feat: 新增 agentfx 任务深链与研判报告入库流程

支持 goPath=agentfx 的 iframe 嵌入问答、内置研判助手种子与报告导入协助卡，并完善深度研究流式报告能力。

Co-authored-by: Cursor <cursoragent@cursor.com>

offline-backend-20260512/backend/packages/harness/deerflow/agents/lead_agent/agent.py

@@ -5,12 +5,12 @@ from langchain.agents.middleware import AgentMiddleware
 from langchain_core.runnables import RunnableConfig
 
 from deerflow.agents.lead_agent.prompt import apply_prompt_template
-from deerflow.agents.middlewares.clarification_middleware import ClarificationMiddleware
-from deerflow.agents.middlewares.forced_research_middleware import (
-    FORCED_RESEARCH_AGENT_ID,
-    ForcedResearchMiddleware,
-    filter_forced_research_tools,
-)
+from deerflow.agents.middlewares.clarification_middleware import ClarificationMiddleware
+from deerflow.agents.middlewares.forced_research_middleware import (
+    FORCED_RESEARCH_AGENT_ID,
+    ForcedResearchMiddleware,
+    filter_forced_research_tools,
+)
 from deerflow.agents.middlewares.loop_detection_middleware import LoopDetectionMiddleware
 from deerflow.agents.middlewares.memory_middleware import MemoryMiddleware
 from deerflow.agents.middlewares.prompt_prefix_middleware import PromptPrefixMiddleware
@@ -373,21 +373,21 @@ def _build_middlewares(
 
         middlewares.append(SkillReviewMiddleware())
 
-    # PositionArtifactCapMiddleware — position-roundtable seats (and summary)
+    # PositionArtifactCapMiddleware — position-roundtable seats (and summary)
     # may only emit N outputs write_file calls per user turn. Action-plan skips
     # the context flag so it can still write report + JSON.
     from deerflow.agents.middlewares.position_artifact_cap_middleware import PositionArtifactCapMiddleware
 
-    middlewares.append(PositionArtifactCapMiddleware())
-
-    # The built-in forced-research responder has a hard per-turn collection
-    # gate plus temporary-Python-only sandbox IO.  This is runtime enforcement,
-    # not merely SOUL prompt steering, so weak models cannot answer before a
-    # successful retrieval/skill-script result exists.
-    if agent_name == FORCED_RESEARCH_AGENT_ID:
-        middlewares.append(ForcedResearchMiddleware())
-
-    # LoopDetectionMiddleware — detect and break repetitive tool call loops
+    middlewares.append(PositionArtifactCapMiddleware())
+
+    # The built-in forced-research responder has a hard per-turn collection
+    # gate plus temporary-Python-only sandbox IO.  This is runtime enforcement,
+    # not merely SOUL prompt steering, so weak models cannot answer before a
+    # successful retrieval/skill-script result exists.
+    if agent_name == FORCED_RESEARCH_AGENT_ID:
+        middlewares.append(ForcedResearchMiddleware())
+
+    # LoopDetectionMiddleware — detect and break repetitive tool call loops
     middlewares.append(LoopDetectionMiddleware())
 
     # Inject custom middlewares before ClarificationMiddleware
@@ -638,20 +638,32 @@ def _make_lead_agent(config: RunnableConfig, *, app_config: AppConfig):
     # Default lead agent (unchanged behavior)
     is_notebook_mode = bool(cfg.get("notebook_search_space_id"))
     notebook_source_ids: list[int] = cfg.get("notebook_source_ids") or []
-    default_tools = get_available_tools(
+    default_tools = get_available_tools(
         model_name=model_name,
         groups=agent_config.tool_groups if agent_config else None,
         subagent_enabled=subagent_enabled,
         is_scheduled_run=cfg.get("is_scheduled_run", False),
         excluded_tools=requested_excluded_tools,
-        app_config=resolved_app_config,
-    )
-    if agent_id == FORCED_RESEARCH_AGENT_ID:
-        default_tools = filter_forced_research_tools(default_tools)
+        app_config=resolved_app_config,
+    )
+    if agent_id == FORCED_RESEARCH_AGENT_ID:
+        default_tools = filter_forced_research_tools(default_tools)
     if is_notebook_mode:
         from deerflow.tools.builtins.notebook_search_tool import notebook_search_tool
 
         default_tools = [notebook_search_tool] + default_tools
+    # 写作模式首轮（尚无 md 产物）：挂深度研究报告工具，报告由管线生成而非
+    # 模型手工 write_file。续写轮（已有 writing_artifact_path）不挂，保持
+    # 原有「直接编辑该文件」行为。不受 tool_groups/excluded_tools 过滤——
+    # 它是写作模式合约的核心交付手段。
+    if writing_mode and not writing_artifact_path:
+        from deerflow.tools.builtins.deep_research_report_tool import deep_research_report_tool
+
+        default_tools = [*default_tools, deep_research_report_tool]
+        logger.info(
+            "Writing mode first report turn: deep_research_report tool mounted for agent=%s",
+            agent_id,
+        )
 
     # Custom agents are steered ONLY by their own SOUL.md — no generic
     # super-agent system template and no personal memory injection. We branch to