powerful! An open source multi-agent management framework that flexibly handles complex multi-round conversations and truly realizes the all-round AI.
The multi-agent coordinator is mainly used to manage multiple AI agents and optimize the way they handle complex conversations.
What I will introduce to you today is multi-agent-orchestrator, a multi-agent coordination framework that uses an intelligent routing mechanism to allocate user queries to the most suitable agent based on the conversation content while maintaining the continuity of the conversation.
To put it simply, it is like an intelligent traffic command center, but it commands various AI agents to help them conduct better conversations. When you ask the system a question, it can determine which AI agent is best at answering your question and send the question to that AI agent. In this way, no matter how complex your question is, the system can answer you smoothly and accurately.
Project introduction
multi-agent-orchestrator is a flexible framework designed to manage multiple artificial intelligence agents and handle complex conversations. The system dynamically routes queries to the most appropriate agent through intelligent intent classification, while maintaining and leveraging session context across multiple agents for coherent interactions. It supports streaming and non-streaming responses and runs on any cloud platform, whether it's AWS Lambda or an on-premises environment. The framework also supports Python and TypeScript, making it easy to extend and customize agents to suit specific needs, making it suitable for a wide range of applications from simple chatbots to complex AI systems.
Architecture process
- The process starts with user input, which is first analyzed by a classifier.
- The classifier selects the most appropriate agent to perform the task based on the characteristics of the agent and its conversation history.
- Once the agent is selected, it begins processing user input.
- The coordinator then saves the conversation content, updates the agent's conversation history, and delivers the response to the user.
- Travel Agent: Powered by Amazon Lex Bot
- Weather Proxy: Tools utilizing Bedrock LLM proxies and querying the open-meteo API
- Restaurant Proxy: Implemented as an Amazon Bedrock Proxy
- Math Agents: Utilizes the Bedrock LLM agent and two tools to perform mathematical operations
- Technology Agent: A Bedrock LLM agent designed to answer questions about technology topics
- Health Agent: Bedrock LLM Agent Specialized in Handling Health-Related Inquiries
The system can move seamlessly between topics, from booking flights to checking the weather, solving math problems, and providing health information. Choose the appropriate agent for each query to maintain coherence even for brief follow-up inputs.
Project link