[Add] Haystack and Microsoft Agent Framework Docs

docs/marketplace-docs/guides/haystack/index.md

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+---
+title: "Deploy Haystack"
+description: "This tutorial will show you how to deploy Haystack as a Quick Deploy App."
+published: 2026-07-21
+keywords: ['AI Framework', 'AI']
+tags: ["quick deploy apps", "linode platform", "cloud manager"]
+external_resources:
+- '[Haystack Documentation](https://docs.haystack.deepset.ai/docs/get-started)'
+aliases: ['/products/tools/marketplace/guides/haystack/', '/guides/haystack/']
+authors: ["Akamai"]
+contributors: ["Akamai"]
+license: '[CC BY-ND 4.0](https://creativecommons.org/licenses/by-nd/4.0)'
+marketplace_app_id: 2165521
+marketplace_app_name: "Haystack"
+---
+
+Haystack is an open-source framework for building applications that use large language models (LLMs). Instead of requiring developers to write custom orchestration logic for every use case, it provides a structured way to assemble AI functionality through reusable components connected in pipelines. This approach makes it well suited for creating retrieval-augmented generation (RAG) systems, AI agents, search experiences, chat applications, and other LLM-driven solutions.
+
+## Deploying a Quick Deploy App
+
+{{% content "deploy-marketplace-apps-shortguide" %}}
+
+{{% content "marketplace-verify-standard-shortguide" %}}
+
+{{< note >}}
+**Estimated deployment time:** Haystack should be fully installed within 5 minutes after the Compute Instance has finished provisioning.
+{{< /note >}}
+
+## Configuration Options
+
+- **Supported distributions:** Ubuntu 24.04 LTS
+- **Recommended plan:** All plan types and sizes can be used.
+
+## Haystack Options
+
+{{% content "marketplace-required-limited-user-fields-shortguide" %}}
+
+{{% content "marketplace-special-character-limitations-shortguide" %}}
+
+## Getting Started after Deployment
+
+### Testing Agent
+
+Once the deployment is complete, the `haystack-ai` library should already be installed on your instance. This will allow you to import the library into your software.
+
+1. To get started, create an example directory called `science`.
+
+    ```command
+    mkdir science
+    ```
+
+2. To get started, create a test Python file called `agent.py` that will allow us to use our AI model.
+
+```
+vim agent.py
+```
+
+3. Enter the following content into the `agent.py` Python file.
+
+```python
+from haystack.components.agents import Agent
+from haystack.components.generators.chat import OpenAIChatGenerator
+from haystack.dataclasses import ChatMessage
+from haystack.tools import ComponentTool
+from haystack.utils import Secret
+
+agent = Agent(
+    chat_generator=OpenAIChatGenerator(
+        api_base_url="http://localhost:8000/v1",
+        api_key=Secret.from_token("EMPTY"),
+        model="Qwen/Qwen3-14B-AWQ",
+    ),
+    system_prompt="You are a helpful assistant that can search the web for information.",
+)
+
+result = agent.run(
+    messages=[ChatMessage.from_user("What is Haystack AI?")]
+)
+
+print(result["last_message"].text)
+```
+
+4. Once you save the file you can execute it with the following command.  
+
+```command
+python3 sky.py
+```
+
+This example uses a self-hosted model exposed via vLLM's API. If you want to use a provider model you can refer to Haystack documentation.
+
+{{% content "marketplace-update-note-shortguide" %}}

docs/marketplace-docs/guides/microsoft-agent-framework/index.md

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+---
+title: "Deploy Microsoft Agent Framework"
+description: "This tutorial will show you how to deploy Microsoft Agent Framework as a Quick Deploy App."
+published: 2026-07-21
+keywords: ['AI Framework', 'AI']
+tags: ["quick deploy apps", "linode platform", "cloud manager"]
+external_resources:
+- '[Microsoft Agent Framework Documentation](https://learn.microsoft.com/en-us/agent-framework/)'
+aliases: ['/products/tools/marketplace/guides/microsoft-agent-framework/', '/guides/microsoft-agent-framework/']
+authors: ["Akamai"]
+contributors: ["Akamai"]
+license: '[CC BY-ND 4.0](https://creativecommons.org/licenses/by-nd/4.0)'
+marketplace_app_id: 2165612
+marketplace_app_name: "Microsoft Agent Framework"
+---
+
+Microsoft Agent Framework is an open-source framework for building, orchestrating, and deploying AI agents and multi-agent applications. It provides developers with the tools to create intelligent agents that can reason, use tools, collaborate, and automate complex workflows.
+
+## Deploying a Quick Deploy App
+
+{{% content "deploy-marketplace-apps-shortguide" %}}
+
+{{% content "marketplace-verify-standard-shortguide" %}}
+
+{{< note >}}
+**Estimated deployment time:** Microsoft Agent Framework should be fully installed within 5 minutes after the Compute Instance has finished provisioning.
+{{< /note >}}
+
+## Configuration Options
+
+- **Supported distributions:** Ubuntu 24.04 LTS
+- **Recommended plan:** All plan types and sizes can be used.
+
+## CrewAI Options
+
+{{% content "marketplace-required-limited-user-fields-shortguide" %}}
+
+{{% content "marketplace-special-character-limitations-shortguide" %}}
+
+## Getting Started after Deployment
+
+### Testing Python's SDK
+
+Once the deployment is complete, the `agent-framework` library should already be installed on your instance. This will allow you to import the library into your software.
+
+1. To get started, create an example directory called `science`.
+
+    ```command
+    mkdir science
+    ```
+
+2. Create a test Python file called `sky.py` that will allow us to use our AI model.
+
+```
+cd science
+vim sky.py
+```
+
+3. Enter the following content into the `sky.py` Python file.
+
+```python
+import asyncio
+from openai import AsyncOpenAI
+from agent_framework import Agent
+from agent_framework.openai import OpenAIChatClient
+
+async def main():
+    client = OpenAIChatClient(
+        model="Qwen/Qwen3-14B-AWQ",
+        base_url="http://localhost:8000/v1",
+        api_key="dummy",
+    )
+
+    agent = client.as_agent(
+        name="Assistant",
+        instructions="You are a helpful assistant.",
+    )
+
+    result = await agent.run("Why is the sky blue?")
+
+    print(result)
+
+if __name__ == "__main__":
+    asyncio.run(main())
+```
+
+4. Once you save the file you can execute it with the following command.  
+
+```command
+python3 sky.py
+```
+
+This example uses a self-hosted model exposed via vLLM's API. If you want to use a provider model you can refer to Microsoft Agent Framework documentation.
+
+{{% content "marketplace-update-note-shortguide" %}}