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

Open chat interface

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

The interface automatically saves all conversations, allowing you to resume any session at any time.

Step 2: Managing Sessions

Sessions list

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

Filters and advanced search

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

Multiple selection and batch actions

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

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

Start new chat

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

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

How to edit session data

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

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

Text messages

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"

Media upload

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

Audio recording

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

Debug functionality

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

Sequential agents debug

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)

Workflows debug

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

Loop Agents debug

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

Function calls debug

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": "[e-mail]", "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

Efficient tag system

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

Advanced search

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

History analysis

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

Optimizing conversations

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

Data management and privacy

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

Teamwork

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

Common problems

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

Configure Agents

Create and configure agents for your conversations

Knowledge Base

Configure knowledge base for more precise agents

Workflow Agents

Understand debugging of complex workflows

Settings

Configure tools and integrations

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💬 Now you master the chat interface! Use these features to have productive conversations, debug complex agents, and organize your history efficiently!