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‎articles/ai-foundry/agents/how-to/tools/deep-research-samples.md

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@@ -10,6 +10,7 @@ ms.date: 07/10/2025
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author: aahill
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ms.author: aahi
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ms.custom: references_regions
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zone_pivot_groups: selection-deep-research
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---
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# How to use the Deep Research tool
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## Prerequisites
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* The requirements in the [Deep Research overview](./deep-research.md).
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* The Deep Research tool requires the latest prerelease versions of the `azure-ai-projects` library. First we recommend creating a [virtual environment](https://docs.python.org/3/library/venv.html) to work in:
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```console
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python -m venv env
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# after creating the virtual environment, activate it with:
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.\env\Scripts\activate
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```
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You can install the package with the following command:
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```console
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pip install --pre azure-ai-projects
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```
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* Your Azure AI Foundry Project endpoint.
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You can find your endpoint in the main project **overview** for your project in the [Azure AI Foundry portal](https://ai.azure.com/?cid=learnDocs), under **Endpoint and keys** > **Libraries** > **Azure AI Foundry**.
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## Create an agent with the Deep Research tool
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:::zone pivot="csharp"
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>[!NOTE]
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> You need version `1.1.0-beta.4` or later of the `Azure.AI.Agents.Persistent` package, and the `Azure.Identity` package.
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```csharp
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using Azure;
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using Azure.AI.Agents.Persistent;
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using Azure.Identity;
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using System.Collections.Generic;
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using System.Text;
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var projectEndpoint = System.Environment.GetEnvironmentVariable("PROJECT_ENDPOINT");
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var modelDeploymentName = System.Environment.GetEnvironmentVariable("MODEL_DEPLOYMENT_NAME");
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var deepResearchModelDeploymentName = System.Environment.GetEnvironmentVariable("DEEP_RESEARCH_MODEL_DEPLOYMENT_NAME");
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var connectionId = System.Environment.GetEnvironmentVariable("AZURE_BING_CONECTION_ID");
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PersistentAgentsClient client = new(projectEndpoint, new DefaultAzureCredential());
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// DeepResearchToolDefinition should be initialized with the name of deep research model and the Bing connection ID,
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// needed to perform the search in the internet.
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DeepResearchToolDefinition deepResearch = new(
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new DeepResearchDetails(
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model: deepResearchModelDeploymentName,
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bingGroundingConnections: [
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new DeepResearchBingGroundingConnection(connectionId)
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]
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)
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);
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// NOTE: To reuse existing agent, fetch it with get_agent(agent_id)
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PersistentAgent agent = client.Administration.CreateAgent(
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model: modelDeploymentName,
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name: "Science Tutor",
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instructions: "You are a helpful Agent that assists in researching scientific topics.",
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tools: [deepResearch]
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);
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//Create a thread and run and wait for the run to complete.
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PersistentAgentThreadCreationOptions threadOp = new();
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threadOp.Messages.Add(new ThreadMessageOptions(
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role: MessageRole.User,
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content: "Research the current state of studies on orca intelligence and orca language, " +
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"including what is currently known about orcas' cognitive capabilities, " +
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"communication systems and problem-solving reflected in recent publications in top thier scientific " +
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"journals like Science, Nature and PNAS."
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));
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ThreadAndRunOptions opts = new()
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{
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ThreadOptions = threadOp,
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};
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ThreadRun run = client.CreateThreadAndRun(
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assistantId: agent.Id,
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options: opts
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);
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Console.WriteLine("Start processing the message... this may take a few minutes to finish. Be patient!");
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do
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{
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Thread.Sleep(TimeSpan.FromMilliseconds(500));
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run = client.Runs.GetRun(run.ThreadId, run.Id);
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}
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while (run.Status == RunStatus.Queued
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|| run.Status == RunStatus.InProgress);
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// We will create a helper function PrintMessagesAndSaveSummary, which prints the response from the agent,
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// and replaces the reference placeholders by links in Markdown format.
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// It also saves the research summary in the file for convenience.
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static void PrintMessagesAndSaveSummary(IEnumerable<PersistentThreadMessage> messages, string summaryFilePath)
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{
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string lastAgentMessage = default;
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foreach (PersistentThreadMessage threadMessage in messages)
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{
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StringBuilder sbAgentMessage = new();
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Console.Write($"{threadMessage.CreatedAt:yyyy-MM-dd HH:mm:ss} - {threadMessage.Role,10}: ");
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foreach (MessageContent contentItem in threadMessage.ContentItems)
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{
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if (contentItem is MessageTextContent textItem)
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{
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string response = textItem.Text;
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if (textItem.Annotations != null)
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{
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foreach (MessageTextAnnotation annotation in textItem.Annotations)
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{
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if (annotation is MessageTextUriCitationAnnotation uriAnnotation)
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{
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response = response.Replace(uriAnnotation.Text, $" [{uriAnnotation.UriCitation.Title}]({uriAnnotation.UriCitation.Uri})");
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}
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}
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}
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if (threadMessage.Role == MessageRole.Agent)
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sbAgentMessage.Append(response);
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Console.Write($"Agent response: {response}");
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}
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else if (contentItem is MessageImageFileContent imageFileItem)
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{
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Console.Write($"<image from ID: {imageFileItem.FileId}");
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}
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Console.WriteLine();
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}
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if (threadMessage.Role == MessageRole.Agent)
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lastAgentMessage = sbAgentMessage.ToString();
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}
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if (!string.IsNullOrEmpty(lastAgentMessage))
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{
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File.WriteAllText(
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path: summaryFilePath,
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contents: lastAgentMessage);
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}
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}
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//List the messages, print them and save the result in research_summary.md file.
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//The file will be saved next to the compiled executable.
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Pageable<PersistentThreadMessage> messages
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= client.Messages.GetMessages(
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threadId: run.ThreadId, order: ListSortOrder.Ascending);
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PrintMessagesAndSaveSummary([.. messages], "research_summary.md");
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// NOTE: Comment out these two lines if you want to delete the agent.
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client.Threads.DeleteThread(threadId: run.ThreadId);
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client.Administration.DeleteAgent(agentId: agent.Id);
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```
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:::zone-end
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:::zone pivot="javascript"
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```javascript
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TBD
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```
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:::zone-end
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:::zone pivot="python"
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The Deep Research tool requires the latest prerelease versions of the `azure-ai-projects` library. First we recommend creating a [virtual environment](https://docs.python.org/3/library/venv.html) to work in:
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```console
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python -m venv env
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# after creating the virtual environment, activate it with:
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.\env\Scripts\activate
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```
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You can install the package with the following command:
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```console
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pip install --pre azure-ai-projects
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```
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```python
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import os, time
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from typing import Optional
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print("Deleted agent")
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```
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:::zone-end
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> [!NOTE]
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> Limitation: The Deep Research tool is currently recommended only in nonstreaming scenarios. Using it with streaming can work, but it might occasionally time out and is therefore not recommended.
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‎zone-pivots/zone-pivot-groups.yml

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@@ -1098,6 +1098,18 @@ groups:
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title: Python
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- id: rest-api
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title: REST API
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- id: selection-deep-research
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title: Selections
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prompt: What would you like to see?
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pivots:
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- id: portal
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title: Azure AI Foundry portal
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- id: csharp
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title: C#
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- id: javascript
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title: JavaScript
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- id: python
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title: Python
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# owner: aahi
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- id: selection-fabric-data-agent
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title: Selections

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