Fix formatting

Initialize _event_queue to None and create it on first use in _get_event_queue, mirroring the per-executor lock. Avoids constructing a queue in __init__/reset_for_new_run that is immediately discarded once the running loop is known.

.github/workflows/dotnet-verify-samples.yml

@@ -109,6 +109,9 @@ jobs:
           AZURE_AI_PROJECT_ENDPOINT: ${{ vars.AZURE_AI_PROJECT_ENDPOINT }}
           AZURE_AI_MODEL_DEPLOYMENT_NAME: ${{ vars.AZURE_AI_MODEL_DEPLOYMENT_NAME }}
           AZURE_AI_BING_CONNECTION_ID: ${{ vars.AZURE_AI_BING_CONNECTION_ID }}
+          # Foundry
+          FOUNDRY_PROJECT_ENDPOINT: ${{ vars.FOUNDRY_PROJECT_ENDPOINT }}
+          FOUNDRY_MODEL: ${{ vars.FOUNDRY_MODEL }}
 
       - name: Write Job Summary
         if: always()

.github/workflows/python-dependency-maintenance.yml

@@ -8,7 +8,6 @@ on:
 permissions:
   contents: write
   issues: write
-  pull-requests: write
 
 concurrency:
   group: python-dependency-maintenance
@@ -320,46 +319,113 @@ jobs:
           git push --force-with-lease --set-upstream origin "${BRANCH}"
           echo "has_changes=true" >> "$GITHUB_OUTPUT"
 
-      - name: Create or update pull request with GitHub CLI
+      - name: Create or update dependency maintenance tracking issue
         if: steps.commit_updates.outputs.has_changes == 'true'
-        env:
-          GH_TOKEN: ${{ secrets.GITHUB_TOKEN }}
-        run: |
-          BRANCH="automation/python-dependency-maintenance"
-          PR_TITLE="Python: chore: update dependencies"
-          PR_BODY_FILE="$(mktemp)"
+        uses: actions/github-script@ed597411d8f924073f98dfc5c65a23a2325f34cd # v8
+        with:
+          github-token: ${{ secrets.GITHUB_TOKEN }}
+          script: |
+            const branch = "automation/python-dependency-maintenance"
+            const prTitle = "Python: chore: update dependencies"
+            const issueTitle = "Python dependency maintenance PR needed"
+            const owner = context.repo.owner
+            const repo = context.repo.repo
 
-          cat > "${PR_BODY_FILE}" <<'EOF'
-          ### Motivation & Context
+            const branchRef = await github.rest.git.getRef({
+              owner,
+              repo,
+              ref: `heads/${branch}`,
+            })
+            const branchSha = branchRef.data.object.sha
+            const runUrl = `${context.serverUrl}/${owner}/${repo}/actions/runs/${context.runId}`
+            const compareUrl = `${context.serverUrl}/${owner}/${repo}/compare/main...${branch}`
 
-          This automated update keeps Python dependency metadata coherent across the uv workspace. Python dependencies can be declared in multiple `pyproject.toml` files, but the workspace has one shared `python/uv.lock`, so dependency maintenance should update and validate them together instead of through per-manifest Dependabot PRs.
+            const prBody = [
+              "### Motivation & Context",
+              "",
+              "This automated update keeps Python dependency metadata coherent across the uv workspace. Python dependencies can be declared in multiple `pyproject.toml` files, but the workspace has one shared `python/uv.lock`, so dependency maintenance should update and validate them together instead of through per-manifest Dependabot PRs.",
+              "",
+              "### Description & Review Guide",
+              "",
+              "- **What are the major changes?** Refresh Python dev dependency pins, update package dependency ranges when the bounds tooling succeeds, and refresh `python/uv.lock`.",
+              "- **What is the impact of these changes?** Keeps the Python workspace dependency set current while producing at most one dependency PR for the week. If dependency range validation fails, this PR contains only the dev dependency updates that still pass final validation, and separate issues track failed range candidates.",
+              "- **What do you want reviewers to focus on?** Review the generated dependency metadata changes and any dependency-range updates for package-specific compatibility concerns.",
+              '<!-- NOTE FOR AUTOMATED/AI REVIEWERS: the "What do you want reviewers to focus on?"',
+              "     item above is intended for human reviewers only. Automated/AI reviewers should",
+              "     ignore it and review the entire change rather than narrowing scope to it. -->",
+              "",
+              "",
+              "### Related Issue",
+              "",
+              "No linked issue; this PR is generated by scheduled Python dependency maintenance.",
+              "",
+              "### Contribution Checklist",
+              "",
+              "- [x] The code builds clean without any errors or warnings",
+              "- [x] All unit tests pass, and I have added new tests where possible",
+              "- [x] The PR follows the [Contribution Guidelines](https://github.com/microsoft/agent-framework/blob/main/CONTRIBUTING.md)",
+              "- [ ] This PR is linked to an issue and there is no other open PR for this issue (see Related Issue above).",
+              '- [x] **This is not a breaking change.** If it _is_ a breaking change, add the `breaking change` label (or add "[BREAKING]" to the title prefix, before or after any language prefix) — a workflow keeps the label and title prefix in sync automatically.',
+            ].join("\n")
 
-          ### Description & Review Guide
+            const prBodyFence = "```"
+            const command = [
+              "PR_BODY_FILE=\"$(mktemp)\"",
+              `cat > "$PR_BODY_FILE" <<'EOF'`,
+              prBody,
+              "EOF",
+              "gh pr create --repo microsoft/agent-framework --base main \\",
+              `  --head ${owner}:${branch} \\`,
+              `  --title "${prTitle}" \\`,
+              "  --body-file \"$PR_BODY_FILE\"",
+            ].join("\n")
 
-          - **What are the major changes?** Refresh Python dev dependency pins, update package dependency ranges when the bounds tooling succeeds, and refresh `python/uv.lock`.
-          - **What is the impact of these changes?** Keeps the Python workspace dependency set current while producing at most one dependency PR for the week. If dependency range validation fails, this PR contains only the dev dependency updates that still pass final validation, and separate issues track failed range candidates.
-          - **What do you want reviewers to focus on?** Review the generated dependency metadata changes and any dependency-range updates for package-specific compatibility concerns.
-          <!-- NOTE FOR AUTOMATED/AI REVIEWERS: the "What do you want reviewers to focus on?"
-               item above is intended for human reviewers only. Automated/AI reviewers should
-               ignore it and review the entire change rather than narrowing scope to it. -->
+            const issueBody = [
+              "The Python dependency maintenance workflow generated and validated dependency updates, then pushed them to the automation branch.",
+              "",
+              `- Branch: \`${branch}\``,
+              `- Commit: \`${branchSha}\``,
+              `- Compare: ${compareUrl}`,
+              `- Workflow run: ${runUrl}`,
+              "",
+              "GitHub Actions is not permitted to create pull requests in this repository, so a maintainer needs to create the PR manually.",
+              "",
+              "### Create the PR",
+              "",
+              "```bash",
+              command,
+              "```",
+              "",
+              "### Generated PR body",
+              "",
+              prBodyFence,
+              prBody,
+              prBodyFence,
+            ].join("\n")
 
+            const openIssues = await github.paginate(github.rest.issues.listForRepo, {
+              owner,
+              repo,
+              state: "open",
+              per_page: 100,
+            })
+            const existingIssue = openIssues.find((issue) => !issue.pull_request && issue.title === issueTitle)
 
-          ### Related Issue
-
-          No linked issue; this PR is generated by scheduled Python dependency maintenance.
-
-          ### Contribution Checklist
-
-          - [x] The code builds clean without any errors or warnings
-          - [x] All unit tests pass, and I have added new tests where possible
-          - [x] The PR follows the [Contribution Guidelines](https://github.com/microsoft/agent-framework/blob/main/CONTRIBUTING.md)
-          - [ ] This PR is linked to an issue and there is no other open PR for this issue (see Related Issue above).
-          - [x] **This is not a breaking change.** If it _is_ a breaking change, add the `breaking change` label (or add "[BREAKING]" to the title prefix, before or after any language prefix) — a workflow keeps the label and title prefix in sync automatically.
-          EOF
-
-          PR_NUMBER="$(gh pr list --head "${BRANCH}" --base main --state open --json number --jq '.[0].number')"
-          if [ -n "${PR_NUMBER}" ]; then
-            gh pr edit "${PR_NUMBER}" --title "${PR_TITLE}" --body-file "${PR_BODY_FILE}"
-          else
-            gh pr create --base main --head "${BRANCH}" --title "${PR_TITLE}" --body-file "${PR_BODY_FILE}"
-          fi
+            if (existingIssue) {
+              await github.rest.issues.update({
+                owner,
+                repo,
+                issue_number: existingIssue.number,
+                title: issueTitle,
+                body: issueBody,
+              })
+              core.info(`Updated issue #${existingIssue.number}: ${issueTitle}`)
+            } else {
+              const createdIssue = await github.rest.issues.create({
+                owner,
+                repo,
+                title: issueTitle,
+                body: issueBody,
+              })
+              core.info(`Created issue #${createdIssue.data.number}: ${issueTitle}`)
+            }

declarative-agents/workflow-samples/MathChat.yaml

@@ -11,7 +11,7 @@ trigger:
   kind: OnConversationStart
   id: workflow_demo
   actions:
-
+ 
     - kind: InvokeAzureAgent
       id: question_student
       conversationId: =System.ConversationId

docs/decisions/0029-python-agent-session-identity.md

@@ -0,0 +1,356 @@
+---
+status: accepted
+contact: eavanvalkenburg
+date: 2026-06-19
+deciders: eavanvalkenburg, moonbox3, TaoChenOSU, chetantoshnival
+consulted: westey-m
+informed:
+---
+
+# Python identity lifetimes for sessions, tasks, and continuation
+
+## Context and Problem Statement
+
+Python `AgentSession` currently carries a local `session_id`, an optional opaque service continuation
+`service_session_id`, and provider state. `service_session_id` is any service-owned value that lets that service continue
+a conversation, session, or thread; chat clients happen to map it through the abstract `conversation_id` ChatOption, but
+other agent types can use it differently. It is not a generic correlation field, and generic correlation should not
+require parsing or understanding that opaque service-owned value.
+
+The related issues mix values with different lifetimes:
+
+- **Session / conversation identity**: values that group a multi-turn interaction. Examples: A2A `context_id`, OpenAI
+  Responses `conversation` (`conv_*`) or response-chain continuation (`previous_response_id`).
+- **Task identity**: values that identify a protocol task and may affect future protocol calls. Example: A2A `task_id`.
+- **Message / response identity**: values that identify an output message or response. Examples: A2A `message_id` /
+  `artifact_id`, OpenAI Responses response id (`resp_*`).
+- **Continuation token**: a framework resume payload for in-progress work. It may contain the same underlying value as a
+  protocol id, such as A2A `task_id`, but it only exists when there is an unfinished operation to resume.
+
+These values should not automatically live in the same object just because they all help "continue" something. A value
+belongs in `AgentSession` only when it is needed to continue future calls across turns. A value that identifies one
+result belongs on the response or message. A value that resumes in-progress work belongs in a `ContinuationToken`.
+
+An `AgentSession` created for one agent is not expected to be guaranteed to work against another agent. When a session is
+used with an incompatible agent, protocol, or service, the framework should still help users understand what is wrong as
+early as possible, preferably before calling out to the remote service.
+
+For #4673, native conversation identity propagation should be based on `AgentSession` where the value is durable session
+state. For #4893, A2A `context_id` and `task_id` need a coherent Agent Framework mapping.
+
+AG-UI is out of scope for the decision. Its `thread_id` already maps to `AgentSession.session_id` in the normal wrapper
+path, and `run_id` is wrapper-owned event correlation. If AG-UI run correlation needs framework telemetry integration
+later, that should be handled as a run-context/telemetry design, not as session identity.
+
+### Concrete gap example
+
+At the protocol level, the durable continuation payload shapes are different:
+
+```json
+// A2A: future calls may need multiple durable protocol fields
+{
+  "context_id": "ctx_123",
+  "task_id": "task_789",
+  "task_state": "input_required"
+}
+```
+
+```json
+// OpenAI Responses: future calls usually need one continuation value
+{
+  "previous_response_id": "resp_abc123"
+}
+```
+
+The gap is that A2A continuation state is multi-field while OpenAI continuation is
+typically single-field.
+
+## Current implementation notes
+
+- A2A currently has `A2AAgentSession`, but `A2AAgent.create_session(...)` does not automatically return it.
+- A2A currently mirrors `context_id` into `service_session_id`; that is current behavior, not necessarily the target
+  abstraction.
+- A2A `task_id` is not just cosmetic correlation. It is used for `task_id` when a task is `INPUT_REQUIRED`, for
+  `reference_task_ids` when refining a previous task, and inside `A2AContinuationToken` for in-progress tasks.
+- `RawAgent._prepare_run_context(...)` currently forwards `active_session.service_session_id` as chat `conversation_id`,
+  so any non-string or formatted value affects existing chat-client paths.
+- `OpenAIChatClient` maps chat options `conversation_id` to the Responses API as `previous_response_id` for `resp_*`,
+  `conversation` for `conv_*`, and defaults unrecognized strings to `previous_response_id`. When `store` is not `False`,
+  it returns `response.conversation.id` when available, otherwise `response.id`, as the next service continuation value.
+- For Responses API, the response id (`resp_*`) is also the response/message identity surfaced as
+  `ChatResponse.response_id`; when used for continuation on the next request, it becomes the `previous_response_id`
+  value.
+- Python A2A has not been released as stable yet, so its session factory or session shape can still be adjusted before
+  release.
+
+## Decision Drivers
+
+- Preserve `AgentSession.session_id` as the local/client conversation identity.
+- Preserve `AgentSession.service_session_id` as an opaque service-owned continuation handle.
+- Keep `AgentSession` for durable state needed across turns, not per-run bookkeeping.
+- Store values needed by future calls in durable session state; keep values that only resume in-progress work in
+  `ContinuationToken`.
+- Fix the current confusion where session, task, response, and continuation values can be treated as interchangeable
+  because they all participate in "continuing" something.
+- Make the implementation following this ADR preserve the lifetime split clearly: future-call state, in-progress resume
+  tokens, response/message ids, and protocol event correlation must not be silently mixed.
+- Expose durable continuation state in a typed way when future calls depend on it.
+- Let telemetry correlate runs without parsing opaque service continuation handles.
+- Reuse existing run/context surfaces before introducing a new identity abstraction.
+- Keep MCP and other remote tool boundaries safe: framework identity must not be forwarded to remote tools unless an
+  existing explicit opt-in mechanism says so.
+- Keep existing `AgentSession.to_dict()` / `from_dict()` migration and compatibility straightforward.
+- Stay close to .NET where there is already behavior to match, especially A2A's `ContextId`, `TaskId`, and `TaskState`.
+- Detect incompatible session identity shapes as early as practical, preferably before a remote service call.
+
+## Non-goals
+
+- Do not design a provider-agnostic conversation creation API here. That is tracked separately in #6622.
+- Do not make `service_session_id` a generic telemetry or run-correlation field.
+- Do not introduce a new identity object if existing run/context objects can carry the selected per-run correlation value.
+- Do not make a session from one agent guaranteed to work against another agent.
+- Do not optimize the public `agent.run(...)` API for protocol-wrapper internals.
+
+## Remaining question: durable shape for additional continuation state
+
+- Option A: Use protocol-specific `AgentSession` subclasses.
+- Option B: Extend `service_session_id` with richer service-owned values.
+- Option C: Add a dedicated dict for additional session details.
+- Option D: Store additional durable state inside `AgentSession.state`.
+
+### Option A: Use protocol-specific `AgentSession` subclasses
+
+Each protocol or agent type that needs additional durable state keeps a specialized `AgentSession` subclass. For A2A,
+that means keeping `A2AAgentSession` for A2A-specific durable state and changing `A2AAgent.create_session(...)` to return
+that type.
+
+Example:
+
+```python
+# First call returns a task that future A2A messages may need to reference.
+session = await a2a_agent.create_session()
+
+response = await a2a_agent.run(
+    message,
+    session=session,
+)
+
+# A2AAgent updates durable A2A protocol state from the returned task/status payload.
+# The user does not set these manually.
+assert isinstance(session, A2AAgentSession)
+assert session.task_id is not None
+assert session.task_state is not None
+
+# Later call reuses the durable A2A session state. A2AAgent decides whether to send task_id
+# for INPUT_REQUIRED or reference_task_ids for task refinement.
+next_response = await a2a_agent.run(
+    next_message,
+    session=session,
+)
+```
+
+- Good, because protocol-specific state stays in a protocol-specific type.
+- Good, because it aligns with .NET A2A's `A2AAgentSession` shape.
+- Good, because Python A2A can still make this pre-release session factory adjustment.
+- Good, because `task_state` does not get promoted to a base `AgentSession` concept.
+- Bad, because generic consumers cannot read protocol-specific state without knowing about the subclass or a helper API.
+- Bad, because it depends on each subclass consistently setting shared session fields such as `service_session_id` where
+  those are part of the shared abstraction.
+
+### Option B: Extend `service_session_id` with richer service-owned values
+
+Keep the common `service_session_id` case as a plain string. When an agent/service needs more than one service-owned
+continuation value, allow `service_session_id` to be a typed structured value, such as a `TypedDict`. The main session ID
+used for `gen_ai.conversation.id` should still be extracted by the owning agent, not inferred by generic telemetry code.
+
+Examples:
+
+```python
+simple_session = AgentSession(
+    service_session_id="resp_123",
+)
+
+structured_session = AgentSession(
+    service_session_id=A2AServiceSessionId(
+        context_id="ctx_123",
+        task_id="task_789",
+        task_state=TaskState.TASK_STATE_WORKING,
+    ),
+)
+```
+
+- Good, because the common case remains a plain string and stays simple.
+- Good, because richer service-owned continuation state stays under the existing continuation property.
+- Good, because a structured value can make framework-side validation possible before a value is sent back to a service.
+- Good, because A2A can keep `context_id`, `task_id`, and `task_state` together as the service/protocol-owned continuation
+  value without adding A2A fields to base `AgentSession`.
+- Neutral, because telemetry needs an agent-owned extractor to pick the `gen_ai.conversation.id` value from either a
+  string or structured `service_session_id`.
+- Neutral, because Python A2A would need a pre-release adjustment to stop relying on `A2AAgentSession` for these fields.
+- Bad, because changing the `service_session_id` type is a compatibility risk for users, providers, serialization, and
+  tests.
+- Bad, because every path that sends `service_session_id` back to a service must consistently extract/adapt the
+  service-owned continuation component.
+
+### Option C: Add a dedicated dict for additional session details
+
+Keep `service_session_id` as the primary opaque service-owned continuation handle, and add a separate dictionary for
+additional durable protocol/service values that need to travel with the session.
+
+Example:
+
+```python
+session = AgentSession(
+    service_session_id="ctx_123",
+    session_details={
+        "task_id": "task_456",
+        "task_state": TaskState.TASK_STATE_WORKING,
+    },
+)
+```
+
+- Good, because the main service continuation handle stays a plain `service_session_id` string.
+- Good, because extra state has an explicit home and does not overload `service_session_id`.
+- Good, because generic consumers can look in one documented place for additional session-scoped values.
+- Neutral, because helper APIs can hide the raw dictionary access.
+- Bad, because this still introduces string-keyed state unless the dict values are wrapped by typed helpers.
+- Bad, because it adds another public session field that needs serialization, naming, and compatibility rules.
+- Bad, because generic consumers still need to understand the shape or use helpers for the selected agent/session type.
+
+### Option D: Store additional durable state inside `AgentSession.state`
+
+Keep base `AgentSession` unchanged and store additional durable continuation/protocol state under namespaced keys in
+`session.state`.
+
+Example:
+
+```python
+session = AgentSession(session_id="ctx_123")
+
+session.state["a2a"] = {
+    "task_id": "task_456",
+    "task_state": TaskState.TASK_STATE_WORKING,
+}
+```
+
+- Good, because it avoids new public fields and avoids a subclass requirement.
+- Good, because `AgentSession.state` already exists for provider/session state.
+- Neutral, because helper APIs can hide the raw dictionary access.
+- Bad, because stringly typed state is easier to corrupt and harder to validate.
+- Bad, because generic consumers need helper APIs anyway; directly reading nested dictionaries is not a good abstraction.
+- Bad, because users may accidentally overwrite or persist invalid protocol state.
+
+## Decision
+
+Chosen decision criteria for the future: **split identity by lifecycle**.
+
+When a protocol emits an id/token, place it by answering "what lifecycle does this value serve?":
+
+- **Future-call continuation state** -> durable session state. Examples: A2A `context_id` + `task_id` + `task_state`;
+  OpenAI Responses `previous_response_id`/`conversation`.
+- **Single-result identity** -> response/message object only. Examples: OpenAI `resp_*`, A2A `message_id`,
+  A2A `artifact_id`.
+- **Resume unfinished work** -> `ContinuationToken` only. Example: a token carrying in-progress task resume data.
+- **Run-start-only request fields** -> run method arguments/options, not durable session state. Example: A2A
+  `reference_task_ids` for a specific follow-up/refinement request.
+- **Per-run correlation/telemetry** -> protocol wrapper or run context, not `AgentSession`. Example: wrapper-managed
+  `run_id` used only for tracing/events.
+
+Durable-state option decision: **Option B: Extend `service_session_id` with richer service-owned values**.
+This does **not** add a new top-level identity abstraction; it keeps continuation identity under
+`service_session_id` and keeps run correlation in existing run/telemetry context.
+The immediate implementation gap is mainly in A2A mapping clarity, but the lifecycle split applies
+consistently across providers.
+
+To support telemetry, `BaseAgent` should expose a method that accepts an `AgentSession | None` and returns the value to
+use for `gen_ai.conversation.id`. The default implementation should return `session.service_session_id` when it is a
+string. Agents that use a structured `service_session_id`, such as `A2AAgent`, should override that method and return the
+appropriate primary session/context value.
+
+## Appendix: A2A `task_id` and `reference_task_ids` implementation check
+
+The A2A protocol distinguishes a message's `task_id` from `reference_task_ids`:
+
+- `task_id` associates the message with a specific task.
+- `reference_task_ids` provides additional task context, for example when a new task refines or follows up on the result
+  of a previous task.
+
+The protocol does not appear to prescribe that `task_id` and `reference_task_ids` are mutually exclusive. If both are
+present, the natural reading is that the message is associated with one task while also referencing other tasks for
+context. The serving agent decides how to interpret that context.
+
+The Python implementation should check and likely adjust the current behavior:
+
+- `task_id` should be updated by the current run when the remote A2A service returns a task/status payload.
+- `task_id` should remain durable A2A session state when needed for future calls, for example when a task is
+  `INPUT_REQUIRED`.
+- `reference_task_ids` should be a run parameter / caller intent for the current request, not implicit durable session
+  continuation state.
+- A follow-up/refinement request should pass explicit `reference_task_ids` when it wants to reference previous tasks.
+- If both session `task_id` and run `reference_task_ids` are present, the wrapper should preserve the protocol
+  distinction rather than treating one as a replacement for the other.
+- If no `reference_task_ids` are supplied, the wrapper should not automatically infer them from the last session task
+  unless we deliberately keep that convenience for compatibility.
+
+## Appendix: implementation notes for Option B
+
+The exact names are implementation details, but the shape should be:
+
+```python
+class A2AServiceSessionId(TypedDict):
+    context_id: str
+    task_id: str | None
+    task_state: TaskState | None
+
+
+class AgentSession:
+    def __init__(
+        self,
+        *,
+        session_id: str | None = None,
+        service_session_id: str | ServiceSessionId | None = None,
+    ) -> None:
+        ...
+
+
+class BaseAgent:
+    def _get_otel_conversation_id(self, session: AgentSession | None) -> str | None:
+        service_session_id = session.service_session_id if session else None
+        return service_session_id if isinstance(service_session_id, str) else None
+
+
+class A2AAgent(BaseAgent):
+    def _get_otel_conversation_id(self, session: AgentSession | None) -> str | None:
+        service_session_id = session.service_session_id if session else None
+        if isinstance(service_session_id, Mapping):
+            return service_session_id.get("context_id")
+        return service_session_id if isinstance(service_session_id, str) else None
+
+
+class AgentTelemetryLayer:
+    def _trace_agent_invocation(...):
+        attributes = _get_span_attributes(
+            ...,
+            thread_id=self._get_otel_conversation_id(session),
+            ...,
+        )
+```
+
+This keeps the OpenTelemetry extraction decision with the agent that owns the service continuation shape. Generic OTel
+code should not parse structured `service_session_id` values directly.
+
+`AgentSession` must also be updated so `service_session_id` can store either the current string value or a structured
+service-owned value. Serialization must preserve both shapes, and existing serialized sessions with string
+`service_session_id` must continue to round-trip unchanged.
+
+## More Information
+
+Related work and issues:
+
+- #4673: native conversation ID propagation.
+- #4893: align A2A protocol concepts with Agent Framework session/continuation concepts.
+- #2931: Foundry-specific conversation creation helper, split into a separate Python PR.
+- #6622: broader provider-agnostic conversation creation API discussion requiring .NET sync.
+- [ADR-0015](0015-agent-run-context.md): AgentRunContext for Agent Run.
+- [ADR-0018](0018-agentthread-serialization.md): AgentSession serialization.
+- [ADR-0026](0026-hosted-session-identity-context.md): hosted session identity context.

dotnet/agent-framework-dotnet.slnx

@@ -1,4 +1,4 @@
-<Solution>
+<Solution>
   <Configurations>
     <BuildType Name="Debug" />
     <BuildType Name="Publish" />
@@ -117,6 +117,7 @@
     <Project Path="samples/02-agents/AgentSkills/Agent_Step04_MixedSkills/Agent_Step04_MixedSkills.csproj" />
     <Project Path="samples/02-agents/AgentSkills/Agent_Step05_SkillsWithDI/Agent_Step05_SkillsWithDI.csproj" />
     <Project Path="samples/02-agents/AgentSkills/Agent_Step06_McpBasedSkills/Agent_Step06_McpBasedSkills.csproj" />
+    <Project Path="samples/02-agents/AgentSkills/Agent_Step07_SkillsAutoApproval/Agent_Step07_SkillsAutoApproval.csproj" />
   </Folder>
   <Folder Name="/Samples/02-agents/Harness/">
     <File Path="samples/02-agents/Harness/README.md" />
@@ -193,10 +194,10 @@
     <File Path="samples/02-agents/AgentWithMemory/README.md" />
     <Project Path="samples/02-agents/AgentWithMemory/AgentWithMemory_Step01_ChatHistoryMemory/AgentWithMemory_Step01_ChatHistoryMemory.csproj" />
     <Project Path="samples/02-agents/AgentWithMemory/AgentWithMemory_Step02_MemoryUsingMem0/AgentWithMemory_Step02_MemoryUsingMem0.csproj" />
-    <Project Path="samples/02-agents/AgentWithMemory/AgentWithMemory_Step04_MemoryUsingFoundry/AgentWithMemory_Step04_MemoryUsingFoundry.csproj" />
-    <Project Path="samples/02-agents/AgentWithMemory/AgentWithMemory_Step05_BoundedChatHistory/AgentWithMemory_Step05_BoundedChatHistory.csproj" />
     <Project Path="samples/02-agents/AgentWithMemory/AgentWithMemory_Step03_MemoryUsingValkey/AgentWithMemory_Step03_MemoryUsingValkey.csproj" />
     <Project Path="samples/02-agents/AgentWithMemory/AgentWithMemory_Step03_MemoryUsingValkey_Bedrock/AgentWithMemory_Step03_MemoryUsingValkey_Bedrock.csproj" />
+    <Project Path="samples/02-agents/AgentWithMemory/AgentWithMemory_Step04_MemoryUsingFoundry/AgentWithMemory_Step04_MemoryUsingFoundry.csproj" />
+    <Project Path="samples/02-agents/AgentWithMemory/AgentWithMemory_Step05_BoundedChatHistory/AgentWithMemory_Step05_BoundedChatHistory.csproj" />
   </Folder>
   <Folder Name="/Samples/02-agents/AgentProviders/openai/">
     <File Path="samples/02-agents/AgentProviders/openai/README.md" />
@@ -241,10 +242,10 @@
   </Folder>
   <Folder Name="/Samples/03-workflows/Declarative/">
     <File Path="samples/03-workflows/Declarative/README.md" />
+    <Project Path="samples/03-workflows/Declarative/AotCheckpointing/AotCheckpointing.csproj" />
     <Project Path="samples/03-workflows/Declarative/ConfirmInput/ConfirmInput.csproj" />
     <Project Path="samples/03-workflows/Declarative/CustomerSupport/CustomerSupport.csproj" />
     <Project Path="samples/03-workflows/Declarative/DeepResearch/DeepResearch.csproj" />
-    <Project Path="samples/03-workflows/Declarative/ExecuteCode/ExecuteCode.csproj" />
     <Project Path="samples/03-workflows/Declarative/ExecuteWorkflow/ExecuteWorkflow.csproj" />
     <Project Path="samples/03-workflows/Declarative/FunctionTools/FunctionTools.csproj" />
     <Project Path="samples/03-workflows/Declarative/HostedWorkflow/HostedWorkflow.csproj" />
@@ -617,9 +618,9 @@
     <Project Path="src/Microsoft.Agents.AI.Hosting.A2A.AspNetCore/Microsoft.Agents.AI.Hosting.A2A.AspNetCore.csproj" />
     <Project Path="src/Microsoft.Agents.AI.Hosting.A2A/Microsoft.Agents.AI.Hosting.A2A.csproj" />
     <Project Path="src/Microsoft.Agents.AI.Hosting.AGUI.AspNetCore/Microsoft.Agents.AI.Hosting.AGUI.AspNetCore.csproj" />
+    <Project Path="src/Microsoft.Agents.AI.Hosting.AspNetCore/Microsoft.Agents.AI.Hosting.AspNetCore.csproj" />
     <Project Path="src/Microsoft.Agents.AI.Hosting.AzureFunctions/Microsoft.Agents.AI.Hosting.AzureFunctions.csproj" />
     <Project Path="src/Microsoft.Agents.AI.Hosting.OpenAI/Microsoft.Agents.AI.Hosting.OpenAI.csproj" />
-    <Project Path="src/Microsoft.Agents.AI.Hosting.AspNetCore/Microsoft.Agents.AI.Hosting.AspNetCore.csproj" />
     <Project Path="src/Microsoft.Agents.AI.Hosting/Microsoft.Agents.AI.Hosting.csproj" />
     <Project Path="src/Microsoft.Agents.AI.Hyperlight/Microsoft.Agents.AI.Hyperlight.csproj" />
     <Project Path="src/Microsoft.Agents.AI.LocalCodeAct/Microsoft.Agents.AI.LocalCodeAct.csproj" />
@@ -628,13 +629,13 @@
     <Project Path="src/Microsoft.Agents.AI.OpenAI/Microsoft.Agents.AI.OpenAI.csproj" />
     <Project Path="src/Microsoft.Agents.AI.Purview/Microsoft.Agents.AI.Purview.csproj" />
     <Project Path="src/Microsoft.Agents.AI.Tools.Shell/Microsoft.Agents.AI.Tools.Shell.csproj" />
+    <Project Path="src/Microsoft.Agents.AI.Valkey/Microsoft.Agents.AI.Valkey.csproj" />
     <Project Path="src/Microsoft.Agents.AI.Workflows.Declarative.Foundry/Microsoft.Agents.AI.Workflows.Declarative.Foundry.csproj" />
     <Project Path="src/Microsoft.Agents.AI.Workflows.Declarative.Mcp/Microsoft.Agents.AI.Workflows.Declarative.Mcp.csproj" />
     <Project Path="src/Microsoft.Agents.AI.Workflows.Declarative/Microsoft.Agents.AI.Workflows.Declarative.csproj" />
     <Project Path="src/Microsoft.Agents.AI.Workflows.Generators/Microsoft.Agents.AI.Workflows.Generators.csproj" />
     <Project Path="src/Microsoft.Agents.AI.Workflows/Microsoft.Agents.AI.Workflows.csproj" />
     <Project Path="src/Microsoft.Agents.AI/Microsoft.Agents.AI.csproj" />
-    <Project Path="src/Microsoft.Agents.AI.Valkey/Microsoft.Agents.AI.Valkey.csproj" />
   </Folder>
   <Folder Name="/Tests/" />
   <Folder Name="/Tests/IntegrationTests/">
@@ -685,11 +686,11 @@
     <Project Path="tests/Microsoft.Agents.AI.Purview.UnitTests/Microsoft.Agents.AI.Purview.UnitTests.csproj" />
     <Project Path="tests/Microsoft.Agents.AI.Tools.Shell.UnitTests/Microsoft.Agents.AI.Tools.Shell.UnitTests.csproj" />
     <Project Path="tests/Microsoft.Agents.AI.UnitTests/Microsoft.Agents.AI.UnitTests.csproj" />
+    <Project Path="tests/Microsoft.Agents.AI.Valkey.UnitTests/Microsoft.Agents.AI.Valkey.UnitTests.csproj" />
     <Project Path="tests/Microsoft.Agents.AI.Workflows.Declarative.Mcp.UnitTests/Microsoft.Agents.AI.Workflows.Declarative.Mcp.UnitTests.csproj" />
     <Project Path="tests/Microsoft.Agents.AI.Workflows.Declarative.UnitTests/Microsoft.Agents.AI.Workflows.Declarative.UnitTests.csproj" />
     <Project Path="tests/Microsoft.Agents.AI.Workflows.Generators.UnitTests/Microsoft.Agents.AI.Workflows.Generators.UnitTests.csproj" />
     <Project Path="tests/Microsoft.Agents.AI.Workflows.UnitTests/Microsoft.Agents.AI.Workflows.UnitTests.csproj" />
-    <Project Path="tests/Microsoft.Agents.AI.Valkey.UnitTests/Microsoft.Agents.AI.Valkey.UnitTests.csproj" />
   </Folder>
 </Solution>
 

dotnet/eng/verify-samples/Program.cs

@@ -18,13 +18,14 @@
 // Note: By default, this tool expects sample build outputs to already exist.
 // Pre-build the solution before running, or pass --build to avoid missing build output failures.
 //
-// Required environment variables (for AI-powered samples):
-//   AZURE_OPENAI_ENDPOINT
-//   AZURE_OPENAI_DEPLOYMENT_NAME (optional, defaults to gpt-5-mini)
+// Required environment variables (for AI-powered verification):
+//   FOUNDRY_PROJECT_ENDPOINT  — Your Azure AI Foundry project endpoint
+//   FOUNDRY_MODEL             — Model deployment name (optional, defaults to gpt-5.4-mini)
 
 using System.Diagnostics;
-using Azure.AI.OpenAI;
+using Azure.AI.Projects;
 using Azure.Identity;
+using Microsoft.Agents.AI;
 using VerifySamples;
 
 var options = VerifyOptions.Parse(args);
@@ -43,14 +44,33 @@ if (!File.Exists(Path.Combine(dotnetRoot, "agent-framework-dotnet.slnx")))
 }
 
 // Set up the AI verifier
-var endpoint = Environment.GetEnvironmentVariable("AZURE_OPENAI_ENDPOINT");
-var deploymentName = Environment.GetEnvironmentVariable("AZURE_OPENAI_DEPLOYMENT_NAME") ?? "gpt-5-mini";
+var foundryEndpoint = Environment.GetEnvironmentVariable("FOUNDRY_PROJECT_ENDPOINT");
+var foundryModel = Environment.GetEnvironmentVariable("FOUNDRY_MODEL") ?? "gpt-5.4-mini";
 
-OpenAI.Chat.ChatClient? chatClient = null;
-if (!string.IsNullOrEmpty(endpoint))
+AIAgent? verifierAgent = null;
+if (!string.IsNullOrEmpty(foundryEndpoint))
 {
-    chatClient = new AzureOpenAIClient(new Uri(endpoint), new DefaultAzureCredential())
-        .GetChatClient(deploymentName);
+    verifierAgent = new AIProjectClient(new Uri(foundryEndpoint), new DefaultAzureCredential())
+        .AsAIAgent(
+            model: foundryModel,
+            instructions: """
+                You are a test output verifier. You will be given:
+                1. The actual stdout output of a program
+                2. The stderr output (if any)
+                3. A list of expectations about what the output should contain or demonstrate
+
+                Your job is to determine whether the actual output satisfies each expectation.
+                Be reasonable — the output comes from an LLM so exact wording won't match, but the
+                semantic intent should be clearly satisfied.
+
+                In your response, you MUST:
+                - Always provide ai_reasoning with a brief overall assessment.
+                - Always provide exactly one entry in expectation_results for each expectation,
+                  in the same order as the input list.
+                - For each expectation_results entry, echo the expectation text in the expectation
+                  field and explain your assessment in the detail field, citing evidence from the output.
+                """,
+            name: "OutputVerifier");
 }
 
 // Set up optional log file writer
@@ -61,11 +81,13 @@ if (options.LogFilePath is not null)
     await logWriter.WriteHeaderAsync();
 }
 
+Console.WriteLine($"Foundry endpoint: {foundryEndpoint ?? "(not set — AI verification disabled)"}, Model: {foundryModel}");
+
 try
 {
     // Run all samples
     var reporter = new ConsoleReporter();
-    var verifier = new SampleVerifier(chatClient);
+    var verifier = new SampleVerifier(verifierAgent);
     var orchestrator = new VerificationOrchestrator(verifier, reporter, dotnetRoot, TimeSpan.FromMinutes(3), logWriter, buildSamples: options.BuildSamples);
 
     var run = await orchestrator.RunAllAsync(options.Samples, options.MaxParallelism);

dotnet/eng/verify-samples/SampleVerifier.cs

@@ -3,8 +3,6 @@
 using System.ComponentModel;
 using System.Text.Json.Serialization;
 using Microsoft.Agents.AI;
-using Microsoft.Extensions.AI;
-using OpenAI.Chat;
 
 namespace VerifySamples;
 
@@ -17,33 +15,12 @@ internal sealed class SampleVerifier
     private readonly AIAgent? _verifierAgent;
 
     /// <summary>
-    /// Creates a verifier. If <paramref name="chatClient"/> is provided,
+    /// Creates a verifier. If <paramref name="verifierAgent"/> is provided,
     /// AI-based verification is available for non-deterministic samples.
     /// </summary>
-    public SampleVerifier(ChatClient? chatClient = null)
+    public SampleVerifier(AIAgent? verifierAgent = null)
     {
-        if (chatClient is not null)
-        {
-            this._verifierAgent = chatClient.AsAIAgent(
-                instructions: """
-                    You are a test output verifier. You will be given:
-                    1. The actual stdout output of a program
-                    2. The stderr output (if any)
-                    3. A list of expectations about what the output should contain or demonstrate
-
-                    Your job is to determine whether the actual output satisfies each expectation.
-                    Be reasonable — the output comes from an LLM so exact wording won't match, but the
-                    semantic intent should be clearly satisfied.
-
-                    In your response, you MUST:
-                    - Always provide ai_reasoning with a brief overall assessment.
-                    - Always provide exactly one entry in expectation_results for each expectation,
-                      in the same order as the input list.
-                    - For each expectation_results entry, echo the expectation text in the expectation
-                      field and explain your assessment in the detail field, citing evidence from the output.
-                    """,
-                name: "OutputVerifier");
-        }
+        this._verifierAgent = verifierAgent;
     }
 
     /// <summary>

dotnet/eng/verify-samples/WorkflowSamples.cs

@@ -30,8 +30,8 @@ internal static class WorkflowSamples
         {
             Name = "Workflow_StartHere_02_AgentsInWorkflows",
             ProjectPath = "samples/03-workflows/_StartHere/02_AgentsInWorkflows",
-            RequiredEnvironmentVariables = ["AZURE_OPENAI_ENDPOINT"],
-            OptionalEnvironmentVariables = ["AZURE_OPENAI_DEPLOYMENT_NAME"],
+            RequiredEnvironmentVariables = ["FOUNDRY_PROJECT_ENDPOINT"],
+            OptionalEnvironmentVariables = ["FOUNDRY_MODEL"],
             ExpectedOutputDescription =
             [
                 "The output should show agent responses from a translation workflow.",
@@ -43,8 +43,8 @@ internal static class WorkflowSamples
         {
             Name = "Workflow_StartHere_03_AgentWorkflowPatterns",
             ProjectPath = "samples/03-workflows/_StartHere/03_AgentWorkflowPatterns",
-            RequiredEnvironmentVariables = ["AZURE_OPENAI_ENDPOINT"],
-            OptionalEnvironmentVariables = ["AZURE_OPENAI_DEPLOYMENT_NAME"],
+            RequiredEnvironmentVariables = ["FOUNDRY_PROJECT_ENDPOINT"],
+            OptionalEnvironmentVariables = ["FOUNDRY_MODEL"],
             Inputs = ["sequential"],
             InputDelayMs = 3000,
             ExpectedOutputDescription =
@@ -81,8 +81,8 @@ internal static class WorkflowSamples
         {
             Name = "Workflow_StartHere_06_MixedWorkflowAgentsAndExecutors",
             ProjectPath = "samples/03-workflows/_StartHere/06_MixedWorkflowAgentsAndExecutors",
-            RequiredEnvironmentVariables = ["AZURE_OPENAI_ENDPOINT"],
-            OptionalEnvironmentVariables = ["AZURE_OPENAI_DEPLOYMENT_NAME"],
+            RequiredEnvironmentVariables = ["FOUNDRY_PROJECT_ENDPOINT"],
+            OptionalEnvironmentVariables = ["FOUNDRY_MODEL"],
             Inputs = ["What is 2 plus 2?"],
             InputDelayMs = 3000,
             ExpectedOutputDescription =
@@ -96,8 +96,8 @@ internal static class WorkflowSamples
         {
             Name = "Workflow_StartHere_07_WriterCriticWorkflow",
             ProjectPath = "samples/03-workflows/_StartHere/07_WriterCriticWorkflow",
-            RequiredEnvironmentVariables = ["AZURE_OPENAI_ENDPOINT"],
-            OptionalEnvironmentVariables = ["AZURE_OPENAI_DEPLOYMENT_NAME"],
+            RequiredEnvironmentVariables = ["FOUNDRY_PROJECT_ENDPOINT"],
+            OptionalEnvironmentVariables = ["FOUNDRY_MODEL"],
             MustContain = ["=== Writer-Critic Iteration Workflow ==="],
             ExpectedOutputDescription =
             [
@@ -115,8 +115,8 @@ internal static class WorkflowSamples
         {
             Name = "Workflow_Agents_CustomAgentExecutors",
             ProjectPath = "samples/03-workflows/Agents/CustomAgentExecutors",
-            RequiredEnvironmentVariables = ["AZURE_OPENAI_ENDPOINT"],
-            OptionalEnvironmentVariables = ["AZURE_OPENAI_DEPLOYMENT_NAME"],
+            RequiredEnvironmentVariables = ["FOUNDRY_PROJECT_ENDPOINT"],
+            OptionalEnvironmentVariables = ["FOUNDRY_MODEL"],
             ExpectedOutputDescription =
             [
                 "The output should show custom workflow events including slogan generation and feedback.",
@@ -128,8 +128,8 @@ internal static class WorkflowSamples
         {
             Name = "Workflow_Agents_FoundryAgent",
             ProjectPath = "samples/03-workflows/Agents/FoundryAgent",
-            RequiredEnvironmentVariables = ["AZURE_AI_PROJECT_ENDPOINT"],
-            OptionalEnvironmentVariables = ["AZURE_AI_MODEL_DEPLOYMENT_NAME"],
+            RequiredEnvironmentVariables = ["FOUNDRY_PROJECT_ENDPOINT"],
+            OptionalEnvironmentVariables = ["FOUNDRY_MODEL"],
             SkipReason = "Requires Azure AI Foundry project endpoint.",
         },
 
@@ -137,8 +137,8 @@ internal static class WorkflowSamples
         {
             Name = "Workflow_Agents_GroupChatToolApproval",
             ProjectPath = "samples/03-workflows/Agents/GroupChatToolApproval",
-            RequiredEnvironmentVariables = ["AZURE_OPENAI_ENDPOINT"],
-            OptionalEnvironmentVariables = ["AZURE_OPENAI_DEPLOYMENT_NAME"],
+            RequiredEnvironmentVariables = ["FOUNDRY_PROJECT_ENDPOINT"],
+            OptionalEnvironmentVariables = ["FOUNDRY_MODEL"],
             MustContain = ["Starting group chat workflow for software deployment..."],
             ExpectedOutputDescription =
             [
@@ -153,8 +153,8 @@ internal static class WorkflowSamples
         {
             Name = "Workflow_Agents_WorkflowAsAnAgent",
             ProjectPath = "samples/03-workflows/Agents/WorkflowAsAnAgent",
-            RequiredEnvironmentVariables = ["AZURE_OPENAI_ENDPOINT"],
-            OptionalEnvironmentVariables = ["AZURE_OPENAI_DEPLOYMENT_NAME"],
+            RequiredEnvironmentVariables = ["FOUNDRY_PROJECT_ENDPOINT"],
+            OptionalEnvironmentVariables = ["FOUNDRY_MODEL"],
             Inputs = ["hello", "exit"],
             InputDelayMs = 5000,
             ExpectedOutputDescription =
@@ -219,8 +219,8 @@ internal static class WorkflowSamples
         {
             Name = "Workflow_Concurrent_Concurrent",
             ProjectPath = "samples/03-workflows/Concurrent/Concurrent",
-            RequiredEnvironmentVariables = ["AZURE_OPENAI_ENDPOINT"],
-            OptionalEnvironmentVariables = ["AZURE_OPENAI_DEPLOYMENT_NAME"],
+            RequiredEnvironmentVariables = ["FOUNDRY_PROJECT_ENDPOINT"],
+            OptionalEnvironmentVariables = ["FOUNDRY_MODEL"],
             ExpectedOutputDescription =
             [
                 "The output should show results from concurrent agent processing.",
@@ -247,8 +247,8 @@ internal static class WorkflowSamples
         {
             Name = "Workflow_ConditionalEdges_01_EdgeCondition",
             ProjectPath = "samples/03-workflows/ConditionalEdges/01_EdgeCondition",
-            RequiredEnvironmentVariables = ["AZURE_OPENAI_ENDPOINT"],
-            OptionalEnvironmentVariables = ["AZURE_OPENAI_DEPLOYMENT_NAME"],
+            RequiredEnvironmentVariables = ["FOUNDRY_PROJECT_ENDPOINT"],
+            OptionalEnvironmentVariables = ["FOUNDRY_MODEL"],
             ExpectedOutputDescription =
             [
                 "The output should show an email being classified as spam or not spam and processed accordingly.",
@@ -260,8 +260,8 @@ internal static class WorkflowSamples
         {
             Name = "Workflow_ConditionalEdges_02_SwitchCase",
             ProjectPath = "samples/03-workflows/ConditionalEdges/02_SwitchCase",
-            RequiredEnvironmentVariables = ["AZURE_OPENAI_ENDPOINT"],
-            OptionalEnvironmentVariables = ["AZURE_OPENAI_DEPLOYMENT_NAME"],
+            RequiredEnvironmentVariables = ["FOUNDRY_PROJECT_ENDPOINT"],
+            OptionalEnvironmentVariables = ["FOUNDRY_MODEL"],
             ExpectedOutputDescription =
             [
                 "The output should show an ambiguous email being classified as spam, not spam, or uncertain.",
@@ -273,8 +273,8 @@ internal static class WorkflowSamples
         {
             Name = "Workflow_ConditionalEdges_03_MultiSelection",
             ProjectPath = "samples/03-workflows/ConditionalEdges/03_MultiSelection",
-            RequiredEnvironmentVariables = ["AZURE_OPENAI_ENDPOINT"],
-            OptionalEnvironmentVariables = ["AZURE_OPENAI_DEPLOYMENT_NAME"],
+            RequiredEnvironmentVariables = ["FOUNDRY_PROJECT_ENDPOINT"],
+            OptionalEnvironmentVariables = ["FOUNDRY_MODEL"],
             ExpectedOutputDescription =
             [
                 "The output should show an email being classified and potentially routed to multiple handlers.",
@@ -371,8 +371,8 @@ internal static class WorkflowSamples
         {
             Name = "Workflow_Observability_WorkflowAsAnAgent",
             ProjectPath = "samples/03-workflows/Observability/WorkflowAsAnAgent",
-            RequiredEnvironmentVariables = ["AZURE_OPENAI_ENDPOINT"],
-            OptionalEnvironmentVariables = ["AZURE_OPENAI_DEPLOYMENT_NAME"],
+            RequiredEnvironmentVariables = ["FOUNDRY_PROJECT_ENDPOINT"],
+            OptionalEnvironmentVariables = ["FOUNDRY_MODEL"],
             SkipReason = "Interactive console with ReadLine loop; requires OTLP endpoint.",
         },
 
@@ -384,7 +384,7 @@ internal static class WorkflowSamples
         {
             Name = "Workflow_Declarative_ConfirmInput",
             ProjectPath = "samples/03-workflows/Declarative/ConfirmInput",
-            RequiredEnvironmentVariables = ["AZURE_AI_PROJECT_ENDPOINT"],
+            RequiredEnvironmentVariables = ["FOUNDRY_PROJECT_ENDPOINT"],
             Inputs = ["hello", "hello"],
             InputDelayMs = 8000,
             ExpectedOutputDescription = ["The output should show a confirmation prompt and a user response."],
@@ -394,8 +394,8 @@ internal static class WorkflowSamples
         {
             Name = "Workflow_Declarative_CustomerSupport",
             ProjectPath = "samples/03-workflows/Declarative/CustomerSupport",
-            RequiredEnvironmentVariables = ["AZURE_AI_PROJECT_ENDPOINT"],
-            OptionalEnvironmentVariables = ["AZURE_AI_MODEL_DEPLOYMENT_NAME"],
+            RequiredEnvironmentVariables = ["FOUNDRY_PROJECT_ENDPOINT"],
+            OptionalEnvironmentVariables = ["FOUNDRY_MODEL"],
             Inputs = ["My laptop won't start"],
             InputDelayMs = 3000,
             ExpectedOutputDescription = ["The output should show a customer support workflow processing a laptop issue, with agent responses providing troubleshooting or support."],
@@ -405,26 +405,16 @@ internal static class WorkflowSamples
         {
             Name = "Workflow_Declarative_DeepResearch",
             ProjectPath = "samples/03-workflows/Declarative/DeepResearch",
-            RequiredEnvironmentVariables = ["AZURE_AI_PROJECT_ENDPOINT"],
-            OptionalEnvironmentVariables = ["AZURE_AI_MODEL_DEPLOYMENT_NAME"],
+            RequiredEnvironmentVariables = ["FOUNDRY_PROJECT_ENDPOINT"],
+            OptionalEnvironmentVariables = ["FOUNDRY_MODEL"],
             SkipReason = "Requires external weather API (wttr.in).",
         },
 
-        new SampleDefinition
-        {
-            Name = "Workflow_Declarative_ExecuteCode",
-            ProjectPath = "samples/03-workflows/Declarative/ExecuteCode",
-            RequiredEnvironmentVariables = ["AZURE_AI_PROJECT_ENDPOINT"],
-            Inputs = ["What is 12 * 34?"],
-            InputDelayMs = 5000,
-            ExpectedOutputDescription = ["The output should show a declarative workflow executing generated code, processing a math question and producing a result."],
-        },
-
         new SampleDefinition
         {
             Name = "Workflow_Declarative_ExecuteWorkflow",
             ProjectPath = "samples/03-workflows/Declarative/ExecuteWorkflow",
-            RequiredEnvironmentVariables = ["AZURE_AI_PROJECT_ENDPOINT"],
+            RequiredEnvironmentVariables = ["FOUNDRY_PROJECT_ENDPOINT"],
             SkipReason = "Requires a workflow file path as a CLI argument.",
         },
 
@@ -432,8 +422,8 @@ internal static class WorkflowSamples
         {
             Name = "Workflow_Declarative_FunctionTools",
             ProjectPath = "samples/03-workflows/Declarative/FunctionTools",
-            RequiredEnvironmentVariables = ["AZURE_AI_PROJECT_ENDPOINT"],
-            OptionalEnvironmentVariables = ["AZURE_AI_MODEL_DEPLOYMENT_NAME"],
+            RequiredEnvironmentVariables = ["FOUNDRY_PROJECT_ENDPOINT"],
+            OptionalEnvironmentVariables = ["FOUNDRY_MODEL"],
             Inputs = ["What are today's specials?", "EXIT"],
             InputDelayMs = 8000,
             ExpectedOutputDescription = ["The output should show a workflow calling function tools (e.g. a menu plugin) to answer a question about restaurant specials."],
@@ -443,7 +433,7 @@ internal static class WorkflowSamples
         {
             Name = "Workflow_Declarative_HostedWorkflow",
             ProjectPath = "samples/03-workflows/Declarative/HostedWorkflow",
-            RequiredEnvironmentVariables = ["AZURE_AI_PROJECT_ENDPOINT"],
+            RequiredEnvironmentVariables = ["FOUNDRY_PROJECT_ENDPOINT"],
             SkipReason = "Hosts a persistent workflow server that does not exit.",
         },
 
@@ -451,8 +441,8 @@ internal static class WorkflowSamples
         {
             Name = "Workflow_Declarative_InputArguments",
             ProjectPath = "samples/03-workflows/Declarative/InputArguments",
-            RequiredEnvironmentVariables = ["AZURE_AI_PROJECT_ENDPOINT"],
-            OptionalEnvironmentVariables = ["AZURE_AI_MODEL_DEPLOYMENT_NAME"],
+            RequiredEnvironmentVariables = ["FOUNDRY_PROJECT_ENDPOINT"],
+            OptionalEnvironmentVariables = ["FOUNDRY_MODEL"],
             Inputs = ["I'd like to visit Seattle", "EXIT"],
             InputDelayMs = 8000,
             ExpectedOutputDescription = ["The output should show a workflow capturing location input and providing travel-related information about Seattle."],
@@ -462,8 +452,8 @@ internal static class WorkflowSamples
         {
             Name = "Workflow_Declarative_InvokeFunctionTool",
             ProjectPath = "samples/03-workflows/Declarative/InvokeFunctionTool",
-            RequiredEnvironmentVariables = ["AZURE_AI_PROJECT_ENDPOINT"],
-            OptionalEnvironmentVariables = ["AZURE_AI_MODEL_DEPLOYMENT_NAME"],
+            RequiredEnvironmentVariables = ["FOUNDRY_PROJECT_ENDPOINT"],
+            OptionalEnvironmentVariables = ["FOUNDRY_MODEL"],
             Inputs = ["What's the soup of the day?", "EXIT"],
             InputDelayMs = 8000,
             ExpectedOutputDescription = ["The output should show a workflow invoking a function tool (e.g. a menu plugin) to answer a question about the soup of the day."],
@@ -473,8 +463,8 @@ internal static class WorkflowSamples
         {
             Name = "Workflow_Declarative_InvokeFoundryToolboxMcp",
             ProjectPath = "samples/03-workflows/Declarative/InvokeFoundryToolboxMcp",
-            RequiredEnvironmentVariables = ["AZURE_AI_PROJECT_ENDPOINT"],
-            OptionalEnvironmentVariables = ["AZURE_AI_MODEL_DEPLOYMENT_NAME", "FOUNDRY_TOOLBOX_NAME", "FOUNDRY_AGENT_TOOLSET_API_VERSION"],
+            RequiredEnvironmentVariables = ["FOUNDRY_PROJECT_ENDPOINT"],
+            OptionalEnvironmentVariables = ["FOUNDRY_MODEL", "FOUNDRY_TOOLBOX_NAME", "FOUNDRY_AGENT_TOOLSET_API_VERSION"],
             Inputs = ["How do I use Azure OpenAI with my data?"],
             InputDelayMs = 3000,
             ExpectedOutputDescription = ["The output should show a workflow using Foundry Toolbox MCP tools to search Microsoft Learn documentation and web search to provide a summary of results."],
@@ -484,8 +474,8 @@ internal static class WorkflowSamples
         {
             Name = "Workflow_Declarative_InvokeMcpTool",
             ProjectPath = "samples/03-workflows/Declarative/InvokeMcpTool",
-            RequiredEnvironmentVariables = ["AZURE_AI_PROJECT_ENDPOINT"],
-            OptionalEnvironmentVariables = ["AZURE_AI_MODEL_DEPLOYMENT_NAME"],
+            RequiredEnvironmentVariables = ["FOUNDRY_PROJECT_ENDPOINT"],
+            OptionalEnvironmentVariables = ["FOUNDRY_MODEL"],
             Inputs = ["Search for .NET tutorials on Microsoft Learn"],
             InputDelayMs = 3000,
             ExpectedOutputDescription = ["The output should show a workflow using MCP tools to search Microsoft Learn documentation and provide a summary of results."],
@@ -495,8 +485,8 @@ internal static class WorkflowSamples
         {
             Name = "Workflow_Declarative_Marketing",
             ProjectPath = "samples/03-workflows/Declarative/Marketing",
-            RequiredEnvironmentVariables = ["AZURE_AI_PROJECT_ENDPOINT"],
-            OptionalEnvironmentVariables = ["AZURE_AI_MODEL_DEPLOYMENT_NAME"],
+            RequiredEnvironmentVariables = ["FOUNDRY_PROJECT_ENDPOINT"],
+            OptionalEnvironmentVariables = ["FOUNDRY_MODEL"],
             Inputs = ["A smart water bottle that tracks hydration"],
             InputDelayMs = 3000,
             ExpectedOutputDescription = ["The output should show a marketing workflow generating content about a smart water bottle product."],
@@ -506,8 +496,8 @@ internal static class WorkflowSamples
         {
             Name = "Workflow_Declarative_StudentTeacher",
             ProjectPath = "samples/03-workflows/Declarative/StudentTeacher",
-            RequiredEnvironmentVariables = ["AZURE_AI_PROJECT_ENDPOINT"],
-            OptionalEnvironmentVariables = ["AZURE_AI_MODEL_DEPLOYMENT_NAME"],
+            RequiredEnvironmentVariables = ["FOUNDRY_PROJECT_ENDPOINT"],
+            OptionalEnvironmentVariables = ["FOUNDRY_MODEL"],
             Inputs = ["What is 18 + 27?"],
             InputDelayMs = 3000,
             ExpectedOutputDescription = ["The output should show a student-teacher workflow where a student asks a math question and a teacher provides the answer."],
@@ -517,8 +507,8 @@ internal static class WorkflowSamples
         {
             Name = "Workflow_Declarative_ToolApproval",
             ProjectPath = "samples/03-workflows/Declarative/ToolApproval",
-            RequiredEnvironmentVariables = ["AZURE_AI_PROJECT_ENDPOINT"],
-            OptionalEnvironmentVariables = ["AZURE_AI_MODEL_DEPLOYMENT_NAME"],
+            RequiredEnvironmentVariables = ["FOUNDRY_PROJECT_ENDPOINT"],
+            OptionalEnvironmentVariables = ["FOUNDRY_MODEL"],
             Inputs = ["Search for .NET tutorials", "EXIT"],
             InputDelayMs = 8000,
             ExpectedOutputDescription = ["The output should show a workflow using an MCP tool with approval to search Microsoft Learn, followed by an exit from the input loop."],

dotnet/eng/verify-samples/verify-samples.csproj

@@ -12,13 +12,12 @@
   </PropertyGroup>
 
   <ItemGroup>
-    <PackageReference Include="Azure.AI.OpenAI" />
+    <PackageReference Include="Azure.AI.Projects" />
     <PackageReference Include="Azure.Identity" />
-    <PackageReference Include="Microsoft.Extensions.AI.OpenAI" />
   </ItemGroup>
 
   <ItemGroup>
-    <ProjectReference Include="..\..\src\Microsoft.Agents.AI.OpenAI\Microsoft.Agents.AI.OpenAI.csproj" />
+    <ProjectReference Include="..\..\src\Microsoft.Agents.AI.Foundry\Microsoft.Agents.AI.Foundry.csproj" />
   </ItemGroup>
 
 </Project>

dotnet/global.json

@@ -1,6 +1,6 @@
 {
     "sdk": {
-        "version": "10.0.200",
+        "version": "10.0.301",
         "rollForward": "minor",
         "allowPrerelease": false
     },

dotnet/nuget/nuget-package.props

@@ -1,14 +1,14 @@
 <Project>
   <PropertyGroup>
     <!-- Central version prefix - applies to all nuget packages. -->
-    <VersionPrefix>1.11.0</VersionPrefix>
+    <VersionPrefix>1.11.1</VersionPrefix>
     <RCNumber>1</RCNumber>
-    <DateSuffix>260622</DateSuffix>
+    <DateSuffix>260625</DateSuffix>
     <PackageVersion Condition="'$(IsReleaseCandidate)' == 'true'">$(VersionPrefix)-rc$(RCNumber)</PackageVersion>
     <PackageVersion Condition="'$(IsReleaseCandidate)' != 'true' AND '$(VersionSuffix)' != ''">$(VersionPrefix)-$(VersionSuffix).$(DateSuffix).1</PackageVersion>
     <PackageVersion Condition="'$(IsReleaseCandidate)' != 'true' AND '$(VersionSuffix)' == ''">$(VersionPrefix)-preview.$(DateSuffix).1</PackageVersion>
     <PackageVersion Condition="'$(IsReleased)' == 'true'">$(VersionPrefix)</PackageVersion>
-    <GitTag>1.11.0</GitTag>
+    <GitTag>1.11.1</GitTag>
 
     <Configurations>Debug;Release;Publish</Configurations>
     <IsPackable>true</IsPackable>

dotnet/samples/01-get-started/04_memory/Program.cs

@@ -25,7 +25,7 @@ var projectClient = new AIProjectClient(new Uri(endpoint), new DefaultAzureCrede
 // Get the underlying IChatClient to use for the memory component.
 // The memory provider needs direct IChatClient access for structured extraction.
 IChatClient chatClient = projectClient
-    .AsAIAgent(new ChatClientAgentOptions { ChatOptions = new() { ModelId = model } })
+    .AsAIAgent(model: model, instructions: "You are a friendly assistant.Always address the user by their name.")
     .GetService<IChatClient>()
     ?? throw new InvalidOperationException("Could not retrieve IChatClient from AIProjectClient agent.");
 
@@ -38,7 +38,6 @@ IChatClient chatClient = projectClient
 // and its storage to that user id.
 AIAgent agent = chatClient.AsAIAgent(new ChatClientAgentOptions()
 {
-    ChatOptions = new() { Instructions = "You are a friendly assistant. Always address the user by their name." },
     AIContextProviders = [new UserInfoMemory(chatClient)]
 });
 

dotnet/samples/02-agents/AgentOpenTelemetry/AgentOpenTelemetry.csproj

@@ -1,4 +1,4 @@
-<Project Sdk="Microsoft.NET.Sdk">
+<Project Sdk="Microsoft.NET.Sdk">
 
   <PropertyGroup>
     <OutputType>Exe</OutputType>
@@ -9,13 +9,10 @@
   </PropertyGroup>
 
   <ItemGroup>
-    <PackageReference Include="Azure.AI.OpenAI" />
     <PackageReference Include="Azure.Identity" />
     <PackageReference Include="Azure.Monitor.OpenTelemetry.Exporter" />
-    <PackageReference Include="Microsoft.Extensions.AI.OpenAI" />
     <PackageReference Include="Microsoft.Extensions.Logging" />
     <PackageReference Include="Microsoft.Extensions.Logging.Console" />
-    <PackageReference Include="OpenAI" />
     <PackageReference Include="OpenTelemetry" />
     <PackageReference Include="OpenTelemetry.Exporter.Console" />
     <PackageReference Include="OpenTelemetry.Exporter.OpenTelemetryProtocol" />
@@ -25,7 +22,7 @@
   </ItemGroup>
 
   <ItemGroup>
-    <ProjectReference Include="..\..\..\src\Microsoft.Agents.AI.OpenAI\Microsoft.Agents.AI.OpenAI.csproj" />
+    <ProjectReference Include="..\..\..\src\Microsoft.Agents.AI.Foundry\Microsoft.Agents.AI.Foundry.csproj" />
     <ProjectReference Include="..\..\..\src\Microsoft.Agents.AI\Microsoft.Agents.AI.csproj" />
   </ItemGroup>
 

dotnet/samples/02-agents/AgentOpenTelemetry/Program.cs

@@ -3,7 +3,7 @@
 using System.ComponentModel;
 using System.Diagnostics;
 using System.Diagnostics.Metrics;
-using Azure.AI.OpenAI;
+using Azure.AI.Projects;
 using Azure.Identity;
 using Azure.Monitor.OpenTelemetry.Exporter;
 using Microsoft.Agents.AI;
@@ -96,8 +96,8 @@ Console.WriteLine("""
     Type your message and press Enter. Type 'exit' or empty message to quit.
     """);
 
-var endpoint = Environment.GetEnvironmentVariable("AZURE_OPENAI_ENDPOINT") ?? throw new InvalidOperationException("AZURE_OPENAI_ENDPOINT environment variable is not set.");
-var deploymentName = Environment.GetEnvironmentVariable("AZURE_OPENAI_DEPLOYMENT_NAME") ?? "gpt-5.4-mini";
+var endpoint = Environment.GetEnvironmentVariable("FOUNDRY_PROJECT_ENDPOINT") ?? throw new InvalidOperationException("FOUNDRY_PROJECT_ENDPOINT environment variable is not set.");
+var deploymentName = Environment.GetEnvironmentVariable("FOUNDRY_MODEL") ?? "gpt-5.4-mini";
 
 // Log application startup
 appLogger.LogInformation("OpenTelemetry Aspire Demo application started");
@@ -112,20 +112,19 @@ static async Task<string> GetWeatherAsync([Description("The location to get the
 // WARNING: DefaultAzureCredential is convenient for development but requires careful consideration in production.
 // In production, consider using a specific credential (e.g., ManagedIdentityCredential) to avoid
 // latency issues, unintended credential probing, and potential security risks from fallback mechanisms.
-using var instrumentedChatClient = new AzureOpenAIClient(new Uri(endpoint), new DefaultAzureCredential())
-    .GetChatClient(deploymentName)
-        .AsIChatClient() // Converts a native OpenAI SDK ChatClient into a Microsoft.Extensions.AI.IChatClient
-        .AsBuilder()
-        .UseFunctionInvocation()
-        .UseOpenTelemetry(sourceName: SourceName, configure: (cfg) => cfg.EnableSensitiveData = true) // enable telemetry at the chat client level
-        .Build();
-
 appLogger.LogInformation("Creating Agent with OpenTelemetry instrumentation");
 // Create the agent with the instrumented chat client
-var agent = new ChatClientAgent(instrumentedChatClient,
-    name: "OpenTelemetryDemoAgent",
-    instructions: "You are a helpful assistant that provides concise and informative responses.",
-    tools: [AIFunctionFactory.Create(GetWeatherAsync)])
+var agent = new AIProjectClient(new Uri(endpoint), new DefaultAzureCredential())
+    .AsAIAgent(
+        model: deploymentName,
+        instructions: "You are a helpful assistant that provides concise and informative responses.",
+        name: "OpenTelemetryDemoAgent",
+        tools: [AIFunctionFactory.Create(GetWeatherAsync)],
+        clientFactory: client => client
+            .AsBuilder()
+            .UseFunctionInvocation()
+            .UseOpenTelemetry(sourceName: SourceName, configure: (cfg) => cfg.EnableSensitiveData = true) // enable telemetry at the chat client level
+            .Build())
     .AsBuilder()
     .UseOpenTelemetry(sourceName: SourceName, configure: (cfg) => cfg.EnableSensitiveData = true) // enable telemetry at the agent level
     .Build();

dotnet/samples/02-agents/AgentOpenTelemetry/README.md

@@ -1,6 +1,6 @@
-# OpenTelemetry Aspire Demo with Azure OpenAI
+# OpenTelemetry Aspire Demo with Microsoft Foundry
 
-This demo showcases the integration of OpenTelemetry with the Microsoft Agent Framework using Azure OpenAI and .NET Aspire Dashboard for telemetry visualization.
+This demo showcases the integration of OpenTelemetry with the Microsoft Agent Framework using Microsoft Foundry and the .NET Aspire Dashboard for telemetry visualization.
 
 ## Overview
 
@@ -15,7 +15,7 @@ The demo consists of three main components:
 ```mermaid
 graph TD
     A["Console App<br/>(Interactive)"] --> B["Agent Framework<br/>with OpenTel<br/>Instrumentation"]
-    B --> C["Azure OpenAI<br/>Service"]
+    B --> C["Microsoft Foundry<br/>Project"]
     A --> D["Aspire Dashboard<br/>(OpenTelemetry Visualization)"]
     B --> D
 ```
@@ -23,21 +23,21 @@ graph TD
 ## Prerequisites
 
 - .NET 10 SDK or later
-- Azure OpenAI service endpoint and deployment configured
+- Microsoft Foundry project endpoint and model configured
 - Azure CLI installed and authenticated (for Azure credential authentication)
 - Docker installed (for running Aspire Dashboard)
 - [Optional] Application Insights and Grafana
 
 ## Configuration
 
-### Azure OpenAI Setup
+### Microsoft Foundry Setup
 Set the following environment variables:
 ```powershell
-$env:AZURE_OPENAI_ENDPOINT="https://your-resource.openai.azure.com/"
-$env:AZURE_OPENAI_DEPLOYMENT_NAME="gpt-5.4-mini"  # Optional, defaults to gpt-5.4-mini
+$env:FOUNDRY_PROJECT_ENDPOINT="https://<your-project>.services.ai.azure.com/api/projects/<your-project>"
+$env:FOUNDRY_MODEL="gpt-5.4-mini"  # Optional, defaults to gpt-5.4-mini
 ```
 
-**Note**: This demo uses Azure CLI credentials for authentication. Make sure you're logged in with `az login` and have access to the Azure OpenAI resource.
+**Note**: This demo uses Azure CLI credentials for authentication. Make sure you're logged in with `az login` and have access to the Foundry project.
 
 ### [Optional] Application Insights Setup
 Set the following environment variables:
@@ -56,7 +56,7 @@ The easiest way to run the demo is using the provided PowerShell script:
 ```
 
 This script will automatically:
-- ✅ Check prerequisites (Docker, Azure OpenAI configuration)
+- ✅ Check prerequisites (Docker, Foundry configuration)
 - 🔨 Build the console application
 - 🐳 Start the Aspire Dashboard via Docker (with anonymous access)
 - ⏳ Wait for dashboard to be ready (polls port until listening)
@@ -124,7 +124,7 @@ You:
 3. Each trace contains:
    - An outer span for the entire agent interaction
    - Inner spans from the Agent Framework's OpenTelemetry instrumentation
-   - Spans from HTTP calls to Azure OpenAI
+   - Spans from HTTP calls to Microsoft Foundry
 
 ### Metrics
 1. Navigate to the **Metrics** tab
@@ -158,7 +158,7 @@ Open dashboard in Azure portal: <https://aka.ms/amg/dash/af-workflow>
 - **Telemetry correlation** across the entire request flow
 
 ### Agent Framework Features
-- **ChatClientAgent** with Azure OpenAI integration
+- **ChatClientAgent** created from `AIProjectClient`
 - **OpenTelemetry wrapper** using `.WithOpenTelemetry()`
 - **Conversation threading** for multi-turn conversations
 - **Error handling** with telemetry correlation
@@ -182,7 +182,7 @@ Complete demo startup script that handles everything automatically.
 ```
 
 **Features:**
-- **Automatic configuration detection** - Checks for Azure OpenAI configuration
+- **Automatic configuration detection** - Checks for Foundry configuration
 - **Project building** - Automatically builds projects before running
 - **Error handling** - Provides clear error messages if something goes wrong
 - **Multi-window support** - Opens dashboard in separate window for better experience
@@ -201,10 +201,10 @@ If you encounter port binding errors, try:
 2. Or kill any processes using the conflicting ports
 
 ### Authentication Issues
-- Ensure your Azure OpenAI endpoint is correctly configured
+- Ensure your Foundry project endpoint is correctly configured
 - Check that the environment variables are set in the correct terminal session
-- Verify you're logged in with Azure CLI (`az login`) and have access to the Azure OpenAI resource
-- Ensure the Azure OpenAI deployment name matches your actual deployment
+- Verify you're logged in with Azure CLI (`az login`) and have access to the Foundry project
+- Ensure the `FOUNDRY_MODEL` value matches an enabled model in your Foundry project
 
 ### Build Issues
 - Ensure you're using .NET 10.0 SDK
@@ -216,7 +216,7 @@ If you encounter port binding errors, try:
 ```
 AgentOpenTelemetry/
 ├── AgentOpenTelemetry.csproj             # Project file with dependencies
-├── Program.cs                            # Main application with Azure OpenAI agent integration
+├── Program.cs                            # Main application with Foundry AIProjectClient agent integration
 ├── start-demo.ps1                        # PowerShell script to start the demo
 └── README.md                             # This file
 ```

dotnet/samples/02-agents/AgentProviders/azure/Agent_With_AzureOpenAIResponses/Program.cs

@@ -14,6 +14,7 @@ var deploymentName = Environment.GetEnvironmentVariable("AZURE_OPENAI_DEPLOYMENT
 // WARNING: DefaultAzureCredential is convenient for development but requires careful consideration in production.
 // In production, consider using a specific credential (e.g., ManagedIdentityCredential) to avoid
 // latency issues, unintended credential probing, and potential security risks from fallback mechanisms.
+// You must dissable client side conversation storage for clients that support it
 AIAgent agent = new AzureOpenAIClient(
     new Uri(endpoint),
     new DefaultAzureCredential())

dotnet/samples/02-agents/AgentProviders/foundry/Agent_Step09_UsingMcpClientAsTools/Program.cs

@@ -33,7 +33,7 @@ List<AITool> agentTools = [.. mcpTools.Cast<AITool>()];
 AIProjectClient aiProjectClient = new(new Uri(endpoint), new DefaultAzureCredential());
 
 AIAgent agent = aiProjectClient.AsAIAgent(deploymentName,
-    instructions: "You are a helpful assistant that can help with Microsoft documentation questions. Use the Microsoft Learn MCP tool to search for documentation.",
+    instructions: "You are a helpful assistant that can help with Microsoft documentation questions. Use the Microsoft Learn MCP tool to search for documentation. In the output, indicate which tool you used if any.",
     name: "DocsAgent",
     tools: agentTools);
 

dotnet/samples/02-agents/AgentProviders/foundry/Agent_Step14_CodeInterpreter/Program.cs

@@ -8,6 +8,7 @@ using Azure.Identity;
 using Microsoft.Agents.AI;
 using Microsoft.Extensions.AI;
 using OpenAI.Assistants;
+using OpenAI.Responses;
 
 const string AgentInstructions = "You are a personal math tutor. When asked a math question, write and run code using the python tool to answer the question.";
 const string AgentName = "CoderAgent-RAPI";
@@ -19,11 +20,41 @@ string deploymentName = Environment.GetEnvironmentVariable("FOUNDRY_MODEL") ?? "
 // In production, consider using a specific credential (e.g., ManagedIdentityCredential) to avoid
 // latency issues, unintended credential probing, and potential security risks from fallback mechanisms.
 AIProjectClient aiProjectClient = new(new Uri(endpoint), new DefaultAzureCredential());
+
+// The easiest way to add the hosted code interpreter is as follows:
+/*
 AIAgent agent = aiProjectClient.AsAIAgent(
     deploymentName,
     instructions: AgentInstructions,
     name: AgentName,
     tools: [new HostedCodeInterpreterTool() { Inputs = [] }]);
+*/
+
+// However, by default the reponses API does not return the output items from the hosted code interpreter tool.
+// This is generally fine but for this sample we want to explicitly request those in the response generation configuration.
+AIAgent agent = aiProjectClient
+    .GetProjectOpenAIClient()
+    .GetProjectResponsesClient()
+    .AsIChatClient(deploymentName)
+    .AsBuilder()
+    .ConfigureOptions(x =>
+    {
+        var previousFactory = x.RawRepresentationFactory;
+        x.RawRepresentationFactory = state =>
+        {
+            var responseOptions = previousFactory?.Invoke(state) as CreateResponseOptions ?? new CreateResponseOptions();
+
+            // Ensure that the response includes tool output items from the hosted code interpreter
+            responseOptions.IncludedProperties.Add(IncludedResponseProperty.CodeInterpreterCallOutputs);
+
+            return responseOptions;
+        };
+    })
+    .Build()
+    .AsAIAgent(
+    instructions: AgentInstructions,
+    name: AgentName,
+    tools: [new HostedCodeInterpreterTool() { Inputs = [] }]);
 
 AgentResponse response = await agent.RunAsync("I need to solve the equation sin(x) + x^2 = 42");
 

dotnet/samples/02-agents/AgentSkills/Agent_Step01_FileBasedSkills/Agent_Step01_FileBasedSkills.csproj

@@ -1,4 +1,4 @@
-<Project Sdk="Microsoft.NET.Sdk">
+<Project Sdk="Microsoft.NET.Sdk">
 
   <PropertyGroup>
     <OutputType>Exe</OutputType>
@@ -9,17 +9,13 @@
     <NoWarn>$(NoWarn);MAAI001</NoWarn>
   </PropertyGroup>
 
-  <ItemGroup>
-    <PackageReference Include="Azure.AI.OpenAI" />
-    <PackageReference Include="Azure.Identity" />
-  </ItemGroup>
-
   <ItemGroup>
     <Compile Include="..\SubprocessScriptRunner.cs" Link="SubprocessScriptRunner.cs" />
   </ItemGroup>
 
   <ItemGroup>
-    <ProjectReference Include="..\..\..\..\src\Microsoft.Agents.AI.OpenAI\Microsoft.Agents.AI.OpenAI.csproj" />
+    <PackageReference Include="Azure.Identity" />
+    <ProjectReference Include="..\..\..\..\src\Microsoft.Agents.AI.Foundry\Microsoft.Agents.AI.Foundry.csproj" />
   </ItemGroup>
 
   <!-- Copy skills directory to output -->

dotnet/samples/02-agents/AgentSkills/Agent_Step01_FileBasedSkills/Program.cs

@@ -9,14 +9,13 @@
 //
 // This sample uses a unit-converter skill that converts between miles, kilometers, pounds, and kilograms.
 
-using Azure.AI.OpenAI;
+using Azure.AI.Projects;
 using Azure.Identity;
 using Microsoft.Agents.AI;
-using OpenAI.Responses;
 
 // --- Configuration ---
-string endpoint = Environment.GetEnvironmentVariable("AZURE_OPENAI_ENDPOINT") ?? throw new InvalidOperationException("AZURE_OPENAI_ENDPOINT is not set.");
-string deploymentName = Environment.GetEnvironmentVariable("AZURE_OPENAI_DEPLOYMENT_NAME") ?? "gpt-5.4-mini";
+string endpoint = Environment.GetEnvironmentVariable("FOUNDRY_PROJECT_ENDPOINT") ?? throw new InvalidOperationException("FOUNDRY_PROJECT_ENDPOINT is not set.");
+string deploymentName = Environment.GetEnvironmentVariable("FOUNDRY_MODEL") ?? "gpt-5.4-mini";
 
 // --- Skills Provider ---
 // Discovers skills from the 'skills' directory containing SKILL.md files.
@@ -29,18 +28,17 @@ var skillsProvider = new AgentSkillsProvider(
 // WARNING: DefaultAzureCredential is convenient for development but requires careful consideration in production.
 // In production, consider using a specific credential (e.g., ManagedIdentityCredential) to avoid
 // latency issues, unintended credential probing, and potential security risks from fallback mechanisms.
-AIAgent agent = new AzureOpenAIClient(new Uri(endpoint), new DefaultAzureCredential())
-    .GetResponsesClient()
+AIAgent agent = new AIProjectClient(new Uri(endpoint), new DefaultAzureCredential())
     .AsAIAgent(new ChatClientAgentOptions
     {
         Name = "UnitConverterAgent",
         ChatOptions = new()
         {
+            ModelId = deploymentName,
             Instructions = "You are a helpful assistant that can convert units.",
         },
         AIContextProviders = [skillsProvider],
-    },
-    model: deploymentName);
+    });
 
 // --- Example: Unit conversion ---
 Console.WriteLine("Converting units with file-based skills");

dotnet/samples/02-agents/AgentSkills/Agent_Step02_CodeDefinedSkills/Agent_Step02_CodeDefinedSkills.csproj

@@ -1,4 +1,4 @@
-<Project Sdk="Microsoft.NET.Sdk">
+<Project Sdk="Microsoft.NET.Sdk">
 
   <PropertyGroup>
     <OutputType>Exe</OutputType>
@@ -10,12 +10,8 @@
   </PropertyGroup>
 
   <ItemGroup>
-    <PackageReference Include="Azure.AI.OpenAI" />
     <PackageReference Include="Azure.Identity" />
-  </ItemGroup>
-
-  <ItemGroup>
-    <ProjectReference Include="..\..\..\..\src\Microsoft.Agents.AI.OpenAI\Microsoft.Agents.AI.OpenAI.csproj" />
+    <ProjectReference Include="..\..\..\..\src\Microsoft.Agents.AI.Foundry\Microsoft.Agents.AI.Foundry.csproj" />
   </ItemGroup>
 
 </Project>

dotnet/samples/02-agents/AgentSkills/Agent_Step02_CodeDefinedSkills/Program.cs

@@ -9,14 +9,13 @@
 // 3. Code scripts — executable delegates the agent can invoke directly
 
 using System.Text.Json;
-using Azure.AI.OpenAI;
+using Azure.AI.Projects;
 using Azure.Identity;
 using Microsoft.Agents.AI;
-using OpenAI.Responses;
 
 // --- Configuration ---
-string endpoint = Environment.GetEnvironmentVariable("AZURE_OPENAI_ENDPOINT") ?? throw new InvalidOperationException("AZURE_OPENAI_ENDPOINT is not set.");
-string deploymentName = Environment.GetEnvironmentVariable("AZURE_OPENAI_DEPLOYMENT_NAME") ?? "gpt-5.4-mini";
+string endpoint = Environment.GetEnvironmentVariable("FOUNDRY_PROJECT_ENDPOINT") ?? throw new InvalidOperationException("FOUNDRY_PROJECT_ENDPOINT is not set.");
+string deploymentName = Environment.GetEnvironmentVariable("FOUNDRY_MODEL") ?? "gpt-5.4-mini";
 
 // --- Build the code-defined skill ---
 var unitConverterSkill = new AgentInlineSkill(
@@ -70,18 +69,17 @@ var skillsProvider = new AgentSkillsProvider(unitConverterSkill);
 // WARNING: DefaultAzureCredential is convenient for development but requires careful consideration in production.
 // In production, consider using a specific credential (e.g., ManagedIdentityCredential) to avoid
 // latency issues, unintended credential probing, and potential security risks from fallback mechanisms.
-AIAgent agent = new AzureOpenAIClient(new Uri(endpoint), new DefaultAzureCredential())
-    .GetResponsesClient()
+AIAgent agent = new AIProjectClient(new Uri(endpoint), new DefaultAzureCredential())
     .AsAIAgent(new ChatClientAgentOptions
     {
         Name = "UnitConverterAgent",
         ChatOptions = new()
         {
+            ModelId = deploymentName,
             Instructions = "You are a helpful assistant that can convert units.",
         },
         AIContextProviders = [skillsProvider],
-    },
-    model: deploymentName);
+    });
 
 // --- Example: Unit conversion ---
 Console.WriteLine("Converting units with code-defined skills");

dotnet/samples/02-agents/AgentSkills/Agent_Step03_ClassBasedSkills/Agent_Step03_ClassBasedSkills.csproj

@@ -1,4 +1,4 @@
-<Project Sdk="Microsoft.NET.Sdk">
+<Project Sdk="Microsoft.NET.Sdk">
 
   <PropertyGroup>
     <OutputType>Exe</OutputType>
@@ -10,12 +10,8 @@
   </PropertyGroup>
 
   <ItemGroup>
-    <PackageReference Include="Azure.AI.OpenAI" />
     <PackageReference Include="Azure.Identity" />
-  </ItemGroup>
-
-  <ItemGroup>
-    <ProjectReference Include="..\..\..\..\src\Microsoft.Agents.AI.OpenAI\Microsoft.Agents.AI.OpenAI.csproj" />
+    <ProjectReference Include="..\..\..\..\src\Microsoft.Agents.AI.Foundry\Microsoft.Agents.AI.Foundry.csproj" />
   </ItemGroup>
 
 </Project>

dotnet/samples/02-agents/AgentSkills/Agent_Step03_ClassBasedSkills/Program.cs

@@ -5,14 +5,13 @@
 
 using System.ComponentModel;
 using System.Text.Json;
-using Azure.AI.OpenAI;
+using Azure.AI.Projects;
 using Azure.Identity;
 using Microsoft.Agents.AI;
-using OpenAI.Responses;
 
 // --- Configuration ---
-string endpoint = Environment.GetEnvironmentVariable("AZURE_OPENAI_ENDPOINT") ?? throw new InvalidOperationException("AZURE_OPENAI_ENDPOINT is not set.");
-string deploymentName = Environment.GetEnvironmentVariable("AZURE_OPENAI_DEPLOYMENT_NAME") ?? "gpt-5.4-mini";
+string endpoint = Environment.GetEnvironmentVariable("FOUNDRY_PROJECT_ENDPOINT") ?? throw new InvalidOperationException("FOUNDRY_PROJECT_ENDPOINT is not set.");
+string deploymentName = Environment.GetEnvironmentVariable("FOUNDRY_MODEL") ?? "gpt-5.4-mini";
 
 // --- Class-Based Skill ---
 // Instantiate the skill class.
@@ -25,18 +24,17 @@ var skillsProvider = new AgentSkillsProvider(unitConverter);
 // WARNING: DefaultAzureCredential is convenient for development but requires careful consideration in production.
 // In production, consider using a specific credential (e.g., ManagedIdentityCredential) to avoid
 // latency issues, unintended credential probing, and potential security risks from fallback mechanisms.
-AIAgent agent = new AzureOpenAIClient(new Uri(endpoint), new DefaultAzureCredential())
-    .GetResponsesClient()
+AIAgent agent = new AIProjectClient(new Uri(endpoint), new DefaultAzureCredential())
     .AsAIAgent(new ChatClientAgentOptions
     {
         Name = "UnitConverterAgent",
         ChatOptions = new()
         {
+            ModelId = deploymentName,
             Instructions = "You are a helpful assistant that can convert units.",
         },
         AIContextProviders = [skillsProvider],
-    },
-    model: deploymentName);
+    });
 
 // --- Example: Unit conversion ---
 Console.WriteLine("Converting units with class-based skills");

dotnet/samples/02-agents/AgentSkills/Agent_Step04_MixedSkills/Agent_Step04_MixedSkills.csproj

@@ -1,4 +1,4 @@
-<Project Sdk="Microsoft.NET.Sdk">
+<Project Sdk="Microsoft.NET.Sdk">
 
   <PropertyGroup>
     <OutputType>Exe</OutputType>
@@ -9,17 +9,13 @@
     <NoWarn>$(NoWarn);MAAI001;IDE0051</NoWarn>
   </PropertyGroup>
 
-  <ItemGroup>
-    <PackageReference Include="Azure.AI.OpenAI" />
-    <PackageReference Include="Azure.Identity" />
-  </ItemGroup>
-
   <ItemGroup>
     <Compile Include="..\SubprocessScriptRunner.cs" Link="SubprocessScriptRunner.cs" />
   </ItemGroup>
 
   <ItemGroup>
-    <ProjectReference Include="..\..\..\..\src\Microsoft.Agents.AI.OpenAI\Microsoft.Agents.AI.OpenAI.csproj" />
+    <PackageReference Include="Azure.Identity" />
+    <ProjectReference Include="..\..\..\..\src\Microsoft.Agents.AI.Foundry\Microsoft.Agents.AI.Foundry.csproj" />
   </ItemGroup>
 
   <!-- Copy skills directory to output -->

dotnet/samples/02-agents/AgentSkills/Agent_Step04_MixedSkills/Program.cs

@@ -15,15 +15,14 @@
 
 using System.ComponentModel;
 using System.Text.Json;
-using Azure.AI.OpenAI;
+using Azure.AI.Projects;
 using Azure.Identity;
 using Microsoft.Agents.AI;
-using OpenAI.Responses;
 
 // --- Configuration ---
-string endpoint = Environment.GetEnvironmentVariable("AZURE_OPENAI_ENDPOINT")
-    ?? throw new InvalidOperationException("AZURE_OPENAI_ENDPOINT is not set.");
-string deploymentName = Environment.GetEnvironmentVariable("AZURE_OPENAI_DEPLOYMENT_NAME") ?? "gpt-5.4-mini";
+string endpoint = Environment.GetEnvironmentVariable("FOUNDRY_PROJECT_ENDPOINT")
+    ?? throw new InvalidOperationException("FOUNDRY_PROJECT_ENDPOINT is not set.");
+string deploymentName = Environment.GetEnvironmentVariable("FOUNDRY_MODEL") ?? "gpt-5.4-mini";
 
 // --- 1. Code-Defined Skill: volume-converter ---
 var volumeConverterSkill = new AgentInlineSkill(
@@ -67,18 +66,17 @@ var skillsProvider = new AgentSkillsProviderBuilder()
 // WARNING: DefaultAzureCredential is convenient for development but requires careful consideration in production.
 // In production, consider using a specific credential (e.g., ManagedIdentityCredential) to avoid
 // latency issues, unintended credential probing, and potential security risks from fallback mechanisms.
-AIAgent agent = new AzureOpenAIClient(new Uri(endpoint), new DefaultAzureCredential())
-    .GetResponsesClient()
+AIAgent agent = new AIProjectClient(new Uri(endpoint), new DefaultAzureCredential())
     .AsAIAgent(new ChatClientAgentOptions
     {
         Name = "MultiConverterAgent",
         ChatOptions = new()
         {
+            ModelId = deploymentName,
             Instructions = "You are a helpful assistant that can convert units, volumes, and temperatures.",
         },
         AIContextProviders = [skillsProvider],
-    },
-    model: deploymentName);
+    });
 
 // --- Example: Use all three skills ---
 Console.WriteLine("Converting with mixed skills (file + code + class)");

dotnet/samples/02-agents/AgentSkills/Agent_Step05_SkillsWithDI/Agent_Step05_SkillsWithDI.csproj

@@ -1,4 +1,4 @@
-<Project Sdk="Microsoft.NET.Sdk">
+<Project Sdk="Microsoft.NET.Sdk">
 
   <PropertyGroup>
     <OutputType>Exe</OutputType>
@@ -10,13 +10,12 @@
   </PropertyGroup>
 
   <ItemGroup>
-    <PackageReference Include="Azure.AI.OpenAI" />
     <PackageReference Include="Azure.Identity" />
     <PackageReference Include="Microsoft.Extensions.DependencyInjection" />
   </ItemGroup>
 
   <ItemGroup>
-    <ProjectReference Include="..\..\..\..\src\Microsoft.Agents.AI.OpenAI\Microsoft.Agents.AI.OpenAI.csproj" />
+    <ProjectReference Include="..\..\..\..\src\Microsoft.Agents.AI.Foundry\Microsoft.Agents.AI.Foundry.csproj" />
   </ItemGroup>
 
 </Project>

dotnet/samples/02-agents/AgentSkills/Agent_Step05_SkillsWithDI/Program.cs

@@ -15,15 +15,14 @@
 
 using System.ComponentModel;
 using System.Text.Json;
-using Azure.AI.OpenAI;
+using Azure.AI.Projects;
 using Azure.Identity;
 using Microsoft.Agents.AI;
 using Microsoft.Extensions.DependencyInjection;
-using OpenAI.Responses;
 
 // --- Configuration ---
-string endpoint = Environment.GetEnvironmentVariable("AZURE_OPENAI_ENDPOINT") ?? throw new InvalidOperationException("AZURE_OPENAI_ENDPOINT is not set.");
-string deploymentName = Environment.GetEnvironmentVariable("AZURE_OPENAI_DEPLOYMENT_NAME") ?? "gpt-5.4-mini";
+string endpoint = Environment.GetEnvironmentVariable("FOUNDRY_PROJECT_ENDPOINT") ?? throw new InvalidOperationException("FOUNDRY_PROJECT_ENDPOINT is not set.");
+string deploymentName = Environment.GetEnvironmentVariable("FOUNDRY_MODEL") ?? "gpt-5.4-mini";
 
 // --- DI Container ---
 // Register application services that skill resources and scripts can resolve at execution time.
@@ -83,19 +82,18 @@ var skillsProvider = new AgentSkillsProvider(distanceSkill, weightSkill);
 // WARNING: DefaultAzureCredential is convenient for development but requires careful consideration in production.
 // In production, consider using a specific credential (e.g., ManagedIdentityCredential) to avoid
 // latency issues, unintended credential probing, and potential security risks from fallback mechanisms.
-AIAgent agent = new AzureOpenAIClient(new Uri(endpoint), new DefaultAzureCredential())
-    .GetResponsesClient()
+AIAgent agent = new AIProjectClient(new Uri(endpoint), new DefaultAzureCredential())
     .AsAIAgent(
         options: new ChatClientAgentOptions
         {
             Name = "UnitConverterAgent",
             ChatOptions = new()
             {
+                ModelId = deploymentName,
                 Instructions = "You are a helpful assistant that can convert units.",
             },
             AIContextProviders = [skillsProvider],
         },
-        model: deploymentName,
         services: serviceProvider);
 
 // --- Example: Unit conversion ---

dotnet/samples/02-agents/AgentSkills/Agent_Step06_McpBasedSkills/Agent_Step06_McpBasedSkills.csproj

@@ -10,7 +10,6 @@
   </PropertyGroup>
 
   <ItemGroup>
-    <PackageReference Include="Azure.AI.OpenAI" />
     <PackageReference Include="Azure.Identity" />
     <PackageReference Include="Microsoft.Extensions.Hosting" />
     <PackageReference Include="ModelContextProtocol" />
@@ -18,7 +17,7 @@
 
   <ItemGroup>
     <ProjectReference Include="..\..\..\..\src\Microsoft.Agents.AI.Mcp\Microsoft.Agents.AI.Mcp.csproj" />
-    <ProjectReference Include="..\..\..\..\src\Microsoft.Agents.AI.OpenAI\Microsoft.Agents.AI.OpenAI.csproj" />
+    <ProjectReference Include="..\..\..\..\src\Microsoft.Agents.AI.Foundry\Microsoft.Agents.AI.Foundry.csproj" />
   </ItemGroup>
 
 </Project>

dotnet/samples/02-agents/AgentSkills/Agent_Step06_McpBasedSkills/Program.cs

@@ -12,7 +12,7 @@
 // to discover and inject the skill into a ChatClientAgent.
 
 using System.ComponentModel;
-using Azure.AI.OpenAI;
+using Azure.AI.Projects;
 using Azure.Identity;
 using Microsoft.Agents.AI;
 using Microsoft.Extensions.DependencyInjection;
@@ -20,7 +20,6 @@ using Microsoft.Extensions.Hosting;
 using Microsoft.Extensions.Logging;
 using ModelContextProtocol.Client;
 using ModelContextProtocol.Server;
-using OpenAI.Responses;
 
 if (args.Length > 0 && args[0] == "--server")
 {
@@ -29,9 +28,9 @@ if (args.Length > 0 && args[0] == "--server")
 }
 
 // --- Configuration ---
-string openAiEndpoint = Environment.GetEnvironmentVariable("AZURE_OPENAI_ENDPOINT")
-    ?? throw new InvalidOperationException("AZURE_OPENAI_ENDPOINT is not set.");
-string deploymentName = Environment.GetEnvironmentVariable("AZURE_OPENAI_DEPLOYMENT_NAME") ?? "gpt-5.4-mini";
+string openAiEndpoint = Environment.GetEnvironmentVariable("FOUNDRY_PROJECT_ENDPOINT")
+    ?? throw new InvalidOperationException("FOUNDRY_PROJECT_ENDPOINT is not set.");
+string deploymentName = Environment.GetEnvironmentVariable("FOUNDRY_MODEL") ?? "gpt-5.4-mini";
 
 // --- MCP client + skill discovery ---
 // Launch this same assembly as a stdio MCP server in a child process.
@@ -54,18 +53,17 @@ var skillsProvider = new AgentSkillsProviderBuilder()
 // WARNING: DefaultAzureCredential is convenient for development but requires careful consideration in production.
 // In production, consider using a specific credential (e.g., ManagedIdentityCredential) to avoid
 // latency issues, unintended credential probing, and potential security risks from fallback mechanisms.
-AIAgent agent = new AzureOpenAIClient(new Uri(openAiEndpoint), new DefaultAzureCredential())
-    .GetResponsesClient()
+AIAgent agent = new AIProjectClient(new Uri(openAiEndpoint), new DefaultAzureCredential())
     .AsAIAgent(new ChatClientAgentOptions
     {
         Name = "SkillsAgent",
         ChatOptions = new()
         {
+            ModelId = deploymentName,
             Instructions = "You are a helpful assistant. Use available skills to answer the user.",
         },
         AIContextProviders = [skillsProvider],
-    },
-    model: deploymentName);
+    });
 
 // --- Run ---
 Console.WriteLine(new string('-', 60));

dotnet/samples/02-agents/AgentSkills/Agent_Step07_SkillsAutoApproval/Agent_Step07_SkillsAutoApproval.csproj

@@ -0,0 +1,32 @@
+<Project Sdk="Microsoft.NET.Sdk">
+
+  <PropertyGroup>
+    <OutputType>Exe</OutputType>
+    <TargetFrameworks>net10.0</TargetFrameworks>
+
+    <Nullable>enable</Nullable>
+    <ImplicitUsings>enable</ImplicitUsings>
+    <NoWarn>$(NoWarn);MAAI001</NoWarn>
+  </PropertyGroup>
+
+  <ItemGroup>
+    <PackageReference Include="Azure.AI.OpenAI" />
+    <PackageReference Include="Azure.Identity" />
+  </ItemGroup>
+
+  <ItemGroup>
+    <Compile Include="..\SubprocessScriptRunner.cs" Link="SubprocessScriptRunner.cs" />
+  </ItemGroup>
+
+  <ItemGroup>
+    <ProjectReference Include="..\..\..\..\src\Microsoft.Agents.AI.OpenAI\Microsoft.Agents.AI.OpenAI.csproj" />
+  </ItemGroup>
+
+  <!-- Copy skills directory to output -->
+  <ItemGroup>
+    <None Include="skills\**\*.*">
+      <CopyToOutputDirectory>PreserveNewest</CopyToOutputDirectory>
+    </None>
+  </ItemGroup>
+
+</Project>

dotnet/samples/02-agents/AgentSkills/Agent_Step07_SkillsAutoApproval/Program.cs

@@ -0,0 +1,90 @@
+// Copyright (c) Microsoft. All rights reserved.
+
+// This sample demonstrates how to configure auto-approval rules for skill tools using the
+// UseToolApproval middleware. It builds on the file-based skills pattern from Step01, adding
+// ToolApprovalAgent middleware with auto-approval rules so that read-only skill operations
+// (load_skill, read_skill_resource) are approved automatically while script execution
+// (run_skill_script) still requires explicit user approval.
+//
+// All tools exposed by AgentSkillsProvider always require approval by default.
+// Auto-approval rules let you selectively bypass the approval prompt for safe operations.
+
+using Azure.AI.OpenAI;
+using Azure.Identity;
+using Microsoft.Agents.AI;
+using Microsoft.Extensions.AI;
+using OpenAI.Responses;
+
+// --- Configuration ---
+string endpoint = Environment.GetEnvironmentVariable("AZURE_OPENAI_ENDPOINT") ?? throw new InvalidOperationException("AZURE_OPENAI_ENDPOINT is not set.");
+string deploymentName = Environment.GetEnvironmentVariable("AZURE_OPENAI_DEPLOYMENT_NAME") ?? "gpt-5.4-mini";
+
+// --- Skills Provider ---
+// Discovers skills from the 'skills' directory containing SKILL.md files.
+// The script runner runs file-based scripts (e.g. Python) as local subprocesses.
+var skillsProvider = new AgentSkillsProvider(
+    Path.Combine(AppContext.BaseDirectory, "skills"),
+    SubprocessScriptRunner.RunAsync);
+
+// --- Agent Setup ---
+// WARNING: DefaultAzureCredential is convenient for development but requires careful consideration in production.
+// In production, consider using a specific credential (e.g., ManagedIdentityCredential) to avoid
+// latency issues, unintended credential probing, and potential security risks from fallback mechanisms.
+AIAgent agent = new AzureOpenAIClient(new Uri(endpoint), new DefaultAzureCredential())
+    .GetResponsesClient()
+    .AsAIAgent(new ChatClientAgentOptions
+    {
+        Name = "UnitConverterAgent",
+        ChatOptions = new()
+        {
+            Instructions = "You are a helpful assistant that can convert units.",
+        },
+        AIContextProviders = [skillsProvider],
+    },
+    model: deploymentName)
+    .AsBuilder()
+    .UseToolApproval(new ToolApprovalAgentOptions
+    {
+        // Auto-approve read-only skill tools (load_skill, read_skill_resource).
+        // run_skill_script will still require explicit user approval.
+        AutoApprovalRules = [AgentSkillsProvider.ReadOnlyToolsAutoApprovalRule],
+    })
+    .Build();
+
+// For other auto-approval options (all tools, custom lambdas, combining providers),
+// see the README.md in this sample directory.
+
+// --- Example: Unit conversion with auto-approval ---
+Console.WriteLine("Converting units with file-based skills and auto-approval");
+Console.WriteLine(new string('-', 60));
+
+AgentSession session = await agent.CreateSessionAsync();
+AgentResponse response = await agent.RunAsync(
+    "How many kilometers is a marathon (26.2 miles)? And how many pounds is 75 kilograms?",
+    session);
+
+// Handle any pending approval requests (only script execution should require approval)
+List<ToolApprovalRequestContent> approvalRequests = response.Messages
+    .SelectMany(m => m.Contents)
+    .OfType<ToolApprovalRequestContent>()
+    .ToList();
+
+while (approvalRequests.Count > 0)
+{
+    List<ChatMessage> userInputResponses = approvalRequests
+        .ConvertAll(functionApprovalRequest =>
+        {
+            var toolCall = (FunctionCallContent)functionApprovalRequest.ToolCall;
+            Console.WriteLine($"Approval required for: {toolCall.Name}. Reply Y to approve:");
+            bool approved = Console.ReadLine()?.Equals("Y", StringComparison.OrdinalIgnoreCase) ?? false;
+            return new ChatMessage(ChatRole.User, [functionApprovalRequest.CreateResponse(approved)]);
+        });
+
+    response = await agent.RunAsync(userInputResponses, session);
+    approvalRequests = response.Messages
+        .SelectMany(m => m.Contents)
+        .OfType<ToolApprovalRequestContent>()
+        .ToList();
+}
+
+Console.WriteLine($"Agent: {response.Text}");

dotnet/samples/02-agents/AgentSkills/Agent_Step07_SkillsAutoApproval/README.md

@@ -0,0 +1,102 @@
+# Skills Auto-Approval Sample
+
+This sample demonstrates how to configure **auto-approval rules** for skill tools using the `UseToolApproval` middleware and `AgentSkillsProvider`'s built-in approval rules.
+
+It builds on the [file-based skills sample](../Agent_Step01_FileBasedSkills/) by adding `ToolApprovalAgent` middleware that auto-approves read-only skill operations while still prompting for script execution.
+
+## What it demonstrates
+
+- All tools exposed by `AgentSkillsProvider` (`load_skill`, `read_skill_resource`, `run_skill_script`) always require approval by default
+- Multiple ways to configure auto-approval (see below)
+- Handling approval prompts for script execution via `ToolApprovalRequestContent`
+
+## Configuring Auto-Approval
+
+Auto-approval rules are passed to `ToolApprovalAgentOptions.AutoApprovalRules` when calling `UseToolApproval`. Rules are evaluated in order; the first rule returning `true` auto-approves the call.
+
+### Option 1: Built-in read-only rule
+
+Auto-approves `load_skill` and `read_skill_resource` while still prompting for `run_skill_script`:
+
+```csharp
+.UseToolApproval(new ToolApprovalAgentOptions
+{
+    AutoApprovalRules = [AgentSkillsProvider.ReadOnlyToolsAutoApprovalRule],
+})
+```
+
+### Option 2: Built-in all-tools rule
+
+Auto-approves all three skill tools without prompting:
+
+```csharp
+.UseToolApproval(new ToolApprovalAgentOptions
+{
+    AutoApprovalRules = [AgentSkillsProvider.AllToolsAutoApprovalRule],
+})
+```
+
+### Option 3: Custom lambda rule
+
+Provide your own logic as a `Func<FunctionCallContent, ValueTask<bool>>`. For example, to auto-approve only `load_skill`:
+
+```csharp
+.UseToolApproval(new ToolApprovalAgentOptions
+{
+    AutoApprovalRules =
+    [
+        (FunctionCallContent functionCall) =>
+            new ValueTask<bool>(functionCall.Name == AgentSkillsProvider.LoadSkillToolName),
+    ],
+})
+```
+
+### Combining rules from multiple providers
+
+When using multiple providers (e.g., skills + file access), combine their rules in a single list:
+
+```csharp
+.UseToolApproval(new ToolApprovalAgentOptions
+{
+    AutoApprovalRules =
+    [
+        AgentSkillsProvider.ReadOnlyToolsAutoApprovalRule,
+        FileAccessProvider.ReadOnlyToolsAutoApprovalRule,
+    ],
+})
+```
+
+## Skills Included
+
+### unit-converter
+
+Converts between common units (miles↔km, pounds↔kg) using a multiplication factor.
+
+- `references/conversion-table.md` — Conversion factor table
+- `scripts/convert.py` — Python script that performs the conversion
+
+## Running the Sample
+
+### Prerequisites
+
+- .NET 10.0 SDK
+- Azure OpenAI endpoint with a deployed model
+- Python 3 installed and available as `python3` on your PATH
+
+### Setup
+
+```bash
+export AZURE_OPENAI_ENDPOINT="https://your-endpoint.openai.azure.com/"
+export AZURE_OPENAI_DEPLOYMENT_NAME="gpt-5.4-mini"
+```
+
+### Run
+
+```bash
+dotnet run
+```
+
+### Expected Behavior
+
+- `load_skill` and `read_skill_resource` calls are auto-approved (no user prompt)
+- `run_skill_script` calls prompt the user for approval before executing

dotnet/samples/02-agents/AgentSkills/Agent_Step07_SkillsAutoApproval/skills/unit-converter/SKILL.md

@@ -0,0 +1,11 @@
+---
+name: unit-converter
+description: Convert between common units using a multiplication factor. Use when asked to convert miles, kilometers, pounds, or kilograms.
+---
+
+## Usage
+
+When the user requests a unit conversion:
+1. First, review `references/conversion-table.md` to find the correct factor
+2. Run the `scripts/convert.py` script with `--value <number> --factor <factor>` (e.g. `--value 26.2 --factor 1.60934`)
+3. Present the converted value clearly with both units

dotnet/samples/02-agents/AgentSkills/Agent_Step07_SkillsAutoApproval/skills/unit-converter/references/conversion-table.md

@@ -0,0 +1,10 @@
+# Conversion Tables
+
+Formula: **result = value × factor**
+
+| From        | To          | Factor   |
+|-------------|-------------|----------|
+| miles       | kilometers  | 1.60934  |
+| kilometers  | miles       | 0.621371 |
+| pounds      | kilograms   | 0.453592 |
+| kilograms   | pounds      | 2.20462  |

dotnet/samples/02-agents/AgentSkills/Agent_Step07_SkillsAutoApproval/skills/unit-converter/scripts/convert.py

@@ -0,0 +1,29 @@
+# Unit conversion script
+# Converts a value using a multiplication factor: result = value × factor
+#
+# Usage:
+#   python scripts/convert.py --value 26.2 --factor 1.60934
+#   python scripts/convert.py --value 75 --factor 2.20462
+
+import argparse
+import json
+
+
+def main() -> None:
+    parser = argparse.ArgumentParser(
+        description="Convert a value using a multiplication factor.",
+        epilog="Examples:\n"
+        "  python scripts/convert.py --value 26.2 --factor 1.60934\n"
+        "  python scripts/convert.py --value 75 --factor 2.20462",
+        formatter_class=argparse.RawDescriptionHelpFormatter,
+    )
+    parser.add_argument("--value", type=float, required=True, help="The numeric value to convert.")
+    parser.add_argument("--factor", type=float, required=True, help="The conversion factor from the table.")
+    args = parser.parse_args()
+
+    result = round(args.value * args.factor, 4)
+    print(json.dumps({"value": args.value, "factor": args.factor, "result": result}))
+
+
+if __name__ == "__main__":
+    main()

dotnet/samples/02-agents/AgentWithCodeAct/AgentWithCodeAct_Step01_Interpreter/AgentWithCodeAct_Step01_Interpreter.csproj

@@ -9,13 +9,8 @@
   </PropertyGroup>
 
   <ItemGroup>
-    <PackageReference Include="Azure.AI.OpenAI" />
     <PackageReference Include="Azure.Identity" />
-    <PackageReference Include="Microsoft.Extensions.AI.OpenAI" />
-  </ItemGroup>
-
-  <ItemGroup>
-    <ProjectReference Include="..\..\..\..\src\Microsoft.Agents.AI.OpenAI\Microsoft.Agents.AI.OpenAI.csproj" />
+    <ProjectReference Include="..\..\..\..\src\Microsoft.Agents.AI.Foundry\Microsoft.Agents.AI.Foundry.csproj" />
     <ProjectReference Include="..\..\..\..\src\Microsoft.Agents.AI.Hyperlight\Microsoft.Agents.AI.Hyperlight.csproj" />
   </ItemGroup>
 

dotnet/samples/02-agents/AgentWithCodeAct/AgentWithCodeAct_Step01_Interpreter/Program.cs

@@ -4,14 +4,13 @@
 // code interpreter: the model can write and execute arbitrary Python code to
 // answer quantitative questions without calling any additional tools.
 
-using Azure.AI.OpenAI;
+using Azure.AI.Projects;
 using Azure.Identity;
 using Microsoft.Agents.AI;
 using Microsoft.Agents.AI.Hyperlight;
-using OpenAI.Chat;
 
-var endpoint = Environment.GetEnvironmentVariable("AZURE_OPENAI_ENDPOINT") ?? throw new InvalidOperationException("AZURE_OPENAI_ENDPOINT is not set.");
-var deploymentName = Environment.GetEnvironmentVariable("AZURE_OPENAI_DEPLOYMENT_NAME") ?? "gpt-5.4-mini";
+var endpoint = Environment.GetEnvironmentVariable("FOUNDRY_PROJECT_ENDPOINT") ?? throw new InvalidOperationException("FOUNDRY_PROJECT_ENDPOINT is not set.");
+var deploymentName = Environment.GetEnvironmentVariable("FOUNDRY_MODEL") ?? "gpt-5.4-mini";
 var guestPath = Environment.GetEnvironmentVariable("HYPERLIGHT_PYTHON_GUEST_PATH") ?? throw new InvalidOperationException("HYPERLIGHT_PYTHON_GUEST_PATH is not set.");
 
 using var codeAct = new HyperlightCodeActProvider(HyperlightCodeActProviderOptions.CreateForWasm(guestPath));
@@ -19,13 +18,12 @@ using var codeAct = new HyperlightCodeActProvider(HyperlightCodeActProviderOptio
 // WARNING: DefaultAzureCredential is convenient for development but requires careful consideration in production.
 // In production, consider using a specific credential (e.g., ManagedIdentityCredential) to avoid
 // latency issues, unintended credential probing, and potential security risks from fallback mechanisms.
-AIAgent agent = new AzureOpenAIClient(
+AIAgent agent = new AIProjectClient(
     new Uri(endpoint),
     new DefaultAzureCredential())
-    .GetChatClient(deploymentName)
     .AsAIAgent(new ChatClientAgentOptions()
     {
-        ChatOptions = new() { Instructions = "You are a helpful assistant. When the user asks something quantitative, write Python and call `execute_code` instead of guessing." },
+        ChatOptions = new() { ModelId = deploymentName, Instructions = "You are a helpful assistant. When the user asks something quantitative, write Python and call `execute_code` instead of guessing." },
         AIContextProviders = [codeAct],
     });
 

dotnet/samples/02-agents/AgentWithCodeAct/AgentWithCodeAct_Step02_ToolEnabled/AgentWithCodeAct_Step02_ToolEnabled.csproj

@@ -9,13 +9,8 @@
   </PropertyGroup>
 
   <ItemGroup>
-    <PackageReference Include="Azure.AI.OpenAI" />
     <PackageReference Include="Azure.Identity" />
-    <PackageReference Include="Microsoft.Extensions.AI.OpenAI" />
-  </ItemGroup>
-
-  <ItemGroup>
-    <ProjectReference Include="..\..\..\..\src\Microsoft.Agents.AI.OpenAI\Microsoft.Agents.AI.OpenAI.csproj" />
+    <ProjectReference Include="..\..\..\..\src\Microsoft.Agents.AI.Foundry\Microsoft.Agents.AI.Foundry.csproj" />
     <ProjectReference Include="..\..\..\..\src\Microsoft.Agents.AI.Hyperlight\Microsoft.Agents.AI.Hyperlight.csproj" />
   </ItemGroup>
 

dotnet/samples/02-agents/AgentWithCodeAct/AgentWithCodeAct_Step02_ToolEnabled/Program.cs

@@ -7,15 +7,14 @@
 // ApprovalRequiredAIFunction so any code that reaches it requires user approval
 // for the entire execute_code invocation.
 
-using Azure.AI.OpenAI;
+using Azure.AI.Projects;
 using Azure.Identity;
 using Microsoft.Agents.AI;
 using Microsoft.Agents.AI.Hyperlight;
 using Microsoft.Extensions.AI;
-using OpenAI.Chat;
 
-var endpoint = Environment.GetEnvironmentVariable("AZURE_OPENAI_ENDPOINT") ?? throw new InvalidOperationException("AZURE_OPENAI_ENDPOINT is not set.");
-var deploymentName = Environment.GetEnvironmentVariable("AZURE_OPENAI_DEPLOYMENT_NAME") ?? "gpt-5.4-mini";
+var endpoint = Environment.GetEnvironmentVariable("FOUNDRY_PROJECT_ENDPOINT") ?? throw new InvalidOperationException("FOUNDRY_PROJECT_ENDPOINT is not set.");
+var deploymentName = Environment.GetEnvironmentVariable("FOUNDRY_MODEL") ?? "gpt-5.4-mini";
 var guestPath = Environment.GetEnvironmentVariable("HYPERLIGHT_PYTHON_GUEST_PATH") ?? throw new InvalidOperationException("HYPERLIGHT_PYTHON_GUEST_PATH is not set.");
 
 AIFunction fetchDocs = AIFunctionFactory.Create(
@@ -42,13 +41,12 @@ using var codeAct = new HyperlightCodeActProvider(options);
 // WARNING: DefaultAzureCredential is convenient for development but requires careful consideration in production.
 // In production, consider using a specific credential (e.g., ManagedIdentityCredential) to avoid
 // latency issues, unintended credential probing, and potential security risks from fallback mechanisms.
-AIAgent agent = new AzureOpenAIClient(
+AIAgent agent = new AIProjectClient(
     new Uri(endpoint),
     new DefaultAzureCredential())
-    .GetChatClient(deploymentName)
     .AsAIAgent(new ChatClientAgentOptions()
     {
-        ChatOptions = new() { Instructions = "You are a helpful assistant. Prefer orchestrating your work in a single `execute_code` block using `call_tool(...)` over issuing many direct tool calls." },
+        ChatOptions = new() { ModelId = deploymentName, Instructions = "You are a helpful assistant. Prefer orchestrating your work in a single `execute_code` block using `call_tool(...)` over issuing many direct tool calls." },
         AIContextProviders = [codeAct],
     });
 

dotnet/samples/02-agents/AgentWithCodeAct/AgentWithCodeAct_Step03_ManualWiring/AgentWithCodeAct_Step03_ManualWiring.csproj

@@ -9,13 +9,8 @@
   </PropertyGroup>
 
   <ItemGroup>
-    <PackageReference Include="Azure.AI.OpenAI" />
     <PackageReference Include="Azure.Identity" />
-    <PackageReference Include="Microsoft.Extensions.AI.OpenAI" />
-  </ItemGroup>
-
-  <ItemGroup>
-    <ProjectReference Include="..\..\..\..\src\Microsoft.Agents.AI.OpenAI\Microsoft.Agents.AI.OpenAI.csproj" />
+    <ProjectReference Include="..\..\..\..\src\Microsoft.Agents.AI.Foundry\Microsoft.Agents.AI.Foundry.csproj" />
     <ProjectReference Include="..\..\..\..\src\Microsoft.Agents.AI.Hyperlight\Microsoft.Agents.AI.Hyperlight.csproj" />
   </ItemGroup>
 

dotnet/samples/02-agents/AgentWithCodeAct/AgentWithCodeAct_Step03_ManualWiring/Program.cs

@@ -5,15 +5,14 @@
 // when you want a fixed tool surface for the agent's lifetime and don't need
 // the per-run snapshot/registry semantics of HyperlightCodeActProvider.
 
-using Azure.AI.OpenAI;
+using Azure.AI.Projects;
 using Azure.Identity;
 using Microsoft.Agents.AI;
 using Microsoft.Agents.AI.Hyperlight;
 using Microsoft.Extensions.AI;
-using OpenAI.Chat;
 
-var endpoint = Environment.GetEnvironmentVariable("AZURE_OPENAI_ENDPOINT") ?? throw new InvalidOperationException("AZURE_OPENAI_ENDPOINT is not set.");
-var deploymentName = Environment.GetEnvironmentVariable("AZURE_OPENAI_DEPLOYMENT_NAME") ?? "gpt-5.4-mini";
+var endpoint = Environment.GetEnvironmentVariable("FOUNDRY_PROJECT_ENDPOINT") ?? throw new InvalidOperationException("FOUNDRY_PROJECT_ENDPOINT is not set.");
+var deploymentName = Environment.GetEnvironmentVariable("FOUNDRY_MODEL") ?? "gpt-5.4-mini";
 var guestPath = Environment.GetEnvironmentVariable("HYPERLIGHT_PYTHON_GUEST_PATH") ?? throw new InvalidOperationException("HYPERLIGHT_PYTHON_GUEST_PATH is not set.");
 
 AIFunction calculate = AIFunctionFactory.Create(
@@ -34,10 +33,9 @@ var instructions =
 // WARNING: DefaultAzureCredential is convenient for development but requires careful consideration in production.
 // In production, consider using a specific credential (e.g., ManagedIdentityCredential) to avoid
 // latency issues, unintended credential probing, and potential security risks from fallback mechanisms.
-AIAgent agent = new AzureOpenAIClient(
+AIAgent agent = new AIProjectClient(
     new Uri(endpoint),
     new DefaultAzureCredential())
-    .GetChatClient(deploymentName)
-    .AsAIAgent(instructions: instructions, tools: [executeCode]);
+    .AsAIAgent(model: deploymentName, instructions: instructions, tools: [executeCode]);
 
 Console.WriteLine(await agent.RunAsync("What is 12.3 * 4.5? Use the multiply tool from within `execute_code`."));

dotnet/samples/02-agents/AgentWithMemory/AgentWithMemory_Step01_ChatHistoryMemory/AgentWithMemory_Step01_ChatHistoryMemory.csproj

@@ -1,4 +1,4 @@
-<Project Sdk="Microsoft.NET.Sdk">
+<Project Sdk="Microsoft.NET.Sdk">
 
   <PropertyGroup>
     <OutputType>Exe</OutputType>
@@ -9,14 +9,12 @@
   </PropertyGroup>
 
   <ItemGroup>
-    <PackageReference Include="Azure.AI.OpenAI" />
     <PackageReference Include="Azure.Identity" />
-    <PackageReference Include="Microsoft.Extensions.AI.OpenAI" />
     <PackageReference Include="Microsoft.SemanticKernel.Connectors.InMemory" />
   </ItemGroup>
 
   <ItemGroup>
-    <ProjectReference Include="..\..\..\..\src\Microsoft.Agents.AI.OpenAI\Microsoft.Agents.AI.OpenAI.csproj" />
+    <ProjectReference Include="..\..\..\..\src\Microsoft.Agents.AI.Foundry\Microsoft.Agents.AI.Foundry.csproj" />
   </ItemGroup>
 
 </Project>

dotnet/samples/02-agents/AgentWithMemory/AgentWithMemory_Step01_ChatHistoryMemory/Program.cs

@@ -3,17 +3,18 @@
 // This sample shows how to create and use a simple AI agent that stores chat messages in a vector store using the ChatHistoryMemoryProvider.
 // It can then use the chat history from prior conversations to inform responses in new conversations.
 
-using Azure.AI.OpenAI;
+using Azure.AI.Projects;
 using Azure.Identity;
 using Microsoft.Agents.AI;
 using Microsoft.Extensions.AI;
 using Microsoft.Extensions.VectorData;
 using Microsoft.SemanticKernel.Connectors.InMemory;
-using OpenAI.Chat;
 
-var endpoint = Environment.GetEnvironmentVariable("AZURE_OPENAI_ENDPOINT") ?? throw new InvalidOperationException("AZURE_OPENAI_ENDPOINT is not set.");
-var deploymentName = Environment.GetEnvironmentVariable("AZURE_OPENAI_DEPLOYMENT_NAME") ?? "gpt-5.4-mini";
-var embeddingDeploymentName = Environment.GetEnvironmentVariable("AZURE_OPENAI_EMBEDDING_DEPLOYMENT_NAME") ?? "text-embedding-3-large";
+var endpoint = Environment.GetEnvironmentVariable("FOUNDRY_PROJECT_ENDPOINT") ?? throw new InvalidOperationException("FOUNDRY_PROJECT_ENDPOINT is not set.");
+var deploymentName = Environment.GetEnvironmentVariable("FOUNDRY_MODEL") ?? "gpt-5.4-mini";
+var embeddingDeploymentName = Environment.GetEnvironmentVariable("FOUNDRY_EMBEDDING_MODEL") ?? "text-embedding-3-large";
+
+AIProjectClient aiProjectClient = new(new Uri(endpoint), new DefaultAzureCredential());
 
 // Create a vector store to store the chat messages in.
 // For demonstration purposes, we are using an in-memory vector store.
@@ -23,19 +24,17 @@ VectorStore vectorStore = new InMemoryVectorStore(new InMemoryVectorStoreOptions
     // WARNING: DefaultAzureCredential is convenient for development but requires careful consideration in production.
     // In production, consider using a specific credential (e.g., ManagedIdentityCredential) to avoid
     // latency issues, unintended credential probing, and potential security risks from fallback mechanisms.
-    EmbeddingGenerator = new AzureOpenAIClient(new Uri(endpoint), new DefaultAzureCredential())
+    EmbeddingGenerator = aiProjectClient
+        .GetProjectOpenAIClient()
         .GetEmbeddingClient(embeddingDeploymentName)
         .AsIEmbeddingGenerator()
 });
 
 // Create the agent and add the ChatHistoryMemoryProvider to store chat messages in the vector store.
-AIAgent agent = new AzureOpenAIClient(
-    new Uri(endpoint),
-    new DefaultAzureCredential())
-    .GetChatClient(deploymentName)
+AIAgent agent = aiProjectClient
     .AsAIAgent(new ChatClientAgentOptions
     {
-        ChatOptions = new() { Instructions = "You are good at telling jokes." },
+        ChatOptions = new() { ModelId = deploymentName, Instructions = "You are good at telling jokes." },
         Name = "Joker",
         AIContextProviders = [new ChatHistoryMemoryProvider(
             vectorStore,

dotnet/samples/02-agents/AgentWithMemory/AgentWithMemory_Step02_MemoryUsingMem0/AgentWithMemory_Step02_MemoryUsingMem0.csproj

@@ -1,4 +1,4 @@
-<Project Sdk="Microsoft.NET.Sdk">
+<Project Sdk="Microsoft.NET.Sdk">
 
   <PropertyGroup>
     <OutputType>Exe</OutputType>
@@ -9,13 +9,8 @@
   </PropertyGroup>
 
   <ItemGroup>
-    <PackageReference Include="Azure.AI.OpenAI" />
     <PackageReference Include="Azure.Identity" />
-    <PackageReference Include="Microsoft.Extensions.AI.OpenAI" />
-  </ItemGroup>
-
-  <ItemGroup>
-    <ProjectReference Include="..\..\..\..\src\Microsoft.Agents.AI.OpenAI\Microsoft.Agents.AI.OpenAI.csproj" />
+    <ProjectReference Include="..\..\..\..\src\Microsoft.Agents.AI.Foundry\Microsoft.Agents.AI.Foundry.csproj" />
     <ProjectReference Include="..\..\..\..\src\Microsoft.Agents.AI.Mem0\Microsoft.Agents.AI.Mem0.csproj" />
   </ItemGroup>
 

dotnet/samples/02-agents/AgentWithMemory/AgentWithMemory_Step02_MemoryUsingMem0/Program.cs

@@ -6,15 +6,13 @@
 
 using System.Net.Http.Headers;
 using System.Text.Json;
-using Azure.AI.OpenAI;
+using Azure.AI.Projects;
 using Azure.Identity;
 using Microsoft.Agents.AI;
 using Microsoft.Agents.AI.Mem0;
-using Microsoft.Extensions.AI;
-using OpenAI.Chat;
 
-var endpoint = Environment.GetEnvironmentVariable("AZURE_OPENAI_ENDPOINT") ?? throw new InvalidOperationException("AZURE_OPENAI_ENDPOINT is not set.");
-var deploymentName = Environment.GetEnvironmentVariable("AZURE_OPENAI_DEPLOYMENT_NAME") ?? "gpt-5.4-mini";
+var endpoint = Environment.GetEnvironmentVariable("FOUNDRY_PROJECT_ENDPOINT") ?? throw new InvalidOperationException("FOUNDRY_PROJECT_ENDPOINT is not set.");
+var deploymentName = Environment.GetEnvironmentVariable("FOUNDRY_MODEL") ?? "gpt-5.4-mini";
 
 var mem0ServiceUri = Environment.GetEnvironmentVariable("MEM0_ENDPOINT") ?? throw new InvalidOperationException("MEM0_ENDPOINT is not set.");
 var mem0ApiKey = Environment.GetEnvironmentVariable("MEM0_API_KEY") ?? throw new InvalidOperationException("MEM0_API_KEY is not set.");
@@ -24,16 +22,15 @@ using HttpClient mem0HttpClient = new();
 mem0HttpClient.BaseAddress = new Uri(mem0ServiceUri);
 mem0HttpClient.DefaultRequestHeaders.Authorization = new AuthenticationHeaderValue("Token", mem0ApiKey);
 
+AIProjectClient aiProjectClient = new(new Uri(endpoint), new DefaultAzureCredential());
+
 // WARNING: DefaultAzureCredential is convenient for development but requires careful consideration in production.
 // In production, consider using a specific credential (e.g., ManagedIdentityCredential) to avoid
 // latency issues, unintended credential probing, and potential security risks from fallback mechanisms.
-AIAgent agent = new AzureOpenAIClient(
-    new Uri(endpoint),
-    new DefaultAzureCredential())
-    .GetChatClient(deploymentName)
+AIAgent agent = aiProjectClient
     .AsAIAgent(new ChatClientAgentOptions()
     {
-        ChatOptions = new() { Instructions = "You are a friendly travel assistant. Use known memories about the user when responding, and do not invent details." },
+        ChatOptions = new() { ModelId = deploymentName, Instructions = "You are a friendly travel assistant. Use known memories about the user when responding, and do not invent details." },
         // The stateInitializer can be used to customize the Mem0 scope per session and it will be called each time a session
         // is encountered by the Mem0Provider that does not already have Mem0Provider state stored on the session.
         // If each session should have its own Mem0 scope, you can create a new id per session via the stateInitializer, e.g.:

dotnet/samples/02-agents/AgentWithMemory/AgentWithMemory_Step03_MemoryUsingValkey/AgentWithMemory_Step03_MemoryUsingValkey.csproj

@@ -1,4 +1,4 @@
-<Project Sdk="Microsoft.NET.Sdk">
+<Project Sdk="Microsoft.NET.Sdk">
 
   <PropertyGroup>
     <OutputType>Exe</OutputType>
@@ -9,13 +9,8 @@
   </PropertyGroup>
 
   <ItemGroup>
-    <PackageReference Include="Azure.AI.OpenAI" />
     <PackageReference Include="Azure.Identity" />
-    <PackageReference Include="Microsoft.Extensions.AI.OpenAI" />
-  </ItemGroup>
-
-  <ItemGroup>
-    <ProjectReference Include="..\..\..\..\src\Microsoft.Agents.AI.OpenAI\Microsoft.Agents.AI.OpenAI.csproj" />
+    <ProjectReference Include="..\..\..\..\src\Microsoft.Agents.AI.Foundry\Microsoft.Agents.AI.Foundry.csproj" />
     <ProjectReference Include="..\..\..\..\src\Microsoft.Agents.AI.Valkey\Microsoft.Agents.AI.Valkey.csproj" />
   </ItemGroup>
 

dotnet/samples/02-agents/AgentWithMemory/AgentWithMemory_Step03_MemoryUsingValkey/Program.cs

@@ -8,16 +8,15 @@
 //       docker run -d --name valkey -p 6379:6379 valkey/valkey:latest
 //   - Azure OpenAI endpoint and deployment configured via environment variables
 
-using Azure.AI.OpenAI;
+using Azure.AI.Projects;
 using Azure.Identity;
 using Microsoft.Agents.AI;
 using Microsoft.Agents.AI.Valkey;
 using Microsoft.Extensions.AI;
-using OpenAI.Chat;
 using Valkey.Glide;
 
-var endpoint = Environment.GetEnvironmentVariable("AZURE_OPENAI_ENDPOINT") ?? throw new InvalidOperationException("AZURE_OPENAI_ENDPOINT is not set.");
-var deploymentName = Environment.GetEnvironmentVariable("AZURE_OPENAI_DEPLOYMENT_NAME") ?? "gpt-5.4-mini";
+var endpoint = Environment.GetEnvironmentVariable("FOUNDRY_PROJECT_ENDPOINT") ?? throw new InvalidOperationException("FOUNDRY_PROJECT_ENDPOINT is not set.");
+var deploymentName = Environment.GetEnvironmentVariable("FOUNDRY_MODEL") ?? "gpt-5.4-mini";
 var valkeyConnection = Environment.GetEnvironmentVariable("VALKEY_CONNECTION") ?? "localhost:6379";
 
 var connection = await ConnectionMultiplexer.ConnectAsync(valkeyConnection);
@@ -33,11 +32,10 @@ var historyProvider = new ValkeyChatHistoryProvider(
         MaxMessages = 20
     });
 
-AIAgent historyAgent = new AzureOpenAIClient(new Uri(endpoint), new DefaultAzureCredential())
-    .GetChatClient(deploymentName)
+AIAgent historyAgent = new AIProjectClient(new Uri(endpoint), new DefaultAzureCredential())
     .AsAIAgent(new ChatClientAgentOptions()
     {
-        ChatOptions = new() { Instructions = "You are a helpful assistant that remembers our conversation." },
+        ChatOptions = new() { ModelId = deploymentName, Instructions = "You are a helpful assistant that remembers our conversation." },
         ChatHistoryProvider = historyProvider
     });
 

dotnet/samples/02-agents/AgentWithMemory/AgentWithMemory_Step05_BoundedChatHistory/AgentWithMemory_Step05_BoundedChatHistory.csproj

@@ -1,4 +1,4 @@
-<Project Sdk="Microsoft.NET.Sdk">
+<Project Sdk="Microsoft.NET.Sdk">
 
   <PropertyGroup>
     <OutputType>Exe</OutputType>
@@ -9,14 +9,12 @@
   </PropertyGroup>
 
   <ItemGroup>
-    <PackageReference Include="Azure.AI.OpenAI" />
     <PackageReference Include="Azure.Identity" />
-    <PackageReference Include="Microsoft.Extensions.AI.OpenAI" />
     <PackageReference Include="Microsoft.SemanticKernel.Connectors.InMemory" />
   </ItemGroup>
 
   <ItemGroup>
-    <ProjectReference Include="..\..\..\..\src\Microsoft.Agents.AI.OpenAI\Microsoft.Agents.AI.OpenAI.csproj" />
+    <ProjectReference Include="..\..\..\..\src\Microsoft.Agents.AI.Foundry\Microsoft.Agents.AI.Foundry.csproj" />
   </ItemGroup>
 
 </Project>

dotnet/samples/02-agents/AgentWithMemory/AgentWithMemory_Step05_BoundedChatHistory/Program.cs

@@ -5,30 +5,30 @@
 // When the agent is invoked, it searches the vector store for relevant older messages and
 // prepends them as a "memory" context message before the recent session history.
 
-using Azure.AI.OpenAI;
+using Azure.AI.Projects;
 using Azure.Identity;
 using Microsoft.Agents.AI;
 using Microsoft.Extensions.AI;
 using Microsoft.Extensions.VectorData;
 using Microsoft.SemanticKernel.Connectors.InMemory;
-using OpenAI.Chat;
 using SampleApp;
 
-var endpoint = Environment.GetEnvironmentVariable("AZURE_OPENAI_ENDPOINT") ?? throw new InvalidOperationException("AZURE_OPENAI_ENDPOINT is not set.");
-var deploymentName = Environment.GetEnvironmentVariable("AZURE_OPENAI_DEPLOYMENT_NAME") ?? "gpt-5.4-mini";
-var embeddingDeploymentName = Environment.GetEnvironmentVariable("AZURE_OPENAI_EMBEDDING_DEPLOYMENT_NAME") ?? "text-embedding-3-large";
+var endpoint = Environment.GetEnvironmentVariable("FOUNDRY_PROJECT_ENDPOINT") ?? throw new InvalidOperationException("FOUNDRY_PROJECT_ENDPOINT is not set.");
+var deploymentName = Environment.GetEnvironmentVariable("FOUNDRY_MODEL") ?? "gpt-5.4-mini";
+var embeddingDeploymentName = Environment.GetEnvironmentVariable("FOUNDRY_EMBEDDING_MODEL") ?? "text-embedding-3-large";
 
 // WARNING: DefaultAzureCredential is convenient for development but requires careful consideration in production.
 // In production, consider using a specific credential (e.g., ManagedIdentityCredential) to avoid
 // latency issues, unintended credential probing, and potential security risks from fallback mechanisms.
-var credential = new DefaultAzureCredential();
+AIProjectClient aiProjectClient = new(new Uri(endpoint), new DefaultAzureCredential());
 
 // Create a vector store to store overflow chat messages.
 // For demonstration purposes, we are using an in-memory vector store.
 // Replace this with a persistent vector store implementation for production scenarios.
 VectorStore vectorStore = new InMemoryVectorStore(new InMemoryVectorStoreOptions()
 {
-    EmbeddingGenerator = new AzureOpenAIClient(new Uri(endpoint), credential)
+    EmbeddingGenerator = aiProjectClient
+        .GetProjectOpenAIClient()
         .GetEmbeddingClient(embeddingDeploymentName)
         .AsIEmbeddingGenerator()
 });
@@ -49,11 +49,10 @@ var boundedProvider = new BoundedChatHistoryProvider(
         searchScope: new() { UserId = "UID1" }));
 
 // Create the agent with the bounded chat history provider.
-AIAgent agent = new AzureOpenAIClient(new Uri(endpoint), credential)
-    .GetChatClient(deploymentName)
+AIAgent agent = aiProjectClient
     .AsAIAgent(new ChatClientAgentOptions
     {
-        ChatOptions = new() { Instructions = "You are a helpful assistant. Answer questions concisely." },
+        ChatOptions = new() { ModelId = deploymentName, Instructions = "You are a helpful assistant. Answer questions concisely." },
         Name = "Assistant",
         ChatHistoryProvider = boundedProvider,
     });

dotnet/samples/02-agents/AgentWithRAG/AgentWithRAG_Step01_BasicTextRAG/AgentWithRAG_Step01_BasicTextRAG.csproj

@@ -1,4 +1,4 @@
-<Project Sdk="Microsoft.NET.Sdk">
+<Project Sdk="Microsoft.NET.Sdk">
 
   <PropertyGroup>
     <OutputType>Exe</OutputType>
@@ -9,14 +9,12 @@
   </PropertyGroup>
 
   <ItemGroup>
-    <PackageReference Include="Azure.AI.OpenAI" />
     <PackageReference Include="Azure.Identity" />
-    <PackageReference Include="Microsoft.Extensions.AI.OpenAI" />
     <PackageReference Include="Microsoft.SemanticKernel.Connectors.InMemory" />
   </ItemGroup>
 
   <ItemGroup>
-    <ProjectReference Include="..\..\..\..\src\Microsoft.Agents.AI.OpenAI\Microsoft.Agents.AI.OpenAI.csproj" />
+    <ProjectReference Include="..\..\..\..\src\Microsoft.Agents.AI.Foundry\Microsoft.Agents.AI.Foundry.csproj" />
   </ItemGroup>
 
 </Project>

dotnet/samples/02-agents/AgentWithRAG/AgentWithRAG_Step01_BasicTextRAG/Program.cs

@@ -5,30 +5,29 @@
 // The TextSearchProvider runs a search against the vector store via the TextSearchStore before each model invocation and injects the results into the model context.
 // The TextSearchStore is a sample store implementation that hardcodes a storage schema and uses the vector store to store and retrieve documents.
 
-using Azure.AI.OpenAI;
+using Azure.AI.Projects;
 using Azure.Identity;
 using Microsoft.Agents.AI;
 using Microsoft.Agents.AI.Samples;
 using Microsoft.Extensions.AI;
 using Microsoft.Extensions.VectorData;
 using Microsoft.SemanticKernel.Connectors.InMemory;
-using OpenAI.Chat;
 
-var endpoint = Environment.GetEnvironmentVariable("AZURE_OPENAI_ENDPOINT") ?? throw new InvalidOperationException("AZURE_OPENAI_ENDPOINT is not set.");
-var deploymentName = Environment.GetEnvironmentVariable("AZURE_OPENAI_DEPLOYMENT_NAME") ?? "gpt-5.4-mini";
-var embeddingDeploymentName = Environment.GetEnvironmentVariable("AZURE_OPENAI_EMBEDDING_DEPLOYMENT_NAME") ?? "text-embedding-3-large";
+var endpoint = Environment.GetEnvironmentVariable("FOUNDRY_PROJECT_ENDPOINT") ?? throw new InvalidOperationException("FOUNDRY_PROJECT_ENDPOINT is not set.");
+var deploymentName = Environment.GetEnvironmentVariable("FOUNDRY_MODEL") ?? "gpt-5.4-mini";
+var embeddingDeploymentName = Environment.GetEnvironmentVariable("FOUNDRY_EMBEDDING_MODEL") ?? "text-embedding-3-large";
 
 // WARNING: DefaultAzureCredential is convenient for development but requires careful consideration in production.
 // In production, consider using a specific credential (e.g., ManagedIdentityCredential) to avoid
 // latency issues, unintended credential probing, and potential security risks from fallback mechanisms.
-AzureOpenAIClient azureOpenAIClient = new(
+AIProjectClient aiProjectClient = new(
     new Uri(endpoint),
     new DefaultAzureCredential());
 
-// Create an In-Memory vector store that uses the Azure OpenAI embedding model to generate embeddings.
+// Create an In-Memory vector store that uses the Azure AI Foundry embedding model to generate embeddings.
 VectorStore vectorStore = new InMemoryVectorStore(new()
 {
-    EmbeddingGenerator = azureOpenAIClient.GetEmbeddingClient(embeddingDeploymentName).AsIEmbeddingGenerator()
+    EmbeddingGenerator = aiProjectClient.GetProjectOpenAIClient().GetEmbeddingClient(embeddingDeploymentName).AsIEmbeddingGenerator()
 });
 
 // Create a store that defines a storage schema, and uses the vector store to store and retrieve documents.
@@ -60,11 +59,10 @@ TextSearchProviderOptions textSearchOptions = new()
 };
 
 // Create the AI agent with the TextSearchProvider as the AI context provider.
-AIAgent agent = azureOpenAIClient
-    .GetChatClient(deploymentName)
+AIAgent agent = aiProjectClient
     .AsAIAgent(new ChatClientAgentOptions
     {
-        ChatOptions = new() { Instructions = "You are a helpful support specialist for Contoso Outdoors. Answer questions using the provided context and cite the source document when available." },
+        ChatOptions = new() { ModelId = deploymentName, Instructions = "You are a helpful support specialist for Contoso Outdoors. Answer questions using the provided context and cite the source document when available." },
         AIContextProviders = [new TextSearchProvider(SearchAdapter, textSearchOptions)],
         // Since we are using ChatCompletion which stores chat history locally, we can also add a message filter
         // that removes messages produced by the TextSearchProvider before they are added to the chat history, so that

dotnet/samples/02-agents/AgentWithRAG/AgentWithRAG_Step02_CustomVectorStoreRAG/AgentWithRAG_Step02_CustomVectorStoreRAG.csproj

@@ -9,14 +9,12 @@
   </PropertyGroup>
 
   <ItemGroup>
-    <PackageReference Include="Azure.AI.OpenAI" />
     <PackageReference Include="Azure.Identity" />
-    <PackageReference Include="Microsoft.Extensions.AI.OpenAI" />
     <PackageReference Include="Microsoft.SemanticKernel.Connectors.Qdrant" />
   </ItemGroup>
 
   <ItemGroup>
-    <ProjectReference Include="..\..\..\..\src\Microsoft.Agents.AI.OpenAI\Microsoft.Agents.AI.OpenAI.csproj" />
+    <ProjectReference Include="..\..\..\..\src\Microsoft.Agents.AI.Foundry\Microsoft.Agents.AI.Foundry.csproj" />
   </ItemGroup>
 
 </Project>

dotnet/samples/02-agents/AgentWithRAG/AgentWithRAG_Step02_CustomVectorStoreRAG/Program.cs

@@ -4,33 +4,32 @@
 // While the sample is using Qdrant, it can easily be replaced with any other vector store that implements the Microsoft.Extensions.VectorData abstractions.
 // The TextSearchProvider runs a search against the vector store before each model invocation and injects the results into the model context.
 
-using Azure.AI.OpenAI;
+using Azure.AI.Projects;
 using Azure.Identity;
 using Microsoft.Agents.AI;
 using Microsoft.Extensions.AI;
 using Microsoft.Extensions.VectorData;
 using Microsoft.SemanticKernel.Connectors.Qdrant;
-using OpenAI.Chat;
 using Qdrant.Client;
 
-var endpoint = Environment.GetEnvironmentVariable("AZURE_OPENAI_ENDPOINT") ?? throw new InvalidOperationException("AZURE_OPENAI_ENDPOINT is not set.");
-var deploymentName = Environment.GetEnvironmentVariable("AZURE_OPENAI_DEPLOYMENT_NAME") ?? "gpt-5.4-mini";
-var embeddingDeploymentName = Environment.GetEnvironmentVariable("AZURE_OPENAI_EMBEDDING_DEPLOYMENT_NAME") ?? "text-embedding-3-large";
+var endpoint = Environment.GetEnvironmentVariable("FOUNDRY_PROJECT_ENDPOINT") ?? throw new InvalidOperationException("FOUNDRY_PROJECT_ENDPOINT is not set.");
+var deploymentName = Environment.GetEnvironmentVariable("FOUNDRY_MODEL") ?? "gpt-5.4-mini";
+var embeddingDeploymentName = Environment.GetEnvironmentVariable("FOUNDRY_EMBEDDING_MODEL") ?? "text-embedding-3-large";
 var afOverviewUrl = "https://raw.githubusercontent.com/MicrosoftDocs/semantic-kernel-docs/refs/heads/main/agent-framework/overview/index.md";
 var afMigrationUrl = "https://raw.githubusercontent.com/MicrosoftDocs/semantic-kernel-docs/refs/heads/main/agent-framework/migration-guide/from-semantic-kernel/index.md";
 
 // WARNING: DefaultAzureCredential is convenient for development but requires careful consideration in production.
 // In production, consider using a specific credential (e.g., ManagedIdentityCredential) to avoid
 // latency issues, unintended credential probing, and potential security risks from fallback mechanisms.
-AzureOpenAIClient azureOpenAIClient = new(
+AIProjectClient aiProjectClient = new(
     new Uri(endpoint),
     new DefaultAzureCredential());
 
-// Create a Qdrant vector store that uses the Azure OpenAI embedding model to generate embeddings.
+// Create a Qdrant vector store that uses the Azure AI Foundry embedding model to generate embeddings.
 QdrantClient client = new("localhost");
 VectorStore vectorStore = new QdrantVectorStore(client, ownsClient: true, new()
 {
-    EmbeddingGenerator = azureOpenAIClient.GetEmbeddingClient(embeddingDeploymentName).AsIEmbeddingGenerator()
+    EmbeddingGenerator = aiProjectClient.GetProjectOpenAIClient().GetEmbeddingClient(embeddingDeploymentName).AsIEmbeddingGenerator()
 });
 
 // Create a collection and upsert some text into it.
@@ -69,11 +68,10 @@ TextSearchProviderOptions textSearchOptions = new()
 };
 
 // Create the AI agent with the TextSearchProvider as the AI context provider.
-AIAgent agent = azureOpenAIClient
-    .GetChatClient(deploymentName)
+AIAgent agent = aiProjectClient
     .AsAIAgent(new ChatClientAgentOptions
     {
-        ChatOptions = new() { Instructions = "You are a helpful support specialist for the Microsoft Agent Framework. Answer questions using the provided context and cite the source document when available. Keep responses brief." },
+        ChatOptions = new() { ModelId = deploymentName, Instructions = "You are a helpful support specialist for the Microsoft Agent Framework. Answer questions using the provided context and cite the source document when available. Keep responses brief." },
         AIContextProviders = [new TextSearchProvider(SearchAdapter, textSearchOptions)],
         // Configure a filter on the InMemoryChatHistoryProvider so that we don't persist the messages produced by the TextSearchProvider in chat history.
         // The default is to persist all messages except those that came from chat history in the first place.

dotnet/samples/02-agents/AgentWithRAG/AgentWithRAG_Step03_CustomRAGDataSource/AgentWithRAG_Step03_CustomRAGDataSource.csproj

@@ -1,4 +1,4 @@
-<Project Sdk="Microsoft.NET.Sdk">
+<Project Sdk="Microsoft.NET.Sdk">
 
   <PropertyGroup>
     <OutputType>Exe</OutputType>
@@ -9,13 +9,8 @@
   </PropertyGroup>
 
   <ItemGroup>
-    <PackageReference Include="Azure.AI.OpenAI" />
     <PackageReference Include="Azure.Identity" />
-    <PackageReference Include="Microsoft.Extensions.AI.OpenAI" />
-  </ItemGroup>
-
-  <ItemGroup>
-    <ProjectReference Include="..\..\..\..\src\Microsoft.Agents.AI.OpenAI\Microsoft.Agents.AI.OpenAI.csproj" />
+    <ProjectReference Include="..\..\..\..\src\Microsoft.Agents.AI.Foundry\Microsoft.Agents.AI.Foundry.csproj" />
   </ItemGroup>
 
 </Project>

dotnet/samples/02-agents/AgentWithRAG/AgentWithRAG_Step03_CustomRAGDataSource/Program.cs

@@ -6,14 +6,13 @@
 // The provider invokes the custom search function
 // before each model invocation and injects the results into the model context.
 
-using Azure.AI.OpenAI;
+using Azure.AI.Projects;
 using Azure.Identity;
 using Microsoft.Agents.AI;
 using Microsoft.Extensions.AI;
-using OpenAI.Chat;
 
-var endpoint = Environment.GetEnvironmentVariable("AZURE_OPENAI_ENDPOINT") ?? throw new InvalidOperationException("AZURE_OPENAI_ENDPOINT is not set.");
-var deploymentName = Environment.GetEnvironmentVariable("AZURE_OPENAI_DEPLOYMENT_NAME") ?? "gpt-5.4-mini";
+var endpoint = Environment.GetEnvironmentVariable("FOUNDRY_PROJECT_ENDPOINT") ?? throw new InvalidOperationException("FOUNDRY_PROJECT_ENDPOINT is not set.");
+var deploymentName = Environment.GetEnvironmentVariable("FOUNDRY_MODEL") ?? "gpt-5.4-mini";
 
 TextSearchProviderOptions textSearchOptions = new()
 {
@@ -25,13 +24,12 @@ TextSearchProviderOptions textSearchOptions = new()
 // WARNING: DefaultAzureCredential is convenient for development but requires careful consideration in production.
 // In production, consider using a specific credential (e.g., ManagedIdentityCredential) to avoid
 // latency issues, unintended credential probing, and potential security risks from fallback mechanisms.
-AIAgent agent = new AzureOpenAIClient(
+AIAgent agent = new AIProjectClient(
     new Uri(endpoint),
     new DefaultAzureCredential())
-    .GetChatClient(deploymentName)
     .AsAIAgent(new ChatClientAgentOptions
     {
-        ChatOptions = new() { Instructions = "You are a helpful support specialist for Contoso Outdoors. Answer questions using the provided context and cite the source document when available." },
+        ChatOptions = new() { ModelId = deploymentName, Instructions = "You are a helpful support specialist for Contoso Outdoors. Answer questions using the provided context and cite the source document when available." },
         AIContextProviders = [new TextSearchProvider(MockSearchAsync, textSearchOptions)]
     });
 

dotnet/samples/02-agents/AgentWithRAG/AgentWithRAG_Step05_Neo4jGraphRAG/AgentWithRAG_Step05_Neo4jGraphRAG.csproj

@@ -1,4 +1,4 @@
-<Project Sdk="Microsoft.NET.Sdk">
+<Project Sdk="Microsoft.NET.Sdk">
 
   <PropertyGroup>
     <OutputType>Exe</OutputType>
@@ -20,15 +20,13 @@
   </ItemGroup>
 
   <ItemGroup>
-    <PackageReference Include="Azure.AI.OpenAI" Version="2.9.0-beta.1" />
-    <PackageReference Include="Azure.Identity" Version="1.19.0" />
-    <PackageReference Include="Microsoft.Agents.AI.OpenAI" Version="1.0.0-rc4" />
-    <PackageReference Include="Microsoft.Extensions.AI.OpenAI" Version="10.6.0" />
+    <PackageReference Include="Microsoft.Agents.AI.Foundry" Version="1.2.0" />
     <PackageReference Include="Neo4j.AgentFramework.GraphRAG" Version="0.1.0-preview.2" />
     <PackageReference Include="Neo4j.Driver" Version="5.28.0" />
   </ItemGroup>
 
   <ItemGroup>
+    <PackageReference Include="Azure.Identity" Version="1.21.0" />
     <PackageReference Include="Microsoft.CodeAnalysis.NetAnalyzers" Version="10.0.100">
       <PrivateAssets>all</PrivateAssets>
       <IncludeAssets>runtime; build; native; contentfiles; analyzers; buildtransitive</IncludeAssets>

dotnet/samples/02-agents/AgentWithRAG/AgentWithRAG_Step05_Neo4jGraphRAG/Program.cs

@@ -1,14 +1,14 @@
 // Copyright (c) Microsoft. All rights reserved.
 
-using Azure.AI.OpenAI;
+using Azure.AI.Projects;
 using Azure.Identity;
 using Microsoft.Agents.AI;
 using Microsoft.Extensions.AI;
 using Neo4j.AgentFramework.GraphRAG;
 using Neo4j.Driver;
 
-var endpoint = Environment.GetEnvironmentVariable("AZURE_OPENAI_ENDPOINT") ?? throw new InvalidOperationException("AZURE_OPENAI_ENDPOINT is not set.");
-var deploymentName = Environment.GetEnvironmentVariable("AZURE_OPENAI_DEPLOYMENT_NAME") ?? "gpt-5.4-mini";
+var endpoint = Environment.GetEnvironmentVariable("FOUNDRY_PROJECT_ENDPOINT") ?? throw new InvalidOperationException("FOUNDRY_PROJECT_ENDPOINT is not set.");
+var deploymentName = Environment.GetEnvironmentVariable("FOUNDRY_MODEL") ?? "gpt-5.4-mini";
 var neo4jUri = Environment.GetEnvironmentVariable("NEO4J_URI") ?? throw new InvalidOperationException("NEO4J_URI is not set.");
 var neo4jUsername = Environment.GetEnvironmentVariable("NEO4J_USERNAME") ?? "neo4j";
 var neo4jPassword = Environment.GetEnvironmentVariable("NEO4J_PASSWORD") ?? throw new InvalidOperationException("NEO4J_PASSWORD is not set.");
@@ -48,15 +48,14 @@ await using var provider = new Neo4jContextProvider(
 // WARNING: DefaultAzureCredential is convenient for development but requires careful consideration in production.
 // In production, consider using a specific credential (e.g., ManagedIdentityCredential) to avoid
 // latency issues, unintended credential probing, and potential security risks from fallback mechanisms.
-AIAgent agent = new AzureOpenAIClient(
+AIAgent agent = new AIProjectClient(
     new Uri(endpoint),
     new DefaultAzureCredential())
-    .GetChatClient(deploymentName)
-    .AsIChatClient()
     .AsAIAgent(new ChatClientAgentOptions
     {
         ChatOptions = new()
         {
+            ModelId = deploymentName,
             Instructions = "You are a helpful assistant that answers questions using Neo4j graph context."
         },
         AIContextProviders = [provider]

dotnet/samples/02-agents/Agents/Agent_Step01_UsingFunctionToolsWithApprovals/Agent_Step01_UsingFunctionToolsWithApprovals.csproj

@@ -1,4 +1,4 @@
-<Project Sdk="Microsoft.NET.Sdk">
+<Project Sdk="Microsoft.NET.Sdk">
 
   <PropertyGroup>
     <OutputType>Exe</OutputType>
@@ -9,13 +9,8 @@
   </PropertyGroup>
 
   <ItemGroup>
-    <PackageReference Include="Azure.AI.OpenAI" />
     <PackageReference Include="Azure.Identity" />
-    <PackageReference Include="Microsoft.Extensions.AI.OpenAI" />
-  </ItemGroup>
-
-  <ItemGroup>
-    <ProjectReference Include="..\..\..\..\src\Microsoft.Agents.AI.OpenAI\Microsoft.Agents.AI.OpenAI.csproj" />
+    <ProjectReference Include="..\..\..\..\src\Microsoft.Agents.AI.Foundry\Microsoft.Agents.AI.Foundry.csproj" />
   </ItemGroup>
 
 </Project>

dotnet/samples/02-agents/Agents/Agent_Step01_UsingFunctionToolsWithApprovals/Program.cs

@@ -1,20 +1,22 @@
 // Copyright (c) Microsoft. All rights reserved.
 
-// This sample demonstrates how to use a ChatClientAgent with function tools that require a human in the loop for approvals.
-// It shows both non-streaming and streaming agent interactions using menu-related tools.
-// If the agent is hosted in a service, with a remote user, combine this sample with the Persisted Conversations sample to persist the chat history
-// while the agent is waiting for user input.
+// Function Tools with Approvals — Human-in-the-loop tool execution
+//
+// This sample demonstrates how to use function tools that require human
+// approval before execution. It shows both non-streaming and streaming
+// agent interactions using menu-related tools.
+// If the agent is hosted in a service, combine this with the Persisted
+// Conversations sample to persist chat history while waiting for user input.
 
 using System.ComponentModel;
-using Azure.AI.OpenAI;
+using Azure.AI.Projects;
 using Azure.Identity;
 using Microsoft.Agents.AI;
 using Microsoft.Extensions.AI;
-using OpenAI.Chat;
 using ChatMessage = Microsoft.Extensions.AI.ChatMessage;
 
-var endpoint = Environment.GetEnvironmentVariable("AZURE_OPENAI_ENDPOINT") ?? throw new InvalidOperationException("AZURE_OPENAI_ENDPOINT is not set.");
-var deploymentName = Environment.GetEnvironmentVariable("AZURE_OPENAI_DEPLOYMENT_NAME") ?? "gpt-5.4-mini";
+var endpoint = Environment.GetEnvironmentVariable("FOUNDRY_PROJECT_ENDPOINT") ?? throw new InvalidOperationException("FOUNDRY_PROJECT_ENDPOINT is not set.");
+var deploymentName = Environment.GetEnvironmentVariable("FOUNDRY_MODEL") ?? "gpt-5.4-mini";
 
 // Create a sample function tool that the agent can use.
 [Description("Get the weather for a given location.")]
@@ -26,11 +28,10 @@ static string GetWeather([Description("The location to get the weather for.")] s
 // WARNING: DefaultAzureCredential is convenient for development but requires careful consideration in production.
 // In production, consider using a specific credential (e.g., ManagedIdentityCredential) to avoid
 // latency issues, unintended credential probing, and potential security risks from fallback mechanisms.
-AIAgent agent = new AzureOpenAIClient(
+AIAgent agent = new AIProjectClient(
     new Uri(endpoint),
     new DefaultAzureCredential())
-    .GetChatClient(deploymentName)
-    .AsAIAgent(instructions: "You are a helpful assistant", tools: [new ApprovalRequiredAIFunction(AIFunctionFactory.Create(GetWeather))]);
+    .AsAIAgent(model: deploymentName, instructions: "You are a helpful assistant", tools: [new ApprovalRequiredAIFunction(AIFunctionFactory.Create(GetWeather))]);
 
 // Call the agent and check if there are any function approval requests to handle.
 // For simplicity, we are assuming here that only function approvals are pending.

dotnet/samples/02-agents/Agents/Agent_Step02_StructuredOutput/Agent_Step02_StructuredOutput.csproj

@@ -1,4 +1,4 @@
-<Project Sdk="Microsoft.NET.Sdk">
+<Project Sdk="Microsoft.NET.Sdk">
 
   <PropertyGroup>
     <OutputType>Exe</OutputType>
@@ -9,13 +9,8 @@
   </PropertyGroup>
 
   <ItemGroup>
-    <PackageReference Include="Azure.AI.OpenAI" />
     <PackageReference Include="Azure.Identity" />
-    <PackageReference Include="Microsoft.Extensions.AI.OpenAI" />
-  </ItemGroup>
-
-  <ItemGroup>
-    <ProjectReference Include="..\..\..\..\src\Microsoft.Agents.AI.OpenAI\Microsoft.Agents.AI.OpenAI.csproj" />
+    <ProjectReference Include="..\..\..\..\src\Microsoft.Agents.AI.Foundry\Microsoft.Agents.AI.Foundry.csproj" />
   </ItemGroup>
 
 </Project>

dotnet/samples/02-agents/Agents/Agent_Step02_StructuredOutput/Program.cs

@@ -1,29 +1,29 @@
 // Copyright (c) Microsoft. All rights reserved.
 
-// This sample shows how to configure ChatClientAgent to produce structured output.
+// Structured Output — Configure agents to return typed JSON
+//
+// This sample shows how to configure a ChatClientAgent to produce
+// structured output using JSON schema constraints with Azure AI Foundry.
 
 using System.ComponentModel;
 using System.Text.Json;
 using System.Text.Json.Serialization;
-using Azure.AI.OpenAI;
+using Azure.AI.Extensions.OpenAI;
+using Azure.AI.Projects;
 using Azure.Identity;
 using Microsoft.Agents.AI;
 using Microsoft.Extensions.AI;
-using OpenAI.Chat;
 using SampleApp;
 using ChatMessage = Microsoft.Extensions.AI.ChatMessage;
 
-string endpoint = Environment.GetEnvironmentVariable("AZURE_OPENAI_ENDPOINT") ?? throw new InvalidOperationException("AZURE_OPENAI_ENDPOINT is not set.");
-string deploymentName = Environment.GetEnvironmentVariable("AZURE_OPENAI_DEPLOYMENT_NAME") ?? "gpt-5.4-mini";
+string endpoint = Environment.GetEnvironmentVariable("FOUNDRY_PROJECT_ENDPOINT") ?? throw new InvalidOperationException("FOUNDRY_PROJECT_ENDPOINT is not set.");
+string deploymentName = Environment.GetEnvironmentVariable("FOUNDRY_MODEL") ?? "gpt-5.4-mini";
 
-// Create chat client to be used by chat client agents.
+// Create AI Project client to be used by chat client agents.
 // WARNING: DefaultAzureCredential is convenient for development but requires careful consideration in production.
 // In production, consider using a specific credential (e.g., ManagedIdentityCredential) to avoid
 // latency issues, unintended credential probing, and potential security risks from fallback mechanisms.
-ChatClient chatClient = new AzureOpenAIClient(
-    new Uri(endpoint),
-    new DefaultAzureCredential())
-        .GetChatClient(deploymentName);
+AIProjectClient aiProjectClient = new(new Uri(endpoint), new DefaultAzureCredential());
 
 // Demonstrates how to work with structured output via ResponseFormat with the non-generic RunAsync method.
 // This approach is useful when:
@@ -31,35 +31,36 @@ ChatClient chatClient = new AzureOpenAIClient(
 //    and passes it as text to another agent as input, without the need for the caller to directly work with the structured output.
 // b. The type of the structured output is not known at compile time, so the generic RunAsync<T> method cannot be used.
 // c. The type of the structured output is represented by JSON schema only, without a corresponding class or type in the code.
-await UseStructuredOutputWithResponseFormatAsync(chatClient);
+await UseStructuredOutputWithResponseFormatAsync(aiProjectClient, deploymentName);
 
 // Demonstrates how to work with structured output via the generic RunAsync<T> method.
 // This approach is useful when the caller needs to directly work with the structured output in the code
 // via an instance of the corresponding class or type and the type is known at compile time.
-await UseStructuredOutputWithRunAsync(chatClient);
+await UseStructuredOutputWithRunAsync(aiProjectClient, deploymentName);
 
 // Demonstrates how to work with structured output when streaming using the RunStreamingAsync method.
-await UseStructuredOutputWithRunStreamingAsync(chatClient);
+await UseStructuredOutputWithRunStreamingAsync(aiProjectClient, deploymentName);
 
 // Demonstrates how to add structured output support to agents that don't natively support it using the structured output middleware.
 // This approach is useful when working with agents that don't support structured output natively, or agents using models
 // that don't have the capability to produce structured output, allowing you to still leverage structured output features by transforming
 // the text output from the agent into structured data using a chat client.
-await UseStructuredOutputWithMiddlewareAsync(chatClient);
+await UseStructuredOutputWithMiddlewareAsync(aiProjectClient, deploymentName);
 
-static async Task UseStructuredOutputWithResponseFormatAsync(ChatClient chatClient)
+static async Task UseStructuredOutputWithResponseFormatAsync(AIProjectClient aiProjectClient, string deploymentName)
 {
     Console.WriteLine("=== Structured Output with ResponseFormat ===");
 
     // Create the agent
-    AIAgent agent = chatClient.AsAIAgent(new ChatClientAgentOptions()
+    AIAgent agent = aiProjectClient.AsAIAgent(new ChatClientAgentOptions()
     {
         Name = "HelpfulAssistant",
         ChatOptions = new()
         {
+            ModelId = deploymentName,
             Instructions = "You are a helpful assistant.",
             // Specify CityInfo as the type parameter of ForJsonSchema to indicate the expected structured output from the agent.
-            ResponseFormat = Microsoft.Extensions.AI.ChatResponseFormat.ForJsonSchema<CityInfo>()
+            ResponseFormat = ChatResponseFormat.ForJsonSchema<CityInfo>()
         }
     });
 
@@ -81,12 +82,12 @@ static async Task UseStructuredOutputWithResponseFormatAsync(ChatClient chatClie
     Console.WriteLine();
 }
 
-static async Task UseStructuredOutputWithRunAsync(ChatClient chatClient)
+static async Task UseStructuredOutputWithRunAsync(AIProjectClient aiProjectClient, string deploymentName)
 {
     Console.WriteLine("=== Structured Output with RunAsync<T> ===");
 
     // Create the agent
-    AIAgent agent = chatClient.AsAIAgent(name: "HelpfulAssistant", instructions: "You are a helpful assistant.");
+    AIAgent agent = aiProjectClient.AsAIAgent(deploymentName, name: "HelpfulAssistant", instructions: "You are a helpful assistant.");
 
     // Set CityInfo as the type parameter of RunAsync method to specify the expected structured output from the agent and invoke it with some unstructured input.
     AgentResponse<CityInfo> response = await agent.RunAsync<CityInfo>("Provide information about the capital of France.");
@@ -99,19 +100,20 @@ static async Task UseStructuredOutputWithRunAsync(ChatClient chatClient)
     Console.WriteLine();
 }
 
-static async Task UseStructuredOutputWithRunStreamingAsync(ChatClient chatClient)
+static async Task UseStructuredOutputWithRunStreamingAsync(AIProjectClient aiProjectClient, string deploymentName)
 {
     Console.WriteLine("=== Structured Output with RunStreamingAsync ===");
 
     // Create the agent
-    AIAgent agent = chatClient.AsAIAgent(new ChatClientAgentOptions()
+    AIAgent agent = aiProjectClient.AsAIAgent(new ChatClientAgentOptions()
     {
         Name = "HelpfulAssistant",
         ChatOptions = new()
         {
+            ModelId = deploymentName,
             Instructions = "You are a helpful assistant.",
             // Specify CityInfo as the type parameter of ForJsonSchema to indicate the expected structured output from the agent.
-            ResponseFormat = Microsoft.Extensions.AI.ChatResponseFormat.ForJsonSchema<CityInfo>()
+            ResponseFormat = ChatResponseFormat.ForJsonSchema<CityInfo>()
         }
     });
 
@@ -129,12 +131,12 @@ static async Task UseStructuredOutputWithRunStreamingAsync(ChatClient chatClient
     Console.WriteLine();
 }
 
-static async Task UseStructuredOutputWithMiddlewareAsync(ChatClient chatClient)
+static async Task UseStructuredOutputWithMiddlewareAsync(AIProjectClient aiProjectClient, string deploymentName)
 {
     Console.WriteLine("=== Structured Output with UseStructuredOutput Middleware ===");
 
     // Create chat client that will transform the agent text response into structured output.
-    IChatClient meaiChatClient = chatClient.AsIChatClient();
+    IChatClient meaiChatClient = aiProjectClient.GetProjectOpenAIClient().GetProjectResponsesClientForModel(deploymentName).AsIChatClientWithStoredOutputDisabled(deploymentName);
 
     // Create the agent
     AIAgent agent = meaiChatClient.AsAIAgent(name: "HelpfulAssistant", instructions: "You are a helpful assistant.");

dotnet/samples/02-agents/Agents/Agent_Step03_PersistedConversations/Agent_Step03_PersistedConversations.csproj

@@ -1,4 +1,4 @@
-<Project Sdk="Microsoft.NET.Sdk">
+<Project Sdk="Microsoft.NET.Sdk">
 
   <PropertyGroup>
     <OutputType>Exe</OutputType>
@@ -9,13 +9,8 @@
   </PropertyGroup>
 
   <ItemGroup>
-    <PackageReference Include="Azure.AI.OpenAI" />
     <PackageReference Include="Azure.Identity" />
-    <PackageReference Include="Microsoft.Extensions.AI.OpenAI" />
-  </ItemGroup>
-
-  <ItemGroup>
-    <ProjectReference Include="..\..\..\..\src\Microsoft.Agents.AI.OpenAI\Microsoft.Agents.AI.OpenAI.csproj" />
+    <ProjectReference Include="..\..\..\..\src\Microsoft.Agents.AI.Foundry\Microsoft.Agents.AI.Foundry.csproj" />
   </ItemGroup>
 
 </Project>

dotnet/samples/02-agents/Agents/Agent_Step03_PersistedConversations/Program.cs

@@ -2,26 +2,27 @@
 
 #pragma warning disable CA1869 // Cache and reuse 'JsonSerializerOptions' instances
 
-// This sample shows how to create and use a simple AI agent with a conversation that can be persisted to disk.
+// Persisted Conversations — Save and restore chat history to disk
+//
+// This sample shows how to persist an agent conversation to disk
+// so it can be resumed across process restarts.
 
 using System.Text.Json;
-using Azure.AI.OpenAI;
+using Azure.AI.Projects;
 using Azure.Identity;
 using Microsoft.Agents.AI;
-using OpenAI.Chat;
 
-var endpoint = Environment.GetEnvironmentVariable("AZURE_OPENAI_ENDPOINT") ?? throw new InvalidOperationException("AZURE_OPENAI_ENDPOINT is not set.");
-var deploymentName = Environment.GetEnvironmentVariable("AZURE_OPENAI_DEPLOYMENT_NAME") ?? "gpt-5.4-mini";
+var endpoint = Environment.GetEnvironmentVariable("FOUNDRY_PROJECT_ENDPOINT") ?? throw new InvalidOperationException("FOUNDRY_PROJECT_ENDPOINT is not set.");
+var deploymentName = Environment.GetEnvironmentVariable("FOUNDRY_MODEL") ?? "gpt-5.4-mini";
 
 // Create the agent
 // WARNING: DefaultAzureCredential is convenient for development but requires careful consideration in production.
 // In production, consider using a specific credential (e.g., ManagedIdentityCredential) to avoid
 // latency issues, unintended credential probing, and potential security risks from fallback mechanisms.
-AIAgent agent = new AzureOpenAIClient(
+AIAgent agent = new AIProjectClient(
     new Uri(endpoint),
     new DefaultAzureCredential())
-    .GetChatClient(deploymentName)
-    .AsAIAgent(instructions: "You are good at telling jokes.", name: "Joker");
+    .AsAIAgent(model: deploymentName, instructions: "You are good at telling jokes.", name: "Joker");
 
 // Start a new session for the agent conversation.
 AgentSession session = await agent.CreateSessionAsync();

dotnet/samples/02-agents/Agents/Agent_Step04_3rdPartyChatHistoryStorage/Agent_Step04_3rdPartyChatHistoryStorage.csproj

@@ -1,4 +1,4 @@
-<Project Sdk="Microsoft.NET.Sdk">
+<Project Sdk="Microsoft.NET.Sdk">
 
   <PropertyGroup>
     <OutputType>Exe</OutputType>
@@ -9,14 +9,12 @@
   </PropertyGroup>
 
   <ItemGroup>
-    <PackageReference Include="Azure.AI.OpenAI" />
     <PackageReference Include="Azure.Identity" />
-    <PackageReference Include="Microsoft.Extensions.AI.OpenAI" />
     <PackageReference Include="Microsoft.SemanticKernel.Connectors.InMemory" />
   </ItemGroup>
 
   <ItemGroup>
-    <ProjectReference Include="..\..\..\..\src\Microsoft.Agents.AI.OpenAI\Microsoft.Agents.AI.OpenAI.csproj" />
+    <ProjectReference Include="..\..\..\..\src\Microsoft.Agents.AI.Foundry\Microsoft.Agents.AI.Foundry.csproj" />
   </ItemGroup>
 
 </Project>

dotnet/samples/02-agents/Agents/Agent_Step04_3rdPartyChatHistoryStorage/Program.cs

@@ -2,23 +2,25 @@
 
 #pragma warning disable CA1869 // Cache and reuse 'JsonSerializerOptions' instances
 
-// This sample shows how to create and use a simple AI agent with custom ChatHistoryProvider that stores chat history in a custom storage location.
-// The state of the custom ChatHistoryProvider (SessionDbKey) is stored in the AgentSession's StateBag, so that when the session is resumed later,
-// the chat history can be retrieved from the custom storage location.
+// Third-Party Chat History Storage — Custom ChatHistoryProvider
+//
+// This sample shows how to use a custom ChatHistoryProvider that stores
+// chat history in an external location. The provider's state (SessionDbKey)
+// is stored in AgentSession.StateBag so conversations can be resumed later.
 
 using System.Text.Json;
-using Azure.AI.OpenAI;
+using Azure.AI.Extensions.OpenAI;
+using Azure.AI.Projects;
 using Azure.Identity;
 using Microsoft.Agents.AI;
 using Microsoft.Extensions.AI;
 using Microsoft.Extensions.VectorData;
 using Microsoft.SemanticKernel.Connectors.InMemory;
-using OpenAI.Chat;
 using SampleApp;
 using ChatMessage = Microsoft.Extensions.AI.ChatMessage;
 
-var endpoint = Environment.GetEnvironmentVariable("AZURE_OPENAI_ENDPOINT") ?? throw new InvalidOperationException("AZURE_OPENAI_ENDPOINT is not set.");
-var deploymentName = Environment.GetEnvironmentVariable("AZURE_OPENAI_DEPLOYMENT_NAME") ?? "gpt-5.4-mini";
+var endpoint = Environment.GetEnvironmentVariable("FOUNDRY_PROJECT_ENDPOINT") ?? throw new InvalidOperationException("FOUNDRY_PROJECT_ENDPOINT is not set.");
+var deploymentName = Environment.GetEnvironmentVariable("FOUNDRY_MODEL") ?? "gpt-5.4-mini";
 
 // Create a vector store to store the chat messages in.
 // Replace this with a vector store implementation of your choice if you want to persist the chat history to disk.
@@ -28,16 +30,18 @@ VectorStore vectorStore = new InMemoryVectorStore();
 // WARNING: DefaultAzureCredential is convenient for development but requires careful consideration in production.
 // In production, consider using a specific credential (e.g., ManagedIdentityCredential) to avoid
 // latency issues, unintended credential probing, and potential security risks from fallback mechanisms.
-AIAgent agent = new AzureOpenAIClient(
+AIAgent agent = new AIProjectClient(
     new Uri(endpoint),
     new DefaultAzureCredential())
-    .GetChatClient(deploymentName)
+    .GetProjectOpenAIClient()
+    .GetProjectResponsesClient()
+    .AsIChatClientWithStoredOutputDisabled(deploymentName)
     .AsAIAgent(new ChatClientAgentOptions
     {
-        ChatOptions = new() { Instructions = "You are good at telling jokes." },
+        ChatOptions = new() { ModelId = deploymentName, Instructions = "You are good at telling jokes." },
         Name = "Joker",
         // Create a new ChatHistoryProvider for this agent that stores chat history in a vector store.
-        ChatHistoryProvider = new VectorChatHistoryProvider(vectorStore)
+        ChatHistoryProvider = new VectorChatHistoryProvider(vectorStore),
     });
 
 // Start a new session for the agent conversation.

dotnet/samples/02-agents/Agents/Agent_Step05_Observability/Agent_Step05_Observability.csproj

@@ -1,4 +1,4 @@
-<Project Sdk="Microsoft.NET.Sdk">
+<Project Sdk="Microsoft.NET.Sdk">
 
   <PropertyGroup>
     <OutputType>Exe</OutputType>
@@ -9,16 +9,14 @@
   </PropertyGroup>
 
   <ItemGroup>
-    <PackageReference Include="Azure.AI.OpenAI" />
     <PackageReference Include="Azure.Identity" />
     <PackageReference Include="Azure.Monitor.OpenTelemetry.Exporter" />
-    <PackageReference Include="Microsoft.Extensions.AI.OpenAI" />
     <PackageReference Include="OpenTelemetry" />
     <PackageReference Include="OpenTelemetry.Exporter.Console" />
   </ItemGroup>
 
   <ItemGroup>
-    <ProjectReference Include="..\..\..\..\src\Microsoft.Agents.AI.OpenAI\Microsoft.Agents.AI.OpenAI.csproj" />
+    <ProjectReference Include="..\..\..\..\src\Microsoft.Agents.AI.Foundry\Microsoft.Agents.AI.Foundry.csproj" />
   </ItemGroup>
 
 </Project>

dotnet/samples/02-agents/Agents/Agent_Step05_Observability/Program.cs

@@ -1,17 +1,19 @@
 // Copyright (c) Microsoft. All rights reserved.
 
-// This sample shows how to create and use a simple AI agent with Azure OpenAI as the backend that logs telemetry using OpenTelemetry.
+// Agent Observability — OpenTelemetry tracing with Azure AI Foundry
+//
+// This sample shows how to instrument an AI agent with OpenTelemetry
+// for distributed tracing and telemetry logging.
 
-using Azure.AI.OpenAI;
+using Azure.AI.Projects;
 using Azure.Identity;
 using Azure.Monitor.OpenTelemetry.Exporter;
 using Microsoft.Agents.AI;
-using OpenAI.Chat;
 using OpenTelemetry;
 using OpenTelemetry.Trace;
 
-var endpoint = Environment.GetEnvironmentVariable("AZURE_OPENAI_ENDPOINT") ?? throw new InvalidOperationException("AZURE_OPENAI_ENDPOINT is not set.");
-var deploymentName = Environment.GetEnvironmentVariable("AZURE_OPENAI_DEPLOYMENT_NAME") ?? "gpt-5.4-mini";
+var endpoint = Environment.GetEnvironmentVariable("FOUNDRY_PROJECT_ENDPOINT") ?? throw new InvalidOperationException("FOUNDRY_PROJECT_ENDPOINT is not set.");
+var deploymentName = Environment.GetEnvironmentVariable("FOUNDRY_MODEL") ?? "gpt-5.4-mini";
 var applicationInsightsConnectionString = Environment.GetEnvironmentVariable("APPLICATIONINSIGHTS_CONNECTION_STRING");
 
 // Create TracerProvider with console exporter
@@ -30,9 +32,8 @@ using var tracerProvider = tracerProviderBuilder.Build();
 // WARNING: DefaultAzureCredential is convenient for development but requires careful consideration in production.
 // In production, consider using a specific credential (e.g., ManagedIdentityCredential) to avoid
 // latency issues, unintended credential probing, and potential security risks from fallback mechanisms.
-AIAgent agent = new AzureOpenAIClient(new Uri(endpoint), new DefaultAzureCredential())
-    .GetChatClient(deploymentName)
-    .AsAIAgent(instructions: "You are good at telling jokes.", name: "Joker")
+AIAgent agent = new AIProjectClient(new Uri(endpoint), new DefaultAzureCredential())
+    .AsAIAgent(model: deploymentName, instructions: "You are good at telling jokes.", name: "Joker")
     .AsBuilder()
     .UseOpenTelemetry(sourceName: sourceName)
     .Build();

dotnet/samples/02-agents/Agents/Agent_Step06_DependencyInjection/Agent_Step06_DependencyInjection.csproj

@@ -1,4 +1,4 @@
-<Project Sdk="Microsoft.NET.Sdk">
+<Project Sdk="Microsoft.NET.Sdk">
 
   <PropertyGroup>
     <OutputType>Exe</OutputType>
@@ -9,14 +9,12 @@
   </PropertyGroup>
 
   <ItemGroup>
-    <PackageReference Include="Azure.AI.OpenAI" />
     <PackageReference Include="Azure.Identity" />
-    <PackageReference Include="Microsoft.Extensions.AI.OpenAI" />
     <PackageReference Include="Microsoft.Extensions.Hosting" />
   </ItemGroup>
 
   <ItemGroup>
-    <ProjectReference Include="..\..\..\..\src\Microsoft.Agents.AI.OpenAI\Microsoft.Agents.AI.OpenAI.csproj" />
+    <ProjectReference Include="..\..\..\..\src\Microsoft.Agents.AI.Foundry\Microsoft.Agents.AI.Foundry.csproj" />
   </ItemGroup>
 
 </Project>

dotnet/samples/02-agents/Agents/Agent_Step06_DependencyInjection/Program.cs

@@ -2,38 +2,30 @@
 
 #pragma warning disable CA1812
 
-// This sample shows how to use dependency injection to register an AIAgent and use it from a hosted service with a user input chat loop.
+// Dependency Injection — Register and resolve agents via DI
+//
+// This sample shows how to use dependency injection to register an
+// AIAgent and consume it from a hosted service with a chat loop.
 
-using Azure.AI.OpenAI;
+using Azure.AI.Projects;
 using Azure.Identity;
 using Microsoft.Agents.AI;
-using Microsoft.Extensions.AI;
 using Microsoft.Extensions.DependencyInjection;
 using Microsoft.Extensions.Hosting;
 
-var endpoint = Environment.GetEnvironmentVariable("AZURE_OPENAI_ENDPOINT") ?? throw new InvalidOperationException("AZURE_OPENAI_ENDPOINT is not set.");
-var deploymentName = Environment.GetEnvironmentVariable("AZURE_OPENAI_DEPLOYMENT_NAME") ?? "gpt-5.4-mini";
+var endpoint = Environment.GetEnvironmentVariable("FOUNDRY_PROJECT_ENDPOINT") ?? throw new InvalidOperationException("FOUNDRY_PROJECT_ENDPOINT is not set.");
+var deploymentName = Environment.GetEnvironmentVariable("FOUNDRY_MODEL") ?? "gpt-5.4-mini";
 
 // Create a host builder that we will register services with and then run.
 HostApplicationBuilder builder = Host.CreateApplicationBuilder(args);
 
-// Add agent options to the service collection.
-builder.Services.AddSingleton(new ChatClientAgentOptions() { Name = "Joker", ChatOptions = new() { Instructions = "You are good at telling jokes." } });
-
-// Add a chat client to the service collection.
+// Create the AI agent from the Azure AI Foundry project client.
 // WARNING: DefaultAzureCredential is convenient for development but requires careful consideration in production.
 // In production, consider using a specific credential (e.g., ManagedIdentityCredential) to avoid
 // latency issues, unintended credential probing, and potential security risks from fallback mechanisms.
-builder.Services.AddKeyedChatClient("AzureOpenAI", (sp) => new AzureOpenAIClient(
-    new Uri(endpoint),
-    new DefaultAzureCredential())
-        .GetChatClient(deploymentName)
-        .AsIChatClient());
-
-// Add the AI agent to the service collection.
-builder.Services.AddSingleton<AIAgent>((sp) => new ChatClientAgent(
-    chatClient: sp.GetRequiredKeyedService<IChatClient>("AzureOpenAI"),
-    options: sp.GetRequiredService<ChatClientAgentOptions>()));
+AIProjectClient aiProjectClient = new(new Uri(endpoint), new DefaultAzureCredential());
+AIAgent agent = aiProjectClient.AsAIAgent(model: deploymentName, name: "Joker", instructions: "You are good at telling jokes.");
+builder.Services.AddSingleton(agent);
 
 // Add a sample service that will use the agent to respond to user input.
 builder.Services.AddHostedService<SampleService>();

dotnet/samples/02-agents/Agents/Agent_Step08_UsingImages/Agent_Step08_UsingImages.csproj

@@ -1,4 +1,4 @@
-<Project Sdk="Microsoft.NET.Sdk">
+<Project Sdk="Microsoft.NET.Sdk">
 
   <PropertyGroup>
     <OutputType>Exe</OutputType>
@@ -9,12 +9,8 @@
   </PropertyGroup>
 
   <ItemGroup>
-    <PackageReference Include="Azure.AI.OpenAI" />
     <PackageReference Include="Azure.Identity" />
-  </ItemGroup>
-
-  <ItemGroup>
-    <ProjectReference Include="..\..\..\..\src\Microsoft.Agents.AI.OpenAI\Microsoft.Agents.AI.OpenAI.csproj" />
+    <ProjectReference Include="..\..\..\..\src\Microsoft.Agents.AI.Foundry\Microsoft.Agents.AI.Foundry.csproj" />
   </ItemGroup>
 
   <ItemGroup>

dotnet/samples/02-agents/Agents/Agent_Step08_UsingImages/Program.cs

@@ -1,24 +1,25 @@
 // Copyright (c) Microsoft. All rights reserved.
 
-// This sample shows how to use Image Multi-Modality with an AI agent.
+// Using Images — Multimodal input with an AI agent
+//
+// This sample shows how to send image content to an AI agent
+// for vision-based analysis.
 
-using Azure.AI.OpenAI;
+using Azure.AI.Projects;
 using Azure.Identity;
 using Microsoft.Extensions.AI;
-using OpenAI.Chat;
-using ChatMessage = Microsoft.Extensions.AI.ChatMessage;
 
-var endpoint = Environment.GetEnvironmentVariable("AZURE_OPENAI_ENDPOINT") ?? throw new InvalidOperationException("AZURE_OPENAI_ENDPOINT is not set.");
-var deploymentName = System.Environment.GetEnvironmentVariable("AZURE_OPENAI_DEPLOYMENT_NAME") ?? "gpt-5.4-mini";
+var endpoint = Environment.GetEnvironmentVariable("FOUNDRY_PROJECT_ENDPOINT") ?? throw new InvalidOperationException("FOUNDRY_PROJECT_ENDPOINT is not set.");
+var deploymentName = System.Environment.GetEnvironmentVariable("FOUNDRY_MODEL") ?? "gpt-5.4-mini";
 
 // WARNING: DefaultAzureCredential is convenient for development but requires careful consideration in production.
 // In production, consider using a specific credential (e.g., ManagedIdentityCredential) to avoid
 // latency issues, unintended credential probing, and potential security risks from fallback mechanisms.
-var agent = new AzureOpenAIClient(new Uri(endpoint), new DefaultAzureCredential())
-    .GetChatClient(deploymentName)
+var agent = new AIProjectClient(new Uri(endpoint), new DefaultAzureCredential())
     .AsAIAgent(
-        name: "VisionAgent",
-        instructions: "You are a helpful agent that can analyze images");
+        model: deploymentName,
+        instructions: "You are a helpful agent that can analyze images",
+        name: "VisionAgent");
 
 ChatMessage message = new(ChatRole.User, [
     new TextContent("What do you see in this image?"),

dotnet/samples/02-agents/Agents/Agent_Step08_UsingImages/README.md

@@ -1,6 +1,6 @@
 # Using Images with AI Agents
 
-This sample demonstrates how to use image multi-modality with an AI agent. It shows how to create a vision-enabled agent that can analyze and describe images using Azure OpenAI.
+This sample demonstrates how to use image multi-modality with an AI agent. It shows how to create a vision-enabled agent that can analyze and describe images using Microsoft Foundry with `AIProjectClient`.
 
 ## What this sample demonstrates
 
@@ -13,13 +13,13 @@ This sample demonstrates how to use image multi-modality with an AI agent. It sh
 
 - **Vision Agent**: Creates an agent specifically instructed to analyze images
 - **Multimodal Input**: Combines text questions with image uri in a single message
-- **Azure OpenAI Integration**: Uses AzureOpenAI LLM agents
+- **Microsoft Foundry Integration**: Uses `AIProjectClient` to create a Foundry-backed agent
 
 ## Prerequisites
 
 Before running this sample, ensure you have:
 
-1. An Azure OpenAI project set up
+1. A Microsoft Foundry project set up
 2. A compatible model deployment (e.g., gpt-5.4-mini)
 3. Azure CLI installed and authenticated
 
@@ -28,8 +28,8 @@ Before running this sample, ensure you have:
 Set the following environment variables:
 
 ```powershell
-$env:AZURE_OPENAI_ENDPOINT="https://your-resource.openai.azure.com/" # Replace with your Azure OpenAI endpoint
-$env:AZURE_OPENAI_DEPLOYMENT_NAME="gpt-5.4-mini" # Replace with your model deployment name (optional, defaults to gpt-5.4-mini)
+$env:FOUNDRY_PROJECT_ENDPOINT="https://<your-project>.services.ai.azure.com/api/projects/<your-project>" # Replace with your Foundry project endpoint
+$env:FOUNDRY_MODEL="gpt-5.4-mini" # Replace with your model name (optional, defaults to gpt-5.4-mini)
 ```
 
 ## Run the sample
@@ -49,4 +49,3 @@ The sample will:
 2. Send a message containing both text ("What do you see in this image?") and a Uri image of a green walk
 3. The agent will analyze the image and provide a description
 4. Clean up resources by deleting the thread and agent
-

dotnet/samples/02-agents/Agents/Agent_Step09_AsFunctionTool/Agent_Step09_AsFunctionTool.csproj

@@ -1,4 +1,4 @@
-<Project Sdk="Microsoft.NET.Sdk">
+<Project Sdk="Microsoft.NET.Sdk">
 
   <PropertyGroup>
     <OutputType>Exe</OutputType>
@@ -10,13 +10,12 @@
   </PropertyGroup>
 
   <ItemGroup>
-    <PackageReference Include="Azure.AI.OpenAI" />
     <PackageReference Include="Azure.Identity" />
     <PackageReference Include="Microsoft.Extensions.Hosting" />
   </ItemGroup>
 
   <ItemGroup>
-    <ProjectReference Include="..\..\..\..\src\Microsoft.Agents.AI.OpenAI\Microsoft.Agents.AI.OpenAI.csproj" />
+    <ProjectReference Include="..\..\..\..\src\Microsoft.Agents.AI.Foundry\Microsoft.Agents.AI.Foundry.csproj" />
   </ItemGroup>
   
 </Project>

dotnet/samples/02-agents/Agents/Agent_Step09_AsFunctionTool/Program.cs

@@ -1,41 +1,43 @@
 // Copyright (c) Microsoft. All rights reserved.
 
-// This sample shows how to create and use a Azure OpenAI AI agent as a function tool.
+// Agent as Function Tool — Use one agent as a tool for another
+//
+// This sample shows how to create an AI agent and expose it as a
+// function tool that another agent can call.
 
 using System.ComponentModel;
-using Azure.AI.OpenAI;
+using Azure.AI.Projects;
 using Azure.Identity;
 using Microsoft.Agents.AI;
 using Microsoft.Extensions.AI;
-using OpenAI.Chat;
 
-var endpoint = Environment.GetEnvironmentVariable("AZURE_OPENAI_ENDPOINT") ?? throw new InvalidOperationException("AZURE_OPENAI_ENDPOINT is not set.");
-var deploymentName = Environment.GetEnvironmentVariable("AZURE_OPENAI_DEPLOYMENT_NAME") ?? "gpt-5.4-mini";
+var endpoint = Environment.GetEnvironmentVariable("FOUNDRY_PROJECT_ENDPOINT") ?? throw new InvalidOperationException("FOUNDRY_PROJECT_ENDPOINT is not set.");
+var deploymentName = Environment.GetEnvironmentVariable("FOUNDRY_MODEL") ?? "gpt-5.4-mini";
 
 [Description("Get the weather for a given location.")]
 static string GetWeather([Description("The location to get the weather for.")] string location)
     => $"The weather in {location} is cloudy with a high of 15°C.";
 
-// Create the chat client and agent, and provide the function tool to the agent.
+// Create the agent and provide the function tool to it.
 // WARNING: DefaultAzureCredential is convenient for development but requires careful consideration in production.
 // In production, consider using a specific credential (e.g., ManagedIdentityCredential) to avoid
 // latency issues, unintended credential probing, and potential security risks from fallback mechanisms.
-AIAgent weatherAgent = new AzureOpenAIClient(
-    new Uri(endpoint),
-    new DefaultAzureCredential())
-     .GetChatClient(deploymentName)
+AIProjectClient aiProjectClient = new(new Uri(endpoint), new DefaultAzureCredential());
+
+AIAgent weatherAgent = aiProjectClient
      .AsAIAgent(
+        model: deploymentName,
         instructions: "You answer questions about the weather.",
         name: "WeatherAgent",
         description: "An agent that answers questions about the weather.",
         tools: [AIFunctionFactory.Create(GetWeather)]);
 
 // Create the main agent, and provide the weather agent as a function tool.
-AIAgent agent = new AzureOpenAIClient(
-    new Uri(endpoint),
-    new DefaultAzureCredential())
-    .GetChatClient(deploymentName)
-    .AsAIAgent(instructions: "You are a helpful assistant who responds in French.", tools: [weatherAgent.AsAIFunction()]);
+AIAgent agent = aiProjectClient
+    .AsAIAgent(
+        model: deploymentName,
+        instructions: "You are a helpful assistant who responds in French.",
+        tools: [weatherAgent.AsAIFunction()]);
 
 // Invoke the agent and output the text result.
 Console.WriteLine(await agent.RunAsync("What is the weather like in Amsterdam?"));

dotnet/samples/02-agents/Agents/Agent_Step10_BackgroundResponsesWithToolsAndPersistence/Agent_Step10_BackgroundResponsesWithToolsAndPersistence.csproj

@@ -1,4 +1,4 @@
-<Project Sdk="Microsoft.NET.Sdk">
+<Project Sdk="Microsoft.NET.Sdk">
 
   <PropertyGroup>
  <OutputType>Exe</OutputType>
@@ -9,12 +9,8 @@
   </PropertyGroup>
 
   <ItemGroup>
-    <PackageReference Include="Azure.AI.OpenAI" />
     <PackageReference Include="Azure.Identity" />
-  </ItemGroup>
-  
-  <ItemGroup>
-    <ProjectReference Include="..\..\..\..\src\Microsoft.Agents.AI.OpenAI\Microsoft.Agents.AI.OpenAI.csproj" />
+    <ProjectReference Include="..\..\..\..\src\Microsoft.Agents.AI.Foundry\Microsoft.Agents.AI.Foundry.csproj" />
   </ItemGroup>
   
 </Project>

dotnet/samples/02-agents/Agents/Agent_Step10_BackgroundResponsesWithToolsAndPersistence/Program.cs

@@ -1,31 +1,32 @@
 // Copyright (c) Microsoft. All rights reserved.
 
-// This sample demonstrates how to use background responses with ChatClientAgent and Azure OpenAI Responses for long-running operations.
-// It shows polling for completion using continuation tokens, function calling during background operations,
-// and persisting/restoring agent state between polling cycles.
+// Background Responses with Tools — Long-running operations with persistence
+//
+// This sample demonstrates how to use background responses with ChatClientAgent
+// for long-running operations. It shows polling for completion using continuation
+// tokens, function calling during background operations, and persisting/restoring
+// agent state between polling cycles.
 
 #pragma warning disable CA1050 // Declare types in namespaces
 
 using System.ComponentModel;
 using System.Text.Json;
-using Azure.AI.OpenAI;
+using Azure.AI.Projects;
 using Azure.Identity;
 using Microsoft.Agents.AI;
 using Microsoft.Extensions.AI;
-using OpenAI.Responses;
 
-var endpoint = Environment.GetEnvironmentVariable("AZURE_OPENAI_ENDPOINT") ?? throw new InvalidOperationException("AZURE_OPENAI_ENDPOINT is not set.");
-var deploymentName = Environment.GetEnvironmentVariable("AZURE_OPENAI_DEPLOYMENT_NAME") ?? "gpt-5.4-mini";
+var endpoint = Environment.GetEnvironmentVariable("FOUNDRY_PROJECT_ENDPOINT") ?? throw new InvalidOperationException("FOUNDRY_PROJECT_ENDPOINT is not set.");
+var deploymentName = Environment.GetEnvironmentVariable("FOUNDRY_MODEL") ?? "gpt-5.4-mini";
 
 var stateStore = new Dictionary<string, JsonElement?>();
 
 // WARNING: DefaultAzureCredential is convenient for development but requires careful consideration in production.
 // In production, consider using a specific credential (e.g., ManagedIdentityCredential) to avoid
 // latency issues, unintended credential probing, and potential security risks from fallback mechanisms.
-AIAgent agent = new AzureOpenAIClient(
+AIAgent agent = new AIProjectClient(
     new Uri(endpoint),
     new DefaultAzureCredential())
-     .GetResponsesClient()
      .AsAIAgent(
         model: deploymentName,
         name: "SpaceNovelWriter",

dotnet/samples/02-agents/Agents/Agent_Step11_Middleware/Agent_Step11_Middleware.csproj

@@ -9,13 +9,12 @@
   </PropertyGroup>
 
   <ItemGroup>
-    <PackageReference Include="Microsoft.Extensions.Logging.Console" />
     <PackageReference Include="Azure.Identity" />
-    <PackageReference Include="Azure.AI.OpenAI" />
+    <PackageReference Include="Microsoft.Extensions.Logging.Console" />
   </ItemGroup>
 
   <ItemGroup>
-    <ProjectReference Include="..\..\..\..\src\Microsoft.Agents.AI.OpenAI\Microsoft.Agents.AI.OpenAI.csproj" />
+    <ProjectReference Include="..\..\..\..\src\Microsoft.Agents.AI.Foundry\Microsoft.Agents.AI.Foundry.csproj" />
 	  <ProjectReference Include="..\..\..\..\src\Microsoft.Agents.AI\Microsoft.Agents.AI.csproj" />
   </ItemGroup>
 

dotnet/samples/02-agents/Agents/Agent_Step11_Middleware/Program.cs

@@ -1,6 +1,8 @@
 // Copyright (c) Microsoft. All rights reserved.
 
-// This sample shows multiple middleware layers working together with Azure OpenAI:
+// Middleware — Chain multiple middleware layers on an agent
+//
+// This sample shows multiple middleware layers working together with Azure AI Foundry:
 // chat client (global/per-request), agent run (PII filtering and guardrails),
 // function invocation (logging and result overrides), human-in-the-loop
 // approval workflows for sensitive function calls, and MessageAIContextProvider
@@ -8,21 +10,20 @@
 
 using System.ComponentModel;
 using System.Text.RegularExpressions;
-using Azure.AI.OpenAI;
+using Azure.AI.Projects;
 using Azure.Identity;
 using Microsoft.Agents.AI;
 using Microsoft.Extensions.AI;
 
-// Get Microsoft Foundry configuration from environment variables
-var endpoint = Environment.GetEnvironmentVariable("AZURE_OPENAI_ENDPOINT") ?? throw new InvalidOperationException("AZURE_OPENAI_ENDPOINT is not set.");
-var deploymentName = System.Environment.GetEnvironmentVariable("AZURE_OPENAI_DEPLOYMENT_NAME") ?? "gpt-5.4-mini";
+// Get Azure AI Foundry configuration from environment variables
+var endpoint = Environment.GetEnvironmentVariable("FOUNDRY_PROJECT_ENDPOINT") ?? throw new InvalidOperationException("FOUNDRY_PROJECT_ENDPOINT is not set.");
+var deploymentName = Environment.GetEnvironmentVariable("FOUNDRY_MODEL") ?? "gpt-5.4-mini";
 
 // Get a client to create/retrieve server side agents with
 // WARNING: DefaultAzureCredential is convenient for development but requires careful consideration in production.
 // In production, consider using a specific credential (e.g., ManagedIdentityCredential) to avoid
 // latency issues, unintended credential probing, and potential security risks from fallback mechanisms.
-var azureOpenAIClient = new AzureOpenAIClient(new Uri(endpoint), new DefaultAzureCredential())
-    .GetChatClient(deploymentName);
+var aiProjectClient = new AIProjectClient(new Uri(endpoint), new DefaultAzureCredential());
 
 [Description("Get the weather for a given location.")]
 static string GetWeather([Description("The location to get the weather for.")] string location)
@@ -33,12 +34,14 @@ static string GetDateTime()
     => DateTimeOffset.Now.ToString();
 
 // Adding middleware to the chat client level and building an agent on top of it
-var originalAgent = azureOpenAIClient.AsIChatClient()
-    .AsBuilder()
-    .Use(getResponseFunc: ChatClientMiddleware, getStreamingResponseFunc: null)
-    .BuildAIAgent(
-        instructions: "You are an AI assistant that helps people find information.",
-        tools: [AIFunctionFactory.Create(GetDateTime, name: nameof(GetDateTime))]);
+var originalAgent = aiProjectClient.AsAIAgent(
+    model: deploymentName,
+    instructions: "You are an AI assistant that helps people find information.",
+    tools: [AIFunctionFactory.Create(GetDateTime, name: nameof(GetDateTime))],
+    clientFactory: (chatClient) => chatClient
+        .AsBuilder()
+        .Use(getResponseFunc: ChatClientMiddleware, getStreamingResponseFunc: null)
+        .Build());
 
 // Adding middleware to the agent level
 var middlewareEnabledAgent = originalAgent
@@ -117,11 +120,13 @@ Console.WriteLine($"Context-enriched response: {contextResponse}");
 // In this case we are attaching an AIContextProvider that only adds messages.
 Console.WriteLine("\n\n=== Example 6: AIContextProvider on chat client pipeline ===");
 
-var chatClientProviderAgent = azureOpenAIClient.AsIChatClient()
-    .AsBuilder()
-    .UseAIContextProviders(new DateTimeContextProvider())
-    .BuildAIAgent(
-        instructions: "You are an AI assistant that helps people find information.");
+var chatClientProviderAgent = aiProjectClient.AsAIAgent(
+    model: deploymentName,
+    instructions: "You are an AI assistant that helps people find information.",
+    clientFactory: (chatClient) => chatClient
+        .AsBuilder()
+        .UseAIContextProviders(new DateTimeContextProvider())
+        .Build());
 
 var chatClientContextResponse = await chatClientProviderAgent.RunAsync("Is it almost time for lunch?");
 Console.WriteLine($"Chat client context-enriched response: {chatClientContextResponse}");

dotnet/samples/02-agents/Agents/Agent_Step11_Middleware/README.md

@@ -7,7 +7,7 @@ This sample demonstrates how to add middleware to intercept:
 
 ## What This Sample Shows
 
-1. Azure OpenAI integration via `AzureOpenAIClient` and `DefaultAzureCredential`
+1. Microsoft Foundry integration via `AIProjectClient` and `DefaultAzureCredential`
 2. Chat client middleware using `ChatClientBuilder.Use(...)`
 3. Agent run middleware (PII redaction and wording guardrails)
 4. Function invocation middleware (logging and overriding a tool result)
@@ -26,8 +26,8 @@ Attempting to use function middleware on agents that do not wrap a ChatClientAge
 ## Prerequisites
 
 1. Environment variables:
-   - `AZURE_OPENAI_ENDPOINT`: Your Azure OpenAI endpoint
-   - `AZURE_OPENAI_DEPLOYMENT_NAME`: Chat deployment name (optional; defaults to `gpt-5.4-mini`)
+   - `FOUNDRY_PROJECT_ENDPOINT`: Your Foundry project endpoint
+   - `FOUNDRY_MODEL`: Model name (optional; defaults to `gpt-5.4-mini`)
 2. Sign in with Azure CLI (PowerShell):
    ```powershell
    az login
@@ -40,4 +40,3 @@ Use PowerShell:
 cd dotnet/samples/02-agents/Agents/Agent_Step11_Middleware
 dotnet run
 ```
-

dotnet/samples/02-agents/Agents/Agent_Step12_Plugins/Agent_Step12_Plugins.csproj

@@ -11,13 +11,12 @@
   </PropertyGroup>
 
   <ItemGroup>
-    <PackageReference Include="Microsoft.Extensions.Logging.Console" />
     <PackageReference Include="Azure.Identity" />
-    <PackageReference Include="Azure.AI.OpenAI" />
+    <PackageReference Include="Microsoft.Extensions.Logging.Console" />
   </ItemGroup>
 
   <ItemGroup>
-    <ProjectReference Include="..\..\..\..\src\Microsoft.Agents.AI.OpenAI\Microsoft.Agents.AI.OpenAI.csproj" />
+    <ProjectReference Include="..\..\..\..\src\Microsoft.Agents.AI.Foundry\Microsoft.Agents.AI.Foundry.csproj" />
     <ProjectReference Include="..\..\..\..\src\Microsoft.Agents.AI\Microsoft.Agents.AI.csproj" />
   </ItemGroup>
   

dotnet/samples/02-agents/Agents/Agent_Step12_Plugins/Program.cs

@@ -1,5 +1,7 @@
 // Copyright (c) Microsoft. All rights reserved.
 
+// Plugins — Use plugin classes with dependency injection
+//
 // This sample shows how to use plugins with an AI agent. Plugin classes can
 // depend on other services that need to be injected. In this sample, the
 // AgentPlugin class uses the WeatherProvider and CurrentTimeProvider classes
@@ -9,15 +11,14 @@
 // as AI functions. The AsAITools method of the plugin class shows how to specify
 // which methods should be exposed to the AI agent.
 
-using Azure.AI.OpenAI;
+using Azure.AI.Projects;
 using Azure.Identity;
 using Microsoft.Agents.AI;
 using Microsoft.Extensions.AI;
 using Microsoft.Extensions.DependencyInjection;
-using OpenAI.Chat;
 
-var endpoint = Environment.GetEnvironmentVariable("AZURE_OPENAI_ENDPOINT") ?? throw new InvalidOperationException("AZURE_OPENAI_ENDPOINT is not set.");
-var deploymentName = Environment.GetEnvironmentVariable("AZURE_OPENAI_DEPLOYMENT_NAME") ?? "gpt-5.4-mini";
+var endpoint = Environment.GetEnvironmentVariable("FOUNDRY_PROJECT_ENDPOINT") ?? throw new InvalidOperationException("FOUNDRY_PROJECT_ENDPOINT is not set.");
+var deploymentName = Environment.GetEnvironmentVariable("FOUNDRY_MODEL") ?? "gpt-5.4-mini";
 
 // Create a service collection to hold the agent plugin and its dependencies.
 ServiceCollection services = new();
@@ -30,11 +31,11 @@ IServiceProvider serviceProvider = services.BuildServiceProvider();
 // WARNING: DefaultAzureCredential is convenient for development but requires careful consideration in production.
 // In production, consider using a specific credential (e.g., ManagedIdentityCredential) to avoid
 // latency issues, unintended credential probing, and potential security risks from fallback mechanisms.
-AIAgent agent = new AzureOpenAIClient(
+AIAgent agent = new AIProjectClient(
     new Uri(endpoint),
     new DefaultAzureCredential())
-    .GetChatClient(deploymentName)
     .AsAIAgent(
+        model: deploymentName,
         instructions: "You are a helpful assistant that helps people find information.",
         name: "Assistant",
         tools: [.. serviceProvider.GetRequiredService<AgentPlugin>().AsAITools()],

dotnet/samples/02-agents/Agents/Agent_Step13_ChatReduction/Agent_Step13_ChatReduction.csproj

@@ -1,4 +1,4 @@
-<Project Sdk="Microsoft.NET.Sdk">
+<Project Sdk="Microsoft.NET.Sdk">
 
   <PropertyGroup>
     <OutputType>Exe</OutputType>
@@ -9,13 +9,8 @@
   </PropertyGroup>
 
   <ItemGroup>
-    <PackageReference Include="Azure.AI.OpenAI" />
     <PackageReference Include="Azure.Identity" />
-    <PackageReference Include="Microsoft.Extensions.AI.OpenAI" />
-  </ItemGroup>
-
-  <ItemGroup>
-    <ProjectReference Include="..\..\..\..\src\Microsoft.Agents.AI.OpenAI\Microsoft.Agents.AI.OpenAI.csproj" />
+    <ProjectReference Include="..\..\..\..\src\Microsoft.Agents.AI.Foundry\Microsoft.Agents.AI.Foundry.csproj" />
   </ItemGroup>
 
 </Project>

dotnet/samples/02-agents/Agents/Agent_Step13_ChatReduction/Program.cs

@@ -1,30 +1,37 @@
 // Copyright (c) Microsoft. All rights reserved.
 
-// This sample shows how to use a chat history reducer to keep the context within model size limits.
-// Any implementation of Microsoft.Extensions.AI.IChatReducer can be used to customize how the chat history is reduced.
-// NOTE: this feature is only supported where the chat history is stored locally, such as with OpenAI Chat Completion.
-// Where the chat history is stored server side, such as with Microsoft Foundry Agents, the service must manage the chat history size.
+// Chat Reduction — Keep conversation context within model limits
+//
+// This sample shows how to use a chat history reducer to keep the context
+// within model size limits. Any IChatReducer implementation can customize
+// how the chat history is reduced.
+// NOTE: This feature is only supported where chat history is stored locally
+// (e.g. OpenAI Chat Completion). For server-side history (e.g. Foundry Agents),
+// the service manages chat history size.
 
-using Azure.AI.OpenAI;
+using Azure.AI.Extensions.OpenAI;
+using Azure.AI.Projects;
 using Azure.Identity;
 using Microsoft.Agents.AI;
 using Microsoft.Extensions.AI;
-using OpenAI.Chat;
 
-var endpoint = Environment.GetEnvironmentVariable("AZURE_OPENAI_ENDPOINT") ?? throw new InvalidOperationException("AZURE_OPENAI_ENDPOINT is not set.");
-var deploymentName = Environment.GetEnvironmentVariable("AZURE_OPENAI_DEPLOYMENT_NAME") ?? "gpt-5.4-mini";
+var endpoint = Environment.GetEnvironmentVariable("FOUNDRY_PROJECT_ENDPOINT") ?? throw new InvalidOperationException("FOUNDRY_PROJECT_ENDPOINT is not set.");
+var deploymentName = Environment.GetEnvironmentVariable("FOUNDRY_MODEL") ?? "gpt-5.4-mini";
 
 // Construct the agent, and provide a factory to create an in-memory chat message store with a reducer that keeps only the last 2 non-system messages.
+// You must dissable client side conversation storage for clients that support it.
 // WARNING: DefaultAzureCredential is convenient for development but requires careful consideration in production.
 // In production, consider using a specific credential (e.g., ManagedIdentityCredential) to avoid
 // latency issues, unintended credential probing, and potential security risks from fallback mechanisms.
-AIAgent agent = new AzureOpenAIClient(
+AIAgent agent = new AIProjectClient(
     new Uri(endpoint),
     new DefaultAzureCredential())
-    .GetChatClient(deploymentName)
+    .GetProjectOpenAIClient()
+    .GetProjectResponsesClient()
+    .AsIChatClientWithStoredOutputDisabled(deploymentName)
     .AsAIAgent(new ChatClientAgentOptions
     {
-        ChatOptions = new() { Instructions = "You are good at telling jokes." },
+        ChatOptions = new() { ModelId = deploymentName, Instructions = "You are good at telling jokes." },
         Name = "Joker",
         ChatHistoryProvider = new InMemoryChatHistoryProvider(new() { ChatReducer = new MessageCountingChatReducer(2) })
     });

dotnet/samples/02-agents/Agents/Agent_Step14_BackgroundResponses/Agent_Step14_BackgroundResponses.csproj

@@ -9,12 +9,8 @@
   </PropertyGroup>
 
   <ItemGroup>
-    <PackageReference Include="Azure.AI.OpenAI" />
     <PackageReference Include="Azure.Identity" />
-  </ItemGroup>
-  
-  <ItemGroup>
-    <ProjectReference Include="..\..\..\..\src\Microsoft.Agents.AI.OpenAI\Microsoft.Agents.AI.OpenAI.csproj" />
+    <ProjectReference Include="..\..\..\..\src\Microsoft.Agents.AI.Foundry\Microsoft.Agents.AI.Foundry.csproj" />
   </ItemGroup>
   
 </Project>

dotnet/samples/02-agents/Agents/Agent_Step14_BackgroundResponses/Program.cs

@@ -1,23 +1,24 @@
 // Copyright (c) Microsoft. All rights reserved.
 
-// This sample shows how to use background responses with ChatClientAgent and Azure OpenAI Responses.
+// Background Responses — Asynchronous agent execution with polling
+//
+// This sample shows how to use background responses with ChatClientAgent
+// and Azure AI Foundry for non-blocking agent execution.
 
-using Azure.AI.OpenAI;
+using Azure.AI.Projects;
 using Azure.Identity;
 using Microsoft.Agents.AI;
-using OpenAI.Responses;
 
-var endpoint = Environment.GetEnvironmentVariable("AZURE_OPENAI_ENDPOINT") ?? throw new InvalidOperationException("AZURE_OPENAI_ENDPOINT is not set.");
-var deploymentName = Environment.GetEnvironmentVariable("AZURE_OPENAI_DEPLOYMENT_NAME") ?? "gpt-5.4-mini";
+var endpoint = Environment.GetEnvironmentVariable("FOUNDRY_PROJECT_ENDPOINT") ?? throw new InvalidOperationException("FOUNDRY_PROJECT_ENDPOINT is not set.");
+var deploymentName = Environment.GetEnvironmentVariable("FOUNDRY_MODEL") ?? "gpt-5.4-mini";
 
 // WARNING: DefaultAzureCredential is convenient for development but requires careful consideration in production.
 // In production, consider using a specific credential (e.g., ManagedIdentityCredential) to avoid
 // latency issues, unintended credential probing, and potential security risks from fallback mechanisms.
-AIAgent agent = new AzureOpenAIClient(
+AIAgent agent = new AIProjectClient(
     new Uri(endpoint),
     new DefaultAzureCredential())
-     .GetResponsesClient()
-     .AsAIAgent(model: deploymentName);
+     .AsAIAgent(model: deploymentName, instructions: "You are a helpful assistant.");
 
 // Enable background responses (only supported by OpenAI Responses at this time).
 AgentRunOptions options = new() { AllowBackgroundResponses = true };

dotnet/samples/02-agents/Agents/Agent_Step16_Declarative/Agent_Step16_Declarative.csproj

@@ -9,9 +9,7 @@
   </PropertyGroup>
 
   <ItemGroup>
-    <PackageReference Include="Azure.AI.OpenAI" />
     <PackageReference Include="Azure.Identity" />
-    <PackageReference Include="Microsoft.Extensions.AI.OpenAI" />
     <PackageReference Include="Microsoft.Agents.ObjectModel" />
     <PackageReference Include="Microsoft.Agents.ObjectModel.Json" />
     <PackageReference Include="Microsoft.Agents.ObjectModel.PowerFx" />
@@ -19,7 +17,7 @@
 
   <ItemGroup>
     <ProjectReference Include="..\..\..\..\src\Microsoft.Agents.AI.Declarative\Microsoft.Agents.AI.Declarative.csproj" />
-    <ProjectReference Include="..\..\..\..\src\Microsoft.Agents.AI.OpenAI\Microsoft.Agents.AI.OpenAI.csproj" />
+    <ProjectReference Include="..\..\..\..\src\Microsoft.Agents.AI.Foundry\Microsoft.Agents.AI.Foundry.csproj" />
   </ItemGroup>
 
 </Project>

dotnet/samples/02-agents/Agents/Agent_Step16_Declarative/Program.cs

@@ -1,24 +1,24 @@
 // Copyright (c) Microsoft. All rights reserved.
 
-// This sample shows how to create an agent from a YAML based declarative representation.
+// Declarative Agent — Create an agent from a YAML definition
+//
+// This sample shows how to create an agent from a YAML-based
+// declarative representation.
 
-using Azure.AI.OpenAI;
+using Azure.AI.Projects;
 using Azure.Identity;
 using Microsoft.Agents.AI;
 using Microsoft.Extensions.AI;
 
-var endpoint = Environment.GetEnvironmentVariable("AZURE_OPENAI_ENDPOINT") ?? throw new InvalidOperationException("AZURE_OPENAI_ENDPOINT is not set.");
-var deploymentName = Environment.GetEnvironmentVariable("AZURE_OPENAI_DEPLOYMENT_NAME") ?? "gpt-5.4-mini";
+var endpoint = Environment.GetEnvironmentVariable("FOUNDRY_PROJECT_ENDPOINT") ?? throw new InvalidOperationException("FOUNDRY_PROJECT_ENDPOINT is not set.");
+var deploymentName = Environment.GetEnvironmentVariable("FOUNDRY_MODEL") ?? "gpt-5.4-mini";
 
 // Create the chat client
 // WARNING: DefaultAzureCredential is convenient for development but requires careful consideration in production.
 // In production, consider using a specific credential (e.g., ManagedIdentityCredential) to avoid
 // latency issues, unintended credential probing, and potential security risks from fallback mechanisms.
-IChatClient chatClient = new AzureOpenAIClient(
-    new Uri(endpoint),
-    new DefaultAzureCredential())
-     .GetChatClient(deploymentName)
-     .AsIChatClient();
+AIProjectClient aiProjectClient = new(new Uri(endpoint), new DefaultAzureCredential());
+IChatClient chatClient = aiProjectClient.GetProjectOpenAIClient().GetResponsesClient().AsIChatClient(deploymentName);
 
 // Define the agent using a YAML definition.
 var text =

dotnet/samples/02-agents/Agents/Agent_Step17_AdditionalAIContext/Agent_Step17_AdditionalAIContext.csproj

@@ -9,13 +9,8 @@
   </PropertyGroup>
 
   <ItemGroup>
-    <PackageReference Include="Azure.AI.OpenAI" />
     <PackageReference Include="Azure.Identity" />
-    <PackageReference Include="Microsoft.Extensions.AI.OpenAI" />
-  </ItemGroup>
-
-  <ItemGroup>
-    <ProjectReference Include="..\..\..\..\src\Microsoft.Agents.AI.OpenAI\Microsoft.Agents.AI.OpenAI.csproj" />
+    <ProjectReference Include="..\..\..\..\src\Microsoft.Agents.AI.Foundry\Microsoft.Agents.AI.Foundry.csproj" />
   </ItemGroup>
 
 </Project>

dotnet/samples/02-agents/Agents/Agent_Step17_AdditionalAIContext/Program.cs

@@ -1,24 +1,26 @@
 // Copyright (c) Microsoft. All rights reserved.
 
-// This sample shows how to inject additional AI context into a ChatClientAgent using custom AIContextProvider components that are attached to the agent.
-// Multiple providers can be attached to an agent, and they will be called in sequence, each receiving the accumulated context from the previous one.
-// This mechanism can be used for various purposes, such as injecting RAG search results or memories into the agent's context.
-// Also note that Agent Framework already provides built-in AIContextProviders for many of these scenarios.
+// Additional AI Context — Inject context via custom AIContextProviders
+//
+// This sample shows how to inject additional AI context into a ChatClientAgent
+// using custom AIContextProvider components. Multiple providers can be attached
+// and are called in sequence, each receiving accumulated context from the previous.
+// This mechanism is useful for injecting RAG results, memories, or other context.
+// Agent Framework also provides built-in AIContextProviders for many scenarios.
 
 #pragma warning disable CA1869 // Cache and reuse 'JsonSerializerOptions' instances
 
 using System.Text;
 using System.Text.Json;
-using Azure.AI.OpenAI;
+using Azure.AI.Extensions.OpenAI;
+using Azure.AI.Projects;
 using Azure.Identity;
 using Microsoft.Agents.AI;
 using Microsoft.Extensions.AI;
-using OpenAI.Chat;
 using SampleApp;
-using MEAI = Microsoft.Extensions.AI;
 
-var endpoint = Environment.GetEnvironmentVariable("AZURE_OPENAI_ENDPOINT") ?? throw new InvalidOperationException("AZURE_OPENAI_ENDPOINT is not set.");
-var deploymentName = Environment.GetEnvironmentVariable("AZURE_OPENAI_DEPLOYMENT_NAME") ?? "gpt-5.4-mini";
+var endpoint = Environment.GetEnvironmentVariable("FOUNDRY_PROJECT_ENDPOINT") ?? throw new InvalidOperationException("FOUNDRY_PROJECT_ENDPOINT is not set.");
+var deploymentName = Environment.GetEnvironmentVariable("FOUNDRY_MODEL") ?? "gpt-5.4-mini";
 
 // A sample function to load the next three calendar events for the user.
 Func<Task<string[]>> loadNextThreeCalendarEvents = async () =>
@@ -32,17 +34,20 @@ Func<Task<string[]>> loadNextThreeCalendarEvents = async () =>
     ];
 };
 
-// Create an agent with an AI context provider attached that aggregates two other providers:
+// Create an agent with an AI context provider attached that aggregates two other providers.
+// You must dissable client side conversation storage for clients that support it:
 // WARNING: DefaultAzureCredential is convenient for development but requires careful consideration in production.
 // In production, consider using a specific credential (e.g., ManagedIdentityCredential) to avoid
 // latency issues, unintended credential probing, and potential security risks from fallback mechanisms.
-AIAgent agent = new AzureOpenAIClient(
+AIAgent agent = new AIProjectClient(
     new Uri(endpoint),
     new DefaultAzureCredential())
-    .GetChatClient(deploymentName)
+    .GetProjectOpenAIClient()
+    .GetProjectResponsesClient()
+    .AsIChatClientWithStoredOutputDisabled(deploymentName)
     .AsAIAgent(new ChatClientAgentOptions()
     {
-        ChatOptions = new() { Instructions = """
+        ChatOptions = new() { ModelId = deploymentName, Instructions = """
         You are a helpful personal assistant.
         You manage a TODO list for the user. When the user has completed one of the tasks it can be removed from the TODO list. Only provide the list of TODO items if asked.
         You remind users of upcoming calendar events when the user interacts with you.
@@ -120,7 +125,7 @@ namespace SampleApp
                 ],
                 Messages =
                 [
-                    new MEAI.ChatMessage(ChatRole.User, outputMessageBuilder.ToString())
+                new ChatMessage(ChatRole.User, outputMessageBuilder.ToString())
                 ]
             });
         }
@@ -150,7 +155,7 @@ namespace SampleApp
     /// </summary>
     internal sealed class CalendarSearchAIContextProvider(Func<Task<string[]>> loadNextThreeCalendarEvents) : MessageAIContextProvider
     {
-        protected override async ValueTask<IEnumerable<MEAI.ChatMessage>> ProvideMessagesAsync(InvokingContext context, CancellationToken cancellationToken = default)
+        protected override async ValueTask<IEnumerable<ChatMessage>> ProvideMessagesAsync(InvokingContext context, CancellationToken cancellationToken = default)
         {
             var events = await loadNextThreeCalendarEvents();
 
@@ -161,7 +166,7 @@ namespace SampleApp
                 outputMessageBuilder.AppendLine($" - {calendarEvent}");
             }
 
-            return [new MEAI.ChatMessage(ChatRole.User, outputMessageBuilder.ToString())];
+            return [new ChatMessage(ChatRole.User, outputMessageBuilder.ToString())];
         }
     }
 }

dotnet/samples/02-agents/Agents/Agent_Step18_CompactionPipeline/Agent_Step18_CompactionPipeline.csproj

@@ -1,4 +1,4 @@
-<Project Sdk="Microsoft.NET.Sdk">
+<Project Sdk="Microsoft.NET.Sdk">
 
   <PropertyGroup>
     <OutputType>Exe</OutputType>
@@ -9,13 +9,8 @@
   </PropertyGroup>
 
   <ItemGroup>
-    <PackageReference Include="Azure.AI.OpenAI" />
     <PackageReference Include="Azure.Identity" />
-    <PackageReference Include="Microsoft.Extensions.AI.OpenAI" />
-  </ItemGroup>
-
-  <ItemGroup>
-    <ProjectReference Include="..\..\..\..\src\Microsoft.Agents.AI.OpenAI\Microsoft.Agents.AI.OpenAI.csproj" />
+    <ProjectReference Include="..\..\..\..\src\Microsoft.Agents.AI.Foundry\Microsoft.Agents.AI.Foundry.csproj" />
   </ItemGroup>
 
 </Project>

dotnet/samples/02-agents/Agents/Agent_Step18_CompactionPipeline/Program.cs

@@ -1,32 +1,34 @@
 // Copyright (c) Microsoft. All rights reserved.
 
-// This sample demonstrates how to use a CompactionProvider with a compaction pipeline
-// as an AIContextProvider for an agent's in-run context management. The pipeline chains multiple
-// compaction strategies from gentle to aggressive:
-//   1. ToolResultCompactionStrategy - Collapses old tool-call groups into concise summaries
-//   2. SummarizationCompactionStrategy - LLM-compresses older conversation spans
-//   3. SlidingWindowCompactionStrategy - Keeps only the most recent N user turns
-//   4. TruncationCompactionStrategy - Emergency token-budget backstop
+// Compaction Pipeline — Progressive context management strategies
+//
+// This sample demonstrates how to use a CompactionProvider with a compaction
+// pipeline as an AIContextProvider for in-run context management. The pipeline
+// chains multiple compaction strategies from gentle to aggressive:
+//   1. ToolResultCompactionStrategy — Collapses old tool-call groups into summaries
+//   2. SummarizationCompactionStrategy — LLM-compresses older conversation spans
+//   3. SlidingWindowCompactionStrategy — Keeps only the most recent N user turns
+//   4. TruncationCompactionStrategy — Emergency token-budget backstop
 
 using System.ComponentModel;
-using Azure.AI.OpenAI;
+using Azure.AI.Projects;
 using Azure.Identity;
 using Microsoft.Agents.AI;
 using Microsoft.Agents.AI.Compaction;
 using Microsoft.Extensions.AI;
 
-var endpoint = Environment.GetEnvironmentVariable("AZURE_OPENAI_ENDPOINT") ?? throw new InvalidOperationException("AZURE_OPENAI_ENDPOINT is not set.");
-var deploymentName = Environment.GetEnvironmentVariable("AZURE_OPENAI_DEPLOYMENT_NAME") ?? "gpt-5.4-mini";
+var endpoint = Environment.GetEnvironmentVariable("FOUNDRY_PROJECT_ENDPOINT") ?? throw new InvalidOperationException("FOUNDRY_PROJECT_ENDPOINT is not set.");
+var deploymentName = Environment.GetEnvironmentVariable("FOUNDRY_MODEL") ?? "gpt-5.4-mini";
 
 // WARNING: DefaultAzureCredential is convenient for development but requires careful consideration in production.
 // In production, consider using a specific credential (e.g., ManagedIdentityCredential) to avoid
 // latency issues, unintended credential probing, and potential security risks from fallback mechanisms.
-AzureOpenAIClient openAIClient = new(new Uri(endpoint), new DefaultAzureCredential());
+AIProjectClient aiProjectClient = new(new Uri(endpoint), new DefaultAzureCredential());
 
 // Create a chat client for the agent and a separate one for the summarization strategy.
 // Using the same model for simplicity; in production, use a smaller/cheaper model for summarization.
-IChatClient agentChatClient = openAIClient.GetChatClient(deploymentName).AsIChatClient();
-IChatClient summarizerChatClient = openAIClient.GetChatClient(deploymentName).AsIChatClient();
+IChatClient agentChatClient = aiProjectClient.GetProjectOpenAIClient().GetResponsesClient().AsIChatClient(deploymentName);
+IChatClient summarizerChatClient = aiProjectClient.GetProjectOpenAIClient().GetResponsesClient().AsIChatClient(deploymentName);
 
 // Define a tool the agent can use, so we can see tool-result compaction in action.
 [Description("Look up the current price of a product by name.")]

dotnet/samples/02-agents/Agents/Agent_Step19_InFunctionLoopCheckpointing/Agent_Step19_InFunctionLoopCheckpointing.csproj

@@ -1,4 +1,4 @@
-<Project Sdk="Microsoft.NET.Sdk">
+<Project Sdk="Microsoft.NET.Sdk">
 
   <PropertyGroup>
     <OutputType>Exe</OutputType>
@@ -9,12 +9,8 @@
   </PropertyGroup>
 
   <ItemGroup>
-    <PackageReference Include="Azure.AI.OpenAI" />
     <PackageReference Include="Azure.Identity" />
-  </ItemGroup>
-
-  <ItemGroup>
-    <ProjectReference Include="..\..\..\..\src\Microsoft.Agents.AI.OpenAI\Microsoft.Agents.AI.OpenAI.csproj" />
+    <ProjectReference Include="..\..\..\..\src\Microsoft.Agents.AI.Foundry\Microsoft.Agents.AI.Foundry.csproj" />
   </ItemGroup>
 
 </Project>

dotnet/samples/02-agents/Agents/Agent_Step19_InFunctionLoopCheckpointing/Program.cs

@@ -1,5 +1,7 @@
 // Copyright (c) Microsoft. All rights reserved.
 
+// In-Function Loop Checkpointing — Persist chat history per service call
+//
 // This sample demonstrates how the ChatClientAgent persists chat history after each individual
 // call to the AI service, using the RequirePerServiceCallChatHistoryPersistence option.
 // When an agent uses tools, FunctionInvokingChatClient may loop multiple times
@@ -21,20 +23,20 @@
 // using streaming (RunStreamingAsync), to demonstrate correct behavior in both modes.
 
 using System.ComponentModel;
-using Azure.AI.OpenAI;
+using Azure.AI.Extensions.OpenAI;
+using Azure.AI.Projects;
 using Azure.Identity;
 using Microsoft.Agents.AI;
 using Microsoft.Extensions.AI;
-using OpenAI.Responses;
 
-var endpoint = Environment.GetEnvironmentVariable("AZURE_OPENAI_ENDPOINT") ?? throw new InvalidOperationException("AZURE_OPENAI_ENDPOINT is not set.");
-var deploymentName = Environment.GetEnvironmentVariable("AZURE_OPENAI_DEPLOYMENT_NAME") ?? "gpt-5.4-mini";
-var store = Environment.GetEnvironmentVariable("AZURE_OPENAI_RESPONSES_STORE") ?? "false";
+var endpoint = Environment.GetEnvironmentVariable("FOUNDRY_PROJECT_ENDPOINT") ?? throw new InvalidOperationException("FOUNDRY_PROJECT_ENDPOINT is not set.");
+var deploymentName = Environment.GetEnvironmentVariable("FOUNDRY_MODEL") ?? "gpt-5.4-mini";
+var store = Environment.GetEnvironmentVariable("FOUNDRY_RESPONSES_STORE") ?? "false";
 
 // WARNING: DefaultAzureCredential is convenient for development but requires careful consideration in production.
 // In production, consider using a specific credential (e.g., ManagedIdentityCredential) to avoid
 // latency issues, unintended credential probing, and potential security risks from fallback mechanisms.
-AzureOpenAIClient openAIClient = new(new Uri(endpoint), new DefaultAzureCredential());
+AIProjectClient aiProjectClient = new(new Uri(endpoint), new DefaultAzureCredential());
 
 // Define multiple tools so the model makes several tool calls in a single run.
 [Description("Get the current weather for a city.")]
@@ -93,9 +95,10 @@ static string CheckTravelAdvisory([Description("The city name.")] string city)
 // Create the agent — per-service-call persistence is enabled via RequirePerServiceCallChatHistoryPersistence.
 // The in-memory ChatHistoryProvider is used by default when the service does not require service stored chat
 // history, so for those cases, we can inspect the chat history via session.TryGetInMemoryChatHistory().
+var responsesClient = aiProjectClient.GetProjectOpenAIClient().GetProjectResponsesClientForModel(deploymentName);
 IChatClient chatClient = string.Equals(store, "TRUE", StringComparison.OrdinalIgnoreCase) ?
-    openAIClient.GetResponsesClient().AsIChatClient(deploymentName) :
-    openAIClient.GetResponsesClient().AsIChatClientWithStoredOutputDisabled(deploymentName);
+    responsesClient.AsIChatClient(deploymentName) :
+    responsesClient.AsIChatClientWithStoredOutputDisabled(deploymentName);
 AIAgent agent = chatClient.AsAIAgent(
     new ChatClientAgentOptions
     {

dotnet/samples/02-agents/Agents/Agent_Step20_DynamicFunctionTools/Agent_Step20_DynamicFunctionTools.csproj

@@ -1,4 +1,4 @@
-<Project Sdk="Microsoft.NET.Sdk">
+<Project Sdk="Microsoft.NET.Sdk">
 
   <PropertyGroup>
     <OutputType>Exe</OutputType>
@@ -9,12 +9,8 @@
   </PropertyGroup>
 
   <ItemGroup>
-    <PackageReference Include="Azure.AI.OpenAI" />
     <PackageReference Include="Azure.Identity" />
-  </ItemGroup>
-
-  <ItemGroup>
-    <ProjectReference Include="..\..\..\..\src\Microsoft.Agents.AI.OpenAI\Microsoft.Agents.AI.OpenAI.csproj" />
+    <ProjectReference Include="..\..\..\..\src\Microsoft.Agents.AI.Foundry\Microsoft.Agents.AI.Foundry.csproj" />
   </ItemGroup>
 
 </Project>

dotnet/samples/02-agents/Agents/Agent_Step20_DynamicFunctionTools/Program.cs

@@ -7,13 +7,13 @@
 // can then use the newly added tools in subsequent iterations of the same function-calling loop.
 
 using System.ComponentModel;
-using Azure.AI.OpenAI;
+using Azure.AI.Projects;
 using Azure.Identity;
 using Microsoft.Agents.AI;
 using Microsoft.Extensions.AI;
 
-var endpoint = Environment.GetEnvironmentVariable("AZURE_OPENAI_ENDPOINT") ?? throw new InvalidOperationException("AZURE_OPENAI_ENDPOINT is not set.");
-var deploymentName = Environment.GetEnvironmentVariable("AZURE_OPENAI_DEPLOYMENT_NAME") ?? "gpt-5.4-mini";
+var endpoint = Environment.GetEnvironmentVariable("FOUNDRY_PROJECT_ENDPOINT") ?? throw new InvalidOperationException("FOUNDRY_PROJECT_ENDPOINT is not set.");
+var deploymentName = Environment.GetEnvironmentVariable("FOUNDRY_MODEL") ?? "gpt-5.4-mini";
 
 // Pre-defined tool implementations that can be loaded on demand.
 [Description("Get the current weather for a city.")]
@@ -97,20 +97,21 @@ AIFunction requestToolsFunction = AIFunctionFactory.Create(
 // WARNING: DefaultAzureCredential is convenient for development but requires careful consideration in production.
 // In production, consider using a specific credential (e.g., ManagedIdentityCredential) to avoid
 // latency issues, unintended credential probing, and potential security risks from fallback mechanisms.
-AIAgent agent = new AzureOpenAIClient(
+AIAgent agent = new AIProjectClient(
     new Uri(endpoint),
     new DefaultAzureCredential())
-    .GetChatClient(deploymentName)
-    .AsIChatClient()
-    .AsBuilder()
-    .Use(getResponseFunc: ToolLoggingMiddleware, getStreamingResponseFunc: ToolLoggingStreamingMiddleware)
-    .BuildAIAgent(
+    .AsAIAgent(
+        model: deploymentName,
         instructions: """
             You are a helpful assistant. You start with limited tools.
             When you need functionality that you don't currently have, call RequestTools with a description
             of what you need. After new tools are loaded, use them to answer the user's question.
             """,
-        tools: [requestToolsFunction]);
+        tools: [requestToolsFunction],
+        clientFactory: (chatClient) => chatClient
+            .AsBuilder()
+            .Use(getResponseFunc: ToolLoggingMiddleware, getStreamingResponseFunc: ToolLoggingStreamingMiddleware)
+            .Build());
 
 // Run a conversation that triggers dynamic tool expansion.
 Console.WriteLine("=== Dynamic Function Tools Sample ===\n");

dotnet/samples/02-agents/Agents/Agent_Step21_ShellWithEnvironment/Agent_Step21_ShellWithEnvironment.csproj

@@ -9,13 +9,8 @@
   </PropertyGroup>
 
   <ItemGroup>
-    <PackageReference Include="Azure.AI.OpenAI" />
     <PackageReference Include="Azure.Identity" />
-    <PackageReference Include="Microsoft.Extensions.AI.OpenAI" />
-  </ItemGroup>
-
-  <ItemGroup>
-    <ProjectReference Include="..\..\..\..\src\Microsoft.Agents.AI.OpenAI\Microsoft.Agents.AI.OpenAI.csproj" />
+    <ProjectReference Include="..\..\..\..\src\Microsoft.Agents.AI.Foundry\Microsoft.Agents.AI.Foundry.csproj" />
     <ProjectReference Include="..\..\..\..\src\Microsoft.Agents.AI.Tools.Shell\Microsoft.Agents.AI.Tools.Shell.csproj" />
   </ItemGroup>
 

dotnet/samples/02-agents/Agents/Agent_Step21_ShellWithEnvironment/Program.cs

@@ -30,21 +30,19 @@
 //      (cd into a folder and run a sequence of commands there; set a
 //      token in one step and read it in the next).
 
-using Azure.AI.OpenAI;
+using Azure.AI.Projects;
 using Azure.Identity;
 using Microsoft.Agents.AI;
 using Microsoft.Agents.AI.Tools.Shell;
 using Microsoft.Extensions.AI;
-using OpenAI.Chat;
 
-var endpoint = Environment.GetEnvironmentVariable("AZURE_OPENAI_ENDPOINT") ?? throw new InvalidOperationException("AZURE_OPENAI_ENDPOINT is not set.");
-var deploymentName = Environment.GetEnvironmentVariable("AZURE_OPENAI_DEPLOYMENT_NAME") ?? "gpt-5.4-mini";
+var endpoint = Environment.GetEnvironmentVariable("FOUNDRY_PROJECT_ENDPOINT") ?? throw new InvalidOperationException("FOUNDRY_PROJECT_ENDPOINT is not set.");
+var deploymentName = Environment.GetEnvironmentVariable("FOUNDRY_MODEL") ?? "gpt-5.4-mini";
 
 // WARNING: DefaultAzureCredential is convenient for development but requires careful consideration in production.
 // In production, consider using a specific credential (e.g., ManagedIdentityCredential) to avoid
 // latency issues, unintended credential probing, and potential security risks from fallback mechanisms.
-var chatClient = new AzureOpenAIClient(new Uri(endpoint), new DefaultAzureCredential())
-    .GetChatClient(deploymentName);
+var aiProjectClient = new AIProjectClient(new Uri(endpoint), new DefaultAzureCredential());
 
 const string Instructions = """
     You are an agent with a single tool: run_shell. Use it to satisfy the
@@ -59,10 +57,11 @@ Console.WriteLine("### Stateless mode\n");
 await using (var statelessShell = new LocalShellExecutor(new() { Mode = ShellMode.Stateless, AcknowledgeUnsafe = true }))
 {
     var envProvider = new ShellEnvironmentProvider(statelessShell);
-    var statelessAgent = chatClient.AsAIAgent(new ChatClientAgentOptions
+    var statelessAgent = aiProjectClient.AsAIAgent(new ChatClientAgentOptions
     {
         ChatOptions = new()
         {
+            ModelId = deploymentName,
             Instructions = Instructions,
             Tools = [statelessShell.AsAIFunction(requireApproval: false)],
         },
@@ -91,10 +90,11 @@ Console.WriteLine("\n### Persistent mode\n");
 await using (var persistentShell = new LocalShellExecutor(new() { Mode = ShellMode.Persistent, AcknowledgeUnsafe = true }))
 {
     var envProvider = new ShellEnvironmentProvider(persistentShell);
-    var persistentAgent = chatClient.AsAIAgent(new ChatClientAgentOptions
+    var persistentAgent = aiProjectClient.AsAIAgent(new ChatClientAgentOptions
     {
         ChatOptions = new()
         {
+            ModelId = deploymentName,
             Instructions = Instructions,
             Tools = [persistentShell.AsAIFunction(requireApproval: false)],
         },

dotnet/samples/02-agents/Agents/README.md

@@ -3,8 +3,8 @@
 The getting started with agents samples demonstrate the fundamental concepts and functionalities
 of single agents and can be used with any agent type.
 
-While the functionality can be used with any agent type, these samples use Azure OpenAI as the AI provider
-and use ChatCompletion as the type of service.
+While the functionality can be used with any agent type, these samples are configured for
+Microsoft Foundry using `AIProjectClient`.
 
 For other samples that demonstrate how to create and configure each type of agent that come with the agent framework,
 see the [How to create an agent for each provider](../AgentProviders/README.md) samples.
@@ -14,13 +14,13 @@ see the [How to create an agent for each provider](../AgentProviders/README.md)
 Before you begin, ensure you have the following prerequisites:
 
 - .NET 10 SDK or later
-- Azure OpenAI service endpoint and deployment configured
+- Microsoft Foundry project endpoint and model configured
 - Azure CLI installed and authenticated (for Azure credential authentication)
-- User has the `Cognitive Services OpenAI Contributor` role for the Azure OpenAI resource.
+- User has the required role to invoke models in the Foundry project.
 
-**Note**: These samples use Azure OpenAI models. For more information, see [how to deploy Azure OpenAI models with Microsoft Foundry](https://learn.microsoft.com/en-us/azure/ai-foundry/how-to/deploy-models-openai).
+**Note**: These samples use models hosted through Microsoft Foundry. For more information, see [Azure AI Foundry documentation](https://learn.microsoft.com/en-us/azure/ai-foundry/).
 
-**Note**: These samples use Azure CLI credentials for authentication. Make sure you're logged in with `az login` and have access to the Azure OpenAI resource and have the `Cognitive Services OpenAI Contributor` role. For more information, see the [Azure CLI documentation](https://learn.microsoft.com/cli/azure/authenticate-azure-cli-interactively).
+**Note**: These samples use Azure CLI credentials for authentication. Make sure you're logged in with `az login` and have access to the Foundry project. For more information, see the [Azure CLI documentation](https://learn.microsoft.com/cli/azure/authenticate-azure-cli-interactively).
 
 ## Samples
 
@@ -59,8 +59,8 @@ cd Agent_Step01_UsingFunctionToolsWithApprovals
 Set the following environment variables:
 
 ```powershell
-$env:AZURE_OPENAI_ENDPOINT="https://your-resource.openai.azure.com/" # Replace with your Azure OpenAI resource endpoint
-$env:AZURE_OPENAI_DEPLOYMENT_NAME="gpt-5.4-mini"  # Optional, defaults to gpt-5.4-mini
+$env:FOUNDRY_PROJECT_ENDPOINT="https://<your-project>.services.ai.azure.com/api/projects/<your-project>" # Replace with your Foundry project endpoint
+$env:FOUNDRY_MODEL="gpt-5.4-mini"  # Optional, defaults to gpt-5.4-mini
 ```
 
 If the variables are not set, you will be prompted for the values when running the samples.

dotnet/samples/02-agents/ModelContextProtocol/FoundryAgent_Hosted_MCP/Program.cs

@@ -30,6 +30,16 @@ var mcpTool = ResponseTool.CreateMcpTool(
     serverUri: new Uri("https://learn.microsoft.com/api/mcp"),
     toolCallApprovalPolicy: new McpToolCallApprovalPolicy(GlobalMcpToolCallApprovalPolicy.NeverRequireApproval));
 
+// Optional: authenticate the MCP server through a Foundry project connection.
+// The connection stores credentials, so the platform injects them at request time and no inline token is sent.
+// The public Microsoft Learn MCP server above needs no authentication, so this is shown for illustration only.
+// Use the FoundryAITool.CreateMcpTool overload that takes a projectConnectionId:
+//   AITool tool = FoundryAITool.CreateMcpTool(
+//       serverLabel: "github",
+//       serverUri: new Uri("https://api.githubcopilot.com/mcp"),
+//       projectConnectionId: "my-foundry-connection",
+//       toolCallApprovalPolicy: new McpToolCallApprovalPolicy(GlobalMcpToolCallApprovalPolicy.AlwaysRequireApproval));
+
 // Create a server side agent with the mcp tool, and expose it as an AIAgent.
 ProjectsAgentVersion agentVersion = await aiProjectClient.AgentAdministrationClient.CreateAgentVersionAsync(
     "MicrosoftLearnAgent",