diff --git a/Endpoints/ChatEndpoint.cs b/Endpoints/ChatEndpoint.cs
index fd2618b..c7356f5 100644
--- a/Endpoints/ChatEndpoint.cs
+++ b/Endpoints/ChatEndpoint.cs
@@ -240,7 +240,7 @@ public static class ChatEndpoint
                     return;
                 }
 
-                await openAi.StreamChat(res, ctx.ModelId, ctx.SystemPromptText, messages, ctx.ChainId, workspace: ctx.Workspace, sessionId: sessionId, agentId: ctx.AgentId, apiKeyOverride: openAiKeyOverride, workCt: workCt, sseCt: sseCt);
+                await openAi.StreamChat(res, ctx.ModelId, ctx.SystemPromptText, messages, ctx.ChainId, workspace: ctx.Workspace, sessionId: sessionId, agentId: ctx.AgentId, apiKeyOverride: openAiKeyOverride, useAutoContext: ctx.Agent?.UseAutoContext ?? true, workCt: workCt, sseCt: sseCt);
             }
             else
             {
diff --git a/Endpoints/SessionEndpoint.cs b/Endpoints/SessionEndpoint.cs
index f681547..1d37577 100644
--- a/Endpoints/SessionEndpoint.cs
+++ b/Endpoints/SessionEndpoint.cs
@@ -317,8 +317,54 @@ public static class SessionEndpoint
         var toolChoice = hasTools || !string.IsNullOrEmpty(agent.WebSearchVersion) ? "any" : null;
         var effectiveChoice = toolChoice == "any" ? "any" : "auto";
 
-        // Payload zusammenbauen (Anthropic-Format)
-        var payload = new Dictionary<string, object>
+        // OpenAI-kompatible Provider: Payload im tatsächlichen API-Format anzeigen
+        if (previewProvider)
+        {
+            var (provider, _) = OpenAiService.ParseModelString(modelId);
+            var maxTokens = provider == "minimax" ? 32768 : 16384;
+
+            // Messages konvertieren (Anthropic → OpenAI-Format)
+            var openAiMessages = OpenAiService.BuildApiMessages(systemPromptText, messages);
+
+            // Tools konvertieren (input_schema → function/parameters)
+            var openAiTools = tools.Count > 0
+                ? OpenAiService.ConvertTo_openAiToolDefs(tools.ToArray())
+                : new List<object>();
+
+            var payload = new Dictionary<string, object>
+            {
+                ["model"] = modelId,
+                ["max_tokens"] = maxTokens,
+                ["messages"] = openAiMessages,
+                ["stream"] = true,
+                ["stream_options"] = new { include_usage = true }
+            };
+
+            if (openAiTools.Count > 0)
+            {
+                payload["tools"] = openAiTools;
+                payload["tool_choice"] = "auto";
+            }
+
+            if (provider == "minimax")
+                payload["extra_body"] = new { reasoning_split = true };
+
+            if (provider == "nvidia")
+            {
+                var nvidiaModelId = modelId.Contains(":") ? modelId.Split(':')[1] : modelId;
+                if (nvidiaModelId.Contains("kimi-k2", StringComparison.OrdinalIgnoreCase)
+                    || nvidiaModelId.Contains("qwq", StringComparison.OrdinalIgnoreCase)
+                    || nvidiaModelId.Contains("deepseek-r1", StringComparison.OrdinalIgnoreCase))
+                    payload["chat_template_kwargs"] = new { thinking = true };
+            }
+
+            ctx.Response.ContentType = "application/json; charset=utf-8";
+            await ctx.Response.WriteAsync(JsonSerializer.Serialize(payload, new JsonSerializerOptions { WriteIndented = true }));
+            return;
+        }
+
+        // Anthropic-Format Payload
+        var anthropicPayload = new Dictionary<string, object>
         {
             ["model"] = modelId,
             ["max_tokens"] = 16384,
@@ -327,16 +373,16 @@ public static class SessionEndpoint
         };
 
         if (systemPrompt.Count > 0)
-            payload["system"] = systemPrompt;
+            anthropicPayload["system"] = systemPrompt;
 
         if (tools.Count > 0)
         {
-            payload["tools"] = tools;
-            payload["tool_choice"] = new { type = effectiveChoice };
+            anthropicPayload["tools"] = tools;
+            anthropicPayload["tool_choice"] = new { type = effectiveChoice };
         }
 
         ctx.Response.ContentType = "application/json; charset=utf-8";
-        await ctx.Response.WriteAsync(JsonSerializer.Serialize(payload, new JsonSerializerOptions { WriteIndented = true }));
+        await ctx.Response.WriteAsync(JsonSerializer.Serialize(anthropicPayload, new JsonSerializerOptions { WriteIndented = true }));
     }
 
     private static async Task HandleStreaming(HttpContext ctx, DatabaseService db, StreamingTracker streamingTracker)
diff --git a/Program.cs b/Program.cs
index 556a54e..a01247b 100644
--- a/Program.cs
+++ b/Program.cs
@@ -95,7 +95,7 @@ builder.Services.AddSingleton<AnthropicService>(sp =>
 
 // OpenAI-kompatibler Service (Nvidia etc.)
 builder.Services.AddSingleton<OpenAiService>(sp =>
-    new OpenAiService(sp.GetRequiredService<IHttpClientFactory>(), dbService, sp.GetRequiredService<ToolService>(), serverSettings, dataDir));
+    new OpenAiService(sp.GetRequiredService<IHttpClientFactory>(), dbService, sp.GetRequiredService<ToolService>(), serverSettings, dataDir, vectorSearchService));
 
 // Telegram Bot (pro Session – Token aus Session-Config)
 builder.Services.AddSingleton<TelegramService>(sp =>
diff --git a/Services/AnthropicService.Cli.cs b/Services/AnthropicService.Cli.cs
index 606cc21..fd8a99a 100644
--- a/Services/AnthropicService.Cli.cs
+++ b/Services/AnthropicService.Cli.cs
@@ -92,7 +92,7 @@ public partial class AnthropicService
             {
                 try
                 {
-                    var autoCtx = await BuildAutoContextAsync(messages, chainId, agentId);
+                    var autoCtx = await BuildAutoContextAsync(_vectorSearch, messages, chainId, agentId);
                     if (autoCtx != null)
                     {
                         systemText.AppendLine(autoCtx);
diff --git a/Services/AnthropicService.Streaming.cs b/Services/AnthropicService.Streaming.cs
index 0a2a482..c7f24e9 100644
--- a/Services/AnthropicService.Streaming.cs
+++ b/Services/AnthropicService.Streaming.cs
@@ -62,7 +62,7 @@ public partial class AnthropicService
             {
                 try
                 {
-                    var autoCtx = await BuildAutoContextAsync(messages, chainId, agentId);
+                    var autoCtx = await BuildAutoContextAsync(_vectorSearch, messages, chainId, agentId);
                     if (autoCtx != null)
                     {
                         systemPrompt.Add(new { type = "text", text = autoCtx });
diff --git a/Services/AnthropicService.cs b/Services/AnthropicService.cs
index b7ee941..ae7c851 100644
--- a/Services/AnthropicService.cs
+++ b/Services/AnthropicService.cs
@@ -138,7 +138,7 @@ public partial class AnthropicService
     }
 
     /// <summary>Extrahiert die letzten N User-Texte für Query-Erweiterung.</summary>
-    private static string ExtractRecentUserTexts(List<object> messages, int count = 3, int maxLength = 500)
+    internal static string ExtractRecentUserTexts(List<object> messages, int count = 3, int maxLength = 500)
     {
         var texts = new List<string>();
         for (int i = messages.Count - 1; i >= 0 && texts.Count < count; i--)
@@ -171,21 +171,21 @@ public partial class AnthropicService
 
     /// <summary>
     /// Baut den AutoContext-String aus Embedding-Suche.
-    /// Shared zwischen StreamChat und StreamChatViaCli.
+    /// Shared zwischen StreamChat, StreamChatViaCli und OpenAiService.
     /// </summary>
-    private async Task<string> BuildAutoContextAsync(List<object> messages, string chainId, string agentId)
+    internal static async Task<string> BuildAutoContextAsync(VectorSearchService vectorSearch, List<object> messages, string chainId, string agentId)
     {
-        if (_vectorSearch == null || messages.Count == 0) return null;
+        if (vectorSearch == null || messages.Count == 0) return null;
 
         // Query-Erweiterung: letzte 2-3 User-Messages statt nur die letzte
         var queryText = ExtractRecentUserTexts(messages, count: 3, maxLength: 500);
         if (string.IsNullOrWhiteSpace(queryText)) return null;
 
-        var contextResults = await _vectorSearch.SearchAsync(queryText, agentId, topN: 5, minScore: 0.35f);
+        var contextResults = await vectorSearch.SearchAsync(queryText, agentId, topN: 5, minScore: 0.35f);
         // Note: threshold lowered from 0.45 to 0.35 because hybrid search is now more precise
         contextResults.RemoveAll(r => r.ChainId == chainId || (r.TextPreview?.Length ?? 0) < 20);
 
-        var memoryResults = await _vectorSearch.SearchMemoryFilesAsync(queryText, agentId, topN: 3, minScore: 0.35f);
+        var memoryResults = await vectorSearch.SearchMemoryFilesAsync(queryText, agentId, topN: 3, minScore: 0.35f);
 
         if (contextResults.Count == 0 && memoryResults.Count == 0) return null;
 
diff --git a/Services/ChatService.cs b/Services/ChatService.cs
index 5ef007e..2da4950 100644
--- a/Services/ChatService.cs
+++ b/Services/ChatService.cs
@@ -299,12 +299,12 @@ Für welche Plattform soll die App entwickelt werden? Web, Mobile oder Desktop?
                             ["content"] = new List<object> { new { type = "text", text = msg.Content } }
                         });
                     }
-                    else if (msg.Role == "assistant" && msg.Type == "text" && !string.IsNullOrEmpty(msg.Content))
+                    else if (msg.Role == "assistant" && msg.Type == "text" && !string.IsNullOrWhiteSpace(msg.Content))
                     {
                         history.Add(new Dictionary<string, object>
                         {
                             ["role"] = "assistant",
-                            ["content"] = new List<object> { new { type = "text", text = msg.Content } }
+                            ["content"] = new List<object> { new { type = "text", text = msg.Content.Trim() } }
                         });
                     }
                 }
@@ -383,11 +383,14 @@ Für welche Plattform soll die App entwickelt werden? Web, Mobile oder Desktop?
                     }
                     else if (msg.Role == "assistant" && msg.Type == "text")
                     {
-                        if (!string.IsNullOrEmpty(msg.Content))
+                        // Trim: Nach </think>-Extraktion bleibt oft nur "\n" als Text übrig.
+                        // Whitespace-only Text nicht in die History aufnehmen (verursacht leere Bubbles).
+                        var trimmedContent = msg.Content?.Trim();
+                        if (!string.IsNullOrEmpty(trimmedContent))
                         {
                             // Wenn die letzte Message bereits assistant ist (z.B. mit Thinking-Block
                             // aus derselben Iteration), Text dort anhängen statt neue Message zu starten.
-                            AppendToLastMessage(history, "assistant", new { type = "text", text = msg.Content });
+                            AppendToLastMessage(history, "assistant", new { type = "text", text = trimmedContent });
                         }
                     }
                     else if (msg.Role == "assistant" && msg.Type == "tool_use")
@@ -436,8 +439,11 @@ Für welche Plattform soll die App entwickelt werden? Web, Mobile oder Desktop?
 
                         AppendToLastMessage(history, "assistant", resultEl);
                     }
-                    else if (msg.Role == "user" && msg.Type == "tool_result")
+                    else if ((msg.Role == "user" || msg.Role == "tool") && msg.Type == "tool_result")
                     {
+                        // Anthropic speichert tool_results mit Role="user", OpenAI mit Role="tool".
+                        // Beide werden in Anthropic-Format (user-Message mit tool_result-Block) eingefügt,
+                        // da BuildApiMessages später das Format für OpenAI-Provider konvertiert.
                         var toolResultBlock = new { type = "tool_result", tool_use_id = msg.ToolCallId, content = SseHelper.TruncateToolResult(msg.Content ?? ""), is_error = msg.IsError };
                         AppendToLastMessage(history, "user", toolResultBlock);
                     }
diff --git a/Services/OpenAiService.cs b/Services/OpenAiService.cs
index 5252ddb..97876ce 100644
--- a/Services/OpenAiService.cs
+++ b/Services/OpenAiService.cs
@@ -23,6 +23,7 @@ public class OpenAiService
     private readonly IHttpClientFactory _httpFactory;
     private readonly DatabaseService _db;
     private readonly ToolService _toolService;
+    private readonly VectorSearchService _vectorSearch;
 
     // Provider-Keys (statisch für ChatService.ResolveModelId)
     public static readonly HashSet<string> ProviderKeys = new() { "nvidia", "pollinations", "gemini", "openai", "minimax" };
@@ -36,7 +37,7 @@ public class OpenAiService
     /// Konvertiert Anthropic-Format Tool-Definitionen (input_schema) in OpenAI-Format (function/parameters).
     /// Single source of truth: ToolService.GetToolDefinitions()
     /// </summary>
-    private static List<object> ConvertTo_openAiToolDefs(object[] anthropicTools)
+    public static List<object> ConvertTo_openAiToolDefs(object[] anthropicTools)
     {
         var result = new List<object>();
         foreach (var tool in anthropicTools)
@@ -86,11 +87,12 @@ public class OpenAiService
 
     private readonly ServerSettingsService _settings;
 
-    public OpenAiService(IHttpClientFactory httpFactory, DatabaseService db, ToolService toolService, ServerSettingsService settings, string dataDir)
+    public OpenAiService(IHttpClientFactory httpFactory, DatabaseService db, ToolService toolService, ServerSettingsService settings, string dataDir, VectorSearchService vectorSearch = null)
     {
         _httpFactory = httpFactory;
         _db = db;
         _toolService = toolService;
+        _vectorSearch = vectorSearch;
         _settings = settings;
         _cacheFile = Path.Combine(dataDir, "models_cache.json");
         Providers = new Dictionary<string, (string BaseUrl, string ApiKey)>
@@ -295,6 +297,7 @@ public class OpenAiService
         string agentId = null,
         int maxIterations = 10,
         string apiKeyOverride = null,
+        bool useAutoContext = true,
         CancellationToken workCt = default,
         CancellationToken sseCt = default)
     {
@@ -328,6 +331,32 @@ public class OpenAiService
 
         try
         {
+            // ── Automatische Kontext-Injektion via Embedding-Suche ──
+            if (useAutoContext)
+            {
+                try
+                {
+                    var autoCtx = await AnthropicService.BuildAutoContextAsync(_vectorSearch, messages, chainId, agentId);
+                    if (autoCtx != null)
+                    {
+                        // Auto-Context an System-Prompt anhängen
+                        systemPromptText = (systemPromptText ?? "") + "\n" + autoCtx;
+                        await SseHelper.TryWriteSse(response, "auto_context", new { context = autoCtx }, sseCt);
+                        await _db.SaveMessage(new MessageRecord
+                        {
+                            ChainId = chainId,
+                            Role = "system",
+                            Type = "auto_context",
+                            Content = autoCtx
+                        });
+                    }
+                }
+                catch (Exception ex)
+                {
+                    Console.WriteLine($"[AutoContext/OpenAI] Error: {ex.Message}");
+                }
+            }
+
             var apiMessages = BuildApiMessages(systemPromptText, messages);
 
             // Agentic Loop
@@ -786,8 +815,8 @@ public class OpenAiService
                     });
                 }
 
-                // Text in DB speichern
-                var finalText = fullText.ToString();
+                // Text in DB speichern (Whitespace-only nach </think> ignorieren)
+                var finalText = fullText.ToString().Trim();
                 if (!string.IsNullOrEmpty(finalText))
                 {
                     await _db.SaveMessage(new MessageRecord
@@ -925,6 +954,7 @@ public class OpenAiService
                     {
                         name = tcName,
                         id = tcId,
+                        input = tcArgs.ToString(),
                         result = toolResult,
                         isError
                     }, sseCt);
@@ -1029,7 +1059,32 @@ public class OpenAiService
             Model = ctx.ModelId
         };
 
-        var apiMessages = BuildApiMessages(ctx.SystemPromptText, messages);
+        // ── Automatische Kontext-Injektion via Embedding-Suche ──
+        var systemPromptText = ctx.SystemPromptText;
+        if (ctx.Agent?.UseAutoContext ?? true)
+        {
+            try
+            {
+                var autoCtx = await AnthropicService.BuildAutoContextAsync(_vectorSearch, messages, ctx.ChainId, ctx.AgentId);
+                if (autoCtx != null)
+                {
+                    systemPromptText = (systemPromptText ?? "") + "\n" + autoCtx;
+                    await _db.SaveMessage(new MessageRecord
+                    {
+                        ChainId = ctx.ChainId,
+                        Role = "system",
+                        Type = "auto_context",
+                        Content = autoCtx
+                    });
+                }
+            }
+            catch (Exception ex)
+            {
+                Console.WriteLine($"[AutoContext/OpenAI-NonStreaming] Error: {ex.Message}");
+            }
+        }
+
+        var apiMessages = BuildApiMessages(systemPromptText, messages);
 
         try
         {
@@ -1039,12 +1094,13 @@ public class OpenAiService
                 onIteration?.Invoke(iterations);
 
                 var client = CreateClient(providerConfig.ApiKey);
+                var maxTokens = provider == "minimax" ? 32768 : 16384;
                 var payload = new Dictionary<string, object>
                 {
                     ["model"] = modelId,
                     ["messages"] = apiMessages,
                     ["stream"] = false,
-                    ["max_tokens"] = 16384,
+                    ["max_tokens"] = maxTokens,
                     ["tools"] = _openAiToolDefs,
                     ["tool_choice"] = "auto"
                 };
@@ -1052,6 +1108,9 @@ public class OpenAiService
                 if (provider == "nvidia" && IsNvidiaThinkingModel(modelId))
                     payload["chat_template_kwargs"] = new { thinking = true };
 
+                if (provider == "minimax")
+                    payload["extra_body"] = new { reasoning_split = true };
+
                 var jsonPayload = JsonSerializer.Serialize(payload);
                 var httpContent = new StringContent(jsonPayload, Encoding.UTF8, "application/json");
 
@@ -1364,7 +1423,7 @@ public class OpenAiService
     /// <summary>
     /// Konvertiert Anthropic-Format Messages in OpenAI-kompatibles Format.
     /// </summary>
-    private static List<object> BuildApiMessages(string systemPromptText, List<object> messages)
+    public static List<object> BuildApiMessages(string systemPromptText, List<object> messages)
     {
         var apiMessages = new List<object>();
         if (!string.IsNullOrEmpty(systemPromptText))
diff --git a/wwwroot/js/history.js b/wwwroot/js/history.js
index 63a3d2b..6d4d116 100644
--- a/wwwroot/js/history.js
+++ b/wwwroot/js/history.js
@@ -220,7 +220,7 @@ function _renderMessagesIntoFragment(fragment, msgs, imageMessageIds, opts) {
     // ── Grouped Review/Preparer/Finalizer Bubble (tag-based) ──
     if (msg.tag && (msg.tag === 'review' || msg.tag === 'preparer' || msg.tag === 'finalizer')) {
       // Collect all consecutive messages with the same tag
-      var tagGroup = [msg];
+      const tagGroup = [msg];
       while (_msgIdx + 1 < msgs.length && msgs[_msgIdx + 1].tag === msg.tag) {
         _msgIdx++;
         tagGroup.push(msgs[_msgIdx]);
@@ -250,6 +250,8 @@ function _renderMessagesIntoFragment(fragment, msgs, imageMessageIds, opts) {
           _addBubbleActions(div, { timestamp: msg.createdAt, messageId: msg.id, deletedOn: msg.deletedOn, isMarked: msg.isMarked });
           fragment.appendChild(div);
         } else if (msg.role === 'assistant') {
+          // Whitespace-only Content nicht rendern (z.B. "\n" nach </think>-Extraktion)
+          if (!msg.content || !msg.content.trim()) break;
           const div = _createMessageBubble({ role: 'assistant', timestamp: msg.createdAt, messageId: msg.id, isMarked: msg.isMarked, deletedOn: msg.deletedOn, archived });
           div.innerHTML = renderMarkdown(msg.content);
           parseFluentEmojis(div);
@@ -261,12 +263,12 @@ function _renderMessagesIntoFragment(fragment, msgs, imageMessageIds, opts) {
       }
       case 'auto_context': {
         if (msg.content) {
-          var acDiv = document.createElement('div');
+          const acDiv = document.createElement('div');
           acDiv.className = 'tool-box auto-context-box' + (archived ? ' archived' : '');
-          var acHeader = document.createElement('div');
+          const acHeader = document.createElement('div');
           acHeader.className = 'tool-box-header clickable';
           acHeader.innerHTML = '<span class="tool-name">Auto-Context</span>';
-          var acContent = document.createElement('div');
+          const acContent = document.createElement('div');
           acContent.className = 'auto-context-content markdown';
           acContent.innerHTML = renderMarkdown(msg.content.replace(/<\/?recalled_context>/g, '').trim());
           acContent.hidden = true;
@@ -279,12 +281,12 @@ function _renderMessagesIntoFragment(fragment, msgs, imageMessageIds, opts) {
       }
       case 'thinking': {
         if (msg.role === 'assistant' && msg.content) {
-          var thinkDiv = document.createElement('div');
+          const thinkDiv = document.createElement('div');
           thinkDiv.className = 'tool-box thinking-box' + (archived ? ' archived' : '');
-          var thinkHeader = document.createElement('div');
+          const thinkHeader = document.createElement('div');
           thinkHeader.className = 'tool-box-header clickable';
           thinkHeader.innerHTML = '<span class="tool-name">Denkprozess</span>';
-          var thinkContent = document.createElement('div');
+          const thinkContent = document.createElement('div');
           thinkContent.className = 'thinking-content markdown';
           thinkContent.innerHTML = renderMarkdown(msg.content);
           thinkContent.hidden = true;
@@ -301,17 +303,18 @@ function _renderMessagesIntoFragment(fragment, msgs, imageMessageIds, opts) {
           continue;
         }
         if (!isToolVisible(msg.toolName)) continue;
-        var toolDiv = document.createElement('div');
+        const toolDiv = document.createElement('div');
         toolDiv.className = 'tool-box' + (archived ? ' archived' : '');
         toolDiv.dataset.toolName = msg.toolName;
+        if (msg.toolCallId) toolDiv.dataset.toolCallId = msg.toolCallId;
         renderToolBoxContent(toolDiv, msg.toolName, msg.content, null, false);
         fragment.appendChild(toolDiv);
         break;
       case 'web_search_result': {
         if (!isToolVisible('web_search')) continue;
-        var wsSources = _extractWebSearchSources(msg.content);
+        const wsSources = _extractWebSearchSources(msg.content);
         if (wsSources.length === 0) continue;
-        var wsDiv = document.createElement('div');
+        const wsDiv = document.createElement('div');
         wsDiv.className = 'tool-box' + (archived ? ' archived' : '');
         wsDiv.dataset.toolName = 'web_search';
         renderToolBoxContent(wsDiv, 'web_search', null, null, false, wsSources);
@@ -320,10 +323,18 @@ function _renderMessagesIntoFragment(fragment, msgs, imageMessageIds, opts) {
       }
       case 'tool_result': {
         if (msg._choiceHandled) continue;
-        const prevBoxes = fragment.querySelectorAll('.tool-box');
-        const lastToolBox = prevBoxes[prevBoxes.length - 1];
+        // Passendes Tool-Box per toolCallId finden (falls verfügbar),
+        // sonst Fallback auf letztes Tool-Box (ältere DB-Einträge ohne toolCallId)
+        let lastToolBox = null;
+        if (msg.toolCallId) {
+          lastToolBox = fragment.querySelector('.tool-box[data-tool-call-id="' + msg.toolCallId + '"]');
+        }
+        if (!lastToolBox) {
+          const prevBoxes = fragment.querySelectorAll('.tool-box');
+          lastToolBox = prevBoxes[prevBoxes.length - 1];
+        }
         if (lastToolBox) {
-          var existingResult = lastToolBox.querySelector('.tool-result');
+          const existingResult = lastToolBox.querySelector('.tool-result');
           // todo_write: nachträglich die Todo-Liste rendern (sichtbar)
           if (lastToolBox.dataset.toolName === 'todo_write' && !lastToolBox.querySelector('.todo-list')) {
             lastToolBox.appendChild(buildTodoList(msg.content || '', msg.isError));
@@ -339,7 +350,7 @@ function _renderMessagesIntoFragment(fragment, msgs, imageMessageIds, opts) {
             rd.hidden = true;
             lastToolBox.appendChild(rd);
             // Header clickable machen
-            var hdr = lastToolBox.querySelector('.tool-box-header');
+            const hdr = lastToolBox.querySelector('.tool-box-header');
             if (hdr && !hdr.classList.contains('clickable')) {
               hdr.classList.add('clickable');
               hdr.addEventListener('click', function() { rd.hidden = !rd.hidden; });