Azure Ai Agent Demos

jcentner4stars

Azure AI Foundry Agent service demos and resources

1# Azure Ai Agent Demos

README.mdMCP server

Transport: stdio
Status: Local

Power your agent

Add this server

Install from the publisher. Not a PlugMCP endpoint.

Run on PlugMCP

Host a copy with our keys and logs.

No packaged release in the registry yet. Follow the repository README.

RepositoryPublisher installStep-by-step: add this in Claude

Remote URL or a command on your machine? How the two installs differ

Run on PlugMCP

Host a copy with our keys and logs.

A new server you create. It is not this listing's official endpoint. Use the publisher install when you want their URL and their credentials.

Launch

Server facts

Local

Transport: stdio
License: MIT
Last checked: 2026-10-28

Stars: 4
Tools: —
Uptime: —

Tools

No tool list yet. Remote servers get one after the first health check.

FAQ

Questions

How do I install Azure Ai Agent Demos?

Copy the stdio config for Cursor, Claude Desktop, Claude Code, or VS Code on this page. It uses the publisher's install, not a PlugMCP proxy.

Does Azure Ai Agent Demos need an API key?

No required environment variable is listed on this record.

What transport does Azure Ai Agent Demos use?

Azure Ai Agent Demos runs locally over stdio.

When was Azure Ai Agent Demos last checked?

Last checked 2026-10-28. Azure AI Foundry Agent service demos and resources

From the publisher

Documentation

Azure AI Foundry Agents Demos

Sample code and walkthroughs for building Azure AI Foundry Agents using the GA v2 SDK (azure-ai-projects>=2.0.0). Demos cover MCP server integration, Code Interpreter, and enterprise patterns like credential isolation via Foundry project connections.

Demos (v2 — GA SDK + New Foundry UI)

Demo	Description	Tools	Tests
enterprise_github_agent	GitHub integration with code execution	MCP (GitHub) + Code Interpreter	42
mcp_mslearn_agent	Search Microsoft Learn documentation	MCP (MS Learn, public)	41
mcp_local_server_agent	Local MCP server with Chinook SQLite DB	MCP (custom, local + ngrok)	84

Every demo follows a consistent pattern:

create_agent.py — Create the agent (run once)
ask_agent.py — Interactive chat REPL with streaming output
.env.sample — Required environment variables
requirements.txt — Python dependencies
README.md — Setup and walkthrough

Training labs

The training/l200-tools/ suite accompanies the L200 Foundry tools training module for support engineers. Unlike the standalone demos, these labs form one sequence and share a single Python environment:

Toolbox versioning
Azure AI Search grounding
Foundry IQ knowledge base

The training README starts with Python, Git, Azure CLI, virtual-environment, dependency, and configuration setup for learners who have not used Python before.

Quick Start

# Pick a demo directory
cd enterprise_github_agent  # or mcp_mslearn_agent, mcp_local_server_agent

# Set up
python -m venv .venv && source .venv/bin/activate
pip install -r requirements.txt
cp .env.sample .env
# Fill in PROJECT_ENDPOINT and MODEL_DEPLOYMENT_NAME

# Create the agent and chat
python create_agent.py
python ask_agent.py

See each demo's README for detailed setup instructions.

Prerequisites

Python 3.11+
An Azure AI Foundry project with a deployed model
Azure CLI logged in (az login) for DefaultAzureCredential

Architecture

All demos use the GA V2 SDK:

Agent creation via PromptAgentDefinition + create_version()
Conversations via OpenAI Responses API (responses.create())
Credentials in Foundry project connections (not in code)

See docs/architecture/overview.md for details and docs/architecture/decisions/ for ADRs.

Archived Demos (v1)

The original demos from the walkthrough video are preserved in archive/v1/. They target the deprecated v1 Agents SDK (azure-ai-agents) and the legacy Foundry portal UI.

Roadmap

Enterprise GitHub Agent (Phase 1)
MCP Microsoft Learn Agent v2 rewrite (Phase 2)
MCP Local Server Agent v2 rewrite (Phase 3)
Cross-cutting polish — architecture docs, README (Phase 4)
Fabric Data Agent — natural language queries against Fabric lakehouse (Phase 5)
Multi-Tool Knowledge Worker — File Search + Code Interpreter + Web Search (Phase 6)
Browser Automation Agent — browser control via natural language (Phase 7)

Running Tests

pip install pytest
python -m pytest enterprise_github_agent/tests/ mcp_mslearn_agent/tests/ mcp_local_server_agent/tests/ -v
# 167 tests, all structural (no live Azure calls needed)

Azure Ai Agent Demos

jcentner4stars

Azure AI Foundry Agent service demos and resources

Cloud Infrastructure

Install Run on PlugMCP

GitHub

Run on PlugMCP

1# Azure Ai Agent Demos

README.mdMCP server

Transport: stdio
Status: Local

Power your agent

Add this server

Install from the publisher. Not a PlugMCP endpoint.

Run on PlugMCP

Host a copy with our keys and logs.

No packaged release in the registry yet. Follow the repository README.

RepositoryPublisher installStep-by-step: add this in Claude

Remote URL or a command on your machine? How the two installs differ

Run on PlugMCP

Host a copy with our keys and logs.

A new server you create. It is not this listing's official endpoint. Use the publisher install when you want their URL and their credentials.

Launch

Server facts

Local

Transport: stdio
License: MIT
Last checked: 2026-10-28

Stars: 4
Tools: —
Uptime: —

Tools

No tool list yet. Remote servers get one after the first health check.

FAQ

Questions

How do I install Azure Ai Agent Demos?

Copy the stdio config for Cursor, Claude Desktop, Claude Code, or VS Code on this page. It uses the publisher's install, not a PlugMCP proxy.

Does Azure Ai Agent Demos need an API key?

No required environment variable is listed on this record.

What transport does Azure Ai Agent Demos use?

Azure Ai Agent Demos runs locally over stdio.

When was Azure Ai Agent Demos last checked?

Last checked 2026-10-28. Azure AI Foundry Agent service demos and resources

From the publisher

Documentation

Azure AI Foundry Agents Demos

Demos (v2 — GA SDK + New Foundry UI)

Demo	Description	Tools	Tests
enterprise_github_agent	GitHub integration with code execution	MCP (GitHub) + Code Interpreter	42
mcp_mslearn_agent	Search Microsoft Learn documentation	MCP (MS Learn, public)	41
mcp_local_server_agent	Local MCP server with Chinook SQLite DB	MCP (custom, local + ngrok)	84

Every demo follows a consistent pattern:

create_agent.py — Create the agent (run once)
ask_agent.py — Interactive chat REPL with streaming output
.env.sample — Required environment variables
requirements.txt — Python dependencies
README.md — Setup and walkthrough

Training labs

Toolbox versioning
Azure AI Search grounding
Foundry IQ knowledge base

The training README starts with Python, Git, Azure CLI, virtual-environment, dependency, and configuration setup for learners who have not used Python before.

Quick Start

# Pick a demo directory
cd enterprise_github_agent  # or mcp_mslearn_agent, mcp_local_server_agent

# Set up
python -m venv .venv && source .venv/bin/activate
pip install -r requirements.txt
cp .env.sample .env
# Fill in PROJECT_ENDPOINT and MODEL_DEPLOYMENT_NAME

# Create the agent and chat
python create_agent.py
python ask_agent.py

See each demo's README for detailed setup instructions.

Prerequisites

Python 3.11+
An Azure AI Foundry project with a deployed model
Azure CLI logged in (az login) for DefaultAzureCredential

Architecture

All demos use the GA V2 SDK:

Agent creation via PromptAgentDefinition + create_version()
Conversations via OpenAI Responses API (responses.create())
Credentials in Foundry project connections (not in code)

See docs/architecture/overview.md for details and docs/architecture/decisions/ for ADRs.

Archived Demos (v1)

The original demos from the walkthrough video are preserved in archive/v1/. They target the deprecated v1 Agents SDK (azure-ai-agents) and the legacy Foundry portal UI.

Roadmap

Enterprise GitHub Agent (Phase 1)
MCP Microsoft Learn Agent v2 rewrite (Phase 2)
MCP Local Server Agent v2 rewrite (Phase 3)
Cross-cutting polish — architecture docs, README (Phase 4)
Fabric Data Agent — natural language queries against Fabric lakehouse (Phase 5)
Multi-Tool Knowledge Worker — File Search + Code Interpreter + Web Search (Phase 6)
Browser Automation Agent — browser control via natural language (Phase 7)

Running Tests

pip install pytest
python -m pytest enterprise_github_agent/tests/ mcp_mslearn_agent/tests/ mcp_local_server_agent/tests/ -v
# 167 tests, all structural (no live Azure calls needed)