cleanup

Author: waleed

apps/docs/content/docs/de/blocks/router.mdx

@@ -105,7 +105,7 @@ Wählen Sie ein KI-Modell für die Routing-Entscheidung:
 **Lokale Modelle**: Jedes Modell, das auf Ollama läuft 
 
 <div className="w-full max-w-2xl mx-auto overflow-hidden rounded-lg">
-  <Video src="router-model-dropdown.mp4" width={500} height={350} />
+  <Video src="models.mp4" width={500} height={350} />
 </div>
 
 **Empfehlung**: Verwenden Sie Modelle mit starken Reasoning-Fähigkeiten wie GPT-4o oder Claude 3.7 Sonnet für genauere Routing-Entscheidungen.

apps/docs/content/docs/de/execution/index.mdx

@@ -96,7 +96,7 @@ Alle öffentlichen Einstiegspunkte—API, Chat, Zeitplan, Webhook und manuelle A
 
 <div className='flex justify-center my-6'>
   <Image
-    src='/static/execution/deployment-versions-light.png'
+    src='/static/execution/deployment-versions.png'
     alt='Tabelle mit Deployment-Versionen'
     width={500}
     height={280}

apps/docs/content/docs/de/tools/generic_webhook.mdx

@@ -5,6 +5,7 @@ description: Empfangen Sie Webhooks von jedem Dienst durch Konfiguration eines
 ---
 
 import { BlockInfoCard } from "@/components/ui/block-info-card"
+import { Image } from '@/components/ui/image'
 
 <BlockInfoCard 
   type="generic_webhook"
@@ -23,6 +24,16 @@ import { BlockInfoCard } from "@/components/ui/block-info-card"
     </svg>`}
 />
 
+<div className="flex justify-center">
+  <Image
+    src="/static/blocks/webhook.png"
+    alt="Webhook Block Configuration"
+    width={500}
+    height={400}
+    className="my-6"
+  />
+</div>
+
 ## Übersicht
 
 Der Generic Webhook-Block ermöglicht es Ihnen, Webhooks von jedem externen Dienst zu empfangen. Dies ist ein flexibler Auslöser, der jede JSON-Nutzlast verarbeiten kann und sich daher ideal für die Integration mit Diensten eignet, die keinen dedizierten Sim-Block haben.

apps/docs/content/docs/de/triggers/schedule.mdx

@@ -11,7 +11,7 @@ Der Zeitplan-Block löst Workflows automatisch nach einem wiederkehrenden Zeitpl
 
 <div className="flex justify-center">
   <Image
-    src="/static/schedule.png"
+    src="/static/blocks/schedule.png"
     alt="Zeitplan-Block"
     width={500}
     height={400}
@@ -45,10 +45,6 @@ Konfigurieren Sie, wann Ihr Workflow ausgeführt wird, mit den Dropdown-Optionen
 
 ## Zeitpläne konfigurieren
 
-<div className="mx-auto w-full overflow-hidden rounded-lg">
-  <Video src="configure-schedule.mp4" width={700} height={450} />
-</div>
-
 Wenn ein Workflow geplant ist:
 - Wird der Zeitplan **aktiv** und zeigt die nächste Ausführungszeit an
 - Klicken Sie auf die Schaltfläche **"Geplant"**, um den Zeitplan zu deaktivieren
@@ -58,7 +54,7 @@ Wenn ein Workflow geplant ist:
 
 <div className="flex justify-center">
   <Image
-    src="/static/schedule-disabled.png"
+    src="/static/blocks/schedule-3.png"
     alt="Deaktivierter Zeitplan"
     width={500}
     height={350}

apps/docs/content/docs/de/triggers/webhook.mdx

@@ -4,6 +4,7 @@ title: Webhooks
 
 import { Callout } from 'fumadocs-ui/components/callout'
 import { Tab, Tabs } from 'fumadocs-ui/components/tabs'
+import { Image } from '@/components/ui/image'
 import { Video } from '@/components/ui/video'
 
 Webhooks ermöglichen externen Diensten die Ausführung von Workflows durch das Senden von HTTP-Anfragen an Ihren Workflow. Sim unterstützt zwei Ansätze für webhook-basierte Auslöser.
@@ -12,8 +13,14 @@ Webhooks ermöglichen externen Diensten die Ausführung von Workflows durch das
 
 Der generische Webhook-Block erstellt einen flexiblen Endpunkt, der beliebige Payloads empfangen und Ihren Workflow auslösen kann:
 
-<div className="mx-auto w-full overflow-hidden rounded-lg">
-  <Video src="webhooks-1.mp4" width={700} height={450} />
+<div className="flex justify-center">
+  <Image
+    src="/static/blocks/webhook.png"
+    alt="Generic Webhook Configuration"
+    width={700}
+    height={450}
+    className="my-6"
+  />
 </div>
 
 ### Funktionsweise

apps/docs/content/docs/en/blocks/agent.mdx

@@ -3,12 +3,10 @@ title: Agent
 ---
 
 import { Callout } from 'fumadocs-ui/components/callout'
-import { Step, Steps } from 'fumadocs-ui/components/steps'
 import { Tab, Tabs } from 'fumadocs-ui/components/tabs'
 import { Image } from '@/components/ui/image'
-import { Video } from '@/components/ui/video'
 
-The Agent block serves as the interface between your workflow and Large Language Models (LLMs). It executes inference requests against various AI providers, processes natural language inputs according to defined instructions, and generates structured or unstructured outputs for downstream consumption.
+The Agent block connects your workflow to Large Language Models (LLMs). It processes natural language inputs, calls external tools, and generates structured or unstructured outputs.
 
 <div className="flex justify-center">
   <Image
@@ -18,26 +16,7 @@ The Agent block serves as the interface between your workflow and Large Language
     height={400}
     className="my-6"
   />
-</div>
-
-## Overview
-
-The Agent block enables you to:
-
-<Steps>
-  <Step>
-    <strong>Process natural language</strong>: Analyze user input and generate contextual responses
-  </Step>
-  <Step>
-    <strong>Execute AI-powered tasks</strong>: Perform content analysis, generation, and decision-making
-  </Step>
-  <Step>
-    <strong>Call external tools</strong>: Access APIs, databases, and services during processing
-  </Step>
-  <Step>
-    <strong>Generate structured output</strong>: Return JSON data that matches your schema requirements
-  </Step>
-</Steps> 
+</div> 
 
 ## Configuration Options
 
@@ -63,71 +42,39 @@ The user prompt represents the primary input data for inference processing. This
 
 The Agent block supports multiple LLM providers through a unified inference interface. Available models include:
 
-**OpenAI Models**: GPT-5, GPT-4o, o1, o3, o4-mini, gpt-4.1 (API-based inference)
-**Anthropic Models**: Claude 3.7 Sonnet (API-based inference)
-**Google Models**: Gemini 2.5 Pro, Gemini 2.0 Flash (API-based inference)
-**Alternative Providers**: Groq, Cerebras, xAI, DeepSeek (API-based inference)
-**Local Deployment**: Ollama-compatible models (self-hosted inference)
-
-<div className="mx-auto w-3/5 overflow-hidden rounded-lg">
-  <Video src="models.mp4" width={500} height={350} />
-</div>
+- **OpenAI**: GPT-5, GPT-4o, o1, o3, o4-mini, gpt-4.1
+- **Anthropic**: Claude 3.7 Sonnet
+- **Google**: Gemini 2.5 Pro, Gemini 2.0 Flash
+- **Other Providers**: Groq, Cerebras, xAI, DeepSeek
+- **Local Models**: Ollama-compatible models
 
 ### Temperature
 
-Control the creativity and randomness of responses:
-
-<Tabs items={['Low (0-0.3)', 'Medium (0.3-0.7)', 'High (0.7-2.0)']}>
-  <Tab>
-    More deterministic, focused responses. Best for factual tasks, customer support, and
-    situations where accuracy is critical.
-  </Tab>
-  <Tab>
-    Balanced creativity and focus. Suitable for general purpose applications that require both
-    accuracy and some creativity.
-  </Tab>
-  <Tab>
-    More creative, varied responses. Ideal for creative writing, brainstorming, and generating
-    diverse ideas.
-  </Tab>
-</Tabs>
-
-<div className="mt-4 text-sm text-gray-600 dark:text-gray-400">
-  The temperature range (0-1 or 0-2) varies depending on the selected model.
-</div>
+Controls response randomness and creativity:
+
+- **Low (0-0.3)**: Deterministic and focused. Best for factual tasks and accuracy.
+- **Medium (0.3-0.7)**: Balanced creativity and focus. Good for general use.
+- **High (0.7-2.0)**: Creative and varied. Ideal for brainstorming and content generation.
 
 ### API Key
 
 Your API key for the selected LLM provider. This is securely stored and used for authentication.
 
 ### Tools
 
-Tools extend the agent's capabilities through external API integrations and service connections. The tool system enables function calling, allowing the agent to execute operations beyond text generation.
-
-**Tool Integration Process**:
-1. Access the Tools configuration section within the Agent block
-2. Select from 60+ pre-built integrations or define custom functions
-3. Configure authentication parameters and operational constraints
-
-<div className="mx-auto w-3/5 overflow-hidden rounded-lg">
-  <Video src="tools.mp4" width={500} height={350} />
-</div>
+Extend agent capabilities with external integrations. Select from 60+ pre-built tools or define custom functions.
 
-**Available Tool Categories**:
+**Available Categories:**
 - **Communication**: Gmail, Slack, Telegram, WhatsApp, Microsoft Teams
 - **Data Sources**: Notion, Google Sheets, Airtable, Supabase, Pinecone
 - **Web Services**: Firecrawl, Google Search, Exa AI, browser automation
-- **Development**: GitHub, Jira, Linear repository and issue management
+- **Development**: GitHub, Jira, Linear
 - **AI Services**: OpenAI, Perplexity, Hugging Face, ElevenLabs
 
-**Tool Execution Control**:
-- **Auto**: Model determines tool invocation based on context and necessity
-- **Required**: Tool must be called during every inference request
-- **None**: Tool definition available but excluded from model context
-
-<div className="mx-auto w-3/5 overflow-hidden rounded-lg">
-  <Video src="granular-tool-control.mp4" width={500} height={350} />
-</div>
+**Execution Modes:**
+- **Auto**: Model decides when to use tools based on context
+- **Required**: Tool must be called in every request
+- **None**: Tool available but not suggested to model
 
 ### Response Format
 
@@ -177,101 +124,29 @@ Use a `Memory` block with a consistent `id` (for example, `chat`) to persist mes
 
 See the [`Memory`](/tools/memory) block reference for details.
 
-## Inputs and Outputs
-
-<Tabs items={['Configuration', 'Variables', 'Results']}>
-  <Tab>
-    <ul className="list-disc space-y-2 pl-6">
-      <li>
-        <strong>System Prompt</strong>: Instructions defining agent behavior and role
-      </li>
-      <li>
-        <strong>User Prompt</strong>: Input text or data to process
-      </li>
-      <li>
-        <strong>Model</strong>: AI model selection (OpenAI, Anthropic, Google, etc.)
-      </li>
-      <li>
-        <strong>Temperature</strong>: Response randomness control (0-2)
-      </li>
-      <li>
-        <strong>Tools</strong>: Array of available tools for function calling
-      </li>
-      <li>
-        <strong>Response Format</strong>: JSON Schema for structured output
-      </li>
-    </ul>
-  </Tab>
-  <Tab>
-    <ul className="list-disc space-y-2 pl-6">
-      <li>
-        <strong>agent.content</strong>: Agent's response text or structured data
-      </li>
-      <li>
-        <strong>agent.tokens</strong>: Token usage statistics object
-      </li>
-      <li>
-        <strong>agent.tool_calls</strong>: Array of tool execution details
-      </li>
-      <li>
-        <strong>agent.cost</strong>: Estimated API call cost (if available)
-      </li>
-    </ul>
-  </Tab>
-  <Tab>
-    <ul className="list-disc space-y-2 pl-6">
-      <li>
-        <strong>Content</strong>: Primary response output from the agent
-      </li>
-      <li>
-        <strong>Metadata</strong>: Usage statistics and execution details
-      </li>
-      <li>
-        <strong>Access</strong>: Available in blocks after the agent
-      </li>
-    </ul>
-  </Tab>
-</Tabs>
+## Outputs
+
+- **`<agent.content>`**: Agent's response text
+- **`<agent.tokens>`**: Token usage statistics
+- **`<agent.tool_calls>`**: Tool execution details
+- **`<agent.cost>`**: Estimated API call cost
 
 ## Example Use Cases
 
-### Customer Support Automation
-
-<div className="mb-4 rounded-md border p-4">
-  <h4 className="font-medium">Scenario: Handle customer inquiries with database access</h4>
-  <ol className="list-decimal pl-5 text-sm">
-    <li>User submits a support ticket via the API block</li>
-    <li>Agent checks orders/subscriptions in Postgres and searches the knowledge base for guidance</li>
-    <li>If escalation is needed, the Agent creates a Linear issue with relevant context</li>
-    <li>Agent drafts a clear email reply</li>
-    <li>Gmail sends the reply to the customer</li>
-    <li>Conversation is saved to Memory to maintain history for future messages</li>
-  </ol>
-</div>
-
-### Multi-Model Content Analysis
-
-<div className="mb-4 rounded-md border p-4">
-  <h4 className="font-medium">Scenario: Analyze content with different AI models</h4>
-  <ol className="list-decimal pl-5 text-sm">
-    <li>Function block processes uploaded document</li>
-    <li>Agent with GPT-4o performs technical analysis</li>
-    <li>Agent with Claude analyzes sentiment and tone</li>
-    <li>Function block combines results for final report</li>
-  </ol>
-</div>
-
-### Tool-Powered Research Assistant
-
-<div className="mb-4 rounded-md border p-4">
-  <h4 className="font-medium">Scenario: Research assistant with web search and document access</h4>
-  <ol className="list-decimal pl-5 text-sm">
-    <li>User query received via input</li>
-    <li>Agent searches web using Google Search tool</li>
-    <li>Agent accesses Notion database for internal docs</li>
-    <li>Agent compiles comprehensive research report</li>
-  </ol>
-</div>
+**Customer Support Automation** - Handle inquiries with database and tool access
+```
+API (Ticket) → Agent (Postgres, KB, Linear) → Gmail (Reply) → Memory (Save)
+```
+
+**Multi-Model Content Analysis** - Analyze content with different AI models
+```
+Function (Process) → Agent (GPT-4o Technical) → Agent (Claude Sentiment) → Function (Report)
+```
+
+**Tool-Powered Research Assistant** - Research with web search and document access
+```
+Input → Agent (Google Search, Notion) → Function (Compile Report)
+```
 
 ## Best Practices