f"best addresses the original task.\n\n"
             + "\n---\n".join(summaries)
@@ -400,12 +586,12 @@ class TeamOrchestrator:
         try:
             response = await gateway.chat(
                 messages=[{"role": "user", "content": prompt}],
-                model="default",
+                model=self._get_model(lead),
             )
             return {
                 "content": response.content.strip(),
                 "strategy": "best",
-                "subtasks_completed": len(results),
+                "phases_completed": len(results),
             }
         except Exception as e:
             logger.warning(f"LLM synthesis failed, falling back to concatenation: {e}")
@@ -415,16 +601,16 @@ class TeamOrchestrator:
             return {
                 "content": combined,
                 "strategy": "best",
-                "subtasks_completed": len(results),
+                "phases_completed": len(results),
             }
 
     async def _fallback_to_single_agent(
         self,
         task: str,
         plan: TeamPlan,
-        subtask_results: dict[str, dict[str, Any]],
+        phase_results: dict[str, dict[str, Any]],
     ) -> dict[str, Any]:
-        """Fallback to single agent mode when hub-and-spoke execution fails.
+        """Fallback to single agent mode when pipeline execution fails.
 
         Uses the lead expert (or first active expert) to complete the original task.
         """
@@ -446,7 +632,7 @@ class TeamOrchestrator:
                     priority=0,
                     input_data={
                         "task": task,
-                        "subtask_results": subtask_results,
+                        "phase_results": phase_results,
                         "team_id": self._team.team_id,
                     },
                     callback_url=None,
@@ -465,10 +651,23 @@ class TeamOrchestrator:
         return {
             "status": "fallback",
             "result": fallback_result,
-            "subtask_results": subtask_results,
+            "phase_results": phase_results,
             "plan": plan,
         }
 
+    def _get_model(self, expert: Expert | None = None) -> str:
+        """Get LLM model name from expert config.
+
+        Reads expert.config.llm (dict[str, Any] | None) and returns the model
+        name. Falls back to "default" if not configured.
+
+        V4 verified: ExpertConfig.llm is dict[str, Any] | None.
+        """
+        target = expert or self._team.lead_expert
+        if target and target.config.llm:
+            return target.config.llm.get("model", "default")
+        return "default"
+
     def _get_llm_gateway(self, expert: Expert | None = None) -> Any:
         """Get LLM gateway from the given expert or the lead expert's agent.
 
@@ -492,7 +691,8 @@ class TeamOrchestrator:
 
         Events are emitted via handoff_transport for WebSocket relay.
         Supported event types: team_formed, expert_step, expert_result,
-        plan_update, team_synthesis, team_dissolved.
+        plan_update, phase_started, phase_completed, phase_failed,
+        team_synthesis, team_dissolved.
         """
         if self._team.handoff_transport:
             try:

src/agentkit/experts/team.py

@@ -29,9 +29,17 @@ logger = logging.getLogger(__name__)
 
 
 class TeamStatus(str, enum.Enum):
-    """ExpertTeam lifecycle states."""
+    """ExpertTeam lifecycle states.
+
+    流水线模式生命周期：
+    FORMING → PLANNING → EXECUTING → SYNTHESIZING → COMPLETED → DISSOLVED
+
+    PLANNING 状态在 Lead Expert 分解任务为阶段时设置（KTD6），
+    与前端 IExpertTeamState.status 的 'planning' 值对齐。
+    """
 
     FORMING = "forming"
+    PLANNING = "planning"
     EXECUTING = "executing"
     SYNTHESIZING = "synthesizing"
     COMPLETED = "completed"
@@ -108,6 +116,15 @@ class ExpertTeam:
         """Public read access to the team's communication channel."""
         return self._team_channel
 
+    @property
+    def pool(self) -> AgentPool | None:
+        """Public read access to the team's AgentPool.
+
+        Used by TeamOrchestrator to create independent ConfigDrivenAgent
+        instances for context isolation in pipeline mode (KTD3).
+        """
+        return self._pool
+
     def get_expert(self, name: str) -> Expert | None:
         """Get an expert by name. Returns None if not found."""
         return self._experts.get(name)
@@ -149,7 +166,8 @@ class ExpertTeam:
             for config in member_configs:
                 await self._add_expert_internal(config, team_context)
 
-        self._status = TeamStatus.EXECUTING
+        # KTD6: 设置 PLANNING 状态（Lead Expert 即将分解任务为阶段）
+        self._status = TeamStatus.PLANNING
 
     async def add_expert(self, config_or_template: ExpertConfig | str) -> Expert:
         """Add an Expert to the team dynamically.

tests/unit/experts/test_team.py

@@ -146,7 +146,7 @@ class TestExpertTeamCreateTeam:
 
         assert team._lead_expert_name == "lead"
         assert team.lead_expert is mock_expert
-        assert team.status == TeamStatus.EXECUTING
+        assert team.status == TeamStatus.PLANNING
         assert mock_expert.team_id == team.team_id
 
     @pytest.mark.asyncio
@@ -168,7 +168,7 @@ class TestExpertTeamCreateTeam:
 
         assert len(team.experts) == 2
         assert team._lead_expert_name == "lead"
-        assert team.status == TeamStatus.EXECUTING
+        assert team.status == TeamStatus.PLANNING
 
     @pytest.mark.asyncio
     async def test_create_team_without_pool_raises(self):