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diff --git a/docs/docs/plugins/anthropic.md b/docs/docs/plugins/anthropic.md
@@ -32,38 +32,83 @@ Install the plugin in your project with your favorite package manager:
 
 ## Usage
 
+### Initialize
+
+```typescript
+import 'dotenv/config';
+
+import { configureGenkit } from '@genkit-ai/core';
+import { defineFlow, startFlowsServer } from '@genkit-ai/flow';
+import { anthropic } from 'genkitx-anthropicai';
+
+configureGenkit({
+  plugins: [
+    // Anthropic API key is required and defaults to the ANTHROPIC_API_KEY environment variable
+    anthropic({ apiKey: process.env.ANTHROPIC_API_KEY }),
+  ],
+  logLevel: 'debug',
+  enableTracingAndMetrics: true,
+});
+```
+
 ### Basic examples
 
 The simplest way to call the text generation model is by using the helper function `generate`:
 
-```
-// Basic usage of an LLM
+```typescript
+// ...configure Genkit (as shown above)...
+
 const response = await generate({
-    model: claude3Haiku,
-    prompt: 'Tell me a joke.',
+  model: claude3Haiku, // model imported from genkitx-anthropicai
+  prompt: 'Tell me a joke.',
 });
 
 console.log(await response.text());
 ```
 
-Using the same interface, you can prompt a multimodal model:
+### Multi-modal prompt
+
+```typescript
+// ...configure Genkit (as shown above)...
 
-```
 const response = await generate({
   model: claude3Haiku,
   prompt: [
     { text: 'What animal is in the photo?' },
-    { media: { url: imageUrl} },
+    { media: { url: imageUrl } },
   ],
-  config:{
+  config: {
     // control of the level of visual detail when processing image embeddings
     // Low detail level also decreases the token usage
     visualDetailLevel: 'low',
-  }
+  },
 });
 console.log(await response.text());
 ```
 
+### Within a flow
+
+```typescript
+// ...configure Genkit (as shown above)...
+
+export const myFlow = defineFlow(
+  {
+    name: 'menuSuggestionFlow',
+    inputSchema: z.string(),
+    outputSchema: z.string(),
+  },
+  async (subject) => {
+    const llmResponse = await generate({
+      prompt: `Suggest an item for the menu of a ${subject} themed restaurant`,
+      model: claude3Opus,
+    });
+
+    return llmResponse.text();
+  }
+);
+startFlowsServer();
+```
+
 ## Contributing
 
 Want to contribute to the project? That's awesome! Head over to our [Contribution Guidelines](https://github.com/TheFireCo/genkit-plugins/blob/main/CONTRIBUTING.md).

diff --git a/docs/docs/plugins/azure-openai.md b/docs/docs/plugins/azure-openai.md
@@ -0,0 +1,151 @@
+---
+id: genkitx-azure-openai
+title:  🚧 genkitx-azure-openai
+---
+
+:::warning
+
+This plugin is currently a work in progress.
+:::
+
+<h1 align="center">
+   Firebase Genkit - Azure OpenAI Plugin
+</h1>
+
+<h4 align="center">Azure OpenAI Community Plugin for Google Firebase Genkit</h4>
+
+<div align="center">
+   <img alt="Github lerna version" src="https://img.shields.io/github/lerna-json/v/TheFireCo/genkit-plugins?label=version"/>
+   <img alt="NPM Downloads" src="https://img.shields.io/npm/dw/genkitx-azure-openai"/>
+   <img alt="GitHub Org's stars" src="https://img.shields.io/github/stars/TheFireCo?style=social"/>
+   <img alt="GitHub License" src="https://img.shields.io/github/license/TheFireCo/genkit-plugins"/>
+   <img alt="Static Badge" src="https://img.shields.io/badge/yes-a?label=maintained"/>
+</div>
+
+<div align="center">
+   <img alt="GitHub Issues or Pull Requests" src="https://img.shields.io/github/issues/TheFireCo/genkit-plugins?color=blue"/>
+   <img alt="GitHub Issues or Pull Requests" src="https://img.shields.io/github/issues-pr/TheFireCo/genkit-plugins?color=blue"/>
+   <img alt="GitHub commit activity" src="https://img.shields.io/github/commit-activity/m/TheFireCo/genkit-plugins"/>
+</div>
+
+**`genkitx-azure-openai`** is a community plugin for using Azure OpenAI APIs with
+[Firebase GenKit](https://github.com/firebase/genkit). Built by [**The Fire Company**](https://github.com/TheFireCo). 🔥
+
+## Installation
+
+Install the plugin in your project with your favorite package manager:
+
+- `npm install genkitx-azure-openai`
+- `yarn add genkitx-azure-openai`
+- `pnpm add genkitx-azure-openai`
+
+## Usage
+
+> The interface to the models of this plugin is the same as for the [OpenAI plugin](../openai/).
+
+### Initialize
+
+You'll also need to have an Azure OpenAI instance deployed. You can deploy a version on Azure Portal following [this guide](https://learn.microsoft.com/azure/ai-services/openai/how-to/create-resource?pivots=web-portal).
+
+Once you have your instance running, make sure you have the endpoint and key. You can find them in the Azure Portal, under the "Keys and Endpoint" section of your instance.
+
+You can then define the following environment variables to use the service:
+
+```
+AZURE_OPENAI_API_ENDPOINT=<YOUR_ENDPOINT>
+AZURE_OPENAI_API_KEY=<YOUR_KEY>
+AZURE_OPENAI_API_EMBEDDING_DEPLOYMENT_NAME=<YOUR_EMBEDDING_DEPLOYMENT
+```
+
+Alternatively, you can pass the values directly to the `azureOpenAI` constructor:
+
+```typescript
+import { azureOpenAI } from 'genkitx-azure-openai';
+
+export default configureGenkit({
+  plugins: [
+    azureOpenAI({
+      apiKey: '<your_key>',
+      azureOpenAIEndpoint: '<your_endpoint>',
+      azureOpenAIApiDeploymentName: '<your_embedding_deployment_name',
+    }),
+    // other plugins
+  ],
+});
+```
+
+If you're using Azure Managed Identity, you can also pass the credentials directly to the constructor:
+
+```typescript
+import { azureOpenAI } from 'genkitx-azure-openai';
+import { DefaultAzureCredential } from '@azure/identity';
+
+const credential = new DefaultAzureCredential();
+
+export default configureGenkit({
+  plugins: [
+    azureOpenAI({
+      credential,
+      azureOpenAIEndpoint: '<your_endpoint>',
+      azureOpenAIApiDeploymentName: '<your_embedding_deployment_name',
+    }),
+    // other plugins
+  ],
+});
+```
+
+### Basic examples
+
+The simplest way to call the text generation model is by using the helper function `generate`:
+
+```typescript
+// Basic usage of an LLM
+const response = await generate({
+  model: gpt35Turbo,
+  prompt: 'Tell me a joke.',
+});
+
+console.log(await response.text());
+```
+
+Using the same interface, you can prompt a multimodal model:
+
+```typescript
+const response = await generate({
+  model: gpt4o,
+  prompt: [
+    { text: 'What animal is in the photo?' },
+    { media: { url: imageUrl } },
+  ],
+  config: {
+    // control of the level of visual detail when processing image embeddings
+    // Low detail level also decreases the token usage
+    visualDetailLevel: 'low',
+  },
+});
+console.log(await response.text());
+```
+
+For more detailed examples and the explanation of other functionalities, refer to the examples in the [official Github repo of the plugin](https://github.com/TheFireCo/genkit-plugins/blob/main/examples/README.md) or in the [official Genkit documentation](https://firebase.google.com/docs/genkit/get-started).
+
+## Contributing
+
+Want to contribute to the project? That's awesome! Head over to our [Contribution Guidelines](https://github.com/TheFireCo/genkit-plugins/blob/main/CONTRIBUTING.md).
+
+## Need support?
+
+:::info
+This repository depends on Google's Firebase Genkit. For issues and questions related to GenKit, please refer to instructions available in [GenKit's repository](https://github.com/firebase/genkit).
+:::
+
+Reach out by opening a discussion on [Github Discussions](https://github.com/TheFireCo/genkit-plugins/discussions).
+
+## Credits
+
+This plugin is proudly maintained by the team at [**The Fire Company**](https://github.com/TheFireCo). 🔥
+
+## License
+
+This project is licensed under the [Apache 2.0 License](https://github.com/TheFireCo/genkit-plugins/blob/main/LICENSE).
+
+[![License: Apache 2.0](https://img.shields.io/badge/License-Apache%202%2E0-lightgrey.svg)](https://github.com/TheFireCo/genkit-plugins/blob/main/LICENSE)
diff --git a/docs/docs/plugins/cohere.md b/docs/docs/plugins/cohere.md
@@ -34,36 +34,95 @@ Install the plugin in your project with your favorite package manager:
 
 ## Usage
 
+### Initialize
+
+```typescript
+import 'dotenv/config';
+
+import { configureGenkit } from '@genkit-ai/core';
+import { defineFlow, startFlowsServer } from '@genkit-ai/flow';
+import { cohere } from 'genkitx-cohere';
+
+configureGenkit({
+  plugins: [
+    // Cohere API key is required and defaults to the COHERE_API_KEY environment variable
+    cohere({ apiKey: process.env.COHERE_API_KEY }),
+  ],
+  logLevel: 'debug',
+  enableTracingAndMetrics: true,
+});
+```
+
 ### Basic examples
 
 The simplest way to call the text generation model is by using the helper function `generate`:
 
 ```typescript
-// Basic usage of an LLM
+// ...configure Genkit (as shown above)...
+
 const response = await generate({
-  model: commandRPlus,
+  model: commandRPlus, // model imported from genkitx-cohere
   prompt: 'Tell me a joke.',
 });
 
 console.log(await response.text());
 ```
 
-Using the same interface, you can prompt a multimodal model:
+### Within a flow
 
 ```typescript
-const response = await generate({
-  model: commandRPlus,
-  prompt: [
-    { text: 'What animal is in the photo?' },
-    { media: { url: imageUrl } },
-  ],
-  config: {
-    // control of the level of visual detail when processing image embeddings
-    // Low detail level also decreases the token usage
-    visualDetailLevel: 'low',
+// ...configure Genkit (as shown above)...
+
+export const myFlow = defineFlow(
+  {
+    name: 'menuSuggestionFlow',
+    inputSchema: z.string(),
+    outputSchema: z.string(),
   },
+  async (subject) => {
+    const llmResponse = await generate({
+      prompt: `Suggest an item for the menu of a ${subject} themed restaurant`,
+      model: commandRPlus,
+    });
+
+    return llmResponse.text();
+  }
+);
+startFlowsServer();
+```
+
+### Tool use
+
+```typescript
+// ...configure Genkit (as shown above)...
+
+const createReminder = defineTool(
+  {
+    name: 'createReminder',
+    description: 'Use this to create reminders for things in the future',
+    inputSchema: z.object({
+      time: z
+        .string()
+        .describe('ISO timestamp string, e.g. 2024-04-03T12:23:00Z'),
+      reminder: z.string().describe('the content of the reminder'),
+    }),
+    outputSchema: z.number().describe('the ID of the created reminder'),
+  },
+  (reminder) => Promise.resolve(3)
+);
+
+const result = generate({
+  model: llama3x70b,
+  tools: [createReminder],
+  prompt: `
+  You are a reminder assistant.
+  If you create a reminder, describe in text the reminder you created as a response.
+
+  Query: I have a meeting with Anna at 3 for dinner - can you set a reminder for the time?
+  `,
 });
-console.log(await response.text());
+
+console.log(result.then((res) => res.text()));
 ```
 
 ## Contributing