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Done: Foundry Memory
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Tao Chen
+2
-5
@@ -1,9 +1,6 @@
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FOUNDRY_PROJECT_ENDPOINT="..."
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AZURE_AI_MODEL_DEPLOYMENT_NAME="..."
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# Embedding model deployment (only needed by provision_memory_store.py).
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AZURE_OPENAI_EMBEDDING_MODEL="text-embedding-3-small"
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AZURE_AI_EMBEDDING_MODEL_DEPLOYMENT_NAME="text-embedding-3-small"
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# Name of the Foundry Memory Store the agent should read/write to.
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FOUNDRY_MEMORY_STORE_NAME="agent_framework_memory"
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# Optional scope (e.g., user id) used to isolate memories. Falls back to the
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# per-session id when unset, which limits memories to a single session.
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FOUNDRY_MEMORY_SCOPE="user_123"
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MEMORY_STORE_NAME="agent_framework_memory"
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+11
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@@ -44,8 +44,8 @@ From this directory, with the venv activated and `az login` done:
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```bash
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export FOUNDRY_PROJECT_ENDPOINT="https://<account>.services.ai.azure.com/api/projects/<project>"
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export AZURE_AI_MODEL_DEPLOYMENT_NAME="gpt-4.1-mini"
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export AZURE_OPENAI_EMBEDDING_MODEL="text-embedding-3-small"
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export FOUNDRY_MEMORY_STORE_NAME="agent_framework_memory"
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export AZURE_AI_EMBEDDING_MODEL_DEPLOYMENT_NAME="text-embedding-3-small"
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export MEMORY_STORE_NAME="agent_framework_memory"
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python provision_memory_store.py
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```
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@@ -54,8 +54,8 @@ Or in PowerShell:
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```powershell
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$env:FOUNDRY_PROJECT_ENDPOINT="https://<account>.services.ai.azure.com/api/projects/<project>"
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$env:AZURE_AI_MODEL_DEPLOYMENT_NAME="gpt-4.1-mini"
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$env:AZURE_OPENAI_EMBEDDING_MODEL="text-embedding-3-small"
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$env:FOUNDRY_MEMORY_STORE_NAME="agent_framework_memory"
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$env:AZURE_AI_EMBEDDING_MODEL_DEPLOYMENT_NAME="text-embedding-3-small"
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$env:MEMORY_STORE_NAME="agent_framework_memory"
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python provision_memory_store.py
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```
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@@ -75,16 +75,13 @@ Follow the instructions in the [Running the Agent Host Locally](../../README.md#
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In addition to the standard environment variables, this sample requires:
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```bash
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export FOUNDRY_MEMORY_STORE_NAME="agent_framework_memory"
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# Optional — defaults to "user_123" in main.py if unset.
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export FOUNDRY_MEMORY_SCOPE="user_123"
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export MEMORY_STORE_NAME="agent_framework_memory"
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```
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Or in PowerShell:
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```powershell
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$env:FOUNDRY_MEMORY_STORE_NAME="agent_framework_memory"
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$env:FOUNDRY_MEMORY_SCOPE="user_123"
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$env:MEMORY_STORE_NAME="agent_framework_memory"
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```
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You can also place these in a `.env` file next to `main.py` — see [`.env.example`](.env.example).
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@@ -93,7 +90,9 @@ You can also place these in a `.env` file next to `main.py` — see [`.env.examp
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> Depending on how you run the agent host, you can invoke the agent using `curl` (`Invoke-WebRequest` in PowerShell) or `azd`. Please refer to the [parent README](../../README.md) for more details.
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Send a POST request to the server with a JSON body containing an `"input"` field to interact with the agent. The first request seeds a memory; subsequent requests (especially in new sessions) should be able to recall it because memories are scoped to `FOUNDRY_MEMORY_SCOPE`, not to a particular session.
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Send a POST request to the server with a JSON body containing an `"input"` field to interact with the agent. The first request seeds a memory; subsequent requests (especially in new sessions) should be able to recall it because memories are persisted across Foundry Hosted Agents sessions.
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> In this sample, the memory is scoped to the user by specifying `scope="{{$userId}}"`, thus memories are isolated across different users but shared across different sessions from the same user.
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```bash
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# 1. Tell the agent something to remember.
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@@ -108,17 +107,14 @@ curl -X POST http://localhost:8088/responses -H "Content-Type: application/json"
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-d '{"input": "What do you remember about my preferences?"}'
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```
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The agent's answers to the follow-up turns should reflect the preferences shared in the first turn, even though `default_options={"store": False}` is set (so the Responses service is not persisting conversation history) — the recall is coming exclusively from the Foundry Memory Store.
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## Deploying the Agent to Foundry
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To host the agent on Foundry, follow the instructions in the [Deploying the Agent to Foundry](../../README.md#deploying-the-agent-to-foundry) section of the README in the parent directory.
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When deploying, make sure `FOUNDRY_MEMORY_STORE_NAME` and `FOUNDRY_MEMORY_SCOPE` are set in your `azd` environment so they get injected into the hosted container per [`agent.manifest.yaml`](agent.manifest.yaml):
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When deploying, make sure `MEMORY_STORE_NAME` and `FOUNDRY_MEMORY_SCOPE` are set in your `azd` environment so they get injected into the hosted container per [`agent.manifest.yaml`](agent.manifest.yaml):
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```bash
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azd env set FOUNDRY_MEMORY_STORE_NAME "agent_framework_memory"
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azd env set FOUNDRY_MEMORY_SCOPE "user_123"
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azd env set MEMORY_STORE_NAME "agent_framework_memory"
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```
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If these are not set, running `azd ai agent init -m <agent-manifest.yaml>` will prompt you to enter them interactively.
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+3
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@@ -20,18 +20,13 @@ template:
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environment_variables:
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- name: AZURE_AI_MODEL_DEPLOYMENT_NAME
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value: "{{AZURE_AI_MODEL_DEPLOYMENT_NAME}}"
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- name: FOUNDRY_MEMORY_STORE_NAME
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value: "{{FOUNDRY_MEMORY_STORE_NAME}}"
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- name: FOUNDRY_MEMORY_SCOPE
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value: "{{FOUNDRY_MEMORY_SCOPE}}"
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- name: MEMORY_STORE_NAME
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value: "{{MEMORY_STORE_NAME}}"
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parameters:
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properties:
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- name: FOUNDRY_MEMORY_STORE_NAME
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- name: MEMORY_STORE_NAME
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secret: false
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description: The name of the pre-provisioned Foundry Memory Store the agent will use (e.g., agent_framework_memory)
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- name: FOUNDRY_MEMORY_SCOPE
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secret: false
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description: Scope (e.g., user id) used to isolate memories within the store (e.g., user_123)
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resources:
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- kind: model
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id: gpt-4.1-mini
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+2
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@@ -10,7 +10,5 @@ resources:
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environment_variables:
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- name: AZURE_AI_MODEL_DEPLOYMENT_NAME
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value: ${AZURE_AI_MODEL_DEPLOYMENT_NAME}
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- name: FOUNDRY_MEMORY_STORE_NAME
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value: ${FOUNDRY_MEMORY_STORE_NAME}
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- name: FOUNDRY_MEMORY_SCOPE
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value: ${FOUNDRY_MEMORY_SCOPE}
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- name: MEMORY_STORE_NAME
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value: ${MEMORY_STORE_NAME}
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+7
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@@ -6,7 +6,7 @@ This agent uses :class:`FoundryMemoryProvider` to give an otherwise stateless
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hosted agent persistent, semantic memory backed by an Azure AI Foundry
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Memory Store. The store itself is provisioned once via
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``provision_memory_store.py`` and its name is passed in through the
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``FOUNDRY_MEMORY_STORE_NAME`` environment variable.
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``MEMORY_STORE_NAME`` environment variable.
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Unlike the standalone ``azure_ai_foundry_memory.py`` sample, here we construct
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the :class:`FoundryChatClient` first and then reuse its underlying
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@@ -41,15 +41,12 @@ async def main() -> None:
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# constructing a second one for the memory provider.
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memory_provider = FoundryMemoryProvider(
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project_client=client.project_client,
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memory_store_name=os.environ["FOUNDRY_MEMORY_STORE_NAME"],
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# Scope namespaces memories (e.g., per end-user). When unset, the
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# provider falls back to the session id, which limits memories to a
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# single session.
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scope=os.environ.get("FOUNDRY_MEMORY_SCOPE", "user_123"),
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# In production, leave update_delay at its default to batch updates and
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# reduce cost. We use 0 here so memories are written immediately, which
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# makes the sample easier to demo.
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update_delay=0,
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memory_store_name=os.environ["MEMORY_STORE_NAME"],
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# Scope memories by user id, so each user that interacts with the agent
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# has their own isolated memories in the store (assuming those users are
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# granted access). `{{userId}}` is a special placeholder that the hosting
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# infrastructure will replace with the actual user id at runtime.
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scope="{{$userId}}",
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)
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agent = Agent(
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+21
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"""Provision the Azure AI Foundry Memory Store used by this sample.
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Creates the memory store named by ``FOUNDRY_MEMORY_STORE_NAME`` if it does not
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Creates the memory store named by ``MEMORY_STORE_NAME`` if it does not
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already exist. The store is configured with the user-profile capability so the
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agent can remember stable facts about a user across sessions; chat-summary is
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disabled to keep the demo focused on durable preferences. Safe to re-run: if a
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@@ -14,10 +14,10 @@ Usage (from this directory, with the venv activated and ``az login`` done):
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Required env vars (also read from a local ``.env`` file if present):
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FOUNDRY_PROJECT_ENDPOINT e.g. https://<account>.services.ai.azure.com/api/projects/<project>
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AZURE_AI_MODEL_DEPLOYMENT_NAME Chat model deployment used by the memory store
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AZURE_OPENAI_EMBEDDING_MODEL Embedding model deployment used by the memory store
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FOUNDRY_MEMORY_STORE_NAME Name of the memory store to create
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FOUNDRY_PROJECT_ENDPOINT e.g. https://<account>.services.ai.azure.com/api/projects/<project>
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AZURE_AI_MODEL_DEPLOYMENT_NAME Chat model deployment used by the memory store
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AZURE_AI_EMBEDDING_MODEL_DEPLOYMENT_NAME Embedding model deployment used by the memory store
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MEMORY_STORE_NAME Name of the memory store to create
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Your identity needs ``Azure AI User`` on the Foundry project scope.
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"""
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@@ -34,14 +34,14 @@ from azure.core.exceptions import ResourceNotFoundError
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from azure.identity.aio import DefaultAzureCredential
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from dotenv import load_dotenv
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load_dotenv()
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async def main() -> None:
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load_dotenv()
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endpoint = os.environ["FOUNDRY_PROJECT_ENDPOINT"]
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memory_store_name = os.environ["FOUNDRY_MEMORY_STORE_NAME"]
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memory_store_name = os.environ["MEMORY_STORE_NAME"]
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chat_model = os.environ["AZURE_AI_MODEL_DEPLOYMENT_NAME"]
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embedding_model = os.environ["AZURE_OPENAI_EMBEDDING_MODEL"]
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embedding_model = os.environ["AZURE_AI_EMBEDDING_MODEL_DEPLOYMENT_NAME"]
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async with (
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DefaultAzureCredential() as credential,
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@@ -73,6 +73,18 @@ async def main() -> None:
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)
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print(f"Created memory store '{created.name}' (id={created.id}).")
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# Verify the store actually exists on the service by reading it back.
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# ``create`` returns the requested definition, but a follow-up ``get``
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# confirms the store is persisted and reachable for the agent at runtime.
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try:
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verified = await project.beta.memory_stores.get(name=memory_store_name)
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except ResourceNotFoundError as exc:
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raise RuntimeError(
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f"Memory store '{memory_store_name}' was not found after creation; "
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"the service may not have persisted it."
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) from exc
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print(f"Verified memory store '{verified.name}' is available on the service (id={verified.id}).")
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if __name__ == "__main__":
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asyncio.run(main())
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