chore: import upstream snapshot with attribution

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+# Basic Chat — Microsoft Agent Framework
+
+This is the [Microsoft Agent Framework (MAF)](https://devblogs.microsoft.com/agent-framework/microsoft-agent-framework-version-1-0/) version of the [chat-basic](../chat-basic/) Prompt Flow example.
+
+It implements the same behaviour: a helpful assistant chatbot that remembers conversation history and responds to user questions.
+
+## Architecture
+
+```
+[InputExecutor] ──→ [ChatExecutor]
+  (question +          (Agent with
+   chat_history)        FoundryChatClient)
+```
+
+| Prompt Flow concept | MAF equivalent |
+|---|---|
+| `flow.dag.yaml` | `WorkflowBuilder` in `chat_flow.py` |
+| `chat.jinja2` (system prompt) | `Agent(instructions="You are a helpful assistant.")` |
+| LLM node (`api: chat`) | `FoundryChatClient` + `Agent.run()` |
+| `chat_history` input | Message list assembled in `InputExecutor` |
+| `open_ai_connection` | Environment variables (`FOUNDRY_PROJECT_ENDPOINT`, `FOUNDRY_MODEL`) + `DefaultAzureCredential` |
+
+## Prerequisites
+
+- Python 3.10+
+- An Azure subscription with a Microsoft Foundry project (or Azure OpenAI resource)
+- `az login` completed
+
+## Setup
+
+```bash
+pip install -r requirements.txt
+
+cp .env.example .env
+# Edit .env with your Foundry project endpoint and model deployment name
+```
+
+## Run
+
+```bash
+python chat_flow.py
+```
+
+This runs two test interactions:
+1. A single-turn question with no history
+2. A follow-up question with one prior turn of chat history