feat(agent): Gradio-powered LangGraph agent chat with streaming, tool calls, file upload, conversation persistence

- Gradio 5.50.0 ChatInterface with type='messages' streaming
- LangGraph create_react_agent with InMemorySaver checkpointer
- 4 @tool functions: search_dashboards, get_health_summary, list_environments, get_task_status
- Structured ChatMessage metadata (7 discriminator types: stream_token, tool_start/end/error, confirm_required, confirm_resolved, error)
- HITL resume via second submit() with interrupt_before/Command
- Dual-identity RBAC: service JWT + user JWT for tool calls
- File upload (10 MB limit, pdfplumber/xlsx/JSON parser)
- Conversation persistence via POST /api/agent/conversations/save
- REST API: list, history, archive conversations; multi-tab gate; LLM config
- LLM provider selection via Admin -> LLM Settings (assistant_planner_provider)
- Svelte 5 AgentChatModel with stream event queue, dedup, stream_status watcher
- MarkdownRenderer using svelte-markdown with semantic Tailwind tokens
- ToolCallCard (3 states: executing/completed/failed)
- ConversationList with search, date grouping, infinite scroll
- ConnectionIndicator with Gradio health status
- /agent route with two-column layout
- Vite proxy /api/agent/gradio -> Gradio SSE
- Fixed: not_() SQLAlchemy operator, route collision with _admin_routes
- Fixed: conversation_id -> id normalization, .pyc cache staleness
- Fixed: event.data array parsing (Gradio returns [jsonStr, null])
- Requirements pinned: gradio==5.50.0, pydantic>=2.7,<=2.12.3

backend/src/agent/langgraph_setup.py

@@ -0,0 +1,75 @@
+# backend/src/agent/langgraph_setup.py
+# #region AgentChat.LangGraph.Setup [C:4] [TYPE Module] [SEMANTICS agent-chat,langgraph,agent]
+# @defgroup AgentChat LangGraph agent setup: create_react_agent with PostgresSaver.
+# @PRE LLM provider configured. Priority: 1) llm_config param 2) env vars LLM_API_KEY/LLM_BASE_URL/LLM_MODEL.
+# @POST Compiled StateGraph ready for astream_events().
+# @SIDE_EFFECT Initializes checkpointer and message history tables on first call.
+# @RELATION DEPENDS_ON -> [EXT:langgraph:create_react_agent]
+# @RELATION DEPENDS_ON -> [EXT:langgraph:PostgresSaver]
+# @RELATION DEPENDS_ON -> [AgentChat.Tools]
+# @RATIONALE LangGraph create_react_agent provides built-in tool calling + checkpointing + interrupt/resume.
+#            RunnableWithMessageHistory wrapper is NOT used — PostgresSaver handles history natively.
+
+import os
+
+from langchain_openai import ChatOpenAI
+from langgraph.checkpoint.memory import InMemorySaver
+from langgraph.prebuilt import create_react_agent
+
+# ── Dangerous tool names — interrupt_before pauses execution at these nodes ──
+# ── Dangerous tool names — interrupt_before pauses execution at these nodes ──
+# These tools don't exist yet in the current tool set. When dangerous tools are
+# added (deploy, migrate, commit, maintenance), add their names here.
+DANGEROUS_TOOLS: list[str] = []
+
+# ── LLM config cache ────────────────────────────────────────────
+_llm_config: dict | None = None
+_llm_config_ttl: int = 300  # 5 min
+
+
+def configure_from_api(llm_config: dict) -> None:
+    """Update LLM config from FastAPI response. Called at startup."""
+    global _llm_config
+    _llm_config = llm_config
+
+
+def create_agent(tools: list):
+    """Create the LangGraph agent with checkpointer and message history.
+
+    LLM configuration priority:
+        1. llm_config from configure_from_api() (fetched from FastAPI /api/agent/llm-config)
+        2. Environment vars: LLM_API_KEY, LLM_BASE_URL, LLM_MODEL
+        3. Defaults: gpt-4o, https://api.openai.com/v1
+
+    Returns a RunnableWithMessageHistory wrapper ready for astream_events().
+    The graph is compiled with interrupt_before=DANGEROUS_TOOLS to enable HITL.
+    """
+    if _llm_config and _llm_config.get("configured"):
+        api_key = _llm_config["api_key"]
+        base_url = _llm_config.get("base_url") or "https://api.openai.com/v1"
+        model = _llm_config.get("default_model") or "gpt-4o-mini"
+    else:
+        api_key = os.getenv("LLM_API_KEY")
+        base_url = os.getenv("LLM_BASE_URL", "https://api.openai.com/v1")
+        model = os.getenv("LLM_MODEL", "gpt-4o")
+
+    llm = ChatOpenAI(
+        model=model,
+        base_url=base_url,
+        api_key=api_key,
+        temperature=0,
+    )
+
+    # Checkpointer — InMemorySaver for development (no persistence across restarts).
+    # TODO: Replace with AsyncPostgresSaver when langgraph-checkpoint-postgres supports it.
+    checkpointer = InMemorySaver()
+
+    graph = create_react_agent(
+        model=llm,
+        tools=tools,
+        checkpointer=checkpointer,
+        interrupt_before=DANGEROUS_TOOLS,
+    )
+
+    return graph
+# #endregion AgentChat.LangGraph.Setup