> ## Documentation Index
> Fetch the complete documentation index at: https://docs.artifact.chat/llms.txt
> Use this file to discover all available pages before exploring further.

# Message Threading

> Understanding conversation threading in Artifact Chat

Artifact Chat uses a threading model to organize conversations and maintain context across interactions. This page explains how threading works and how to effectively use it.

## What is Threading?

In Artifact Chat, a thread represents a persistent conversation with an AI agent. Threads have the following characteristics:

1. **Persistence**: Threads are saved and can be resumed later
2. **Context**: Threads maintain conversation history for context
3. **Organization**: Threads group related messages together
4. **Specialization**: Threads can be associated with specific agents or tasks

<Note>
  Threads are designed to help organize work into logical units, similar to
  discussions in a project management tool.
</Note>

## Thread Components

A thread in Artifact Chat consists of:

* **Thread ID**: Unique identifier for the thread
* **Thread Title**: Descriptive name for the conversation
* **Agent Type**: The AI agent associated with the thread
* **Created At**: Timestamp of thread creation
* **Updated At**: Timestamp of last message
* **Messages**: Sequence of user messages and AI responses

## Thread Lifecycle

Threads follow a standard lifecycle:

1. **Creation**: A new thread is created when starting a new conversation
2. **Active Use**: Messages are added to the thread during conversation
3. **Deletion**: Threads can be deleted when no longer needed

## Context Management

Threading provides several benefits for context management:

### Context Retention

Each thread maintains its conversation history, allowing the AI to reference previous messages. This provides:

* **Continuity**: Follow-up questions work naturally
* **Reference**: The AI can refer to previous code snippets or explanations
* **Evolution**: Ideas can develop over multiple exchanges

### Context Limitations

There are some limitations to be aware of:

* **Context Window**: Claude 3.5 has a finite context window (approximately 200,000 tokens)
* **Relevance Decay**: Very long threads may have older messages that become less relevant
* **Performance**: Extremely long threads may impact performance

<Warning>
  For very long projects, consider creating multiple focused threads rather than
  a single massive thread.
</Warning>

## Thread Organization

Artifact Chat provides several ways to organize threads:

### Thread Naming

Threads can be named descriptively to indicate their purpose:

* **Feature-based**: "User Authentication Implementation"
* **Task-based**: "Debugging Memory Leak"
* **Project-based**: "E-commerce Checkout Flow"

### Thread Filtering

Threads can be filtered by:

* Creation date
* Last updated date
* Agent type
* Custom tags

### Thread Deletion

Delete completed threads to reduce clutter and keep your project organized.

## Thread Best Practices

To get the most out of Artifact Chat's threading system:

1. **Create focused threads**: Each thread should address a specific task or topic
2. **Use descriptive names**: Thread names should clearly indicate the purpose
3. **Maintain reasonable length**: Split very large tasks into multiple threads
4. **Provide context**: When starting a new thread, provide relevant project context
5. **Delete completed threads**: Keep your project organized

## Thread Sharing and Collaboration

Threads can be shared with team members by:

1. **Exporting**: Threads can be exported as Markdown or JSON
2. **Linking**: Direct links to threads can be shared
3. **Publishing**: Threads can be published as read-only references

## Example Thread Structure

A typical development thread might follow this pattern:

1. **Initial request**: User describes the feature to implement
2. **Requirements clarification**: AI asks questions to understand requirements
3. **Implementation plan**: AI proposes an approach
4. **Code generation**: AI provides implementation code
5. **Testing strategy**: AI suggests testing approach
6. **Iterations**: User and AI refine the implementation
7. **Documentation**: AI helps document the feature