swarms/docs/swarms/structs/group_chat.md

# GroupChat Swarm Documentation

A production-grade multi-agent system enabling sophisticated group conversations between AI agents with customizable speaking patterns, parallel processing capabilities, and comprehensive conversation tracking.

## Advanced Configuration

### Agent Parameters

| Parameter | Type | Default | Description |
|-----------|------|---------|-------------|
| agent_name | str | Required | Unique identifier for the agent |
| system_prompt | str | Required | Role and behavior instructions |
| llm | Any | Required | Language model instance |
| max_loops | int | 1 | Maximum conversation turns |
| autosave | bool | False | Enable conversation saving |
| dashboard | bool | False | Enable monitoring dashboard |
| verbose | bool | True | Enable detailed logging |
| dynamic_temperature | bool | True | Enable dynamic temperature |
| retry_attempts | int | 1 | Failed request retry count |
| context_length | int | 200000 | Maximum context window |
| output_type | str | "string" | Response format type |
| streaming_on | bool | False | Enable streaming responses |

### GroupChat Parameters

| Parameter | Type | Default | Description |
|-----------|------|---------|-------------|
| name | str | "GroupChat" | Chat group identifier |
| description | str | "" | Purpose description |
| agents | List[Agent] | [] | Participating agents |
| speaker_fn | Callable | round_robin | Speaker selection function |
| max_loops | int | 10 | Maximum conversation turns |


## Table of Contents

- [Installation](#installation)
- [Core Concepts](#core-concepts)
- [Basic Usage](#basic-usage)
- [Advanced Configuration](#advanced-configuration)
- [Speaker Functions](#speaker-functions)
- [Response Models](#response-models)
- [Advanced Examples](#advanced-examples)
- [API Reference](#api-reference)
- [Best Practices](#best-practices)

## Installation

```bash
pip3 install swarms swarm-models loguru
```

## Core Concepts

The GroupChat system consists of several key components:

1. **Agents**: Individual AI agents with specialized knowledge and roles
2. **Speaker Functions**: Control mechanisms for conversation flow
3. **Chat History**: Structured conversation tracking
4. **Response Models**: Pydantic models for data validation

## Basic Usage

```python

import os
from dotenv import load_dotenv
from swarm_models import OpenAIChat
from swarms import Agent, GroupChat, expertise_based


if __name__ == "__main__":

    load_dotenv()

    # Get the OpenAI API key from the environment variable
    api_key = os.getenv("OPENAI_API_KEY")

    # Create an instance of the OpenAIChat class
    model = OpenAIChat(
        openai_api_key=api_key,
        model_name="gpt-4o-mini",
        temperature=0.1,
    )

    # Example agents
    agent1 = Agent(
        agent_name="Financial-Analysis-Agent",
        system_prompt="You are a financial analyst specializing in investment strategies.",
        llm=model,
        max_loops=1,
        autosave=False,
        dashboard=False,
        verbose=True,
        dynamic_temperature_enabled=True,
        user_name="swarms_corp",
        retry_attempts=1,
        context_length=200000,
        output_type="string",
        streaming_on=False,
    )

    agent2 = Agent(
        agent_name="Tax-Adviser-Agent",
        system_prompt="You are a tax adviser who provides clear and concise guidance on tax-related queries.",
        llm=model,
        max_loops=1,
        autosave=False,
        dashboard=False,
        verbose=True,
        dynamic_temperature_enabled=True,
        user_name="swarms_corp",
        retry_attempts=1,
        context_length=200000,
        output_type="string",
        streaming_on=False,
    )

    agents = [agent1, agent2]

    chat = GroupChat(
        name="Investment Advisory",
        description="Financial and tax analysis group",
        agents=agents,
        speaker_fn=expertise_based,
    )

    history = chat.run(
        "How to optimize tax strategy for investments?"
    )
    print(history.model_dump_json(indent=2))

```

## Speaker Functions


### Built-in Functions

```python
def round_robin(history: List[str], agent: Agent) -> bool:
    """
    Enables agents to speak in turns.
    Returns True for each agent in sequence.
    """
    return True

def expertise_based(history: List[str], agent: Agent) -> bool:
    """
    Enables agents to speak based on their expertise.
    Returns True if agent's role matches conversation context.
    """
    return agent.system_prompt.lower() in history[-1].lower() if history else True

def random_selection(history: List[str], agent: Agent) -> bool:
    """
    Randomly selects speaking agents.
    Returns True/False with 50% probability.
    """
    import random
    return random.choice([True, False])

def most_recent(history: List[str], agent: Agent) -> bool:
    """
    Enables agents to respond to their mentions.
    Returns True if agent was last speaker.
    """
    return agent.agent_name == history[-1].split(":")[0].strip() if history else True
```

### Custom Speaker Function Example

```python
def custom_speaker(history: List[str], agent: Agent) -> bool:
    """
    Custom speaker function with complex logic.
    
    Args:
        history: Previous conversation messages
        agent: Current agent being evaluated
        
    Returns:
        bool: Whether agent should speak
    """
    # No history - let everyone speak
    if not history:
        return True
        
    last_message = history[-1].lower()
    
    # Check for agent expertise keywords
    expertise_relevant = any(
        keyword in last_message 
        for keyword in agent.expertise_keywords
    )
    
    # Check for direct mentions
    mentioned = agent.agent_name.lower() in last_message
    
    # Check if agent hasn't spoken recently
    not_recent_speaker = not any(
        agent.agent_name in msg 
        for msg in history[-3:]
    )
    
    return expertise_relevant or mentioned or not_recent_speaker

# Usage
chat = GroupChat(
    agents=[agent1, agent2],
    speaker_fn=custom_speaker
)
```

## Response Models

### Complete Schema

```python
class AgentResponse(BaseModel):
    """Individual agent response in a conversation turn"""
    agent_name: str
    role: str
    message: str
    timestamp: datetime = Field(default_factory=datetime.now)
    turn_number: int
    preceding_context: List[str] = Field(default_factory=list)

class ChatTurn(BaseModel):
    """Single turn in the conversation"""
    turn_number: int
    responses: List[AgentResponse]
    task: str
    timestamp: datetime = Field(default_factory=datetime.now)

class ChatHistory(BaseModel):
    """Complete conversation history"""
    turns: List[ChatTurn]
    total_messages: int
    name: str
    description: str
    start_time: datetime = Field(default_factory=datetime.now)
```

## Advanced Examples

### Multi-Agent Analysis Team

```python
# Create specialized agents
data_analyst = Agent(
    agent_name="Data-Analyst",
    system_prompt="You analyze numerical data and patterns",
    llm=model
)

market_expert = Agent(
    agent_name="Market-Expert",
    system_prompt="You provide market insights and trends",
    llm=model
)

strategy_advisor = Agent(
    agent_name="Strategy-Advisor",
    system_prompt="You formulate strategic recommendations",
    llm=model
)

# Create analysis team
analysis_team = GroupChat(
    name="Market Analysis Team",
    description="Comprehensive market analysis group",
    agents=[data_analyst, market_expert, strategy_advisor],
    speaker_fn=expertise_based,
    max_loops=15
)

# Run complex analysis
history = analysis_team.run("""
    Analyze the current market conditions:
    1. Identify key trends
    2. Evaluate risks
    3. Recommend investment strategy
""")
```

### Parallel Processing

```python
# Define multiple analysis tasks
tasks = [
    "Analyze tech sector trends",
    "Evaluate real estate market",
    "Review commodity prices",
    "Assess global economic indicators"
]

# Run tasks concurrently
histories = chat.concurrent_run(tasks)

# Process results
for task, history in zip(tasks, histories):
    print(f"\nAnalysis for: {task}")
    for turn in history.turns:
        for response in turn.responses:
            print(f"{response.agent_name}: {response.message}")
```

## Best Practices

1. **Agent Design**
   - Give agents clear, specific roles
   - Use detailed system prompts
   - Set appropriate context lengths
   - Enable retries for reliability

2. **Speaker Functions**
   - Match function to use case
   - Consider conversation flow
   - Handle edge cases
   - Add appropriate logging

3. **Error Handling**
   - Use try-except blocks
   - Log errors appropriately
   - Implement retry logic
   - Provide fallback responses

4. **Performance**
   - Use concurrent processing for multiple tasks
   - Monitor context lengths
   - Implement proper cleanup
   - Cache responses when appropriate

## API Reference

### GroupChat Methods

| Method | Description | Arguments | Returns |
|--------|-------------|-----------|---------|
| run | Run single conversation | task: str | ChatHistory |
| batched_run | Run multiple sequential tasks | tasks: List[str] | List[ChatHistory] |
| concurrent_run | Run multiple parallel tasks | tasks: List[str] | List[ChatHistory] |
| get_recent_messages | Get recent messages | n: int = 3 | List[str] |

### Agent Methods

| Method | Description | Returns |
|--------|-------------|---------|
| run | Process single task | str |
| generate_response | Generate LLM response | str |
| save_context | Save conversation context | None |