use separate readme for python and typescript

.github/workflows/npm-publish.yml

@@ -19,7 +19,6 @@ jobs:
           node-version: 20
           registry-url: https://registry.npmjs.org/
       - run: cp ../LICENSE .
-      - run: cp ../README.md .
       - run: npm install
       - run: npm run build
       - run: npm pack

.github/workflows/pypi-publish.yml

@@ -15,7 +15,6 @@ jobs:
     - name: Copy files
       run: |
         cp ../LICENSE .
-        cp ../README.md .
 
     - name: Set up Python
       uses: actions/setup-python@2bd53f9a4d1dd1cd21eaffcc01a7b91a8e73ea4c

README.md

@@ -159,11 +159,11 @@ 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.

python/README.md

@@ -0,0 +1,105 @@
+<h2 align="center">Multi-Agent Orchestrator&nbsp;<img alt="Static Badge" src="https://img.shields.io/badge/Beta-4e9bcd"></h2>
+
+<p align="center">Flexible and powerful framework for managing multiple AI agents and handling complex conversations.</p>
+
+<p align="center">
+<a href="https://github.com/awslabs/multi-agent-orchestrator"><img alt="GitHub Repo" src="https://img.shields.io/badge/GitHub-Repo-green.svg" /></a>
+</p>
+
+## 🔖 Features
+
+- 🧠 **Intelligent Intent Classification** — Dynamically route queries to the most suitable agent based on context and content.
+- 🌊 **Flexible Agent Responses** — Support for both streaming and non-streaming responses from different agents.
+- 📚 **Context Management** — Maintain and utilize conversation context across multiple agents for coherent interactions.
+- 🔧 **Extensible Architecture** — Easily integrate new agents or customize existing ones to fit your specific needs.
+- 🌐 **Universal Deployment** — Run anywhere - from AWS Lambda to your local environment or any cloud platform.
+- 📦 **Pre-built Agents and Classifiers** — A variety of ready-to-use agents and multiple classifier implementations available.
+- 🔤 **TypeScript Support** — Native TypeScript implementation available.
+
+## 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 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.
+
+
+## 🏗️ High-level architecture flow diagram
+
+<br /><br />
+
+![High-level architecture flow diagram](https://raw.githubusercontent.com/awslabs/multi-agent-orchestrator/main/img/flow.jpg)
+
+<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. 
+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. 
+
+
+## 💬 Demo App
+
+To quickly get a feel for the Multi-Agent Orchestrator, we've provided a Demo App with a few basic agents. This interactive demo showcases the orchestrator's capabilities in a user-friendly interface. To learn more about setting up and running the demo app, please refer to our [Demo App](https://awslabs.github.io/multi-agent-orchestrator/deployment/demo-web-app/) section.
+
+<br>
+
+In the screen recording below, we demonstrate an extended version of the demo app that uses 6 specialized agents:
+- **Travel Agent**: Powered by an Amazon Lex Bot
+- **Weather Agent**: Utilizes a Bedrock LLM Agent with a tool to query the open-meteo API
+- **Restaurant Agent**: Implemented as an Amazon Bedrock Agent
+- **Math Agent**: Utilizes a Bedrock LLM Agent with two tools for executing mathematical operations
+- **Tech Agent**: A Bedrock LLM Agent designed to answer questions on technical topics
+- **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. 
+
+The demo highlights the system's ability to handle complex, multi-turn conversations while preserving context and leveraging specialized agents across various domains.
+
+Click on the image below to see a screen recording of the demo app on the GitHub repository of the project:
+<a href="https://github.com/awslabs/multi-agent-orchestrator" target="_blank">
+  <img src="https://raw.githubusercontent.com/awslabs/multi-agent-orchestrator/main/img/demo-app.jpg" alt="Demo App Screen Recording" style="max-width: 100%; height: auto;">
+</a>
+
+
+
+## 🚀 Getting Started
+
+Check out our [documentation](https://awslabs.github.io/multi-agent-orchestrator/) for comprehensive guides on setting up and using the Multi-Agent Orchestrator!
+
+
+### Installation
+
+```bash
+pip install multi-agent-orchestrator
+```
+
+### Usage
+
+The following example demonstrates how to use the Multi-Agent Orchestrator with two different types of agents: a Bedrock LLM Agent with Converse API support and a Lex Bot Agent. This showcases the flexibility of the system in integrating various AI services.
+
+```python
+
+```
+
+This example showcases:
+1. The use of a Bedrock LLM Agent with Converse API support, allowing for multi-turn conversations.
+2. Integration of a Lex Bot Agent for specialized tasks (in this case, travel-related queries).
+3. The orchestrator's ability to route requests to the most appropriate agent based on the input.
+4. Handling of both streaming and non-streaming responses from different types of agents.
+
+
+## 🤝 Contributing
+
+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](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.
+For full license details, see [FONT-LICENSE.md](https://github.com/JetBrains/JetBrainsMono/blob/master/OFL.txt).

python/setup.cfg

@@ -1,6 +1,6 @@
 [metadata]
 name = multi_agent_orchestrator
-version = 0.0.7
+version = 0.0.8
 author = Anthony Bernabeu, Corneliu Croitoru
 author_email = [email protected], [email protected]
 description = Multi-agent orchestrator framework

typescript/README.md

@@ -0,0 +1,168 @@
+<h2 align="center">Multi-Agent Orchestrator&nbsp;<img alt="Static Badge" src="https://img.shields.io/badge/Beta-4e9bcd"></h2>
+
+<p align="center">Flexible and powerful framework for managing multiple AI agents and handling complex conversations.</p>
+
+<p align="center">
+<a href="https://github.com/awslabs/multi-agent-orchestrator"><img alt="GitHub Repo" src="https://img.shields.io/badge/GitHub-Repo-green.svg" /></a>
+</p>
+
+## 🔖 Features
+
+- 🧠 **Intelligent Intent Classification** — Dynamically route queries to the most suitable agent based on context and content.
+- 🌊 **Flexible Agent Responses** — Support for both streaming and non-streaming responses from different agents.
+- 📚 **Context Management** — Maintain and utilize conversation context across multiple agents for coherent interactions.
+- 🔧 **Extensible Architecture** — Easily integrate new agents or customize existing ones to fit your specific needs.
+- 🌐 **Universal Deployment** — Run anywhere - from AWS Lambda to your local environment or any cloud platform.
+- 📦 **Pre-built Agents and Classifiers** — A variety of ready-to-use agents and multiple classifier implementations available.
+- 🔤 **TypeScript Support** — Native TypeScript implementation available.
+
+## 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 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.
+
+
+## 🏗️ High-level architecture flow diagram
+
+<br /><br />
+
+![High-level architecture flow diagram](https://raw.githubusercontent.com/awslabs/multi-agent-orchestrator/main/img/flow.jpg)
+
+<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. 
+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. 
+
+
+## 💬 Demo App
+
+To quickly get a feel for the Multi-Agent Orchestrator, we've provided a Demo App with a few basic agents. This interactive demo showcases the orchestrator's capabilities in a user-friendly interface. To learn more about setting up and running the demo app, please refer to our [Demo App](https://awslabs.github.io/multi-agent-orchestrator/deployment/demo-web-app/) section.
+
+<br>
+
+In the screen recording below, we demonstrate an extended version of the demo app that uses 6 specialized agents:
+- **Travel Agent**: Powered by an Amazon Lex Bot
+- **Weather Agent**: Utilizes a Bedrock LLM Agent with a tool to query the open-meteo API
+- **Restaurant Agent**: Implemented as an Amazon Bedrock Agent
+- **Math Agent**: Utilizes a Bedrock LLM Agent with two tools for executing mathematical operations
+- **Tech Agent**: A Bedrock LLM Agent designed to answer questions on technical topics
+- **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. 
+
+The demo highlights the system's ability to handle complex, multi-turn conversations while preserving context and leveraging specialized agents across various domains.
+
+![](https://raw.githubusercontent.com/awslabs/multi-agent-orchestrator/main/img/demo-app.gif?raw=true)
+
+## 🚀 Getting Started
+
+Check out our [documentation](https://awslabs.github.io/multi-agent-orchestrator/) for comprehensive guides on setting up and using the Multi-Agent Orchestrator!
+
+### Installation
+
+```bash
+npm install multi-agent-orchestrator
+```
+
+### Usage
+
+The following example demonstrates how to use the Multi-Agent Orchestrator with two different types of agents: a Bedrock LLM Agent with Converse API support and a Lex Bot Agent. This showcases the flexibility of the system in integrating various AI services.
+
+```typescript
+import { MultiAgentOrchestrator, BedrockLLMAgent, LexBotAgent } from "multi-agent-orchestrator";
+
+const orchestrator = new MultiAgentOrchestrator();
+
+// Add a Bedrock LLM Agent with Converse API support
+orchestrator.addAgent(
+  new BedrockLLMAgent({
+      name: "Tech Agent",
+      description:
+        "Specializes in technology areas including software development, hardware, AI, cybersecurity, blockchain, cloud computing, emerging tech innovations, and pricing/costs related to technology products and services.",
+      streaming: true
+  })
+);
+
+// Add a Lex Bot Agent for handling travel-related queries
+orchestrator.addAgent(
+  new LexBotAgent({
+    name: "Travel Agent",
+    description: "Helps users book and manage their flight reservations",
+    botId: process.env.LEX_BOT_ID,
+    botAliasId: process.env.LEX_BOT_ALIAS_ID,
+    localeId: "en_US",
+  })
+);
+
+// Example usage
+const response = await orchestrator.routeRequest(
+  "I want to book a flight",
+  'user123',
+  'session456'
+);
+
+// Handle the response (streaming or non-streaming)
+if (response.streaming == true) {
+    console.log("\n** RESPONSE STREAMING ** \n");
+    // Send metadata immediately
+    console.log(`> Agent ID: ${response.metadata.agentId}`);
+    console.log(`> Agent Name: ${response.metadata.agentName}`);
+    console.log(`> User Input: ${response.metadata.userInput}`);
+    console.log(`> User ID: ${response.metadata.userId}`);
+    console.log(`> Session ID: ${response.metadata.sessionId}`);
+    console.log(
+      `> Additional Parameters:`,
+      response.metadata.additionalParams
+    );
+    console.log(`\n> Response: `);
+
+    // Stream the content
+    for await (const chunk of response.output) {
+      if (typeof chunk === "string") {
+        process.stdout.write(chunk);
+      } else {
+        console.error("Received unexpected chunk type:", typeof chunk);
+      }
+    }
+
+} else {
+    // Handle non-streaming response (AgentProcessingResult)
+    console.log("\n** RESPONSE ** \n");
+    console.log(`> Agent ID: ${response.metadata.agentId}`);
+    console.log(`> Agent Name: ${response.metadata.agentName}`);
+    console.log(`> User Input: ${response.metadata.userInput}`);
+    console.log(`> User ID: ${response.metadata.userId}`);
+    console.log(`> Session ID: ${response.metadata.sessionId}`);
+    console.log(
+      `> Additional Parameters:`,
+      response.metadata.additionalParams
+    );
+    console.log(`\n> Response: ${response.output}`);
+}
+```
+
+This example showcases:
+1. The use of a Bedrock LLM Agent with Converse API support, allowing for multi-turn conversations.
+2. Integration of a Lex Bot Agent for specialized tasks (in this case, travel-related queries).
+3. The orchestrator's ability to route requests to the most appropriate agent based on the input.
+4. Handling of both streaming and non-streaming responses from different types of agents.
+
+
+## 🤝 Contributing
+
+We welcome contributions! Please see our [Contributing Guide](CONTRIBUTING.md) for more details.
+
+## 📄 LICENSE
+
+This project is licensed under the Apache 2.0 licence - see the [LICENSE](LICENSE) file for details.
+
+## 📄 Font License
+This project uses the JetBrainsMono NF font, licensed under the SIL Open Font License 1.1.
+For full license details, see [FONT-LICENSE.md](https://github.com/JetBrains/JetBrainsMono/blob/master/OFL.txt).