Multi AI Agent Systems is an open-source Python framework for orchestrating interactions among customizable AI agents. Developers define agent roles, communication protocols, and memory settings in simple configuration files. The built-in orchestrator manages message routing and workflow execution, enabling scalable multi-agent collaboration without deep infrastructure overhead.
AI Platforms & Frameworks
Workflow Automation
#AI Agents
#multi-agent systems
#orchestration framework
#dynamic routing
#persistent memory
#customer support automation
#document generation
#Docker deployment
#role-based agents
#JSON communication
#modular architecture
#Open-source AI
#LLM Integration
#conversational AI
#Scalable Workflows
#Collaboration Tools
#Task Automation
#AI Frameworks
#software development
#agent configuration
...
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Multi AI Agent Systems
Multi AI Agent Systems is an open-source Python framework for orchestrating interactions among customizable AI agents. Developers define agent roles, communication protocols, and memory settings in simple configuration files. The built-in orchestrator manages message routing and workflow execution, enabling scalable multi-agent collaboration without deep infrastructure overhead.
AI Platforms & Frameworks
Workflow Automation
#AI Agents
#multi-agent systems
#orchestration framework
#dynamic routing
#persistent memory
#customer support automation
#document generation
#Docker deployment
#role-based agents
#JSON communication
#modular architecture
#Open-source AI
#LLM Integration
#conversational AI
#Scalable Workflows
#Collaboration Tools
#Task Automation
#AI Frameworks
#software development
#agent configuration
...
Featured
What is Multi AI Agent Systems?
This framework allows users to design, configure, and deploy multiple AI agents that communicate via JSON messages through a central orchestrator. Each agent can have distinct roles, prompts, and memory modules, and you can plug in any LLM provider by implementing a provider interface. The system supports persistent conversation history, dynamic routing, and modular extensions. Ideal for simulating debates, automating customer support flows, or coordinating multi-step document generation, it runs on Python, with Docker support for containerized deployments.
Who will use Multi AI Agent Systems?
- Developers building AI workflows
- Researchers studying agent interactions
- Automation engineers
- Businesses seeking multi-agent orchestration
How to use the Multi AI Agent Systems?
- Step1: Clone the GitHub repository
- Step2: Install Python dependencies via `pip install -r requirements.txt`
- Step3: Define agents, roles, and prompts in the configuration files
- Step4: Configure LLM providers and memory backends
- Step5: Run the orchestrator via CLI or API endpoint
- Step6: Monitor and log agent interactions through provided logging tools
Platform
- mac
- windows
- linux
Multi AI Agent Systems's Core Features & Benefits
The Core Features
- Multi-agent orchestration with central message bus
- Role-based agent definitions and behaviors
- Configurable communication protocols (JSON schemas)
- Plugin support for custom LLM providers
- Persistent conversation memory modules
- Dockerfile for containerized deployments
The Benefits
- Modular and extensible architecture
- Accelerates development of multi-agent systems
- Open-source and MIT-licensed
- Scalable workflows without complex infrastructure
- Enhances collaboration among autonomous AI agents
Multi AI Agent Systems's Main Use Cases & Applications
- Simulating agent-based debates for academic research
- Building automated customer support workflows
- Coordinating multi-step document or report generation
- Testing AI coordination strategies and protocols
- Automating complex business process flows
FAQs of Multi AI Agent Systems
Multi AI Agent Systems Company Information
- kicka42
Multi AI Agent Systems Reviews
5/5
Multi AI Agent Systems's Main Competitors and alternatives?
- LangChain Agents
- Microsoft Semantic Kernel
- AutoGPT
- AgentOS
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