Merge branch 'main' into dev/dotnet_workflow/fix_concurrent_sample

* Python: Adds sample documentation for two separate Neo4j context providers for retrieval and memory

* adding pypi links

* adding dotnot examples

* adding dotnot examples

* merge upstream samples

* fixing docs

* fix relative paths

---------

Co-authored-by: Ben Lackey <ben.lackey@neo4j.com>

dotnet/samples/03-workflows/Concurrent/Concurrent/Concurrent.csproj

@@ -9,7 +9,7 @@
   </PropertyGroup>
 
   <ItemGroup>
-    <PackageReference Include="Azure.AI.OpenAI" />
+    <PackageReference Include="Azure.AI.Projects" />
     <PackageReference Include="Azure.Identity" />
     <PackageReference Include="Microsoft.Extensions.AI.OpenAI" />
   </ItemGroup>

dotnet/samples/03-workflows/Concurrent/Concurrent/Program.cs

@@ -1,6 +1,7 @@
 // Copyright (c) Microsoft. All rights reserved.
 
-using Azure.AI.OpenAI;
+using System.Text;
+using Azure.AI.Projects;
 using Azure.Identity;
 using Microsoft.Agents.AI;
 using Microsoft.Agents.AI.Workflows;
@@ -31,22 +32,26 @@ public static class Program
 {
     private static async Task Main()
     {
-        // Set up the Azure OpenAI client
-        var endpoint = Environment.GetEnvironmentVariable("AZURE_OPENAI_ENDPOINT") ?? throw new InvalidOperationException("AZURE_OPENAI_ENDPOINT is not set.");
-        var deploymentName = Environment.GetEnvironmentVariable("AZURE_OPENAI_DEPLOYMENT_NAME") ?? "gpt-4o-mini";
-        var chatClient = new AzureOpenAIClient(new Uri(endpoint), new AzureCliCredential()).GetChatClient(deploymentName).AsIChatClient();
+        // Set up the Azure AI Project client
+        var endpoint = Environment.GetEnvironmentVariable("AZURE_AI_PROJECT_ENDPOINT")
+            ?? throw new InvalidOperationException("AZURE_AI_PROJECT_ENDPOINT is not set.");
+        var deploymentName = Environment.GetEnvironmentVariable("AZURE_AI_MODEL_DEPLOYMENT_NAME") ?? "gpt-4o-mini";
+        var chatClient = new AIProjectClient(new Uri(endpoint), new AzureCliCredential())
+                            .ProjectOpenAIClient.GetChatClient(deploymentName).AsIChatClient();
 
         // Create the executors
-        ChatClientAgent physicist = new(
+        var physicist = new ChatClientAgent(
             chatClient,
             name: "Physicist",
             instructions: "You are an expert in physics. You answer questions from a physics perspective."
-        );
-        ChatClientAgent chemist = new(
+        ).BindAsExecutor(new AIAgentHostOptions { ForwardIncomingMessages = false });
+
+        var chemist = new ChatClientAgent(
             chatClient,
             name: "Chemist",
             instructions: "You are an expert in chemistry. You answer questions from a chemistry perspective."
-        );
+        ).BindAsExecutor(new AIAgentHostOptions { ForwardIncomingMessages = false });
+
         var startExecutor = new ConcurrentStartExecutor();
         var aggregationExecutor = new ConcurrentAggregationExecutor();
 
@@ -61,11 +66,30 @@ public static class Program
         await using StreamingRun run = await InProcessExecution.RunStreamingAsync(workflow, input: "What is temperature?");
         await foreach (WorkflowEvent evt in run.WatchStreamAsync())
         {
-            if (evt is WorkflowOutputEvent output)
+            switch (evt)
             {
-                Console.WriteLine($"Workflow completed with results:\n{output.Data}");
+                case WorkflowOutputEvent workflowOutput:
+                    Console.WriteLine($"Workflow completed with results:\n{workflowOutput.Data}");
+                    break;
+
+                case WorkflowErrorEvent workflowError:
+                    WriteError(workflowError.Exception?.ToString() ?? "Unknown workflow error occurred");
+                    break;
+
+                case ExecutorFailedEvent executorFailed:
+                    WriteError($"Executor '{executorFailed.ExecutorId}' failed with {(
+                        executorFailed.Data == null ? "unknown error" : $"exception {executorFailed.Data}"
+                        )}.");
+                    break;
             }
         }
+
+        void WriteError(string error)
+        {
+            Console.ForegroundColor = ConsoleColor.Red;
+            Console.Write(error);
+            Console.ResetColor();
+        }
     }
 }
 
@@ -92,7 +116,7 @@ internal sealed partial class ConcurrentStartExecutor() :
         // the message but will not start processing until they receive a turn token.
         await context.SendMessageAsync(new ChatMessage(ChatRole.User, message), cancellationToken: cancellationToken);
         // Broadcast the turn token to kick off the agents.
-        await context.SendMessageAsync(new TurnToken(emitEvents: true), cancellationToken: cancellationToken);
+        await context.SendMessageAsync(new TurnToken(emitEvents: false), cancellationToken: cancellationToken);
     }
 }
 
@@ -116,11 +140,19 @@ internal sealed partial class ConcurrentAggregationExecutor() :
     public override async ValueTask HandleAsync(List<ChatMessage> message, IWorkflowContext context, CancellationToken cancellationToken = default)
     {
         this._messages.AddRange(message);
+    }
 
-        if (this._messages.Count == 2)
+    protected override ValueTask OnMessageDeliveryFinishedAsync(IWorkflowContext context, CancellationToken cancellationToken = default)
+    {
+        StringBuilder resultBuilder = new();
+        foreach (ChatMessage m in this._messages)
         {
-            var formattedMessages = string.Join(Environment.NewLine, this._messages.Select(m => $"{m.AuthorName}: {m.Text}"));
-            await context.YieldOutputAsync(formattedMessages, cancellationToken);
+            resultBuilder.AppendLine($"{m.AuthorName}: {m.Text}");
+            resultBuilder.AppendLine();
         }
+
+        this._messages.Clear();
+
+        return context.YieldOutputAsync(resultBuilder.ToString(), cancellationToken);
     }
 }

python/samples/02-agents/context_providers/neo4j/README.md

@@ -0,0 +1,19 @@
+# Neo4j Context Providers
+
+Neo4j offers two context providers for the Agent Framework, each serving a different purpose:
+
+| | [Neo4j Memory](../neo4j_memory/README.md) | [Neo4j GraphRAG](../../../05-end-to-end/neo4j_graphrag/README.md) |
+|---|---|---|
+| **What it does** | Read-write memory — stores conversations, builds knowledge graphs, learns from interactions | Read-only retrieval from a pre-existing knowledge base with optional graph traversal |
+| **Data source** | Agent interactions (grows over time) | Pre-loaded documents and indexes |
+| **Python package** | [`neo4j-agent-memory`](https://pypi.org/project/neo4j-agent-memory/) | [`agent-framework-neo4j`](https://pypi.org/project/agent-framework-neo4j/) |
+| **Database setup** | Empty — creates its own schema | Requires pre-indexed documents with vector or fulltext indexes |
+| **Example use case** | "Remember my preferences", "What did we discuss last time?" | "Search our documents", "What risks does Acme Corp face?" |
+
+## Which should I use?
+
+**Use [Neo4j Memory](../neo4j_memory/README.md)** when your agent needs to remember things across sessions — user preferences, past conversations, extracted entities, and reasoning traces. The memory provider writes to the database on every interaction, building a knowledge graph that grows over time.
+
+**Use [Neo4j GraphRAG](../../../05-end-to-end/neo4j_graphrag/README.md)** when your agent needs to search an existing knowledge base — documents, articles, product catalogs — and optionally enrich results by traversing graph relationships. The GraphRAG provider is read-only and does not modify your data.
+
+You can use both together: GraphRAG for domain knowledge retrieval, Memory for personalization and learning.

python/samples/02-agents/context_providers/neo4j_memory/README.md

@@ -0,0 +1,9 @@
+# Neo4j Memory Context Provider
+
+[Neo4j Agent Memory](https://github.com/neo4j-labs/agent-memory) is a graph-native memory system for AI agents that stores conversations, builds knowledge graphs from interactions, and lets agents learn from their own reasoning — all backed by Neo4j.
+
+For full documentation, installation instructions, code examples, and configuration details, see the [Neo4j Memory integration guide on Microsoft Learn](https://learn.microsoft.com/agent-framework/integrations/neo4j-memory).
+
+For a runnable example, see the [retail assistant sample](https://github.com/neo4j-labs/agent-memory/tree/main/examples/microsoft_agent_retail_assistant).
+
+For help choosing between the Memory and GraphRAG providers, see the [Neo4j Context Providers overview](../neo4j/README.md).

python/samples/05-end-to-end/neo4j_graphrag/README.md

@@ -4,6 +4,8 @@ The [Neo4j GraphRAG context provider](https://github.com/neo4j-labs/neo4j-maf-pr
 
 This sample keeps setup lightweight by using a pre-built Neo4j fulltext index plus a graph-enrichment query.
 
+For full documentation, see the [Neo4j GraphRAG integration guide on Microsoft Learn](https://learn.microsoft.com/agent-framework/integrations/neo4j-graphrag).
+
 ## Example
 
 | File | Description |