Creating Conversations
This guide explains how to create conversation-type tasks in the Wegent frontend to interact with AI agents.
π Table of Contentsβ
- What is a Conversation
- Creating a Conversation
- Conversation Interface
- Advanced Features
- Best Practices
- Common Issues
- Related Resources
π¬ What is a Conversationβ
In Wegent, a conversation is the primary way users interact with AI agents. Each conversation creates a Task that records the complete conversation history.
Core Concept:
Conversation = User Message + Agent + Context + Conversation History
Conversation Componentsβ
| Component | Description | Example |
|---|---|---|
| Message | User's question or instruction | "Help me analyze this report" |
| Agent | AI team executing the task | Data Analysis Agent |
| Context | Attached knowledge bases, files, etc. | Project docs, data files |
| Status | Conversation execution status | PENDING β RUNNING β COMPLETED |
π Creating a Conversationβ
Step 1: Navigate to Chat Pageβ
- Click "Chat" in the left navigation bar
- The system displays the conversation list and input area
Step 2: Select an Agentβ
Above the input area, click the agent selector:
- Click the agent dropdown - Shows available agents
- Select an appropriate agent
Step 3: Configure Conversation Options (Optional)β
Configure the following options as needed:
Model Selectionβ
Click the model selector to override the agent's default model:
- Select model: Choose from the dropdown list
- Force override: When enabled, uses your selected model even if the agent has a configured model
Code Repository (Code Type)β
If you selected a Code type agent:
- Select repository: Click the repository selector, choose the target repository
- Select branch: Choose the branch to work on
Knowledge Base Contextβ
Click the context button to add knowledge bases:
- Click the "#" button - Opens the context selector
- Select knowledge bases - Check the ones to add
- Confirm selection - Knowledge bases appear as tags
File Attachmentsβ
Click the attachment button to upload files:
- Click the attachment icon - Opens file selector
- Select files - Supports images, documents, code files
- Wait for upload - Files show preview after upload
Some system recommendation presets can add attachments automatically. Preset attachments are shown in the input attachment area and can be removed before sending. If you upload your own attachment, Wegent removes the attachments added by the current preset and sends only the files you uploaded.
Skill Selectionβ
If the agent supports skills:
- Click the skill button - Opens skill selector
- Select skills - Check the needed skills
- Or use "/" command - Type
/in the input box to trigger skill selection
Step 4: Enter Message and Sendβ
- Type your message in the input box - Describe your needs
- Press Enter to send - Or click the send button
- Wait for response - Agent starts processing and streams results
π₯οΈ Conversation Interfaceβ
Input Areaβ
The input area contains the following controls:
| Control | Function | Location |
|---|---|---|
| Agent Selector | Select the agent for the task | Above input box |
| Model Selector | Override default model | Control bar |
| Context Button | Add knowledge bases | Control bar |
| Attachment Button | Upload files | Control bar |
| Skill Button | Select skills | Control bar |
| Follow-up Button | Enable smart follow-up mode | Control bar |
| Cross-Validation Button | Enable AI cross-validation | Control bar |
| Send Button | Send message | Right side of input box |
Message Areaβ
The message area displays conversation history:
- User Messages: Your sent messages, displayed on the right
- AI Responses: Agent's replies, displayed on the left
- Text content
- Code blocks (with syntax highlighting)
- Thinking process (if enabled)
- Tool call indicators
Sidebarβ
The left sidebar displays:
- Conversation List: All historical conversations
- Search Box: Search conversation content
- New Chat Button: Start a new conversation
β‘ Advanced Featuresβ
Per-Conversation Model Selectionβ
Select a different model for a single conversation without modifying agent configuration:
- Click the model selector - In the input control bar
- Select a model - From the available model list
- Enable force override - Ensures the selected model is used
Use Cases:
- Use a more powerful model for complex tasks
- Use a faster/cheaper model for simple queries
- Test different models' effectiveness
Smart Follow-up Modeβ
Enable smart follow-up mode for the agent to confirm requirements before execution:
- Click the follow-up icon (π¬) - In the input control bar
- Agent asks questions first - Confirms your requirement details
- After answering questions - Agent starts executing the task
π For detailed information, see Smart Follow-up Mode Guide
AI Cross-Validationβ
Enable AI cross-validation to have another AI model verify and improve responses:
- Click the cross-validation icon (β) - In the input control bar
- Select validation model - Choose from the popup dialog
- View evaluation results - Scores and improvement suggestions appear after agent responds
- Apply improvements (optional) - Click "Apply" button to adopt the improved version
π For detailed information, see AI Cross-Validation Guide
File Attachmentsβ
Upload files to provide context:
- Click the attachment button (π)
- Select files - Supports multiple formats
- Supported formats: Images, PDF, Word, code files
- Paste upload: Directly paste images from clipboard
Knowledge Base Contextβ
Add knowledge bases to enhance agent capabilities:
- Click the context selector
- Select knowledge bases - Multiple selection supported
- Agent searches knowledge bases - Provides more accurate answers
Skill Selectionβ
Add additional capabilities to the agent:
- Click the skill button - Opens skill selector
- Select skills - Check the needed skills
- Or type "/" command - Quick skill selection
- Skills load on-demand - Dynamically loaded during execution
β¨ Best Practicesβ
1. Writing Effective Promptsβ
β Be Specific and Clearβ
- Clearly describe your needs
- Provide necessary background information
- Specify expected output format
- Include acceptance criteria
β Avoid Vague Requestsβ
- Avoid overly brief descriptions
- Avoid requests lacking context
- Avoid asking too many things at once
2. Choosing the Right Agentβ
3. Task Granularityβ
Recommended granularity:
- Small task: Single clear objective
- Medium task: Contains a few related steps
- Large task: Split into multiple smaller tasks
4. Providing Sufficient Contextβ
- Upload relevant files
- Add knowledge bases
- Reference previous conversation content
- Specify technical constraints or preferences
5. Leveraging Conversation Historyβ
- Continue conversations for iterative refinement
- Reference previous messages for context
β οΈ Common Issuesβ
Q1: Conversation stuck in PENDING status?β
Possible reasons:
- Agent is unavailable
- System resources are limited
- Repository configuration error
Solutions:
- Check agent status in Settings β Agents
- Verify repository access permissions
- Try selecting a different agent
Q2: Agent response is incomplete?β
Solutions:
- Click "Continue" to resume generation
- Select a model with larger context window
- Split complex tasks into smaller parts
Q3: How to stop a running conversation?β
Method:
- Click the "Stop" button in the input area
- Agent stops processing
- You can continue the conversation or start a new task
Q4: How to retry a failed conversation?β
Method:
- Click the "Retry" button on the failed message
- Optionally modify the message before retrying
- Agent will attempt the task again
Q5: How to share a conversation?β
Method:
- Click the share button in the message area
- Copy the generated link
- Share with team members (requires access permissions)
Q6: How to export conversation history?β
Method:
- Click the export button in the task menu
- Choose export format (Markdown, JSON)
- Download the conversation history
π Related Resourcesβ
Prerequisitesβ
- Agent Settings - Configure agents and bots
- Configuring Models - Set up AI models
- Configuring Shells - Configure execution environments
Reference Documentationβ
- Core Concepts - Understand Wegent concepts
- Collaboration Models - Multi-agent collaboration
Detailed Feature Documentationβ
For more details on advanced features, see:
- Smart Follow-up Mode Guide - Let agent confirm requirements before execution
- AI Cross-Validation Guide - Use another model to verify and improve responses
- IM Channel Integration - Integrate enterprise IM
π¬ Get Helpβ
Need assistance?
- π Check FAQ
- π Submit GitHub Issue
- π¬ Join community discussions
Start your first conversation and let AI agents work for you! π