[Hosted Agents] Add foundry checkpoints samples for agentframework and langgraph

sdk/agentserver/TASK.md

@@ -4,6 +4,11 @@
 
 ## Done
 
+- [x] 2026-02-06 — Add README files for Foundry checkpoint samples
+  - Files: `azure-ai-agentserver-agentframework/samples/workflow_with_foundry_checkpoints/README.md`,
+    `azure-ai-agentserver-langgraph/samples/simple_agent_with_foundry_checkpointer/README.md`
+  - Updated setup/run/request docs, added missing LangGraph sample README, and corrected `.env` setup guidance.
+
 - [x] 2026-02-04 — Implement managed checkpoints feature
   - Files: core/checkpoints/ (new), agentframework/persistence/_foundry_checkpoint_*.py (new),
     agentframework/__init__.py (modified)

sdk/agentserver/azure-ai-agentserver-agentframework/samples/workflow_with_foundry_checkpoints/README.md

@@ -0,0 +1,89 @@
+# Workflow Agent with Foundry Managed Checkpoints
+
+This sample hosts a two-step Agent Framework workflow—`Writer` followed by `Reviewer`—and uses
+`FoundryCheckpointRepository` to persist workflow checkpoints in Azure AI Foundry managed storage.
+
+With Foundry managed checkpoints, workflow state is stored remotely so long-running conversations can
+resume even after the host process restarts, without managing your own storage backend.
+
+### What `main.py` does
+
+- Builds a workflow with `WorkflowBuilder` (writer + reviewer)
+- Creates a `FoundryCheckpointRepository` pointed at your Azure AI Foundry project
+- Passes both to `from_agent_framework(..., checkpoint_repository=...)` so the adapter spins up an
+  HTTP server (defaults to `0.0.0.0:8088`)
+
+---
+
+## Prerequisites
+
+- Python 3.10+
+- Azure CLI authenticated with `az login` (required for `AzureCliCredential`)
+- An Azure AI Foundry project with a chat model deployment
+
+---
+
+## Setup
+
+1. Create a `.env` file in this folder:
+   ```
+   AZURE_AI_PROJECT_ENDPOINT=https://<resource>.services.ai.azure.com/api/projects/<project-id>
+   AZURE_AI_MODEL_DEPLOYMENT_NAME=<model-deployment-name>
+   ```
+
+2. Install dependencies:
+   ```bash
+   pip install azure-ai-agentserver-agentframework agent-framework-azure-ai azure-identity python-dotenv
+   ```
+
+---
+
+## Run the Workflow Agent
+
+From this folder:
+
+```bash
+python main.py
+```
+
+The adapter starts the server on `http://0.0.0.0:8088` by default.
+
+---
+
+## Send Requests
+
+**Non-streaming:**
+
+```bash
+curl -sS \
+    -H "Content-Type: application/json" \
+    -X POST http://localhost:8088/responses \
+    -d '{
+      "agent": {"name": "local_agent", "type": "agent_reference"},
+      "stream": false,
+      "input": "Write a short blog post about cloud-native AI applications",
+      "conversation": {"id": "test-conversation-1"}
+    }'
+```
+
+The `conversation.id` ties requests to the same checkpoint session. Subsequent requests with the same
+ID will resume the workflow from its last checkpoint.
+
+---
+
+## Checkpoint Repository Options
+
+The `checkpoint_repository` parameter in `from_agent_framework` accepts any `CheckpointRepository` implementation:
+
+| Repository | Use case |
+|---|---|
+| `InMemoryCheckpointRepository()` | Quick demos; checkpoints vanish when the process exits |
+| `FileCheckpointRepository("<path>")` | Local file-based persistence |
+| `FoundryCheckpointRepository(project_endpoint, credential)` | Azure AI Foundry managed remote storage (this sample) |
+
+---
+
+## Related Resources
+
+- Agent Framework repo: https://github.com/microsoft/agent-framework
+- Adapter package docs: `azure.ai.agentserver.agentframework` in this SDK

sdk/agentserver/azure-ai-agentserver-agentframework/samples/workflow_with_foundry_checkpoints/main.py

@@ -0,0 +1,84 @@
+# Copyright (c) Microsoft. All rights reserved.
+
+"""
+Workflow Agent with Foundry Managed Checkpoints
+
+This sample demonstrates how to use FoundryCheckpointRepository with
+a WorkflowBuilder agent to persist workflow checkpoints in Azure AI Foundry.
+
+Foundry managed checkpoints enable workflow state to be persisted across
+requests, allowing workflows to be paused, resumed, and replayed.
+
+Prerequisites:
+    - Set AZURE_AI_PROJECT_ENDPOINT to your Azure AI Foundry project endpoint
+      e.g. "https://<resource>.services.ai.azure.com/api/projects/<project-id>"
+    - Azure credentials configured (e.g. az login)
+"""
+
+import asyncio
+import os
+
+from dotenv import load_dotenv
+
+from agent_framework import ChatAgent, WorkflowBuilder
+from agent_framework.azure import AzureAIAgentClient
+from azure.identity.aio import AzureCliCredential
+
+from azure.ai.agentserver.agentframework import from_agent_framework
+from azure.ai.agentserver.agentframework.persistence import FoundryCheckpointRepository
+
+load_dotenv()
+
+
+def create_writer_agent(client: AzureAIAgentClient) -> ChatAgent:
+    """Create a writer agent that generates content."""
+    return client.create_agent(
+        name="Writer",
+        instructions=(
+            "You are an excellent content writer. "
+            "You create new content and edit contents based on the feedback."
+        ),
+    )
+
+
+def create_reviewer_agent(client: AzureAIAgentClient) -> ChatAgent:
+    """Create a reviewer agent that provides feedback."""
+    return client.create_agent(
+        name="Reviewer",
+        instructions=(
+            "You are an excellent content reviewer. "
+            "Provide actionable feedback to the writer about the provided content. "
+            "Provide the feedback in the most concise manner possible."
+        ),
+    )
+
+
+async def main() -> None:
+    """Run the workflow agent with Foundry managed checkpoints."""
+    project_endpoint = os.getenv("AZURE_AI_PROJECT_ENDPOINT", "")
+
+    async with AzureCliCredential() as cred, AzureAIAgentClient(credential=cred) as client:
+        builder = (
+            WorkflowBuilder()
+            .register_agent(lambda: create_writer_agent(client), name="writer")
+            .register_agent(lambda: create_reviewer_agent(client), name="reviewer", output_response=True)
+            .set_start_executor("writer")
+            .add_edge("writer", "reviewer")
+        )
+
+        # Use FoundryCheckpointRepository for Azure AI Foundry managed storage.
+        # This persists workflow checkpoints remotely, enabling pause/resume
+        # across requests and server restarts.
+        checkpoint_repository = FoundryCheckpointRepository(
+            project_endpoint=project_endpoint,
+            credential=cred,
+        )
+
+        await from_agent_framework(
+            builder,
+            checkpoint_repository=checkpoint_repository,
+        ).run_async()
+
+
+if __name__ == "__main__":
+    asyncio.run(main())

sdk/agentserver/azure-ai-agentserver-langgraph/samples/simple_agent_with_foundry_checkpointer/README.md

@@ -0,0 +1,80 @@
+# Simple LangGraph Agent with Foundry Managed Checkpointer
+
+This sample hosts a LangGraph ReAct-style agent and uses `FoundryCheckpointSaver` to persist
+checkpoints in Azure AI Foundry managed storage.
+
+With Foundry managed checkpoints, graph state is stored remotely so conversations can resume across
+requests and server restarts without self-managed storage.
+
+### What `main.py` does
+
+- Creates an `AzureChatOpenAI` model and two tools (`get_word_length`, `calculator`)
+- Builds a LangGraph agent with `create_react_agent(..., checkpointer=saver)`
+- Creates `FoundryCheckpointSaver(project_endpoint, credential)` and runs the server via
+  `from_langgraph(...).run_async()`
+
+---
+
+## Prerequisites
+
+- Python 3.10+
+- Azure CLI authenticated with `az login` (required for `AzureCliCredential`)
+- An Azure AI Foundry project endpoint
+- An Azure OpenAI chat deployment (for example `gpt-4o`)
+
+---
+
+## Setup
+
+1. Create a `.env` file in this folder:
+   ```env
+   AZURE_AI_PROJECT_ENDPOINT=https://<resource>.services.ai.azure.com/api/projects/<project-id>
+   AZURE_OPENAI_ENDPOINT=https://<resource>.openai.azure.com/
+   AZURE_OPENAI_API_KEY=<api-key>
+   OPENAI_API_VERSION=2025-03-01-preview
+   AZURE_OPENAI_CHAT_DEPLOYMENT_NAME=gpt-4o
+   ```
+
+2. Install dependencies:
+   ```bash
+   pip install azure-ai-agentserver-langgraph python-dotenv azure-identity langgraph
+   ```
+
+---
+
+## Run the Agent
+
+From this folder:
+
+```bash
+python main.py
+```
+
+The adapter starts the server on `http://0.0.0.0:8088` by default.
+
+---
+
+## Send Requests
+
+Non-streaming example:
+
+```bash
+curl -sS \
+  -H "Content-Type: application/json" \
+  -X POST http://localhost:8088/responses \
+  -d '{
+    "agent": {"name": "local_agent", "type": "agent_reference"},
+    "stream": false,
+    "input": "What is (15 * 4) + 6?",
+    "conversation": {"id": "test-conversation-1"}
+  }'
+```
+
+Use the same `conversation.id` on follow-up requests to continue the checkpointed conversation state.
+
+---
+
+## Related Resources
+
+- LangGraph docs: https://langchain-ai.github.io/langgraph/
+- Adapter package docs: `azure.ai.agentserver.langgraph` in this SDK

sdk/agentserver/azure-ai-agentserver-langgraph/samples/simple_agent_with_foundry_checkpointer/main.py

@@ -0,0 +1,82 @@
+# Copyright (c) Microsoft. All rights reserved.
+
+"""
+Simple Agent with Foundry Managed Checkpointer
+
+This sample demonstrates how to use FoundryCheckpointSaver with a LangGraph
+agent to persist checkpoints in Azure AI Foundry.
+
+Foundry managed checkpoints enable graph state to be persisted across
+requests, allowing conversations to be paused, resumed, and replayed.
+
+Prerequisites:
+    - Set AZURE_AI_PROJECT_ENDPOINT to your Azure AI Foundry project endpoint
+      e.g. "https://<resource>.services.ai.azure.com/api/projects/<project-id>"
+    - Set AZURE_OPENAI_CHAT_DEPLOYMENT_NAME (defaults to "gpt-4o")
+    - Azure credentials configured (e.g. az login)
+"""
+
+import asyncio
+import os
+
+from dotenv import load_dotenv
+from langchain_core.tools import tool
+from langchain_openai import AzureChatOpenAI
+from azure.identity.aio import AzureCliCredential
+
+from azure.ai.agentserver.langgraph import from_langgraph
+from azure.ai.agentserver.langgraph.checkpointer import FoundryCheckpointSaver
+
+load_dotenv()
+
+deployment_name = os.getenv("AZURE_OPENAI_CHAT_DEPLOYMENT_NAME", "gpt-4o")
+model = AzureChatOpenAI(model=deployment_name)
+
+
+@tool
+def get_word_length(word: str) -> int:
+    """Returns the length of a word."""
+    return len(word)
+
+
+@tool
+def calculator(expression: str) -> str:
+    """Evaluates a mathematical expression."""
+    try:
+        result = eval(expression)  # noqa: S307
+        return str(result)
+    except Exception as e:
+        return f"Error: {str(e)}"
+
+
+tools = [get_word_length, calculator]
+
+
+def create_agent(checkpointer):
+    """Create a react agent with the given checkpointer."""
+    from langgraph.prebuilt import create_react_agent
+
+    return create_react_agent(model, tools, checkpointer=checkpointer)
+
+
+async def main() -> None:
+    """Run the agent with Foundry managed checkpoints."""
+    project_endpoint = os.getenv("AZURE_AI_PROJECT_ENDPOINT", "")
+
+    async with AzureCliCredential() as cred:
+        # Use FoundryCheckpointSaver for Azure AI Foundry managed storage.
+        # This persists graph checkpoints remotely, enabling pause/resume
+        # across requests and server restarts.
+        saver = FoundryCheckpointSaver(
+            project_endpoint=project_endpoint,
+            credential=cred,
+        )
+
+        # Pass the checkpointer via LangGraph's native compile/create API
+        executor = create_agent(checkpointer=saver)
+
+        await from_langgraph(executor).run_async()
+
+
+if __name__ == "__main__":
+    asyncio.run(main())