NTG.Adk 1.8.8

NTG.Adk - Agent Development Kit for .NET

Production-ready C# implementation of Google's Agent Development Kit with 99% feature parity

NTG.Adk is a complete C# port of Google ADK Python, following strict Abstract Driven Development (A.D.D) V3 architecture principles for enterprise-grade agent systems.

✨ Key Features

• 🏗️ A.D.D V3 Architecture - Five-layer fractal design with zero coupling
• 🤖 Multi-Agent Orchestration - Sequential, parallel, and loop workflows
• 🔄 Session Management - Multi-user with app/user/session state hierarchy
• 💾 Artifact & Memory Services - File storage and long-term agent memory
• ⚙️ RunConfig - Configurable limits and streaming (MaxLlmCalls: 500, StreamingMode)
• 📡 Token Streaming - Real-time SSE streaming with partial event detection
• 🌐 A2A Protocol - Seamless interoperability with Google Agent ecosystem
• 🔌 MCP Protocol - Connect to MCP servers and use their tools (stdio, SSE, HTTP)
• 🌐 OpenAPI Toolset - Auto-generate tools from any REST API (JSON/YAML specs)
• 🚀 Runner Pattern - Production-ready orchestration with integrated services
• 🧩 LLM Adapters - Gemini, OpenAI, and OpenAI-compatible endpoints (Ollama, LocalAI, vLLM)
• 🛠️ Tool Ecosystem - Function calling, custom tools, and built-in tools (Google Search, Code Execution)

📊 Status

Version: 1.8.8 Target Framework: .NET 8.0 LTS (supported until Nov 2026) Production Readiness: 100% ✅ Core Feature Parity with Python ADK: 99% ✅ (Retrieval/RAG: 50%) A2A Interoperability: 100% ✅ MCP Protocol Support: 100% ✅ OpenAPI Toolset: 100% ✅

See docs/STATUS.md for detailed metrics.

⚡ Quick Start

Basic Agent

using NTG.Adk.Implementations.Models;
using NTG.Adk.Operators.Agents;
using NTG.Adk.Operators.Runners;

// Create agent with LLM
var llm = new GeminiLlm("gemini-2.0-flash-exp");
var agent = new LlmAgent(llm, "gemini-2.0-flash-exp")
{
    Name = "Assistant",
    Instruction = "You are a helpful assistant"
};

// Run with InMemoryRunner
var runner = new InMemoryRunner(agent, appName: "MyApp");

await foreach (var evt in runner.RunAsync("user001", "session001", "Hello!"))
{
    if (evt.Content?.Parts != null)
    {
        foreach (var part in evt.Content.Parts)
        {
            if (part.Text != null)
                Console.WriteLine($"[{evt.Author}] {part.Text}");
        }
    }
}

OpenAI-Compatible Endpoints (Ollama, LocalAI, vLLM)

using NTG.Adk.Implementations.Models;
using NTG.Adk.Operators.Agents;
using NTG.Adk.Operators.Runners;

// Use Ollama local models
var llm = new OpenAILlm(
    modelName: "llama3",
    apiKey: "ollama",  // Any string works
    endpoint: new Uri("http://localhost:11434/v1")
);

var agent = new LlmAgent(llm, "llama3")
{
    Name = "LocalAssistant",
    Instruction = "You are a helpful assistant running locally"
};

var runner = new InMemoryRunner(agent, appName: "LocalApp");
await foreach (var evt in runner.RunAsync("user001", "session001", "Hello!"))
{
    // Handle events
}

Supports: Ollama, LocalAI, vLLM, LM Studio, and any OpenAI-compatible endpoint.

Multi-Agent Workflow

using NTG.Adk.Operators.Workflows;

// Define agents
var validator = new LlmAgent(llm, "gemini-2.0-flash-exp")
{
    Name = "Validator",
    Instruction = "Validate input data",
    OutputKey = "validation"
};

var processor = new LlmAgent(llm, "gemini-2.0-flash-exp")
{
    Name = "Processor",
    Instruction = "Process validated data",
    OutputKey = "result"
};

// Sequential pipeline
var pipeline = new SequentialAgent("DataPipeline", [validator, processor]);

var runner = new InMemoryRunner(pipeline, appName: "PipelineApp");
await foreach (var evt in runner.RunAsync("user001", "session001", "Process this data"))
{
    // Handle events
}

A2A Interoperability

using NTG.Adk.Operators.A2A;

// Create ADK agent
var agent = new LlmAgent(llm, "gemini-2.0-flash-exp")
{
    Name = "A2AAgent",
    Instruction = "Answer questions via A2A protocol"
};

var runner = new InMemoryRunner(agent, appName: "A2AApp");

// Wrap with A2A executor
var a2aExecutor = new A2aAgentExecutor(runner);

// Handle A2A messages
var a2aMessage = new A2A.AgentMessage
{
    MessageId = Guid.NewGuid().ToString(),
    Role = A2A.MessageRole.User,
    Parts = [new A2A.TextPart { Text = "Hello from A2A!" }]
};

await foreach (var a2aEvent in a2aExecutor.ExecuteAsync(
    a2aMessage,
    taskId: Guid.NewGuid().ToString(),
    contextId: "ADK/A2AApp/user001/session001"))
{
    // Handle A2A events (TaskStatusUpdateEvent, TaskArtifactUpdateEvent)
}

MCP Protocol Integration

using NTG.Adk.Boundary.Mcp;
using NTG.Adk.Implementations.Mcp;

// Connect to MCP server via stdio
var connectionParams = new StdioConnectionParams
{
    Command = "npx",
    Arguments = ["-y", "@modelcontextprotocol/server-filesystem", "/tmp"]
};

var mcpToolset = new McpToolset(connectionParams);

// Connect and get tools
await mcpToolset.ConnectAsync();
var tools = await mcpToolset.GetToolsAsync();

// Use MCP tools with agent
var agent = new LlmAgent(llm, "gemini-2.0-flash-exp")
{
    Name = "McpAssistant",
    Instruction = "You have access to MCP tools",
    Tools = tools.ToList()
};

var runner = new InMemoryRunner(agent, appName: "McpApp");
await foreach (var evt in runner.RunAsync("user001", "session001", "List files"))
{
    // Handle events
}

OpenAPI Toolset

using NTG.Adk.Implementations.Tools.OpenApi;
using NTG.Adk.Boundary.Tools.Auth;

// Load OpenAPI spec (JSON or YAML)
var openApiSpec = File.ReadAllText("petstore-openapi.json");

// Create toolset from spec
var toolset = new OpenAPIToolset(openApiSpec, "json");

// Optional: Add authentication
var authScheme = new ApiKeyAuthScheme
{
    In = "header",
    Name = "X-API-Key"
};
var authCredential = new ApiKeyCredential
{
    ApiKey = Environment.GetEnvironmentVariable("API_KEY")!
};
toolset = new OpenAPIToolset(openApiSpec, "json", authScheme, authCredential);

// Get all tools from spec
var tools = toolset.GetTools();

// Use with agent
var agent = new LlmAgent(llm, "gemini-2.0-flash-exp")
{
    Name = "ApiAgent",
    Instruction = "You can interact with the API using these tools",
    Tools = tools
};

var runner = new InMemoryRunner(agent, appName: "ApiApp");
await foreach (var evt in runner.RunAsync("user001", "session001", "List all pets"))
{
    // Handle events
}

⚙️ Advanced Configuration

RunConfig - Execution Control

Configure agent execution limits and streaming (matches Python ADK):

using NTG.Adk.CoreAbstractions.Agents;

// Default configuration (matches Python ADK defaults)
var runConfig = new RunConfig
{
    MaxLlmCalls = 500,                  // Max LLM calls per invocation (prevents infinite loops)
    StreamingMode = StreamingMode.None  // No streaming by default
};

var runner = new Runner(agent, "MyApp", sessionService, runConfig: runConfig);

Token Streaming

Enable real-time token-by-token streaming:

// Enable SSE streaming
var runConfig = new RunConfig
{
    StreamingMode = StreamingMode.Sse  // Server-sent events streaming
};

var runner = new Runner(agent, "MyApp", sessionService, runConfig: runConfig);

await foreach (var evt in runner.RunAsync("user001", "session001", "Hello"))
{
    if (evt.Partial)
    {
        // Streaming chunk - arrives in real-time as tokens are generated
        Console.Write(evt.Content?.Parts?.FirstOrDefault()?.Text ?? "");
    }
    else
    {
        // Complete response
        Console.WriteLine("\n[Complete]");
    }
}

Streaming Modes:

• StreamingMode.None - Buffer complete response (default, matches Python ADK)
• StreamingMode.Sse - Server-sent events, token-by-token streaming
• StreamingMode.Bidi - Bidirectional streaming (reserved for future use)

LLM Call Limits

Prevent infinite loops with configurable limits:

var runConfig = new RunConfig
{
    MaxLlmCalls = 100  // Custom limit
};

try
{
    await foreach (var evt in runner.RunAsync("user001", "session001", "Complex task"))
    {
        // Process events
    }
}
catch (LlmCallsLimitExceededError ex)
{
    Console.WriteLine($"Limit exceeded: {ex.Message}");
    // Output: "Max number of LLM calls limit of 100 exceeded"
}

Default: 500 calls per invocation (matches Python ADK)

📚 Documentation

• Getting Started Guide - Detailed setup and usage
• Architecture - A.D.D V3 five-layer design
• Features - Complete feature list with examples
• Compatibility - Python ADK API mapping
• Status - Current implementation status
• Changelog - Version history

🏗️ Architecture Overview

NTG.Adk follows A.D.D V3 strict five-layer architecture:

NTG.Adk/
├── Boundary/              # Layer 1: DTOs, Events (no dependencies)
├── CoreAbstractions/      # Layer 2: Interfaces/Ports (no dependencies)
├── Implementations/       # Layer 3: Adapters (depends on CoreAbstractions)
├── Operators/             # Layer 4: Business Logic (depends on CoreAbstractions + Boundary)
└── Bootstrap/             # Layer 5: Composition Root (depends on all)

Key Principles:

• ✅ Operators call ports (interfaces), never implementations
• ✅ Zero coupling between layers (except explicit dependencies)
• ✅ Dependency inversion at all boundaries
• ✅ Technology-agnostic core abstractions

See docs/ARCHITECTURE.md for details.

🔗 Python ADK Compatibility

NTG.Adk maintains 99% feature parity with Google ADK Python (100% core features):

See docs/COMPATIBILITY.md for complete mapping.

📦 Project Structure

E:\repos\adk-csharp/
├── src/
│   ├── NTG.Adk.Boundary/           # Layer 1: DTOs
│   ├── NTG.Adk.CoreAbstractions/   # Layer 2: Ports
│   ├── NTG.Adk.Implementations/    # Layer 3: Adapters
│   ├── NTG.Adk.Operators/          # Layer 4: Orchestration
│   └── NTG.Adk.Bootstrap/          # Layer 5: DI/Entry
├── samples/
│   ├── HelloWorldAgent/            # Basic agent demo
│   ├── GeminiAgent/                # Gemini LLM integration
│   ├── OpenAIAgent/                # OpenAI integration
│   ├── AutoFlowAgent/              # AutoFlow orchestration
│   ├── StoryFlowAgent/             # Multi-agent workflow
│   ├── A2AInteropSample/           # A2A protocol demo
│   ├── McpToolsSample/             # MCP Protocol integration
│   ├── OpenApiToolsSample/         # OpenAPI Toolset demo
│   └── BuiltInToolsSample/         # Built-in tools demo
├── docs/                           # Documentation
└── README.md                       # This file

🧪 Samples

Explore working examples in the samples/ directory:

1. HelloWorldAgent - Simple echo agent with InMemoryRunner
2. GeminiAgent - Google Gemini 2.0 Flash integration
3. OpenAIAgent - OpenAI GPT-4 integration
4. AutoFlowAgent - Dynamic multi-agent routing
5. StoryFlowAgent - Sequential story generation workflow
6. A2AInteropSample - A2A protocol interoperability
7. McpToolsSample - MCP Protocol integration (stdio, SSE, HTTP transports)
8. OpenApiToolsSample - OpenAPI Toolset with REST API integration
9. BuiltInToolsSample - Built-in tools (Google Search, Code Execution)

Run a sample:

cd samples/HelloWorldAgent
dotnet run

🔧 Requirements

• .NET 8.0 LTS (supported until November 2026)
• C# 12 language features
• Visual Studio 2022 or VS Code with C# Dev Kit

🛠️ Build

# Clone repository
git clone <repository-url>
cd adk-csharp

# Restore packages
dotnet restore

# Build solution
dotnet build

# Run tests (if available)
dotnet test

# Run a sample
cd samples/HelloWorldAgent
dotnet run

🗺️ Roadmap

Phase 1 - Core Infrastructure ✅ COMPLETE

• ✅ Multi-Agent Orchestration (Sequential, Parallel, Loop)
• ✅ Session/Artifact/Memory Services (In-Memory)
• ✅ A2A Protocol Integration
• ✅ MCP Protocol Support (stdio, SSE, HTTP)
• ✅ OpenAPI Toolset (REST API auto-integration)
• ✅ Built-in Tools (Google Search, Code Execution)
• ✅ LLM Adapters (Gemini, OpenAI)
• ✅ .NET 8.0 LTS Migration

Phase 2 - Persistent Storage (In Progress)

• ⏳ DatabaseSessionService - SQL persistence for sessions
  • PostgreSQL, MySQL, SQLite support
  • Production-ready session storage
  • Multi-instance deployment support
• ⏳ FileArtifactService - Local file system storage
  • File-based artifact persistence
  • Blob storage support (Azure, AWS S3, GCP)
  • Artifact versioning and cleanup

Phase 3 - Advanced Features (Planned)

• 📋 Planner System - Agent reasoning and planning
  • BuiltInPlanner with extended thinking
  • PlanReActPlanner for ReAct pattern
  • Custom planner support
• 📋 FilesRetrieval - RAG and document search
  • Directory-based retrieval
  • Semantic search capabilities
  • LlamaIndex integration
• 📋 AgentEvaluator - Testing and quality assurance
  • Automated agent testing
  • Benchmark framework
  • Performance metrics

Phase 4 - Cloud Integration (Future)

• 📋 Cloud Storage Services
  • GCS Artifact Service (Google Cloud Storage)
  • Azure Blob Artifact Service
  • AWS S3 Artifact Service
• 📋 Vertex AI Integration
  • Vertex AI RAG Retrieval
  • Vertex AI Search Tool
  • Vertex AI Example Store
• 📋 Enterprise Features
  • Authentication & Authorization
  • Rate limiting and quotas
  • Audit logging and monitoring

Community Contributions Welcome! 🤝

We welcome contributions for:

• New LLM adapters (Anthropic Claude, Cohere, etc.)
• Additional built-in tools
• Cloud provider integrations
• Performance optimizations
• Documentation improvements

See CONTRIBUTING.md for guidelines.

🔖 Version Update Checklist

When bumping version, update these files:

1. README.md - Version field in Status section
2. docs/CHANGELOG.md - Add new version entry
3. docs/FEATURES.md - Version + Last Updated
4. docs/STATUS.md - Version + Last Updated
5. llms-full.txt - Version (line 12) + VERSION INFORMATION section
6. src/NTG.Adk.*/NTG.Adk.*.csproj - All 4 package Version properties

📄 License

Apache 2.0 License - see LICENSE file.

🙏 Credits

• Based on Google ADK Python
• Architecture: Abstract Driven Development (A.D.D) V3
• A2A Protocol: a2a-dotnet SDK

Built with Abstract Driven Development (A.D.D) V3 🚀