fixed workflow to accept 999 status code

.github/workflows/ts-run-lint.yml

@@ -20,15 +20,15 @@ jobs:
     runs-on: ubuntu-latest
     defaults:
       run:
-        working-directory: typescript    
+        working-directory: typescript
     steps:
       - name: Checkout repository
         uses: actions/checkout@9a9194f87191a7e9055e3e9b95b8cfb13023bb08
       - name: Link Checker
         uses: lycheeverse/lychee-action@c053181aa0c3d17606addfe97a9075a32723548a
         with:
           fail: true
-          args: --scheme=https . --exclude-all-private
+          args: --scheme=https . --exclude-all-private --accept 999
       - name: Install dependencies
         run: npm install
       - name: Run linting

README.md

@@ -22,9 +22,9 @@
 
 ## 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.
 
@@ -36,10 +36,10 @@ This adaptability makes it suitable for a wide range of applications, from simpl
 
 <br /><br />
 
-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
 
@@ -56,7 +56,7 @@ 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.
 
@@ -276,7 +276,7 @@ We welcome contributions! Please see our [Contributing Guide](https://raw.github
 # Authors
 
 - [Corneliu Croitoru](https://www.linkedin.com/in/corneliucroitoru/)
-- [Anthony Bernabeu](https://www.linkedin.com/in/anthony-bernabeu-74228160/)
+- [Anthony Bernabeu](https://www.linkedin.com/in/anthonybernabeu/)
 
 # Contributors
 [![contributors](https://contrib.rocks/image?repo=awslabs/multi-agent-orchestrator&max=2000)](https://github.com/awslabs/multi-agent-orchestrator/graphs/contributors)