Multi-Agent-Conversation-AutoGen is a Python framework that uses OpenAI's GPT models to produce realistic multi-agent dialogues. Developers define agent personalities, conversation parameters, and turn-taking rules to automatically generate extensive chat logs. It supports customizable roles, adjustable conversation length, and flexible prompt templates. This tool accelerates scenario creation for chatbot testing, research studies, and educational purposes by delivering reproducible, diverse dialogue samples with minimal manual effort.
AI Platforms & Frameworks
Workflow Automation
#AI conversation automation
#multi-agent interaction
#dialogue generation tool
#conversational testing framework
#dynamic dialogue simulation
#interactive AI agents
#Chatbot Development Tool
#research conversation scenarios
#educational dialogue creation
#customizable agent profiles
#dynamic prompt templates
#API integration for dialogue
#json log export
#conversation control features
#automated dialogue sequences
#group discussion simulation
#AI testing scenarios
#script-free dialog generation
#modular AI framework
#fast dialogue data production
...
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Multi-Agent Conversation AutoGen
Multi-Agent-Conversation-AutoGen is a Python framework that uses OpenAI's GPT models to produce realistic multi-agent dialogues. Developers define agent personalities, conversation parameters, and turn-taking rules to automatically generate extensive chat logs. It supports customizable roles, adjustable conversation length, and flexible prompt templates. This tool accelerates scenario creation for chatbot testing, research studies, and educational purposes by delivering reproducible, diverse dialogue samples with minimal manual effort.
AI Platforms & Frameworks
Workflow Automation
#AI conversation automation
#multi-agent interaction
#dialogue generation tool
#conversational testing framework
#dynamic dialogue simulation
#interactive AI agents
#Chatbot Development Tool
#research conversation scenarios
#educational dialogue creation
#customizable agent profiles
#dynamic prompt templates
#API integration for dialogue
#json log export
#conversation control features
#automated dialogue sequences
#group discussion simulation
#AI testing scenarios
#script-free dialog generation
#modular AI framework
#fast dialogue data production
...
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What is Multi-Agent Conversation AutoGen?
Multi-Agent-Conversation-AutoGen is engineered to automate the creation of interactive dialogue sequences among multiple AI agents for testing, research, and educational applications. Users supply a configuration file to define agent profiles, personas, and conversation flows. The framework orchestrates turn-based interactions, leveraging OpenAI GPT APIs to generate each message dynamically. Key features include customizable prompt templates, flexible API integration, conversation length control, and exportable logs in JSON or text formats. With this tool, developers can simulate complex group discussions, stress-test conversational agents in diverse scenarios, and rapidly produce large sets of dialogue data without manual scripting. The modular architecture allows extension to other LLM providers and integration into existing development pipelines.
Who will use Multi-Agent Conversation AutoGen?
- AI researchers
- Chatbot developers
- Educational content creators
- UX designers testing conversational flows
- QA engineers for dialog systems
How to use the Multi-Agent Conversation AutoGen?
- Step1: Clone the repository using git clone https://github.com/shaadclt/Multi-Agent-Conversation-AutoGen.git
- Step2: Install Python 3.8+ and dependencies via pip install -r requirements.txt
- Step3: Set OPENAI_API_KEY in your environment variables or config file
- Step4: Edit the config.yaml file to define agent personas, roles, and conversation parameters
- Step5: Run python autogen.py --config config.yaml to generate multi-agent dialogues
- Step6: Review the output logs in the output directory and adjust parameters as needed
Platform
- mac
- windows
- linux
Multi-Agent Conversation AutoGen's Core Features & Benefits
The Core Features
- Multi-agent dialog orchestration
- Customizable agent persona definitions
- Dynamic prompt template support
- Conversation length and turn control
- Exportable logs in JSON and text
The Benefits
- Accelerates scenario creation
- Enhances chatbot testing coverage
- Produces reproducible dialogue samples
- Reduces manual scripting effort
- Modular and extendable architecture
Multi-Agent Conversation AutoGen's Main Use Cases & Applications
- Stress-testing conversational agents in various scenarios
- Generating training data for NLP models
- Simulating group discussions for educational tools
- Researching multi-agent communication strategies
- Creating sample dialogues for UX validation
FAQs of Multi-Agent Conversation AutoGen
Multi-Agent Conversation AutoGen Company Information
Multi-Agent Conversation AutoGen Reviews
5/5
Multi-Agent Conversation AutoGen's Main Competitors and alternatives?
- Microsoft Autogen
- LangChain multi-agent
- AI2E MultiAgentToolkit
- ConvLab-3
- Rasa
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