diff --git a/.github/workflows/ts-run-lint.yml b/.github/workflows/ts-run-lint.yml
index cc9bd45..5d2d036 100644
--- a/.github/workflows/ts-run-lint.yml
+++ b/.github/workflows/ts-run-lint.yml
@@ -20,7 +20,7 @@ jobs:
runs-on: ubuntu-latest
defaults:
run:
- working-directory: typescript
+ working-directory: typescript
steps:
- name: Checkout repository
uses: actions/checkout@9a9194f87191a7e9055e3e9b95b8cfb13023bb08
@@ -28,7 +28,7 @@ jobs:
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
diff --git a/README.md b/README.md
index 1851d10..88519fd 100644
--- a/README.md
+++ b/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
-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
[](https://github.com/awslabs/multi-agent-orchestrator/graphs/contributors)