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287 changed files with 5195 additions and 18066 deletions
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@@ -1,165 +0,0 @@
name: DevFlow PR Review
on:
pull_request_target:
types:
- opened
- reopened
- ready_for_review
workflow_dispatch:
inputs:
pr_number:
description: Pull request number to review
required: true
type: string
permissions:
contents: read
issues: write
pull-requests: write
concurrency:
group: devflow-pr-review-${{ github.repository }}-${{ github.event.pull_request.number || inputs.pr_number || github.run_id }}
cancel-in-progress: true
env:
DEVFLOW_REPOSITORY: ${{ vars.DF_REPO }}
DEVFLOW_REF: main
TARGET_REPO_PATH: ${{ github.workspace }}/target-repo
DEVFLOW_PATH: ${{ github.workspace }}/devflow
jobs:
team_check:
runs-on: ubuntu-latest
outputs:
is_team_member: ${{ steps.check.outputs.is_team_member }}
pr_number: ${{ steps.pr.outputs.pr_number }}
pr_url: ${{ steps.pr.outputs.pr_url }}
repo: ${{ steps.pr.outputs.repo }}
steps:
- name: Resolve PR metadata
id: pr
shell: bash
env:
PR_HTML_URL: ${{ github.event.pull_request.html_url }}
PR_NUMBER_EVENT: ${{ github.event.pull_request.number }}
PR_NUMBER_INPUT: ${{ inputs.pr_number }}
run: |
set -euo pipefail
if [[ "${GITHUB_EVENT_NAME}" == "pull_request_target" ]]; then
pr_number="${PR_NUMBER_EVENT}"
pr_url="${PR_HTML_URL}"
else
pr_number="${PR_NUMBER_INPUT}"
pr_url="https://github.com/${GITHUB_REPOSITORY}/pull/${pr_number}"
fi
if [[ ! "$pr_number" =~ ^[1-9][0-9]*$ ]]; then
echo "Could not determine PR number; for workflow_dispatch runs, the 'pr_number' input is required when not running on pull_request_target." >&2
exit 1
fi
echo "pr_url=${pr_url}" >> "$GITHUB_OUTPUT"
echo "pr_number=${pr_number}" >> "$GITHUB_OUTPUT"
echo "repo=${GITHUB_REPOSITORY}" >> "$GITHUB_OUTPUT"
- name: Check PR author team membership
id: check
uses: actions/github-script@v8
env:
TEAM_NAME: ${{ secrets.DEVELOPER_TEAM }}
PR_NUMBER: ${{ steps.pr.outputs.pr_number }}
with:
github-token: ${{ secrets.GH_ACTIONS_PR_WRITE }}
script: |
let author = context.payload.pull_request?.user?.login;
if (!author) {
const { data: pr } = await github.rest.pulls.get({
owner: context.repo.owner,
repo: context.repo.repo,
pull_number: Number(process.env.PR_NUMBER),
});
author = pr.user.login;
}
let isTeamMember = false;
try {
const teamMembership = await github.rest.teams.getMembershipForUserInOrg({
org: context.repo.owner,
team_slug: process.env.TEAM_NAME,
username: author,
});
isTeamMember = teamMembership.data.state === 'active';
} catch (error) {
console.log(`Team membership lookup failed for ${author}: ${error.message}`);
isTeamMember = false;
}
core.setOutput('is_team_member', isTeamMember ? 'true' : 'false');
if (isTeamMember) {
core.info(`Author ${author} is a team member; proceeding with review.`);
} else {
core.info(`Author ${author} is not a member of ${process.env.TEAM_NAME}; skipping review.`);
}
review:
runs-on: ubuntu-latest
needs: team_check
if: ${{ needs.team_check.outputs.is_team_member == 'true' }}
timeout-minutes: 60
# Advisory check: failures here should not block the PR. The reviewer
# posts comments as a best-effort signal; if the pipeline breaks, the
# PR author should still be able to merge without a red required check.
continue-on-error: true
steps:
# Safe checkout: base repo only, not the untrusted PR head.
- name: Checkout target repo base
uses: actions/checkout@v6
with:
ref: ${{ github.event_name == 'pull_request_target' && github.event.pull_request.base.sha || github.sha }}
fetch-depth: 0
persist-credentials: false
path: target-repo
# Private DevFlow checkout: the PAT/token grants access to this repo's code.
- name: Checkout DevFlow
uses: actions/checkout@v6
with:
repository: ${{ env.DEVFLOW_REPOSITORY }}
ref: ${{ env.DEVFLOW_REF }}
token: ${{ secrets.DEVFLOW_TOKEN }}
fetch-depth: 1
persist-credentials: false
path: devflow
- name: Set up Python
uses: actions/setup-python@v5
with:
python-version: "3.13"
- name: Set up uv
uses: astral-sh/setup-uv@v7
with:
version: "0.11.x"
enable-cache: true
- name: Install DevFlow dependencies
working-directory: ${{ env.DEVFLOW_PATH }}
run: uv sync --frozen
- name: Run PR review
id: review
working-directory: ${{ env.DEVFLOW_PATH }}
env:
GITHUB_TOKEN: ${{ secrets.GITHUB_TOKEN }}
GH_COPILOT_TOKEN: ${{ secrets.GH_COPILOT_TOKEN }}
SK_REPO_PATH: ${{ env.TARGET_REPO_PATH }}
AGENT_REPO_PATH: ${{ env.TARGET_REPO_PATH }}
PR_URL: ${{ needs.team_check.outputs.pr_url }}
run: |
uv run python scripts/trigger_pr_review.py \
--pr-url "$PR_URL" \
--github-username "$GITHUB_ACTOR" \
--no-require-comment-selection
+1 -114
View File
@@ -87,14 +87,6 @@ jobs:
-n logical --dist worksteal
--timeout=120 --session-timeout=900 --timeout_method thread
--retries 2 --retry-delay 5
--junitxml=pytest.xml
- name: Upload test results
if: always()
uses: actions/upload-artifact@v7
with:
name: test-results-openai
path: ./python/pytest.xml
if-no-files-found: ignore
# Azure OpenAI integration tests
python-tests-azure-openai:
@@ -138,14 +130,6 @@ jobs:
-n logical --dist worksteal
--timeout=120 --session-timeout=900 --timeout_method thread
--retries 2 --retry-delay 5
--junitxml=pytest.xml
- name: Upload test results
if: always()
uses: actions/upload-artifact@v7
with:
name: test-results-azure-openai
path: ./python/pytest.xml
if-no-files-found: ignore
# Misc integration tests (Anthropic, Hyperlight, Ollama, MCP)
python-tests-misc-integration:
@@ -189,14 +173,6 @@ jobs:
-n logical --dist worksteal
--timeout=120 --session-timeout=900 --timeout_method thread
--retries 2 --retry-delay 30
--junitxml=pytest.xml
- name: Upload test results
if: always()
uses: actions/upload-artifact@v7
with:
name: test-results-misc
path: ./python/pytest.xml
if-no-files-found: ignore
- name: Stop local MCP server
if: always()
shell: bash
@@ -273,14 +249,6 @@ jobs:
-x
--timeout=360 --session-timeout=900 --timeout_method thread
--retries 2 --retry-delay 5
--junitxml=pytest.xml
- name: Upload test results
if: always()
uses: actions/upload-artifact@v7
with:
name: test-results-functions
path: ./python/pytest.xml
if-no-files-found: ignore
# Foundry integration tests
python-tests-foundry:
@@ -327,14 +295,6 @@ jobs:
-n logical --dist worksteal
--timeout=120 --session-timeout=900 --timeout_method thread
--retries 2 --retry-delay 5
--junitxml=pytest.xml
- name: Upload test results
if: always()
uses: actions/upload-artifact@v7
with:
name: test-results-foundry
path: ./python/pytest.xml
if-no-files-found: ignore
# Azure Cosmos integration tests
python-tests-cosmos:
@@ -379,80 +339,7 @@ jobs:
echo "Cosmos DB emulator did not become ready in time." >&2
exit 1
- name: Test with pytest (Cosmos integration)
run: uv run --directory packages/azure-cosmos poe integration-tests -n logical --dist worksteal --timeout=120 --session-timeout=900 --timeout_method thread --retries 2 --retry-delay 5 --junitxml=${{ github.workspace }}/python/pytest.xml
- name: Upload test results
if: always()
uses: actions/upload-artifact@v7
with:
name: test-results-cosmos
path: ./python/pytest.xml
if-no-files-found: ignore
# Flaky test trend report (aggregates per-job JUnit XML results)
python-flaky-test-report:
name: Flaky Test Report
if: >
always() &&
(contains(join(needs.*.result, ','), 'success') ||
contains(join(needs.*.result, ','), 'failure'))
needs:
[
python-tests-openai,
python-tests-azure-openai,
python-tests-misc-integration,
python-tests-functions,
python-tests-foundry,
python-tests-cosmos,
]
runs-on: ubuntu-latest
defaults:
run:
working-directory: python
steps:
- uses: actions/checkout@v6
with:
ref: ${{ inputs.checkout-ref }}
persist-credentials: false
- name: Set up python and install the project
uses: ./.github/actions/python-setup
with:
python-version: ${{ env.UV_PYTHON }}
os: ${{ runner.os }}
- name: Download all test results from current run
uses: actions/download-artifact@v4
with:
pattern: test-results-*
path: test-results/
- name: Restore flaky report history cache
uses: actions/cache/restore@v4
with:
path: python/flaky-report-history.json
key: flaky-report-history-integration-${{ github.run_id }}
restore-keys: |
flaky-report-history-integration-
- name: Generate trend report
run: >
uv run python scripts/flaky_report/aggregate.py
../test-results/
flaky-report-history.json
flaky-test-report.md
- name: Post to Job Summary
if: always()
run: cat flaky-test-report.md >> $GITHUB_STEP_SUMMARY
- name: Save flaky report history cache
if: always()
uses: actions/cache/save@v4
with:
path: python/flaky-report-history.json
key: flaky-report-history-integration-${{ github.run_id }}
- name: Upload unified trend report
if: always()
uses: actions/upload-artifact@v7
with:
name: flaky-test-report
path: |
python/flaky-test-report.md
python/flaky-report-history.json
run: uv run --directory packages/azure-cosmos poe integration-tests -n logical --dist worksteal --timeout=120 --session-timeout=900 --timeout_method thread --retries 2 --retry-delay 5
python-integration-tests-check:
if: always()
+1 -110
View File
@@ -181,13 +181,6 @@ jobs:
display-options: fEX
fail-on-empty: false
title: OpenAI integration test results
- name: Upload test results
if: always()
uses: actions/upload-artifact@v7
with:
name: test-results-openai
path: ./python/pytest.xml
if-no-files-found: ignore
# Azure OpenAI integration tests
python-tests-azure-openai:
@@ -251,13 +244,6 @@ jobs:
display-options: fEX
fail-on-empty: false
title: Azure OpenAI integration test results
- name: Upload test results
if: always()
uses: actions/upload-artifact@v7
with:
name: test-results-azure-openai
path: ./python/pytest.xml
if-no-files-found: ignore
# Misc integration tests (Anthropic, Ollama, MCP)
python-tests-misc-integration:
@@ -335,13 +321,6 @@ jobs:
display-options: fEX
fail-on-empty: false
title: Misc integration test results
- name: Upload test results
if: always()
uses: actions/upload-artifact@v7
with:
name: test-results-misc
path: ./python/pytest.xml
if-no-files-found: ignore
# Azure Functions + Durable Task integration tests
python-tests-functions:
@@ -413,13 +392,6 @@ jobs:
display-options: fEX
fail-on-empty: false
title: Functions integration test results
- name: Upload test results
if: always()
uses: actions/upload-artifact@v7
with:
name: test-results-functions
path: ./python/pytest.xml
if-no-files-found: ignore
python-tests-foundry:
name: Python Integration Tests - Foundry
@@ -437,10 +409,6 @@ jobs:
FOUNDRY_MODEL: ${{ vars.FOUNDRY_MODEL }}
FOUNDRY_AGENT_NAME: ${{ vars.FOUNDRY_AGENT_NAME }}
FOUNDRY_AGENT_VERSION: ${{ vars.FOUNDRY_AGENT_VERSION }}
FOUNDRY_MODELS_ENDPOINT: ${{ vars.FOUNDRY_MODELS_ENDPOINT || '' }}
FOUNDRY_MODELS_API_KEY: ${{ secrets.FOUNDRY_MODELS_API_KEY || '' }}
FOUNDRY_EMBEDDING_MODEL: ${{ vars.FOUNDRY_EMBEDDING_MODEL || '' }}
FOUNDRY_IMAGE_EMBEDDING_MODEL: ${{ vars.FOUNDRY_IMAGE_EMBEDDING_MODEL || '' }}
LOCAL_MCP_URL: ${{ vars.LOCAL_MCP__URL }}
defaults:
run:
@@ -480,13 +448,6 @@ jobs:
display-options: fEX
fail-on-empty: false
title: Test results
- name: Upload test results
if: always()
uses: actions/upload-artifact@v7
with:
name: test-results-foundry
path: ./python/pytest.xml
if-no-files-found: ignore
# TODO: Add python-tests-lab
@@ -536,7 +497,7 @@ jobs:
echo "Cosmos DB emulator did not become ready in time." >&2
exit 1
- name: Test with pytest (Cosmos integration)
run: uv run --directory packages/azure-cosmos poe integration-tests -n logical --dist worksteal --timeout=120 --session-timeout=900 --timeout_method thread --retries 2 --retry-delay 5 --junitxml=${{ github.workspace }}/python/pytest.xml
run: uv run --directory packages/azure-cosmos poe integration-tests -n logical --dist worksteal --timeout=120 --session-timeout=900 --timeout_method thread --retries 2 --retry-delay 5 --junitxml=pytest.xml
working-directory: ./python
- name: Surface failing tests
if: always()
@@ -547,76 +508,6 @@ jobs:
display-options: fEX
fail-on-empty: false
title: Cosmos integration test results
- name: Upload test results
if: always()
uses: actions/upload-artifact@v7
with:
name: test-results-cosmos
path: ./python/pytest.xml
if-no-files-found: ignore
# Flaky test trend report (aggregates per-job JUnit XML results)
python-flaky-test-report:
name: Flaky Test Report
if: >
always() &&
(contains(join(needs.*.result, ','), 'success') ||
contains(join(needs.*.result, ','), 'failure'))
needs:
[
python-tests-openai,
python-tests-azure-openai,
python-tests-misc-integration,
python-tests-functions,
python-tests-foundry,
python-tests-cosmos,
]
runs-on: ubuntu-latest
defaults:
run:
working-directory: python
steps:
- uses: actions/checkout@v6
- name: Set up python and install the project
uses: ./.github/actions/python-setup
with:
python-version: ${{ env.UV_PYTHON }}
os: ${{ runner.os }}
- name: Download all test results from current run
uses: actions/download-artifact@v4
with:
pattern: test-results-*
path: test-results/
- name: Restore flaky report history cache
uses: actions/cache/restore@v4
with:
path: python/flaky-report-history.json
key: flaky-report-history-merge-${{ github.run_id }}
restore-keys: |
flaky-report-history-merge-
- name: Generate trend report
run: >
uv run python scripts/flaky_report/aggregate.py
../test-results/
flaky-report-history.json
flaky-test-report.md
- name: Post to Job Summary
if: always()
run: cat flaky-test-report.md >> $GITHUB_STEP_SUMMARY
- name: Save flaky report history cache
if: always()
uses: actions/cache/save@v4
with:
path: python/flaky-report-history.json
key: flaky-report-history-merge-${{ github.run_id }}
- name: Upload unified trend report
if: always()
uses: actions/upload-artifact@v7
with:
name: flaky-test-report
path: |
python/flaky-test-report.md
python/flaky-report-history.json
python-integration-tests-check:
if: always()
-4
View File
@@ -136,10 +136,6 @@ celerybeat.pid
.venv
env/
venv/
# Foundry agent CLI (contains secrets, auto-generated)
.foundry-agent.json
.foundry-agent-build.log
ENV/
env.bak/
venv.bak/
+1 -8
View File
@@ -402,11 +402,4 @@ FodyWeavers.xsd
*.msp
# JetBrains Rider
*.sln.iml
# Foundry agent CLI config (contains secrets, auto-generated)
.foundry-agent.json
.foundry-agent-build.log
# Pre-published output for Docker builds
out/
*.sln.iml
+15 -20
View File
@@ -22,16 +22,11 @@
<PackageVersion Include="Aspire.Microsoft.Azure.Cosmos" Version="$(AspireAppHostSdkVersion)" />
<PackageVersion Include="CommunityToolkit.Aspire.OllamaSharp" Version="13.0.0" />
<!-- Azure.* -->
<PackageVersion Include="Azure.AI.AgentServer.Core" Version="1.0.0-beta.22" />
<PackageVersion Include="Azure.AI.AgentServer.Invocations" Version="1.0.0-beta.1" />
<PackageVersion Include="Azure.AI.AgentServer.Responses" Version="1.0.0-beta.3" />
<PackageVersion Include="Azure.AI.Projects" Version="2.0.0" />
<PackageVersion Include="Azure.AI.Agents.Persistent" Version="1.2.0-beta.10" />
<PackageVersion Include="Azure.AI.OpenAI" Version="2.9.0-beta.1" />
<PackageVersion Include="Azure.Core" Version="1.53.0" />
<PackageVersion Include="Azure.Identity" Version="1.21.0" />
<PackageVersion Include="DotNetEnv" Version="3.1.1" />
<PackageVersion Include="Azure.Monitor.OpenTelemetry.Exporter" Version="1.5.0" />
<PackageVersion Include="Azure.Identity" Version="1.20.0" />
<PackageVersion Include="Azure.Monitor.OpenTelemetry.Exporter" Version="1.4.0" />
<!-- Google Gemini -->
<PackageVersion Include="Google.GenAI" Version="1.6.0" />
<PackageVersion Include="Mscc.GenerativeAI.Microsoft" Version="2.9.3" />
@@ -42,29 +37,29 @@
<!-- System.* -->
<PackageVersion Include="Microsoft.Bcl.AsyncInterfaces" Version="10.0.6" />
<PackageVersion Include="Microsoft.Bcl.HashCode" Version="6.0.0" />
<PackageVersion Include="Microsoft.Bcl.Memory" Version="10.0.5" />
<PackageVersion Include="Microsoft.Bcl.Memory" Version="10.0.4" />
<PackageVersion Include="System.ClientModel" Version="1.10.0" />
<PackageVersion Include="System.CodeDom" Version="10.0.0" />
<PackageVersion Include="System.Collections.Immutable" Version="10.0.1" />
<PackageVersion Include="System.CommandLine" Version="2.0.0-rc.2.25502.107" />
<PackageVersion Include="System.Diagnostics.DiagnosticSource" Version="10.0.6" />
<PackageVersion Include="System.Linq.AsyncEnumerable" Version="10.0.5" />
<PackageVersion Include="System.Linq.AsyncEnumerable" Version="10.0.4" />
<PackageVersion Include="System.Net.Http.Json" Version="10.0.0" />
<PackageVersion Include="System.Net.ServerSentEvents" Version="10.0.5" />
<PackageVersion Include="System.Net.ServerSentEvents" Version="10.0.4" />
<PackageVersion Include="System.Text.Json" Version="10.0.6" />
<PackageVersion Include="System.Threading.Channels" Version="10.0.6" />
<PackageVersion Include="System.Threading.Tasks.Extensions" Version="4.6.3" />
<PackageVersion Include="System.Net.Security" Version="4.3.2" />
<!-- OpenTelemetry -->
<PackageVersion Include="OpenTelemetry" Version="1.15.0" />
<PackageVersion Include="OpenTelemetry.Api" Version="1.15.0" />
<PackageVersion Include="OpenTelemetry.Exporter.Console" Version="1.15.0" />
<PackageVersion Include="OpenTelemetry.Exporter.InMemory" Version="1.15.0" />
<PackageVersion Include="OpenTelemetry.Exporter.OpenTelemetryProtocol" Version="1.15.0" />
<PackageVersion Include="OpenTelemetry" Version="1.14.0" />
<PackageVersion Include="OpenTelemetry.Api" Version="1.14.0" />
<PackageVersion Include="OpenTelemetry.Exporter.Console" Version="1.14.0" />
<PackageVersion Include="OpenTelemetry.Exporter.InMemory" Version="1.14.0" />
<PackageVersion Include="OpenTelemetry.Exporter.OpenTelemetryProtocol" Version="1.14.0" />
<PackageVersion Include="OpenTelemetry.Extensions.Hosting" Version="1.14.0" />
<PackageVersion Include="OpenTelemetry.Instrumentation.AspNetCore" Version="1.14.0" />
<PackageVersion Include="OpenTelemetry.Instrumentation.Http" Version="1.14.0" />
<PackageVersion Include="OpenTelemetry.Instrumentation.Runtime" Version="1.14.0" />
<PackageVersion Include="OpenTelemetry.Instrumentation.AspNetCore" Version="1.13.0" />
<PackageVersion Include="OpenTelemetry.Instrumentation.Http" Version="1.13.0" />
<PackageVersion Include="OpenTelemetry.Instrumentation.Runtime" Version="1.13.0" />
<!-- Microsoft.AspNetCore.* -->
<PackageVersion Include="Microsoft.AspNetCore.Authentication.JwtBearer" Version="10.0.0" />
<PackageVersion Include="Microsoft.AspNetCore.Authentication.OpenIdConnect" Version="10.0.0" />
@@ -104,8 +99,8 @@
<PackageVersion Include="Microsoft.Agents.Authentication.Msal" Version="1.3.171-beta" />
<PackageVersion Include="Microsoft.Agents.Hosting.AspNetCore" Version="1.3.171-beta" />
<!-- A2A -->
<PackageVersion Include="A2A" Version="1.0.0-preview2" />
<PackageVersion Include="A2A.AspNetCore" Version="1.0.0-preview2" />
<PackageVersion Include="A2A" Version="0.3.4-preview" />
<PackageVersion Include="A2A.AspNetCore" Version="0.3.4-preview" />
<!-- MCP -->
<PackageVersion Include="ModelContextProtocol" Version="1.1.0" />
<!-- Inference SDKs -->
+22 -50
View File
@@ -1,4 +1,4 @@
<Solution>
<Solution>
<Configurations>
<BuildType Name="Debug" />
<BuildType Name="Publish" />
@@ -283,44 +283,7 @@
<Folder Name="/Samples/03-workflows/Evaluation/">
<Project Path="samples/03-workflows/Evaluation/Evaluation_WorkflowEval/Evaluation_WorkflowEval.csproj" />
</Folder>
<Folder Name="/Samples/04-hosting/">
</Folder>
<Folder Name="/Samples/04-hosting/FoundryHostedAgents/" />
<Folder Name="/Samples/04-hosting/FoundryHostedAgents/invocations/" />
<Folder Name="/Samples/04-hosting/FoundryHostedAgents/invocations/Hosted-Invocations-EchoAgent/">
<Project Path="samples/04-hosting/FoundryHostedAgents/invocations/Hosted-Invocations-EchoAgent/Hosted-Invocations-EchoAgent.csproj" />
</Folder>
<Folder Name="/Samples/04-hosting/FoundryHostedAgents/invocations/Using-Samples/">
<Project Path="samples/04-hosting/FoundryHostedAgents/invocations/Using-Samples/SimpleInvocationsAgent/SimpleInvocationsAgent.csproj" />
</Folder>
<Folder Name="/Samples/04-hosting/FoundryHostedAgents/responses/" />
<Folder Name="/Samples/04-hosting/FoundryHostedAgents/responses/Hosted-ChatClientAgent/">
<Project Path="samples/04-hosting/FoundryHostedAgents/responses/Hosted-ChatClientAgent/HostedChatClientAgent.csproj" />
</Folder>
<Folder Name="/Samples/04-hosting/FoundryHostedAgents/responses/Hosted-FoundryAgent/">
<Project Path="samples/04-hosting/FoundryHostedAgents/responses/Hosted-FoundryAgent/HostedFoundryAgent.csproj" />
</Folder>
<Folder Name="/Samples/04-hosting/FoundryHostedAgents/responses/Hosted-LocalTools/">
<Project Path="samples/04-hosting/FoundryHostedAgents/responses/Hosted-LocalTools/HostedLocalTools.csproj" />
</Folder>
<Folder Name="/Samples/04-hosting/FoundryHostedAgents/responses/Hosted-McpTools/">
<Project Path="samples/04-hosting/FoundryHostedAgents/responses/Hosted-McpTools/HostedMcpTools.csproj" />
</Folder>
<Folder Name="/Samples/04-hosting/FoundryHostedAgents/responses/Hosted-Toolbox/">
<Project Path="samples/04-hosting/FoundryHostedAgents/responses/Hosted-Toolbox/HostedToolbox.csproj" />
</Folder>
<Folder Name="/Samples/04-hosting/FoundryHostedAgents/responses/Hosted-TextRag/">
<Project Path="samples/04-hosting/FoundryHostedAgents/responses/Hosted-TextRag/HostedTextRag.csproj" />
</Folder>
<Folder Name="/Samples/04-hosting/FoundryHostedAgents/responses/Hosted-Workflow-Simple/">
<Project Path="samples/04-hosting/FoundryHostedAgents/responses/Hosted-Workflow-Simple/HostedWorkflowSimple.csproj" />
</Folder>
<Folder Name="/Samples/04-hosting/FoundryHostedAgents/responses/Using-Samples/">
<Project Path="samples/04-hosting/FoundryHostedAgents/responses/Using-Samples/SimpleAgent/SimpleAgent.csproj" />
</Folder>
<Folder Name="/Samples/04-hosting/FoundryHostedAgents/responses/Hosted-Workflow-Handoff/">
<Project Path="samples/04-hosting/FoundryHostedAgents/responses/Hosted-Workflow-Handoff/HostedWorkflowHandoff.csproj" />
</Folder>
<Folder Name="/Samples/04-hosting/" />
<Folder Name="/Samples/04-hosting/DurableAgents/" />
<Folder Name="/Samples/04-hosting/DurableAgents/AzureFunctions/">
<File Path="samples/04-hosting/DurableAgents/AzureFunctions/.editorconfig" />
@@ -344,13 +307,11 @@
<Project Path="samples/04-hosting/DurableAgents/ConsoleApps/06_LongRunningTools/06_LongRunningTools.csproj" />
<Project Path="samples/04-hosting/DurableAgents/ConsoleApps/07_ReliableStreaming/07_ReliableStreaming.csproj" />
</Folder>
<Folder Name="/Samples/02-agents/A2A/">
<File Path="samples/02-agents/A2A/README.md" />
<Project Path="samples/02-agents/A2A/A2AAgent_AsFunctionTools/A2AAgent_AsFunctionTools.csproj" />
<Project Path="samples/02-agents/A2A/A2AAgent_PollingForTaskCompletion/A2AAgent_PollingForTaskCompletion.csproj" />
<Project Path="samples/02-agents/A2A/A2AAgent_StreamReconnection/A2AAgent_StreamReconnection.csproj" />
<Project Path="samples/02-agents/A2A/A2AAgent_ProtocolSelection/A2AAgent_ProtocolSelection.csproj" />
</Folder>
<Folder Name="/Samples/04-hosting/A2A/">
<File Path="samples/04-hosting/A2A/README.md" />
<Project Path="samples/04-hosting/A2A/A2AAgent_AsFunctionTools/A2AAgent_AsFunctionTools.csproj" />
<Project Path="samples/04-hosting/A2A/A2AAgent_PollingForTaskCompletion/A2AAgent_PollingForTaskCompletion.csproj" />
</Folder>
<Folder Name="/Samples/05-end-to-end/">
<Project Path="samples/05-end-to-end/AgentWithPurview/AgentWithPurview.csproj" />
<Project Path="samples/05-end-to-end/M365Agent/M365Agent.csproj" />
@@ -377,6 +338,15 @@
<Project Path="samples/05-end-to-end/AGUIClientServer/AGUIDojoServer/AGUIDojoServer.csproj" />
<Project Path="samples/05-end-to-end/AGUIClientServer/AGUIServer/AGUIServer.csproj" />
</Folder>
<Folder Name="/Samples/05-end-to-end/HostedAgents/">
<Project Path="samples/05-end-to-end/HostedAgents/AgentsInWorkflows/AgentsInWorkflows.csproj" />
<Project Path="samples/05-end-to-end/HostedAgents/AgentThreadAndHITL/AgentThreadAndHITL.csproj" />
<Project Path="samples/05-end-to-end/HostedAgents/AgentWithHostedMCP/AgentWithHostedMCP.csproj" />
<Project Path="samples/05-end-to-end/HostedAgents/AgentWithLocalTools/AgentWithLocalTools.csproj" />
<Project Path="samples/05-end-to-end/HostedAgents/AgentWithTextSearchRag/AgentWithTextSearchRag.csproj" />
<Project Path="samples/05-end-to-end/HostedAgents/FoundryMultiAgent/FoundryMultiAgent.csproj" />
<Project Path="samples/05-end-to-end/HostedAgents/FoundrySingleAgent/FoundrySingleAgent.csproj" />
</Folder>
<Folder Name="/Samples/05-end-to-end/AspNetAgentAuthorization/">
<File Path="samples/05-end-to-end/AspNetAgentAuthorization/docker-compose.yml" />
<File Path="samples/05-end-to-end/AspNetAgentAuthorization/README.md" />
@@ -538,13 +508,13 @@
<Project Path="src/Microsoft.Agents.AI.AGUI/Microsoft.Agents.AI.AGUI.csproj" />
<Project Path="src/Microsoft.Agents.AI.Anthropic/Microsoft.Agents.AI.Anthropic.csproj" />
<Project Path="src/Microsoft.Agents.AI.AzureAI.Persistent/Microsoft.Agents.AI.AzureAI.Persistent.csproj" />
<Project Path="src/Microsoft.Agents.AI.Foundry/Microsoft.Agents.AI.Foundry.csproj" />
<Project Path="src/Microsoft.Agents.AI.CopilotStudio/Microsoft.Agents.AI.CopilotStudio.csproj" />
<Project Path="src/Microsoft.Agents.AI.CosmosNoSql/Microsoft.Agents.AI.CosmosNoSql.csproj" />
<Project Path="src/Microsoft.Agents.AI.Declarative/Microsoft.Agents.AI.Declarative.csproj" />
<Project Path="src/Microsoft.Agents.AI.DevUI/Microsoft.Agents.AI.DevUI.csproj" />
<Project Path="src/Microsoft.Agents.AI.DurableTask/Microsoft.Agents.AI.DurableTask.csproj" />
<Project Path="src/Microsoft.Agents.AI.Foundry/Microsoft.Agents.AI.Foundry.csproj" />
<Project Path="src/Microsoft.Agents.AI.Foundry.Hosting/Microsoft.Agents.AI.Foundry.Hosting.csproj" />
<Project Path="src/Microsoft.Agents.AI.GitHub.Copilot/Microsoft.Agents.AI.GitHub.Copilot.csproj" />
<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" />
@@ -566,10 +536,11 @@
<Folder Name="/Tests/IntegrationTests/">
<Project Path="tests/AgentConformance.IntegrationTests/AgentConformance.IntegrationTests.csproj" />
<Project Path="tests/AnthropicChatCompletion.IntegrationTests/AnthropicChatCompletion.IntegrationTests.csproj" />
<Project Path="tests/Foundry.IntegrationTests/Foundry.IntegrationTests.csproj" />
<Project Path="tests/AzureAIAgentsPersistent.IntegrationTests/AzureAIAgentsPersistent.IntegrationTests.csproj" />
<Project Path="tests/CopilotStudio.IntegrationTests/CopilotStudio.IntegrationTests.csproj" />
<Project Path="tests/Foundry.IntegrationTests/Foundry.IntegrationTests.csproj" />
<Project Path="tests/Microsoft.Agents.AI.DurableTask.IntegrationTests/Microsoft.Agents.AI.DurableTask.IntegrationTests.csproj" />
<Project Path="tests/Microsoft.Agents.AI.GitHub.Copilot.IntegrationTests/Microsoft.Agents.AI.GitHub.Copilot.IntegrationTests.csproj" />
<Project Path="tests/Microsoft.Agents.AI.Hosting.AGUI.AspNetCore.IntegrationTests/Microsoft.Agents.AI.Hosting.AGUI.AspNetCore.IntegrationTests.csproj" />
<Project Path="tests/Microsoft.Agents.AI.Hosting.AzureFunctions.IntegrationTests/Microsoft.Agents.AI.Hosting.AzureFunctions.IntegrationTests.csproj" />
@@ -586,11 +557,12 @@
<Project Path="tests/Microsoft.Agents.AI.AGUI.UnitTests/Microsoft.Agents.AI.AGUI.UnitTests.csproj" />
<Project Path="tests/Microsoft.Agents.AI.Anthropic.UnitTests/Microsoft.Agents.AI.Anthropic.UnitTests.csproj" />
<Project Path="tests/Microsoft.Agents.AI.AzureAI.Persistent.UnitTests/Microsoft.Agents.AI.AzureAI.Persistent.UnitTests.csproj" />
<Project Path="tests/Microsoft.Agents.AI.Foundry.UnitTests/Microsoft.Agents.AI.Foundry.UnitTests.csproj" />
<Project Path="tests/Microsoft.Agents.AI.CosmosNoSql.UnitTests/Microsoft.Agents.AI.CosmosNoSql.UnitTests.csproj" />
<Project Path="tests/Microsoft.Agents.AI.Declarative.UnitTests/Microsoft.Agents.AI.Declarative.UnitTests.csproj" />
<Project Path="tests/Microsoft.Agents.AI.DevUI.UnitTests/Microsoft.Agents.AI.DevUI.UnitTests.csproj" />
<Project Path="tests/Microsoft.Agents.AI.DurableTask.UnitTests/Microsoft.Agents.AI.DurableTask.UnitTests.csproj" />
<Project Path="tests/Microsoft.Agents.AI.Foundry.UnitTests/Microsoft.Agents.AI.Foundry.UnitTests.csproj" />
<Project Path="tests/Microsoft.Agents.AI.GitHub.Copilot.UnitTests/Microsoft.Agents.AI.GitHub.Copilot.UnitTests.csproj" />
<Project Path="tests/Microsoft.Agents.AI.Hosting.A2A.UnitTests/Microsoft.Agents.AI.Hosting.A2A.UnitTests.csproj" />
<Project Path="tests/Microsoft.Agents.AI.Hosting.AGUI.AspNetCore.UnitTests/Microsoft.Agents.AI.Hosting.AGUI.AspNetCore.UnitTests.csproj" />
-1
View File
@@ -9,7 +9,6 @@
"src\\Microsoft.Agents.AI.GitHub.Copilot\\Microsoft.Agents.AI.GitHub.Copilot.csproj",
"src\\Microsoft.Agents.AI.AzureAI.Persistent\\Microsoft.Agents.AI.AzureAI.Persistent.csproj",
"src\\Microsoft.Agents.AI.Foundry\\Microsoft.Agents.AI.Foundry.csproj",
"src\\Microsoft.Agents.AI.Foundry.Hosting\\Microsoft.Agents.AI.Foundry.Hosting.csproj",
"src\\Microsoft.Agents.AI.CopilotStudio\\Microsoft.Agents.AI.CopilotStudio.csproj",
"src\\Microsoft.Agents.AI.CosmosNoSql\\Microsoft.Agents.AI.CosmosNoSql.csproj",
"src\\Microsoft.Agents.AI.Declarative\\Microsoft.Agents.AI.Declarative.csproj",
+2 -2
View File
@@ -1,4 +1,4 @@
<?xml version="1.0" encoding="utf-8"?>
<?xml version="1.0" encoding="utf-8"?>
<configuration>
<packageSources>
<clear />
@@ -9,4 +9,4 @@
<package pattern="*" />
</packageSource>
</packageSourceMapping>
</configuration>
</configuration>
+1 -2
View File
@@ -30,8 +30,7 @@
<!-- Report low, moderate, high and critical advisories -->
<NuGetAuditLevel>low</NuGetAuditLevel>
<!-- Default description and tags. Packages can override. -->
<Authors>Microsoft</Authors>
<Company>Microsoft</Company>
@@ -1,19 +0,0 @@
<Project Sdk="Microsoft.NET.Sdk">
<PropertyGroup>
<OutputType>Exe</OutputType>
<TargetFrameworks>net10.0</TargetFrameworks>
<Nullable>enable</Nullable>
<ImplicitUsings>enable</ImplicitUsings>
</PropertyGroup>
<ItemGroup>
<PackageReference Include="A2A" />
</ItemGroup>
<ItemGroup>
<ProjectReference Include="..\..\..\..\src\Microsoft.Agents.AI.A2A\Microsoft.Agents.AI.A2A.csproj" />
</ItemGroup>
</Project>
@@ -1,36 +0,0 @@
// Copyright (c) Microsoft. All rights reserved.
// This sample demonstrates how to select the A2A protocol binding (HTTP+JSON vs JSON-RPC) when
// creating an AIAgent from an A2A agent card using A2AClientOptions.PreferredBindings.
using A2A;
using Microsoft.Agents.AI;
var a2aAgentHost = Environment.GetEnvironmentVariable("A2A_AGENT_HOST") ?? throw new InvalidOperationException("A2A_AGENT_HOST is not set.");
// Initialize an A2ACardResolver to get an A2A agent card.
A2ACardResolver agentCardResolver = new(new Uri(a2aAgentHost));
// Get the agent card
AgentCard agentCard = await agentCardResolver.GetAgentCardAsync();
// Use A2AClientOptions to explicitly select the HTTP+JSON protocol binding.
// This tells the A2A client factory to prefer the HTTP+JSON interface when the agent card
// advertises multiple supported interfaces.
A2AClientOptions options = new()
{
PreferredBindings = [ProtocolBindingNames.HttpJson]
};
// To prefer JSON-RPC instead, use:
// A2AClientOptions options = new()
// {
// PreferredBindings = [ProtocolBindingNames.JsonRpc]
// };
// Create an instance of the AIAgent for an existing A2A agent, using the specified protocol binding.
AIAgent agent = agentCard.AsAIAgent(options: options);
// Invoke the agent and output the text result.
AgentResponse response = await agent.RunAsync("Tell me a joke about a pirate.");
Console.WriteLine(response);
@@ -1,27 +0,0 @@
# A2A Agent Protocol Selection
This sample demonstrates how to select the A2A protocol binding when creating an `AIAgent` from an A2A agent card.
A2A agents can expose multiple interfaces with different protocol bindings (e.g., HTTP+JSON, JSON-RPC). By default, `AsAIAgent()` prefers HTTP+JSON with JSON-RPC as a fallback. This sample shows how to use `A2AClientOptions.PreferredBindings` to explicitly control which protocol binding is used.
The sample:
- Connects to an A2A agent server specified in the `A2A_AGENT_HOST` environment variable
- Configures `A2AClientOptions` to prefer the HTTP+JSON protocol binding
- Creates an `AIAgent` from the resolved agent card using the specified binding
- Sends a message to the agent and displays the response
## Prerequisites
Before you begin, ensure you have the following prerequisites:
- .NET 10.0 SDK or later
- An A2A agent server running and accessible via HTTP
**Note**: These samples need to be run against a valid A2A server. If no A2A server is available, they can be run against the echo-agent that can be spun up locally by following the guidelines at: https://github.com/a2aproject/a2a-dotnet/blob/main/samples/AgentServer/README.md
Set the following environment variable:
```powershell
$env:A2A_AGENT_HOST="http://localhost:5000" # Replace with your A2A agent server host
```
@@ -1,23 +0,0 @@
<Project Sdk="Microsoft.NET.Sdk">
<PropertyGroup>
<OutputType>Exe</OutputType>
<TargetFrameworks>net10.0</TargetFrameworks>
<Nullable>enable</Nullable>
<ImplicitUsings>enable</ImplicitUsings>
</PropertyGroup>
<ItemGroup>
<PackageReference Include="A2A" />
<PackageReference Include="Azure.AI.OpenAI" />
<PackageReference Include="Azure.Identity" />
<PackageReference Include="Microsoft.Extensions.Hosting" />
</ItemGroup>
<ItemGroup>
<ProjectReference Include="..\..\..\..\src\Microsoft.Agents.AI.A2A\Microsoft.Agents.AI.A2A.csproj" />
<ProjectReference Include="..\..\..\..\src\Microsoft.Agents.AI.OpenAI\Microsoft.Agents.AI.OpenAI.csproj" />
</ItemGroup>
</Project>
@@ -1,55 +0,0 @@
// Copyright (c) Microsoft. All rights reserved.
// This sample demonstrates how to reconnect to an A2A agent's streaming response using continuation tokens,
// allowing recovery from stream interruptions without losing progress.
using A2A;
using Microsoft.Agents.AI;
using Microsoft.Extensions.AI;
var a2aAgentHost = Environment.GetEnvironmentVariable("A2A_AGENT_HOST") ?? throw new InvalidOperationException("A2A_AGENT_HOST is not set.");
// Initialize an A2ACardResolver to get an A2A agent card.
A2ACardResolver agentCardResolver = new(new Uri(a2aAgentHost));
// Get the agent card
AgentCard agentCard = await agentCardResolver.GetAgentCardAsync();
// Create an instance of the AIAgent for an existing A2A agent specified by the agent card.
AIAgent agent = agentCard.AsAIAgent();
AgentSession session = await agent.CreateSessionAsync();
ResponseContinuationToken? continuationToken = null;
await foreach (var update in agent.RunStreamingAsync("Conduct a comprehensive analysis of quantum computing applications in cryptography, including recent breakthroughs, implementation challenges, and future roadmap. Please include diagrams and visual representations to illustrate complex concepts.", session))
{
// Saving the continuation token to be able to reconnect to the same response stream later.
// Note: Continuation tokens are only returned for long-running tasks. If the underlying A2A agent
// returns a message instead of a task, the continuation token will not be initialized.
// A2A agents do not support stream resumption from a specific point in the stream,
// but only reconnection to obtain the same response stream from the beginning.
// So, A2A agents will return an initialized continuation token in the first update
// representing the beginning of the stream, and it will be null in all subsequent updates.
if (update.ContinuationToken is { } token)
{
continuationToken = token;
}
// Imitating stream interruption
break;
}
// Reconnect to the same response stream using the continuation token obtained from the previous run.
// As a first update, the agent will return an update representing the current state of the response at the moment of calling
// RunStreamingAsync with the same continuation token, followed by other updates until the end of the stream is reached.
if (continuationToken is not null)
{
await foreach (var update in agent.RunStreamingAsync(session, options: new() { ContinuationToken = continuationToken }))
{
if (!string.IsNullOrEmpty(update.Text))
{
Console.WriteLine(update.Text);
}
}
}
@@ -1,29 +0,0 @@
# A2A Agent Stream Reconnection
This sample demonstrates how to reconnect to an A2A agent's streaming response using continuation tokens, allowing recovery from stream interruptions without losing progress.
The sample:
- Connects to an A2A agent server specified in the `A2A_AGENT_HOST` environment variable
- Sends a request to the agent and begins streaming the response
- Captures a continuation token from the stream for later reconnection
- Simulates a stream interruption by breaking out of the streaming loop
- Reconnects to the same response stream using the captured continuation token
- Displays the response received after reconnection
This pattern is useful when network interruptions or other failures may disrupt an ongoing streaming response, and you need to recover and continue processing.
> **Note:** Continuation tokens are only available when the underlying A2A agent returns a task. If the agent returns a message instead, the continuation token will not be initialized and stream reconnection is not applicable.
# Prerequisites
Before you begin, ensure you have the following prerequisites:
- .NET 10.0 SDK or later
- An A2A agent server running and accessible via HTTP
Set the following environment variable:
```powershell
$env:A2A_AGENT_HOST="http://localhost:5000" # Replace with your A2A agent server host
```
-1
View File
@@ -19,4 +19,3 @@ The getting started samples demonstrate the fundamental concepts and functionali
| [Declarative Agents](./DeclarativeAgents) | Loading and executing AI agents from YAML configuration files |
| [AG-UI](./AGUI/README.md) | Getting started with AG-UI (Agent UI Protocol) servers and clients |
| [Dev UI](./DevUI/README.md) | Interactive web interface for testing and debugging AI agents during development |
| [A2A Agents](./A2A/README.md) | Working with Agent-to-Agent (A2A) specific features |
-1
View File
@@ -1 +0,0 @@
**/Properties/launchSettings.json
@@ -2,7 +2,7 @@
<PropertyGroup>
<OutputType>Exe</OutputType>
<TargetFrameworks>net10.0</TargetFrameworks>
<TargetFramework>net10.0</TargetFramework>
<Nullable>enable</Nullable>
<ImplicitUsings>enable</ImplicitUsings>
@@ -13,6 +13,7 @@
<PackageReference Include="Azure.AI.OpenAI" />
<PackageReference Include="Azure.Identity" />
<PackageReference Include="Microsoft.Extensions.Hosting" />
<PackageReference Include="System.Net.ServerSentEvents" />
</ItemGroup>
<ItemGroup>
@@ -18,12 +18,8 @@ AIAgent agent = agentCard.AsAIAgent();
AgentSession session = await agent.CreateSessionAsync();
// AllowBackgroundResponses must be true so the server returns immediately with a continuation token
// instead of blocking until the task is complete.
AgentRunOptions options = new() { AllowBackgroundResponses = true };
// Start the initial run with a long-running task.
AgentResponse response = await agent.RunAsync("Conduct a comprehensive analysis of quantum computing applications in cryptography, including recent breakthroughs, implementation challenges, and future roadmap. Please include diagrams and visual representations to illustrate complex concepts.", session, options: options);
AgentResponse response = await agent.RunAsync("Conduct a comprehensive analysis of quantum computing applications in cryptography, including recent breakthroughs, implementation challenges, and future roadmap. Please include diagrams and visual representations to illustrate complex concepts.", session);
// Poll until the response is complete.
while (response.ContinuationToken is { } token)
@@ -3,7 +3,7 @@
These samples demonstrate how to work with Agent-to-Agent (A2A) specific features in the Agent Framework.
For other samples that demonstrate how to use AIAgent instances,
see the [Getting Started With Agents](../Agents/README.md) samples.
see the [Getting Started With Agents](../../02-agents/Agents/README.md) samples.
## Prerequisites
@@ -15,8 +15,6 @@ See the README.md for each sample for the prerequisites for that sample.
|---|---|
|[A2A Agent As Function Tools](./A2AAgent_AsFunctionTools/)|This sample demonstrates how to represent an A2A agent as a set of function tools, where each function tool corresponds to a skill of the A2A agent, and register these function tools with another AI agent so it can leverage the A2A agent's skills.|
|[A2A Agent Polling For Task Completion](./A2AAgent_PollingForTaskCompletion/)|This sample demonstrates how to poll for long-running task completion using continuation tokens with an A2A agent.|
|[A2A Agent Stream Reconnection](./A2AAgent_StreamReconnection/)|This sample demonstrates how to reconnect to an A2A agent's streaming response using continuation tokens, allowing recovery from stream interruptions.|
|[A2A Agent Protocol Selection](./A2AAgent_ProtocolSelection/)|This sample demonstrates how to select the A2A protocol binding (HTTP+JSON vs JSON-RPC) when creating an AIAgent from an A2A agent card using A2AClientOptions.|
## Running the samples from the console
@@ -1,2 +0,0 @@
ASPNETCORE_URLS=http://+:8088
ASPNETCORE_ENVIRONMENT=Development
@@ -1,17 +0,0 @@
# Use the official .NET 10.0 ASP.NET runtime as a parent image
FROM mcr.microsoft.com/dotnet/aspnet:10.0 AS base
WORKDIR /app
FROM mcr.microsoft.com/dotnet/sdk:10.0 AS build
WORKDIR /src
COPY . .
RUN dotnet restore
RUN dotnet publish -c Release -o /app/publish
# Final stage
FROM base AS final
WORKDIR /app
COPY --from=build /app/publish .
EXPOSE 8088
ENV ASPNETCORE_URLS=http://+:8088
ENTRYPOINT ["dotnet", "HostedInvocationsEchoAgent.dll"]
@@ -1,19 +0,0 @@
# Dockerfile for contributors building from the agent-framework repository source.
#
# This project uses ProjectReference to the local Microsoft.Agents.AI.Abstractions source,
# which means a standard multi-stage Docker build cannot resolve dependencies outside
# this folder. Instead, pre-publish the app targeting the container runtime and copy
# the output into the container:
#
# dotnet publish -c Debug -f net10.0 -r linux-musl-x64 --self-contained false -o out
# docker build -f Dockerfile.contributor -t hosted-invocations-echo-agent .
# docker run --rm -p 8088:8088 hosted-invocations-echo-agent
#
# For end-users consuming the NuGet package (not ProjectReference), use the standard
# Dockerfile which performs a full dotnet restore + publish inside the container.
FROM mcr.microsoft.com/dotnet/aspnet:10.0-alpine AS final
WORKDIR /app
COPY out/ .
EXPOSE 8088
ENV ASPNETCORE_URLS=http://+:8088
ENTRYPOINT ["dotnet", "HostedInvocationsEchoAgent.dll"]
@@ -1,85 +0,0 @@
// Copyright (c) Microsoft. All rights reserved.
using System.Runtime.CompilerServices;
using System.Text.Json;
using Microsoft.Extensions.AI;
namespace Microsoft.Agents.AI;
/// <summary>
/// A minimal <see cref="AIAgent"/> that echoes the user's input text back as the response.
/// No LLM or external service is required.
/// </summary>
public sealed class EchoAIAgent : AIAgent
{
/// <inheritdoc/>
public override string Name => "echo-agent";
/// <inheritdoc/>
public override string Description => "An agent that echoes back the input message.";
/// <inheritdoc/>
protected override Task<AgentResponse> RunCoreAsync(
IEnumerable<ChatMessage> messages,
AgentSession? session = null,
AgentRunOptions? options = null,
CancellationToken cancellationToken = default)
{
var inputText = GetInputText(messages);
var response = new AgentResponse(new ChatMessage(ChatRole.Assistant, $"Echo: {inputText}"));
return Task.FromResult(response);
}
/// <inheritdoc/>
protected override async IAsyncEnumerable<AgentResponseUpdate> RunCoreStreamingAsync(
IEnumerable<ChatMessage> messages,
AgentSession? session = null,
AgentRunOptions? options = null,
[EnumeratorCancellation] CancellationToken cancellationToken = default)
{
var inputText = GetInputText(messages);
yield return new AgentResponseUpdate
{
Role = ChatRole.Assistant,
Contents = [new TextContent($"Echo: {inputText}")],
};
await Task.CompletedTask;
}
/// <inheritdoc/>
protected override ValueTask<AgentSession> CreateSessionCoreAsync(CancellationToken cancellationToken = default)
=> new(new EchoAgentSession());
/// <inheritdoc/>
protected override ValueTask<JsonElement> SerializeSessionCoreAsync(
AgentSession session,
JsonSerializerOptions? jsonSerializerOptions = null,
CancellationToken cancellationToken = default)
=> new(JsonSerializer.SerializeToElement(new { }, jsonSerializerOptions));
/// <inheritdoc/>
protected override ValueTask<AgentSession> DeserializeSessionCoreAsync(
JsonElement serializedState,
JsonSerializerOptions? jsonSerializerOptions = null,
CancellationToken cancellationToken = default)
=> new(new EchoAgentSession());
private static string GetInputText(IEnumerable<ChatMessage> messages)
{
foreach (var message in messages)
{
if (message.Role == ChatRole.User)
{
return message.Text ?? string.Empty;
}
}
return string.Empty;
}
/// <summary>
/// Minimal session for the echo agent. No state is persisted.
/// </summary>
private sealed class EchoAgentSession : AgentSession;
}
@@ -1,32 +0,0 @@
// Copyright (c) Microsoft. All rights reserved.
using Azure.AI.AgentServer.Invocations;
using Microsoft.Agents.AI;
namespace HostedInvocationsEchoAgent;
/// <summary>
/// An <see cref="InvocationHandler"/> that reads the request body as plain text,
/// passes it to the <see cref="EchoAIAgent"/>, and writes the response back.
/// </summary>
public sealed class EchoInvocationHandler(EchoAIAgent agent) : InvocationHandler
{
/// <inheritdoc/>
public override async Task HandleAsync(
HttpRequest request,
HttpResponse response,
InvocationContext context,
CancellationToken cancellationToken)
{
// Read the raw text from the request body.
using var reader = new StreamReader(request.Body);
var input = await reader.ReadToEndAsync(cancellationToken);
// Run the echo agent with the input text.
var agentResponse = await agent.RunAsync(input, cancellationToken: cancellationToken);
// Write the agent response text back to the HTTP response.
response.ContentType = "text/plain";
await response.WriteAsync(agentResponse.Text, cancellationToken);
}
}
@@ -1,30 +0,0 @@
<Project Sdk="Microsoft.NET.Sdk.Web">
<PropertyGroup>
<TargetFrameworks>net10.0</TargetFrameworks>
<Nullable>enable</Nullable>
<ImplicitUsings>enable</ImplicitUsings>
<CentralPackageTransitivePinningEnabled>false</CentralPackageTransitivePinningEnabled>
<RootNamespace>HostedInvocationsEchoAgent</RootNamespace>
<AssemblyName>HostedInvocationsEchoAgent</AssemblyName>
<NoWarn>$(NoWarn);</NoWarn>
</PropertyGroup>
<ItemGroup>
<PackageReference Include="Azure.AI.AgentServer.Invocations" />
<PackageReference Include="DotNetEnv" />
</ItemGroup>
<!-- For contributors: uses ProjectReference to build against local source -->
<ItemGroup>
<ProjectReference Include="..\..\..\..\..\src\Microsoft.Agents.AI.Abstractions\Microsoft.Agents.AI.Abstractions.csproj" />
</ItemGroup>
<!-- For end-users: uncomment the PackageReference below and remove the ProjectReference above
<ItemGroup>
<PackageReference Include="Microsoft.Agents.AI.Abstractions" Version="1.0.0" />
<PackageReference Include="Azure.AI.AgentServer.Invocations" />
</ItemGroup>
-->
</Project>
@@ -1,28 +0,0 @@
// Copyright (c) Microsoft. All rights reserved.
using Azure.AI.AgentServer.Invocations;
using DotNetEnv;
using HostedInvocationsEchoAgent;
using Microsoft.Agents.AI;
// Load .env file if present (for local development)
Env.TraversePath().Load();
var builder = WebApplication.CreateBuilder(args);
// Register the echo agent as a singleton (no LLM needed).
builder.Services.AddSingleton<EchoAIAgent>();
// Register the Invocations SDK services and wire the handler.
builder.Services.AddInvocationsServer();
builder.Services.AddScoped<InvocationHandler, EchoInvocationHandler>();
var app = builder.Build();
// Map the Invocations protocol endpoints:
// POST /invocations — invoke the agent
// GET /invocations/{id} — get result (not used by this sample)
// POST /invocations/{id}/cancel — cancel (not used by this sample)
app.MapInvocationsServer();
app.Run();
@@ -1,76 +0,0 @@
# Hosted-Invocations-EchoAgent
A minimal echo agent hosted as a Foundry Hosted Agent using the **Invocations protocol**. The agent reads the request body as plain text, passes it through a custom `EchoAIAgent`, and writes the echoed text back in the response. No LLM or Azure credentials are required.
## Prerequisites
- [.NET 10 SDK](https://dotnet.microsoft.com/download/dotnet/10.0)
## Configuration
Copy the template:
```bash
cp .env.example .env
```
> **Note:** `.env` is gitignored. The `.env.example` template is checked in as a reference.
## Running directly (contributors)
This project uses `ProjectReference` to build against the local Agent Framework source.
```bash
cd dotnet/samples/04-hosting/FoundryHostedAgents/invocations/Hosted-Invocations-EchoAgent
dotnet run
```
The agent will start on `http://localhost:8088`.
### Test it
```bash
curl -X POST http://localhost:8088/invocations \
-H "Content-Type: text/plain" \
-d "Hello, world!"
```
Expected response:
```
Echo: Hello, world!
```
## Running with Docker
Since this project uses `ProjectReference`, the standard `Dockerfile` cannot resolve dependencies outside this folder. Use `Dockerfile.contributor` which takes a pre-published output.
### 1. Publish for the container runtime (Linux Alpine)
```bash
dotnet publish -c Debug -f net10.0 -r linux-musl-x64 --self-contained false -o out
```
### 2. Build the Docker image
```bash
docker build -f Dockerfile.contributor -t hosted-invocations-echo-agent .
```
### 3. Run the container
```bash
docker run --rm -p 8088:8088 hosted-invocations-echo-agent
```
### 4. Test it
```bash
curl -X POST http://localhost:8088/invocations \
-H "Content-Type: text/plain" \
-d "Hello from Docker!"
```
## NuGet package users
If you are consuming the Agent Framework as a NuGet package (not building from source), use the standard `Dockerfile` instead of `Dockerfile.contributor`. See the commented section in `Hosted-Invocations-EchoAgent.csproj` for the `PackageReference` alternative.
@@ -1,27 +0,0 @@
# yaml-language-server: $schema=https://raw.githubusercontent.com/microsoft/AgentSchema/refs/heads/main/schemas/v1.0/AgentManifest.yaml
name: hosted-invocations-echo-agent
displayName: "Hosted Invocations Echo Agent"
description: >
A minimal echo agent hosted as a Foundry Hosted Agent using the Invocations
protocol. Reads the request body as plain text, echoes it back in the response.
metadata:
tags:
- AI Agent Hosting
- Azure AI AgentServer
- Invocations Protocol
- Agent Framework
template:
name: hosted-invocations-echo-agent
kind: hosted
protocols:
- protocol: invocations
version: 1.0.0
resources:
cpu: "0.25"
memory: 0.5Gi
parameters:
properties: []
resources: []
@@ -1,9 +0,0 @@
# yaml-language-server: $schema=https://raw.githubusercontent.com/microsoft/AgentSchema/refs/heads/main/schemas/v1.0/ContainerAgent.yaml
kind: hosted
name: hosted-invocations-echo-agent
protocols:
- protocol: invocations
version: 1.0.0
resources:
cpu: "0.25"
memory: 0.5Gi
@@ -1,129 +0,0 @@
// Copyright (c) Microsoft. All rights reserved.
using System.Runtime.CompilerServices;
using System.Text.Json;
using Microsoft.Extensions.AI;
namespace Microsoft.Agents.AI;
/// <summary>
/// An <see cref="AIAgent"/> that invokes a remote agent hosted with the Invocations protocol
/// by sending plain-text HTTP POST requests to the <c>/invocations</c> endpoint.
/// </summary>
public sealed class InvocationsAIAgent : AIAgent
{
private readonly HttpClient _httpClient;
private readonly Uri _invocationsUri;
/// <summary>
/// Initializes a new instance of the <see cref="InvocationsAIAgent"/> class.
/// </summary>
/// <param name="agentEndpoint">
/// The base URI of the hosted agent (e.g., <c>http://localhost:8089</c>).
/// The <c>/invocations</c> path is appended automatically.
/// </param>
/// <param name="httpClient">Optional <see cref="HttpClient"/> to use. If <see langword="null"/>, a new instance is created.</param>
/// <param name="name">Optional name for the agent.</param>
/// <param name="description">Optional description for the agent.</param>
public InvocationsAIAgent(
Uri agentEndpoint,
HttpClient? httpClient = null,
string? name = null,
string? description = null)
{
ArgumentNullException.ThrowIfNull(agentEndpoint);
this._httpClient = httpClient ?? new HttpClient();
// Ensure the base URI ends with a slash so that combining works correctly.
var baseUri = agentEndpoint.AbsoluteUri.EndsWith('/')
? agentEndpoint
: new Uri(agentEndpoint.AbsoluteUri + "/");
this._invocationsUri = new Uri(baseUri, "invocations");
this.Name = name ?? "invocations-agent";
this.Description = description ?? "An agent that calls a remote Invocations protocol endpoint.";
}
/// <inheritdoc/>
public override string? Name { get; }
/// <inheritdoc/>
public override string? Description { get; }
/// <inheritdoc/>
protected override async Task<AgentResponse> RunCoreAsync(
IEnumerable<ChatMessage> messages,
AgentSession? session = null,
AgentRunOptions? options = null,
CancellationToken cancellationToken = default)
{
var inputText = GetLastUserText(messages);
var responseText = await this.SendInvocationAsync(inputText, cancellationToken).ConfigureAwait(false);
return new AgentResponse(new ChatMessage(ChatRole.Assistant, responseText));
}
/// <inheritdoc/>
protected override async IAsyncEnumerable<AgentResponseUpdate> RunCoreStreamingAsync(
IEnumerable<ChatMessage> messages,
AgentSession? session = null,
AgentRunOptions? options = null,
[EnumeratorCancellation] CancellationToken cancellationToken = default)
{
// The Invocations protocol returns a complete response (no SSE streaming),
// so we yield a single update with the full text.
var inputText = GetLastUserText(messages);
var responseText = await this.SendInvocationAsync(inputText, cancellationToken).ConfigureAwait(false);
yield return new AgentResponseUpdate
{
Role = ChatRole.Assistant,
Contents = [new TextContent(responseText)],
};
}
/// <inheritdoc/>
protected override ValueTask<AgentSession> CreateSessionCoreAsync(CancellationToken cancellationToken = default)
=> new(new InvocationsAgentSession());
/// <inheritdoc/>
protected override ValueTask<JsonElement> SerializeSessionCoreAsync(
AgentSession session,
JsonSerializerOptions? jsonSerializerOptions = null,
CancellationToken cancellationToken = default)
=> new(JsonSerializer.SerializeToElement(new { }, jsonSerializerOptions));
/// <inheritdoc/>
protected override ValueTask<AgentSession> DeserializeSessionCoreAsync(
JsonElement serializedState,
JsonSerializerOptions? jsonSerializerOptions = null,
CancellationToken cancellationToken = default)
=> new(new InvocationsAgentSession());
private async Task<string> SendInvocationAsync(string input, CancellationToken cancellationToken)
{
using var content = new StringContent(input, System.Text.Encoding.UTF8, "text/plain");
using var response = await this._httpClient.PostAsync(this._invocationsUri, content, cancellationToken).ConfigureAwait(false);
response.EnsureSuccessStatusCode();
return await response.Content.ReadAsStringAsync(cancellationToken).ConfigureAwait(false);
}
private static string GetLastUserText(IEnumerable<ChatMessage> messages)
{
string? lastUserText = null;
foreach (var message in messages)
{
if (message.Role == ChatRole.User)
{
lastUserText = message.Text;
}
}
return lastUserText ?? string.Empty;
}
/// <summary>
/// Minimal session for the invocations agent. No state is persisted.
/// </summary>
private sealed class InvocationsAgentSession : AgentSession;
}
@@ -1,61 +0,0 @@
// Copyright (c) Microsoft. All rights reserved.
using DotNetEnv;
using Microsoft.Agents.AI;
// Load .env file if present (for local development)
Env.TraversePath().Load();
Uri agentEndpoint = new(Environment.GetEnvironmentVariable("AGENT_ENDPOINT")
?? "http://localhost:8088");
// Create an agent that calls the remote Invocations endpoint.
InvocationsAIAgent agent = new(agentEndpoint);
// REPL
Console.ForegroundColor = ConsoleColor.Cyan;
Console.WriteLine($"""
══════════════════════════════════════════════════════════
Simple Invocations Agent Sample
Connected to: {agentEndpoint}
Type a message or 'quit' to exit
══════════════════════════════════════════════════════════
""");
Console.ResetColor();
Console.WriteLine();
while (true)
{
Console.ForegroundColor = ConsoleColor.Green;
Console.Write("You> ");
Console.ResetColor();
string? input = Console.ReadLine();
if (string.IsNullOrWhiteSpace(input)) { continue; }
if (input.Equals("quit", StringComparison.OrdinalIgnoreCase)) { break; }
try
{
Console.ForegroundColor = ConsoleColor.Yellow;
Console.Write("Agent> ");
Console.ResetColor();
await foreach (var update in agent.RunStreamingAsync(input))
{
Console.Write(update);
}
Console.WriteLine();
}
catch (Exception ex)
{
Console.ForegroundColor = ConsoleColor.Red;
Console.WriteLine($"Error: {ex.Message}");
Console.ResetColor();
}
Console.WriteLine();
}
Console.WriteLine("Goodbye!");
@@ -1,22 +0,0 @@
<Project Sdk="Microsoft.NET.Sdk">
<PropertyGroup>
<OutputType>Exe</OutputType>
<TargetFrameworks>net10.0</TargetFrameworks>
<Nullable>enable</Nullable>
<ImplicitUsings>enable</ImplicitUsings>
<CentralPackageTransitivePinningEnabled>false</CentralPackageTransitivePinningEnabled>
<RootNamespace>SimpleInvocationsAgentClient</RootNamespace>
<AssemblyName>simple-invocations-agent-client</AssemblyName>
<NoWarn>$(NoWarn);NU1605</NoWarn>
</PropertyGroup>
<ItemGroup>
<PackageReference Include="DotNetEnv" />
</ItemGroup>
<ItemGroup>
<ProjectReference Include="..\..\..\..\..\..\src\Microsoft.Agents.AI.Abstractions\Microsoft.Agents.AI.Abstractions.csproj" />
</ItemGroup>
</Project>
@@ -1,6 +0,0 @@
AZURE_AI_PROJECT_ENDPOINT=<your-azure-ai-project-endpoint>
ASPNETCORE_URLS=http://+:8088
ASPNETCORE_ENVIRONMENT=Development
AZURE_AI_MODEL_DEPLOYMENT_NAME=gpt-4o
AGENT_NAME=hosted-chat-client-agent
AZURE_BEARER_TOKEN=DefaultAzureCredential
@@ -1,17 +0,0 @@
# Use the official .NET 10.0 ASP.NET runtime as a parent image
FROM mcr.microsoft.com/dotnet/aspnet:10.0 AS base
WORKDIR /app
FROM mcr.microsoft.com/dotnet/sdk:10.0 AS build
WORKDIR /src
COPY . .
RUN dotnet restore
RUN dotnet publish -c Release -o /app/publish
# Final stage
FROM base AS final
WORKDIR /app
COPY --from=build /app/publish .
EXPOSE 8088
ENV ASPNETCORE_URLS=http://+:8088
ENTRYPOINT ["dotnet", "HostedChatClientAgent.dll"]
@@ -1,19 +0,0 @@
# Dockerfile for contributors building from the agent-framework repository source.
#
# This project uses ProjectReference to the local Microsoft.Agents.AI.Foundry source,
# which means a standard multi-stage Docker build cannot resolve dependencies outside
# this folder. Instead, pre-publish the app targeting the container runtime and copy
# the output into the container:
#
# dotnet publish -c Debug -f net10.0 -r linux-musl-x64 --self-contained false -o out
# docker build -f Dockerfile.contributor -t hosted-chat-client-agent .
# docker run --rm -p 8088:8088 -e AGENT_NAME=hosted-chat-client-agent --env-file .env hosted-chat-client-agent
#
# For end-users consuming the NuGet package (not ProjectReference), use the standard
# Dockerfile which performs a full dotnet restore + publish inside the container.
FROM mcr.microsoft.com/dotnet/aspnet:10.0-alpine AS final
WORKDIR /app
COPY out/ .
EXPOSE 8088
ENV ASPNETCORE_URLS=http://+:8088
ENTRYPOINT ["dotnet", "HostedChatClientAgent.dll"]
@@ -1,32 +0,0 @@
<Project Sdk="Microsoft.NET.Sdk.Web">
<PropertyGroup>
<TargetFrameworks>net10.0</TargetFrameworks>
<Nullable>enable</Nullable>
<ImplicitUsings>enable</ImplicitUsings>
<CentralPackageTransitivePinningEnabled>false</CentralPackageTransitivePinningEnabled>
<RootNamespace>HostedChatClientAgent</RootNamespace>
<AssemblyName>HostedChatClientAgent</AssemblyName>
<NoWarn>$(NoWarn);</NoWarn>
</PropertyGroup>
<ItemGroup>
<PackageReference Include="DotNetEnv" />
</ItemGroup>
<!-- For contributors: uses ProjectReference to build against local source -->
<ItemGroup>
<ProjectReference Include="..\..\..\..\..\src\Microsoft.Agents.AI.Foundry\Microsoft.Agents.AI.Foundry.csproj" />
<ProjectReference Include="..\..\..\..\..\src\Microsoft.Agents.AI.Foundry.Hosting\Microsoft.Agents.AI.Foundry.Hosting.csproj" />
</ItemGroup>
<!-- For end-users: uncomment the PackageReference below and remove the ProjectReference above
<ItemGroup>
<PackageReference Include="Microsoft.Agents.AI.Foundry" Version="1.0.0" />
<PackageReference Include="Microsoft.Agents.AI.Foundry.Hosting" Version="1.0.0" />
<PackageReference Include="Azure.AI.Projects" />
<PackageReference Include="Azure.Identity" />
</ItemGroup>
-->
</Project>
@@ -1,98 +0,0 @@
// Copyright (c) Microsoft. All rights reserved.
using Azure.AI.Projects;
using Azure.Core;
using Azure.Identity;
using DotNetEnv;
using Microsoft.Agents.AI;
using Microsoft.Agents.AI.Foundry.Hosting;
// Load .env file if present (for local development)
Env.TraversePath().Load();
var projectEndpoint = new Uri(Environment.GetEnvironmentVariable("AZURE_AI_PROJECT_ENDPOINT")
?? throw new InvalidOperationException("AZURE_AI_PROJECT_ENDPOINT is not set."));
var agentName = Environment.GetEnvironmentVariable("AGENT_NAME")
?? throw new InvalidOperationException("AGENT_NAME is not set.");
var deployment = Environment.GetEnvironmentVariable("AZURE_AI_MODEL_DEPLOYMENT_NAME") ?? "gpt-4o";
// Use a chained credential: try a temporary dev token first (for local Docker debugging),
// then fall back to DefaultAzureCredential (for local dev via dotnet run / managed identity running in foundry).
TokenCredential credential = new ChainedTokenCredential(
new DevTemporaryTokenCredential(),
new DefaultAzureCredential());
// Create the agent via the AI project client using the Responses API.
AIAgent agent = new AIProjectClient(projectEndpoint, credential)
.AsAIAgent(
model: deployment,
instructions: """
You are a helpful AI assistant hosted as a Foundry Hosted Agent.
You can help with a wide range of tasks including answering questions,
providing explanations, brainstorming ideas, and offering guidance.
Be concise, clear, and helpful in your responses.
""",
name: agentName,
description: "A simple general-purpose AI assistant");
// Host the agent as a Foundry Hosted Agent using the Responses API.
var builder = WebApplication.CreateBuilder(args);
builder.Services.AddFoundryResponses(agent);
var app = builder.Build();
app.MapFoundryResponses();
// In Development, also map the OpenAI-compatible route that AIProjectClient uses.
if (app.Environment.IsDevelopment())
{
app.MapFoundryResponses("openai/v1");
}
app.Run();
/// <summary>
/// A <see cref="TokenCredential"/> for local Docker debugging only.
///
/// When debugging and testing a hosted agent in a local Docker container, Azure CLI
/// and other interactive credentials are not available. This credential reads a
/// pre-fetched bearer token from the <c>AZURE_BEARER_TOKEN</c> environment variable.
///
/// This should NOT be used in production — tokens expire (~1 hour) and cannot be refreshed.
/// In production, the Foundry platform injects a managed identity automatically.
///
/// Generate a token on your host and pass it to the container:
/// export AZURE_BEARER_TOKEN=$(az account get-access-token --resource https://ai.azure.com --query accessToken -o tsv)
/// docker run -e AZURE_BEARER_TOKEN=$AZURE_BEARER_TOKEN ...
/// </summary>
internal sealed class DevTemporaryTokenCredential : TokenCredential
{
private const string EnvironmentVariable = "AZURE_BEARER_TOKEN";
private readonly string? _token;
public DevTemporaryTokenCredential()
{
this._token = Environment.GetEnvironmentVariable(EnvironmentVariable);
}
public override AccessToken GetToken(TokenRequestContext requestContext, CancellationToken cancellationToken)
{
return this.GetAccessToken();
}
public override ValueTask<AccessToken> GetTokenAsync(TokenRequestContext requestContext, CancellationToken cancellationToken)
{
return new ValueTask<AccessToken>(this.GetAccessToken());
}
private AccessToken GetAccessToken()
{
if (string.IsNullOrEmpty(this._token) || this._token == "DefaultAzureCredential")
{
throw new CredentialUnavailableException($"{EnvironmentVariable} environment variable is not set.");
}
return new AccessToken(this._token, DateTimeOffset.UtcNow.AddHours(1));
}
}
@@ -1,109 +0,0 @@
# Hosted-ChatClientAgent
A simple general-purpose AI assistant hosted as a Foundry Hosted Agent using the Agent Framework instance hosting pattern. The agent is created inline via `AIProjectClient.AsAIAgent(model, instructions)` and served using the Responses protocol.
## Prerequisites
- [.NET 10 SDK](https://dotnet.microsoft.com/download/dotnet/10.0)
- An Azure AI Foundry project with a deployed model (e.g., `gpt-4o`)
- Azure CLI logged in (`az login`)
## Configuration
Copy the template and fill in your project endpoint:
```bash
cp .env.example .env
```
Edit `.env` and set your Azure AI Foundry project endpoint:
```env
AZURE_AI_PROJECT_ENDPOINT=https://<your-account>.services.ai.azure.com/api/projects/<your-project>
ASPNETCORE_URLS=http://+:8088
ASPNETCORE_ENVIRONMENT=Development
AZURE_AI_MODEL_DEPLOYMENT_NAME=gpt-4o
```
> **Note:** `.env` is gitignored. The `.env.example` template is checked in as a reference.
## Running directly (contributors)
This project uses `ProjectReference` to build against the local Agent Framework source.
```bash
cd dotnet/samples/04-hosting/FoundryHostedAgents/responses/Hosted-ChatClientAgent
dotnet run
```
The agent will start on `http://localhost:8088`.
### Test it
Using the Azure Developer CLI:
```bash
azd ai agent invoke --local "Hello!"
```
Or with curl (specifying the agent name explicitly):
```bash
curl -X POST http://localhost:8088/responses \
-H "Content-Type: application/json" \
-d '{"input": "Hello!", "model": "hosted-chat-client-agent"}'
```
## Running with Docker
Since this project uses `ProjectReference`, the standard `Dockerfile` cannot resolve dependencies outside this folder. Use `Dockerfile.contributor` which takes a pre-published output.
### 1. Publish for the container runtime (Linux Alpine)
```bash
dotnet publish -c Debug -f net10.0 -r linux-musl-x64 --self-contained false -o out
```
### 2. Build the Docker image
```bash
docker build -f Dockerfile.contributor -t hosted-chat-client-agent .
```
### 3. Run the container
Generate a bearer token on your host and pass it to the container:
```bash
# Generate token (expires in ~1 hour)
export AZURE_BEARER_TOKEN=$(az account get-access-token --resource https://ai.azure.com --query accessToken -o tsv)
# Run with token
docker run --rm -p 8088:8088 \
-e AGENT_NAME=hosted-chat-client-agent \
-e AZURE_BEARER_TOKEN=$AZURE_BEARER_TOKEN \
--env-file .env \
hosted-chat-client-agent
```
> **Note:** `AGENT_NAME` is passed via `-e` to simulate the platform injection. `AZURE_BEARER_TOKEN` provides Azure credentials to the container (tokens expire after ~1 hour). The `.env` file provides the remaining configuration.
### 4. Test it
Using the Azure Developer CLI:
```bash
azd ai agent invoke --local "Hello!"
```
Or with curl (specifying the agent name explicitly):
```bash
curl -X POST http://localhost:8088/responses \
-H "Content-Type: application/json" \
-d '{"input": "Hello!", "model": "hosted-chat-client-agent"}'
```
## NuGet package users
If you are consuming the Agent Framework as a NuGet package (not building from source), use the standard `Dockerfile` instead of `Dockerfile.contributor` — it performs a full `dotnet restore` and `dotnet publish` inside the container. See the commented section in `HostedChatClientAgent.csproj` for the `PackageReference` alternative.
@@ -1,28 +0,0 @@
# yaml-language-server: $schema=https://raw.githubusercontent.com/microsoft/AgentSchema/refs/heads/main/schemas/v1.0/AgentManifest.yaml
name: hosted-chat-client-agent
displayName: "Hosted Chat Client Agent"
description: >
A simple general-purpose AI assistant hosted as a Foundry Hosted Agent
using the Agent Framework instance hosting pattern.
metadata:
tags:
- AI Agent Hosting
- Azure AI AgentServer
- Responses Protocol
- Streaming
- Agent Framework
template:
name: hosted-chat-client-agent
kind: hosted
protocols:
- protocol: responses
version: 1.0.0
resources:
cpu: "0.25"
memory: 0.5Gi
parameters:
properties: []
resources: []
@@ -1,9 +0,0 @@
# yaml-language-server: $schema=https://raw.githubusercontent.com/microsoft/AgentSchema/refs/heads/main/schemas/v1.0/ContainerAgent.yaml
kind: hosted
name: hosted-chat-client-agent
protocols:
- protocol: responses
version: 1.0.0
resources:
cpu: "0.25"
memory: 0.5Gi
@@ -1,5 +0,0 @@
AZURE_AI_PROJECT_ENDPOINT=<your-azure-ai-project-endpoint>
ASPNETCORE_URLS=http://+:8088
ASPNETCORE_ENVIRONMENT=Development
AGENT_NAME=<your-foundry-agent-name>
AZURE_BEARER_TOKEN=DefaultAzureCredential
@@ -1,17 +0,0 @@
# Use the official .NET 10.0 ASP.NET runtime as a parent image
FROM mcr.microsoft.com/dotnet/aspnet:10.0 AS base
WORKDIR /app
FROM mcr.microsoft.com/dotnet/sdk:10.0 AS build
WORKDIR /src
COPY . .
RUN dotnet restore
RUN dotnet publish -c Release -o /app/publish
# Final stage
FROM base AS final
WORKDIR /app
COPY --from=build /app/publish .
EXPOSE 8088
ENV ASPNETCORE_URLS=http://+:8088
ENTRYPOINT ["dotnet", "HostedFoundryAgent.dll"]
@@ -1,19 +0,0 @@
# Dockerfile for contributors building from the agent-framework repository source.
#
# This project uses ProjectReference to the local Microsoft.Agents.AI.Foundry source,
# which means a standard multi-stage Docker build cannot resolve dependencies outside
# this folder. Instead, pre-publish the app targeting the container runtime and copy
# the output into the container:
#
# dotnet publish -c Debug -f net10.0 -r linux-musl-x64 --self-contained false -o out
# docker build -f Dockerfile.contributor -t hosted-foundry-agent .
# docker run --rm -p 8088:8088 -e AGENT_NAME=<your-agent> -e AZURE_BEARER_TOKEN=$AZURE_BEARER_TOKEN --env-file .env hosted-foundry-agent
#
# For end-users consuming the NuGet package (not ProjectReference), use the standard
# Dockerfile which performs a full dotnet restore + publish inside the container.
FROM mcr.microsoft.com/dotnet/aspnet:10.0-alpine AS final
WORKDIR /app
COPY out/ .
EXPOSE 8088
ENV ASPNETCORE_URLS=http://+:8088
ENTRYPOINT ["dotnet", "HostedFoundryAgent.dll"]
@@ -1,32 +0,0 @@
<Project Sdk="Microsoft.NET.Sdk.Web">
<PropertyGroup>
<TargetFrameworks>net10.0</TargetFrameworks>
<Nullable>enable</Nullable>
<ImplicitUsings>enable</ImplicitUsings>
<CentralPackageTransitivePinningEnabled>false</CentralPackageTransitivePinningEnabled>
<RootNamespace>HostedFoundryAgent</RootNamespace>
<AssemblyName>HostedFoundryAgent</AssemblyName>
<NoWarn>$(NoWarn);</NoWarn>
</PropertyGroup>
<ItemGroup>
<PackageReference Include="DotNetEnv" />
</ItemGroup>
<!-- For contributors: uses ProjectReference to build against local source -->
<ItemGroup>
<ProjectReference Include="..\..\..\..\..\src\Microsoft.Agents.AI.Foundry\Microsoft.Agents.AI.Foundry.csproj" />
<ProjectReference Include="..\..\..\..\..\src\Microsoft.Agents.AI.Foundry.Hosting\Microsoft.Agents.AI.Foundry.Hosting.csproj" />
</ItemGroup>
<!-- For end-users: uncomment the PackageReference below and remove the ProjectReference above
<ItemGroup>
<PackageReference Include="Microsoft.Agents.AI.Foundry" Version="1.0.0" />
<PackageReference Include="Microsoft.Agents.AI.Foundry.Hosting" Version="1.0.0" />
<PackageReference Include="Azure.AI.Projects" />
<PackageReference Include="Azure.Identity" />
</ItemGroup>
-->
</Project>
@@ -1,91 +0,0 @@
// Copyright (c) Microsoft. All rights reserved.
using Azure.AI.Projects;
using Azure.AI.Projects.Agents;
using Azure.Core;
using Azure.Identity;
using DotNetEnv;
using Microsoft.Agents.AI.Foundry;
using Microsoft.Agents.AI.Foundry.Hosting;
// Load .env file if present (for local development)
Env.TraversePath().Load();
var projectEndpoint = new Uri(Environment.GetEnvironmentVariable("AZURE_AI_PROJECT_ENDPOINT")
?? throw new InvalidOperationException("AZURE_AI_PROJECT_ENDPOINT is not set."));
var agentName = Environment.GetEnvironmentVariable("AGENT_NAME")
?? throw new InvalidOperationException("AGENT_NAME is not set.");
// Use a chained credential: try a temporary dev token first (for local Docker debugging),
// then fall back to DefaultAzureCredential (for local dev via dotnet run / managed identity running in foundry).
TokenCredential credential = new ChainedTokenCredential(
new DevTemporaryTokenCredential(),
new DefaultAzureCredential());
var aiProjectClient = new AIProjectClient(projectEndpoint, credential);
// Retrieve the Foundry-managed agent by name (latest version).
ProjectsAgentRecord agentRecord = await aiProjectClient
.AgentAdministrationClient.GetAgentAsync(agentName);
FoundryAgent agent = aiProjectClient.AsAIAgent(agentRecord);
// Host the agent as a Foundry Hosted Agent using the Responses API.
var builder = WebApplication.CreateBuilder(args);
builder.Services.AddFoundryResponses(agent);
var app = builder.Build();
app.MapFoundryResponses();
// In Development, also map the OpenAI-compatible route that AIProjectClient uses.
if (app.Environment.IsDevelopment())
{
app.MapFoundryResponses("openai/v1");
}
app.Run();
/// <summary>
/// A <see cref="TokenCredential"/> for local Docker debugging only.
///
/// When debugging and testing a hosted agent in a local Docker container, Azure CLI
/// and other interactive credentials are not available. This credential reads a
/// pre-fetched bearer token from the <c>AZURE_BEARER_TOKEN</c> environment variable.
///
/// This should NOT be used in production — tokens expire (~1 hour) and cannot be refreshed.
/// In production, the Foundry platform injects a managed identity automatically.
///
/// Generate a token on your host and pass it to the container:
/// export AZURE_BEARER_TOKEN=$(az account get-access-token --resource https://ai.azure.com --query accessToken -o tsv)
/// docker run -e AZURE_BEARER_TOKEN=$AZURE_BEARER_TOKEN ...
/// </summary>
internal sealed class DevTemporaryTokenCredential : TokenCredential
{
private const string EnvironmentVariable = "AZURE_BEARER_TOKEN";
private readonly string? _token;
public DevTemporaryTokenCredential()
{
this._token = Environment.GetEnvironmentVariable(EnvironmentVariable);
}
public override AccessToken GetToken(TokenRequestContext requestContext, CancellationToken cancellationToken)
{
return this.GetAccessToken();
}
public override ValueTask<AccessToken> GetTokenAsync(TokenRequestContext requestContext, CancellationToken cancellationToken)
{
return new ValueTask<AccessToken>(this.GetAccessToken());
}
private AccessToken GetAccessToken()
{
if (string.IsNullOrEmpty(this._token) || this._token == "DefaultAzureCredential")
{
throw new CredentialUnavailableException($"{EnvironmentVariable} environment variable is not set.");
}
return new AccessToken(this._token, DateTimeOffset.UtcNow.AddHours(1));
}
}
@@ -1,121 +0,0 @@
# Hosted-FoundryAgent
A hosted agent that delegates to a **Foundry-managed agent definition**. Instead of defining the model, instructions, and tools inline in code, this sample retrieves an existing agent registered in the Foundry platform via `AIProjectClient.AsAIAgent(agentRecord)` and hosts it using the Responses protocol.
This is the **Foundry hosting** pattern — the agent's behavior is configured in the platform (via Foundry UI, CLI, or API), and this server simply wraps and serves it.
## Prerequisites
- [.NET 10 SDK](https://dotnet.microsoft.com/download/dotnet/10.0)
- An Azure AI Foundry project with a **registered agent** (created via Foundry UI, CLI, or API)
- Azure CLI logged in (`az login`)
## Configuration
Copy the template and fill in your project endpoint:
```bash
cp .env.example .env
```
Edit `.env` and set your Azure AI Foundry project endpoint:
```env
AZURE_AI_PROJECT_ENDPOINT=https://<your-account>.services.ai.azure.com/api/projects/<your-project>
ASPNETCORE_URLS=http://+:8088
ASPNETCORE_ENVIRONMENT=Development
```
> **Note:** `.env` is gitignored. The `.env.example` template is checked in as a reference.
You also need to set `AGENT_NAME` — the name of the Foundry-managed agent to host. This is injected automatically by the Foundry platform when deployed. For local development, pass it as an environment variable.
## Running directly (contributors)
This project uses `ProjectReference` to build against the local Agent Framework source.
```bash
cd dotnet/samples/04-hosting/FoundryHostedAgents/responses/Hosted-FoundryAgent
AGENT_NAME=<your-agent-name> dotnet run
```
The agent will start on `http://localhost:8088`.
### Test it
Using the Azure Developer CLI:
```bash
azd ai agent invoke --local "Hello!"
```
Or with curl (specifying the agent name explicitly):
```bash
curl -X POST http://localhost:8088/responses \
-H "Content-Type: application/json" \
-d '{"input": "Hello!", "model": "<your-agent-name>"}'
```
## Running with Docker
Since this project uses `ProjectReference`, the standard `Dockerfile` cannot resolve dependencies outside this folder. Use `Dockerfile.contributor` which takes a pre-published output.
### 1. Publish for the container runtime (Linux Alpine)
```bash
dotnet publish -c Debug -f net10.0 -r linux-musl-x64 --self-contained false -o out
```
### 2. Build the Docker image
```bash
docker build -f Dockerfile.contributor -t hosted-foundry-agent .
```
### 3. Run the container
Generate a bearer token on your host and pass it to the container:
```bash
# Generate token (expires in ~1 hour)
export AZURE_BEARER_TOKEN=$(az account get-access-token --resource https://ai.azure.com --query accessToken -o tsv)
# Run with token
docker run --rm -p 8088:8088 \
-e AGENT_NAME=<your-agent-name> \
-e AZURE_BEARER_TOKEN=$AZURE_BEARER_TOKEN \
--env-file .env \
hosted-foundry-agent
```
> **Note:** `AGENT_NAME` is passed via `-e` to simulate the platform injection. `AZURE_BEARER_TOKEN` provides Azure credentials to the container (tokens expire after ~1 hour). The `.env` file provides the remaining configuration.
### 4. Test it
Using the Azure Developer CLI:
```bash
azd ai agent invoke --local "Hello!"
```
Or with curl (specifying the agent name explicitly):
```bash
curl -X POST http://localhost:8088/responses \
-H "Content-Type: application/json" \
-d '{"input": "Hello!", "model": "<your-agent-name>"}'
```
## NuGet package users
If you are consuming the Agent Framework as a NuGet package (not building from source), use the standard `Dockerfile` instead of `Dockerfile.contributor` — it performs a full `dotnet restore` and `dotnet publish` inside the container. See the commented section in `HostedFoundryAgent.csproj` for the `PackageReference` alternative.
## How it differs from Hosted-ChatClientAgent
| | Hosted-ChatClientAgent | Hosted-FoundryAgent |
|---|---|---|
| **Agent definition** | Inline in code (`AsAIAgent(model, instructions)`) | Managed in Foundry platform (`AsAIAgent(agentRecord)`) |
| **Model/instructions** | Set in `Program.cs` | Set in Foundry UI/CLI/API |
| **Tools** | Defined in code | Configured in the platform |
| **Use case** | Full control over agent behavior | Platform-managed agent with centralized config |
@@ -1,28 +0,0 @@
# yaml-language-server: $schema=https://raw.githubusercontent.com/microsoft/AgentSchema/refs/heads/main/schemas/v1.0/AgentManifest.yaml
name: hosted-foundry-agent
displayName: "Hosted Foundry Agent"
description: >
A simple general-purpose AI assistant hosted as a Foundry Hosted Agent,
backed by a Foundry-managed agent definition.
metadata:
tags:
- AI Agent Hosting
- Azure AI AgentServer
- Responses Protocol
- Streaming
- Agent Framework
template:
name: hosted-foundry-agent
kind: hosted
protocols:
- protocol: responses
version: 1.0.0
resources:
cpu: "0.25"
memory: 0.5Gi
parameters:
properties: []
resources: []
@@ -1,9 +0,0 @@
# yaml-language-server: $schema=https://raw.githubusercontent.com/microsoft/AgentSchema/refs/heads/main/schemas/v1.0/ContainerAgent.yaml
kind: hosted
name: hosted-foundry-agent
protocols:
- protocol: responses
version: 1.0.0
resources:
cpu: "0.25"
memory: 0.5Gi
@@ -1,5 +0,0 @@
AZURE_AI_PROJECT_ENDPOINT=<your-azure-ai-project-endpoint>
ASPNETCORE_URLS=http://+:8088
ASPNETCORE_ENVIRONMENT=Development
AZURE_AI_MODEL_DEPLOYMENT_NAME=gpt-4o
AZURE_BEARER_TOKEN=DefaultAzureCredential
@@ -1,17 +0,0 @@
# Use the official .NET 10.0 ASP.NET runtime as a parent image
FROM mcr.microsoft.com/dotnet/aspnet:10.0 AS base
WORKDIR /app
FROM mcr.microsoft.com/dotnet/sdk:10.0 AS build
WORKDIR /src
COPY . .
RUN dotnet restore
RUN dotnet publish -c Release -o /app/publish
# Final stage
FROM base AS final
WORKDIR /app
COPY --from=build /app/publish .
EXPOSE 8088
ENV ASPNETCORE_URLS=http://+:8088
ENTRYPOINT ["dotnet", "HostedLocalTools.dll"]
@@ -1,19 +0,0 @@
# Dockerfile for contributors building from the agent-framework repository source.
#
# This project uses ProjectReference to the local Microsoft.Agents.AI.Foundry source,
# which means a standard multi-stage Docker build cannot resolve dependencies outside
# this folder. Instead, pre-publish the app targeting the container runtime and copy
# the output into the container:
#
# dotnet publish -c Debug -f net10.0 -r linux-musl-x64 --self-contained false -o out
# docker build -f Dockerfile.contributor -t hosted-local-tools .
# docker run --rm -p 8088:8088 -e AGENT_NAME=hosted-local-tools -e AZURE_BEARER_TOKEN=$AZURE_BEARER_TOKEN --env-file .env hosted-local-tools
#
# For end-users consuming the NuGet package (not ProjectReference), use the standard
# Dockerfile which performs a full dotnet restore + publish inside the container.
FROM mcr.microsoft.com/dotnet/aspnet:10.0-alpine AS final
WORKDIR /app
COPY out/ .
EXPOSE 8088
ENV ASPNETCORE_URLS=http://+:8088
ENTRYPOINT ["dotnet", "HostedLocalTools.dll"]
@@ -1,32 +0,0 @@
<Project Sdk="Microsoft.NET.Sdk.Web">
<PropertyGroup>
<TargetFrameworks>net10.0</TargetFrameworks>
<Nullable>enable</Nullable>
<ImplicitUsings>enable</ImplicitUsings>
<CentralPackageTransitivePinningEnabled>false</CentralPackageTransitivePinningEnabled>
<RootNamespace>HostedLocalTools</RootNamespace>
<AssemblyName>HostedLocalTools</AssemblyName>
<NoWarn>$(NoWarn);</NoWarn>
</PropertyGroup>
<ItemGroup>
<PackageReference Include="Azure.AI.Projects" VersionOverride="2.1.0-beta.1" />
<PackageReference Include="Azure.Identity" />
<PackageReference Include="DotNetEnv" />
</ItemGroup>
<!-- For contributors: uses ProjectReference to build against local source -->
<ItemGroup>
<ProjectReference Include="..\..\..\..\..\src\Microsoft.Agents.AI.Foundry\Microsoft.Agents.AI.Foundry.csproj" />
<ProjectReference Include="..\..\..\..\..\src\Microsoft.Agents.AI.Foundry.Hosting\Microsoft.Agents.AI.Foundry.Hosting.csproj" />
</ItemGroup>
<!-- For end-users: uncomment the PackageReference below and remove the ProjectReference above
<ItemGroup>
<PackageReference Include="Microsoft.Agents.AI.Foundry" Version="1.0.0" />
<PackageReference Include="Microsoft.Agents.AI.Foundry.Hosting" Version="1.0.0" />
</ItemGroup>
-->
</Project>
@@ -1,164 +0,0 @@
// Copyright (c) Microsoft. All rights reserved.
// Seattle Hotel Agent - A hosted agent with local C# function tools.
// Demonstrates how to define and wire local tools that the LLM can invoke,
// a key advantage of code-based hosted agents over prompt agents.
using System.ComponentModel;
using System.Globalization;
using System.Text;
using Azure.AI.Projects;
using Azure.Core;
using Azure.Identity;
using DotNetEnv;
using Microsoft.Agents.AI;
using Microsoft.Agents.AI.Foundry.Hosting;
using Microsoft.Extensions.AI;
// Load .env file if present (for local development)
Env.TraversePath().Load();
string endpoint = Environment.GetEnvironmentVariable("AZURE_AI_PROJECT_ENDPOINT")
?? throw new InvalidOperationException("AZURE_AI_PROJECT_ENDPOINT is not set.");
string deploymentName = Environment.GetEnvironmentVariable("AZURE_AI_MODEL_DEPLOYMENT_NAME") ?? "gpt-4o";
// Use a chained credential: try a temporary dev token first (for local Docker debugging),
// then fall back to DefaultAzureCredential (for local dev via dotnet run / managed identity in production).
TokenCredential credential = new ChainedTokenCredential(
new DevTemporaryTokenCredential(),
new DefaultAzureCredential());
// ── Hotel data ───────────────────────────────────────────────────────────────
Hotel[] seattleHotels =
[
new("Contoso Suites", 189, 4.5, "Downtown"),
new("Fabrikam Residences", 159, 4.2, "Pike Place Market"),
new("Alpine Ski House", 249, 4.7, "Seattle Center"),
new("Margie's Travel Lodge", 219, 4.4, "Waterfront"),
new("Northwind Inn", 139, 4.0, "Capitol Hill"),
new("Relecloud Hotel", 99, 3.8, "University District"),
];
// ── Tool: GetAvailableHotels ─────────────────────────────────────────────────
[Description("Get available hotels in Seattle for the specified dates.")]
string GetAvailableHotels(
[Description("Check-in date in YYYY-MM-DD format")] string checkInDate,
[Description("Check-out date in YYYY-MM-DD format")] string checkOutDate,
[Description("Maximum price per night in USD (optional, defaults to 500)")] int maxPrice = 500)
{
if (!DateTime.TryParseExact(checkInDate, "yyyy-MM-dd", CultureInfo.InvariantCulture, DateTimeStyles.None, out var checkIn))
{
return "Error parsing check-in date. Please use YYYY-MM-DD format.";
}
if (!DateTime.TryParseExact(checkOutDate, "yyyy-MM-dd", CultureInfo.InvariantCulture, DateTimeStyles.None, out var checkOut))
{
return "Error parsing check-out date. Please use YYYY-MM-DD format.";
}
if (checkOut <= checkIn)
{
return "Error: Check-out date must be after check-in date.";
}
int nights = (checkOut - checkIn).Days;
List<Hotel> availableHotels = seattleHotels.Where(h => h.PricePerNight <= maxPrice).ToList();
if (availableHotels.Count == 0)
{
return $"No hotels found in Seattle within your budget of ${maxPrice}/night.";
}
StringBuilder result = new();
result.AppendLine($"Available hotels in Seattle from {checkInDate} to {checkOutDate} ({nights} nights):");
result.AppendLine();
foreach (Hotel hotel in availableHotels)
{
int totalCost = hotel.PricePerNight * nights;
result.AppendLine($"**{hotel.Name}**");
result.AppendLine($" Location: {hotel.Location}");
result.AppendLine($" Rating: {hotel.Rating}/5");
result.AppendLine($" ${hotel.PricePerNight}/night (Total: ${totalCost})");
result.AppendLine();
}
return result.ToString();
}
// ── Create and host the agent ────────────────────────────────────────────────
AIAgent agent = new AIProjectClient(new Uri(endpoint), credential)
.AsAIAgent(
model: deploymentName,
instructions: """
You are a helpful travel assistant specializing in finding hotels in Seattle, Washington.
When a user asks about hotels in Seattle:
1. Ask for their check-in and check-out dates if not provided
2. Ask about their budget preferences if not mentioned
3. Use the GetAvailableHotels tool to find available options
4. Present the results in a friendly, informative way
5. Offer to help with additional questions about the hotels or Seattle
Be conversational and helpful. If users ask about things outside of Seattle hotels,
politely let them know you specialize in Seattle hotel recommendations.
""",
name: Environment.GetEnvironmentVariable("AGENT_NAME") ?? "hosted-local-tools",
description: "Seattle hotel search agent with local function tools",
tools: [AIFunctionFactory.Create(GetAvailableHotels)]);
var builder = WebApplication.CreateBuilder(args);
builder.Services.AddFoundryResponses(agent);
var app = builder.Build();
app.MapFoundryResponses();
if (app.Environment.IsDevelopment())
{
app.MapFoundryResponses("openai/v1");
}
app.Run();
// ── Types ────────────────────────────────────────────────────────────────────
internal sealed record Hotel(string Name, int PricePerNight, double Rating, string Location);
/// <summary>
/// A <see cref="TokenCredential"/> for local Docker debugging only.
/// Reads a pre-fetched bearer token from the <c>AZURE_BEARER_TOKEN</c> environment variable
/// once at startup. This should NOT be used in production.
///
/// Generate a token on your host and pass it to the container:
/// export AZURE_BEARER_TOKEN=$(az account get-access-token --resource https://ai.azure.com --query accessToken -o tsv)
/// docker run -e AZURE_BEARER_TOKEN=$AZURE_BEARER_TOKEN ...
/// </summary>
internal sealed class DevTemporaryTokenCredential : TokenCredential
{
private const string EnvironmentVariable = "AZURE_BEARER_TOKEN";
private readonly string? _token;
public DevTemporaryTokenCredential()
{
this._token = Environment.GetEnvironmentVariable(EnvironmentVariable);
}
public override AccessToken GetToken(TokenRequestContext requestContext, CancellationToken cancellationToken)
=> this.GetAccessToken();
public override ValueTask<AccessToken> GetTokenAsync(TokenRequestContext requestContext, CancellationToken cancellationToken)
=> new(this.GetAccessToken());
private AccessToken GetAccessToken()
{
if (string.IsNullOrEmpty(this._token) || this._token == "DefaultAzureCredential")
{
throw new CredentialUnavailableException($"{EnvironmentVariable} environment variable is not set.");
}
return new AccessToken(this._token, DateTimeOffset.UtcNow.AddHours(1));
}
}
@@ -1,113 +0,0 @@
# Hosted-LocalTools
A hosted agent with **local C# function tools** for hotel search. Demonstrates how to define and wire local tools that the LLM can invoke — a key advantage of code-based hosted agents over prompt agents.
The agent specializes in finding hotels in Seattle, with a `GetAvailableHotels` tool that searches a mock hotel database by dates and budget.
## Prerequisites
- [.NET 10 SDK](https://dotnet.microsoft.com/download/dotnet/10.0)
- An Azure AI Foundry project with a deployed model (e.g., `gpt-4o`)
- Azure CLI logged in (`az login`)
## Configuration
Copy the template and fill in your project endpoint:
```bash
cp .env.example .env
```
Edit `.env` and set your Azure AI Foundry project endpoint:
```env
AZURE_AI_PROJECT_ENDPOINT=https://<your-account>.services.ai.azure.com/api/projects/<your-project>
ASPNETCORE_URLS=http://+:8088
ASPNETCORE_ENVIRONMENT=Development
AZURE_AI_MODEL_DEPLOYMENT_NAME=gpt-4o
```
> **Note:** `.env` is gitignored. The `.env.example` template is checked in as a reference.
## Running directly (contributors)
This project uses `ProjectReference` to build against the local Agent Framework source.
```bash
cd dotnet/samples/04-hosting/FoundryHostedAgents/responses/Hosted-LocalTools
AGENT_NAME=hosted-local-tools dotnet run
```
The agent will start on `http://localhost:8088`.
### Test it
Using the Azure Developer CLI:
```bash
azd ai agent invoke --local "Find me a hotel in Seattle for Dec 20-25 under $200/night"
```
Or with curl:
```bash
curl -X POST http://localhost:8088/responses \
-H "Content-Type: application/json" \
-d '{"input": "Find me a hotel in Seattle for Dec 20-25 under $200/night", "model": "hosted-local-tools"}'
```
## Running with Docker
Since this project uses `ProjectReference`, use `Dockerfile.contributor` which takes a pre-published output.
### 1. Publish for the container runtime (Linux Alpine)
```bash
dotnet publish -c Debug -f net10.0 -r linux-musl-x64 --self-contained false -o out
```
### 2. Build the Docker image
```bash
docker build -f Dockerfile.contributor -t hosted-local-tools .
```
### 3. Run the container
Generate a bearer token on your host and pass it to the container:
```bash
# Generate token (expires in ~1 hour)
export AZURE_BEARER_TOKEN=$(az account get-access-token --resource https://ai.azure.com --query accessToken -o tsv)
# Run with token
docker run --rm -p 8088:8088 \
-e AGENT_NAME=hosted-local-tools \
-e AZURE_BEARER_TOKEN=$AZURE_BEARER_TOKEN \
--env-file .env \
hosted-local-tools
```
### 4. Test it
Using the Azure Developer CLI:
```bash
azd ai agent invoke --local "What hotels are available in Seattle for next weekend?"
```
## How local tools work
The agent has a single tool `GetAvailableHotels` defined as a C# method with `[Description]` attributes. The LLM decides when to call it based on the user's request:
| Parameter | Type | Description |
|-----------|------|-------------|
| `checkInDate` | string | Check-in date (YYYY-MM-DD) |
| `checkOutDate` | string | Check-out date (YYYY-MM-DD) |
| `maxPrice` | int | Max price per night in USD (default: 500) |
The tool searches a mock database of 6 Seattle hotels and returns formatted results with name, location, rating, and pricing.
## NuGet package users
If you are consuming the Agent Framework as a NuGet package (not building from source), use the standard `Dockerfile` instead of `Dockerfile.contributor`. See the commented section in `HostedLocalTools.csproj` for the `PackageReference` alternative.
@@ -1,29 +0,0 @@
# yaml-language-server: $schema=https://raw.githubusercontent.com/microsoft/AgentSchema/refs/heads/main/schemas/v1.0/AgentManifest.yaml
name: hosted-local-tools
displayName: "Seattle Hotel Agent with Local Tools"
description: >
A travel assistant agent that helps users find hotels in Seattle.
Demonstrates local C# tool execution — a key advantage of code-based
hosted agents over prompt agents.
metadata:
tags:
- AI Agent Hosting
- Azure AI AgentServer
- Responses Protocol
- Local Tools
- Agent Framework
template:
name: hosted-local-tools
kind: hosted
protocols:
- protocol: responses
version: 1.0.0
resources:
cpu: "0.25"
memory: 0.5Gi
parameters:
properties: []
resources: []
@@ -1,9 +0,0 @@
# yaml-language-server: $schema=https://raw.githubusercontent.com/microsoft/AgentSchema/refs/heads/main/schemas/v1.0/ContainerAgent.yaml
kind: hosted
name: hosted-local-tools
protocols:
- protocol: responses
version: 1.0.0
resources:
cpu: "0.25"
memory: 0.5Gi
@@ -1,5 +0,0 @@
AZURE_AI_PROJECT_ENDPOINT=<your-azure-ai-project-endpoint>
ASPNETCORE_URLS=http://+:8088
ASPNETCORE_ENVIRONMENT=Development
AZURE_AI_MODEL_DEPLOYMENT_NAME=gpt-4o
AZURE_BEARER_TOKEN=DefaultAzureCredential
@@ -1,17 +0,0 @@
# Use the official .NET 10.0 ASP.NET runtime as a parent image
FROM mcr.microsoft.com/dotnet/aspnet:10.0 AS base
WORKDIR /app
FROM mcr.microsoft.com/dotnet/sdk:10.0 AS build
WORKDIR /src
COPY . .
RUN dotnet restore
RUN dotnet publish -c Release -o /app/publish
# Final stage
FROM base AS final
WORKDIR /app
COPY --from=build /app/publish .
EXPOSE 8088
ENV ASPNETCORE_URLS=http://+:8088
ENTRYPOINT ["dotnet", "HostedMcpTools.dll"]
@@ -1,18 +0,0 @@
# Dockerfile for contributors building from the agent-framework repository source.
#
# This project uses ProjectReference to the local source, which means a standard
# multi-stage Docker build cannot resolve dependencies outside this folder.
# Pre-publish the app targeting the container runtime and copy the output:
#
# dotnet publish -c Debug -f net10.0 -r linux-musl-x64 --self-contained false -o out
# docker build -f Dockerfile.contributor -t hosted-mcp-tools .
# docker run --rm -p 8088:8088 -e AGENT_NAME=mcp-tools -e GITHUB_PAT=$GITHUB_PAT -e AZURE_BEARER_TOKEN=$AZURE_BEARER_TOKEN --env-file .env hosted-mcp-tools
#
# For end-users consuming the NuGet package (not ProjectReference), use the standard
# Dockerfile which performs a full dotnet restore + publish inside the container.
FROM mcr.microsoft.com/dotnet/aspnet:10.0-alpine AS final
WORKDIR /app
COPY out/ .
EXPOSE 8088
ENV ASPNETCORE_URLS=http://+:8088
ENTRYPOINT ["dotnet", "HostedMcpTools.dll"]
@@ -1,33 +0,0 @@
<Project Sdk="Microsoft.NET.Sdk.Web">
<PropertyGroup>
<TargetFrameworks>net10.0</TargetFrameworks>
<Nullable>enable</Nullable>
<ImplicitUsings>enable</ImplicitUsings>
<CentralPackageTransitivePinningEnabled>false</CentralPackageTransitivePinningEnabled>
<RootNamespace>HostedMcpTools</RootNamespace>
<AssemblyName>HostedMcpTools</AssemblyName>
<NoWarn>$(NoWarn);</NoWarn>
</PropertyGroup>
<ItemGroup>
<PackageReference Include="Azure.AI.Projects" VersionOverride="2.1.0-beta.1" />
<PackageReference Include="Azure.Identity" />
<PackageReference Include="ModelContextProtocol" VersionOverride="1.2.0" />
<PackageReference Include="DotNetEnv" />
</ItemGroup>
<!-- For contributors: uses ProjectReference to build against local source -->
<ItemGroup>
<ProjectReference Include="..\..\..\..\..\src\Microsoft.Agents.AI.Foundry\Microsoft.Agents.AI.Foundry.csproj" />
<ProjectReference Include="..\..\..\..\..\src\Microsoft.Agents.AI.Foundry.Hosting\Microsoft.Agents.AI.Foundry.Hosting.csproj" />
</ItemGroup>
<!-- For end-users: uncomment the PackageReference below and remove the ProjectReference above
<ItemGroup>
<PackageReference Include="Microsoft.Agents.AI.Foundry" Version="1.0.0" />
<PackageReference Include="Microsoft.Agents.AI.Foundry.Hosting" Version="1.0.0" />
</ItemGroup>
-->
</Project>
@@ -1,130 +0,0 @@
// Copyright (c) Microsoft. All rights reserved.
// This sample demonstrates a hosted agent with two layers of MCP (Model Context Protocol) tools:
//
// 1. CLIENT-SIDE MCP: The agent connects to the Microsoft Learn MCP server directly via
// McpClient, discovers tools, and handles tool invocations locally within the agent process.
//
// 2. SERVER-SIDE MCP: The agent declares a HostedMcpServerTool for the same MCP server which
// delegates tool discovery and invocation to the LLM provider (Azure OpenAI Responses API).
// The provider calls the MCP server on behalf of the agent — no local connection needed.
//
// Both patterns use the Microsoft Learn MCP server to illustrate the architectural difference:
// client-side tools are resolved and invoked by the agent, while server-side tools are resolved
// and invoked by the LLM provider.
#pragma warning disable MEAI001 // HostedMcpServerTool is experimental
using Azure.AI.Projects;
using Azure.Core;
using Azure.Identity;
using DotNetEnv;
using Microsoft.Agents.AI;
using Microsoft.Agents.AI.Foundry.Hosting;
using Microsoft.Extensions.AI;
using ModelContextProtocol.Client;
// Load .env file if present (for local development)
Env.TraversePath().Load();
var projectEndpoint = new Uri(Environment.GetEnvironmentVariable("AZURE_AI_PROJECT_ENDPOINT")
?? throw new InvalidOperationException("AZURE_AI_PROJECT_ENDPOINT is not set."));
var deployment = Environment.GetEnvironmentVariable("AZURE_AI_MODEL_DEPLOYMENT_NAME") ?? "gpt-4o";
// Use a chained credential: try a temporary dev token first (for local Docker debugging),
// then fall back to DefaultAzureCredential (for local dev via dotnet run / managed identity in production).
TokenCredential credential = new ChainedTokenCredential(
new DevTemporaryTokenCredential(),
new DefaultAzureCredential());
// ── Client-side MCP: Microsoft Learn (local resolution) ──────────────────────
// Connect directly to the MCP server. The agent discovers and invokes tools locally.
Console.WriteLine("Connecting to Microsoft Learn MCP server (client-side)...");
await using var learnMcp = await McpClient.CreateAsync(new HttpClientTransport(new()
{
Endpoint = new Uri("https://learn.microsoft.com/api/mcp"),
Name = "Microsoft Learn (client)",
}));
var clientTools = await learnMcp.ListToolsAsync();
Console.WriteLine($"Client-side MCP tools: {string.Join(", ", clientTools.Select(t => t.Name))}");
// ── Server-side MCP: Microsoft Learn (provider resolution) ───────────────────
// Declare a HostedMcpServerTool — the LLM provider (Responses API) handles tool
// invocations directly. No local MCP connection needed for this pattern.
AITool serverTool = new HostedMcpServerTool(
serverName: "microsoft_learn_hosted",
serverAddress: "https://learn.microsoft.com/api/mcp")
{
AllowedTools = ["microsoft_docs_search"],
ApprovalMode = HostedMcpServerToolApprovalMode.NeverRequire
};
Console.WriteLine("Server-side MCP tool: microsoft_docs_search (via HostedMcpServerTool)");
// ── Combine both tool types into a single agent ──────────────────────────────
// The agent has access to tools from both MCP patterns simultaneously.
List<AITool> allTools = [.. clientTools.Cast<AITool>(), serverTool];
AIAgent agent = new AIProjectClient(projectEndpoint, credential)
.AsAIAgent(
model: deployment,
instructions: """
You are a helpful developer assistant with access to Microsoft Learn documentation.
Use the available tools to search and retrieve documentation.
Be concise and provide direct answers with relevant links.
""",
name: "mcp-tools",
description: "Developer assistant with dual-layer MCP tools (client-side and server-side)",
tools: allTools);
// Host the agent as a Foundry Hosted Agent using the Responses API.
var builder = WebApplication.CreateBuilder(args);
builder.Services.AddFoundryResponses(agent);
var app = builder.Build();
app.MapFoundryResponses();
// In Development, also map the OpenAI-compatible route that AIProjectClient uses.
if (app.Environment.IsDevelopment())
{
app.MapFoundryResponses("openai/v1");
}
app.Run();
/// <summary>
/// A <see cref="TokenCredential"/> for local Docker debugging only.
/// Reads a pre-fetched bearer token from the <c>AZURE_BEARER_TOKEN</c> environment variable
/// once at startup. This should NOT be used in production.
///
/// Generate a token on your host and pass it to the container:
/// export AZURE_BEARER_TOKEN=$(az account get-access-token --resource https://ai.azure.com --query accessToken -o tsv)
/// docker run -e AZURE_BEARER_TOKEN=$AZURE_BEARER_TOKEN ...
/// </summary>
internal sealed class DevTemporaryTokenCredential : TokenCredential
{
private const string EnvironmentVariable = "AZURE_BEARER_TOKEN";
private readonly string? _token;
public DevTemporaryTokenCredential()
{
this._token = Environment.GetEnvironmentVariable(EnvironmentVariable);
}
public override AccessToken GetToken(TokenRequestContext requestContext, CancellationToken cancellationToken)
=> this.GetAccessToken();
public override ValueTask<AccessToken> GetTokenAsync(TokenRequestContext requestContext, CancellationToken cancellationToken)
=> new(this.GetAccessToken());
private AccessToken GetAccessToken()
{
if (string.IsNullOrEmpty(this._token) || this._token == "DefaultAzureCredential")
{
throw new CredentialUnavailableException($"{EnvironmentVariable} environment variable is not set.");
}
return new AccessToken(this._token, DateTimeOffset.UtcNow.AddHours(1));
}
}
@@ -1,83 +0,0 @@
# Hosted-McpTools
A hosted agent demonstrating **two layers of MCP (Model Context Protocol) tool integration**:
1. **Client-side MCP (Microsoft Learn)** — The agent connects directly to the Microsoft Learn MCP server via `McpClient`, discovers tools, and handles tool invocations locally within the agent process.
2. **Server-side MCP (Microsoft Learn)** — The agent declares a `HostedMcpServerTool` which delegates tool discovery and invocation to the LLM provider (Azure OpenAI Responses API). The provider calls the MCP server on behalf of the agent with no local connection needed.
## How the two MCP patterns differ
| | Client-side MCP | Server-side MCP |
|---|---|---|
| **Connection** | Agent connects to MCP server directly | LLM provider connects to MCP server |
| **Tool invocation** | Handled by the agent process | Handled by the Responses API |
| **Auth** | Agent manages credentials | Provider manages credentials |
| **Use case** | Custom/private MCP servers, fine-grained control | Public MCP servers, simpler setup |
| **Example** | Microsoft Learn (`McpClient` + `HttpClientTransport`) | Microsoft Learn (`HostedMcpServerTool`) |
## Prerequisites
- [.NET 10 SDK](https://dotnet.microsoft.com/download/dotnet/10.0)
- An Azure AI Foundry project with a deployed model (e.g., `gpt-4o`)
- Azure CLI logged in (`az login`)
## Configuration
Copy the template and fill in your values:
```bash
cp .env.example .env
```
Edit `.env`:
```env
AZURE_AI_PROJECT_ENDPOINT=https://<your-account>.services.ai.azure.com/api/projects/<your-project>
AZURE_AI_MODEL_DEPLOYMENT_NAME=gpt-4o
```
## Running directly (contributors)
```bash
cd dotnet/samples/04-hosting/FoundryHostedAgents/responses/Hosted-McpTools
dotnet run
```
### Test it
Using the Azure Developer CLI:
```bash
# Uses GitHub MCP (client-side)
azd ai agent invoke --local "Search for the agent-framework repository on GitHub"
# Uses Microsoft Learn MCP (server-side)
azd ai agent invoke --local "How do I create an Azure storage account using az cli?"
```
## Running with Docker
### 1. Publish for the container runtime
```bash
dotnet publish -c Debug -f net10.0 -r linux-musl-x64 --self-contained false -o out
```
### 2. Build and run
```bash
docker build -f Dockerfile.contributor -t hosted-mcp-tools .
export AZURE_BEARER_TOKEN=$(az account get-access-token --resource https://ai.azure.com --query accessToken -o tsv)
docker run --rm -p 8088:8088 \
-e AGENT_NAME=mcp-tools \
-e AZURE_BEARER_TOKEN=$AZURE_BEARER_TOKEN \
--env-file .env \
hosted-mcp-tools
```
## NuGet package users
Use the standard `Dockerfile` instead of `Dockerfile.contributor`. See the commented section in `HostedMcpTools.csproj` for the `PackageReference` alternative.
@@ -1,30 +0,0 @@
# yaml-language-server: $schema=https://raw.githubusercontent.com/microsoft/AgentSchema/refs/heads/main/schemas/v1.0/AgentManifest.yaml
name: mcp-tools
displayName: "MCP Tools Agent"
description: >
A developer assistant demonstrating dual-layer MCP integration:
client-side GitHub MCP tools handled by the agent and server-side
Microsoft Learn MCP tools delegated to the LLM provider.
metadata:
tags:
- AI Agent Hosting
- Azure AI AgentServer
- Responses Protocol
- Agent Framework
- MCP
- Model Context Protocol
template:
name: mcp-tools
kind: hosted
protocols:
- protocol: responses
version: 1.0.0
resources:
cpu: "0.25"
memory: 0.5Gi
parameters:
properties: []
resources: []
@@ -1,9 +0,0 @@
# yaml-language-server: $schema=https://raw.githubusercontent.com/microsoft/AgentSchema/refs/heads/main/schemas/v1.0/ContainerAgent.yaml
kind: hosted
name: mcp-tools
protocols:
- protocol: responses
version: 1.0.0
resources:
cpu: "0.25"
memory: 0.5Gi
@@ -1,5 +0,0 @@
AZURE_AI_PROJECT_ENDPOINT=<your-azure-ai-project-endpoint>
ASPNETCORE_URLS=http://+:8088
ASPNETCORE_ENVIRONMENT=Development
AZURE_AI_MODEL_DEPLOYMENT_NAME=gpt-4o
AZURE_BEARER_TOKEN=DefaultAzureCredential
@@ -1,17 +0,0 @@
# Use the official .NET 10.0 ASP.NET runtime as a parent image
FROM mcr.microsoft.com/dotnet/aspnet:10.0 AS base
WORKDIR /app
FROM mcr.microsoft.com/dotnet/sdk:10.0 AS build
WORKDIR /src
COPY . .
RUN dotnet restore
RUN dotnet publish -c Release -o /app/publish
# Final stage
FROM base AS final
WORKDIR /app
COPY --from=build /app/publish .
EXPOSE 8088
ENV ASPNETCORE_URLS=http://+:8088
ENTRYPOINT ["dotnet", "HostedTextRag.dll"]
@@ -1,19 +0,0 @@
# Dockerfile for contributors building from the agent-framework repository source.
#
# This project uses ProjectReference to the local Microsoft.Agents.AI.Foundry source,
# which means a standard multi-stage Docker build cannot resolve dependencies outside
# this folder. Instead, pre-publish the app targeting the container runtime and copy
# the output into the container:
#
# dotnet publish -c Debug -f net10.0 -r linux-musl-x64 --self-contained false -o out
# docker build -f Dockerfile.contributor -t hosted-text-rag .
# docker run --rm -p 8088:8088 -e AGENT_NAME=hosted-text-rag -e AZURE_BEARER_TOKEN=$AZURE_BEARER_TOKEN --env-file .env hosted-text-rag
#
# For end-users consuming the NuGet package (not ProjectReference), use the standard
# Dockerfile which performs a full dotnet restore + publish inside the container.
FROM mcr.microsoft.com/dotnet/aspnet:10.0-alpine AS final
WORKDIR /app
COPY out/ .
EXPOSE 8088
ENV ASPNETCORE_URLS=http://+:8088
ENTRYPOINT ["dotnet", "HostedTextRag.dll"]
@@ -1,34 +0,0 @@
<Project Sdk="Microsoft.NET.Sdk.Web">
<PropertyGroup>
<TargetFrameworks>net10.0</TargetFrameworks>
<Nullable>enable</Nullable>
<ImplicitUsings>enable</ImplicitUsings>
<CentralPackageTransitivePinningEnabled>false</CentralPackageTransitivePinningEnabled>
<RootNamespace>HostedTextRag</RootNamespace>
<AssemblyName>HostedTextRag</AssemblyName>
<NoWarn>$(NoWarn);</NoWarn>
</PropertyGroup>
<ItemGroup>
<PackageReference Include="Azure.AI.Projects" VersionOverride="2.1.0-beta.1" />
<PackageReference Include="Azure.Identity" />
<PackageReference Include="DotNetEnv" />
</ItemGroup>
<!-- For contributors: uses ProjectReference to build against local source -->
<ItemGroup>
<ProjectReference Include="..\..\..\..\..\src\Microsoft.Agents.AI.Foundry\Microsoft.Agents.AI.Foundry.csproj" />
<ProjectReference Include="..\..\..\..\..\src\Microsoft.Agents.AI.Foundry.Hosting\Microsoft.Agents.AI.Foundry.Hosting.csproj" />
<ProjectReference Include="..\..\..\..\..\src\Microsoft.Agents.AI.OpenAI\Microsoft.Agents.AI.OpenAI.csproj" />
</ItemGroup>
<!-- For end-users: uncomment the PackageReference below and remove the ProjectReferences above
<ItemGroup>
<PackageReference Include="Microsoft.Agents.AI.Foundry" Version="1.0.0" />
<PackageReference Include="Microsoft.Agents.AI.Foundry.Hosting" Version="1.0.0" />
<PackageReference Include="Microsoft.Agents.AI.OpenAI" Version="1.0.0" />
</ItemGroup>
-->
</Project>
@@ -1,130 +0,0 @@
// Copyright (c) Microsoft. All rights reserved.
// This sample shows how to use TextSearchProvider to add retrieval augmented generation (RAG)
// capabilities to a hosted agent. The provider runs a search against an external knowledge base
// before each model invocation and injects the results into the model context.
using Azure.AI.Projects;
using Azure.Core;
using Azure.Identity;
using DotNetEnv;
using Microsoft.Agents.AI;
using Microsoft.Agents.AI.Foundry.Hosting;
using Microsoft.Extensions.AI;
using OpenAI.Chat;
// Load .env file if present (for local development)
Env.TraversePath().Load();
string endpoint = Environment.GetEnvironmentVariable("AZURE_AI_PROJECT_ENDPOINT")
?? throw new InvalidOperationException("AZURE_AI_PROJECT_ENDPOINT is not set.");
string deploymentName = Environment.GetEnvironmentVariable("AZURE_AI_MODEL_DEPLOYMENT_NAME") ?? "gpt-4o";
// Use a chained credential: try a temporary dev token first (for local Docker debugging),
// then fall back to DefaultAzureCredential (for local dev via dotnet run / managed identity in production).
TokenCredential credential = new ChainedTokenCredential(
new DevTemporaryTokenCredential(),
new DefaultAzureCredential());
TextSearchProviderOptions textSearchOptions = new()
{
SearchTime = TextSearchProviderOptions.TextSearchBehavior.BeforeAIInvoke,
RecentMessageMemoryLimit = 6,
};
AIAgent agent = new AIProjectClient(new Uri(endpoint), credential)
.AsAIAgent(new ChatClientAgentOptions
{
Name = Environment.GetEnvironmentVariable("AGENT_NAME") ?? "hosted-text-rag",
ChatOptions = new ChatOptions
{
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)]
});
// Host the agent as a Foundry Hosted Agent using the Responses API.
var builder = WebApplication.CreateBuilder(args);
builder.Services.AddFoundryResponses(agent);
var app = builder.Build();
app.MapFoundryResponses();
if (app.Environment.IsDevelopment())
{
app.MapFoundryResponses("openai/v1");
}
app.Run();
// ── Mock search function ─────────────────────────────────────────────────────
// In production, replace this with a real search provider (e.g., Azure AI Search).
static Task<IEnumerable<TextSearchProvider.TextSearchResult>> MockSearchAsync(string query, CancellationToken cancellationToken)
{
List<TextSearchProvider.TextSearchResult> results = [];
if (query.Contains("return", StringComparison.OrdinalIgnoreCase) || query.Contains("refund", StringComparison.OrdinalIgnoreCase))
{
results.Add(new()
{
SourceName = "Contoso Outdoors Return Policy",
SourceLink = "https://contoso.com/policies/returns",
Text = "Customers may return any item within 30 days of delivery. Items should be unused and include original packaging. Refunds are issued to the original payment method within 5 business days of inspection."
});
}
if (query.Contains("shipping", StringComparison.OrdinalIgnoreCase))
{
results.Add(new()
{
SourceName = "Contoso Outdoors Shipping Guide",
SourceLink = "https://contoso.com/help/shipping",
Text = "Standard shipping is free on orders over $50 and typically arrives in 3-5 business days within the continental United States. Expedited options are available at checkout."
});
}
if (query.Contains("tent", StringComparison.OrdinalIgnoreCase) || query.Contains("fabric", StringComparison.OrdinalIgnoreCase))
{
results.Add(new()
{
SourceName = "TrailRunner Tent Care Instructions",
SourceLink = "https://contoso.com/manuals/trailrunner-tent",
Text = "Clean the tent fabric with lukewarm water and a non-detergent soap. Allow it to air dry completely before storage and avoid prolonged UV exposure to extend the lifespan of the waterproof coating."
});
}
return Task.FromResult<IEnumerable<TextSearchProvider.TextSearchResult>>(results);
}
/// <summary>
/// A <see cref="TokenCredential"/> for local Docker debugging only.
/// Reads a pre-fetched bearer token from the <c>AZURE_BEARER_TOKEN</c> environment variable.
/// This should NOT be used in production — tokens expire (~1 hour) and cannot be refreshed.
///
/// Generate a token on your host and pass it to the container:
/// export AZURE_BEARER_TOKEN=$(az account get-access-token --resource https://ai.azure.com --query accessToken -o tsv)
/// docker run -e AZURE_BEARER_TOKEN=$AZURE_BEARER_TOKEN ...
/// </summary>
internal sealed class DevTemporaryTokenCredential : TokenCredential
{
private const string EnvironmentVariable = "AZURE_BEARER_TOKEN";
public override AccessToken GetToken(TokenRequestContext requestContext, CancellationToken cancellationToken)
=> GetAccessToken();
public override ValueTask<AccessToken> GetTokenAsync(TokenRequestContext requestContext, CancellationToken cancellationToken)
=> new(GetAccessToken());
private static AccessToken GetAccessToken()
{
var token = Environment.GetEnvironmentVariable(EnvironmentVariable);
if (string.IsNullOrEmpty(token) || token == "DefaultAzureCredential")
{
throw new CredentialUnavailableException($"{EnvironmentVariable} environment variable is not set.");
}
return new AccessToken(token, DateTimeOffset.UtcNow.AddHours(1));
}
}
@@ -1,116 +0,0 @@
# Hosted-TextRag
A hosted agent with **Retrieval Augmented Generation (RAG)** capabilities using `TextSearchProvider`. The agent grounds its answers in product documentation by running a search before each model invocation, then citing the source in its response.
This sample demonstrates how to add knowledge grounding to a hosted agent without requiring an external search index — using a mock search function that can be replaced with Azure AI Search or any other provider.
## Prerequisites
- [.NET 10 SDK](https://dotnet.microsoft.com/download/dotnet/10.0)
- An Azure AI Foundry project with a deployed model (e.g., `gpt-4o`)
- Azure CLI logged in (`az login`)
## Configuration
Copy the template and fill in your project endpoint:
```bash
cp .env.example .env
```
Edit `.env` and set your Azure AI Foundry project endpoint:
```env
AZURE_AI_PROJECT_ENDPOINT=https://<your-account>.services.ai.azure.com/api/projects/<your-project>
ASPNETCORE_URLS=http://+:8088
ASPNETCORE_ENVIRONMENT=Development
AZURE_AI_MODEL_DEPLOYMENT_NAME=gpt-4o
AZURE_BEARER_TOKEN=
```
> **Note:** `.env` is gitignored. The `.env.example` template is checked in as a reference.
## Running directly (contributors)
This project uses `ProjectReference` to build against the local Agent Framework source.
```bash
cd dotnet/samples/04-hosting/FoundryHostedAgents/responses/Hosted-TextRag
AGENT_NAME=hosted-text-rag dotnet run
```
The agent will start on `http://localhost:8088`.
### Test it
Using the Azure Developer CLI:
```bash
azd ai agent invoke --local "What is your return policy?"
azd ai agent invoke --local "How long does shipping take?"
azd ai agent invoke --local "How do I clean my tent?"
```
Or with curl:
```bash
curl -X POST http://localhost:8088/responses \
-H "Content-Type: application/json" \
-d '{"input": "What is your return policy?", "model": "hosted-text-rag"}'
```
## Running with Docker
Since this project uses `ProjectReference`, use `Dockerfile.contributor` which takes a pre-published output.
### 1. Publish for the container runtime (Linux Alpine)
```bash
dotnet publish -c Debug -f net10.0 -r linux-musl-x64 --self-contained false -o out
```
### 2. Build the Docker image
```bash
docker build -f Dockerfile.contributor -t hosted-text-rag .
```
### 3. Run the container
Generate a bearer token on your host and pass it to the container:
```bash
# Generate token (expires in ~1 hour)
export AZURE_BEARER_TOKEN=$(az account get-access-token --resource https://ai.azure.com --query accessToken -o tsv)
# Run with token
docker run --rm -p 8088:8088 \
-e AGENT_NAME=hosted-text-rag \
-e AZURE_BEARER_TOKEN=$AZURE_BEARER_TOKEN \
--env-file .env \
hosted-text-rag
```
### 4. Test it
Using the Azure Developer CLI:
```bash
azd ai agent invoke --local "What is your return policy?"
```
## How RAG works in this sample
The `TextSearchProvider` runs a mock search **before each model invocation**:
| User query contains | Search result injected |
|---|---|
| "return" or "refund" | Contoso Outdoors Return Policy |
| "shipping" | Contoso Outdoors Shipping Guide |
| "tent" or "fabric" | TrailRunner Tent Care Instructions |
The model receives the search results as additional context and cites the source in its response. In production, replace `MockSearchAsync` with a call to Azure AI Search or your preferred search provider.
## NuGet package users
If you are consuming the Agent Framework as a NuGet package (not building from source), use the standard `Dockerfile` instead of `Dockerfile.contributor`. See the commented section in `HostedTextRag.csproj` for the `PackageReference` alternative.
@@ -1,30 +0,0 @@
# yaml-language-server: $schema=https://raw.githubusercontent.com/microsoft/AgentSchema/refs/heads/main/schemas/v1.0/AgentManifest.yaml
name: hosted-text-rag
displayName: "Hosted Text RAG Agent"
description: >
A support specialist agent for Contoso Outdoors with RAG capabilities.
Uses TextSearchProvider to ground answers in product documentation
before each model invocation.
metadata:
tags:
- AI Agent Hosting
- Azure AI AgentServer
- Responses Protocol
- RAG
- Text Search
- Agent Framework
template:
name: hosted-text-rag
kind: hosted
protocols:
- protocol: responses
version: 1.0.0
resources:
cpu: "0.25"
memory: 0.5Gi
parameters:
properties: []
resources: []
@@ -1,9 +0,0 @@
# yaml-language-server: $schema=https://raw.githubusercontent.com/microsoft/AgentSchema/refs/heads/main/schemas/v1.0/ContainerAgent.yaml
kind: hosted
name: hosted-text-rag
protocols:
- protocol: responses
version: 1.0.0
resources:
cpu: "0.25"
memory: 0.5Gi
@@ -1,32 +0,0 @@
<Project Sdk="Microsoft.NET.Sdk.Web">
<PropertyGroup>
<TargetFrameworks>net10.0</TargetFrameworks>
<Nullable>enable</Nullable>
<ImplicitUsings>enable</ImplicitUsings>
<CentralPackageTransitivePinningEnabled>false</CentralPackageTransitivePinningEnabled>
<RootNamespace>HostedToolbox</RootNamespace>
<AssemblyName>HostedToolbox</AssemblyName>
<NoWarn>$(NoWarn);</NoWarn>
</PropertyGroup>
<ItemGroup>
<PackageReference Include="Azure.AI.Projects" VersionOverride="2.1.0-beta.1" />
<PackageReference Include="Azure.Identity" />
<PackageReference Include="DotNetEnv" />
</ItemGroup>
<!-- For contributors: uses ProjectReference to build against local source -->
<ItemGroup>
<ProjectReference Include="..\..\..\..\..\src\Microsoft.Agents.AI.Foundry\Microsoft.Agents.AI.Foundry.csproj" />
<ProjectReference Include="..\..\..\..\..\src\Microsoft.Agents.AI.Foundry.Hosting\Microsoft.Agents.AI.Foundry.Hosting.csproj" />
</ItemGroup>
<!-- For end-users: uncomment the PackageReference below and remove the ProjectReference above
<ItemGroup>
<PackageReference Include="Microsoft.Agents.AI.Foundry" Version="1.0.0" />
<PackageReference Include="Microsoft.Agents.AI.Foundry.Hosting" Version="1.0.0" />
</ItemGroup>
-->
</Project>
@@ -1,113 +0,0 @@
// Copyright (c) Microsoft. All rights reserved.
// Foundry Toolbox Agent - A hosted agent that uses Foundry Toolset MCP tools.
//
// Demonstrates how to register one or more Foundry toolsets so the agent can
// call tools provided by the Foundry platform's managed MCP proxy.
//
// Required environment variables:
// AZURE_AI_PROJECT_ENDPOINT - Azure AI Foundry project endpoint
// AZURE_AI_MODEL_DEPLOYMENT_NAME - Model deployment name (default: gpt-4o)
// FOUNDRY_AGENT_TOOLSET_ENDPOINT - Foundry Toolsets proxy base URL
// (injected automatically by Foundry platform at runtime)
//
// Optional:
// FOUNDRY_TOOLBOX_NAME - Name of the toolset to load (default: my-toolset)
// FOUNDRY_AGENT_NAME - Client name reported to MCP server
// FOUNDRY_AGENT_VERSION - Client version reported to MCP server
// FOUNDRY_AGENT_TOOLSET_FEATURES - Feature flags sent to Foundry proxy via header
using Azure.AI.Projects;
using Azure.Core;
using Azure.Identity;
using DotNetEnv;
using Microsoft.Agents.AI;
using Microsoft.Agents.AI.Foundry.Hosting;
// Load .env file if present (for local development)
Env.TraversePath().Load();
string endpoint = Environment.GetEnvironmentVariable("AZURE_AI_PROJECT_ENDPOINT")
?? throw new InvalidOperationException("AZURE_AI_PROJECT_ENDPOINT is not set.");
string deploymentName = Environment.GetEnvironmentVariable("AZURE_AI_MODEL_DEPLOYMENT_NAME") ?? "gpt-4o";
string toolboxName = Environment.GetEnvironmentVariable("FOUNDRY_TOOLBOX_NAME") ?? "my-toolset";
// Use a chained credential: try a temporary dev token first (for local Docker debugging),
// then fall back to DefaultAzureCredential (for local dev via dotnet run / managed identity in production).
TokenCredential credential = new ChainedTokenCredential(
new DevTemporaryTokenCredential(),
new DefaultAzureCredential());
// ── Create agent ─────────────────────────────────────────────────────────────
AIAgent agent = new AIProjectClient(new Uri(endpoint), credential)
.AsAIAgent(
model: deploymentName,
instructions: """
You are a helpful assistant with access to tools provided by the Foundry Toolset.
Use the available tools to answer user questions.
If a tool is not available for a request, let the user know clearly.
""",
name: Environment.GetEnvironmentVariable("AGENT_NAME") ?? "hosted-toolbox-agent",
description: "Hosted agent backed by Foundry Toolset MCP tools");
// ── Build the host ────────────────────────────────────────────────────────────
var builder = WebApplication.CreateBuilder(args);
// Register the agent and response handler
builder.Services.AddFoundryResponses(agent);
// Register Foundry Toolbox: connects to the MCP proxy at startup and makes tools available.
// The toolset name must match a toolset registered in your Foundry project.
// When FOUNDRY_AGENT_TOOLSET_ENDPOINT is absent (e.g., in local development without Foundry
// infrastructure), startup succeeds without error and no toolbox tools are loaded.
builder.Services.AddFoundryToolboxes(toolboxName);
var app = builder.Build();
app.MapFoundryResponses();
if (app.Environment.IsDevelopment())
{
app.MapFoundryResponses("openai/v1");
}
app.Run();
// ── DevTemporaryTokenCredential ───────────────────────────────────────────────
/// <summary>
/// A <see cref="TokenCredential"/> for local Docker debugging only.
/// Reads a pre-fetched bearer token from the <c>AZURE_BEARER_TOKEN</c> environment variable
/// once at startup. This should NOT be used in production.
///
/// Generate a token on your host and pass it to the container:
/// export AZURE_BEARER_TOKEN=$(az account get-access-token --resource https://ai.azure.com --query accessToken -o tsv)
/// docker run -e AZURE_BEARER_TOKEN=$AZURE_BEARER_TOKEN ...
/// </summary>
internal sealed class DevTemporaryTokenCredential : TokenCredential
{
private const string EnvironmentVariable = "AZURE_BEARER_TOKEN";
private readonly string? _token;
public DevTemporaryTokenCredential()
{
this._token = Environment.GetEnvironmentVariable(EnvironmentVariable);
}
public override AccessToken GetToken(TokenRequestContext requestContext, CancellationToken cancellationToken)
=> this.GetAccessToken();
public override ValueTask<AccessToken> GetTokenAsync(TokenRequestContext requestContext, CancellationToken cancellationToken)
=> new(this.GetAccessToken());
private AccessToken GetAccessToken()
{
if (string.IsNullOrEmpty(this._token) || this._token == "DefaultAzureCredential")
{
throw new CredentialUnavailableException($"{EnvironmentVariable} environment variable is not set.");
}
return new AccessToken(this._token, DateTimeOffset.MaxValue);
}
}
@@ -1,5 +0,0 @@
AZURE_OPENAI_ENDPOINT=https://<your-account>.openai.azure.com/
AZURE_OPENAI_DEPLOYMENT=gpt-4o
AZURE_BEARER_TOKEN=DefaultAzureCredential
ASPNETCORE_URLS=http://+:8088
ASPNETCORE_ENVIRONMENT=Development
@@ -1,17 +0,0 @@
# Use the official .NET 10.0 ASP.NET runtime as a parent image
FROM mcr.microsoft.com/dotnet/aspnet:10.0 AS base
WORKDIR /app
FROM mcr.microsoft.com/dotnet/sdk:10.0 AS build
WORKDIR /src
COPY . .
RUN dotnet restore
RUN dotnet publish -c Release -o /app/publish
# Final stage
FROM base AS final
WORKDIR /app
COPY --from=build /app/publish .
EXPOSE 8088
ENV ASPNETCORE_URLS=http://+:8088
ENTRYPOINT ["dotnet", "HostedWorkflowHandoff.dll"]
@@ -1,19 +0,0 @@
# Dockerfile for contributors building from the agent-framework repository source.
#
# This project uses ProjectReference to the local Microsoft.Agents.AI.Foundry source,
# which means a standard multi-stage Docker build cannot resolve dependencies outside
# this folder. Instead, pre-publish the app targeting the container runtime and copy
# the output into the container:
#
# dotnet publish -c Debug -f net10.0 -r linux-musl-x64 --self-contained false -o out
# docker build -f Dockerfile.contributor -t hosted-workflow-handoff .
# docker run --rm -p 8088:8088 -e AZURE_BEARER_TOKEN=$AZURE_BEARER_TOKEN --env-file .env hosted-workflow-handoff
#
# For end-users consuming the NuGet package (not ProjectReference), use the standard
# Dockerfile which performs a full dotnet restore + publish inside the container.
FROM mcr.microsoft.com/dotnet/aspnet:10.0-alpine AS final
WORKDIR /app
COPY out/ .
EXPOSE 8088
ENV ASPNETCORE_URLS=http://+:8088
ENTRYPOINT ["dotnet", "HostedWorkflowHandoff.dll"]
@@ -1,42 +0,0 @@
<Project Sdk="Microsoft.NET.Sdk.Web">
<PropertyGroup>
<OutputType>Exe</OutputType>
<TargetFrameworks>net10.0</TargetFrameworks>
<Nullable>enable</Nullable>
<ImplicitUsings>enable</ImplicitUsings>
<RootNamespace>HostedWorkflowHandoff</RootNamespace>
<AssemblyName>HostedWorkflowHandoff</AssemblyName>
<CentralPackageTransitivePinningEnabled>false</CentralPackageTransitivePinningEnabled>
<NoWarn>$(NoWarn);NU1605;MAAIW001</NoWarn>
</PropertyGroup>
<ItemGroup>
<PackageReference Include="Azure.AI.OpenAI" />
<PackageReference Include="Azure.Core" />
<PackageReference Include="Azure.Identity" />
<PackageReference Include="DotNetEnv" />
<PackageReference Include="Microsoft.Extensions.AI.OpenAI" />
<PackageReference Include="ModelContextProtocol" />
</ItemGroup>
<!-- For contributors: uses ProjectReference to build against local source -->
<ItemGroup>
<ProjectReference Include="..\..\..\..\..\src\Microsoft.Agents.AI.Foundry\Microsoft.Agents.AI.Foundry.csproj" />
<ProjectReference Include="..\..\..\..\..\src\Microsoft.Agents.AI.Foundry.Hosting\Microsoft.Agents.AI.Foundry.Hosting.csproj" />
<ProjectReference Include="..\..\..\..\..\src\Microsoft.Agents.AI.Hosting\Microsoft.Agents.AI.Hosting.csproj" />
<ProjectReference Include="..\..\..\..\..\src\Microsoft.Agents.AI.OpenAI\Microsoft.Agents.AI.OpenAI.csproj" />
<ProjectReference Include="..\..\..\..\..\src\Microsoft.Agents.AI.Workflows\Microsoft.Agents.AI.Workflows.csproj" />
</ItemGroup>
<!-- For end-users: uncomment the PackageReference below and remove the ProjectReference above
<ItemGroup>
<PackageReference Include="Microsoft.Agents.AI.Foundry" />
<PackageReference Include="Microsoft.Agents.AI.Foundry.Hosting" />
<PackageReference Include="Microsoft.Agents.AI.Hosting" />
<PackageReference Include="Microsoft.Agents.AI.OpenAI" />
<PackageReference Include="Microsoft.Agents.AI.Workflows" />
</ItemGroup>
-->
</Project>
@@ -1,470 +0,0 @@
// Copyright (c) Microsoft. All rights reserved.
/// <summary>
/// Static HTML pages served by the sample application.
/// </summary>
internal static class Pages
{
// ═══════════════════════════════════════════════════════════════════════
// Homepage
// ═══════════════════════════════════════════════════════════════════════
internal const string Home = """
<!DOCTYPE html>
<html>
<head>
<meta charset="utf-8" />
<title>Foundry Responses Hosting Demos</title>
<style>
* { box-sizing: border-box; margin: 0; padding: 0; }
body { font-family: system-ui, sans-serif; background: #f5f5f5; display: flex; justify-content: center; padding: 2rem; }
main { width: 100%; max-width: 700px; }
h1 { font-size: 1.5rem; margin-bottom: .5rem; color: #1a1a1a; }
.subtitle { color: #555; margin-bottom: 2rem; line-height: 1.5; }
.cards { display: flex; flex-direction: column; gap: 1rem; }
.card { background: #fff; border: 1px solid #ddd; border-radius: 10px; padding: 1.5rem; text-decoration: none; color: inherit; transition: box-shadow .15s, transform .15s; }
.card:hover { box-shadow: 0 4px 16px rgba(0,0,0,.1); transform: translateY(-2px); }
.card h2 { font-size: 1.15rem; color: #0066cc; margin-bottom: .4rem; }
.card p { color: #555; line-height: 1.5; font-size: .9rem; }
.card .tags { margin-top: .6rem; display: flex; gap: .4rem; flex-wrap: wrap; }
.card .tag { background: #e8f0fe; color: #1a73e8; padding: .15rem .5rem; border-radius: 12px; font-size: .75rem; }
footer { margin-top: 2rem; font-size: .8rem; color: #999; text-align: center; }
</style>
</head>
<body>
<main>
<h1>🚀 Foundry Responses Hosting</h1>
<p class="subtitle">
Agent-framework agents hosted via the Azure AI Responses Server SDK.<br/>
Each demo registers a different agent and serves it through <code>POST /responses</code>.
</p>
<div class="cards">
<a class="card" href="/tool-demo">
<h2>🔧 Tool Demo</h2>
<p>An agent with local function tools (time, weather) and remote MCP tools from
Microsoft Learn for documentation search.</p>
<div class="tags">
<span class="tag">Local Tools</span>
<span class="tag">MCP</span>
<span class="tag">Microsoft Learn</span>
<span class="tag">Streaming</span>
</div>
</a>
<a class="card" href="/workflow-demo">
<h2>🔀 Workflow Demo</h2>
<p>A triage workflow that routes questions to specialist agents a Code Expert
or a Creative Writer using agent handoffs.</p>
<div class="tags">
<span class="tag">Workflow</span>
<span class="tag">Handoffs</span>
<span class="tag">Multi-Agent</span>
<span class="tag">Triage</span>
</div>
</a>
</div>
<footer>
All demos share the same <code>/responses</code> endpoint.
The <code>model</code> field in the request selects which agent handles it.
</footer>
</main>
</body>
</html>
""";
// ═══════════════════════════════════════════════════════════════════════
// Tool Demo
// ═══════════════════════════════════════════════════════════════════════
internal const string ToolDemo = """
<!DOCTYPE html>
<html>
<head>
<meta charset="utf-8" />
<title>Tool Demo Foundry Responses Hosting</title>
<style>
* { box-sizing: border-box; margin: 0; padding: 0; }
body { font-family: system-ui, sans-serif; background: #f5f5f5; display: flex; justify-content: center; padding: 2rem; }
main { width: 100%; max-width: 800px; }
h1 { font-size: 1.2rem; margin-bottom: .3rem; color: #333; }
.subtitle { font-size: .85rem; color: #666; margin-bottom: .8rem; }
a.back { font-size: .85rem; color: #0066cc; text-decoration: none; display: inline-block; margin-bottom: 1rem; }
#chat { background: #fff; border: 1px solid #ddd; border-radius: 8px; padding: 1rem; height: 56vh; overflow-y: auto; margin-bottom: 1rem; }
.msg { margin-bottom: .75rem; line-height: 1.6; }
.msg.user { color: #0066cc; }
.msg.assistant { color: #333; }
.msg .role { font-weight: 600; margin-right: .25rem; }
.tool-call { background: #f0f4ff; border-left: 3px solid #4a90d9; padding: .4rem .6rem; margin: .4rem 0; border-radius: 4px; font-size: .85rem; color: #555; font-family: 'Cascadia Code', 'Fira Code', monospace; }
.tool-call .tool-icon { margin-right: .3rem; }
form { display: flex; gap: .5rem; }
input { flex: 1; padding: .6rem .8rem; border: 1px solid #ccc; border-radius: 6px; font-size: 1rem; }
button { padding: .6rem 1.2rem; background: #0066cc; color: #fff; border: none; border-radius: 6px; font-size: 1rem; cursor: pointer; }
button:disabled { opacity: .5; cursor: not-allowed; }
#status { font-size: .85rem; color: #888; margin-top: .5rem; }
.suggestions { display: flex; flex-wrap: wrap; gap: .4rem; margin-bottom: 1rem; }
.suggestions button { padding: .3rem .7rem; font-size: .8rem; background: #e8f0fe; color: #1a73e8; border: 1px solid #c5d8f8; border-radius: 16px; cursor: pointer; }
.suggestions button:hover { background: #d2e3fc; }
</style>
</head>
<body>
<main>
<a class="back" href="/"> Back to demos</a>
<h1>🔧 Tool Demo</h1>
<p class="subtitle">Agent with local tools (time, weather) + Microsoft Learn MCP (docs search)</p>
<div class="suggestions">
<button onclick="sendText('What time is it in Tokyo?')">🕐 Time in Tokyo</button>
<button onclick="sendText('What is the weather in Seattle?')">🌤 Weather in Seattle</button>
<button onclick="sendText('How do I create an Azure Function using the CLI?')">📚 Azure Functions docs</button>
<button onclick="sendText('What is Microsoft Agent Framework?')">📚 Agent Framework</button>
</div>
<div id="chat"></div>
<form id="form">
<input id="input" placeholder="Try: 'What time is it?' or 'Search docs for Azure AI Foundry'" autocomplete="off" autofocus />
<button type="submit">Send</button>
</form>
<div id="status"></div>
</main>
<script src="/js/sse-validator.js"></script>
<script>
const AGENT = 'tool-agent';
const chat = document.getElementById('chat');
const form = document.getElementById('form');
const input = document.getElementById('input');
const status = document.getElementById('status');
function escapeHtml(s) { return s.replace(/&/g,'&amp;').replace(/</g,'&lt;').replace(/>/g,'&gt;'); }
function addMsg(role, html) {
const d = document.createElement('div');
d.className = 'msg ' + role; d.innerHTML = html;
chat.appendChild(d); chat.scrollTop = chat.scrollHeight; return d;
}
function addToolCall(name) {
const d = document.createElement('div');
d.className = 'tool-call';
d.innerHTML = '<span class="tool-icon">🔧</span> Calling <b>' + escapeHtml(name) + '</b>';
chat.appendChild(d); chat.scrollTop = chat.scrollHeight; return d;
}
function sendText(t) { input.value = t; form.dispatchEvent(new Event('submit')); }
form.addEventListener('submit', async e => {
e.preventDefault();
const text = input.value.trim(); if (!text) return;
input.value = '';
addMsg('user', '<span class="role">You:</span>' + escapeHtml(text));
const btn = form.querySelector('button[type="submit"]');
btn.disabled = true; status.textContent = 'Streaming';
let fullText = '', assistantDiv = null;
const toolCalls = {};
const validator = new SseValidator();
try {
const resp = await fetch('/responses', {
method: 'POST', headers: { 'Content-Type': 'application/json' },
body: JSON.stringify({ model: AGENT, stream: true, input: text })
});
if (!resp.ok) { status.textContent = 'Error ' + resp.status; btn.disabled = false; return; }
const reader = resp.body.getReader();
const decoder = new TextDecoder();
let buf = '', curEvt = null;
while (true) {
const { done, value } = await reader.read(); if (done) break;
buf += decoder.decode(value, { stream: true });
const lines = buf.split('\n'); buf = lines.pop();
for (const line of lines) {
if (line.startsWith('event: ')) { curEvt = line.slice(7).trim(); continue; }
if (!line.startsWith('data: ')) continue;
const d = line.slice(6).trim(); if (d === '[DONE]') continue;
try {
const evt = JSON.parse(d);
validator.capture(curEvt || evt.type || 'unknown', d);
curEvt = null;
if (evt.type === 'response.output_item.added' && evt.item?.type === 'function_call') {
const id = evt.item.id;
toolCalls[id] = { name: evt.item.name || '?', args: '', el: addToolCall(evt.item.name || '?') };
status.textContent = 'Calling tool: ' + (evt.item.name || '…');
}
if (evt.type === 'response.function_call_arguments.delta' && evt.item_id && toolCalls[evt.item_id])
toolCalls[evt.item_id].args += (evt.delta || '');
if (evt.type === 'response.function_call_arguments.done' && evt.item_id && toolCalls[evt.item_id]) {
const tc = toolCalls[evt.item_id];
let args = tc.args; try { args = JSON.stringify(JSON.parse(args), null, 0); } catch {}
tc.el.innerHTML = '<span class="tool-icon"></span> Called <b>' + escapeHtml(tc.name) + '</b>(' + escapeHtml(args) + ')';
}
if (evt.type === 'response.output_text.delta') {
if (!assistantDiv) assistantDiv = addMsg('assistant', '<span class="role">Agent:</span>');
fullText += evt.delta;
assistantDiv.innerHTML = '<span class="role">Agent:</span>' + escapeHtml(fullText);
chat.scrollTop = chat.scrollHeight;
status.textContent = 'Streaming';
}
} catch {}
}
}
if (!fullText && !assistantDiv) addMsg('assistant', '<span class="role">Agent:</span><em>(empty)</em>');
status.textContent = '';
} catch (err) { status.textContent = 'Error: ' + err.message; }
if (validator.events.length > 0) {
try { const vr = await validator.validate(); chat.appendChild(validator.renderElement(vr)); chat.scrollTop = chat.scrollHeight; } catch {}
}
btn.disabled = false; input.focus();
});
</script>
</body>
</html>
""";
// ═══════════════════════════════════════════════════════════════════════
// Workflow Demo
// ═══════════════════════════════════════════════════════════════════════
internal const string WorkflowDemo = """
<!DOCTYPE html>
<html>
<head>
<meta charset="utf-8" />
<title>Workflow Demo Foundry Responses Hosting</title>
<style>
* { box-sizing: border-box; margin: 0; padding: 0; }
body { font-family: system-ui, sans-serif; background: #f5f5f5; display: flex; justify-content: center; padding: 2rem; }
main { width: 100%; max-width: 800px; }
h1 { font-size: 1.2rem; margin-bottom: .3rem; color: #333; }
.subtitle { font-size: .85rem; color: #666; margin-bottom: .8rem; }
a.back { font-size: .85rem; color: #0066cc; text-decoration: none; display: inline-block; margin-bottom: 1rem; }
#chat { background: #fff; border: 1px solid #ddd; border-radius: 8px; padding: 1rem; height: 56vh; overflow-y: auto; margin-bottom: 1rem; }
.msg { margin-bottom: .75rem; line-height: 1.6; }
.msg.user { color: #0066cc; }
.msg.assistant { color: #333; }
.msg .role { font-weight: 600; margin-right: .25rem; }
.workflow-evt { background: #f0f9f0; border-left: 3px solid #4caf50; padding: .4rem .6rem; margin: .4rem 0; border-radius: 4px; font-size: .85rem; color: #555; }
.workflow-evt.failed { background: #fef0f0; border-left-color: #e53935; }
.tool-call { background: #f0f4ff; border-left: 3px solid #4a90d9; padding: .4rem .6rem; margin: .4rem 0; border-radius: 4px; font-size: .85rem; color: #555; font-family: 'Cascadia Code', 'Fira Code', monospace; }
form { display: flex; gap: .5rem; }
input { flex: 1; padding: .6rem .8rem; border: 1px solid #ccc; border-radius: 6px; font-size: 1rem; }
button { padding: .6rem 1.2rem; background: #0066cc; color: #fff; border: none; border-radius: 6px; font-size: 1rem; cursor: pointer; }
button:disabled { opacity: .5; cursor: not-allowed; }
#status { font-size: .85rem; color: #888; margin-top: .5rem; }
.suggestions { display: flex; flex-wrap: wrap; gap: .4rem; margin-bottom: 1rem; }
.suggestions button { padding: .3rem .7rem; font-size: .8rem; background: #e8f0fe; color: #1a73e8; border: 1px solid #c5d8f8; border-radius: 16px; cursor: pointer; }
.suggestions button:hover { background: #d2e3fc; }
.agent-diagram { background: #fff; border: 1px solid #ddd; border-radius: 8px; padding: 1rem; margin-bottom: 1rem; font-size: .85rem; text-align: center; color: #555; }
.agent-diagram .flow { font-size: 1.1rem; letter-spacing: 2px; }
</style>
</head>
<body>
<main>
<a class="back" href="/"> Back to demos</a>
<h1>🔀 Workflow Demo Agent Handoffs</h1>
<p class="subtitle">A triage agent routes your question to a specialist (Code Expert or Creative Writer)</p>
<div class="agent-diagram">
<div class="flow">👤 User 🔀 <b>Triage</b> 💻 <b>Code Expert</b> / <b>Creative Writer</b></div>
</div>
<div class="suggestions">
<button onclick="sendText('Write a Python function to reverse a linked list')">💻 Reverse linked list</button>
<button onclick="sendText('Write me a haiku about cloud computing')"> Cloud haiku</button>
<button onclick="sendText('Explain the difference between async and threads in C#')">💻 Async vs threads</button>
<button onclick="sendText('Write a short story about an AI that learns to paint')"> AI painter story</button>
</div>
<div id="chat"></div>
<form id="form">
<input id="input" placeholder="Ask a coding question or request creative writing…" autocomplete="off" autofocus />
<button type="submit">Send</button>
</form>
<div id="status"></div>
</main>
<script src="/js/sse-validator.js"></script>
<script>
const AGENT = 'triage-workflow';
const chat = document.getElementById('chat');
const form = document.getElementById('form');
const input = document.getElementById('input');
const status = document.getElementById('status');
function escapeHtml(s) { return s.replace(/&/g,'&amp;').replace(/</g,'&lt;').replace(/>/g,'&gt;'); }
function addMsg(role, html) {
const d = document.createElement('div');
d.className = 'msg ' + role; d.innerHTML = html;
chat.appendChild(d); chat.scrollTop = chat.scrollHeight; return d;
}
function addWorkflowEvent(icon, text, failed) {
const d = document.createElement('div');
d.className = 'workflow-evt' + (failed ? ' failed' : '');
d.innerHTML = icon + ' ' + escapeHtml(text);
chat.appendChild(d); chat.scrollTop = chat.scrollHeight;
}
function addToolCall(name) {
const d = document.createElement('div');
d.className = 'tool-call';
d.innerHTML = '🔀 Handoff: <b>' + escapeHtml(name) + '</b>';
chat.appendChild(d); chat.scrollTop = chat.scrollHeight; return d;
}
function sendText(t) { input.value = t; form.dispatchEvent(new Event('submit')); }
form.addEventListener('submit', async e => {
e.preventDefault();
const text = input.value.trim(); if (!text) return;
input.value = '';
addMsg('user', '<span class="role">You:</span>' + escapeHtml(text));
const btn = form.querySelector('button[type="submit"]');
btn.disabled = true; status.textContent = 'Running workflow';
let fullText = '', assistantDiv = null;
const toolCalls = {};
const validator = new SseValidator();
try {
const resp = await fetch('/responses', {
method: 'POST', headers: { 'Content-Type': 'application/json' },
body: JSON.stringify({ model: AGENT, stream: true, input: text })
});
if (!resp.ok) { status.textContent = 'Error ' + resp.status; btn.disabled = false; return; }
const reader = resp.body.getReader();
const decoder = new TextDecoder();
let buf = '', curEvt = null;
while (true) {
const { done, value } = await reader.read(); if (done) break;
buf += decoder.decode(value, { stream: true });
const lines = buf.split('\n'); buf = lines.pop();
for (const line of lines) {
if (line.startsWith('event: ')) { curEvt = line.slice(7).trim(); continue; }
if (!line.startsWith('data: ')) continue;
const d = line.slice(6).trim(); if (d === '[DONE]') continue;
try {
const evt = JSON.parse(d);
validator.capture(curEvt || evt.type || 'unknown', d);
curEvt = null;
// Workflow events (executor invoked/completed/failed)
if (evt.type === 'response.output_item.added' && evt.item?.type === 'workflow_action') {
const s = evt.item.status;
const id = evt.item.action_id || evt.item.actionId || '?';
if (s === 'in_progress' || s === 'InProgress')
addWorkflowEvent('', 'Agent invoked: ' + id);
else if (s === 'completed' || s === 'Completed')
addWorkflowEvent('✅', 'Agent completed: ' + id);
else if (s === 'failed' || s === 'Failed')
addWorkflowEvent('❌', 'Agent failed: ' + id, true);
}
// Handoff function calls
if (evt.type === 'response.output_item.added' && evt.item?.type === 'function_call') {
const id = evt.item.id;
toolCalls[id] = { name: evt.item.name || '?', args: '', el: addToolCall(evt.item.name || '?') };
status.textContent = 'Handoff: ' + (evt.item.name || '…');
}
if (evt.type === 'response.function_call_arguments.delta' && evt.item_id && toolCalls[evt.item_id])
toolCalls[evt.item_id].args += (evt.delta || '');
if (evt.type === 'response.function_call_arguments.done' && evt.item_id && toolCalls[evt.item_id]) {
const tc = toolCalls[evt.item_id];
let args = tc.args; try { args = JSON.stringify(JSON.parse(args), null, 0); } catch {}
tc.el.innerHTML = '🔀 Handoff: <b>' + escapeHtml(tc.name) + '</b>(' + escapeHtml(args) + ')';
}
// Text streaming from the specialist agent
if (evt.type === 'response.output_text.delta') {
if (!assistantDiv) assistantDiv = addMsg('assistant', '<span class="role">Agent:</span>');
fullText += evt.delta;
assistantDiv.innerHTML = '<span class="role">Agent:</span>' + escapeHtml(fullText);
chat.scrollTop = chat.scrollHeight;
status.textContent = 'Streaming';
}
} catch {}
}
}
if (!fullText && !assistantDiv) addMsg('assistant', '<span class="role">Agent:</span><em>(empty)</em>');
status.textContent = '';
} catch (err) { status.textContent = 'Error: ' + err.message; }
if (validator.events.length > 0) {
try { const vr = await validator.validate(); chat.appendChild(validator.renderElement(vr)); chat.scrollTop = chat.scrollHeight; } catch {}
}
btn.disabled = false; input.focus();
});
</script>
</body>
</html>
""";
// ═══════════════════════════════════════════════════════════════════════
// SSE Validator Script (shared by all demo pages)
// ═══════════════════════════════════════════════════════════════════════
internal const string ValidationScript = """
// SseValidator - inline SSE stream validation for Foundry Responses demos
// Captures events during streaming and validates against the API behaviour contract.
(function() {
const style = document.createElement('style');
style.textContent = `
.sse-val { margin: .4rem 0 .6rem; padding: .3rem .5rem; font-size: .75rem; color: #aaa; border-top: 1px dashed #e8e8e8; }
.val-ok { color: #7ab88a; }
.val-err { color: #d47272; font-weight: 500; }
.val-issues { margin: .2rem 0; }
.val-issue { color: #c06060; font-size: .72rem; padding: .1rem 0; }
.val-issue b { color: #b04040; }
.val-at { color: #ccc; font-size: .68rem; }
.val-log summary { cursor: pointer; color: #bbb; font-size: .72rem; }
.val-log-items { max-height: 120px; overflow-y: auto; font-size: .7rem; background: #fafafa;
padding: .3rem; border-radius: 3px; margin-top: .15rem;
font-family: 'Cascadia Code', 'Fira Code', monospace; }
.val-i { color: #ccc; display: inline-block; width: 1.8rem; text-align: right; margin-right: .3rem; }
.val-t { color: #8ab4d0; }
`;
document.head.appendChild(style);
})();
class SseValidator {
constructor() { this.events = []; }
reset() { this.events = []; }
capture(eventType, data) { this.events.push({ eventType, data }); }
async validate() {
const resp = await fetch('/api/validate', {
method: 'POST',
headers: { 'Content-Type': 'application/json' },
body: JSON.stringify({ events: this.events })
});
return await resp.json();
}
renderElement(result) {
const el = document.createElement('div');
el.className = 'sse-val';
const n = result.eventCount;
const ok = result.isValid;
const vs = result.violations || [];
const esc = s => String(s).replace(/&/g,'&amp;').replace(/</g,'&lt;').replace(/>/g,'&gt;');
let h = ok
? `<span class="val-ok">${n} events all rules passed </span>`
: `<span class="val-err">${n} events ${vs.length} violation(s)</span>`;
if (vs.length) {
h += '<div class="val-issues">';
vs.forEach(v => {
h += `<div class="val-issue"><b>[${esc(v.ruleId)}]</b> ${esc(v.message)} <span class="val-at">#${v.eventIndex}</span></div>`;
});
h += '</div>';
}
h += `<details class="val-log"><summary>Event log (${this.events.length})</summary><div class="val-log-items">`;
this.events.forEach((e, i) => {
h += `<div><span class="val-i">${i}</span> <span class="val-t">${esc(e.eventType)}</span></div>`;
});
h += '</div></details>';
el.innerHTML = h;
return el;
}
}
""";
}
@@ -1,221 +0,0 @@
// Copyright (c) Microsoft. All rights reserved.
// This sample demonstrates hosting agent-framework agents as Foundry Hosted Agents
// using the Azure AI Responses Server SDK.
//
// Demos:
// / - Homepage listing all demos
// /tool-demo - Agent with local tools + remote MCP tools
// /workflow-demo - Triage workflow routing to specialist agents
//
// Prerequisites:
// - Azure OpenAI resource with a deployed model
//
// Environment variables:
// - AZURE_OPENAI_ENDPOINT - your Azure OpenAI endpoint
// - AZURE_OPENAI_DEPLOYMENT - the model deployment name (default: "gpt-4o")
using System.ComponentModel;
using Azure.AI.OpenAI;
using Azure.Core;
using Azure.Identity;
using DotNetEnv;
using Microsoft.Agents.AI;
using Microsoft.Agents.AI.Foundry.Hosting;
using Microsoft.Agents.AI.Hosting;
using Microsoft.Agents.AI.Workflows;
using Microsoft.Extensions.AI;
using ModelContextProtocol.Client;
// Load .env file if present (for local development)
Env.TraversePath().Load();
var builder = WebApplication.CreateBuilder(args);
// ---------------------------------------------------------------------------
// 1. Create the shared Azure OpenAI chat client
// ---------------------------------------------------------------------------
var endpoint = new Uri(Environment.GetEnvironmentVariable("AZURE_OPENAI_ENDPOINT") ?? throw new InvalidOperationException("AZURE_OPENAI_ENDPOINT is not set."));
var deployment = Environment.GetEnvironmentVariable("AZURE_OPENAI_DEPLOYMENT") ?? "gpt-4o";
var azureClient = new AzureOpenAIClient(endpoint, new ChainedTokenCredential(
new DevTemporaryTokenCredential(),
new DefaultAzureCredential()));
IChatClient chatClient = azureClient.GetResponsesClient().AsIChatClient(deployment);
// ---------------------------------------------------------------------------
// 2. DEMO 1: Tool Agent — local tools + Microsoft Learn MCP
// ---------------------------------------------------------------------------
Console.WriteLine("Connecting to Microsoft Learn MCP server...");
McpClient mcpClient = await McpClient.CreateAsync(new HttpClientTransport(new()
{
Endpoint = new Uri("https://learn.microsoft.com/api/mcp"),
Name = "Microsoft Learn MCP",
}));
var mcpTools = await mcpClient.ListToolsAsync();
Console.WriteLine($"MCP tools available: {string.Join(", ", mcpTools.Select(t => t.Name))}");
builder.AddAIAgent(
name: "tool-agent",
instructions: """
You are a helpful assistant hosted as a Foundry Hosted Agent.
You have access to several tools - use them proactively:
- GetCurrentTime: Returns the current date/time in any timezone.
- GetWeather: Returns weather conditions for any location.
- Microsoft Learn MCP tools: Search and fetch Microsoft documentation.
When a user asks a technical question about Microsoft products, use the
documentation search tools to give accurate, up-to-date answers.
""",
chatClient: chatClient)
.WithAITool(AIFunctionFactory.Create(GetCurrentTime))
.WithAITool(AIFunctionFactory.Create(GetWeather))
.WithAITools(mcpTools.Cast<AITool>().ToArray());
// ---------------------------------------------------------------------------
// 3. DEMO 2: Triage Workflow — routes to specialist agents
// ---------------------------------------------------------------------------
ChatClientAgent triageAgent = new(
chatClient,
instructions: """
You are a triage agent that determines which specialist to hand off to.
Based on the user's question, ALWAYS hand off to one of the available agents.
Do NOT answer the question yourself - just route it.
""",
name: "triage_agent",
description: "Routes messages to the appropriate specialist agent");
ChatClientAgent codeExpert = new(
chatClient,
instructions: """
You are a coding and technology expert. You help with programming questions,
explain technical concepts, debug code, and suggest best practices.
Provide clear, well-structured answers with code examples when appropriate.
""",
name: "code_expert",
description: "Specialist agent for programming and technology questions");
ChatClientAgent creativeWriter = new(
chatClient,
instructions: """
You are a creative writing specialist. You help write stories, poems,
marketing copy, emails, and other creative content. You have a flair
for engaging language and vivid descriptions.
""",
name: "creative_writer",
description: "Specialist agent for creative writing and content tasks");
Workflow triageWorkflow = AgentWorkflowBuilder.CreateHandoffBuilderWith(triageAgent)
.WithHandoffs(triageAgent, [codeExpert, creativeWriter])
.WithHandoffs([codeExpert, creativeWriter], triageAgent)
.Build();
builder.AddAIAgent("triage-workflow", (_, key) =>
triageWorkflow.AsAIAgent(name: key));
// Register triage-workflow as the non-keyed default so azd invoke (no model) works
builder.Services.AddSingleton(sp =>
sp.GetRequiredKeyedService<AIAgent>("triage-workflow"));
// ---------------------------------------------------------------------------
// 4. Wire up the agent-framework handler and Responses Server SDK
// ---------------------------------------------------------------------------
builder.Services.AddFoundryResponses();
var app = builder.Build();
// Dispose the MCP client on shutdown
app.Lifetime.ApplicationStopping.Register(() =>
mcpClient.DisposeAsync().AsTask().GetAwaiter().GetResult());
// ---------------------------------------------------------------------------
// 5. Routes
// ---------------------------------------------------------------------------
app.MapGet("/ready", () => Results.Ok("ready"));
app.MapFoundryResponses();
app.MapGet("/", () => Results.Content(Pages.Home, "text/html"));
app.MapGet("/tool-demo", () => Results.Content(Pages.ToolDemo, "text/html"));
app.MapGet("/workflow-demo", () => Results.Content(Pages.WorkflowDemo, "text/html"));
app.MapGet("/js/sse-validator.js", () => Results.Content(Pages.ValidationScript, "application/javascript"));
// Validation endpoint: accepts captured SSE lines and validates them
app.MapPost("/api/validate", (HostedWorkflowHandoff.CapturedSseStream captured) =>
{
var validator = new HostedWorkflowHandoff.ResponseStreamValidator();
foreach (var evt in captured.Events)
{
validator.ProcessEvent(evt.EventType, evt.Data);
}
validator.Complete();
return Results.Json(validator.GetResult());
});
app.Run();
// ---------------------------------------------------------------------------
// Local tool definitions
// ---------------------------------------------------------------------------
// ---------------------------------------------------------------------------
// Dev-only credential: reads a pre-fetched bearer token from AZURE_BEARER_TOKEN.
// When the value is missing or set to "DefaultAzureCredential", this credential
// throws CredentialUnavailableException so the ChainedTokenCredential falls
// through to DefaultAzureCredential.
// ---------------------------------------------------------------------------
[Description("Gets the current date and time in the specified timezone.")]
static string GetCurrentTime(
[Description("IANA timezone (e.g. 'America/New_York', 'Europe/London', 'UTC'). Defaults to UTC.")]
string timezone = "UTC")
{
try
{
var tz = TimeZoneInfo.FindSystemTimeZoneById(timezone);
return TimeZoneInfo.ConvertTimeFromUtc(DateTime.UtcNow, tz).ToString("F");
}
catch
{
return DateTime.UtcNow.ToString("F") + " (UTC - unknown timezone: " + timezone + ")";
}
}
[Description("Gets the current weather for a location. Returns temperature, conditions, and humidity.")]
static string GetWeather(
[Description("The city or location (e.g. 'Seattle', 'London, UK').")]
string location)
{
// Simulated weather - deterministic per location for demo consistency
var rng = new Random(location.ToUpperInvariant().GetHashCode());
var temp = rng.Next(-5, 35);
string[] conditions = ["sunny", "partly cloudy", "overcast", "rainy", "snowy", "windy", "foggy"];
var condition = conditions[rng.Next(conditions.Length)];
return $"Weather in {location}: {temp}C, {condition}. Humidity: {rng.Next(30, 90)}%. Wind: {rng.Next(5, 30)} km/h.";
}
internal sealed class DevTemporaryTokenCredential : TokenCredential
{
private const string EnvironmentVariable = "AZURE_BEARER_TOKEN";
private readonly string? _token;
public DevTemporaryTokenCredential()
{
this._token = Environment.GetEnvironmentVariable(EnvironmentVariable);
}
public override AccessToken GetToken(TokenRequestContext requestContext, CancellationToken cancellationToken)
=> this.GetAccessToken();
public override ValueTask<AccessToken> GetTokenAsync(TokenRequestContext requestContext, CancellationToken cancellationToken)
=> new(this.GetAccessToken());
private AccessToken GetAccessToken()
{
if (string.IsNullOrEmpty(this._token) || this._token == "DefaultAzureCredential")
{
throw new CredentialUnavailableException($"{EnvironmentVariable} environment variable is not set.");
}
return new AccessToken(this._token, DateTimeOffset.UtcNow.AddHours(1));
}
}
@@ -1,126 +0,0 @@
# Hosted-Workflow-Handoff
A hosted agent server demonstrating two patterns in a single app:
- **`tool-agent`** — an agent with local tools (time, weather) plus remote Microsoft Learn MCP tools
- **`triage-workflow`** — a handoff workflow that routes conversations to specialist agents (code expert or creative writer) using `AgentWorkflowBuilder`
Both agents are served over the Responses protocol. The server also exposes interactive web demos at `/tool-demo` and `/workflow-demo`.
> Unlike the other samples in this folder, this one connects to an **Azure OpenAI** resource directly (not an Azure AI Foundry project endpoint).
## Prerequisites
- [.NET 10 SDK](https://dotnet.microsoft.com/download/dotnet/10.0)
- An Azure OpenAI resource with a deployed model (e.g., `gpt-4o`)
- Azure CLI logged in (`az login`)
## Configuration
Copy the template and fill in your values:
```bash
cp .env.example .env
```
Edit `.env`:
```env
AZURE_OPENAI_ENDPOINT=https://<your-account>.openai.azure.com/
AZURE_OPENAI_DEPLOYMENT=gpt-4o
AZURE_BEARER_TOKEN=DefaultAzureCredential
ASPNETCORE_URLS=http://+:8088
ASPNETCORE_ENVIRONMENT=Development
```
`AZURE_BEARER_TOKEN=DefaultAzureCredential` is a sentinel value that tells the app to skip the bearer token and fall through to `DefaultAzureCredential` (requires `az login`). Set it to a real token only when running in Docker.
> **Note:** `.env` is gitignored. The `.env.example` template is checked in as a reference.
## Running directly (contributors)
```bash
cd dotnet/samples/04-hosting/FoundryHostedAgents/responses/Hosted-Workflow-Handoff
dotnet run
```
The server starts on `http://localhost:8088`. Open `http://localhost:8088` to see the demo index page.
### Test it
Using the Azure Developer CLI (invokes `triage-workflow` — the primary/default agent):
```bash
azd ai agent invoke --local "Write me a short poem about coding"
```
To target a specific agent by name, use curl:
```bash
# Invoke triage-workflow explicitly
curl -X POST http://localhost:8088/responses \
-H "Content-Type: application/json" \
-d '{"input": "Write me a haiku about autumn", "model": "triage-workflow"}'
```
```bash
# Invoke tool-agent (local tools + MCP)
curl -X POST http://localhost:8088/responses \
-H "Content-Type: application/json" \
-d '{"input": "What time is it in Tokyo?", "model": "tool-agent"}'
```
## Running with Docker
### 1. Publish for the container runtime
```bash
dotnet publish -c Debug -f net10.0 -r linux-musl-x64 --self-contained false -o out
```
### 2. Build the Docker image
```bash
docker build -f Dockerfile.contributor -t hosted-workflow-handoff .
```
### 3. Run the container
```bash
export AZURE_BEARER_TOKEN=$(az account get-access-token --resource https://ai.azure.com --query accessToken -o tsv)
docker run --rm -p 8088:8088 \
-e AZURE_BEARER_TOKEN=$AZURE_BEARER_TOKEN \
--env-file .env \
hosted-workflow-handoff
```
### 4. Test it
```bash
azd ai agent invoke --local "Explain async/await in C#"
```
## How the triage workflow works
```
User message
┌──────────────┐
│ Triage Agent │ ──routes──▶ ┌─────────────┐
│ (router) │ │ Code Expert │
└──────────────┘ └─────────────┘
▲ │
│◀──────────────────────────────┘
└──routes──▶ ┌─────────────────┐
│ Creative Writer │
└─────────────────┘
```
The triage agent receives every message and hands off to the appropriate specialist. Specialists route back to the triage agent after responding, allowing for multi-turn conversations.
## NuGet package users
Use the standard `Dockerfile` instead of `Dockerfile.contributor`. See the commented section in `HostedWorkflowHandoff.csproj` for the `PackageReference` alternative.
@@ -1,601 +0,0 @@
// Copyright (c) Microsoft. All rights reserved.
using System.Text.Json;
using System.Text.Json.Serialization;
namespace HostedWorkflowHandoff;
/// <summary>Captured SSE event for validation.</summary>
[System.Diagnostics.CodeAnalysis.SuppressMessage("Microsoft.Performance", "CA1812:AvoidUninstantiatedInternalClasses", Justification = "Instantiated by JSON deserialization")]
internal sealed record CapturedSseEvent(
[property: JsonPropertyName("eventType")] string EventType,
[property: JsonPropertyName("data")] string Data);
/// <summary>Captured SSE stream sent from the client for server-side validation.</summary>
[System.Diagnostics.CodeAnalysis.SuppressMessage("Microsoft.Performance", "CA1812:AvoidUninstantiatedInternalClasses", Justification = "Instantiated by JSON deserialization")]
internal sealed record CapturedSseStream(
[property: JsonPropertyName("events")] List<CapturedSseEvent> Events);
/// <summary>
/// Validates an SSE event stream from the Azure AI Responses Server SDK against
/// the API behaviour contract. Feed events sequentially via <see cref="ProcessEvent"/>
/// and call <see cref="Complete"/> when the stream ends.
/// </summary>
internal sealed class ResponseStreamValidator
{
private readonly List<ValidationViolation> _violations = [];
private int _eventCount;
private int _expectedSequenceNumber;
private StreamState _state = StreamState.Initial;
private string? _responseId;
private readonly HashSet<int> _addedItemIndices = [];
private readonly HashSet<int> _doneItemIndices = [];
private readonly HashSet<string> _addedContentParts = []; // "outputIdx:partIdx"
private readonly HashSet<string> _doneContentParts = [];
private readonly Dictionary<string, string> _textAccumulators = []; // "outputIdx:contentIdx" → accumulated text
private bool _hasTerminal;
/// <summary>All violations found so far.</summary>
internal IReadOnlyList<ValidationViolation> Violations => this._violations;
/// <summary>
/// Processes a single SSE event line pair (event type + JSON data).
/// </summary>
/// <param name="eventType">The SSE event type (e.g. "response.created").</param>
/// <param name="jsonData">The raw JSON data payload.</param>
internal void ProcessEvent(string eventType, string jsonData)
{
JsonElement data;
try
{
data = JsonDocument.Parse(jsonData).RootElement;
}
catch (JsonException ex)
{
this.Fail("PARSE-01", $"Invalid JSON in event data: {ex.Message}");
return;
}
this._eventCount++;
// ── Sequence number validation ──────────────────────────────────
if (data.TryGetProperty("sequence_number", out var seqProp) && seqProp.ValueKind == JsonValueKind.Number)
{
int seq = seqProp.GetInt32();
if (seq != this._expectedSequenceNumber)
{
this.Fail("SEQ-01", $"Expected sequence_number {this._expectedSequenceNumber}, got {seq}");
}
this._expectedSequenceNumber = seq + 1;
}
else if (this._state != StreamState.Initial || eventType != "error")
{
// Pre-creation error events may not have sequence_number
this.Fail("SEQ-02", $"Missing sequence_number on event '{eventType}'");
}
// ── Post-terminal guard ─────────────────────────────────────────
if (this._hasTerminal)
{
this.Fail("TERM-01", $"Event '{eventType}' received after terminal event");
return;
}
// ── Dispatch by event type ──────────────────────────────────────
switch (eventType)
{
case "response.created":
this.ValidateResponseCreated(data);
break;
case "response.queued":
this.ValidateStateTransition(eventType, StreamState.Created, StreamState.Queued);
this.ValidateResponseEnvelope(data, eventType);
break;
case "response.in_progress":
if (this._state is StreamState.Created or StreamState.Queued)
{
this._state = StreamState.InProgress;
}
else
{
this.Fail("ORDER-02", $"'response.in_progress' received in state {this._state} (expected Created or Queued)");
}
this.ValidateResponseEnvelope(data, eventType);
break;
case "response.output_item.added":
case "output_item.added":
this.ValidateInProgress(eventType);
this.ValidateOutputItemAdded(data);
break;
case "response.output_item.done":
case "output_item.done":
this.ValidateInProgress(eventType);
this.ValidateOutputItemDone(data);
break;
case "response.content_part.added":
case "content_part.added":
this.ValidateInProgress(eventType);
this.ValidateContentPartAdded(data);
break;
case "response.content_part.done":
case "content_part.done":
this.ValidateInProgress(eventType);
this.ValidateContentPartDone(data);
break;
case "response.output_text.delta":
case "output_text.delta":
this.ValidateInProgress(eventType);
this.ValidateTextDelta(data);
break;
case "response.output_text.done":
case "output_text.done":
this.ValidateInProgress(eventType);
this.ValidateTextDone(data);
break;
case "response.function_call_arguments.delta":
case "function_call_arguments.delta":
this.ValidateInProgress(eventType);
break;
case "response.function_call_arguments.done":
case "function_call_arguments.done":
this.ValidateInProgress(eventType);
break;
case "response.completed":
this.ValidateTerminal(data, "completed");
break;
case "response.failed":
this.ValidateTerminal(data, "failed");
break;
case "response.incomplete":
this.ValidateTerminal(data, "incomplete");
break;
case "error":
// Pre-creation error — standalone, no response.created precedes it
if (this._state != StreamState.Initial)
{
this.Fail("ERR-01", "'error' event received after response.created — should use response.failed instead");
}
this._hasTerminal = true;
break;
default:
// Unknown events are not violations — the spec may evolve
break;
}
}
/// <summary>
/// Call after the stream ends. Checks that a terminal event was received.
/// </summary>
internal void Complete()
{
if (!this._hasTerminal && this._state != StreamState.Initial)
{
this.Fail("TERM-02", "Stream ended without a terminal event (response.completed, response.failed, or response.incomplete)");
}
if (this._state == StreamState.Initial && this._eventCount == 0)
{
this.Fail("EMPTY-01", "No events received in the stream");
}
// Check for output items that were added but never completed
foreach (int idx in this._addedItemIndices)
{
if (!this._doneItemIndices.Contains(idx))
{
this.Fail("ITEM-03", $"Output item at index {idx} was added but never received output_item.done");
}
}
// Check for content parts that were added but never completed
foreach (string key in this._addedContentParts)
{
if (!this._doneContentParts.Contains(key))
{
this.Fail("CONTENT-03", $"Content part '{key}' was added but never received content_part.done");
}
}
}
/// <summary>
/// Returns a summary of all validation results.
/// </summary>
internal ValidationResult GetResult()
{
return new ValidationResult(
EventCount: this._eventCount,
IsValid: this._violations.Count == 0,
Violations: [.. this._violations]);
}
// ═══════════════════════════════════════════════════════════════════════
// Event-specific validators
// ═══════════════════════════════════════════════════════════════════════
private void ValidateResponseCreated(JsonElement data)
{
if (this._state != StreamState.Initial)
{
this.Fail("ORDER-01", $"'response.created' received in state {this._state} (expected Initial — must be first event)");
return;
}
this._state = StreamState.Created;
// Must have a response envelope
if (!data.TryGetProperty("response", out var resp))
{
this.Fail("FIELD-01", "'response.created' missing 'response' object");
return;
}
// Required response fields
this.ValidateRequiredResponseFields(resp, "response.created");
// Capture response ID for cross-event checks
if (resp.TryGetProperty("id", out var idProp))
{
this._responseId = idProp.GetString();
}
// Status must be non-terminal
if (resp.TryGetProperty("status", out var statusProp))
{
string? status = statusProp.GetString();
if (status is "completed" or "failed" or "incomplete" or "cancelled")
{
this.Fail("STATUS-01", $"'response.created' has terminal status '{status}' — must be 'queued' or 'in_progress'");
}
}
}
private void ValidateTerminal(JsonElement data, string expectedKind)
{
if (this._state is StreamState.Initial or StreamState.Created)
{
this.Fail("ORDER-03", $"Terminal event 'response.{expectedKind}' received before 'response.in_progress'");
}
this._hasTerminal = true;
this._state = StreamState.Terminal;
if (!data.TryGetProperty("response", out var resp))
{
this.Fail("FIELD-01", $"'response.{expectedKind}' missing 'response' object");
return;
}
this.ValidateRequiredResponseFields(resp, $"response.{expectedKind}");
if (resp.TryGetProperty("status", out var statusProp))
{
string? status = statusProp.GetString();
// completed_at validation (B6)
bool hasCompletedAt = resp.TryGetProperty("completed_at", out var catProp)
&& catProp.ValueKind != JsonValueKind.Null;
if (status == "completed" && !hasCompletedAt)
{
this.Fail("FIELD-02", "'completed_at' must be non-null when status is 'completed'");
}
if (status != "completed" && hasCompletedAt)
{
this.Fail("FIELD-03", $"'completed_at' must be null when status is '{status}'");
}
// error field validation
bool hasError = resp.TryGetProperty("error", out var errProp)
&& errProp.ValueKind != JsonValueKind.Null;
if (status == "failed" && !hasError)
{
this.Fail("FIELD-04", "'error' must be non-null when status is 'failed'");
}
if (status is "completed" or "incomplete" && hasError)
{
this.Fail("FIELD-05", $"'error' must be null when status is '{status}'");
}
// error structure validation
if (hasError)
{
this.ValidateErrorObject(errProp, $"response.{expectedKind}");
}
// cancelled output must be empty (B11)
if (status == "cancelled" && resp.TryGetProperty("output", out var outputProp)
&& outputProp.ValueKind == JsonValueKind.Array && outputProp.GetArrayLength() > 0)
{
this.Fail("CANCEL-01", "Cancelled response must have empty output array (B11)");
}
// response ID consistency
if (this._responseId is not null && resp.TryGetProperty("id", out var idProp)
&& idProp.GetString() != this._responseId)
{
this.Fail("ID-01", $"Response ID changed: was '{this._responseId}', now '{idProp.GetString()}'");
}
}
// Usage validation (optional, but if present must be structured correctly)
if (resp.TryGetProperty("usage", out var usageProp) && usageProp.ValueKind == JsonValueKind.Object)
{
this.ValidateUsage(usageProp, $"response.{expectedKind}");
}
}
private void ValidateOutputItemAdded(JsonElement data)
{
if (data.TryGetProperty("output_index", out var idxProp) && idxProp.ValueKind == JsonValueKind.Number)
{
int index = idxProp.GetInt32();
if (!this._addedItemIndices.Add(index))
{
this.Fail("ITEM-01", $"Duplicate output_item.added for output_index {index}");
}
}
else
{
this.Fail("FIELD-06", "output_item.added missing 'output_index' field");
}
if (!data.TryGetProperty("item", out _))
{
this.Fail("FIELD-07", "output_item.added missing 'item' object");
}
}
private void ValidateOutputItemDone(JsonElement data)
{
if (data.TryGetProperty("output_index", out var idxProp) && idxProp.ValueKind == JsonValueKind.Number)
{
int index = idxProp.GetInt32();
if (!this._addedItemIndices.Contains(index))
{
this.Fail("ITEM-02", $"output_item.done for output_index {index} without preceding output_item.added");
}
this._doneItemIndices.Add(index);
}
else
{
this.Fail("FIELD-06", "output_item.done missing 'output_index' field");
}
}
private void ValidateContentPartAdded(JsonElement data)
{
string key = GetContentPartKey(data);
if (!this._addedContentParts.Add(key))
{
this.Fail("CONTENT-01", $"Duplicate content_part.added for {key}");
}
}
private void ValidateContentPartDone(JsonElement data)
{
string key = GetContentPartKey(data);
if (!this._addedContentParts.Contains(key))
{
this.Fail("CONTENT-02", $"content_part.done for {key} without preceding content_part.added");
}
this._doneContentParts.Add(key);
}
private void ValidateTextDelta(JsonElement data)
{
string key = GetTextKey(data);
string delta = data.TryGetProperty("delta", out var deltaProp)
? deltaProp.GetString() ?? string.Empty
: string.Empty;
if (!this._textAccumulators.TryGetValue(key, out string? existing))
{
this._textAccumulators[key] = delta;
}
else
{
this._textAccumulators[key] = existing + delta;
}
}
private void ValidateTextDone(JsonElement data)
{
string key = GetTextKey(data);
string? finalText = data.TryGetProperty("text", out var textProp)
? textProp.GetString()
: null;
if (finalText is null)
{
this.Fail("TEXT-01", $"output_text.done for {key} missing 'text' field");
return;
}
if (this._textAccumulators.TryGetValue(key, out string? accumulated) && accumulated != finalText)
{
this.Fail("TEXT-02", $"output_text.done text for {key} does not match accumulated deltas (accumulated {accumulated.Length} chars, done has {finalText.Length} chars)");
}
}
// ═══════════════════════════════════════════════════════════════════════
// Shared field validators
// ═══════════════════════════════════════════════════════════════════════
private void ValidateRequiredResponseFields(JsonElement resp, string context)
{
if (!HasNonNullString(resp, "id"))
{
this.Fail("FIELD-01", $"{context}: response missing 'id'");
}
if (resp.TryGetProperty("object", out var objProp))
{
if (objProp.GetString() != "response")
{
this.Fail("FIELD-08", $"{context}: response.object must be 'response', got '{objProp.GetString()}'");
}
}
else
{
this.Fail("FIELD-08", $"{context}: response missing 'object' field");
}
if (!resp.TryGetProperty("created_at", out var catProp) || catProp.ValueKind == JsonValueKind.Null)
{
this.Fail("FIELD-09", $"{context}: response missing 'created_at'");
}
if (!resp.TryGetProperty("status", out _))
{
this.Fail("FIELD-10", $"{context}: response missing 'status'");
}
if (!resp.TryGetProperty("output", out var outputProp) || outputProp.ValueKind != JsonValueKind.Array)
{
this.Fail("FIELD-11", $"{context}: response missing 'output' array");
}
}
private void ValidateErrorObject(JsonElement error, string context)
{
if (!HasNonNullString(error, "code"))
{
this.Fail("ERR-02", $"{context}: error object missing 'code' field");
}
if (!HasNonNullString(error, "message"))
{
this.Fail("ERR-03", $"{context}: error object missing 'message' field");
}
}
private void ValidateUsage(JsonElement usage, string context)
{
if (!usage.TryGetProperty("input_tokens", out _))
{
this.Fail("USAGE-01", $"{context}: usage missing 'input_tokens'");
}
if (!usage.TryGetProperty("output_tokens", out _))
{
this.Fail("USAGE-02", $"{context}: usage missing 'output_tokens'");
}
if (!usage.TryGetProperty("total_tokens", out _))
{
this.Fail("USAGE-03", $"{context}: usage missing 'total_tokens'");
}
}
private void ValidateResponseEnvelope(JsonElement data, string eventType)
{
if (!data.TryGetProperty("response", out var resp))
{
this.Fail("FIELD-01", $"'{eventType}' missing 'response' object");
return;
}
this.ValidateRequiredResponseFields(resp, eventType);
// Response ID consistency
if (this._responseId is not null && resp.TryGetProperty("id", out var idProp)
&& idProp.GetString() != this._responseId)
{
this.Fail("ID-01", $"Response ID changed: was '{this._responseId}', now '{idProp.GetString()}'");
}
}
// ═══════════════════════════════════════════════════════════════════════
// Helpers
// ═══════════════════════════════════════════════════════════════════════
private void ValidateInProgress(string eventType)
{
if (this._state != StreamState.InProgress)
{
this.Fail("ORDER-04", $"'{eventType}' received in state {this._state} (expected InProgress)");
}
}
private void ValidateStateTransition(string eventType, StreamState expected, StreamState next)
{
if (this._state != expected)
{
this.Fail("ORDER-05", $"'{eventType}' received in state {this._state} (expected {expected})");
}
else
{
this._state = next;
}
}
private void Fail(string ruleId, string message)
{
this._violations.Add(new ValidationViolation(ruleId, message, this._eventCount));
}
private static bool HasNonNullString(JsonElement obj, string property)
{
return obj.TryGetProperty(property, out var prop)
&& prop.ValueKind == JsonValueKind.String
&& !string.IsNullOrEmpty(prop.GetString());
}
private static string GetContentPartKey(JsonElement data)
{
int outputIdx = data.TryGetProperty("output_index", out var oi) ? oi.GetInt32() : -1;
int partIdx = data.TryGetProperty("content_index", out var pi) ? pi.GetInt32() : -1;
return $"{outputIdx}:{partIdx}";
}
private static string GetTextKey(JsonElement data)
{
int outputIdx = data.TryGetProperty("output_index", out var oi) ? oi.GetInt32() : -1;
int contentIdx = data.TryGetProperty("content_index", out var ci) ? ci.GetInt32() : -1;
return $"{outputIdx}:{contentIdx}";
}
private enum StreamState
{
Initial,
Created,
Queued,
InProgress,
Terminal,
}
}
/// <summary>A single validation violation.</summary>
/// <param name="RuleId">The rule identifier (e.g. SEQ-01, FIELD-02).</param>
/// <param name="Message">Human-readable description of the violation.</param>
/// <param name="EventIndex">1-based index of the event that triggered this violation.</param>
internal sealed record ValidationViolation(string RuleId, string Message, int EventIndex);
/// <summary>Overall validation result.</summary>
/// <param name="EventCount">Total number of events processed.</param>
/// <param name="IsValid">True if no violations were found.</param>
/// <param name="Violations">List of all violations.</param>
internal sealed record ValidationResult(int EventCount, bool IsValid, IReadOnlyList<ValidationViolation> Violations);
@@ -1,30 +0,0 @@
# yaml-language-server: $schema=https://raw.githubusercontent.com/microsoft/AgentSchema/refs/heads/main/schemas/v1.0/AgentManifest.yaml
name: triage-workflow
displayName: "Triage Handoff Workflow Agent"
description: >
A hosted agent demonstrating two patterns in a single server: a tool-equipped agent
with local tools and remote MCP tools, and a triage workflow that routes conversations
to specialist agents (code expert or creative writer) via handoff orchestration.
metadata:
tags:
- AI Agent Hosting
- Azure AI AgentServer
- Responses Protocol
- Workflows
- Handoff
- Agent Framework
template:
name: triage-workflow
kind: hosted
protocols:
- protocol: responses
version: 1.0.0
resources:
cpu: "0.25"
memory: 0.5Gi
parameters:
properties: []
resources: []
@@ -1,9 +0,0 @@
# yaml-language-server: $schema=https://raw.githubusercontent.com/microsoft/AgentSchema/refs/heads/main/schemas/v1.0/ContainerAgent.yaml
kind: hosted
name: triage-workflow
protocols:
- protocol: responses
version: 1.0.0
resources:
cpu: "0.25"
memory: 0.5Gi
@@ -1,5 +0,0 @@
AZURE_AI_PROJECT_ENDPOINT=<your-azure-ai-project-endpoint>
ASPNETCORE_URLS=http://+:8088
ASPNETCORE_ENVIRONMENT=Development
AZURE_AI_MODEL_DEPLOYMENT_NAME=gpt-4o
AZURE_BEARER_TOKEN=DefaultAzureCredential
@@ -1,17 +0,0 @@
# Use the official .NET 10.0 ASP.NET runtime as a parent image
FROM mcr.microsoft.com/dotnet/aspnet:10.0 AS base
WORKDIR /app
FROM mcr.microsoft.com/dotnet/sdk:10.0 AS build
WORKDIR /src
COPY . .
RUN dotnet restore
RUN dotnet publish -c Release -o /app/publish
# Final stage
FROM base AS final
WORKDIR /app
COPY --from=build /app/publish .
EXPOSE 8088
ENV ASPNETCORE_URLS=http://+:8088
ENTRYPOINT ["dotnet", "HostedWorkflowSimple.dll"]
@@ -1,18 +0,0 @@
# Dockerfile for contributors building from the agent-framework repository source.
#
# This project uses ProjectReference to the local source, which means a standard
# multi-stage Docker build cannot resolve dependencies outside this folder.
# Pre-publish the app targeting the container runtime and copy the output:
#
# dotnet publish -c Debug -f net10.0 -r linux-musl-x64 --self-contained false -o out
# docker build -f Dockerfile.contributor -t hosted-workflow-simple .
# docker run --rm -p 8088:8088 -e AGENT_NAME=hosted-workflow-simple -e AZURE_BEARER_TOKEN=$AZURE_BEARER_TOKEN --env-file .env hosted-workflow-simple
#
# For end-users consuming the NuGet package (not ProjectReference), use the standard
# Dockerfile which performs a full dotnet restore + publish inside the container.
FROM mcr.microsoft.com/dotnet/aspnet:10.0-alpine AS final
WORKDIR /app
COPY out/ .
EXPOSE 8088
ENV ASPNETCORE_URLS=http://+:8088
ENTRYPOINT ["dotnet", "HostedWorkflowSimple.dll"]
@@ -1,36 +0,0 @@
<Project Sdk="Microsoft.NET.Sdk.Web">
<PropertyGroup>
<TargetFrameworks>net10.0</TargetFrameworks>
<Nullable>enable</Nullable>
<ImplicitUsings>enable</ImplicitUsings>
<CentralPackageTransitivePinningEnabled>false</CentralPackageTransitivePinningEnabled>
<RootNamespace>HostedWorkflowSimple</RootNamespace>
<AssemblyName>HostedWorkflowSimple</AssemblyName>
<NoWarn>$(NoWarn);</NoWarn>
</PropertyGroup>
<ItemGroup>
<PackageReference Include="Azure.AI.Projects" VersionOverride="2.1.0-beta.1" />
<PackageReference Include="Azure.Identity" />
<PackageReference Include="DotNetEnv" />
</ItemGroup>
<!-- For contributors: uses ProjectReference to build against local source -->
<ItemGroup>
<ProjectReference Include="..\..\..\..\..\src\Microsoft.Agents.AI.Foundry\Microsoft.Agents.AI.Foundry.csproj" />
<ProjectReference Include="..\..\..\..\..\src\Microsoft.Agents.AI.Foundry.Hosting\Microsoft.Agents.AI.Foundry.Hosting.csproj" />
<ProjectReference Include="..\..\..\..\..\src\Microsoft.Agents.AI.OpenAI\Microsoft.Agents.AI.OpenAI.csproj" />
<ProjectReference Include="..\..\..\..\..\src\Microsoft.Agents.AI.Workflows\Microsoft.Agents.AI.Workflows.csproj" />
</ItemGroup>
<!-- For end-users: uncomment the PackageReference below and remove the ProjectReferences above
<ItemGroup>
<PackageReference Include="Microsoft.Agents.AI.Foundry" Version="1.0.0" />
<PackageReference Include="Microsoft.Agents.AI.Foundry.Hosting" Version="1.0.0" />
<PackageReference Include="Microsoft.Agents.AI.OpenAI" Version="1.0.0" />
<PackageReference Include="Microsoft.Agents.AI.Workflows" Version="1.0.0" />
</ItemGroup>
-->
</Project>
@@ -1,97 +0,0 @@
// Copyright (c) Microsoft. All rights reserved.
// Translation Chain Workflow Agent — demonstrates how to compose multiple AI agents
// into a sequential workflow pipeline. Three translation agents are connected:
// English → French → Spanish → English, showing how agents can be orchestrated
// as workflow executors in a hosted agent.
using Azure.AI.Projects;
using Azure.Core;
using Azure.Identity;
using DotNetEnv;
using Microsoft.Agents.AI;
using Microsoft.Agents.AI.Foundry.Hosting;
using Microsoft.Agents.AI.Workflows;
using Microsoft.Extensions.AI;
// Load .env file if present (for local development)
Env.TraversePath().Load();
string endpoint = Environment.GetEnvironmentVariable("AZURE_AI_PROJECT_ENDPOINT")
?? throw new InvalidOperationException("AZURE_AI_PROJECT_ENDPOINT is not set.");
string deploymentName = Environment.GetEnvironmentVariable("AZURE_AI_MODEL_DEPLOYMENT_NAME") ?? "gpt-4o";
// Use a chained credential: try a temporary dev token first (for local Docker debugging),
// then fall back to DefaultAzureCredential (for local dev via dotnet run / managed identity in production).
TokenCredential credential = new ChainedTokenCredential(
new DevTemporaryTokenCredential(),
new DefaultAzureCredential());
// Create a chat client from the Foundry project
IChatClient chatClient = new AIProjectClient(new Uri(endpoint), credential)
.GetProjectOpenAIClient()
.GetChatClient(deploymentName)
.AsIChatClient();
// Create translation agents
AIAgent frenchAgent = chatClient.AsAIAgent("You are a translation assistant that translates the provided text to French.");
AIAgent spanishAgent = chatClient.AsAIAgent("You are a translation assistant that translates the provided text to Spanish.");
AIAgent englishAgent = chatClient.AsAIAgent("You are a translation assistant that translates the provided text to English.");
// Build the sequential workflow: French → Spanish → English
AIAgent agent = new WorkflowBuilder(frenchAgent)
.AddEdge(frenchAgent, spanishAgent)
.AddEdge(spanishAgent, englishAgent)
.Build()
.AsAIAgent(
name: Environment.GetEnvironmentVariable("AGENT_NAME") ?? "hosted-workflows");
// Host the workflow agent as a Foundry Hosted Agent using the Responses API.
var builder = WebApplication.CreateBuilder(args);
builder.Services.AddFoundryResponses(agent);
var app = builder.Build();
app.MapFoundryResponses();
if (app.Environment.IsDevelopment())
{
app.MapFoundryResponses("openai/v1");
}
app.Run();
/// <summary>
/// A <see cref="TokenCredential"/> for local Docker debugging only.
/// Reads a pre-fetched bearer token from the <c>AZURE_BEARER_TOKEN</c> environment variable
/// once at startup. This should NOT be used in production.
///
/// Generate a token on your host and pass it to the container:
/// export AZURE_BEARER_TOKEN=$(az account get-access-token --resource https://ai.azure.com --query accessToken -o tsv)
/// docker run -e AZURE_BEARER_TOKEN=$AZURE_BEARER_TOKEN ...
/// </summary>
internal sealed class DevTemporaryTokenCredential : TokenCredential
{
private const string EnvironmentVariable = "AZURE_BEARER_TOKEN";
private readonly string? _token;
public DevTemporaryTokenCredential()
{
this._token = Environment.GetEnvironmentVariable(EnvironmentVariable);
}
public override AccessToken GetToken(TokenRequestContext requestContext, CancellationToken cancellationToken)
=> this.GetAccessToken();
public override ValueTask<AccessToken> GetTokenAsync(TokenRequestContext requestContext, CancellationToken cancellationToken)
=> new(this.GetAccessToken());
private AccessToken GetAccessToken()
{
if (string.IsNullOrEmpty(this._token) || this._token == "DefaultAzureCredential")
{
throw new CredentialUnavailableException($"{EnvironmentVariable} environment variable is not set.");
}
return new AccessToken(this._token, DateTimeOffset.UtcNow.AddHours(1));
}
}
@@ -1,109 +0,0 @@
# Hosted-Workflow-Simple
A hosted agent that demonstrates **multi-agent workflow orchestration**. Three translation agents are composed into a sequential pipeline: English → French → Spanish → English, showing how agents can be chained as workflow executors using `WorkflowBuilder`.
## Prerequisites
- [.NET 10 SDK](https://dotnet.microsoft.com/download/dotnet/10.0)
- An Azure AI Foundry project with a deployed model (e.g., `gpt-4o`)
- Azure CLI logged in (`az login`)
## Configuration
Copy the template and fill in your project endpoint:
```bash
cp .env.example .env
```
Edit `.env` and set your Azure AI Foundry project endpoint:
```env
AZURE_AI_PROJECT_ENDPOINT=https://<your-account>.services.ai.azure.com/api/projects/<your-project>
ASPNETCORE_URLS=http://+:8088
ASPNETCORE_ENVIRONMENT=Development
AZURE_AI_MODEL_DEPLOYMENT_NAME=gpt-4o
```
> **Note:** `.env` is gitignored. The `.env.example` template is checked in as a reference.
## Running directly (contributors)
```bash
cd dotnet/samples/04-hosting/FoundryHostedAgents/responses/Hosted-Workflow-Simple
AGENT_NAME=hosted-workflow-simple dotnet run
```
The agent will start on `http://localhost:8088`.
### Test it
Using the Azure Developer CLI:
```bash
azd ai agent invoke --local "The quick brown fox jumps over the lazy dog"
```
Or with curl:
```bash
curl -X POST http://localhost:8088/responses \
-H "Content-Type: application/json" \
-d '{"input": "The quick brown fox jumps over the lazy dog", "model": "hosted-workflow-simple"}'
```
The text will be translated through the chain: English → French → Spanish → English.
## Running with Docker
### 1. Publish for the container runtime
```bash
dotnet publish -c Debug -f net10.0 -r linux-musl-x64 --self-contained false -o out
```
### 2. Build the Docker image
```bash
docker build -f Dockerfile.contributor -t hosted-workflow-simple .
```
### 3. Run the container
```bash
export AZURE_BEARER_TOKEN=$(az account get-access-token --resource https://ai.azure.com --query accessToken -o tsv)
docker run --rm -p 8088:8088 \
-e AGENT_NAME=hosted-workflow-simple \
-e AZURE_BEARER_TOKEN=$AZURE_BEARER_TOKEN \
--env-file .env \
hosted-workflow-simple
```
### 4. Test it
```bash
azd ai agent invoke --local "Hello, how are you today?"
```
## How the workflow works
```
Input text
┌─────────────┐ ┌──────────────┐ ┌──────────────┐
│ French Agent │ → │ Spanish Agent │ → │ English Agent │
│ (translate) │ │ (translate) │ │ (translate) │
└─────────────┘ └──────────────┘ └──────────────┘
Final output
(back in English)
```
Each agent in the chain receives the output of the previous agent. The final result demonstrates how meaning is preserved (or subtly shifted) through multiple translation hops.
## NuGet package users
Use the standard `Dockerfile` instead of `Dockerfile.contributor`. See the commented section in `HostedWorkflowSimple.csproj` for the `PackageReference` alternative.
@@ -1,29 +0,0 @@
# yaml-language-server: $schema=https://raw.githubusercontent.com/microsoft/AgentSchema/refs/heads/main/schemas/v1.0/AgentManifest.yaml
name: hosted-workflows
displayName: "Translation Chain Workflow Agent"
description: >
A workflow agent that performs sequential translation through multiple languages.
Translates text from English to French, then to Spanish, and finally back to English,
demonstrating how AI agents can be composed as workflow executors.
metadata:
tags:
- AI Agent Hosting
- Azure AI AgentServer
- Responses Protocol
- Workflows
- Agent Framework
template:
name: hosted-workflows
kind: hosted
protocols:
- protocol: responses
version: 1.0.0
resources:
cpu: "0.25"
memory: 0.5Gi
parameters:
properties: []
resources: []
@@ -1,9 +0,0 @@
# yaml-language-server: $schema=https://raw.githubusercontent.com/microsoft/AgentSchema/refs/heads/main/schemas/v1.0/ContainerAgent.yaml
kind: hosted
name: hosted-workflow-simple
protocols:
- protocol: responses
version: 1.0.0
resources:
cpu: "0.25"
memory: 0.5Gi
@@ -1,115 +0,0 @@
// Copyright (c) Microsoft. All rights reserved.
using System.ClientModel.Primitives;
using Azure.AI.Extensions.OpenAI;
using Azure.AI.Projects;
using Azure.Identity;
using DotNetEnv;
using Microsoft.Agents.AI;
using Microsoft.Agents.AI.Foundry;
// Load .env file if present (for local development)
Env.TraversePath().Load();
Uri agentEndpoint = new(Environment.GetEnvironmentVariable("AGENT_ENDPOINT")
?? "http://localhost:8088");
var agentName = Environment.GetEnvironmentVariable("AGENT_NAME")
?? throw new InvalidOperationException("AGENT_NAME is not set.");
// ── Create an agent-framework agent backed by the remote agent endpoint ──────
var options = new AIProjectClientOptions();
if (agentEndpoint.Scheme == "http")
{
// For local HTTP dev: tell AIProjectClient the endpoint is HTTPS (to satisfy
// BearerTokenPolicy's TLS check), then swap the scheme back to HTTP right
// before the request hits the wire.
agentEndpoint = new UriBuilder(agentEndpoint) { Scheme = "https" }.Uri;
options.AddPolicy(new HttpSchemeRewritePolicy(), PipelinePosition.BeforeTransport);
}
var aiProjectClient = new AIProjectClient(agentEndpoint, new AzureCliCredential(), options);
FoundryAgent agent = aiProjectClient.AsAIAgent(new AgentReference(agentName));
AgentSession session = await agent.CreateSessionAsync();
// ── REPL ──────────────────────────────────────────────────────────────────────
Console.ForegroundColor = ConsoleColor.Cyan;
Console.WriteLine($"""
══════════════════════════════════════════════════════════
Simple Agent Sample
Connected to: {agentEndpoint}
Type a message or 'quit' to exit
══════════════════════════════════════════════════════════
""");
Console.ResetColor();
Console.WriteLine();
while (true)
{
Console.ForegroundColor = ConsoleColor.Green;
Console.Write("You> ");
Console.ResetColor();
string? input = Console.ReadLine();
if (string.IsNullOrWhiteSpace(input)) { continue; }
if (input.Equals("quit", StringComparison.OrdinalIgnoreCase)) { break; }
try
{
Console.ForegroundColor = ConsoleColor.Yellow;
Console.Write("Agent> ");
Console.ResetColor();
await foreach (var update in agent.RunStreamingAsync(input, session))
{
Console.Write(update);
}
Console.WriteLine();
}
catch (Exception ex)
{
Console.ForegroundColor = ConsoleColor.Red;
Console.WriteLine($"Error: {ex.Message}");
Console.ResetColor();
}
Console.WriteLine();
}
Console.WriteLine("Goodbye!");
/// <summary>
/// For Local Development Only
/// Rewrites HTTPS URIs to HTTP right before transport, allowing AIProjectClient
/// to target a local HTTP dev server while satisfying BearerTokenPolicy's TLS check.
/// </summary>
internal sealed class HttpSchemeRewritePolicy : PipelinePolicy
{
public override void Process(PipelineMessage message, IReadOnlyList<PipelinePolicy> pipeline, int currentIndex)
{
RewriteScheme(message);
ProcessNext(message, pipeline, currentIndex);
}
public override async ValueTask ProcessAsync(PipelineMessage message, IReadOnlyList<PipelinePolicy> pipeline, int currentIndex)
{
RewriteScheme(message);
await ProcessNextAsync(message, pipeline, currentIndex).ConfigureAwait(false);
}
private static void RewriteScheme(PipelineMessage message)
{
var uri = message.Request.Uri!;
if (uri.Scheme == Uri.UriSchemeHttps)
{
message.Request.Uri = new UriBuilder(uri) { Scheme = "http" }.Uri;
}
}
}
@@ -1,24 +0,0 @@
<Project Sdk="Microsoft.NET.Sdk">
<PropertyGroup>
<OutputType>Exe</OutputType>
<TargetFrameworks>net10.0</TargetFrameworks>
<Nullable>enable</Nullable>
<ImplicitUsings>enable</ImplicitUsings>
<CentralPackageTransitivePinningEnabled>false</CentralPackageTransitivePinningEnabled>
<RootNamespace>SimpleAgentClient</RootNamespace>
<AssemblyName>simple-agent-client</AssemblyName>
<NoWarn>$(NoWarn);NU1605;OPENAI001</NoWarn>
</PropertyGroup>
<ItemGroup>
<PackageReference Include="Azure.AI.Projects" />
<PackageReference Include="Azure.Identity" />
<PackageReference Include="DotNetEnv" />
</ItemGroup>
<ItemGroup>
<ProjectReference Include="..\..\..\..\..\..\src\Microsoft.Agents.AI.Foundry\Microsoft.Agents.AI.Foundry.csproj" />
</ItemGroup>
</Project>

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