AgentMesh is an open-source Python framework that enables you to define, register, and orchestrate specialized AI agents into a dynamic mesh. It supports asynchronous messaging, error handling, and routing rules for building scalable, modular automation and conversational systems.
AI Platforms & Frameworks
Workflow Automation
Digital Workers
#AI Agents
#modular infrastructure
#asynchronous communication
#dynamic discovery
#message routing
#error handling
#scalable frameworks
#multi-agent systems
#task orchestration
#plugin support
#fault tolerance
#data processing
#decision support
#conversational AI
#integration with LLMs
#reusable components
#development efficiency
#agent communication
#runtime registration
#message filtering
...
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AgentMesh
AgentMesh is an open-source Python framework that enables you to define, register, and orchestrate specialized AI agents into a dynamic mesh. It supports asynchronous messaging, error handling, and routing rules for building scalable, modular automation and conversational systems.
AI Platforms & Frameworks
Workflow Automation
Digital Workers
#AI Agents
#modular infrastructure
#asynchronous communication
#dynamic discovery
#message routing
#error handling
#scalable frameworks
#multi-agent systems
#task orchestration
#plugin support
#fault tolerance
#data processing
#decision support
#conversational AI
#integration with LLMs
#reusable components
#development efficiency
#agent communication
#runtime registration
#message filtering
...
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What is AgentMesh?
AgentMesh provides a modular infrastructure for developers to create networks of AI agents, each focusing on a specific task or domain. Agents can be dynamically discovered and registered at runtime, exchange messages asynchronously, and follow configurable routing rules. The framework handles retries, fallbacks, and error recovery, allowing multi-agent pipelines for data processing, decision support, or conversational use cases. It integrates easily with existing LLMs and custom models via a simple plugin interface.
Who will use AgentMesh?
- AI developers
- Software engineers
- Research teams
- Automation architects
- Chatbot designers
How to use the AgentMesh?
- Step1: Clone the AgentMesh repository from GitHub.
- Step2: Install dependencies with pip install -r requirements.txt.
- Step3: Define custom agents by extending the BaseAgent class.
- Step4: Register each agent in the mesh orchestrator with routing rules.
- Step5: Launch the MeshServer to start asynchronous message passing.
- Step6: Monitor agent workflows and adjust configurations as needed.
Platform
- mac
- windows
- linux
AgentMesh's Core Features & Benefits
The Core Features
- Asynchronous agent communication
- Dynamic agent discovery and registration
- Configurable routing and message filtering
- Error handling and retry mechanisms
- Plugin and extension support
- Scalable multi-agent pipelines
The Benefits
- Modular and reusable agent components
- Scalable task orchestration
- Improved fault tolerance
- Seamless integration with LLMs
- Reduced development overhead
AgentMesh's Main Use Cases & Applications
- Multi-agent customer support chatbots
- Automated data processing workflows
- Distributed decision-making systems
- Domain-specific conversational assistants
- Business process automation
FAQs of AgentMesh
AgentMesh Company Information
AgentMesh Reviews
5/5
AgentMesh's Main Competitors and alternatives?
- LangChain Agents
- AutoGen
- Haystack
- Microsoft Semantic Kernel
- AIM
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