Add examples

.env.example

@@ -1,5 +1,5 @@
 AGENTUITY_SDK_KEY=
-AGENTUITY_PROJECT_KEY= 
+AGENTUITY_PROJECT_KEY=
 
 # example-composio
 # COMPOSIO_API_KEY=
@@ -11,5 +11,24 @@ AGENTUITY_PROJECT_KEY=
 # SLACK_SIGNING_SECRET=
 # SLACK_BOT_TOKEN=
 
+# example-telegram
+# TELEGRAM_BOT_TOKEN=
+
+# example-teams
+# TEAMS_BOT_APP_ID=
+# TEAMS_BOT_APP_PASSWORD=
+# TEAMS_BOT_TENANT_ID=
+# TEAMS_TEST_USER_KEY= # Get this by chatting with the bot, then check logs for "userKey" field
+
 # gateway-byo-token - Only use if you want to bypass the AI gateway
-# ANTHROPIC_API_KEY=
+# ANTHROPIC_API_KEY=
+
+# io-email - Email address for the io-email agent (configure in Agentuity Console)
+# IO_EMAIL_ADDRESS=
+
+# Checkly Heartbeat URLs - Create monitors at https://app.checklyhq.com
+# CHECKLY_TEST_SUITE_URL=
+# CHECKLY_EXAMPLE_SLACK_URL=
+# CHECKLY_EXAMPLE_TEAMS_URL=
+# CHECKLY_IO_EMAIL_URL=
+# CHECKLY_IO_SMS_URL=

README.md

@@ -94,7 +94,11 @@ Each agent demonstrates specific Agentuity features. Here's what you can explore
 | **example-chat** | Conversational AI with persistent chat history |
 | **example-composio** | Integration with Composio tools (Hacker News) |
 | **example-discord** | Discord webhook notifications |
+| **example-llm-judge** | LLM-as-a-judge pattern for evaluating AI outputs |
 | **example-slack** | Slack bot integration with thread support |
+| **example-streaming** | Real-time data streaming |
+| **example-teams** | Microsoft Teams bot integration with persistent chat history |
+| **example-telegram** | Telegram bot integration |
 
 ## How to Use in DevMode
 

agentuity.yaml

@@ -50,11 +50,11 @@ deployment:
   # You should tune the resources for the deployment
   resources:
     # The memory requirements
-    memory: 2Gi
+    memory: 4Gi
     # The CPU requirements
-    cpu: 2000M
+    cpu: 4000M
     # The disk size requirements
-    disk: 250Mi
+    disk: 3Gi
   # The deployment mode
   mode:
     # on-demand or provisioned
@@ -145,3 +145,15 @@ agents:
   - id: agent_fee72e13a3fdc7f0783abd65220d352d
     name: test-suite
     description: Tests the functionality of all Kitchen Sink example agents
+  - id: agent_52f73139a881ee0e1cdfafb3c6404e70
+    name: example-telegram
+    description: Demonstrates how to integrate with a Telegram bot
+  - id: agent_c38f9a6d8d3edc6bde56047cbfd16c6f
+    name: example-streaming
+    description: Demonstrates advanced agent streaming patterns
+  - id: agent_4268cac212e32dae1f6a7c394d2c6b9d
+    name: example-llm-judge
+    description: Demonstrates LLM-as-a-judge pattern for evaluating AI outputs
+  - id: agent_ebc87cc45db854eff103e3d54cefa24a
+    name: example-teams
+    description: Demonstrates how to integrate with a Microsoft Teams bot

package.json

@@ -28,18 +28,21 @@
     "typescript": "^5"
   },
   "dependencies": {
-    "@agentuity/sdk": "^0.0.146",
+    "@agentuity/sdk": "^0.0.157",
     "@ai-sdk/anthropic": "^2.0.17",
     "@ai-sdk/google": "^2.0.11",
+    "@ai-sdk/groq": "^2.0.24",
     "@ai-sdk/openai": "^2.0.23",
     "@composio/core": "^0.1.52",
     "@composio/vercel": "^0.2.8",
     "@mastra/core": "^0.15.2",
     "@slack/types": "^2.16.0",
     "@slack/web-api": "^7.10.0",
     "ai": "^5.0.29",
+    "botbuilder": "^4.23.3",
     "crypto": "^1.0.1",
-    "source-map-js": "^1.2.1"
+    "source-map-js": "^1.2.1",
+    "zod": "^4.1.12"
   },
   "module": "index.ts"
-}
+}

src/agents/example-composio/README.md

@@ -41,7 +41,7 @@ This example uses the HackerNews toolkit, but Composio offers many more, includi
    ![Composio project settings showing API key](/.github/example-composio/composio-setup-2-api-key.png)
 
    Copy the API key and add it to your `.env` file:
-   ```
+   ```env
    COMPOSIO_API_KEY=your-api-key-here
    ```
 

src/agents/example-discord/README.md

@@ -26,7 +26,7 @@
 2. **Configure Environment Variable**
 
    For local development, add to your `.env` file:
-   ```
+   ```env
    DISCORD_WEBHOOK_URL=https://discord.com/api/webhooks/YOUR_WEBHOOK_ID/YOUR_WEBHOOK_TOKEN
    ```
 

src/agents/example-llm-judge/index.ts

@@ -0,0 +1,82 @@
+import type { AgentContext, AgentRequest, AgentResponse } from '@agentuity/sdk';
+import { openai } from '@ai-sdk/openai';
+import { generateObject } from 'ai';
+import { evaluationSchema, formatReport } from './story-eval';
+
+export default async function Agent(
+  req: AgentRequest,
+  resp: AgentResponse,
+  ctx: AgentContext
+) {
+  try {
+    // Get the prompt from request, or use a default
+    const prompt =
+      (await req.data.text()) ||
+      'Write a short story about an AI learning to paint';
+
+    ctx.logger.info('Starting LLM-as-a-judge evaluation');
+
+    // Get stories from gateway-provider
+    const gatewayAgent = await ctx.getAgent({ name: 'gateway-provider' });
+    const stories = await gatewayAgent.run({ data: prompt });
+    const storiesText = await stories.data.text();
+
+    ctx.logger.debug('Received stories from gateway-provider');
+
+    // Create evaluation prompt
+    const evaluationPrompt = `
+You are evaluating two AI-generated short stories.
+
+Here are the stories:
+
+${storiesText}
+
+Extract each story text:
+- OpenAI story: appears after "### OpenAI (GPT-5 Nano)"
+- Google story: appears after "### Google (Gemini 2.0 Flash)"
+
+For each story, provide:
+1. Creativity score (1-10): How original and imaginative is it?
+2. Quality score (1-10): Overall writing quality
+3. Strengths: What works well (1-2 sentences)
+
+Finally, provide a verdict declaring which story is better and why (2-3 sentences).`;
+
+    ctx.logger.info('Generating structured evaluation');
+
+    // Generate structured evaluation
+    const evaluation = await generateObject({
+      model: openai('gpt-5-nano'),
+      schema: evaluationSchema,
+      system:
+        'You are a literary critic evaluating short AI-generated stories.',
+      prompt: evaluationPrompt,
+    });
+
+    // Log key metrics
+    ctx.logger.debug('Evaluation scores', {
+      openai: evaluation.object.openai.quality,
+      google: evaluation.object.google.quality,
+    });
+
+    // Return formatted report
+    return resp.markdown(formatReport(evaluation.object));
+  } catch (error) {
+    ctx.logger.error('Error in LLM judge evaluation:', error);
+    return resp.text(
+      'Sorry, there was an error running the evaluation. Please ensure the gateway-provider agent is available.'
+    );
+  }
+}
+
+export const welcome = () => {
+  return {
+    welcome: `Welcome to the <span style="color: light-dark(#0AA, #0FF);">LLM-as-a-Judge</span> example agent.\n\n### About\n\nThis agent demonstrates the LLM-as-a-judge pattern, where one AI model evaluates the outputs of other models. It generates content using the gateway-provider agent (which uses two different AI models), then evaluates both outputs with structured scoring and feedback.\n\n### Testing\n\nTry the default prompt about AI learning to paint, or send your own story prompt. The agent calls \`gateway-provider\` to generate two stories (one from each AI model), then provides an evaluation comparing their creativity, quality, and strengths.\n\n### Questions?\n\nThe "Help" command is not available for this agent, as it's a specific example demonstration.`,
+    prompts: [
+      {
+        data: 'Write a short story about an AI learning to paint',
+        contentType: 'text/plain',
+      },
+    ],
+  };
+};