diff --git a/.gitignore b/.gitignore
index 8ed30e17..6df4413d 100644
--- a/.gitignore
+++ b/.gitignore
@@ -14,6 +14,7 @@ Cargo.lock
 .pytest_cache
 static/generated
 conversations/
+next_swarms_update.txt
 runs
 Financial-Analysis-Agent_state.json
 conversations/
diff --git a/docs/examples/index.md b/docs/examples/index.md
new file mode 100644
index 00000000..4781c3d7
--- /dev/null
+++ b/docs/examples/index.md
@@ -0,0 +1,108 @@
+# Swarms Examples Index
+
+A comprehensive index of examples from the [Swarms Examples Repository](https://github.com/The-Swarm-Corporation/swarms-examples).
+
+Additionally, we have more comprehensive examples available in [The Swarms Cookbook](https://github.com/The-Swarm-Corporation/Cookbook).
+
+## Single Agent Examples
+
+### Core Agents
+| Category | Example | Description |
+|----------|---------|-------------|
+| Basic | [Easy Example](https://github.com/The-Swarm-Corporation/swarms-examples/blob/main/examples/agents/easy_example.py) | Basic agent implementation demonstrating core functionality and setup |
+| Settings | [Agent Settings](https://github.com/The-Swarm-Corporation/swarms-examples/blob/main/examples/agents/agent_settings.py) | Comprehensive configuration options for customizing agent behavior and capabilities |
+| YAML | [Agents from YAML](https://github.com/The-Swarm-Corporation/swarms-examples/blob/main/examples/agents/agents_from_yaml_example.py) | Creating and configuring agents using YAML configuration files for easy deployment |
+| Memory | [Agent with Long-term Memory](https://github.com/The-Swarm-Corporation/swarms-examples/blob/main/examples/agents/memory/agents_and_memory/agent_with_longterm_memory.py) | Implementation of persistent memory capabilities for maintaining context across sessions |
+
+### Model Integrations
+| Category | Example | Description |
+|----------|---------|-------------|
+| Azure | [Azure OpenAI Agent](https://github.com/The-Swarm-Corporation/swarms-examples/blob/main/examples/agents/settings/various_models/basic_agent_with_azure_openai.py) | Integration with Azure OpenAI services for enterprise-grade AI capabilities |
+| Groq | [Groq Agent](https://github.com/The-Swarm-Corporation/swarms-examples/blob/main/examples/agents/settings/various_models/groq_agent.py) | High-performance inference using Groq's accelerated computing platform |
+| Custom | [Custom Model Agent](https://github.com/The-Swarm-Corporation/swarms-examples/blob/main/examples/agents/settings/various_models/custom_model_with_agent.py) | Framework for integrating custom ML models into the agent architecture |
+
+### Tools and Function Calling
+| Category | Example | Description |
+|----------|---------|-------------|
+| Basic Tools | [Tool Agent](https://github.com/The-Swarm-Corporation/swarms-examples/blob/main/examples/agents/tools/tool_agent.py) | Basic tool-using agent demonstrating external tool integration capabilities |
+| Advanced Tools | [Agent with Many Tools](https://github.com/The-Swarm-Corporation/swarms-examples/blob/main/examples/agents/tools/agent_with_many_tools.py) | Advanced agent utilizing multiple tools for complex task execution |
+| OpenAI Functions | [OpenAI Function Caller](https://github.com/The-Swarm-Corporation/swarms-examples/blob/main/examples/agents/tools/function_calling/openai_function_caller_example.py) | Integration with OpenAI's function calling API for structured outputs |
+| Command Line | [Command Tool Agent](https://github.com/The-Swarm-Corporation/swarms-examples/blob/main/examples/agents/tools/tool_agent/command_r_tool_agent.py) | Command-line interface tool integration |
+| Jamba | [Jamba Tool Agent](https://github.com/The-Swarm-Corporation/swarms-examples/blob/main/examples/agents/tools/tool_agent/jamba_tool_agent.py) | Integration with Jamba framework for enhanced tool capabilities |
+| Pydantic | [Pydantic Tool Agent](https://github.com/The-Swarm-Corporation/swarms-examples/blob/main/examples/agents/tools/tool_agent/tool_agent_pydantic.py) | Tool validation and schema enforcement using Pydantic |
+
+### Third-Party Integrations
+| Category | Example | Description |
+|----------|---------|-------------|
+| Microsoft | [AutoGen Integration](https://github.com/The-Swarm-Corporation/swarms-examples/blob/main/examples/agents/3rd_party_agents/auto_gen.py) | Integration with Microsoft's AutoGen framework for autonomous agents |
+| LangChain | [LangChain Integration](https://github.com/The-Swarm-Corporation/swarms-examples/blob/main/examples/agents/3rd_party_agents/langchain.py) | Combining LangChain's capabilities with Swarms for enhanced functionality |
+| Browser | [Multion Integration](https://github.com/The-Swarm-Corporation/swarms-examples/blob/main/examples/agents/3rd_party_agents/multion_agent.py) | Web automation and browsing capabilities using Multion |
+| Team AI | [Crew AI](https://github.com/The-Swarm-Corporation/swarms-examples/blob/main/examples/agents/3rd_party_agents/crew_ai.py) | Team-based AI collaboration using Crew AI framework |
+| Development | [Griptape](https://github.com/The-Swarm-Corporation/swarms-examples/blob/main/examples/agents/3rd_party_agents/griptape.py) | Integration with Griptape for structured AI application development |
+
+### Industry-Specific Agents
+| Category | Example | Description |
+|----------|---------|-------------|
+| Finance | [401k Agent](https://github.com/The-Swarm-Corporation/swarms-examples/blob/main/examples/agents/use_cases/finance/401k_agent.py) | Retirement planning assistant with investment strategy recommendations |
+| Finance | [Estate Planning](https://github.com/The-Swarm-Corporation/swarms-examples/blob/main/examples/agents/use_cases/finance/estate_planning_agent.py) | Comprehensive estate planning and wealth management assistant |
+| Security | [Perimeter Defense](https://github.com/The-Swarm-Corporation/swarms-examples/blob/main/examples/agents/use_cases/security/perimeter_defense_agent.py) | Security monitoring and threat detection system |
+| Research | [Perplexity Agent](https://github.com/The-Swarm-Corporation/swarms-examples/blob/main/examples/agents/use_cases/research/perplexity_agent.py) | Advanced research automation using Perplexity AI integration |
+| Legal | [Alberto Agent](https://github.com/The-Swarm-Corporation/swarms-examples/blob/main/examples/agents/use_cases/law/alberto_agent.py) | Legal research and document analysis assistant |
+| Healthcare | [Pharma Agent](https://github.com/The-Swarm-Corporation/swarms-examples/blob/main/examples/agents/use_cases/pharma/pharma_agent_two.py) | Pharmaceutical research and drug interaction analysis |
+
+## Multi-Agent Examples
+
+### Core Architectures
+| Category | Example | Description |
+|----------|---------|-------------|
+| Basic | [Build a Swarm](https://github.com/The-Swarm-Corporation/swarms-examples/blob/main/examples/structs/swarms/base_swarm/build_a_swarm.py) | Foundation for creating custom swarm architectures with multiple agents |
+| Auto Swarm | [Auto Swarm](https://github.com/The-Swarm-Corporation/swarms-examples/blob/main/examples/structs/swarms/auto_swarm/auto_swarm_example.py) | Self-organizing swarm with automatic task distribution and management |
+| Concurrent | [Concurrent Swarm](https://github.com/The-Swarm-Corporation/swarms-examples/blob/main/examples/structs/swarms/concurrent_swarm/concurrent_swarm_example.py) | Parallel execution of tasks across multiple agents for improved performance |
+| Star | [Star Swarm](https://github.com/The-Swarm-Corporation/swarms-examples/blob/main/examples/structs/swarms/different_architectures/star_swarm.py) | Centralized architecture with a hub agent coordinating peripheral agents |
+| Circular | [Circular Swarm](https://github.com/The-Swarm-Corporation/swarms-examples/blob/main/examples/structs/swarms/different_architectures/circular_swarm.py) | Ring topology for cyclic information flow between agents |
+
+### Experimental Architectures
+| Category | Example | Description |
+|----------|---------|-------------|
+| Monte Carlo | [Monte Carlo Swarm](https://github.com/The-Swarm-Corporation/swarms-examples/blob/main/examples/structs/swarms/experimental/monte_carlo_swarm.py) | Probabilistic decision-making using Monte Carlo simulation across agents |
+| Federated | [Federated Swarm](https://github.com/The-Swarm-Corporation/swarms-examples/blob/main/examples/structs/swarms/experimental/federated_swarm.py) | Distributed learning system with privacy-preserving agent collaboration |
+| Ant Colony | [Ant Swarm](https://github.com/The-Swarm-Corporation/swarms-examples/blob/main/examples/structs/swarms/experimental/ant_swarm.py) | Bio-inspired optimization using ant colony algorithms for agent coordination |
+| Matrix | [Agent Matrix](https://github.com/The-Swarm-Corporation/swarms-examples/blob/main/examples/structs/swarms/experimental/agent_matrix.py) | Grid-based agent organization for complex problem-solving |
+| DFS | [DFS Search Swarm](https://github.com/The-Swarm-Corporation/swarms-examples/blob/main/examples/structs/swarms/experimental/dfs_search_swarm.py) | Depth-first search swarm for complex problem exploration |
+| Pulsar | [Pulsar Swarm](https://github.com/The-Swarm-Corporation/swarms-examples/blob/main/examples/structs/swarms/experimental/pulsar_swarm.py) | Pulsar-based coordination for synchronized agent behavior |
+
+### Collaboration Patterns
+| Category | Example | Description |
+|----------|---------|-------------|
+| Delegation | [Agent Delegation](https://github.com/The-Swarm-Corporation/swarms-examples/blob/main/examples/structs/swarms/multi_agent_collaboration/agent_delegation.py) | Task delegation and management system |
+| Communication | [Message Pool](https://github.com/The-Swarm-Corporation/swarms-examples/blob/main/examples/structs/swarms/multi_agent_collaboration/message_pool.py) | Shared communication system for efficient agent interaction |
+| Scheduling | [Round Robin](https://github.com/The-Swarm-Corporation/swarms-examples/blob/main/examples/structs/swarms/multi_agent_collaboration/round_robin_example.py) | Round-robin task scheduling and execution |
+| Load Balancing | [Load Balancer](https://github.com/The-Swarm-Corporation/swarms-examples/blob/main/examples/structs/swarms/multi_agent_collaboration/load_balancer_example.py) | Dynamic task distribution system for optimal resource utilization |
+| Consensus | [Majority Voting](https://github.com/The-Swarm-Corporation/swarms-examples/blob/main/examples/structs/swarms/multi_agent_collaboration/majority_voting.py) | Consensus-building system using democratic voting among agents |
+
+### Industry Applications
+| Category | Example | Description |
+|----------|---------|-------------|
+| Finance | [Accountant Team](https://github.com/The-Swarm-Corporation/swarms-examples/blob/main/examples/applications/demos/accountant_team/account_team2_example.py) | Multi-agent system for financial analysis, bookkeeping, and tax planning |
+| Marketing | [Ad Generation](https://github.com/The-Swarm-Corporation/swarms-examples/blob/main/examples/applications/demos/ad_gen/ad_gen_example.py) | Collaborative ad creation with copywriting and design agents |
+| Aerospace | [Space Traffic Control](https://github.com/The-Swarm-Corporation/swarms-examples/blob/main/examples/applications/demos/agentic_space_traffic_control/game.py) | Complex simulation of space traffic management with multiple coordinating agents |
+| Agriculture | [Plant Biology](https://github.com/The-Swarm-Corporation/swarms-examples/blob/main/examples/applications/demos/plant_biologist_swarm/agricultural_swarm.py) | Agricultural analysis and optimization using specialized biology agents |
+| Urban Dev | [Urban Planning](https://github.com/The-Swarm-Corporation/swarms-examples/blob/main/examples/applications/demos/urban_planning/urban_planning_example.py) | City development planning with multiple specialized urban development agents |
+| Education | [Education System](https://github.com/The-Swarm-Corporation/swarms-examples/blob/main/examples/applications/demos/education/education_example.py) | Personalized learning system with multiple teaching and assessment agents |
+| Security | [Email Phishing Detection](https://github.com/The-Swarm-Corporation/swarms-examples/blob/main/examples/applications/demos/email_phiser/email_swarm.py) | Multi-agent security analysis and threat detection |
+| Fashion | [Personal Stylist](https://github.com/The-Swarm-Corporation/swarms-examples/blob/main/examples/applications/demos/personal_stylist/personal_stylist_example.py) | Fashion recommendation system with style analysis and matching agents |
+| Healthcare | [Healthcare Assistant](https://github.com/The-Swarm-Corporation/swarms-examples/blob/main/examples/applications/demos/positive_med/positive_med_example.py) | Medical diagnosis and treatment planning with specialist consultation agents |
+| Security Ops | [Security Team](https://github.com/The-Swarm-Corporation/swarms-examples/blob/main/examples/applications/demos/security_team/security_team_example.py) | Comprehensive security operations with threat detection and response agents |
+| Medical | [X-Ray Analysis](https://github.com/The-Swarm-Corporation/swarms-examples/blob/main/examples/applications/demos/xray/xray_example.py) | Multi-agent medical imaging analysis and diagnosis |
+| Business | [Business Strategy](https://github.com/The-Swarm-Corporation/swarms-examples/blob/main/examples/applications/business_strategy/business_strategy_graph/growth_agent.py) | Strategic planning and business development swarm |
+| Research | [Astronomy Research](https://github.com/The-Swarm-Corporation/swarms-examples/blob/main/examples/applications/astronomy/multiversal_detection/test.py) | Collaborative space research and astronomical analysis |
+
+## Additional Resources
+
+- [Github](https://github.com/kyegomez/swarms)
+
+- Discord (https://t.co/zlLe07AqUX)
+
+- Telegram (https://t.co/dSRy143zQv)
+
+- X Community (https://x.com/i/communities/1875452887414804745)
\ No newline at end of file
diff --git a/docs/mkdocs.yml b/docs/mkdocs.yml
index 7fac02cd..967a3cc7 100644
--- a/docs/mkdocs.yml
+++ b/docs/mkdocs.yml
@@ -242,6 +242,7 @@ nav:
         - MatrixSwarm: "swarms/structs/matrix_swarm.md"
         - ModelRouter: "swarms/structs/model_router.md"
         - MALT: "swarms/structs/malt.md"
+        - Interactive Group Chat: "swarms/structs/interactive_groupchat.md"
         - Various Execution Methods: "swarms/structs/various_execution_methods.md"
         - Deep Research Swarm: "swarms/structs/deep_research_swarm.md"
         - Swarm Matcher: "swarms/structs/swarm_matcher.md"
@@ -305,6 +306,7 @@ nav:
         - Swarms 5.9.2: "swarms/changelog/changelog_new.md"
     
   - Examples:
+    - Overview: "examples/index.md"
     - Customizing Agents:
       - Basic Agent: "swarms/examples/basic_agent.md"
       - Agents with Callable Tools: "swarms/examples/agent_with_tools.md"
diff --git a/docs/swarms/structs/interactive_groupchat.md b/docs/swarms/structs/interactive_groupchat.md
index c4705aee..207e2a50 100644
--- a/docs/swarms/structs/interactive_groupchat.md
+++ b/docs/swarms/structs/interactive_groupchat.md
@@ -1,11 +1,11 @@
 # InteractiveGroupChat Documentation
 
-The InteractiveGroupChat is a sophisticated multi-agent system that enables interactive conversations between users and AI agents using @mentions. This system allows users to direct messages to specific agents and facilitates collaborative responses when multiple agents are mentioned.
+The InteractiveGroupChat is a sophisticated multi-agent system that enables interactive conversations between users and AI agents using @mentions. This system allows users to direct tasks to specific agents and facilitates collaborative responses when multiple agents are mentioned.
 
 ## Features
 
-- **@mentions Support**: Direct messages to specific agents using @agent_name syntax
-- **Multi-Agent Collaboration**: Multiple mentioned agents can see and respond to each other's messages
+- **@mentions Support**: Direct tasks to specific agents using @agent_name syntax
+- **Multi-Agent Collaboration**: Multiple mentioned agents can see and respond to each other's tasks
 - **Callable Function Support**: Supports both Agent instances and callable functions as chat participants
 - **Comprehensive Error Handling**: Custom error classes for different scenarios
 - **Conversation History**: Maintains a complete history of the conversation
@@ -17,186 +17,248 @@ The InteractiveGroupChat is a sophisticated multi-agent system that enables inte
 pip install swarms
 ```
 
-## Basic Usage
+## Methods Reference
 
+### Constructor (`__init__`)
+
+**Description:**  
+Initializes a new InteractiveGroupChat instance with the specified configuration.
+
+**Arguments:**
+| Parameter | Type | Description | Default |
+|-----------|------|-------------|---------|
+| `id` | str | Unique identifier for the chat | auto-generated key |
+| `name` | str | Name of the group chat | "InteractiveGroupChat" |
+| `description` | str | Description of the chat's purpose | generic description |
+| `agents` | List[Union[Agent, Callable]] | List of participating agents | empty list |
+| `max_loops` | int | Maximum conversation turns | 1 |
+| `output_type` | str | Type of output format | "string" |
+| `interactive` | bool | Whether to enable interactive mode | False |
+
+**Example:**
 ```python
 from swarms import Agent, InteractiveGroupChat
 
-# Initialize agents
+# Create agents
 financial_advisor = Agent(
     agent_name="FinancialAdvisor",
     system_prompt="You are a financial advisor specializing in investment strategies.",
-    model_name="gpt-4o-mini"
+    model_name="gpt-4"
 )
 
 tax_expert = Agent(
     agent_name="TaxExpert",
     system_prompt="You are a tax expert providing tax-related guidance.",
-    model_name="gpt-4o-mini"
+    model_name="gpt-4"
 )
 
-# Create the interactive group chat
+# Initialize group chat
 chat = InteractiveGroupChat(
-    name="Financial Team",
-    description="Financial advisory team",
-    agents=[financial_advisor, tax_expert]
+    id="finance-chat-001",
+    name="Financial Advisory Team",
+    description="Expert financial guidance team",
+    agents=[financial_advisor, tax_expert],
+    max_loops=3,
+    output_type="string",
+    interactive=True
 )
+```
 
-# Send a message to a single agent
-response = chat.run("@FinancialAdvisor what are good investment strategies?")
+### Run Method (`run`)
 
-# Send a message to multiple agents
-response = chat.run("@FinancialAdvisor and @TaxExpert, how can I optimize my investment taxes?")
-```
+**Description:**  
+Processes a task and gets responses from mentioned agents. This is the main method for sending tasks in non-interactive mode.
+
+**Arguments:**
 
-## Advanced Usage
+- `task` (str): The input task containing @mentions to agents
 
-### Using Callable Functions
+**Returns:**
 
+- str: Formatted conversation history including agent responses
+
+**Example:**
 ```python
-def custom_agent(context: str) -> str:
-    """A custom callable function that can act as an agent"""
-    return "Custom response based on: " + context
+# Single agent interaction
+response = chat.run("@FinancialAdvisor what are the best ETFs for 2024?")
+print(response)
 
-# Add both Agent instances and callable functions
-agents = [financial_advisor, tax_expert, custom_agent]
-chat = InteractiveGroupChat(agents=agents)
+# Multiple agent collaboration
+response = chat.run("@FinancialAdvisor and @TaxExpert, how can I minimize taxes on my investments?")
+print(response)
+```
+
+### Start Interactive Session (`start_interactive_session`)
+
+**Description:**  
+Starts an interactive terminal session for real-time chat with agents. This creates a REPL (Read-Eval-Print Loop) interface.
+
+**Arguments:**
+None
 
-# Interact with the callable function
-response = chat.run("@custom_agent what do you think?")
+**Example:**
+
+```python
+# Initialize chat with interactive mode
+chat = InteractiveGroupChat(
+    agents=[financial_advisor, tax_expert],
+    interactive=True
+)
+
+# Start the interactive session
+chat.start_interactive_session()
 ```
 
-## Configuration Options
+### Extract Mentions (`_extract_mentions`)
+
+**Description:**  
 
-| Parameter | Type | Default | Description |
-|-----------|------|---------|-------------|
-| name | str | "InteractiveGroupChat" | Name of the group chat |
-| description | str | "An interactive group chat..." | Description of the chat's purpose |
-| agents | List[Union[Agent, Callable]] | [] | List of agents or callable functions |
-| max_loops | int | 1 | Maximum conversation turns |
-| output_type | str | "string" | Output format type |
+Internal method that extracts @mentions from a task. Used by the run method to identify which agents should respond.
 
-## Error Handling
+**Arguments:**
 
-The system includes several custom error classes:
+- `task` (str): The input task to extract mentions from
 
-- **InteractiveGroupChatError**: Base exception class
-- **AgentNotFoundError**: Raised when a mentioned agent doesn't exist
-- **NoMentionedAgentsError**: Raised when no agents are mentioned
-- **InvalidMessageFormatError**: Raised for invalid message formats
+**Returns:**
 
-Example error handling:
+- List[str]: List of mentioned agent names
 
+**Example:**
 ```python
-try:
-    response = chat.run("@NonExistentAgent hello!")
-except AgentNotFoundError as e:
-    print(f"Agent not found: {e}")
-except NoMentionedAgentsError as e:
-    print(f"No agents mentioned: {e}")
+# Internal usage example (not typically called directly)
+chat = InteractiveGroupChat(agents=[financial_advisor, tax_expert])
+mentions = chat._extract_mentions("@FinancialAdvisor and @TaxExpert, please help")
+print(mentions)  # ['FinancialAdvisor', 'TaxExpert']
 ```
 
-## Best Practices
+### Validate Initialization (`_validate_initialization`)
+
+**Description:**  
 
-1. **Agent Naming**: Use clear, unique names for agents to avoid confusion
-2. **Message Format**: Always use @mentions to direct messages to specific agents
-3. **Error Handling**: Implement proper error handling for various scenarios
-4. **Context Management**: Be aware that agents can see the full conversation history
-5. **Resource Management**: Consider the number of agents and message length to optimize performance
+Internal method that validates the group chat configuration during initialization.
 
-## Logging
+**Arguments:**
+None
 
-The system uses loguru for comprehensive logging:
+**Example:**
 
 ```python
-from loguru import logger
+# Internal validation happens automatically during initialization
+chat = InteractiveGroupChat(
+    agents=[financial_advisor],  # Valid: at least one agent
+    max_loops=5  # Valid: positive number
+)
+```
 
-# Configure logging
-logger.add("groupchat.log", rotation="500 MB")
+### Setup Conversation Context (`_setup_conversation_context`)
 
-# Logs will include:
-# - Agent responses
-# - Error messages
-# - System events
-```
+**Description:**  
 
-## Examples
+Internal method that sets up the initial conversation context with group chat information.
 
-### Basic Interaction
+**Arguments:**
 
-```python
-# Single agent interaction
-response = chat.run("@FinancialAdvisor what are the best investment strategies for 2024?")
+None
 
-# Multiple agent collaboration
-response = chat.run("@TaxExpert and @InvestmentAnalyst, how can we optimize investment taxes?")
+**Example:**
+
+```python
+# Context is automatically set up during initialization
+chat = InteractiveGroupChat(
+    name="Investment Team",
+    description="Expert investment advice",
+    agents=[financial_advisor, tax_expert]
+)
+# The conversation context now includes chat name, description, and agent info
 ```
 
-### Error Handling
+### Update Agent Prompts (`_update_agent_prompts`)
 
-```python
-try:
-    # Invalid agent mention
-    response = chat.run("@NonExistentAgent hello!")
-except AgentNotFoundError as e:
-    print(f"Error: {e}")
+**Description:**  
 
-try:
-    # No mentions
-    response = chat.run("Hello everyone!")
-except NoMentionedAgentsError as e:
-    print(f"Error: {e}")
-```
+Internal method that updates each agent's system prompt with information about other agents and the group chat.
+
+**Arguments:**
 
-### Custom Callable Integration
+None
 
+**Example:**
 ```python
-def market_analyzer(context: str) -> str:
-    """Custom market analysis function"""
-    return "Market analysis based on: " + context
+# Agent prompts are automatically updated during initialization
+chat = InteractiveGroupChat(agents=[financial_advisor, tax_expert])
+# Each agent now knows about the other participants in the chat
+```
+
+## Error Classes
 
-agents = [financial_advisor, tax_expert, market_analyzer]
-chat = InteractiveGroupChat(agents=agents)
+### InteractiveGroupChatError
 
-response = chat.run("@market_analyzer what's your analysis of the current market?")
+**Description:**  
+
+Base exception class for InteractiveGroupChat errors.
+
+**Example:**
+```python
+try:
+    # Some operation that might fail
+    chat.run("@InvalidAgent hello")
+except InteractiveGroupChatError as e:
+    print(f"Chat error occurred: {e}")
 ```
 
-## API Reference
+### AgentNotFoundError
 
-### InteractiveGroupChat Class
+**Description:**  
 
+Raised when a mentioned agent is not found in the group.
+
+**Example:**
 ```python
-class InteractiveGroupChat:
-    def __init__(
-        self,
-        name: str = "InteractiveGroupChat",
-        description: str = "An interactive group chat for multiple agents",
-        agents: List[Union[Agent, Callable]] = [],
-        max_loops: int = 1,
-        output_type: str = "string",
-    ):
-        """Initialize the interactive group chat."""
-        
-    def run(self, message: str) -> str:
-        """Process a message and get responses from mentioned agents."""
+try:
+    chat.run("@NonExistentAgent hello!")
+except AgentNotFoundError as e:
+    print(f"Agent not found: {e}")
 ```
 
-### Custom Error Classes
+### NoMentionedAgentsError
+
+**Description:**  
+
+Raised when no agents are mentioned in the task.
+
+**Example:**
 
 ```python
-class InteractiveGroupChatError(Exception):
-    """Base exception class for InteractiveGroupChat errors"""
+try:
+    chat.run("Hello everyone!")  # No @mentions
+except NoMentionedAgentsError as e:
+    print(f"No agents mentioned: {e}")
+```
 
-class AgentNotFoundError(InteractiveGroupChatError):
-    """Raised when a mentioned agent is not found"""
+### InvalidtaskFormatError
 
-class NoMentionedAgentsError(InteractiveGroupChatError):
-    """Raised when no agents are mentioned"""
+**Description:**  
 
-class InvalidMessageFormatError(InteractiveGroupChatError):
-    """Raised when the message format is invalid"""
+Raised when the task format is invalid.
+
+**Example:**
+```python
+try:
+    chat.run("@Invalid@Format")
+except InvalidtaskFormatError as e:
+    print(f"Invalid task format: {e}")
 ```
 
+## Best Practices
+
+| Best Practice | Description | Example |
+|--------------|-------------|---------|
+| Agent Naming | Use clear, unique names for agents to avoid confusion | `financial_advisor`, `tax_expert` |
+| task Format | Always use @mentions to direct tasks to specific agents | `@financial_advisor What's your investment advice?` |
+| Error Handling | Implement proper error handling for various scenarios | `try/except` blocks for `AgentNotFoundError` |
+| Context Management | Be aware that agents can see the full conversation history | Monitor conversation length and relevance |
+| Resource Management | Consider the number of agents and task length to optimize performance | Limit max_loops and task size |
+
 ## Contributing
 
 Contributions are welcome! Please read our contributing guidelines and submit pull requests to our GitHub repository.
diff --git a/examples/multi_agent/deep_research_example.py b/examples/multi_agent/deep_research_examples/deep_research_example.py
similarity index 100%
rename from examples/multi_agent/deep_research_example.py
rename to examples/multi_agent/deep_research_examples/deep_research_example.py
diff --git a/examples/multi_agent/unique_swarms_examples.py b/examples/multi_agent/utils/unique_swarms_examples.py
similarity index 100%
rename from examples/multi_agent/unique_swarms_examples.py
rename to examples/multi_agent/utils/unique_swarms_examples.py
diff --git a/many_tool_use_demo.py b/examples/tools/multii_tool_use/many_tool_use_demo.py
similarity index 100%
rename from many_tool_use_demo.py
rename to examples/tools/multii_tool_use/many_tool_use_demo.py
diff --git a/swarms/structs/__init__.py b/swarms/structs/__init__.py
index cc49a1fd..778a059a 100644
--- a/swarms/structs/__init__.py
+++ b/swarms/structs/__init__.py
@@ -84,6 +84,7 @@ from swarms.structs.swarming_architectures import (
     staircase_swarm,
     star_swarm,
 )
+from swarms.structs.interactive_groupchat import InteractiveGroupChat
 
 __all__ = [
     "Agent",
@@ -156,4 +157,5 @@ __all__ = [
     "aggregate",
     "find_agent_by_name",
     "run_agent",
+    "InteractiveGroupChat",
 ]
diff --git a/swarms/structs/interactive_groupchat.py b/swarms/structs/interactive_groupchat.py
index 4ffa5533..c8a9411b 100644
--- a/swarms/structs/interactive_groupchat.py
+++ b/swarms/structs/interactive_groupchat.py
@@ -1,12 +1,14 @@
-from typing import List, Union, Callable
 import re
+from typing import Callable, List, Union
+
 from loguru import logger
+
 from swarms.structs.agent import Agent
 from swarms.structs.conversation import Conversation
+from swarms.utils.generate_keys import generate_api_key
 from swarms.utils.history_output_formatter import (
     history_output_formatter,
 )
-from swarms.utils.generate_keys import generate_api_key
 
 
 class InteractiveGroupChatError(Exception):
@@ -22,13 +24,13 @@ class AgentNotFoundError(InteractiveGroupChatError):
 
 
 class NoMentionedAgentsError(InteractiveGroupChatError):
-    """Raised when no agents are mentioned in the message"""
+    """Raised when no agents are mentioned in the task"""
 
     pass
 
 
-class InvalidMessageFormatError(InteractiveGroupChatError):
-    """Raised when the message format is invalid"""
+class InvalidTaskFormatError(InteractiveGroupChatError):
+    """Raised when the task format is invalid"""
 
     pass
 
@@ -38,7 +40,7 @@ class InteractiveGroupChat:
     An interactive group chat system that enables conversations with multiple agents using @mentions.
 
     This class allows users to interact with multiple agents by mentioning them using @agent_name syntax.
-    When multiple agents are mentioned, they can see and respond to each other's messages.
+    When multiple agents are mentioned, they can see and respond to each other's tasks.
 
     Attributes:
         name (str): Name of the group chat
@@ -70,7 +72,7 @@ class InteractiveGroupChat:
         output_type: str = "string",
         interactive: bool = False,
     ):
-        self.id = id 
+        self.id = id
         self.name = name
         self.description = description
         self.agents = agents
@@ -175,22 +177,22 @@ class InteractiveGroupChat:
                     f"Updated system prompt for agent: {agent.agent_name}"
                 )
 
-    def _extract_mentions(self, message: str) -> List[str]:
+    def _extract_mentions(self, task: str) -> List[str]:
         """
-        Extracts @mentions from the message.
+        Extracts @mentions from the task.
 
         Args:
-            message (str): The input message
+            task (str): The input task
 
         Returns:
             List[str]: List of mentioned agent names
 
         Raises:
-            InvalidMessageFormatError: If the message format is invalid
+            InvalidtaskFormatError: If the task format is invalid
         """
         try:
             # Find all @mentions using regex
-            mentions = re.findall(r"@(\w+)", message)
+            mentions = re.findall(r"@(\w+)", task)
             return [
                 mention
                 for mention in mentions
@@ -198,9 +200,7 @@ class InteractiveGroupChat:
             ]
         except Exception as e:
             logger.error(f"Error extracting mentions: {e}")
-            raise InvalidMessageFormatError(
-                f"Invalid message format: {e}"
-            )
+            raise InvalidTaskFormatError(f"Invalid task format: {e}")
 
     def start_interactive_session(self):
         """
@@ -248,7 +248,7 @@ class InteractiveGroupChat:
                     print("\nHelp:")
                     print("1. Mention agents using @agent_name")
                     print(
-                        "2. You can mention multiple agents in one message"
+                        "2. You can mention multiple agents in one task"
                     )
                     print("3. Available agents:")
                     for name in self.agent_map:
@@ -261,7 +261,7 @@ class InteractiveGroupChat:
                 if not user_input:
                     continue
 
-                # Process the message and get responses
+                # Process the task and get responses
                 try:
                     response = self.run(user_input)
                     print("\nChat:")
@@ -285,23 +285,23 @@ class InteractiveGroupChat:
                     "The session will continue. You can type 'exit' to end it."
                 )
 
-    def run(self, message: str) -> str:
+    def run(self, task: str) -> str:
         """
-        Process a message and get responses from mentioned agents.
+        Process a task and get responses from mentioned agents.
         If interactive mode is enabled, this will be called by start_interactive_session().
-        Otherwise, it can be called directly for single message processing.
+        Otherwise, it can be called directly for single task processing.
         """
         try:
             # Extract mentioned agents
-            mentioned_agents = self._extract_mentions(message)
+            mentioned_agents = self._extract_mentions(task)
 
             if not mentioned_agents:
                 raise NoMentionedAgentsError(
-                    "No valid agents mentioned in the message"
+                    "No valid agents mentioned in the task"
                 )
 
-            # Add user message to conversation
-            self.conversation.add(role="User", content=message)
+            # Add user task to conversation
+            self.conversation.add(role="User", content=task)
 
             # Get responses from mentioned agents
             for agent_name in mentioned_agents:
@@ -320,7 +320,7 @@ class InteractiveGroupChat:
                     # Get response from agent
                     if isinstance(agent, Agent):
                         response = agent.run(
-                            task=f"{context}\nPlease respond to the latest message as {agent_name}."
+                            task=f"{context}\nPlease respond to the latest task as {agent_name}."
                         )
                     else:
                         # For callable functions