> ## Documentation Index > Fetch the complete documentation index at: https://docs.evo-ai.co/llms.txt > Use this file to discover all available pages before exploring further. # Chat Interface > Complete interface to chat with your agents, track real-time executions, and manage chat sessions ## Overview The **Chat Interface** is where you interact directly with your AI agents. More than a simple conversation, it's a complete tool that allows you to: * 💬 **Chat** with any created agent * 🔍 **Track executions** in real-time (debug) * 📁 **Manage sessions** with intelligent organization * 🎤 **Send audio, text, and media** for rich interaction * 🏷️ **Organize conversations** with tags and filters **Real-Time Debug**: Even in complex agents (Sequential, Workflow, Loop), you see each execution step, function calls, and returns in real-time, facilitating debugging and optimization. ## Navigating the Chat Interface ### Step 1: Accessing Chat 1. In the main Evo AI menu, click **"Chat"** 2. You'll see an interface divided into two parts: * **Left panel**: List of all chat sessions * **Main area**: Active chat or initial screen 3. To start, you can create a new chat or open an existing session Chat Sessions List

The interface automatically saves all conversations, allowing you to resume any session at any time. ### Step 2: Managing Sessions **Left panel - All your conversations:** Each session shows: * **Session name** (editable) * **Agent used** * **Last message** or summary * **Conversation date** * **Tags** for organization **List functionalities:** * 🔍 **Quick search** by name, description, or content * 🏷️ **Filter by tags** to organize by project/type * 🤖 **Filter by agent** to see specific conversations * 📅 **Sort** by date, name, or recent activity * ✅ **Multiple selection** for batch actions **How to use filters:** **By Agent:** ``` "Agent" Dropdown: Select specific agent Result: Shows only conversations with that agent Example: Filter by "Sales Assistant" → Lists only chats with this agent ``` **By Tags:** ``` "Tags" Field: Type specific tag Result: Shows sessions marked with that tag Example: Tags "vip-client", "support", "sales" → Organization by service category ``` **By Text:** ``` "Search" Field: Type keyword Result: Searches in names, descriptions, and content Example: Search for "notebook" → Finds all conversations about notebooks ``` **Filter combination:** * Use multiple filters simultaneously * Progressively refine results * Mentally save frequent filters **How to select multiple sessions:** 1. **Check the checkboxes** next to each desired session 2. **Action bar** appears at the top when there's selection 3. **Select all**: General checkbox to mark all visible Selected Sessions

**Available batch actions:** **🗑️ Delete Selected:** * Removes multiple sessions at once * Confirms before permanently deleting * Useful for periodic cleanup **🏷️ Add Tags:** * Applies tags to multiple sessions * Organizes conversations by project/client * Facilitates future filters **📁 Move to Folder:** (if available) * Organizes into categories * Separates by client, project, type **💡 Tip:** Use multiple selection to organize old sessions, apply tags in batch, or clean up test conversations. ### Step 3: Creating New Session **How to create a new conversation:** 1. Click **"New Chat"** in the left panel 2. **Select the agent** you want to use in the conversation 3. **Configure session name** (optional, can change later) 4. **Add tags** for organization (optional) 5. Click **"Create"** to start Creating New Chat

**Initial settings:** ``` Session Name: "Customer Service John - Notebooks" Agent: sales_assistant_tech Tags: client-john, sales, notebook, 2024 Result: Organized chat easy to find later ``` **Best practices for names:** * Use consistent pattern: "Type - Client - Subject" * Include date if relevant: "Support-15Jan-LoginProblem" * Be descriptive: "Demo-Product-Client-ABC" * Avoid generic names: "Chat 1", "Test" ### Step 4: Editing Session Information **Accessing edit options:** 1. **In sessions list**: Click the **"Edit"** icon (✏️) next to the session 2. **Or inside chat**: Session options menu 3. **Edit form** opens with editable fields Editing Chat Data

**Editable fields:** **Session Name:** * Change to something more descriptive * Useful when conversation evolved to different subject * Helps in future searches **Description:** * Summary of what was discussed * Important conversation details * Results or next steps **Tags:** * Add new tags as conversation evolves * Remove irrelevant tags * Use status tags: "resolved", "pending", "follow-up" **Agent:** (if allowed) * Switch agent during conversation * Useful to escalate to specialist **Organization example:** ``` Before: "Chat 1" After: "Client John - Gaming Notebook Purchase" Tags: client-john, sales, notebook, gaming, budget-5k Description: "Client interested in gaming notebook up to $5,000. Showed interest in model X. Awaiting proposal." ``` ## Chatting with Agents ### Input Types **Most common standard input:** * Type your message in the text box * Press **Enter** to send * **Shift + Enter** for line break * Supports long text and simple formatting **Tips for better results:** ``` ✅ Be specific: "Need notebook for graphic design, budget $4000" ❌ Too generic: "I want a computer" ✅ Provide context: "I'm a photographer and need to edit 50MB RAW files" ❌ No context: "What's better?" ✅ Include preferences: "Prefer national brands with good warranty" ❌ No direction: "Help me choose" ``` **Attach files and images:** **Supported formats:** * 🖼️ **Images**: PNG, JPG, JPEG, GIF, WebP * 📄 **Documents**: PDF, DOC, DOCX, TXT * 📊 **Spreadsheets**: XLS, XLSX, CSV * 🎵 **Audio**: MP3, WAV, M4A, OGG **How to send:** 1. Click the **"Attach"** icon (📎) 2. **Select file** from your computer 3. **Or drag and drop** directly into chat 4. **Wait for upload** and processing 5. **Add explanatory text** if needed **Use cases:** ``` 📷 Product photo with defect for support 📄 Contract for legal analysis 📊 Data spreadsheet for analysis 🖼️ Design for feedback ``` **Models that support media:** * GPT-4 Vision: Image analysis * Gemini Pro Vision: Complete multimodal * Claude 3: Documents and images **Voice functionality:** **How to record:** 1. Click the **"Microphone"** icon (🎤) 2. **Allow access** to microphone if requested 3. **Speak your message** - indicator shows active recording 4. **Click again** to stop recording 5. **Audio is sent** automatically **⚠️ Important - For audio to work:** **Option 1: Model that accepts audio** ``` Use native models with audio support: - Whisper (OpenAI) - specialized in transcription - Gemini Pro - multimodal with audio - Provider-specific models ``` **Option 2: Speech to Text Tool** ``` Configure speech_to_text tool in agent: 1. Go to LLM agent settings 2. "Tools" section → Add "Speech to Text" 3. Configure provider (OpenAI, Groq, Google) 4. Agent converts audio → text → processes ``` **Audio quality:** * Silent environment for better transcription * Speak clearly and at normal pace * Avoid background noise * Test with short phrases first **Use cases:** ``` 🎤 Report detailed problem by voice 🗣️ Make complex request by speaking 🎙️ Interview or feedback collection 📞 Simulate phone service ``` ### Debug and Real-Time Tracking **Chat as debug tool:** The chat interface isn't just for talking - it's a **complete debug tool** that shows everything happening "behind the scenes" of agent execution. **What you see in real-time:** * 🔄 **Each execution step** in Sequential agents * 🌐 **Calls to APIs** and external tools * 🤖 **LLM decisions** and reasoning * ⚙️ **Workflow execution** step by step * 🔁 **Loop Agent iterations** * 📞 **Function calls** and returns * ⏱️ **Execution times** of each stage **Following sequential execution:** **Example: Sales agent with 3 sub-agents** ``` 🚀 Starting execution... 📍 Step 1: Lead Qualifier ├── Analyzing customer needs... ├── Identifying budget: $5,000 ├── Detecting urgency: Medium └── ✅ Qualification completed 📍 Step 2: Product Demonstrator ├── Consulting product database... ├── Filtering by budget $5,000 ├── Selecting 3 suitable options └── ✅ Demonstration completed 📍 Step 3: Closing Specialist ├── Analyzing qualification data ├── Creating personalized proposal ├── Adding sense of urgency └── ✅ Final proposal generated 🎯 Execution completed in 8.2s ``` **Visible information:** * Status of each sub-agent * Time spent on each stage * Data passed between agents * Errors or failures (if any) **Following complex flows:** **Example: Proposal approval workflow** ``` 🔄 Workflow: Proposal Analysis 🟦 Node: Receive Data ├── Input: Commercial proposal received ├── Validating format... └── ✅ Valid data 🟦 Node: Reviewer Agent ├── Executing content analysis... ├── Checking values and terms... ├── Quality score: 8.5/10 └── ✅ Review approved 🔷 Node: Condition (Score > 8.0?) ├── Evaluating: 8.5 > 8.0 ├── Result: TRUE └── ➡️ Proceeding to approval 🟦 Node: Automatic Approval ├── Generating final contract... ├── Applying templates... └── ✅ Contract generated ⏸️ Node: Delay (5 seconds) ├── Waiting for processing... └── ✅ Delay completed 💬 Node: Final Message └── ✅ "Proposal approved and contract sent!" ``` **Details shown:** * Type of each node (Agent, Condition, Message, Delay) * Data flowing between nodes * Conditional decisions * Delay times * Errors at any point **Following iterations:** **Example: Content refinement loop** ``` 🔁 Loop Agent: Content Refiner === ITERATION 1 === 📝 Generator: Creating initial content ├── Based on user request ├── Generating first version... └── ✅ Content v1 created 📊 Analyzer: Evaluating quality ├── Clarity score: 6.5/10 ├── Persuasion score: 7.0/10 ├── Overall score: 6.8/10 └── ❌ Below threshold (8.0) === ITERATION 2 === 📝 Generator: Refining content ├── Applying analysis feedback ├── Improving clarity... └── ✅ Content v2 created 📊 Analyzer: Re-evaluating ├── Clarity score: 8.2/10 ├── Persuasion score: 8.5/10 ├── Overall score: 8.4/10 └── ✅ Above threshold! 🛑 Checker: Using exit_loop() └── ✅ Loop finished 📋 Finalizer: Generating final response └── ✅ Result consolidated ``` **Complete visibility:** * Current iteration number * Progress of each sub-agent * Quality metrics * Stop conditions * Exact moment of exit\_loop **Monitoring tools and APIs:** **Example: Agent using external tools** ``` 🔧 Function Call: load_knowledge ├── Parameters: {"query": "notebook warranty", "tags": ["support"]} ├── Searching knowledge base... ├── Result: 3 chunks found └── ✅ Knowledge loaded 🔧 Function Call: get_product_info ├── Parameters: {"product_id": "NB001", "include_price": true} ├── Consulting products API... ├── Status: 200 OK ├── Data: {"name": "Notebook Pro", "price": 4500, "stock": 12} └── ✅ Product found 🔧 Function Call: send_email ├── Parameters: {"to": "client@email.com", "subject": "Proposal"} ├── Connecting to SMTP server... ├── Status: Email sent └── ✅ Communication completed ``` **Detailed information:** * Name of called function * Sent parameters * Execution status * Returned data * Response time * Errors (if any) **Debugging advantages:** * Identifies performance bottlenecks * Detects integration failures * Validates sent parameters * Confirms expected returns ## Advanced Session Management ### Organization and Productivity **Organization strategies:** **By client:** ``` client-john, client-company-abc, client-vip → Facilitates finding specific conversations ``` **By service type:** ``` sales, support, onboarding, demo, follow-up → Organizes by activity category ``` **By status:** ``` pending, resolved, awaiting-client, escalated → Controls service workflow ``` **By product/service:** ``` notebook, smartphone, software, consulting → Segments by business area ``` **By priority:** ``` urgent, high, medium, low → Prioritizes service ``` **Useful combinations:** ``` Example: "client-vip + sales + notebook + urgent" → VIP client with urgent notebook demand ``` **Efficient search techniques:** **Content search:** ``` Type keywords of what was discussed: "login problem", "budget 5000", "warranty" ``` **Results search:** ``` "proposal sent", "problem resolved", "purchase completed" ``` **Date search:** ``` Use date filters for specific period "last week", "December 2024" ``` **Filter combination:** ``` Agent: sales_assistant Tag: client-vip Search: "gaming notebook" → VIP conversations about gaming notebooks ``` **Extracting insights from conversations:** **Service patterns:** * Which agents are most used? * What types of questions are most common? * What time has highest activity? **Agent performance:** * Conversations that generated sales * Successfully resolved problems * Average resolution time **Client evolution:** * Complete interaction history * Preferences identified over time * Products or services of interest **Opportunities:** * Clients with unexplored potential * Recurring problems to improve * Agents that need adjustments ## Best Practices **For better results:** **Preparation:** * Choose the right agent for each conversation type * Configure descriptive name and tags from the start * Have clear context of what you want to achieve **During conversation:** * Be specific and provide adequate context * Use media when appropriate (images, documents) * Follow debug to understand reasoning * Ask follow-up questions to deepen **After conversation:** * Update tags based on what was discussed * Add description summarizing results * Mark status (resolved, pending, etc.) * Document next steps if necessary **Important precautions:** **Sensitive information:** * Avoid sharing unnecessary personal data * Use codes or initials when possible * Consider privacy implications **Secure organization:** * Use tags that don't expose confidential information * Be judicious with session names * Periodically clean old conversations * Configure appropriate data retention **Backup and continuity:** * Export important conversations if necessary * Document valuable insights externally * Maintain history of important decisions **Sharing knowledge:** **Standardization:** * Use consistent naming conventions * Define tag patterns for the team * Document organization best practices **Collaboration:** * Share relevant sessions when appropriate * Use status tags for coordination * Document learnings for reuse **Training:** * Use example conversations to train new users * Analyze successful conversations to replicate * Identify success patterns ## Troubleshooting **1. Audio doesn't work:** * ✅ Check microphone permissions in browser * ✅ Configure speech\_to\_text tool in agent if model doesn't support native audio * ✅ Test with short phrase first * ✅ Check if speech\_to\_text provider is configured **2. Media upload fails:** * ✅ Check file size (limits by type) * ✅ Confirm supported format * ✅ Test internet connection * ✅ Try smaller file to test **3. Agent doesn't respond as expected:** * ✅ Use debug functionality to understand execution * ✅ Check if agent is configured correctly * ✅ Be more specific in question * ✅ Provide more context about what you need **4. Search doesn't find conversations:** * ✅ Try different keywords * ✅ Check active filters * ✅ Use tags instead of text search * ✅ Adjust date period if applicable **5. Slow performance:** * ✅ Limit number of loaded sessions * ✅ Use filters to reduce list * ✅ Clean up old sessions * ✅ Check internet connection ## Next Steps Create and configure agents for your conversations Configure knowledge base for more precise agents Understand debugging of complex workflows Configure tools and integrations *** 💬 **Now you master the chat interface!** Use these features to have productive conversations, debug complex agents, and organize your history efficiently!