samples: add AgentMemory (Neo4j-agent memory reimplemented in NET ) shopping assistant sample

dotnet/agent-framework-dotnet.slnx

@@ -200,6 +200,7 @@
     <Project Path="samples/02-agents/AgentWithMemory/AgentWithMemory_Step03_MemoryUsingValkey_Bedrock/AgentWithMemory_Step03_MemoryUsingValkey_Bedrock.csproj" />
     <Project Path="samples/02-agents/AgentWithMemory/AgentWithMemory_Step04_MemoryUsingFoundry/AgentWithMemory_Step04_MemoryUsingFoundry.csproj" />
     <Project Path="samples/02-agents/AgentWithMemory/AgentWithMemory_Step05_BoundedChatHistory/AgentWithMemory_Step05_BoundedChatHistory.csproj" />
+    <Project Path="samples/02-agents/AgentWithMemory/AgentWithMemory_Step06_MemoryUsingAgentMemory/AgentWithMemory_Step06_MemoryUsingAgentMemory.csproj" />
   </Folder>
   <Folder Name="/Samples/02-agents/AgentProviders/openai/">
     <File Path="samples/02-agents/AgentProviders/openai/README.md" />

dotnet/eng/verify-samples/AgentsSamples.cs

@@ -427,6 +427,15 @@ internal static class AgentsSamples
             ],
         },
 
+        new SampleDefinition
+        {
+            Name = "AgentWithMemory_Step06_MemoryUsingAgentMemory",
+            ProjectPath = "samples/02-agents/AgentWithMemory/AgentWithMemory_Step06_MemoryUsingAgentMemory",
+            RequiredEnvironmentVariables = ["AZURE_OPENAI_ENDPOINT"],
+            OptionalEnvironmentVariables = ["AZURE_OPENAI_API_KEY", "FOUNDRY_MODEL", "FOUNDRY_EMBEDDING_MODEL", "NEO4J_URI", "NEO4J_USER", "NEO4J_PASSWORD"],
+            SkipReason = "Requires a running Neo4j instance; standalone sample outside the repo's CPM build.",
+        },
+
         // ── AgentWithRAG ────────────────────────────────────────────────────
 
         new SampleDefinition

dotnet/samples/02-agents/AgentWithMemory/AgentWithMemory_Step06_MemoryUsingAgentMemory/AgentWithMemory_Step06_MemoryUsingAgentMemory.csproj

@@ -0,0 +1,78 @@
+<Project Sdk="Microsoft.NET.Sdk">
+
+  <!--
+    This project is part of the repo's solution and targets .NET 10 like the rest of the repo, but it
+    intentionally opts out of Central Package Management and source-referencing Microsoft.Agents.AI:
+    it consumes the *published* AgentMemory NuGet packages (which target Microsoft.Agents.AI 1.9.0)
+    instead. Run it with `dotnet run` from this folder.
+
+    ManagePackageVersionsCentrally is off, but dotnet/Directory.Packages.props still unconditionally
+    merges its repo-wide analyzer PackageReference items (no Version, resolved via CPM) into every
+    project that imports it — including this one. With CPM off here those versions can't resolve
+    (NU1015), so each is removed and re-added with an explicit version below (matching
+    AgentWithRAG_Step05_Neo4jGraphRAG, which hits the same issue). xunit.analyzers/Moq.Analyzers are
+    dropped rather than re-added since this project has no test code.
+  -->
+  <PropertyGroup>
+    <OutputType>Exe</OutputType>
+    <TargetFrameworks>net10.0</TargetFrameworks>
+    <ImplicitUsings>enable</ImplicitUsings>
+    <Nullable>enable</Nullable>
+    <ManagePackageVersionsCentrally>false</ManagePackageVersionsCentrally>
+    <RootNamespace>AgentMemoryShoppingAssistant</RootNamespace>
+    <!-- OPENAI001: the OpenAIClient(AuthenticationPolicy, options) ctor used for keyless Azure auth is
+         marked experimental in the OpenAI SDK (the MAF Foundry samples use the same pattern). -->
+    <NoWarn>$(NoWarn);OPENAI001</NoWarn>
+  </PropertyGroup>
+
+  <ItemGroup>
+    <PackageReference Remove="Microsoft.CodeAnalysis.NetAnalyzers" />
+    <PackageReference Remove="Microsoft.VisualStudio.Threading.Analyzers" />
+    <PackageReference Remove="xunit.analyzers" />
+    <PackageReference Remove="Moq.Analyzers" />
+    <PackageReference Remove="Roslynator.Analyzers" />
+    <PackageReference Remove="Roslynator.CodeAnalysis.Analyzers" />
+    <PackageReference Remove="Roslynator.Formatting.Analyzers" />
+  </ItemGroup>
+
+  <ItemGroup>
+    <!-- AgentMemory (published) — an unofficial .NET port of the Neo4j Labs agent-memory library + its
+         Microsoft Agent Framework adapter. -->
+    <PackageReference Include="AgentMemory" Version="1.2.0" />
+    <PackageReference Include="AgentMemory.AgentFramework" Version="1.2.0" />
+    <!-- Microsoft Agent Framework (matches AgentMemory's target) + the OpenAI/Foundry chat & embedding clients. -->
+    <PackageReference Include="Microsoft.Agents.AI" Version="1.9.0" />
+    <PackageReference Include="Microsoft.Extensions.AI.OpenAI" Version="10.5.1" />
+    <PackageReference Include="Azure.Identity" Version="1.21.0" />
+    <PackageReference Include="Microsoft.Extensions.Hosting" Version="9.0.17" />
+    <!-- Transitive dependency of Microsoft.Agents.AI; pinned explicitly (CPM is off here) because the
+         version it would otherwise resolve to, 1.12.0, has a known moderate severity vulnerability
+         (GHSA-g94r-2vxg-569j) that fails the repo's NuGet audit (NU1902 as error). Matches the version
+         pinned in dotnet/Directory.Packages.props. -->
+    <PackageReference Include="OpenTelemetry.Api" Version="1.15.3" />
+  </ItemGroup>
+
+  <ItemGroup>
+    <PackageReference Include="Microsoft.CodeAnalysis.NetAnalyzers" Version="10.0.100">
+      <PrivateAssets>all</PrivateAssets>
+      <IncludeAssets>runtime; build; native; contentfiles; analyzers; buildtransitive</IncludeAssets>
+    </PackageReference>
+    <PackageReference Include="Microsoft.VisualStudio.Threading.Analyzers" Version="17.14.15">
+      <PrivateAssets>all</PrivateAssets>
+      <IncludeAssets>runtime; build; native; contentfiles; analyzers; buildtransitive</IncludeAssets>
+    </PackageReference>
+    <PackageReference Include="Roslynator.Analyzers" Version="4.14.1">
+      <PrivateAssets>all</PrivateAssets>
+      <IncludeAssets>runtime; build; native; contentfiles; analyzers; buildtransitive</IncludeAssets>
+    </PackageReference>
+    <PackageReference Include="Roslynator.CodeAnalysis.Analyzers" Version="4.14.1">
+      <PrivateAssets>all</PrivateAssets>
+      <IncludeAssets>runtime; build; native; contentfiles; analyzers; buildtransitive</IncludeAssets>
+    </PackageReference>
+    <PackageReference Include="Roslynator.Formatting.Analyzers" Version="4.14.1">
+      <PrivateAssets>all</PrivateAssets>
+      <IncludeAssets>runtime; build; native; contentfiles; analyzers; buildtransitive</IncludeAssets>
+    </PackageReference>
+  </ItemGroup>
+
+</Project>

dotnet/samples/02-agents/AgentWithMemory/AgentWithMemory_Step06_MemoryUsingAgentMemory/ProductCatalog.cs

@@ -0,0 +1,195 @@
+// Copyright (c) Microsoft. All rights reserved.
+
+using System.ComponentModel;
+using System.Text;
+using AgentMemory.Neo4j.Infrastructure;
+using Microsoft.Extensions.AI;
+using Neo4j.Driver;
+
+namespace AgentMemoryShoppingAssistant;
+
+/// <summary>
+/// A small retail product graph plus the shopping tools that query it — the .NET counterpart of the
+/// Python retail-assistant's <c>get_product_tools</c>. Products live in Neo4j as <c>:Product</c> nodes
+/// linked to <c>:ProductCategory</c> / <c>:ProductBrand</c> nodes, so recommendations and "related
+/// products" come from graph traversals. Cypher runs through the public <see cref="INeo4jTransactionRunner"/>
+/// seam. Exposed as <see cref="AIFunction"/>s so a real chat model can call them during a run — the same
+/// way <c>Neo4jMemoryContextProvider</c> surfaces the memory tools through <c>AIContext.Tools</c> when
+/// <c>ExposeMemoryToolsFromContextProvider</c> is enabled.
+/// </summary>
+public sealed class ProductCatalog(INeo4jTransactionRunner runner)
+{
+    private readonly INeo4jTransactionRunner _runner = runner;
+
+    private static readonly (string Name, string Category, string Brand, double Price, bool InStock, int Inventory, string Description, int Popularity)[] s_seed =
+    [
+        ("Nike Air Zoom Pegasus 40", "shoes",       "Nike",   130, true,  40, "Everyday running shoe with responsive cushioning.",   95),
+        ("Nike Revolution 7",        "shoes",       "Nike",    70, true,  60, "Lightweight, budget-friendly running shoe.",          80),
+        ("Adidas Ultraboost Light",  "shoes",       "Adidas",  190, true, 25, "Premium running shoe with Boost cushioning.",         90),
+        ("Asics Gel-Kayano 31",      "shoes",       "Asics",   165, false,  0, "Stability running shoe for overpronation.",           70),
+        ("Sony WH-1000XM5",          "electronics", "Sony",    350, true,  18, "Industry-leading noise-cancelling headphones.",       92),
+        ("Bose QuietComfort Ultra",  "electronics", "Bose",    330, true,  12, "Premium noise-cancelling over-ear headphones.",       85),
+        ("Apple AirPods Pro 2",      "electronics", "Apple",   250, true,  50, "Wireless earbuds with active noise cancellation.",    88),
+        ("Garmin Forerunner 265",    "electronics", "Garmin",  450, true,   9, "GPS running watch with training metrics.",            78),
+        ("Nike Dri-FIT Running Tee", "apparel",     "Nike",     35, true, 120, "Breathable, moisture-wicking running shirt.",         65),
+        ("Adidas Own the Run Jacket","apparel",     "Adidas",   80, true,  33, "Lightweight, water-repellent running jacket.",        60),
+    ];
+
+    /// <summary>Seeds the sample product graph (idempotent — safe to run every start).</summary>
+    public Task SeedAsync(CancellationToken ct = default) => this._runner.WriteAsync(async r =>
+    {
+        await r.RunAsync(
+            """
+            UNWIND $products AS row
+            MERGE (p:Product {name: row.name})
+              SET p.category = row.category, p.brand = row.brand, p.price = row.price,
+                  p.in_stock = row.in_stock, p.inventory = row.inventory,
+                  p.description = row.description, p.popularity = row.popularity
+            MERGE (c:ProductCategory {name: row.category})
+            MERGE (b:ProductBrand {name: row.brand})
+            MERGE (p)-[:IN_CATEGORY]->(c)
+            MERGE (p)-[:MADE_BY]->(b)
+            """,
+            new
+            {
+                products = s_seed.Select(p => (object)new Dictionary<string, object>
+                {
+                    ["name"] = p.Name, ["category"] = p.Category, ["brand"] = p.Brand, ["price"] = p.Price,
+                    ["in_stock"] = p.InStock, ["inventory"] = p.Inventory, ["description"] = p.Description,
+                    ["popularity"] = p.Popularity,
+                }).ToList(),
+            });
+    }, ct);
+
+    // ── Tools (also usable directly in the scripted demo) ────────────────────────────────────────
+
+    [Description("Search the product catalog for items matching a query, with optional category, brand, and max-price filters.")]
+    public Task<string> SearchProductsAsync(
+        [Description("What the customer is looking for, e.g. 'running shoes'.")] string query,
+        [Description("Optional category filter: shoes, electronics, apparel.")] string? category = null,
+        [Description("Optional brand filter, e.g. 'Nike'.")] string? brand = null,
+        [Description("Optional maximum price.")] double? maxPrice = null,
+        CancellationToken ct = default) => this._runner.ReadAsync(async r =>
+    {
+        const string Cypher =
+            """
+            MATCH (p:Product)
+            WHERE ANY(w IN split(toLower($query), ' ') WHERE
+                      toLower(p.name) CONTAINS w OR toLower(p.description) CONTAINS w OR toLower(p.category) CONTAINS w)
+              AND ($category IS NULL OR p.category = $category)
+              AND ($brand    IS NULL OR p.brand = $brand)
+              AND ($maxPrice IS NULL OR p.price <= $maxPrice)
+            RETURN p.name AS name, p.brand AS brand, p.category AS category,
+                   p.price AS price, p.in_stock AS inStock
+            ORDER BY p.popularity DESC
+            LIMIT 10
+            """;
+        var cursor = await r.RunAsync(Cypher, new { query, category, brand, maxPrice });
+        return Render("Matches", await cursor.ToListAsync());
+    }, ct);
+
+    [Description("Get personalized product recommendations, optionally biased toward a preferred brand and/or category.")]
+    public Task<string> GetRecommendationsAsync(
+        [Description("The customer's preferred brand (from their saved preferences), if known.")] string? preferredBrand = null,
+        [Description("Optional category to recommend within.")] string? category = null,
+        [Description("How many recommendations to return.")] int limit = 5,
+        CancellationToken ct = default) => this._runner.ReadAsync(async r =>
+    {
+        const string Cypher =
+            """
+            MATCH (p:Product)
+            WHERE p.in_stock = true
+              AND ($category IS NULL OR p.category = $category)
+            WITH p, (CASE WHEN $preferredBrand IS NOT NULL AND p.brand = $preferredBrand THEN 1 ELSE 0 END) AS onBrand
+            RETURN p.name AS name, p.brand AS brand, p.category AS category, p.price AS price, p.in_stock AS inStock
+            ORDER BY onBrand DESC, p.popularity DESC
+            LIMIT $limit
+            """;
+        var cursor = await r.RunAsync(Cypher, new { preferredBrand, category, limit });
+        var header = preferredBrand is null ? "Recommended for you" : $"Recommended for you (favoring {preferredBrand})";
+        return Render(header, await cursor.ToListAsync());
+    }, ct);
+
+    [Description("Find products related to a given product — same category or same brand — via graph traversal.")]
+    public Task<string> GetRelatedProductsAsync(
+        [Description("The exact product name to find related items for.")] string productName,
+        CancellationToken ct = default) => this._runner.ReadAsync(async r =>
+    {
+        const string Cypher =
+            """
+            MATCH (p:Product {name: $productName})
+            CALL (p) {
+                MATCH (p)-[:IN_CATEGORY]->(c)<-[:IN_CATEGORY]-(rel:Product) WHERE rel <> p
+                RETURN rel, 'same category' AS reason
+              UNION
+                MATCH (p)-[:MADE_BY]->(b)<-[:MADE_BY]-(rel:Product) WHERE rel <> p
+                RETURN rel, 'same brand' AS reason
+            }
+            WITH rel, collect(DISTINCT reason) AS reasons
+            RETURN rel.name AS name, rel.brand AS brand, rel.category AS category,
+                   rel.price AS price, rel.in_stock AS inStock, rel.popularity AS popularity,
+                   reduce(s = '', x IN reasons | CASE WHEN s = '' THEN x ELSE s + ', ' + x END) AS reason
+            ORDER BY popularity DESC
+            LIMIT 5
+            """;
+        var cursor = await r.RunAsync(Cypher, new { productName });
+        return Render($"Related to {productName}", await cursor.ToListAsync());
+    }, ct);
+
+    [Description("Check whether a product is in stock and how many units are available.")]
+    public Task<string> CheckInventoryAsync(
+        [Description("The exact product name to check.")] string productName,
+        CancellationToken ct = default) => this._runner.ReadAsync(async r =>
+    {
+        var cursor = await r.RunAsync(
+            "MATCH (p:Product {name: $productName}) RETURN p.name AS name, p.in_stock AS inStock, p.inventory AS inventory",
+            new { productName });
+        var rows = await cursor.ToListAsync();
+        if (rows.Count == 0)
+        {
+            return $"'{productName}' was not found in the catalog.";
+        }
+
+        var rec = rows[0];
+        var inStock = rec["inStock"].As<bool>();
+        return inStock
+            ? $"{rec["name"].As<string>()}: In stock ({rec["inventory"].As<long>()} available)."
+            : $"{rec["name"].As<string>()}: Out of stock.";
+    }, ct);
+
+    /// <summary>The retail tools as MAF/MEAI <see cref="AIFunction"/>s (attach to the agent's ChatOptions.Tools).</summary>
+    public IReadOnlyList<AIFunction> CreateAIFunctions() =>
+    [
+        AIFunctionFactory.Create(this.SearchProductsAsync, "search_products",
+            "Search the product catalog with optional category/brand/price filters."),
+        AIFunctionFactory.Create(this.GetRecommendationsAsync, "get_recommendations",
+            "Get personalized recommendations, optionally favoring a preferred brand/category."),
+        AIFunctionFactory.Create(this.GetRelatedProductsAsync, "get_related_products",
+            "Find products related to a given product via the graph."),
+        AIFunctionFactory.Create(this.CheckInventoryAsync, "check_inventory",
+            "Check stock/availability for a product."),
+    ];
+
+    private static string Render(string header, List<IRecord> rows)
+    {
+        if (rows.Count == 0)
+        {
+            return $"{header}: (no matches)";
+        }
+
+        var sb = new StringBuilder().Append(header).Append(':').AppendLine();
+        foreach (var rec in rows)
+        {
+            var stock = rec["inStock"].As<bool>() ? "in stock" : "out of stock";
+            var reason = rec.Keys.Contains("reason") ? $"  [{rec["reason"].As<string>()}]" : string.Empty;
+            sb.Append("  • ")
+              .Append(rec["name"].As<string>())
+              .Append(" — ").Append(rec["brand"].As<string>())
+              .Append(", ").Append(rec["category"].As<string>())
+              .Append(", $").Append(rec["price"].As<double>().ToString("0"))
+              .Append(", ").Append(stock).Append(reason)
+              .AppendLine();
+        }
+        return sb.ToString().TrimEnd();
+    }
+}