AI Toolkit for Visual Studio Code

AI Toolkit for Visual Studio Code helps developers and AI engineers build, test, and deploy AI apps with generative AI models. You can use it locally or in the cloud to manage your full AI app workflow in one place.

AI Toolkit offers seamless integration with popular AI models from providers like OpenAI, Anthropic, Google, and GitHub, while also supporting local models through ONNX and Ollama. From model discovery and experimentation to prompt engineering and deployment, AI Toolkit streamlines your AI development workflow within VS Code.

Key features

Feature	Description	Screenshot
Model Catalog	Discover and access AI models from multiple sources including Microsoft Foundry, Foundry Local, GitHub, ONNX, Ollama, OpenAI, Anthropic, and Google. Compare models side-by-side and find the perfect fit for your use case.
Playground	Interactive chat environment for real-time model testing. Experiment with different prompts, parameters, and multi-modal inputs including images and attachments.
Agent Builder	Streamlined prompt engineering and agent development workflow. Create sophisticated prompts, integrate MCP tools, and generate production-ready code with structured outputs.
Agent Inspector	Debug, visualize, and iterate on AI agents directly within VS Code.
Bulk Run	Execute batch prompt testing across multiple models simultaneously. Ideal for comparing model performance and testing at scale with various input scenarios.
Model Evaluation	Comprehensive model assessment using datasets and standard metrics. Measure performance with built-in evaluators (F1 score, relevance, similarity, coherence) or create custom evaluation criteria.
Fine-tuning	Customize and adapt models for specific domains and requirements. Train models locally with GPU support or use Azure Container Apps for cloud-based fine-tuning.
Model Conversion	Convert, quantize, and optimize machine learning models for local deployment. Transform models from Hugging Face and other sources to run efficiently on Windows with CPU, GPU, or NPU acceleration.
Tracing	Monitor and analyze the performance of your AI applications. Collect and visualize trace data to gain insights into model behavior and performance.
Profiling (Windows ML)	Diagnose the CPU, GPU, NPU resource usages of the process, ONNX model on different execution providers, and Windows Machine Learning events.

Who is AI Toolkit for?

AI Toolkit is designed for anyone working with generative AI, from beginners to experts:

Developers

• App developers building AI-powered applications who need to integrate language models
• Full-stack developers looking to add intelligent features to web and desktop applications
• Mobile developers prototyping AI functionality before production deployment

AI engineers & data scientists

• AI engineers fine-tuning models for specific domains and deploying to production
• Data scientists evaluating model performance and comparing different approaches
• ML engineers converting and optimizing models for efficient local deployment

Researchers and educators

• AI researchers experimenting with different models and prompt engineering techniques
• Educators teaching AI concepts and demonstrating model capabilities
• Students learning about generative AI and hands-on model interaction

Key use cases

• Explore and evaluate models from providers like Anthropic, OpenAI, and GitHub
• Run models locally using ONNX and Ollama for privacy and cost control
• Build and test agents with prompt generation and MCP tool integrations
• Convert and optimize models for deployment across different hardware configurations

Install and setup

Quick installation

The fastest way to get started is by installing the extension through the Visual Studio Marketplace:

Install the AI Toolkit for VS Code

After successful installation, the AI Toolkit icon appears in the Activity Bar.

Manual installation

You can also install AI Toolkit extension manually from the Visual Studio Code Marketplace. Follow the steps detailed in Install an extension.

• Search for AI Toolkit for Visual Studio Code and select Install from search results.

  

• After successful installation, the AI Toolkit icon appears in the Activity Bar.

Explore AI Toolkit

AI Toolkit includes the Foundry sidebar directly, so you manage your Microsoft Foundry resources and AI Toolkit features in one place.

AI Toolkit opens in its own view, with the AI Toolkit icon displayed on the VS Code Activity Bar. The extension has three main sections: My Resources, Developer Tools, and Help and Feedback.

• My Resources: This section contains the resources you have access to in AI Toolkit. The My Resources section is the main view for interacting with your Azure AI resources. It contains the following subsections:
  • Local Resources: This section contains the AI resources you have on your local machine, such as local models, agents, and tools.
  • Your Foundry Project This section shows the Microsoft Foundry project connected to AI Toolkit. Use your Foundry project to manage and deploy AI resources, such as deployed models, prompt agents, hosted agents, connections, tools, vector stores, and classic agents.
  • Connected Resources: This section contains the resources that are connected to AI Toolkit from providers such as GitHub models.
• Developer Tools: This section contains the tools you can use to build and deploy your AI applications. The Developer Tools view is where you can find the tools available to deploy and then work with your deployed models and agents. It contains the following subsections:
  • Discover: This section contains tools to help you discover and manage AI models and tools. It contains the following subsections:
    • Model Catalog: The model catalog lets you discover and access AI models from multiple sources including GitHub, ONNX, Ollama, OpenAI, Anthropic, and Google. Compare models side-by-side and find the right model for your use case.
    • Tool Catalog: Browse and manage the tools available in AI Toolkit.
• Build: This section is where you can find the tools available to deploy and then work with your deployed agents in AI Toolkit. It contains the following subsections:
  • Create Agent: Create and deploy agents easily.
  • Agent Inspector: Debug, visualize, and iterate on AI agents directly within VS Code.
  • Deploy to Microsoft Foundry: Deploy your local agent to Microsoft Foundry as a hosted agent.
  • Hosted Agent Playground: The hosted agent playground provides an interactive environment to experiment with your hosted agents.
  • Model Playground: The model playground provides an interactive environment to experiment with generative AI models.
  • Model Conversion: The model conversion tool helps you convert, quantize, optimize, and evaluate the prebuilt machine learning models on your local Windows platform.
  • Fine-tuning: This tool allows you to use your custom dataset to run fine-tuning jobs on a pre-trained model in a local computing environment with GPU or in the cloud (Azure Container Apps) with GPU.
  • Monitor: This section is where you monitor and analyze the performance of your AI applications. It contains the following subsections:
    • Tracing: Trace capabilities to help you monitor and analyze the performance of your AI applications.
    • Evaluation: Evaluate models, prompts, and agents by comparing their outputs to ground truth data and computing evaluation metrics.
    • Profiling (Windows ML)(Preview): This tool allows you to diagnose the CPU, GPU, NPU resource usages of the process, ONNX model on different execution providers, and Windows Machine Learning events.
• Help and Feedback: This section contains links to the AI Toolkit documentation, feedback, support, and the Microsoft Privacy Statement. It contains the following subsections:
  • View Documentation: The link to the AI Toolkit documentation.
  • What's New: The link to the AI Toolkit release notes.
  • Report Issues: The link to the AI Toolkit GitHub repository issues page.
  • Join Community: Join the AI Toolkit community to share feedback and connect with other users and the AI Toolkit team.

Next steps

• Get more information about adding generative AI models in AI Toolkit
• Use the model playground to interact with models
• Develop agents with the Agent Builder and debug them with the Agent Inspector