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189 lines
7.8 KiB
189 lines
7.8 KiB
# GroupChat
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*Enables dynamic collaboration through chat-based interaction*
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**Swarm Type**: `GroupChat`
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## Overview
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The GroupChat swarm type enables dynamic collaboration between agents through a chat-based interface, facilitating real-time information sharing and decision-making. Agents participate in a conversational workflow where they can build upon each other's contributions, debate ideas, and reach consensus through natural dialogue.
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Key features:
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- **Interactive Dialogue**: Agents communicate through natural conversation
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- **Dynamic Collaboration**: Real-time information sharing and building upon ideas
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- **Consensus Building**: Agents can debate and reach decisions collectively
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- **Flexible Participation**: Agents can contribute when relevant to the discussion
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## Use Cases
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- Brainstorming and ideation sessions
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- Multi-perspective problem analysis
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- Collaborative decision-making processes
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- Creative content development
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## API Usage
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### Basic GroupChat Example
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=== "Shell (curl)"
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```bash
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curl -X POST "https://api.swarms.world/v1/swarm/completions" \
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-H "x-api-key: $SWARMS_API_KEY" \
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-H "Content-Type: application/json" \
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-d '{
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"name": "Product Strategy Discussion",
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"description": "Collaborative chat to develop product strategy",
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"swarm_type": "GroupChat",
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"task": "Discuss and develop a go-to-market strategy for a new AI-powered productivity tool targeting small businesses",
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"agents": [
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{
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"agent_name": "Product Manager",
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"description": "Leads product strategy and development",
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"system_prompt": "You are a senior product manager. Focus on product positioning, features, user needs, and market fit. Ask probing questions and build on others ideas.",
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"model_name": "gpt-4o",
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"max_loops": 3,
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},
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{
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"agent_name": "Marketing Strategist",
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"description": "Develops marketing and positioning strategy",
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"system_prompt": "You are a marketing strategist. Focus on target audience, messaging, channels, and competitive positioning. Contribute marketing insights to the discussion.",
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"model_name": "gpt-4o",
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"max_loops": 3,
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},
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{
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"agent_name": "Sales Director",
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"description": "Provides sales and customer perspective",
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"system_prompt": "You are a sales director with small business experience. Focus on pricing, sales process, customer objections, and market adoption. Share practical sales insights.",
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"model_name": "gpt-4o",
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"max_loops": 3,
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},
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{
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"agent_name": "UX Researcher",
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"description": "Represents user experience and research insights",
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"system_prompt": "You are a UX researcher specializing in small business tools. Focus on user behavior, usability, adoption barriers, and design considerations.",
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"model_name": "gpt-4o",
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"max_loops": 3,
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}
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],
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"max_loops": 3
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}'
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```
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=== "Python (requests)"
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```python
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import requests
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import json
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API_BASE_URL = "https://api.swarms.world"
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API_KEY = "your_api_key_here"
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headers = {
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"x-api-key": API_KEY,
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"Content-Type": "application/json"
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}
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swarm_config = {
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"name": "Product Strategy Discussion",
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"description": "Collaborative chat to develop product strategy",
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"swarm_type": "GroupChat",
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"task": "Discuss and develop a go-to-market strategy for a new AI-powered productivity tool targeting small businesses",
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"agents": [
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{
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"agent_name": "Product Manager",
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"description": "Leads product strategy and development",
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"system_prompt": "You are a senior product manager. Focus on product positioning, features, user needs, and market fit. Ask probing questions and build on others ideas.",
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"model_name": "gpt-4o",
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"max_loops": 3,
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},
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{
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"agent_name": "Marketing Strategist",
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"description": "Develops marketing and positioning strategy",
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"system_prompt": "You are a marketing strategist. Focus on target audience, messaging, channels, and competitive positioning. Contribute marketing insights to the discussion.",
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"model_name": "gpt-4o",
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"max_loops": 3,
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},
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{
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"agent_name": "Sales Director",
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"description": "Provides sales and customer perspective",
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"system_prompt": "You are a sales director with small business experience. Focus on pricing, sales process, customer objections, and market adoption. Share practical sales insights.",
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"model_name": "gpt-4o",
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"max_loops": 3,
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},
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{
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"agent_name": "UX Researcher",
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"description": "Represents user experience and research insights",
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"system_prompt": "You are a UX researcher specializing in small business tools. Focus on user behavior, usability, adoption barriers, and design considerations.",
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"model_name": "gpt-4o",
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"max_loops": 3,
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}
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],
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"max_loops": 3
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}
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response = requests.post(
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f"{API_BASE_URL}/v1/swarm/completions",
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headers=headers,
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json=swarm_config
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)
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if response.status_code == 200:
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result = response.json()
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print("GroupChat swarm completed successfully!")
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print(f"Cost: ${result['metadata']['billing_info']['total_cost']}")
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print(f"Execution time: {result['metadata']['execution_time_seconds']} seconds")
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print(f"Chat discussion: {result['output']}")
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else:
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print(f"Error: {response.status_code} - {response.text}")
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```
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**Example Response**:
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```json
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{
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"job_id": "swarms-2COVtf3k0Fz7jU1BOOHF3b5nuL2x",
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"status": "success",
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"swarm_name": "Product Strategy Discussion",
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"description": "Collaborative chat to develop product strategy",
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"swarm_type": "GroupChat",
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"output": "User: \n\nSystem: \n Group Chat Name: Product Strategy Discussion\nGroup Chat Description: Collaborative chat to develop product strategy\n Agents in your Group Chat: Available Agents for Team: None\n\n\n\n[Agent 1]\nName: Product Manager\nDescription: Leads product strategy and development\nRole.....",
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"number_of_agents": 4,
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"service_tier": "standard",
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"execution_time": 47.36732482910156,
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"usage": {
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"input_tokens": 30,
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"output_tokens": 1633,
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"total_tokens": 1663,
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"billing_info": {
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"cost_breakdown": {
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"agent_cost": 0.04,
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"input_token_cost": 0.00009,
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"output_token_cost": 0.024495,
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"token_counts": {
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"total_input_tokens": 30,
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"total_output_tokens": 1633,
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"total_tokens": 1663
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},
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"num_agents": 4,
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"service_tier": "standard",
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"night_time_discount_applied": false
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},
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"total_cost": 0.064585,
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"discount_active": false,
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"discount_type": "none",
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"discount_percentage": 0
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}
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}
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}
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```
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## Best Practices
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- Set clear discussion goals and objectives
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- Use diverse agent personalities for richer dialogue
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- Allow multiple conversation rounds for idea development
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- Encourage agents to build upon each other's contributions
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## Related Swarm Types
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- [MixtureOfAgents](mixture_of_agents.md) - For complementary expertise
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- [MajorityVoting](majority_voting.md) - For consensus decision-making
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- [AutoSwarmBuilder](auto_swarm_builder.md) - For automatic discussion setup |