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@@ -86,13 +86,13 @@ Hosted agents run any code you can put in a container. These samples cover three
 |                         | **Agent Framework**                                                                | **LangGraph**                                                         | **Bring Your Own**                                                                                                          |
 | ----------------------- | ---------------------------------------------------------------------------------- | --------------------------------------------------------------------- | --------------------------------------------------------------------------------------------------------------------------- |
 | **Best for**            | Starting fresh on Foundry — also supports AutoGen and Semantic Kernel              | Already using LangChain / LangGraph                                   | Already built with CrewAI or your own stack                                                                                 |
-| **SDK**                 | `agent-framework-foundry-hosting` (includes core, openai, foundry, orchestrations) | `azure-ai-agentserver-responses` / `azure-ai-agentserver-invocations` | `azure-ai-agentserver-responses` / `azure-ai-agentserver-invocations`, or `azure-ai-agentserver-core` for fully custom HTTP |
-| **Foundry integration** | Native — sessions, tools, memory, streaming all built in                           | Adapter — sessions and tools wired through LangGraph adapter          | Core adapter hosts the web server and exposes `/invocations` and `/responses` endpoints; you supply the agent logic         |
+| **SDK**                 | `agent-framework-foundry-hosting` (includes core, openai, foundry, orchestrations) | `langchain-azure-ai[hosting]` (`ResponsesHostServer` / `InvocationsHostServer`) | `azure-ai-agentserver-responses` / `azure-ai-agentserver-invocations`, or `azure-ai-agentserver-core` for fully custom HTTP |
+| **Foundry integration** | Native — sessions, tools, memory, streaming all built in                           | Native via `langchain_azure_ai.agents.hosting` — sessions, streaming, and tool-call surfacing built in for LangGraph agents (`create_agent`) and custom `StateGraph`s | Core adapter hosts the web server and exposes `/invocations` and `/responses` endpoints; you supply the agent logic         |
 | **Protocols**           | Responses and Invocations                                                          | Responses and Invocations                                             | Responses and Invocations                                                                                                   |
 | **Language support**    | Python and C#                                                                      | Python only                                                           | Any language (Python and C# samples provided)                                                                               |
-| **Start here**          | [Basic Agent →](agent-framework/responses/01-basic/)                               | [LangGraph Chat →](bring-your-own/responses/langgraph-chat/)          | [Hello World →](bring-your-own/responses/hello-world/)                                                                      |
+| **Start here**          | [Basic Agent →](agent-framework/responses/01-basic/)                               | [LangGraph Chat →](langgraph/responses/01-langgraph-chat/)            | [Hello World →](bring-your-own/responses/hello-world/)                                                                      |
 
-> **Which should I choose?** If you're building a new agent — or already using AutoGen or Semantic Kernel — start with **Agent Framework**. It has the tightest Foundry integration, supports those orchestrators natively, and has the most samples to learn from. If you already have LangGraph code, use the **LangGraph** adapter to bring it to Foundry. If you have an existing agent in another framework (e.g., CrewAI), **Bring Your Own** shows how to containerize and deploy it unchanged.
+> **Which should I choose?** If you're building a new agent — or already using AutoGen or Semantic Kernel — start with **Agent Framework**. It has the tightest Foundry integration, supports those orchestrators natively, and has the most samples to learn from. If you already have LangGraph code, use the **LangGraph** hosting integration (`langchain_azure_ai.agents.hosting`) to bring it to Foundry. If you have an existing agent in another framework (e.g., CrewAI), **Bring Your Own** shows how to containerize and deploy it unchanged.
 
 ---
 
@@ -135,7 +135,26 @@ Full control over the HTTP request/response cycle. You define the payload schema
 
 ## LangGraph samples
 
-LangGraph samples are included in the **Bring Your Own** section below — see [`bring-your-own/responses/langgraph-chat/`](bring-your-own/responses/langgraph-chat/) and [`bring-your-own/invocations/langgraph-chat/`](bring-your-own/invocations/langgraph-chat/).
+Bring your existing [LangGraph](https://langchain-ai.github.io/langgraph/) graphs to Foundry. These samples use [`langchain_azure_ai.agents.hosting`](https://github.com/langchain-ai/langchain-azure/tree/main/libs/azure-ai/langchain_azure_ai/agents/hosting) (`ResponsesHostServer` / `InvocationsHostServer`) to expose LangGraph agents (`create_agent`) and custom `StateGraph`s over the hosted agent protocols, with native Foundry session, streaming, and tool wiring.
+
+See [`langgraph/README.md`](langgraph/) for the full list and the local-run guide.
+
+### Responses protocol
+
+| Sample                                                                  | What it shows                                                                                                                          |
+| ----------------------------------------------------------------------- | -------------------------------------------------------------------------------------------------------------------------------------- |
+| **[Chat](langgraph/responses/01-langgraph-chat/)**                      | Minimal LangGraph agent with two local tools (`get_current_time`, `calculator`); multi-turn via `previous_response_id`.                |
+| **[MCP](langgraph/responses/04-mcp/)**                                  | LangGraph agent that loads tools from a remote MCP server (default: GitHub Copilot MCP) via `langchain_mcp_adapters`.                  |
+| **[Workflows](langgraph/responses/05-workflows/)**                      | Custom `StateGraph` chaining three specialized LLM nodes — slogan writer, legal reviewer, formatter — each seeing only the prior agent's output. |
+| **[Files](langgraph/responses/06-files/)**                              | LangGraph agent with local filesystem tools and a Foundry-Toolbox `code_interpreter` for session-uploaded files.                       |
+| **[Human-in-the-Loop](langgraph/responses/07-human-in-the-loop/)**      | `StateGraph` that drafts a proposal and pauses for approval via `langgraph.types.interrupt`, serialized as `mcp_approval_request` + `function_call`. |
+| **[Observability](langgraph/responses/08-observability/)**              | GenAI OpenTelemetry tracing enabled with `enable_auto_tracing()` — spans, metrics, and logs flow to Application Insights.              |
+
+### Invocations protocol
+
+| Sample                                                | What it shows                                                                                                                              |
+| ----------------------------------------------------- | ------------------------------------------------------------------------------------------------------------------------------------------ |
+| **[Chat](langgraph/invocations/01-langgraph-chat/)**  | Minimal LangGraph agent with local tools; session state via `agent_session_id` (URL param / `x-agent-session-id` header) backed by a LangGraph checkpointer. |
 
 ---
 
 
@@ -0,0 +1,218 @@
+# LangGraph Samples
+
+This directory contains samples that demonstrate how to use [LangGraph](https://langchain-ai.github.io/langgraph/) together with [`langchain_azure_ai.agents.hosting`](https://github.com/langchain-ai/langchain-azure/tree/main/libs/azure-ai/langchain_azure_ai/agents/hosting) to host agents on Foundry with different capabilities and configurations. Each sample includes a README with sample queries and any sample-specific notes.
+
+## Samples
+
+### Responses API
+
+| #   | Sample                                                                | Description                                                                                                                                                                              |
+| --- | --------------------------------------------------------------------- | ---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
+| 1   | [Chat](responses/01-langgraph-chat/)                                  | A minimal LangGraph agent with two local tools (`get_current_time`, `calculator`), demonstrating multi-turn conversation via `previous_response_id`.                                     |
+| 4   | [MCP](responses/04-mcp/)                                              | A LangGraph agent that loads tools from a remote MCP server (default: GitHub Copilot MCP) via `langchain_mcp_adapters.client.MultiServerMCPClient`.                                      |
+| 5   | [Workflows](responses/05-workflows/)                                  | A custom `StateGraph` chaining three specialized LLM nodes — slogan writer, legal reviewer, formatter — each seeing only the previous agent's output.                                    |
+| 6   | [Files](responses/06-files/)                                          | A LangGraph agent with local filesystem tools and a Foundry-Toolbox `code_interpreter`, demonstrating session-uploaded file handling.                                                    |
+| 7   | [Human-in-the-Loop](responses/07-human-in-the-loop/)                  | A LangGraph `StateGraph` that drafts a proposal and pauses for human review via `langgraph.types.interrupt`, serialized as `mcp_approval_request` + `function_call` output items.        |
+| 8   | [Observability](responses/08-observability/)                          | A LangGraph agent with GenAI OpenTelemetry tracing enabled via `enable_auto_tracing()`, emitting spans, metrics, and logs to Application Insights.                                       |
+
+### Invocations API
+
+| #   | Sample                                          | Description                                                                                                                                          |
+| --- | ----------------------------------------------- | ---------------------------------------------------------------------------------------------------------------------------------------------------- |
+| 1   | [Chat](invocations/01-langgraph-chat/)          | A minimal LangGraph agent with two local tools, demonstrating session state via `agent_session_id` (URL param / `x-agent-session-id` response header) backed by a LangGraph checkpointer. |
+
+## Running the Agent Host Locally
+
+### Using `azd`
+
+#### Prerequisites
+
+1. **Azure Developer CLI (`azd`)**
+   - [Install azd](https://learn.microsoft.com/en-us/azure/developer/azure-developer-cli/install-azd) and the AI agent extension: `azd ext install azure.ai.agents`
+   - Authenticated: `azd auth login`
+
+2. **Azure Subscription**
+
+#### Create a new project
+
+**No cloning required**. Create a new folder, point azd at the manifest on GitHub.
+
+```bash
+mkdir hosted-langgraph-agent && cd hosted-langgraph-agent
+
+# Initialize from the manifest (replace with the sample you want to try)
+azd ai agent init -m https://github.com/microsoft-foundry/foundry-samples/blob/main/samples/python/hosted-agents/langgraph/responses/01-langgraph-chat/agent.manifest.yaml
+```
+
+Follow the instructions from `azd ai agent init` to complete the agent initialization. If you don't have an existing Foundry project and a model deployment, `azd ai agent init` will guide you through creating them.
+
+#### Provision Azure Resources
+
+> This step is only needed if you don't have an existing Foundry project and model deployment.
+
+Run the following command to provision the necessary Azure resources:
+
+```bash
+azd provision
+```
+
+This will create the following Azure resources:
+
+- A new resource group named `rg-[project_name]-dev`. In this guide, `[project_name]` will be `hosted-langgraph-agent`.
+- Within the resource group, among other resources, the most important ones are:
+  - A new Foundry instance
+  - A new Foundry project, within which a new model deployment will be created
+  - An Application Insights instance
+  - A container registry, which will be used to store the container images for the hosted agent
+
+#### Set Environment Variables
+
+```bash
+export FOUNDRY_PROJECT_ENDPOINT="https://<account>.services.ai.azure.com/api/projects/<project>"
+export AZURE_AI_MODEL_DEPLOYMENT_NAME="<your-model-deployment-name>"
+# And any other environment variables required by the sample
+```
+
+Or in PowerShell:
+
+```powershell
+$env:FOUNDRY_PROJECT_ENDPOINT="https://<account>.services.ai.azure.com/api/projects/<project>"
+$env:AZURE_AI_MODEL_DEPLOYMENT_NAME="<your-model-deployment-name>"
+# And any other environment variables required by the sample
+```
+
+> Note: The environment variables set above are only for the current session. You will need to set them again if you open a new terminal session. If you want to set the environment variables permanently in the azd environment, you can use `azd env set <name> <value>`.
+
+#### Running the Agent Host
+
+```bash
+azd ai agent run
+```
+
+Right now, the agent host should be running on `http://localhost:8088`
+
+#### Invoking the Agent
+
+Open another terminal, **navigate to the project directory**, and run the following command to invoke the agent:
+
+```bash
+azd ai agent invoke --local "Hello!"
+```
+
+Or in another terminal, without navigating to the project directory, run the following command to invoke the agent:
+
+```bash
+curl -X POST http://localhost:8088/responses -H "Content-Type: application/json" -d '{"input": "Hello!"}'
+```
+
+Or in PowerShell:
+
+```powershell
+(Invoke-WebRequest -Uri http://localhost:8088/responses -Method POST -ContentType "application/json" -Body '{"input": "Hello!"}').Content
+```
+
+> **Invocations protocol note:** The `curl` examples above target the Responses endpoint (`/responses`). For Invocations samples, the endpoint is `/invocations` and the request body uses `"message"` instead of `"input"`. Session continuity uses the `agent_session_id` URL parameter and the `x-agent-session-id` response header. See [`invocations/01-langgraph-chat/`](invocations/01-langgraph-chat/) for full examples.
+
+### Using `python`
+
+#### Prerequisites
+
+1. An existing Foundry project
+2. A deployed model in your Foundry project
+3. Azure CLI installed and authenticated
+4. Python 3.10 or later
+
+#### Running the Agent Host with Python
+
+Clone the repository containing the sample code:
+
+```bash
+git clone https://github.com/microsoft-foundry/foundry-samples.git
+cd foundry-samples/samples/python/hosted-agents/langgraph
+```
+
+#### Environment setup
+
+1. Navigate to the sample directory you want to explore. Create a virtual environment:
+
+   ```bash
+   python -m venv .venv
+
+   # Windows
+   .venv\Scripts\Activate
+
+   # macOS/Linux
+   source .venv/bin/activate
+   ```
+
+2. Install dependencies:
+
+   ```bash
+   pip install -r requirements.txt
+   ```
+
+3. Create a `.env` file with your Foundry configuration following the `.env.example` file in the sample.
+
+4. Make sure you are logged in with the Azure CLI:
+
+   ```bash
+   az login
+   ```
+
+#### Running the Agent Host
+
+```bash
+python main.py
+```
+
+Right now, the agent host should be running on `http://localhost:8088`
+
+#### Invoking the Agent
+
+On another terminal, run the following command to invoke the agent:
+
+```bash
+curl -X POST http://localhost:8088/responses -H "Content-Type: application/json" -d '{"input": "Hello!"}'
+```
+
+Or in PowerShell:
+
+```powershell
+(Invoke-WebRequest -Uri http://localhost:8088/responses -Method POST -ContentType "application/json" -Body '{"input": "Hello!"}').Content
+```
+
+> **Invocations protocol note:** See the [Invocations protocol note](#invoking-the-agent) above for the endpoint, body shape, and session-id mechanics used by Invocations samples.
+
+## Deploying the Agent to Foundry
+
+Once you've tested locally, deploy to Microsoft Foundry.
+
+### With an Existing Foundry Project
+
+If you already have a Foundry project and the necessary Azure resources provisioned, you can skip the setup steps and proceed directly to deploying the agent.
+
+After running `azd ai agent init -m <agent.manifest.yaml>` and following the prompts to configure your agent, you will have a project ready for deployment.
+
+### Setting Up a New Foundry Project
+
+Follow the steps in [Using `azd`](#using-azd) to set up the project and provision the necessary Azure resources for your Foundry deployment.
+
+### Deploying the Agent
+
+Once the project is setup and resources are provisioned, you can deploy the agent to Foundry by running:
+
+```bash
+azd deploy
+```
+
+> The Foundry hosting infrastructure will inject the following environment variables into your agent at runtime:
+>
+> - `FOUNDRY_PROJECT_ENDPOINT`: The endpoint URL for the Foundry project where the agent is deployed.
+> - `AZURE_AI_MODEL_DEPLOYMENT_NAME`: The name of the model deployment in your Foundry project. This is configured during the agent initialization process with `azd ai agent init`.
+> - `APPLICATIONINSIGHTS_CONNECTION_STRING`: The connection string for Application Insights to enable telemetry for your agent.
+
+This will package your agent and deploy it to the Foundry environment, making it accessible through the Foundry project endpoint. Once it's deployed, you can also access the agent through the Foundry UI.
+
+For the full deployment guide, see the [official deployment guide](https://learn.microsoft.com/en-us/azure/foundry/agents/how-to/deploy-hosted-agent).
+
+Once deployed, learn more about how to manage deployed agents in the [official management guide](https://learn.microsoft.com/en-us/azure/foundry/agents/how-to/manage-hosted-agent).
@@ -0,0 +1,26 @@
+**/__pycache__/
+**/*.py[cod]
+**/*.egg-info/
+.eggs/
+
+# Virtual environments
+.venv/
+venv/
+env/
+
+# IDE settings
+.vscode/
+.idea/
+
+# Version control
+.git/
+.gitignore
+
+# Docker files
+.dockerignore
+
+# Docs
+README.md
+
+# Local environment (never bake credentials into the image)
+.env
@@ -0,0 +1,6 @@
+# Foundry project endpoint — auto-injected in hosted containers.
+# Only set manually if running without `azd ai agent run`.
+# FOUNDRY_PROJECT_ENDPOINT=https://<account>.services.ai.azure.com/api/projects/<project>
+
+# Model deployment name — must match a deployment in your Foundry project.
+AZURE_AI_MODEL_DEPLOYMENT_NAME=
@@ -0,0 +1,7 @@
+FROM python:3.12-slim
+WORKDIR /app
+COPY . user_agent/
+WORKDIR /app/user_agent
+RUN pip install --upgrade pip && if [ -f requirements.txt ]; then pip install -r requirements.txt; fi
+EXPOSE 8088
+CMD ["python", "main.py"]