AgentInteraction is an open-source Python library for simulating and orchestrating interactions among multiple AI agents. It provides tools to define custom agent roles, manage dialogue states, integrate with LLM providers, and evaluate conversation outcomes through built-in metrics.
AI Platforms & Frameworks
#Multi-Agent-Framework
#conversational AI
#dialogue management
#agent orchestration
#Python development
#LLM Integration
#Performance Analysis
#Open-source tools
#AI prototyping
#custom agent roles
#dialogue simulation
#context handling
#research accelerator
#experimentation toolkit
#collaborative agents
#competitive dialogues
#flexible API
#user-driven design
#analytics tools
...
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AgentInteraction
AgentInteraction is an open-source Python library for simulating and orchestrating interactions among multiple AI agents. It provides tools to define custom agent roles, manage dialogue states, integrate with LLM providers, and evaluate conversation outcomes through built-in metrics.
AI Platforms & Frameworks
#Multi-Agent-Framework
#conversational AI
#dialogue management
#agent orchestration
#Python development
#LLM Integration
#Performance Analysis
#Open-source tools
#AI prototyping
#custom agent roles
#dialogue simulation
#context handling
#research accelerator
#experimentation toolkit
#collaborative agents
#competitive dialogues
#flexible API
#user-driven design
#analytics tools
...
Featured
What is AgentInteraction?
AgentInteraction is a developer-focused Python framework designed to simulate, coordinate, and evaluate multi-agent interactions using large language models. It allows users to define distinct agent roles, control conversational flow through a central manager, and integrate any LLM provider via a consistent API. With features like message routing, context management, and performance analytics, AgentInteraction streamlines experimentation with collaborative or competitive agent architectures, making it easy to prototype complex dialogue scenarios and measure success rates.
Who will use AgentInteraction?
- AI researchers exploring multi-agent systems
- Developers building conversational AI prototypes
- Data scientists evaluating LLM behaviors
- Educators teaching AI dialogue concepts
- Enthusiasts experimenting with agent collaborations
How to use the AgentInteraction?
- Step1: Install via pip with 'pip install agentinteraction'.
- Step2: Import and configure your preferred LLM provider and API keys.
- Step3: Define custom agent classes by extending the base Agent class.
- Step4: Instantiate an InteractionManager and register all agents.
- Step5: Configure conversation parameters and turn limits.
- Step6: Run the manager to start multi-agent dialogues.
- Step7: Use built-in evaluators to analyze and visualize results.
Platform
- mac
- windows
- linux
AgentInteraction's Core Features & Benefits
The Core Features
- Multi-agent conversation orchestration
- Customizable agent roles and behaviors
- Integration with any LLM provider
- Dialogue state and context management
- Built-in evaluation metrics and analytics
The Benefits
- Accelerates multi-agent research and prototyping
- Simplifies orchestration of complex dialogues
- Flexible API for custom workflows
- Open-source and extensible architecture
- Comprehensive tools for performance evaluation
AgentInteraction's Main Use Cases & Applications
- Researching collaboration and competition among AI agents
- Benchmarking different LLMs in multi-agent settings
- Teaching AI dialogue systems in academic courses
- Prototyping negotiation, debate, or role-play scenarios
- Testing agent coordination strategies in simulations
FAQs of AgentInteraction
AgentInteraction Company Information
- Chiara Paglioni
AgentInteraction Reviews
5/5
AgentInteraction's Main Competitors and alternatives?
- LangChain Multi-Agent Chains
- Auto-GPT
- Microsoft Multi-Agent Systems Toolkit
- Rasa
- OpenAI Function Calling
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