Updated documentation for python installation with anthropic and openai

README.md

@@ -69,8 +69,8 @@ To quickly get a feel for the Multi-Agent Orchestrator, check out our [Demo App]
 Get hands-on experience with the Multi-Agent Orchestrator through our diverse set of examples:
 
 - **Ready-to-run Scripts**: Start locally with our collection of standalone scripts in both Python and TypeScript.
-- **Demo Applications**: 
-  - [Chat Demo App](https://awslabs.github.io/multi-agent-orchestrator/cookbook/examples/chat-demo-app/): 
+- **Demo Applications**:
+  - [Chat Demo App](https://awslabs.github.io/multi-agent-orchestrator/cookbook/examples/chat-demo-app/):
     - Explore multiple specialized agents handling various domains like travel, weather, math, and health
   - [E-commerce Support Simulator](https://awslabs.github.io/multi-agent-orchestrator/cookbook/examples/ecommerce-support-simulator/): Experience AI-powered customer support with:
     - Automated response generation for common queries
@@ -83,8 +83,8 @@ Get hands-on experience with the Multi-Agent Orchestrator through our diverse se
   - [`chat-chainlit-app`](https://github.com/awslabs/multi-agent-orchestrator/tree/main/examples/chat-chainlit-app): Chat application built with Chainlit
   - [`fast-api-streaming`](https://github.com/awslabs/multi-agent-orchestrator/tree/main/examples/fast-api-streaming): FastAPI implementation with streaming support
   - [`text-2-structured-output`](https://github.com/awslabs/multi-agent-orchestrator/tree/main/examples/text-2-structured-output): Natural Language to Structured Data
-  
-  
+
+
 All examples are available in both Python and TypeScript implementations. Check out our [documentation](https://awslabs.github.io/multi-agent-orchestrator/) for comprehensive guides on setting up and using the Multi-Agent Orchestrator!
 
 
@@ -192,7 +192,7 @@ if (response.streaming == true) {
 
 ### Python Version
 
-#### Installation
+#### Core Installation
 
 ```bash
 # Optional: Set up a virtual environment
@@ -201,7 +201,7 @@ source venv/bin/activate  # On Windows use `venv\Scripts\activate`
 pip install multi-agent-orchestrator
 ```
 
-#### Usage
+#### Default Usage
 
 Here's an equivalent Python example demonstrating the use of the Multi-Agent Orchestrator with a Bedrock LLM Agent and a Lex Bot Agent:
 
@@ -289,6 +289,19 @@ These examples showcase:
 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.
 
+### Working with Anthropic or OpenAI
+If you want to use Anthropic or OpenAI for classifier and/or agents, make sure to install the multi-agent-orchestrator with the relevant extra feature.
+```bash
+pip install multi-agent-orchestrator[anthropic]
+pip install multi-agent-orchestrator[openai]
+```
+
+### Full package installation
+For a complete installation (including Anthropic and OpenAi):
+```bash
+pip install multi-agent-orchestrator[all]
+```
+
 
 ## 🤝 Contributing
 

docs/src/content/docs/classifiers/built-in/anthropic-classifier.mdx

@@ -16,6 +16,11 @@ The Anthropic Classifier extends the abstract `Classifier` class and uses the An
 
 ### Basic Usage
 
+⚠️ To use Anthropic Classifier, make sure you have installed the multi-agent-orchestrator with anthropic feature (see [python installation](/multi-agent-orchestrator/general/quickstart#-get-started))
+```bash
+pip install multi-agent-orchestrator[anthropic]
+```
+
 To use the AnthropicClassifier, you need to create an instance with your Anthropic API key and pass it to the Multi-Agent Orchestrator:
 
 import { Tabs, TabItem } from '@astrojs/starlight/components';

docs/src/content/docs/classifiers/built-in/openai-classifier.mdx

@@ -16,6 +16,12 @@ The OpenAI Classifier extends the abstract `Classifier` class and uses the OpenA
 
 ## Basic Usage
 
+⚠️ To use OpenAI Classifier, make sure you have installed the multi-agent-orchestrator with openai feature (see [python installation](/multi-agent-orchestrator/general/quickstart#-get-started))
+
+```bash
+pip install multi-agent-orchestrator[openai]
+```
+
 To use the OpenAIClassifier, you need to create an instance with your OpenAI API key and pass it to the Multi-Agent Orchestrator:
 
 import { Tabs, TabItem } from '@astrojs/starlight/components';

docs/src/content/docs/general/quickstart.mdx

@@ -38,7 +38,7 @@ To help you kickstart with the Multi-Agent Orchestrator framework, we'll walk yo
 
 2. Authenticate with your AWS account
 
-This quickstart demonstrates the use of Amazon Bedrock for both classification and agent responses. 
+This quickstart demonstrates the use of Amazon Bedrock for both classification and agent responses.
 
 To authenticate with your AWS account, follow these steps:
 
@@ -61,8 +61,8 @@ By default, the framework is configured as follows:
 <br/>
 
 > **Important**
-> 
->   These are merely default settings and can be easily changed to suit your needs or preferences. 
+>
+>   These are merely default settings and can be easily changed to suit your needs or preferences.
 
 <br/>
 
@@ -91,7 +91,10 @@ Ensure you have [requested access](https://docs.aws.amazon.com/bedrock/latest/us
   </TabItem>
   <TabItem label="Python" icon="seti:python">
     ```bash
-    pip install multi-agent-orchestrator
+    pip install multi-agent-orchestrator # for core dependencies
+    pip install multi-agent-orchestrator[anthropic] # for Anthropic classifier and agent
+    pip install multi-agent-orchestrator[openai] # for OpenAI classifier and agent
+    pip install multi-agent-orchestrator[all] # for all packages including Anthropic and OpenAI
     ```
   </TabItem>
 </Tabs>
@@ -165,7 +168,7 @@ Ensure you have [requested access](https://docs.aws.amazon.com/bedrock/latest/us
         streaming: true
       })
     );
-    
+
     orchestrator.addAgent(
       new BedrockLLMAgent({
         name: "Health Agent",
@@ -227,7 +230,7 @@ Ensure you have [requested access](https://docs.aws.amazon.com/bedrock/latest/us
             console.error("Received unexpected chunk type:", typeof chunk);
           }
         }
-        console.log(); 
+        console.log();
       } catch (error) {
         console.error("An error occurred:", error);
         // Here you could also add more specific error handling if needed

python/README.md

@@ -72,7 +72,7 @@ Check out our [documentation](https://awslabs.github.io/multi-agent-orchestrator
 
 
 
-### Installation
+### Core Installation
 
 ```bash
 # Optional: Set up a virtual environment
@@ -81,7 +81,7 @@ source venv/bin/activate  # On Windows use `venv\Scripts\activate`
 pip install multi-agent-orchestrator
 ```
 
-### Usage
+#### Default Usage
 
 Here's an equivalent Python example demonstrating the use of the Multi-Agent Orchestrator with a Bedrock LLM Agent and a Lex Bot Agent:
 
@@ -176,6 +176,19 @@ This example showcases:
 4. Handling of both streaming and non-streaming responses from different types of agents.
 
 
+### Working with Anthropic or OpenAI
+If you want to use Anthropic or OpenAI for classifier and/or agents, make sure to install the multi-agent-orchestrator with the relevant extra feature.
+```bash
+pip install multi-agent-orchestrator[anthropic]
+pip install multi-agent-orchestrator[openai]
+```
+
+### Full package installation
+For a complete installation (including Anthropic and OpenAi):
+```bash
+pip install multi-agent-orchestrator[all]
+```
+
 ## 🤝 Contributing
 
 We welcome contributions! Please see our [Contributing Guide](https://raw.githubusercontent.com/awslabs/multi-agent-orchestrator/main/CONTRIBUTING.md) for more details.