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Mahilo
Overview :
Mahilo is a powerful AI agent integration platform designed to connect AI agents from various frameworks for real-time communication and human oversight. It provides a framework-agnostic communication protocol, supporting various popular agent frameworks like LangGraph, Pydantic AI, etc., while also allowing connection to proprietary agents via API. The platform emphasizes intelligent collaboration, organization-level policy management, and human-centered design, ensuring human control while automating tasks. Mahilo offers a flexible solution for building complex multi-agent systems, suitable for various applications from content creation to emergency response. Currently, Mahilo boasts 251 stars on GitHub and over 500 monthly PyPI downloads, showcasing its popularity within the developer community. Mahilo primarily targets developers and enterprise users, helping them quickly build and deploy multi-agent systems to improve efficiency and foster innovation.
Target Users :
Mahilo primarily targets developers and enterprise users, especially those who need to build and manage multi-agent systems. It's suitable for users who want to integrate AI agents from different frameworks into a unified platform for efficient collaboration and real-time communication. Furthermore, Mahilo offers an ideal solution for scenarios requiring human oversight and control in AI automation processes. Whether for content creation, emergency response, or business applications, Mahilo helps users quickly build and deploy complex multi-agent systems, improving efficiency and fostering innovation.
Use Cases
Story Weaver: A multiplayer story creation game where users collaborate with AI agents to create stories. The AI intelligently integrates narratives, supporting real-time collaborative creation, shared context management, and multi-player AI interaction.
911 Emergency Response: In emergency response scenarios, AI agents can coordinate resources, quickly respond, and provide support, improving emergency handling efficiency.
RentMate AI: A real estate matching application where AI agents help users find suitable properties, improving rental efficiency through intelligent matching and information sharing.
Features
Universal Agent Integration: Supports connecting AI agents from various frameworks like LangGraph, Pydantic AI, and proprietary agents via API.
Real-time Communication: Provides instant voice and text chat for integrated agents, enabling real-time communication and seamless human-machine interaction between agents.
Intelligent Collaboration: AI agents can autonomously share context and information, enabling cross-framework information exchange and automated agent queries.
Organization-level Policy Management: Centrally manage policies to ensure consistent behavior and security controls for all integrated agents.
Human-centered Design: Maintains human control while AI handles complex interactions, contacting humans only when necessary and allowing human intervention in AI decisions.
Multi-agent Architecture: Supports building complex agent systems with flexible communication modes, including hierarchical and peer-to-peer.
Multi-user Interaction: Supports multiple users interacting with AI agents simultaneously, featuring sophisticated shared context management and configurable inter-agent communication modes.
Flexible Development Interface: Provides tools like BaseAgent class and AgentManager to facilitate agent definition, agent manager creation, and WebSocket server startup.
How to Use
1. Define agents: Create agents using the BaseAgent class or via integration, such as using LangGraphAgent to connect LangGraph framework agents.
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2. Create an agent manager: Add defined agents to the AgentManager, treating them as a team.
3. Start the WebSocket server: Create and run AgentWebSocketServer for real-time communication between agents.
4. Connect clients: Connect to the WebSocket server using client scripts, specifying the agent name to begin interaction. For example, connect the buyer agent using the command `$ python client.py --agent-name buyer_agent`.
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