LangGraph Multi-Agent Supervisor
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Langgraph Multi Agent Supervisor
Overview :
LangGraph Multi-Agent Supervisor is a Python library built on the LangGraph framework for creating hierarchical multi-agent systems. It allows developers to coordinate multiple specialized agents through a centralized supervisor agent, enabling dynamic task allocation and communication management. The significance of this technology lies in its ability to efficiently organize complex multi-agent tasks, enhancing system flexibility and scalability. It is suitable for scenarios requiring multi-agent collaboration, such as automated task processing and complex problem-solving. This product is positioned for advanced developers and enterprise-level applications. While pricing is not explicitly public, its open-source nature allows users to customize and extend it according to their needs.
Target Users :
This product is designed for advanced developers, researchers, and enterprise users who need to build complex multi-agent systems. It is suitable for those who require intelligent agent collaboration to solve complex problems, automate task processing, or construct intelligent systems. Its flexible architecture and powerful features make it an ideal choice for developing efficient and scalable multi-agent applications.
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Use Cases
Manage a research team and a mathematics team, dynamically assigning problems to the appropriate team based on task requirements.
Build an automated customer service system, where a supervisor agent assigns issues to different specialized agents based on the problem type.
Develop intelligent education applications that provide students with personalized learning paths through a multi-level agent system.
Features
Create a centralized supervisor agent to coordinate multiple specialized agents.
Support tool-based agent handover mechanisms to enable communication between agents.
Flexibly manage conversation history, with options to retain the complete history or only the last message.
Support multi-level hierarchical structures, allowing the creation of multiple agent tiers.
Provide memory support, including short-term and long-term memory capabilities.
How to Use
1. Install the LangGraph Multi-Agent Supervisor library: `pip install langgraph-supervisor`
2. Import necessary modules and define specialized agents and their tools.
3. Create a supervisor agent, specifying the specialized agents it coordinates and the model.
4. Compile and run the supervisor agent workflow, passing in user questions or tasks.
5. Adjust dialogue history management mode or add memory support as needed.
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