From e6eb46e2c2e463a0b31cf9231ae3f0abade2df8c Mon Sep 17 00:00:00 2001 From: Anthony Date: Mon, 14 Oct 2024 10:41:03 +0200 Subject: [PATCH] fixed links --- typescript/README.md | 16 ++++++++-------- 1 file changed, 8 insertions(+), 8 deletions(-) diff --git a/typescript/README.md b/typescript/README.md index f018a17f..360068c4 100644 --- a/typescript/README.md +++ b/typescript/README.md @@ -18,9 +18,9 @@ ## What's the Multi-Agent Orchestrator ❓ -The Multi-Agent Orchestrator is a flexible framework for managing multiple AI agents and handling complex conversations. It intelligently routes queries and maintains context across interactions. +The Multi-Agent Orchestrator is a flexible framework for managing multiple AI agents and handling complex conversations. It intelligently routes queries and maintains context across interactions. -The system offers pre-built components for quick deployment, while also allowing easy integration of custom agents and conversation messages storage solutions. +The system offers pre-built components for quick deployment, while also allowing easy integration of custom agents and conversation messages storage solutions. This adaptability makes it suitable for a wide range of applications, from simple chatbots to sophisticated AI systems, accommodating diverse requirements and scaling efficiently. @@ -34,10 +34,10 @@ This adaptability makes it suitable for a wide range of applications, from simpl

-1. The process begins with user input, which is analyzed by a Classifier. -2. The Classifier leverages both Agents' Characteristics and Agents' Conversation history to select the most appropriate agent for the task. +1. The process begins with user input, which is analyzed by a Classifier. +2. The Classifier leverages both Agents' Characteristics and Agents' Conversation history to select the most appropriate agent for the task. 3. Once an agent is selected, it processes the user input. -4. The orchestrator then saves the conversation, updating the Agents' Conversation history, before delivering the response back to the user. +4. The orchestrator then saves the conversation, updating the Agents' Conversation history, before delivering the response back to the user. ## 💬 Demo App @@ -55,7 +55,7 @@ In the screen recording below, we demonstrate an extended version of the demo ap - **Health Agent**: A Bedrock LLM Agent focused on addressing health-related queries Watch as the system seamlessly switches context between diverse topics, from booking flights to checking weather, solving math problems, and providing health information. -Notice how the appropriate agent is selected for each query, maintaining coherence even with brief follow-up inputs. +Notice how the appropriate agent is selected for each query, maintaining coherence even with brief follow-up inputs. The demo highlights the system's ability to handle complex, multi-turn conversations while preserving context and leveraging specialized agents across various domains. @@ -157,11 +157,11 @@ This example showcases: ## 🤝 Contributing -We welcome contributions! Please see our [Contributing Guide](CONTRIBUTING.md) for more details. +We welcome contributions! Please see our [Contributing Guide](https://raw.githubusercontent.com/awslabs/multi-agent-orchestrator/main/CONTRIBUTING.md) for more details. ## 📄 LICENSE -This project is licensed under the Apache 2.0 licence - see the [LICENSE](LICENSE) file for details. +This project is licensed under the Apache 2.0 licence - see the [LICENSE](https://raw.githubusercontent.com/awslabs/multi-agent-orchestrator/main/LICENSE) file for details. ## 📄 Font License This project uses the JetBrainsMono NF font, licensed under the SIL Open Font License 1.1.