diff --git a/README.md b/README.md index 16510a89..ac9068a7 100644 --- a/README.md +++ b/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 diff --git a/docs/src/content/docs/classifiers/built-in/anthropic-classifier.mdx b/docs/src/content/docs/classifiers/built-in/anthropic-classifier.mdx index a85d9760..cd3ffee3 100644 --- a/docs/src/content/docs/classifiers/built-in/anthropic-classifier.mdx +++ b/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'; diff --git a/docs/src/content/docs/classifiers/built-in/openai-classifier.mdx b/docs/src/content/docs/classifiers/built-in/openai-classifier.mdx index d6e4cd03..8da836aa 100644 --- a/docs/src/content/docs/classifiers/built-in/openai-classifier.mdx +++ b/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'; diff --git a/docs/src/content/docs/general/quickstart.mdx b/docs/src/content/docs/general/quickstart.mdx index 060b2c2e..3643a44b 100644 --- a/docs/src/content/docs/general/quickstart.mdx +++ b/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:
> **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.
@@ -91,7 +91,10 @@ Ensure you have [requested access](https://docs.aws.amazon.com/bedrock/latest/us ```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 ``` @@ -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 diff --git a/python/README.md b/python/README.md index b6b04e8b..d09b65ba 100644 --- a/python/README.md +++ b/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.