https://github.com/okahu-demos/employee-ms-basic-bot
An M365 agentic app built using Microsoft Agent Framework and Azure OpenAI Assistants API to demonstrate open-source Monocle instrumentation and Okahu Cloud observability for AI-enabled automated experiences.
https://github.com/okahu-demos/employee-ms-basic-bot
azure-openai microsoft microsoft-agent-framework monocle2ai observability okahu
Last synced: 2 months ago
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An M365 agentic app built using Microsoft Agent Framework and Azure OpenAI Assistants API to demonstrate open-source Monocle instrumentation and Okahu Cloud observability for AI-enabled automated experiences.
- Host: GitHub
- URL: https://github.com/okahu-demos/employee-ms-basic-bot
- Owner: okahu-demos
- License: apache-2.0
- Created: 2026-01-19T14:49:02.000Z (5 months ago)
- Default Branch: main
- Last Pushed: 2026-03-05T02:42:57.000Z (3 months ago)
- Last Synced: 2026-03-05T07:50:36.665Z (3 months ago)
- Topics: azure-openai, microsoft, microsoft-agent-framework, monocle2ai, observability, okahu
- Language: Python
- Homepage: https://portal.okahu.co
- Size: 9.95 MB
- Stars: 1
- Watchers: 0
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
README
# Basic AI Chatbot for Employees in Teams
This M365 agentic app is built using [Microsoft Agent Framework](https://github.com/microsoft/agent-framework) and Azure OpenAI Assistants API. It demonstrates how open-source [Monocle](#about-monocle) and [Okahu Cloud](#about-okahu) is used to monitor AI-enabled automated experiences.
This demo is managed by *Okahu*.[^1]
### About Monocle
[Monocle](http://monocle2ai.org/) is a GenAI-native community driven open source project created to simplify instrumentation of AI apps so app developers can built high impact, safe and reliable AI apps.
Monocle is currently a [Sandbox](https://lfaidata.foundation/projects/monocle/) project in Linux Foundation AI & Data. In its first iteration, Monocle helps app developers make GenAI apps observable without having to write last-mile monitoring code.
Check out [PyPI](https://pypi.org/project/monocle-apptrace/) for release history of Monocle Python package.
### About Okahu Cloud
[Okahu Cloud](http://portal.okahu.co/) is a GenAI-native observability platform that simplifies observability & evals required for taking agentic and LLM based apps from lab into prod.
Okahu Cloud is built by a team of AI, observability and cloud engineers from [Okahu](https://www.okahu.ai). Okahu is a VC funded bay area startup founded by Stanford, Microsoft & BlueTalon to help AI app developers, platform engineers and engineering leaders to build reliable, accurate and safer AI apps.
#### Requirement
1. VS Code IDE
1. Microsoft 365 Agents Toolkit for VS Code
1. Monocle extension for VS Code
### Configure
1. In VS Code, open the command box and enter `Python: Create Environment` to create and activate your desired virtual environment. Remember to select `src/requirements.txt` as dependencies to install when creating the virtual environment.
1. Create a file `env/.env.playground.user` and enter the following environment variables:
```
AZURE_OPENAI_API_KEY=
AZURE_OPENAI_MODEL_DEPLOYMENT_NAME=
AZURE_OPENAI_ENDPOINT=
OKAHU_API_KEY=
OKAHU_INGESTION_ENDPOINT=
```
These environment variables are automatically loaded when you run the bot in VS Code (F5 → "Start Python") or in the Agents Playground.
### Conversation with agent
1. Select the Microsoft 365 Agents Toolkit icon on the left in the VS Code toolbar.
1. Press F5 to start debugging which launches your app in Teams using a web browser. Select `Debug in Microsoft 365 Agent Playground`.
1. You will receive a welcome message from the agent, or send any message to get a response.
#### Manual (CLI) option
If you prefer to run everything manually from the terminal:
1) Start the bot locally
```bash
# From repo root
python3 -m venv .venv
source .venv/bin/activate
# Install Python dependencies
pip install -r src/requirements.txt
# Provide required environment (use your values)
export AZURE_OPENAI_API_KEY=""
export AZURE_OPENAI_MODEL_DEPLOYMENT_NAME=""
export AZURE_OPENAI_ENDPOINT="https://.openai.azure.com/"
# Start the bot (listens on http://localhost:3978/api/messages)
cd src
python app.py
```
Tip: You can alternatively create a `.env` file in `src/` with the variables above and keep the values out of your shell history.
2) Run the Microsoft Teams App Test Tool against your bot
```bash
# In a new terminal (same repo root)
npx @microsoft/teams-app-test-tool --help
npx @microsoft/teams-app-test-tool -e http://localhost:3978/api/messages -c emulator
```
Notes
- Channel `emulator` requires no Microsoft Entra auth and is ideal for local testing.
- If the port is busy, change `Config.PORT` or pass a different `-e` URL.
### Observability of agent
#### Using Okahu VS Code Extension
View traces directly in VS Code using the Okahu extension:
1. Open the Okahu extension panel in VS Code
2. Monitor trace events in real-time as you interact with the agent:
3. View detailed trace attributes including model calls, token usage, and response times:
#### Using Okahu Cloud Portal
For centralized monitoring and team collaboration:
1. Navigate to the Okahu Cloud Portal (http://portal.okahu.co) and click `Discover Workflows` under **Applications > Components** tab.
2. Once discovered, your workflow will appear in the list as shown below:
3. Click on the `Traces` tab to visualize and monitor agent interactions and performance metrics.
4. To view detailed insights, create a new application by clicking the `+ New` button in the `Applications` navigation.
5. Complete the application setup form:
- Enter an **Application Name**
- Add **Categories** (press Enter after each category to register it)
- Provide a **Description**
- Define **Goals**
- Under **Components**, select the workflow you discovered
- Click **Create** to finalize
6. Your newly created application will now appear under the `Applications` navigation.
**Congratulations**! You are running an application that can now interact with users in Teams and monitor how it works using Okahu Cloud.
### Deploy to Azure + M365
1. Create a file `env/.env.dev.user` and enter the following environment variables:
```
AZURE_OPENAI_API_KEY=
AZURE_OPENAI_MODEL_DEPLOYMENT_NAME=
AZURE_OPENAI_ENDPOINT=
OKAHU_API_KEY=
```
### Experiment
| File | Changes |
| - | - |
|`src/bot.py`| Modify the `_get_instructions()` method to change the system prompt and alter your bot's behavior.|
|`src/config.py`| Configure Azure OpenAI settings and bot credentials.|
[^1]: Use of Okahu hosted demo is covered by Okahu's [terms of service for evaluations](https://www.okahu.ai/agreements/evaluation-agreement).
[Okahu](https://www.okahu.ai) is a team of AI, observability & cloud engineers working to simplify observability for agentic and other GenAI apps. We serve AI app developers, platform engineers and engineering leaders to build reliable, accurate and safer AI apps. We believe in community driven open source software and are a major contributor to GenAI native observability Project Monocle hosted by Linux Foundation.
Connect with us on [Linkedin](https://www.linkedin.com/company/99272699/admin/dashboard/), [Github](https://github.com/okahu) or email us at