Merge branch 'main' into feat/litellm_sambanova_usage

.coveragerc

@@ -0,0 +1,6 @@
+[run]
+omit =
+    */tests/*
+    */llama_stack/providers/*
+    */llama_stack/templates/*
+    .venv/*

.github/workflows/install-script-ci.yml

@@ -0,0 +1,26 @@
+name: Installer CI
+
+on:
+  pull_request:
+    paths:
+      - 'install.sh'
+  push:
+    paths:
+      - 'install.sh'
+  schedule:
+    - cron: '0 2 * * *'  # every day at 02:00 UTC
+
+jobs:
+  lint:
+    runs-on: ubuntu-latest
+    steps:
+      - uses: actions/checkout@11bd71901bbe5b1630ceea73d27597364c9af683 # 4.2.2
+      - name: Run ShellCheck on install.sh
+        run: shellcheck install.sh
+  smoke-test:
+    needs: lint
+    runs-on: ubuntu-latest
+    steps:
+      - uses: actions/checkout@11bd71901bbe5b1630ceea73d27597364c9af683 # 4.2.2
+      - name: Run installer end-to-end
+        run: ./install.sh

.github/workflows/integration-auth-tests.yml

@@ -0,0 +1,136 @@
+name: Integration Auth Tests
+
+on:
+  push:
+    branches: [ main ]
+  pull_request:
+    branches: [ main ]
+    paths:
+      - 'distributions/**'
+      - 'llama_stack/**'
+      - 'tests/integration/**'
+      - 'uv.lock'
+      - 'pyproject.toml'
+      - 'requirements.txt'
+      - '.github/workflows/integration-auth-tests.yml' # This workflow
+
+concurrency:
+  group: ${{ github.workflow }}-${{ github.ref }}
+  cancel-in-progress: true
+
+jobs:
+  test-matrix:
+    runs-on: ubuntu-latest
+    strategy:
+      matrix:
+        auth-provider: [kubernetes]
+      fail-fast: false # we want to run all tests regardless of failure
+
+    steps:
+      - name: Checkout repository
+        uses: actions/checkout@v4
+
+      - name: Install uv
+        uses: astral-sh/setup-uv@v5
+        with:
+          python-version: "3.10"
+
+      - name: Set Up Environment and Install Dependencies
+        run: |
+          uv sync --extra dev --extra test
+          uv pip install -e .
+          llama stack build --template ollama --image-type venv
+
+      - name: Install minikube
+        if: ${{ matrix.auth-provider == 'kubernetes' }}
+        uses: medyagh/setup-minikube@latest
+
+      - name: Start minikube
+        if: ${{ matrix.auth-provider == 'kubernetes' }}
+        run: |
+          minikube start
+          kubectl get pods -A
+
+      - name: Configure Kube Auth
+        if: ${{ matrix.auth-provider == 'kubernetes' }}
+        run: |
+          kubectl create namespace llama-stack
+          kubectl create serviceaccount llama-stack-auth -n llama-stack
+          kubectl create rolebinding llama-stack-auth-rolebinding --clusterrole=admin --serviceaccount=llama-stack:llama-stack-auth -n llama-stack
+          kubectl create token llama-stack-auth -n llama-stack > llama-stack-auth-token
+
+      - name: Set Kubernetes Config
+        if: ${{ matrix.auth-provider == 'kubernetes' }}
+        run: |
+          echo "KUBERNETES_API_SERVER_URL=$(kubectl config view --minify -o jsonpath='{.clusters[0].cluster.server}')" >> $GITHUB_ENV
+          echo "KUBERNETES_CA_CERT_PATH=$(kubectl config view --minify -o jsonpath='{.clusters[0].cluster.certificate-authority}')" >> $GITHUB_ENV
+
+      - name: Set Kube Auth Config and run server
+        env:
+          INFERENCE_MODEL: "meta-llama/Llama-3.2-3B-Instruct"
+        if: ${{ matrix.auth-provider == 'kubernetes' }}
+        run: |
+          run_dir=$(mktemp -d)
+          cat <<'EOF' > $run_dir/run.yaml
+          version: '2'
+          image_name: kube
+          apis:
+          - agents
+          - datasetio
+          - eval
+          - inference
+          - safety
+          - scoring
+          - telemetry
+          - tool_runtime
+          - vector_io
+          providers:
+            agents:
+            - provider_id: meta-reference
+              provider_type: inline::meta-reference
+              config:
+                persistence_store:
+                  type: sqlite
+                  namespace: null
+                  db_path: ${env.SQLITE_STORE_DIR:~/.llama/distributions/ollama}/agents_store.db
+            telemetry:
+            - provider_id: meta-reference
+              provider_type: inline::meta-reference
+              config:
+                service_name: "${env.OTEL_SERVICE_NAME:\u200B}"
+                sinks: ${env.TELEMETRY_SINKS:console,sqlite}
+                sqlite_db_path: ${env.SQLITE_DB_PATH:~/.llama/distributions/ollama/trace_store.db}
+          server:
+            port: 8321
+          EOF
+          yq eval '.server.auth = {"provider_type": "${{ matrix.auth-provider }}"}' -i $run_dir/run.yaml
+          yq eval '.server.auth.config = {"api_server_url": "${{ env.KUBERNETES_API_SERVER_URL }}", "ca_cert_path": "${{ env.KUBERNETES_CA_CERT_PATH }}"}' -i $run_dir/run.yaml
+          cat $run_dir/run.yaml
+
+          source .venv/bin/activate
+          nohup uv run llama stack run $run_dir/run.yaml --image-type venv > server.log 2>&1 &
+
+      - name: Wait for Llama Stack server to be ready
+        run: |
+          echo "Waiting for Llama Stack server..."
+          for i in {1..30}; do
+            if curl -s -L -H "Authorization: Bearer $(cat llama-stack-auth-token)" http://localhost:8321/v1/health | grep -q "OK"; then
+              echo "Llama Stack server is up!"
+              if grep -q "Enabling authentication with provider: ${{ matrix.auth-provider }}" server.log; then
+                echo "Llama Stack server is configured to use ${{ matrix.auth-provider }} auth"
+                exit 0
+              else
+                echo "Llama Stack server is not configured to use ${{ matrix.auth-provider }} auth"
+                cat server.log
+                exit 1
+              fi
+            fi
+            sleep 1
+          done
+          echo "Llama Stack server failed to start"
+          cat server.log
+          exit 1
+
+      - name: Test auth
+        run: |
+          curl -s -L -H "Authorization: Bearer $(cat llama-stack-auth-token)" http://127.0.0.1:8321/v1/providers|jq

.github/workflows/integration-tests.yml

@@ -6,7 +6,6 @@ on:
   pull_request:
     branches: [ main ]
     paths:
-      - 'distributions/**'
       - 'llama_stack/**'
       - 'tests/integration/**'
       - 'uv.lock'
@@ -34,19 +33,24 @@ jobs:
         uses: actions/checkout@11bd71901bbe5b1630ceea73d27597364c9af683 # v4.2.2
 
       - name: Install uv
-        uses: astral-sh/setup-uv@0c5e2b8115b80b4c7c5ddf6ffdd634974642d182 # v5.4.1
+        uses: astral-sh/setup-uv@c7f87aa956e4c323abf06d5dec078e358f6b4d04 # v6.0.0
         with:
           python-version: "3.10"
+          activate-environment: true
 
       - name: Install and start Ollama
         run: |
           # the ollama installer also starts the ollama service
           curl -fsSL https://ollama.com/install.sh | sh
 
-      - name: Pull Ollama image
+      # Do NOT cache models - pulling the cache is actually slower than just pulling the model.
+      # It takes ~45 seconds to pull the models from the cache and unpack it, but only 30 seconds to
+      # pull them directly.
+      # Maybe this is because the cache is being pulled at the same time by all the matrix jobs?
+      - name: Pull Ollama models (instruct and embed)
         run: |
-          # TODO: cache the model. OLLAMA_MODELS defaults to ~ollama/.ollama/models.
           ollama pull llama3.2:3b-instruct-fp16
+          ollama pull all-minilm:latest
 
       - name: Set Up Environment and Install Dependencies
         run: |
@@ -106,3 +110,16 @@ jobs:
             -k "not(builtin_tool or safety_with_image or code_interpreter or test_rag)" \
             --text-model="meta-llama/Llama-3.2-3B-Instruct" \
             --embedding-model=all-MiniLM-L6-v2
+
+      - name: Write ollama logs to file
+        run: |
+          sudo journalctl -u ollama.service > ollama.log
+
+      - name: Upload all logs to artifacts
+        if: always()
+        uses: actions/upload-artifact@v4
+        with:
+          name: logs-${{ github.run_id }}-${{ github.run_attempt }}-${{ matrix.client-type }}-${{ matrix.test-type }}
+          path: |
+            *.log
+          retention-days: 1

.github/workflows/pre-commit.yml

@@ -18,7 +18,7 @@ jobs:
         uses: actions/checkout@11bd71901bbe5b1630ceea73d27597364c9af683 # v4.2.2
 
       - name: Set up Python
-        uses: actions/setup-python@8d9ed9ac5c53483de85588cdf95a591a75ab9f55 # v5.5.0
+        uses: actions/setup-python@a26af69be951a213d495a4c3e4e4022e16d87065 # v5.6.0
         with:
           python-version: '3.11'
           cache: pip
@@ -27,6 +27,8 @@ jobs:
             .pre-commit-config.yaml
 
       - uses: pre-commit/action@2c7b3805fd2a0fd8c1884dcaebf91fc102a13ecd # v3.0.1
+        env:
+          SKIP: no-commit-to-branch
 
       - name: Verify if there are any diff files after pre-commit
         run: |

.github/workflows/providers-build.yml

@@ -51,12 +51,12 @@ jobs:
         uses: actions/checkout@11bd71901bbe5b1630ceea73d27597364c9af683 # v4.2.2
 
       - name: Set up Python
-        uses: actions/setup-python@8d9ed9ac5c53483de85588cdf95a591a75ab9f55 # v5.5.0
+        uses: actions/setup-python@a26af69be951a213d495a4c3e4e4022e16d87065 # v5.6.0
         with:
           python-version: '3.10'
 
       - name: Install uv
-        uses: astral-sh/setup-uv@0c5e2b8115b80b4c7c5ddf6ffdd634974642d182 # v5.4.1
+        uses: astral-sh/setup-uv@c7f87aa956e4c323abf06d5dec078e358f6b4d04 # v6.0.0
         with:
           python-version: "3.10"
 
@@ -81,3 +81,120 @@ jobs:
         run: |
           source test/bin/activate
           uv pip list
+
+  build-single-provider:
+    runs-on: ubuntu-latest
+    steps:
+      - name: Checkout repository
+        uses: actions/checkout@11bd71901bbe5b1630ceea73d27597364c9af683 # v4.2.2
+
+      - name: Set up Python
+        uses: actions/setup-python@a26af69be951a213d495a4c3e4e4022e16d87065 # v5.6.0
+        with:
+          python-version: '3.10'
+
+      - name: Install uv
+        uses: astral-sh/setup-uv@c7f87aa956e4c323abf06d5dec078e358f6b4d04 # v6.0.0
+        with:
+          python-version: "3.10"
+
+      - name: Install LlamaStack
+        run: |
+          uv venv
+          source .venv/bin/activate
+          uv pip install -e .
+
+      - name: Build a single provider
+        run: |
+          USE_COPY_NOT_MOUNT=true LLAMA_STACK_DIR=. uv run llama stack build --image-type venv --image-name test --providers inference=remote::ollama
+
+  build-custom-container-distribution:
+    runs-on: ubuntu-latest
+    steps:
+      - name: Checkout repository
+        uses: actions/checkout@11bd71901bbe5b1630ceea73d27597364c9af683 # v4.2.2
+
+      - name: Set up Python
+        uses: actions/setup-python@a26af69be951a213d495a4c3e4e4022e16d87065 # v5.6.0
+        with:
+          python-version: '3.10'
+
+      - name: Install uv
+        uses: astral-sh/setup-uv@c7f87aa956e4c323abf06d5dec078e358f6b4d04 # v6.0.0
+        with:
+          python-version: "3.10"
+
+      - name: Install LlamaStack
+        run: |
+          uv venv
+          source .venv/bin/activate
+          uv pip install -e .
+
+      - name: Build a single provider
+        run: |
+          yq -i '.image_type = "container"' llama_stack/templates/dev/build.yaml
+          yq -i '.image_name = "test"' llama_stack/templates/dev/build.yaml
+          USE_COPY_NOT_MOUNT=true LLAMA_STACK_DIR=. uv run llama stack build --config llama_stack/templates/dev/build.yaml
+
+      - name: Inspect the container image entrypoint
+        run: |
+          IMAGE_ID=$(docker images --format "{{.Repository}}:{{.Tag}}" | head -n 1)
+          entrypoint=$(docker inspect --format '{{ .Config.Entrypoint }}' $IMAGE_ID)
+          echo "Entrypoint: $entrypoint"
+          if [ "$entrypoint" != "[python -m llama_stack.distribution.server.server --config /app/run.yaml]" ]; then
+            echo "Entrypoint is not correct"
+            exit 1
+          fi
+
+  build-ubi9-container-distribution:
+    runs-on: ubuntu-latest
+    steps:
+      - name: Checkout repository
+        uses: actions/checkout@11bd71901bbe5b1630ceea73d27597364c9af683 # v4.2.2
+
+      - name: Set up Python
+        uses: actions/setup-python@a26af69be951a213d495a4c3e4e4022e16d87065 # v5.6.0
+        with:
+          python-version: '3.10'
+
+      - name: Install uv
+        uses: astral-sh/setup-uv@0c5e2b8115b80b4c7c5ddf6ffdd634974642d182 # v5.4.1
+        with:
+          python-version: "3.10"
+
+      - name: Install LlamaStack
+        run: |
+          uv venv
+          source .venv/bin/activate
+          uv pip install -e .
+
+      - name: Pin template to UBI9 base
+        run: |
+          yq -i '
+            .image_type    = "container" |
+            .image_name    = "ubi9-test" |
+            .distribution_spec.container_image = "registry.access.redhat.com/ubi9:latest"
+          ' llama_stack/templates/dev/build.yaml
+
+      - name: Build dev container (UBI9)
+        env:
+          USE_COPY_NOT_MOUNT: "true"
+          LLAMA_STACK_DIR: "."
+        run: |
+          uv run llama stack build --config llama_stack/templates/dev/build.yaml
+
+      - name: Inspect UBI9 image
+        run: |
+          IMAGE_ID=$(docker images --format "{{.Repository}}:{{.Tag}}" | head -n 1)
+          entrypoint=$(docker inspect --format '{{ .Config.Entrypoint }}' $IMAGE_ID)
+          echo "Entrypoint: $entrypoint"
+          if [ "$entrypoint" != "[python -m llama_stack.distribution.server.server --config /app/run.yaml]" ]; then
+            echo "Entrypoint is not correct"
+            exit 1
+          fi
+
+          echo "Checking /etc/os-release in $IMAGE_ID"
+          docker run --rm --entrypoint sh "$IMAGE_ID" -c \
+              'source /etc/os-release && echo "$ID"' \
+              | grep -qE '^(rhel|ubi)$' \
+              || { echo "Base image is not UBI 9!"; exit 1; }

.github/workflows/test-external-providers.yml

@@ -5,89 +5,74 @@ on:
     branches: [ main ]
   pull_request:
     branches: [ main ]
+    paths:
+      - 'llama_stack/**'
+      - 'tests/integration/**'
+      - 'uv.lock'
+      - 'pyproject.toml'
+      - 'requirements.txt'
+      - '.github/workflows/test-external-providers.yml' # This workflow
 
 jobs:
   test-external-providers:
     runs-on: ubuntu-latest
+    strategy:
+      matrix:
+        image-type: [venv]
+        # We don't do container yet, it's tricky to install a package from the host into the
+        # container and point 'uv pip install' to the correct path...
     steps:
       - name: Checkout repository
         uses: actions/checkout@v4
 
       - name: Install uv
-        uses: astral-sh/setup-uv@v5
+        uses: astral-sh/setup-uv@v6
         with:
           python-version: "3.10"
 
-      - name: Install Ollama
-        run: |
-          curl -fsSL https://ollama.com/install.sh | sh
-
-      - name: Pull Ollama image
-        run: |
-          ollama pull llama3.2:3b-instruct-fp16
-
-      - name: Start Ollama in background
-        run: |
-          nohup ollama run llama3.2:3b-instruct-fp16 --keepalive=30m > ollama.log 2>&1 &
-
       - name: Set Up Environment and Install Dependencies
         run: |
           uv sync --extra dev --extra test
           uv pip install -e .
 
-      - name: Install Ollama custom provider
+      - name: Apply image type to config file
+        run: |
+          yq -i '.image_type = "${{ matrix.image-type }}"' tests/external-provider/llama-stack-provider-ollama/custom-distro.yaml
+          cat tests/external-provider/llama-stack-provider-ollama/custom-distro.yaml
+
+      - name: Setup directory for Ollama custom provider
         run: |
           mkdir -p tests/external-provider/llama-stack-provider-ollama/src/
           cp -a llama_stack/providers/remote/inference/ollama/ tests/external-provider/llama-stack-provider-ollama/src/llama_stack_provider_ollama
-          uv pip install tests/external-provider/llama-stack-provider-ollama
 
       - name: Create provider configuration
         run: |
           mkdir -p /tmp/providers.d/remote/inference
           cp tests/external-provider/llama-stack-provider-ollama/custom_ollama.yaml /tmp/providers.d/remote/inference/custom_ollama.yaml
 
-      - name: Wait for Ollama to start
+      - name: Build distro from config file
         run: |
-          echo "Waiting for Ollama..."
+          USE_COPY_NOT_MOUNT=true LLAMA_STACK_DIR=. uv run llama stack build --config tests/external-provider/llama-stack-provider-ollama/custom-distro.yaml
-          for i in {1..30}; do
-            if curl -s http://localhost:11434 | grep -q "Ollama is running"; then
-              echo "Ollama is running!"
-              exit 0
-            fi
-            sleep 1
-          done
-          echo "Ollama failed to start"
-          ollama ps
-          ollama.log
-          exit 1
 
       - name: Start Llama Stack server in background
+        if: ${{ matrix.image-type }} == 'venv'
         env:
           INFERENCE_MODEL: "meta-llama/Llama-3.2-3B-Instruct"
         run: |
-          source .venv/bin/activate
+          source ci-test/bin/activate
-          nohup uv run llama stack run tests/external-provider/llama-stack-provider-ollama/run.yaml --image-type venv > server.log 2>&1 &
+          uv run pip list
+          nohup uv run --active llama stack run tests/external-provider/llama-stack-provider-ollama/run.yaml --image-type ${{ matrix.image-type }} > server.log 2>&1 &
 
       - name: Wait for Llama Stack server to be ready
         run: |
-          echo "Waiting for Llama Stack server..."
           for i in {1..30}; do
-            if curl -s http://localhost:8321/v1/health | grep -q "OK"; then
+            if ! grep -q "remote::custom_ollama from /tmp/providers.d/remote/inference/custom_ollama.yaml" server.log; then
-              echo "Llama Stack server is up!"
+              echo "Waiting for Llama Stack server to load the provider..."
-              if grep -q "remote::custom_ollama from /tmp/providers.d/remote/inference/custom_ollama.yaml" server.log; then
+              sleep 1
-                echo "Llama Stack server is using custom Ollama provider"
+            else
-                exit 0
+              echo "Provider loaded"
-              else
+              exit 0
-                echo "Llama Stack server is not using custom Ollama provider"
-                exit 1
-              fi
             fi
-            sleep 1
           done
-          echo "Llama Stack server failed to start"
+          echo "Provider failed to load"
-          cat server.log
           exit 1
-
-      - name: run inference tests
-        run: |
-          uv run pytest -v tests/integration/inference/test_text_inference.py --stack-config="http://localhost:8321" --text-model="meta-llama/Llama-3.2-3B-Instruct" --embedding-model=all-MiniLM-L6-v2

.github/workflows/unit-tests.yml

@@ -6,7 +6,6 @@ on:
   pull_request:
     branches: [ main ]
     paths:
-      - 'distributions/**'
       - 'llama_stack/**'
       - 'tests/unit/**'
       - 'uv.lock'
@@ -34,11 +33,11 @@ jobs:
       - uses: actions/checkout@11bd71901bbe5b1630ceea73d27597364c9af683 # v4.2.2
 
       - name: Set up Python ${{ matrix.python }}
-        uses: actions/setup-python@8d9ed9ac5c53483de85588cdf95a591a75ab9f55 # v5.5.0
+        uses: actions/setup-python@a26af69be951a213d495a4c3e4e4022e16d87065 # v5.6.0
         with:
           python-version: ${{ matrix.python }}
 
-      - uses: astral-sh/setup-uv@0c5e2b8115b80b4c7c5ddf6ffdd634974642d182 # v5.4.1
+      - uses: astral-sh/setup-uv@c7f87aa956e4c323abf06d5dec078e358f6b4d04 # v6.0.0
         with:
           python-version: ${{ matrix.python }}
           enable-cache: false

.github/workflows/update-readthedocs.yml

@@ -36,12 +36,12 @@ jobs:
         uses: actions/checkout@11bd71901bbe5b1630ceea73d27597364c9af683 # v4.2.2
 
       - name: Set up Python
-        uses: actions/setup-python@8d9ed9ac5c53483de85588cdf95a591a75ab9f55 # v5.5.0
+        uses: actions/setup-python@a26af69be951a213d495a4c3e4e4022e16d87065 # v5.6.0
         with:
           python-version: '3.11'
 
       - name: Install the latest version of uv
-        uses: astral-sh/setup-uv@0c5e2b8115b80b4c7c5ddf6ffdd634974642d182 # v5.4.1
+        uses: astral-sh/setup-uv@c7f87aa956e4c323abf06d5dec078e358f6b4d04 # v6.0.0
 
       - name: Sync with uv
         run: uv sync --extra docs

.pre-commit-config.yaml

@@ -15,6 +15,18 @@ repos:
         args: ['--maxkb=1000']
     -   id: end-of-file-fixer
         exclude: '^(.*\.svg)$'
+    -   id: no-commit-to-branch
+    -   id: check-yaml
+        args: ["--unsafe"]
+    -   id: detect-private-key
+    -   id: requirements-txt-fixer
+    -   id: mixed-line-ending
+        args: [--fix=lf] # Forces to replace line ending by LF (line feed)
+    -   id: check-executables-have-shebangs
+    -   id: check-json
+    -   id: check-shebang-scripts-are-executable
+    -   id: check-symlinks
+    -   id: check-toml
 
 -   repo: https://github.com/Lucas-C/pre-commit-hooks
     rev: v1.5.4

CHANGELOG.md

@@ -1,5 +1,33 @@
 # Changelog
 
+# v0.2.3
+Published on: 2025-04-25T22:46:21Z
+
+## Highlights
+
+* OpenAI compatible inference endpoints and client-SDK support. `client.chat.completions.create()` now works.
+* significant improvements and functionality added to the nVIDIA distribution
+* many improvements to the test verification suite.
+* new inference providers: Ramalama, IBM WatsonX
+* many improvements to the Playground UI
+
+
+---
+
+# v0.2.2
+Published on: 2025-04-13T01:19:49Z
+
+## Main changes
+
+- Bring Your Own Provider (@leseb) - use out-of-tree provider code to execute the distribution server
+- OpenAI compatible inference API in progress (@bbrowning)
+- Provider verifications (@ehhuang)
+- Many updates and fixes to playground
+- Several llama4 related fixes
+
+
+---
+
 # v0.2.1
 Published on: 2025-04-05T23:13:00Z
 
@@ -10,10 +38,10 @@ Published on: 2025-04-05T23:13:00Z
 # v0.2.0
 Published on: 2025-04-05T19:04:29Z
 
-## Llama 4 Support
+## Llama 4 Support
-
+
-Checkout more at https://www.llama.com
+Checkout more at https://www.llama.com
-
+
 
 
 ---
@@ -21,58 +49,58 @@ Checkout more at https://www.llama.com
 # v0.1.9
 Published on: 2025-03-29T00:52:23Z
 
-### Build and Test Agents
+### Build and Test Agents
-* Agents: Entire document context with attachments
+* Agents: Entire document context with attachments
-* RAG: Documentation with sqlite-vec faiss comparison
+* RAG: Documentation with sqlite-vec faiss comparison
-* Getting started: Fixes to getting started notebook.
+* Getting started: Fixes to getting started notebook.
-
+
-### Agent Evals and Model Customization
+### Agent Evals and Model Customization
-* (**New**) Post-training: Add nemo customizer
+* (**New**) Post-training: Add nemo customizer
-
+
-### Better Engineering
+### Better Engineering
-* Moved sqlite-vec to non-blocking calls
+* Moved sqlite-vec to non-blocking calls
-* Don't return a payload on file delete
+* Don't return a payload on file delete
-
+
-
+
 
 ---
 
 # v0.1.8
 Published on: 2025-03-24T01:28:50Z
 
-# v0.1.8 Release Notes
+# v0.1.8 Release Notes
-
+
-### Build and Test Agents
+### Build and Test Agents
-* Safety: Integrated NVIDIA as a safety provider.
+* Safety: Integrated NVIDIA as a safety provider.
-* VectorDB: Added Qdrant as an inline provider.
+* VectorDB: Added Qdrant as an inline provider.
-* Agents: Added support for multiple tool groups in agents.
+* Agents: Added support for multiple tool groups in agents.
-* Agents: Simplified imports for Agents in client package
+* Agents: Simplified imports for Agents in client package
-
+
-
+
-### Agent Evals and Model Customization
+### Agent Evals and Model Customization
-* Introduced DocVQA and IfEval benchmarks.
+* Introduced DocVQA and IfEval benchmarks.
-
+
-### Deploying and Monitoring Agents
+### Deploying and Monitoring Agents
-* Introduced a Containerfile and image workflow for the Playground.
+* Introduced a Containerfile and image workflow for the Playground.
-* Implemented support for Bearer (API Key) authentication.
+* Implemented support for Bearer (API Key) authentication.
-* Added attribute-based access control for resources.
+* Added attribute-based access control for resources.
-* Fixes on docker deployments: use --pull always and standardized the default port to 8321
+* Fixes on docker deployments: use --pull always and standardized the default port to 8321
-* Deprecated: /v1/inspect/providers use /v1/providers/ instead
+* Deprecated: /v1/inspect/providers use /v1/providers/ instead
-
+
-### Better Engineering
+### Better Engineering
-* Consolidated scripts under the ./scripts directory.
+* Consolidated scripts under the ./scripts directory.
-* Addressed mypy violations in various modules.
+* Addressed mypy violations in various modules.
-* Added Dependabot scans for Python dependencies.
+* Added Dependabot scans for Python dependencies.
-* Implemented a scheduled workflow to update the changelog automatically.
+* Implemented a scheduled workflow to update the changelog automatically.
-* Enforced concurrency to reduce CI loads.
+* Enforced concurrency to reduce CI loads.
-
+
-
+
-### New Contributors
+### New Contributors
-* @cmodi-meta made their first contribution in https://github.com/meta-llama/llama-stack/pull/1650
+* @cmodi-meta made their first contribution in https://github.com/meta-llama/llama-stack/pull/1650
-* @jeffmaury made their first contribution in https://github.com/meta-llama/llama-stack/pull/1671
+* @jeffmaury made their first contribution in https://github.com/meta-llama/llama-stack/pull/1671
-* @derekhiggins made their first contribution in https://github.com/meta-llama/llama-stack/pull/1698
+* @derekhiggins made their first contribution in https://github.com/meta-llama/llama-stack/pull/1698
-* @Bobbins228 made their first contribution in https://github.com/meta-llama/llama-stack/pull/1745
+* @Bobbins228 made their first contribution in https://github.com/meta-llama/llama-stack/pull/1745
-
+
 **Full Changelog**: https://github.com/meta-llama/llama-stack/compare/v0.1.7...v0.1.8
 
 ---
@@ -80,73 +108,73 @@ Published on: 2025-03-24T01:28:50Z
 # v0.1.7
 Published on: 2025-03-14T22:30:51Z
 
-## 0.1.7 Release Notes
+## 0.1.7 Release Notes
-
+
-###  Build and Test Agents
+###  Build and Test Agents
-* Inference: ImageType is now refactored to LlamaStackImageType
+* Inference: ImageType is now refactored to LlamaStackImageType
-* Inference: Added tests to measure TTFT
+* Inference: Added tests to measure TTFT
-* Inference: Bring back usage metrics
+* Inference: Bring back usage metrics
-* Agents: Added endpoint for get agent, list agents and list sessions
+* Agents: Added endpoint for get agent, list agents and list sessions
-* Agents: Automated conversion of type hints in client tool for lite llm format
+* Agents: Automated conversion of type hints in client tool for lite llm format
-* Agents: Deprecated ToolResponseMessage in agent.resume API
+* Agents: Deprecated ToolResponseMessage in agent.resume API
-* Added Provider API for listing and inspecting provider info
+* Added Provider API for listing and inspecting provider info
-
+
-### Agent Evals and Model Customization
+### Agent Evals and Model Customization
-* Eval: Added new eval benchmarks Math 500 and BFCL v3
+* Eval: Added new eval benchmarks Math 500 and BFCL v3
-* Deploy and Monitoring of Agents
+* Deploy and Monitoring of Agents
-* Telemetry: Fix tracing to work across coroutines
+* Telemetry: Fix tracing to work across coroutines
-
+
-###  Better Engineering
+###  Better Engineering
-* Display code coverage for unit tests
+* Display code coverage for unit tests
-* Updated call sites (inference, tool calls, agents) to move to async non blocking calls
+* Updated call sites (inference, tool calls, agents) to move to async non blocking calls
-* Unit tests also run on Python 3.11, 3.12, and 3.13
+* Unit tests also run on Python 3.11, 3.12, and 3.13
-* Added ollama inference to Integration tests CI
+* Added ollama inference to Integration tests CI
-* Improved documentation across examples, testing, CLI, updated providers table )
+* Improved documentation across examples, testing, CLI, updated providers table )
-
+
-
+
-
+
 
 ---
 
 # v0.1.6
 Published on: 2025-03-08T04:35:08Z
 
-## 0.1.6 Release Notes
+## 0.1.6 Release Notes
-
+
-### Build and Test Agents
+### Build and Test Agents
-* Inference: Fixed support for inline vllm provider
+* Inference: Fixed support for inline vllm provider
-* (**New**) Agent: Build & Monitor Agent Workflows with Llama Stack + Anthropic's Best Practice [Notebook](https://github.com/meta-llama/llama-stack/blob/main/docs/notebooks/Llama_Stack_Agent_Workflows.ipynb)
+* (**New**) Agent: Build & Monitor Agent Workflows with Llama Stack + Anthropic's Best Practice [Notebook](https://github.com/meta-llama/llama-stack/blob/main/docs/notebooks/Llama_Stack_Agent_Workflows.ipynb)
-* (**New**) Agent: Revamped agent [documentation](https://llama-stack.readthedocs.io/en/latest/building_applications/agent.html) with more details and examples
+* (**New**) Agent: Revamped agent [documentation](https://llama-stack.readthedocs.io/en/latest/building_applications/agent.html) with more details and examples
-* Agent: Unify tools and Python SDK Agents API
+* Agent: Unify tools and Python SDK Agents API
-* Agent: AsyncAgent Python SDK wrapper supporting async client tool calls
+* Agent: AsyncAgent Python SDK wrapper supporting async client tool calls
-* Agent: Support python functions without @client_tool decorator as client tools
+* Agent: Support python functions without @client_tool decorator as client tools
-* Agent: deprecation for allow_resume_turn flag, and remove need to specify tool_prompt_format
+* Agent: deprecation for allow_resume_turn flag, and remove need to specify tool_prompt_format
-* VectorIO: MilvusDB support added
+* VectorIO: MilvusDB support added
-
+
-### Agent Evals and Model Customization
+### Agent Evals and Model Customization
-* (**New**) Agent: Llama Stack RAG Lifecycle [Notebook](https://github.com/meta-llama/llama-stack/blob/main/docs/notebooks/Llama_Stack_RAG_Lifecycle.ipynb)
+* (**New**) Agent: Llama Stack RAG Lifecycle [Notebook](https://github.com/meta-llama/llama-stack/blob/main/docs/notebooks/Llama_Stack_RAG_Lifecycle.ipynb)
-* Eval: Documentation for eval, scoring, adding new benchmarks
+* Eval: Documentation for eval, scoring, adding new benchmarks
-* Eval: Distribution template to run benchmarks on llama & non-llama models
+* Eval: Distribution template to run benchmarks on llama & non-llama models
-* Eval: Ability to register new custom LLM-as-judge scoring functions
+* Eval: Ability to register new custom LLM-as-judge scoring functions
-* (**New**) Looking for contributors for open benchmarks. See [documentation](https://llama-stack.readthedocs.io/en/latest/references/evals_reference/index.html#open-benchmark-contributing-guide) for details.
+* (**New**) Looking for contributors for open benchmarks. See [documentation](https://llama-stack.readthedocs.io/en/latest/references/evals_reference/index.html#open-benchmark-contributing-guide) for details.
-
+
-### Deploy and Monitoring of Agents
+### Deploy and Monitoring of Agents
-* Better support for different log levels across all components for better monitoring
+* Better support for different log levels across all components for better monitoring
-
+
-### Better Engineering
+### Better Engineering
-* Enhance OpenAPI spec to include Error types across all APIs
+* Enhance OpenAPI spec to include Error types across all APIs
-* Moved all tests to /tests and created unit tests to run on each PR
+* Moved all tests to /tests and created unit tests to run on each PR
-* Removed all dependencies on llama-models repo
+* Removed all dependencies on llama-models repo
-
+
 
 ---
 
 # v0.1.5.1
 Published on: 2025-02-28T22:37:44Z
 
-## 0.1.5.1 Release Notes
+## 0.1.5.1 Release Notes
-* Fixes for security risk in https://github.com/meta-llama/llama-stack/pull/1327 and https://github.com/meta-llama/llama-stack/pull/1328
+* Fixes for security risk in https://github.com/meta-llama/llama-stack/pull/1327 and https://github.com/meta-llama/llama-stack/pull/1328
-
+
 **Full Changelog**: https://github.com/meta-llama/llama-stack/compare/v0.1.5...v0.1.5.1
 
 ---
@@ -154,176 +182,176 @@ Published on: 2025-02-28T22:37:44Z
 # v0.1.5
 Published on: 2025-02-28T18:14:01Z
 
-## 0.1.5 Release Notes
+## 0.1.5 Release Notes
-###  Build Agents
+###  Build Agents
-* Inference: Support more non-llama models (openai, anthropic, gemini)
+* Inference: Support more non-llama models (openai, anthropic, gemini)
-* Inference: Can use the provider's model name in addition to the HF alias
+* Inference: Can use the provider's model name in addition to the HF alias
-* Inference: Fixed issues with calling tools that weren't specified in the prompt
+* Inference: Fixed issues with calling tools that weren't specified in the prompt
-* RAG: Improved system prompt for RAG and no more need for hard-coded rag-tool calling
+* RAG: Improved system prompt for RAG and no more need for hard-coded rag-tool calling
-* Embeddings: Added support for Nemo retriever embedding models
+* Embeddings: Added support for Nemo retriever embedding models
-* Tools: Added support for MCP tools in Ollama Distribution
+* Tools: Added support for MCP tools in Ollama Distribution
-* Distributions: Added new Groq distribution
+* Distributions: Added new Groq distribution
-
+
-### Customize Models
+### Customize Models
-* Save post-trained checkpoint in SafeTensor format to allow Ollama inference provider to use the post-trained model
+* Save post-trained checkpoint in SafeTensor format to allow Ollama inference provider to use the post-trained model
-
+
-### Monitor agents
+### Monitor agents
-* More comprehensive logging of agent steps including client tools
+* More comprehensive logging of agent steps including client tools
-* Telemetry inputs/outputs are now structured and queryable
+* Telemetry inputs/outputs are now structured and queryable
-* Ability to retrieve agents session, turn, step by ids
+* Ability to retrieve agents session, turn, step by ids
-
+
-### Better Engineering
+### Better Engineering
-* Moved executorch Swift code out of this repo into the llama-stack-client-swift repo, similar to kotlin
+* Moved executorch Swift code out of this repo into the llama-stack-client-swift repo, similar to kotlin
-* Move most logging to use logger instead of prints
+* Move most logging to use logger instead of prints
-* Completed text /chat-completion and /completion tests
+* Completed text /chat-completion and /completion tests
-
+
 
 ---
 
 # v0.1.4
 Published on: 2025-02-25T00:02:43Z
 
-## v0.1.4 Release Notes
+## v0.1.4 Release Notes
-Here are the key changes coming as part of this release:
+Here are the key changes coming as part of this release:
-
+
-### Build and Test Agents
+### Build and Test Agents
-* Inference: Added support for non-llama models
+* Inference: Added support for non-llama models
-* Inference: Added option to list all downloaded models and remove models
+* Inference: Added option to list all downloaded models and remove models
-* Agent: Introduce new api agents.resume_turn to include client side tool execution in the same turn
+* Agent: Introduce new api agents.resume_turn to include client side tool execution in the same turn
-* Agent: AgentConfig introduces new variable “tool_config” that allows for better tool configuration and system prompt overrides
+* Agent: AgentConfig introduces new variable “tool_config” that allows for better tool configuration and system prompt overrides
-* Agent: Added logging for agent step start and completion times
+* Agent: Added logging for agent step start and completion times
-* Agent: Added support for logging for tool execution metadata
+* Agent: Added support for logging for tool execution metadata
-* Embedding: Updated /inference/embeddings to support asymmetric models, truncation and variable sized outputs
+* Embedding: Updated /inference/embeddings to support asymmetric models, truncation and variable sized outputs
-* Embedding: Updated embedding models for Ollama, Together, and Fireworks with available defaults
+* Embedding: Updated embedding models for Ollama, Together, and Fireworks with available defaults
-* VectorIO: Improved performance of sqlite-vec using chunked writes
+* VectorIO: Improved performance of sqlite-vec using chunked writes
-### Agent Evals and Model Customization
+### Agent Evals and Model Customization
-* Deprecated api /eval-tasks. Use /eval/benchmark  instead
+* Deprecated api /eval-tasks. Use /eval/benchmark  instead
-* Added CPU training support for TorchTune
+* Added CPU training support for TorchTune
-### Deploy and Monitoring of Agents
+### Deploy and Monitoring of Agents
-* Consistent view of client and server tool calls in telemetry
+* Consistent view of client and server tool calls in telemetry
-### Better Engineering
+### Better Engineering
-* Made tests more data-driven for consistent evaluation
+* Made tests more data-driven for consistent evaluation
-* Fixed documentation links and improved API reference generation
+* Fixed documentation links and improved API reference generation
-* Various small fixes for build scripts and system reliability
+* Various small fixes for build scripts and system reliability
-
+
-
+
 
 ---
 
 # v0.1.3
 Published on: 2025-02-14T20:24:32Z
 
-## v0.1.3 Release
+## v0.1.3 Release
-
+
-Here are some key changes that are coming as part of this release.
+Here are some key changes that are coming as part of this release.
-
+
-### Build and Test Agents
+### Build and Test Agents
-Streamlined the initial development experience
+Streamlined the initial development experience
-- Added support for  llama stack run --image-type venv
+- Added support for  llama stack run --image-type venv
-- Enhanced vector store options with new sqlite-vec provider and improved Qdrant integration
+- Enhanced vector store options with new sqlite-vec provider and improved Qdrant integration
-- vLLM improvements for tool calling and logprobs
+- vLLM improvements for tool calling and logprobs
-- Better handling of sporadic code_interpreter tool calls
+- Better handling of sporadic code_interpreter tool calls
-
+
-### Agent Evals
+### Agent Evals
-Better benchmarking and Agent performance assessment
+Better benchmarking and Agent performance assessment
-- Renamed eval API /eval-task to /benchmarks
+- Renamed eval API /eval-task to /benchmarks
-- Improved documentation and notebooks for RAG and evals
+- Improved documentation and notebooks for RAG and evals
-
+
-### Deploy and Monitoring of Agents
+### Deploy and Monitoring of Agents
-Improved production readiness
+Improved production readiness
-- Added usage metrics collection for chat completions
+- Added usage metrics collection for chat completions
-- CLI improvements for provider information
+- CLI improvements for provider information
-- Improved error handling and system reliability
+- Improved error handling and system reliability
-- Better model endpoint handling and accessibility
+- Better model endpoint handling and accessibility
-- Improved signal handling on distro server
+- Improved signal handling on distro server
-
+
-### Better Engineering
+### Better Engineering
-Infrastructure and code quality improvements
+Infrastructure and code quality improvements
-- Faster text-based chat completion tests
+- Faster text-based chat completion tests
-- Improved testing for non-streaming agent apis
+- Improved testing for non-streaming agent apis
-- Standardized import formatting with ruff linter
+- Standardized import formatting with ruff linter
-- Added conventional commits standard
+- Added conventional commits standard
-- Fixed documentation parsing issues
+- Fixed documentation parsing issues
-
+
 
 ---
 
 # v0.1.2
 Published on: 2025-02-07T22:06:49Z
 
-# TL;DR
+# TL;DR
-- Several stabilizations to development flows after the switch to `uv`
+- Several stabilizations to development flows after the switch to `uv`
-- Migrated CI workflows to new OSS repo - [llama-stack-ops](https://github.com/meta-llama/llama-stack-ops)
+- Migrated CI workflows to new OSS repo - [llama-stack-ops](https://github.com/meta-llama/llama-stack-ops)
-- Added automated rebuilds for ReadTheDocs
+- Added automated rebuilds for ReadTheDocs
-- Llama Stack server supports HTTPS
+- Llama Stack server supports HTTPS
-- Added system prompt overrides support
+- Added system prompt overrides support
-- Several bug fixes and improvements to documentation (check out Kubernetes deployment guide by @terrytangyuan )
+- Several bug fixes and improvements to documentation (check out Kubernetes deployment guide by @terrytangyuan )
-
+
 
 ---
 
 # v0.1.1
 Published on: 2025-02-02T02:29:24Z
 
-A bunch of small / big improvements everywhere including support for Windows, switching to `uv` and many provider improvements.
+A bunch of small / big improvements everywhere including support for Windows, switching to `uv` and many provider improvements.
-
+
 
 ---
 
 # v0.1.0
 Published on: 2025-01-24T17:47:47Z
 
-We are excited to announce a stable API release of Llama Stack, which enables developers to build RAG applications and Agents using tools and safety shields, monitor and those agents with telemetry, and evaluate the agent with scoring functions.
+We are excited to announce a stable API release of Llama Stack, which enables developers to build RAG applications and Agents using tools and safety shields, monitor and those agents with telemetry, and evaluate the agent with scoring functions.
-
+
-## Context
+## Context
-GenAI application developers need more than just an LLM - they need to integrate tools, connect with their data sources, establish guardrails, and ground the LLM responses effectively. Currently, developers must piece together various tools and APIs, complicating the development lifecycle and increasing costs. The result is that developers are spending more time on these integrations rather than focusing on the application logic itself. The bespoke coupling of components also makes it challenging to adopt state-of-the-art solutions in the rapidly evolving GenAI space. This is particularly difficult for open models like Llama, as best practices are not widely established in the open.
+GenAI application developers need more than just an LLM - they need to integrate tools, connect with their data sources, establish guardrails, and ground the LLM responses effectively. Currently, developers must piece together various tools and APIs, complicating the development lifecycle and increasing costs. The result is that developers are spending more time on these integrations rather than focusing on the application logic itself. The bespoke coupling of components also makes it challenging to adopt state-of-the-art solutions in the rapidly evolving GenAI space. This is particularly difficult for open models like Llama, as best practices are not widely established in the open.
-
+
-Llama Stack was created to provide developers with a comprehensive and coherent interface that simplifies AI application development and codifies best practices across the Llama ecosystem. Since our launch in September 2024, we have seen a huge uptick in interest in Llama Stack APIs by both AI developers and from partners building AI services with Llama models. Partners like Nvidia, Fireworks, and Ollama have collaborated with us to develop implementations across various APIs, including inference, memory, and safety.
+Llama Stack was created to provide developers with a comprehensive and coherent interface that simplifies AI application development and codifies best practices across the Llama ecosystem. Since our launch in September 2024, we have seen a huge uptick in interest in Llama Stack APIs by both AI developers and from partners building AI services with Llama models. Partners like Nvidia, Fireworks, and Ollama have collaborated with us to develop implementations across various APIs, including inference, memory, and safety.
-
+
-With Llama Stack, you can easily build a RAG agent which can also search the web, do complex math, and custom tool calling. You can use telemetry to inspect those traces, and convert telemetry into evals datasets. And with Llama Stack’s plugin architecture and prepackage distributions, you choose to run your agent anywhere - in the cloud with our partners, deploy your own environment using virtualenv, conda, or Docker, operate locally with Ollama, or even run on mobile devices with our SDKs. Llama Stack offers unprecedented flexibility while also simplifying the developer experience.
+With Llama Stack, you can easily build a RAG agent which can also search the web, do complex math, and custom tool calling. You can use telemetry to inspect those traces, and convert telemetry into evals datasets. And with Llama Stack’s plugin architecture and prepackage distributions, you choose to run your agent anywhere - in the cloud with our partners, deploy your own environment using virtualenv, conda, or Docker, operate locally with Ollama, or even run on mobile devices with our SDKs. Llama Stack offers unprecedented flexibility while also simplifying the developer experience.
-
+
-## Release
+## Release
-After iterating on the APIs for the last 3 months, today we’re launching a stable release (V1) of the Llama Stack APIs and the corresponding llama-stack server and client packages(v0.1.0). We now have automated tests for providers. These tests make sure that all provider implementations are verified. Developers can now easily and reliably select distributions or providers based on their specific requirements.
+After iterating on the APIs for the last 3 months, today we’re launching a stable release (V1) of the Llama Stack APIs and the corresponding llama-stack server and client packages(v0.1.0). We now have automated tests for providers. These tests make sure that all provider implementations are verified. Developers can now easily and reliably select distributions or providers based on their specific requirements.
-
+
-There are example standalone apps in llama-stack-apps.
+There are example standalone apps in llama-stack-apps.
-
+
-
+
-## Key Features of this release
+## Key Features of this release
-
+
-- **Unified API Layer**
+- **Unified API Layer**
-  - Inference: Run LLM models
+  - Inference: Run LLM models
-  - RAG: Store and retrieve knowledge for RAG
+  - RAG: Store and retrieve knowledge for RAG
-  - Agents: Build multi-step agentic workflows
+  - Agents: Build multi-step agentic workflows
-  - Tools: Register tools that can be called by the agent
+  - Tools: Register tools that can be called by the agent
-  - Safety: Apply content filtering and safety policies
+  - Safety: Apply content filtering and safety policies
-  - Evaluation: Test model and agent quality
+  - Evaluation: Test model and agent quality
-  - Telemetry: Collect and analyze usage data and complex agentic traces
+  - Telemetry: Collect and analyze usage data and complex agentic traces
-  - Post Training ( Coming Soon ): Fine tune models for specific use cases
+  - Post Training ( Coming Soon ): Fine tune models for specific use cases
-
+
-- **Rich Provider Ecosystem**
+- **Rich Provider Ecosystem**
-  - Local Development: Meta's Reference, Ollama
+  - Local Development: Meta's Reference, Ollama
-  - Cloud: Fireworks, Together, Nvidia, AWS Bedrock, Groq, Cerebras
+  - Cloud: Fireworks, Together, Nvidia, AWS Bedrock, Groq, Cerebras
-  - On-premises: Nvidia NIM, vLLM, TGI, Dell-TGI
+  - On-premises: Nvidia NIM, vLLM, TGI, Dell-TGI
-  - On-device: iOS and Android support
+  - On-device: iOS and Android support
-
+
-- **Built for Production**
+- **Built for Production**
-  - Pre-packaged distributions for common deployment scenarios
+  - Pre-packaged distributions for common deployment scenarios
-  - Backwards compatibility across model versions
+  - Backwards compatibility across model versions
-  - Comprehensive evaluation capabilities
+  - Comprehensive evaluation capabilities
-  - Full observability and monitoring
+  - Full observability and monitoring
-
+
-- **Multiple developer interfaces**
+- **Multiple developer interfaces**
-  - CLI: Command line interface
+  - CLI: Command line interface
-  - Python SDK
+  - Python SDK
-  - Swift iOS SDK
+  - Swift iOS SDK
-  - Kotlin Android SDK
+  - Kotlin Android SDK
-
+
-- **Sample llama stack applications**
+- **Sample llama stack applications**
-  - Python
+  - Python
-  - iOS
+  - iOS
-  - Android
+  - Android
-
+
-
+
 
 ---
 
@@ -337,8 +365,8 @@ Published on: 2025-01-22T22:24:01Z
 # v0.0.63
 Published on: 2024-12-18T07:17:43Z
 
-A small but important bug-fix release to update the URL datatype for the client-SDKs. The issue affected multimodal agentic turns especially.
+A small but important bug-fix release to update the URL datatype for the client-SDKs. The issue affected multimodal agentic turns especially.
-
+
 **Full Changelog**: https://github.com/meta-llama/llama-stack/compare/v0.0.62...v0.0.63
 
 ---
@@ -374,39 +402,39 @@ Published on: 2024-11-22T00:36:09Z
 # v0.0.53
 Published on: 2024-11-20T22:18:00Z
 
-🚀  Initial Release Notes for Llama Stack!
+🚀  Initial Release Notes for Llama Stack!
-
+
-### Added
+### Added
-- Resource-oriented design for models, shields, memory banks, datasets and eval tasks
+- Resource-oriented design for models, shields, memory banks, datasets and eval tasks
-- Persistence for registered objects with distribution
+- Persistence for registered objects with distribution
-- Ability to persist memory banks created for FAISS
+- Ability to persist memory banks created for FAISS
-- PostgreSQL KVStore implementation
+- PostgreSQL KVStore implementation
-- Environment variable placeholder support in run.yaml files
+- Environment variable placeholder support in run.yaml files
-- Comprehensive Zero-to-Hero notebooks and quickstart guides
+- Comprehensive Zero-to-Hero notebooks and quickstart guides
-- Support for quantized models in Ollama
+- Support for quantized models in Ollama
-- Vision models support for Together, Fireworks, Meta-Reference, and Ollama, and vLLM
+- Vision models support for Together, Fireworks, Meta-Reference, and Ollama, and vLLM
-- Bedrock distribution with safety shields support
+- Bedrock distribution with safety shields support
-- Evals API with task registration and scoring functions
+- Evals API with task registration and scoring functions
-- MMLU and SimpleQA benchmark scoring functions
+- MMLU and SimpleQA benchmark scoring functions
-- Huggingface dataset provider integration for benchmarks
+- Huggingface dataset provider integration for benchmarks
-- Support for custom dataset registration from local paths
+- Support for custom dataset registration from local paths
-- Benchmark evaluation CLI tools with visualization tables
+- Benchmark evaluation CLI tools with visualization tables
-- RAG evaluation scoring functions and metrics
+- RAG evaluation scoring functions and metrics
-- Local persistence for datasets and eval tasks
+- Local persistence for datasets and eval tasks
-
+
-### Changed
+### Changed
-- Split safety into distinct providers (llama-guard, prompt-guard, code-scanner)
+- Split safety into distinct providers (llama-guard, prompt-guard, code-scanner)
-- Changed provider naming convention (`impls` → `inline`, `adapters` → `remote`)
+- Changed provider naming convention (`impls` → `inline`, `adapters` → `remote`)
-- Updated API signatures for dataset and eval task registration
+- Updated API signatures for dataset and eval task registration
-- Restructured folder organization for providers
+- Restructured folder organization for providers
-- Enhanced Docker build configuration
+- Enhanced Docker build configuration
-- Added version prefixing for REST API routes
+- Added version prefixing for REST API routes
-- Enhanced evaluation task registration workflow
+- Enhanced evaluation task registration workflow
-- Improved benchmark evaluation output formatting
+- Improved benchmark evaluation output formatting
-- Restructured evals folder organization for better modularity
+- Restructured evals folder organization for better modularity
-
+
-### Removed
+### Removed
-- `llama stack configure` command
+- `llama stack configure` command
-
+
 
 ---

CONTRIBUTING.md

@@ -141,11 +141,18 @@ uv sync
 
 ## Coding Style
 
-* Comments should provide meaningful insights into the code. Avoid filler comments that simply describe the next step, as they create unnecessary clutter, same goes for docstrings.
+* Comments should provide meaningful insights into the code. Avoid filler comments that simply
-* Prefer comments to clarify surprising behavior and/or relationships between parts of the code rather than explain what the next line of code does.
+  describe the next step, as they create unnecessary clutter, same goes for docstrings.
-* Catching exceptions, prefer using a specific exception type rather than a broad catch-all like `Exception`.
+* Prefer comments to clarify surprising behavior and/or relationships between parts of the code
+  rather than explain what the next line of code does.
+* Catching exceptions, prefer using a specific exception type rather than a broad catch-all like
+  `Exception`.
 * Error messages should be prefixed with "Failed to ..."
-* 4 spaces for indentation rather than tabs
+* 4 spaces for indentation rather than tab
+* When using `# noqa` to suppress a style or linter warning, include a comment explaining the
+  justification for bypassing the check.
+* When using `# type: ignore` to suppress a mypy warning, include a comment explaining the
+  justification for bypassing the check.
 
 ## Common Tasks
 

README.md

@@ -70,6 +70,13 @@ As more providers start supporting Llama 4, you can use them in Llama Stack as w
 
 </details>
 
+### 🚀 One-Line Installer 🚀
+
+To try Llama Stack locally, run:
+
+```bash
+curl -LsSf https://github.com/meta-llama/llama-stack/raw/main/install.sh | sh
+```
 
 ### Overview
 
@@ -119,6 +126,7 @@ Here is a list of the various API providers and available distributions that can
 |          OpenAI          |         Hosted         |            |       ✅       |            |            |               |
 |        Anthropic         |         Hosted         |            |       ✅       |            |            |               |
 |          Gemini          |         Hosted         |            |       ✅       |            |            |               |
+|          watsonx         |         Hosted         |            |       ✅       |            |            |               |
 
 
 ### Distributions
@@ -128,7 +136,6 @@ A Llama Stack Distribution (or "distro") is a pre-configured bundle of provider
 |               **Distribution**                |                                                                    **Llama Stack Docker**                                                                     |                                                 Start This Distribution                                                  |
 |:---------------------------------------------:|:-------------------------------------------------------------------------------------------------------------------------------------------------------------:|:------------------------------------------------------------------------------------------------------------------------:|
 |                Meta Reference                 |           [llamastack/distribution-meta-reference-gpu](https://hub.docker.com/repository/docker/llamastack/distribution-meta-reference-gpu/general)           |      [Guide](https://llama-stack.readthedocs.io/en/latest/distributions/self_hosted_distro/meta-reference-gpu.html)      |
-|           Meta Reference Quantized            | [llamastack/distribution-meta-reference-quantized-gpu](https://hub.docker.com/repository/docker/llamastack/distribution-meta-reference-quantized-gpu/general) | [Guide](https://llama-stack.readthedocs.io/en/latest/distributions/self_hosted_distro/meta-reference-quantized-gpu.html) |
 |                   SambaNova                   |                     [llamastack/distribution-sambanova](https://hub.docker.com/repository/docker/llamastack/distribution-sambanova/general)                     |   [Guide](https://llama-stack.readthedocs.io/en/latest/distributions/self_hosted_distro/sambanova.html)   |
 |                   Cerebras                    |                     [llamastack/distribution-cerebras](https://hub.docker.com/repository/docker/llamastack/distribution-cerebras/general)                     |   [Guide](https://llama-stack.readthedocs.io/en/latest/distributions/self_hosted_distro/cerebras.html)   |
 |                    Ollama                     |                       [llamastack/distribution-ollama](https://hub.docker.com/repository/docker/llamastack/distribution-ollama/general)                       |            [Guide](https://llama-stack.readthedocs.io/en/latest/distributions/self_hosted_distro/ollama.html)            |

docs/_static/css/my_theme.css

@@ -27,3 +27,9 @@ pre {
     white-space: pre-wrap !important;
     word-break: break-all;
 }
+
+[data-theme="dark"] .mermaid {
+    background-color: #f4f4f6 !important;
+    border-radius: 6px;
+    padding: 0.5em;
+  }

docs/_static/js/detect_theme.js

@@ -1,9 +1,32 @@
 document.addEventListener("DOMContentLoaded", function () {
   const prefersDark = window.matchMedia("(prefers-color-scheme: dark)").matches;
   const htmlElement = document.documentElement;
-  if (prefersDark) {
+
-    htmlElement.setAttribute("data-theme", "dark");
+  // Check if theme is saved in localStorage
+  const savedTheme = localStorage.getItem("sphinx-rtd-theme");
+
+  if (savedTheme) {
+    // Use the saved theme preference
+    htmlElement.setAttribute("data-theme", savedTheme);
+    document.body.classList.toggle("dark", savedTheme === "dark");
   } else {
-    htmlElement.setAttribute("data-theme", "light");
+    // Fall back to system preference
+    const theme = prefersDark ? "dark" : "light";
+    htmlElement.setAttribute("data-theme", theme);
+    document.body.classList.toggle("dark", theme === "dark");
+    // Save initial preference
+    localStorage.setItem("sphinx-rtd-theme", theme);
   }
+
+  // Listen for theme changes from the existing toggle
+  const observer = new MutationObserver(function(mutations) {
+    mutations.forEach(function(mutation) {
+      if (mutation.attributeName === "data-theme") {
+        const currentTheme = htmlElement.getAttribute("data-theme");
+        localStorage.setItem("sphinx-rtd-theme", currentTheme);
+      }
+    });
+  });
+
+  observer.observe(htmlElement, { attributes: true });
 });

docs/_static/llama-stack-spec.html

@@ -497,6 +497,54 @@
                 }
             }
         },
+        "/v1/openai/v1/responses": {
+            "post": {
+                "responses": {
+                    "200": {
+                        "description": "Runtime representation of an annotated type.",
+                        "content": {
+                            "application/json": {
+                                "schema": {
+                                    "$ref": "#/components/schemas/OpenAIResponseObject"
+                                }
+                            },
+                            "text/event-stream": {
+                                "schema": {
+                                    "$ref": "#/components/schemas/OpenAIResponseObjectStream"
+                                }
+                            }
+                        }
+                    },
+                    "400": {
+                        "$ref": "#/components/responses/BadRequest400"
+                    },
+                    "429": {
+                        "$ref": "#/components/responses/TooManyRequests429"
+                    },
+                    "500": {
+                        "$ref": "#/components/responses/InternalServerError500"
+                    },
+                    "default": {
+                        "$ref": "#/components/responses/DefaultError"
+                    }
+                },
+                "tags": [
+                    "Agents"
+                ],
+                "description": "Create a new OpenAI response.",
+                "parameters": [],
+                "requestBody": {
+                    "content": {
+                        "application/json": {
+                            "schema": {
+                                "$ref": "#/components/schemas/CreateOpenaiResponseRequest"
+                            }
+                        }
+                    },
+                    "required": true
+                }
+            }
+        },
         "/v1/files": {
             "get": {
                 "responses": {
@@ -1278,6 +1326,49 @@
                 ]
             }
         },
+        "/v1/openai/v1/responses/{id}": {
+            "get": {
+                "responses": {
+                    "200": {
+                        "description": "An OpenAIResponseObject.",
+                        "content": {
+                            "application/json": {
+                                "schema": {
+                                    "$ref": "#/components/schemas/OpenAIResponseObject"
+                                }
+                            }
+                        }
+                    },
+                    "400": {
+                        "$ref": "#/components/responses/BadRequest400"
+                    },
+                    "429": {
+                        "$ref": "#/components/responses/TooManyRequests429"
+                    },
+                    "500": {
+                        "$ref": "#/components/responses/InternalServerError500"
+                    },
+                    "default": {
+                        "$ref": "#/components/responses/DefaultError"
+                    }
+                },
+                "tags": [
+                    "Agents"
+                ],
+                "description": "Retrieve an OpenAI response by its ID.",
+                "parameters": [
+                    {
+                        "name": "id",
+                        "in": "path",
+                        "description": "The ID of the OpenAI response to retrieve.",
+                        "required": true,
+                        "schema": {
+                            "type": "string"
+                        }
+                    }
+                ]
+            }
+        },
         "/v1/scoring-functions/{scoring_fn_id}": {
             "get": {
                 "responses": {
@@ -5221,17 +5312,25 @@
                         "default": 10
                     },
                     "model": {
-                        "type": "string"
+                        "type": "string",
+                        "description": "The model identifier to use for the agent"
                     },
                     "instructions": {
-                        "type": "string"
+                        "type": "string",
+                        "description": "The system instructions for the agent"
+                    },
+                    "name": {
+                        "type": "string",
+                        "description": "Optional name for the agent, used in telemetry and identification"
                     },
                     "enable_session_persistence": {
                         "type": "boolean",
-                        "default": false
+                        "default": false,
+                        "description": "Optional flag indicating whether session data has to be persisted"
                     },
                     "response_format": {
-                        "$ref": "#/components/schemas/ResponseFormat"
+                        "$ref": "#/components/schemas/ResponseFormat",
+                        "description": "Optional response format configuration"
                     }
                 },
                 "additionalProperties": false,
@@ -5239,7 +5338,8 @@
                     "model",
                     "instructions"
                 ],
-                "title": "AgentConfig"
+                "title": "AgentConfig",
+                "description": "Configuration for an agent."
             },
             "AgentTool": {
                 "oneOf": [
@@ -6183,6 +6283,430 @@
                 ],
                 "title": "AgentTurnResponseTurnStartPayload"
             },
+            "OpenAIResponseInputMessage": {
+                "type": "object",
+                "properties": {
+                    "content": {
+                        "oneOf": [
+                            {
+                                "type": "string"
+                            },
+                            {
+                                "type": "array",
+                                "items": {
+                                    "$ref": "#/components/schemas/OpenAIResponseInputMessageContent"
+                                }
+                            }
+                        ]
+                    },
+                    "role": {
+                        "oneOf": [
+                            {
+                                "type": "string",
+                                "const": "system"
+                            },
+                            {
+                                "type": "string",
+                                "const": "developer"
+                            },
+                            {
+                                "type": "string",
+                                "const": "user"
+                            },
+                            {
+                                "type": "string",
+                                "const": "assistant"
+                            }
+                        ]
+                    },
+                    "type": {
+                        "type": "string",
+                        "const": "message",
+                        "default": "message"
+                    }
+                },
+                "additionalProperties": false,
+                "required": [
+                    "content",
+                    "role"
+                ],
+                "title": "OpenAIResponseInputMessage"
+            },
+            "OpenAIResponseInputMessageContent": {
+                "oneOf": [
+                    {
+                        "$ref": "#/components/schemas/OpenAIResponseInputMessageContentText"
+                    },
+                    {
+                        "$ref": "#/components/schemas/OpenAIResponseInputMessageContentImage"
+                    }
+                ],
+                "discriminator": {
+                    "propertyName": "type",
+                    "mapping": {
+                        "input_text": "#/components/schemas/OpenAIResponseInputMessageContentText",
+                        "input_image": "#/components/schemas/OpenAIResponseInputMessageContentImage"
+                    }
+                }
+            },
+            "OpenAIResponseInputMessageContentImage": {
+                "type": "object",
+                "properties": {
+                    "detail": {
+                        "oneOf": [
+                            {
+                                "type": "string",
+                                "const": "low"
+                            },
+                            {
+                                "type": "string",
+                                "const": "high"
+                            },
+                            {
+                                "type": "string",
+                                "const": "auto"
+                            }
+                        ],
+                        "default": "auto"
+                    },
+                    "type": {
+                        "type": "string",
+                        "const": "input_image",
+                        "default": "input_image"
+                    },
+                    "image_url": {
+                        "type": "string"
+                    }
+                },
+                "additionalProperties": false,
+                "required": [
+                    "detail",
+                    "type"
+                ],
+                "title": "OpenAIResponseInputMessageContentImage"
+            },
+            "OpenAIResponseInputMessageContentText": {
+                "type": "object",
+                "properties": {
+                    "text": {
+                        "type": "string"
+                    },
+                    "type": {
+                        "type": "string",
+                        "const": "input_text",
+                        "default": "input_text"
+                    }
+                },
+                "additionalProperties": false,
+                "required": [
+                    "text",
+                    "type"
+                ],
+                "title": "OpenAIResponseInputMessageContentText"
+            },
+            "OpenAIResponseInputTool": {
+                "type": "object",
+                "properties": {
+                    "type": {
+                        "oneOf": [
+                            {
+                                "type": "string",
+                                "const": "web_search"
+                            },
+                            {
+                                "type": "string",
+                                "const": "web_search_preview_2025_03_11"
+                            }
+                        ],
+                        "default": "web_search"
+                    },
+                    "search_context_size": {
+                        "type": "string",
+                        "default": "medium"
+                    }
+                },
+                "additionalProperties": false,
+                "required": [
+                    "type"
+                ],
+                "title": "OpenAIResponseInputToolWebSearch"
+            },
+            "CreateOpenaiResponseRequest": {
+                "type": "object",
+                "properties": {
+                    "input": {
+                        "oneOf": [
+                            {
+                                "type": "string"
+                            },
+                            {
+                                "type": "array",
+                                "items": {
+                                    "$ref": "#/components/schemas/OpenAIResponseInputMessage"
+                                }
+                            }
+                        ],
+                        "description": "Input message(s) to create the response."
+                    },
+                    "model": {
+                        "type": "string",
+                        "description": "The underlying LLM used for completions."
+                    },
+                    "previous_response_id": {
+                        "type": "string",
+                        "description": "(Optional) if specified, the new response will be a continuation of the previous response. This can be used to easily fork-off new responses from existing responses."
+                    },
+                    "store": {
+                        "type": "boolean"
+                    },
+                    "stream": {
+                        "type": "boolean"
+                    },
+                    "temperature": {
+                        "type": "number"
+                    },
+                    "tools": {
+                        "type": "array",
+                        "items": {
+                            "$ref": "#/components/schemas/OpenAIResponseInputTool"
+                        }
+                    }
+                },
+                "additionalProperties": false,
+                "required": [
+                    "input",
+                    "model"
+                ],
+                "title": "CreateOpenaiResponseRequest"
+            },
+            "OpenAIResponseError": {
+                "type": "object",
+                "properties": {
+                    "code": {
+                        "type": "string"
+                    },
+                    "message": {
+                        "type": "string"
+                    }
+                },
+                "additionalProperties": false,
+                "required": [
+                    "code",
+                    "message"
+                ],
+                "title": "OpenAIResponseError"
+            },
+            "OpenAIResponseObject": {
+                "type": "object",
+                "properties": {
+                    "created_at": {
+                        "type": "integer"
+                    },
+                    "error": {
+                        "$ref": "#/components/schemas/OpenAIResponseError"
+                    },
+                    "id": {
+                        "type": "string"
+                    },
+                    "model": {
+                        "type": "string"
+                    },
+                    "object": {
+                        "type": "string",
+                        "const": "response",
+                        "default": "response"
+                    },
+                    "output": {
+                        "type": "array",
+                        "items": {
+                            "$ref": "#/components/schemas/OpenAIResponseOutput"
+                        }
+                    },
+                    "parallel_tool_calls": {
+                        "type": "boolean",
+                        "default": false
+                    },
+                    "previous_response_id": {
+                        "type": "string"
+                    },
+                    "status": {
+                        "type": "string"
+                    },
+                    "temperature": {
+                        "type": "number"
+                    },
+                    "top_p": {
+                        "type": "number"
+                    },
+                    "truncation": {
+                        "type": "string"
+                    },
+                    "user": {
+                        "type": "string"
+                    }
+                },
+                "additionalProperties": false,
+                "required": [
+                    "created_at",
+                    "id",
+                    "model",
+                    "object",
+                    "output",
+                    "parallel_tool_calls",
+                    "status"
+                ],
+                "title": "OpenAIResponseObject"
+            },
+            "OpenAIResponseOutput": {
+                "oneOf": [
+                    {
+                        "$ref": "#/components/schemas/OpenAIResponseOutputMessage"
+                    },
+                    {
+                        "$ref": "#/components/schemas/OpenAIResponseOutputMessageWebSearchToolCall"
+                    }
+                ],
+                "discriminator": {
+                    "propertyName": "type",
+                    "mapping": {
+                        "message": "#/components/schemas/OpenAIResponseOutputMessage",
+                        "web_search_call": "#/components/schemas/OpenAIResponseOutputMessageWebSearchToolCall"
+                    }
+                }
+            },
+            "OpenAIResponseOutputMessage": {
+                "type": "object",
+                "properties": {
+                    "id": {
+                        "type": "string"
+                    },
+                    "content": {
+                        "type": "array",
+                        "items": {
+                            "$ref": "#/components/schemas/OpenAIResponseOutputMessageContent"
+                        }
+                    },
+                    "role": {
+                        "type": "string",
+                        "const": "assistant",
+                        "default": "assistant"
+                    },
+                    "status": {
+                        "type": "string"
+                    },
+                    "type": {
+                        "type": "string",
+                        "const": "message",
+                        "default": "message"
+                    }
+                },
+                "additionalProperties": false,
+                "required": [
+                    "id",
+                    "content",
+                    "role",
+                    "status",
+                    "type"
+                ],
+                "title": "OpenAIResponseOutputMessage"
+            },
+            "OpenAIResponseOutputMessageContent": {
+                "type": "object",
+                "properties": {
+                    "text": {
+                        "type": "string"
+                    },
+                    "type": {
+                        "type": "string",
+                        "const": "output_text",
+                        "default": "output_text"
+                    }
+                },
+                "additionalProperties": false,
+                "required": [
+                    "text",
+                    "type"
+                ],
+                "title": "OpenAIResponseOutputMessageContentOutputText"
+            },
+            "OpenAIResponseOutputMessageWebSearchToolCall": {
+                "type": "object",
+                "properties": {
+                    "id": {
+                        "type": "string"
+                    },
+                    "status": {
+                        "type": "string"
+                    },
+                    "type": {
+                        "type": "string",
+                        "const": "web_search_call",
+                        "default": "web_search_call"
+                    }
+                },
+                "additionalProperties": false,
+                "required": [
+                    "id",
+                    "status",
+                    "type"
+                ],
+                "title": "OpenAIResponseOutputMessageWebSearchToolCall"
+            },
+            "OpenAIResponseObjectStream": {
+                "oneOf": [
+                    {
+                        "$ref": "#/components/schemas/OpenAIResponseObjectStreamResponseCreated"
+                    },
+                    {
+                        "$ref": "#/components/schemas/OpenAIResponseObjectStreamResponseCompleted"
+                    }
+                ],
+                "discriminator": {
+                    "propertyName": "type",
+                    "mapping": {
+                        "response.created": "#/components/schemas/OpenAIResponseObjectStreamResponseCreated",
+                        "response.completed": "#/components/schemas/OpenAIResponseObjectStreamResponseCompleted"
+                    }
+                }
+            },
+            "OpenAIResponseObjectStreamResponseCompleted": {
+                "type": "object",
+                "properties": {
+                    "response": {
+                        "$ref": "#/components/schemas/OpenAIResponseObject"
+                    },
+                    "type": {
+                        "type": "string",
+                        "const": "response.completed",
+                        "default": "response.completed"
+                    }
+                },
+                "additionalProperties": false,
+                "required": [
+                    "response",
+                    "type"
+                ],
+                "title": "OpenAIResponseObjectStreamResponseCompleted"
+            },
+            "OpenAIResponseObjectStreamResponseCreated": {
+                "type": "object",
+                "properties": {
+                    "response": {
+                        "$ref": "#/components/schemas/OpenAIResponseObject"
+                    },
+                    "type": {
+                        "type": "string",
+                        "const": "response.created",
+                        "default": "response.created"
+                    }
+                },
+                "additionalProperties": false,
+                "required": [
+                    "response",
+                    "type"
+                ],
+                "title": "OpenAIResponseObjectStreamResponseCreated"
+            },
             "CreateUploadSessionRequest": {
                 "type": "object",
                 "properties": {
@@ -8891,8 +9415,7 @@
                 },
                 "additionalProperties": false,
                 "required": [
-                    "role",
+                    "role"
-                    "content"
                 ],
                 "title": "OpenAIAssistantMessageParam",
                 "description": "A message containing the model's (assistant) response in an OpenAI-compatible chat completion request."

docs/_static/llama-stack-spec.yaml

@@ -330,6 +330,39 @@ paths:
             schema:
               $ref: '#/components/schemas/CreateAgentTurnRequest'
         required: true
+  /v1/openai/v1/responses:
+    post:
+      responses:
+        '200':
+          description: >-
+            Runtime representation of an annotated type.
+          content:
+            application/json:
+              schema:
+                $ref: '#/components/schemas/OpenAIResponseObject'
+            text/event-stream:
+              schema:
+                $ref: '#/components/schemas/OpenAIResponseObjectStream'
+        '400':
+          $ref: '#/components/responses/BadRequest400'
+        '429':
+          $ref: >-
+            #/components/responses/TooManyRequests429
+        '500':
+          $ref: >-
+            #/components/responses/InternalServerError500
+        default:
+          $ref: '#/components/responses/DefaultError'
+      tags:
+        - Agents
+      description: Create a new OpenAI response.
+      parameters: []
+      requestBody:
+        content:
+          application/json:
+            schema:
+              $ref: '#/components/schemas/CreateOpenaiResponseRequest'
+        required: true
   /v1/files:
     get:
       responses:
@@ -875,6 +908,36 @@ paths:
           required: true
           schema:
             type: string
+  /v1/openai/v1/responses/{id}:
+    get:
+      responses:
+        '200':
+          description: An OpenAIResponseObject.
+          content:
+            application/json:
+              schema:
+                $ref: '#/components/schemas/OpenAIResponseObject'
+        '400':
+          $ref: '#/components/responses/BadRequest400'
+        '429':
+          $ref: >-
+            #/components/responses/TooManyRequests429
+        '500':
+          $ref: >-
+            #/components/responses/InternalServerError500
+        default:
+          $ref: '#/components/responses/DefaultError'
+      tags:
+        - Agents
+      description: Retrieve an OpenAI response by its ID.
+      parameters:
+        - name: id
+          in: path
+          description: >-
+            The ID of the OpenAI response to retrieve.
+          required: true
+          schema:
+            type: string
   /v1/scoring-functions/{scoring_fn_id}:
     get:
       responses:
@@ -3686,18 +3749,29 @@ components:
           default: 10
         model:
           type: string
+          description: >-
+            The model identifier to use for the agent
         instructions:
           type: string
+          description: The system instructions for the agent
+        name:
+          type: string
+          description: >-
+            Optional name for the agent, used in telemetry and identification
         enable_session_persistence:
           type: boolean
           default: false
+          description: >-
+            Optional flag indicating whether session data has to be persisted
         response_format:
           $ref: '#/components/schemas/ResponseFormat'
+          description: Optional response format configuration
       additionalProperties: false
       required:
         - model
         - instructions
       title: AgentConfig
+      description: Configuration for an agent.
     AgentTool:
       oneOf:
         - type: string
@@ -4318,6 +4392,295 @@ components:
         - event_type
         - turn_id
       title: AgentTurnResponseTurnStartPayload
+    OpenAIResponseInputMessage:
+      type: object
+      properties:
+        content:
+          oneOf:
+            - type: string
+            - type: array
+              items:
+                $ref: '#/components/schemas/OpenAIResponseInputMessageContent'
+        role:
+          oneOf:
+            - type: string
+              const: system
+            - type: string
+              const: developer
+            - type: string
+              const: user
+            - type: string
+              const: assistant
+        type:
+          type: string
+          const: message
+          default: message
+      additionalProperties: false
+      required:
+        - content
+        - role
+      title: OpenAIResponseInputMessage
+    OpenAIResponseInputMessageContent:
+      oneOf:
+        - $ref: '#/components/schemas/OpenAIResponseInputMessageContentText'
+        - $ref: '#/components/schemas/OpenAIResponseInputMessageContentImage'
+      discriminator:
+        propertyName: type
+        mapping:
+          input_text: '#/components/schemas/OpenAIResponseInputMessageContentText'
+          input_image: '#/components/schemas/OpenAIResponseInputMessageContentImage'
+    OpenAIResponseInputMessageContentImage:
+      type: object
+      properties:
+        detail:
+          oneOf:
+            - type: string
+              const: low
+            - type: string
+              const: high
+            - type: string
+              const: auto
+          default: auto
+        type:
+          type: string
+          const: input_image
+          default: input_image
+        image_url:
+          type: string
+      additionalProperties: false
+      required:
+        - detail
+        - type
+      title: OpenAIResponseInputMessageContentImage
+    OpenAIResponseInputMessageContentText:
+      type: object
+      properties:
+        text:
+          type: string
+        type:
+          type: string
+          const: input_text
+          default: input_text
+      additionalProperties: false
+      required:
+        - text
+        - type
+      title: OpenAIResponseInputMessageContentText
+    OpenAIResponseInputTool:
+      type: object
+      properties:
+        type:
+          oneOf:
+            - type: string
+              const: web_search
+            - type: string
+              const: web_search_preview_2025_03_11
+          default: web_search
+        search_context_size:
+          type: string
+          default: medium
+      additionalProperties: false
+      required:
+        - type
+      title: OpenAIResponseInputToolWebSearch
+    CreateOpenaiResponseRequest:
+      type: object
+      properties:
+        input:
+          oneOf:
+            - type: string
+            - type: array
+              items:
+                $ref: '#/components/schemas/OpenAIResponseInputMessage'
+          description: Input message(s) to create the response.
+        model:
+          type: string
+          description: The underlying LLM used for completions.
+        previous_response_id:
+          type: string
+          description: >-
+            (Optional) if specified, the new response will be a continuation of the
+            previous response. This can be used to easily fork-off new responses from
+            existing responses.
+        store:
+          type: boolean
+        stream:
+          type: boolean
+        temperature:
+          type: number
+        tools:
+          type: array
+          items:
+            $ref: '#/components/schemas/OpenAIResponseInputTool'
+      additionalProperties: false
+      required:
+        - input
+        - model
+      title: CreateOpenaiResponseRequest
+    OpenAIResponseError:
+      type: object
+      properties:
+        code:
+          type: string
+        message:
+          type: string
+      additionalProperties: false
+      required:
+        - code
+        - message
+      title: OpenAIResponseError
+    OpenAIResponseObject:
+      type: object
+      properties:
+        created_at:
+          type: integer
+        error:
+          $ref: '#/components/schemas/OpenAIResponseError'
+        id:
+          type: string
+        model:
+          type: string
+        object:
+          type: string
+          const: response
+          default: response
+        output:
+          type: array
+          items:
+            $ref: '#/components/schemas/OpenAIResponseOutput'
+        parallel_tool_calls:
+          type: boolean
+          default: false
+        previous_response_id:
+          type: string
+        status:
+          type: string
+        temperature:
+          type: number
+        top_p:
+          type: number
+        truncation:
+          type: string
+        user:
+          type: string
+      additionalProperties: false
+      required:
+        - created_at
+        - id
+        - model
+        - object
+        - output
+        - parallel_tool_calls
+        - status
+      title: OpenAIResponseObject
+    OpenAIResponseOutput:
+      oneOf:
+        - $ref: '#/components/schemas/OpenAIResponseOutputMessage'
+        - $ref: '#/components/schemas/OpenAIResponseOutputMessageWebSearchToolCall'
+      discriminator:
+        propertyName: type
+        mapping:
+          message: '#/components/schemas/OpenAIResponseOutputMessage'
+          web_search_call: '#/components/schemas/OpenAIResponseOutputMessageWebSearchToolCall'
+    OpenAIResponseOutputMessage:
+      type: object
+      properties:
+        id:
+          type: string
+        content:
+          type: array
+          items:
+            $ref: '#/components/schemas/OpenAIResponseOutputMessageContent'
+        role:
+          type: string
+          const: assistant
+          default: assistant
+        status:
+          type: string
+        type:
+          type: string
+          const: message
+          default: message
+      additionalProperties: false
+      required:
+        - id
+        - content
+        - role
+        - status
+        - type
+      title: OpenAIResponseOutputMessage
+    OpenAIResponseOutputMessageContent:
+      type: object
+      properties:
+        text:
+          type: string
+        type:
+          type: string
+          const: output_text
+          default: output_text
+      additionalProperties: false
+      required:
+        - text
+        - type
+      title: >-
+        OpenAIResponseOutputMessageContentOutputText
+    "OpenAIResponseOutputMessageWebSearchToolCall":
+      type: object
+      properties:
+        id:
+          type: string
+        status:
+          type: string
+        type:
+          type: string
+          const: web_search_call
+          default: web_search_call
+      additionalProperties: false
+      required:
+        - id
+        - status
+        - type
+      title: >-
+        OpenAIResponseOutputMessageWebSearchToolCall
+    OpenAIResponseObjectStream:
+      oneOf:
+        - $ref: '#/components/schemas/OpenAIResponseObjectStreamResponseCreated'
+        - $ref: '#/components/schemas/OpenAIResponseObjectStreamResponseCompleted'
+      discriminator:
+        propertyName: type
+        mapping:
+          response.created: '#/components/schemas/OpenAIResponseObjectStreamResponseCreated'
+          response.completed: '#/components/schemas/OpenAIResponseObjectStreamResponseCompleted'
+    "OpenAIResponseObjectStreamResponseCompleted":
+      type: object
+      properties:
+        response:
+          $ref: '#/components/schemas/OpenAIResponseObject'
+        type:
+          type: string
+          const: response.completed
+          default: response.completed
+      additionalProperties: false
+      required:
+        - response
+        - type
+      title: >-
+        OpenAIResponseObjectStreamResponseCompleted
+    "OpenAIResponseObjectStreamResponseCreated":
+      type: object
+      properties:
+        response:
+          $ref: '#/components/schemas/OpenAIResponseObject'
+        type:
+          type: string
+          const: response.created
+          default: response.created
+      additionalProperties: false
+      required:
+        - response
+        - type
+      title: >-
+        OpenAIResponseObjectStreamResponseCreated
     CreateUploadSessionRequest:
       type: object
       properties:
@@ -6097,7 +6460,6 @@ components:
       additionalProperties: false
       required:
         - role
-        - content
       title: OpenAIAssistantMessageParam
       description: >-
         A message containing the model's (assistant) response in an OpenAI-compatible

docs/make.bat

@@ -1,35 +1,35 @@
-@ECHO OFF
+@ECHO OFF
-
+
-pushd %~dp0
+pushd %~dp0
-
+
-REM Command file for Sphinx documentation
+REM Command file for Sphinx documentation
-
+
-if "%SPHINXBUILD%" == "" (
+if "%SPHINXBUILD%" == "" (
-	set SPHINXBUILD=sphinx-build
+	set SPHINXBUILD=sphinx-build
-)
+)
-set SOURCEDIR=.
+set SOURCEDIR=.
-set BUILDDIR=_build
+set BUILDDIR=_build
-
+
-%SPHINXBUILD% >NUL 2>NUL
+%SPHINXBUILD% >NUL 2>NUL
-if errorlevel 9009 (
+if errorlevel 9009 (
-	echo.
+	echo.
-	echo.The 'sphinx-build' command was not found. Make sure you have Sphinx
+	echo.The 'sphinx-build' command was not found. Make sure you have Sphinx
-	echo.installed, then set the SPHINXBUILD environment variable to point
+	echo.installed, then set the SPHINXBUILD environment variable to point
-	echo.to the full path of the 'sphinx-build' executable. Alternatively you
+	echo.to the full path of the 'sphinx-build' executable. Alternatively you
-	echo.may add the Sphinx directory to PATH.
+	echo.may add the Sphinx directory to PATH.
-	echo.
+	echo.
-	echo.If you don't have Sphinx installed, grab it from
+	echo.If you don't have Sphinx installed, grab it from
-	echo.https://www.sphinx-doc.org/
+	echo.https://www.sphinx-doc.org/
-	exit /b 1
+	exit /b 1
-)
+)
-
+
-if "%1" == "" goto help
+if "%1" == "" goto help
-
+
-%SPHINXBUILD% -M %1 %SOURCEDIR% %BUILDDIR% %SPHINXOPTS% %O%
+%SPHINXBUILD% -M %1 %SOURCEDIR% %BUILDDIR% %SPHINXOPTS% %O%
-goto end
+goto end
-
+
-:help
+:help
-%SPHINXBUILD% -M help %SOURCEDIR% %BUILDDIR% %SPHINXOPTS% %O%
+%SPHINXBUILD% -M help %SOURCEDIR% %BUILDDIR% %SPHINXOPTS% %O%
-
+
-:end
+:end
-popd
+popd

docs/openapi_generator/pyopenapi/generator.py

@@ -6,6 +6,7 @@
 
 import hashlib
 import ipaddress
+import types
 import typing
 from dataclasses import make_dataclass
 from typing import Any, Dict, Set, Union
@@ -179,7 +180,7 @@ class ContentBuilder:
         "Creates the content subtree for a request or response."
 
         def is_iterator_type(t):
-            return "StreamChunk" in str(t)
+            return "StreamChunk" in str(t) or "OpenAIResponseObjectStream" in str(t)
 
         def get_media_type(t):
             if is_generic_list(t):
@@ -189,7 +190,7 @@ class ContentBuilder:
             else:
                 return "application/json"
 
-        if typing.get_origin(payload_type) is typing.Union:
+        if typing.get_origin(payload_type) in (typing.Union, types.UnionType):
             media_types = []
             item_types = []
             for x in typing.get_args(payload_type):

docs/requirements.txt

@@ -1,16 +1,16 @@
-sphinx==8.1.3
-myst-parser
 linkify
+myst-parser
 -e git+https://github.com/pytorch/pytorch_sphinx_theme.git#egg=pytorch_sphinx_theme
-sphinx-rtd-theme>=1.0.0
+sphinx==8.1.3
-sphinx_autobuild
 sphinx-copybutton
 sphinx-design
 sphinx-pdj-theme
-sphinx_rtd_dark_mode
+sphinx-rtd-theme>=1.0.0
 sphinx-tabs
+sphinx_autobuild
+sphinx_rtd_dark_mode
+sphinxcontrib-mermaid
 sphinxcontrib-openapi
 sphinxcontrib-redoc
-sphinxcontrib-mermaid
 sphinxcontrib-video
 tomli

docs/source/building_applications/rag.md

@@ -68,7 +68,8 @@ chunks_response = client.vector_io.query(
 ### Using the RAG Tool
 
 A better way to ingest documents is to use the RAG Tool. This tool allows you to ingest documents from URLs, files, etc.
-and automatically chunks them into smaller pieces.
+and automatically chunks them into smaller pieces. More examples for how to format a RAGDocument can be found in the
+[appendix](#more-ragdocument-examples).
 
 ```python
 from llama_stack_client import RAGDocument
@@ -178,3 +179,38 @@ for vector_db_id in client.vector_dbs.list():
     print(f"Unregistering vector database: {vector_db_id.identifier}")
     client.vector_dbs.unregister(vector_db_id=vector_db_id.identifier)
 ```
+
+### Appendix
+
+#### More RAGDocument Examples
+```python
+from llama_stack_client import RAGDocument
+import base64
+
+RAGDocument(document_id="num-0", content={"uri": "file://path/to/file"})
+RAGDocument(document_id="num-1", content="plain text")
+RAGDocument(
+    document_id="num-2",
+    content={
+        "type": "text",
+        "text": "plain text input",
+    },  # for inputs that should be treated as text explicitly
+)
+RAGDocument(
+    document_id="num-3",
+    content={
+        "type": "image",
+        "image": {"url": {"uri": "https://mywebsite.com/image.jpg"}},
+    },
+)
+B64_ENCODED_IMAGE = base64.b64encode(
+    requests.get(
+        "https://raw.githubusercontent.com/meta-llama/llama-stack/refs/heads/main/docs/_static/llama-stack.png"
+    ).content
+)
+RAGDocuemnt(
+    document_id="num-4",
+    content={"type": "image", "image": {"data": B64_ENCODED_IMAGE}},
+)
+```
+for more strongly typed interaction use the typed dicts found [here](https://github.com/meta-llama/llama-stack-client-python/blob/38cd91c9e396f2be0bec1ee96a19771582ba6f17/src/llama_stack_client/types/shared_params/document.py).

docs/source/building_applications/tools.md

@@ -41,30 +41,9 @@ client.toolgroups.register(
 
 The tool requires an API key which can be provided either in the configuration or through the request header `X-LlamaStack-Provider-Data`. The format of the header is `{"<provider_name>_api_key": <your api key>}`.
 
+> **NOTE:** When using Tavily Search and Bing Search, the inference output will still display "Brave Search." This is because Llama models have been trained with Brave Search as a built-in tool. Tavily and bing is just being used in lieu of Brave search.
 
 
-#### Code Interpreter
-
-The Code Interpreter allows execution of Python code within a controlled environment.
-
-```python
-# Register Code Interpreter tool group
-client.toolgroups.register(
-    toolgroup_id="builtin::code_interpreter", provider_id="code_interpreter"
-)
-```
-
-Features:
-- Secure execution environment using `bwrap` sandboxing
-- Matplotlib support for generating plots
-- Disabled dangerous system operations
-- Configurable execution timeouts
-
-> ⚠️ Important: The code interpreter tool can operate in a controlled environment locally or on Podman containers. To ensure proper functionality in containerized environments:
-> - The container requires privileged access (e.g., --privileged).
-> - Users without sufficient permissions may encounter permission errors. (`bwrap: Can't mount devpts on /newroot/dev/pts: Permission denied`)
-> - 🔒 Security Warning: Privileged mode grants elevated access and bypasses security restrictions. Use only in local, isolated, or controlled environments.
-
 #### WolframAlpha
 
 The WolframAlpha tool provides access to computational knowledge through the WolframAlpha API.
@@ -102,7 +81,7 @@ Features:
 - Context retrieval with token limits
 
 
-> **Note:** By default, llama stack run.yaml defines toolgroups for web search, code interpreter and rag, that are provided by tavily-search, code-interpreter and rag providers.
+> **Note:** By default, llama stack run.yaml defines toolgroups for web search, wolfram alpha and rag, that are provided by tavily-search, wolfram-alpha and rag providers.
 
 ## Model Context Protocol (MCP) Tools
 
@@ -214,3 +193,69 @@ response = agent.create_turn(
     session_id=session_id,
 )
 ```
+## Simple Example 2: Using an Agent with the Web Search Tool
+1. Start by registering a Tavily API key at [Tavily](https://tavily.com/).
+2. [Optional] Provide the API key directly to the Llama Stack server
+```bash
+export TAVILY_SEARCH_API_KEY="your key"
+```
+```bash
+--env TAVILY_SEARCH_API_KEY=${TAVILY_SEARCH_API_KEY}
+```
+3. Run the following script.
+```python
+from llama_stack_client.lib.agents.agent import Agent
+from llama_stack_client.types.agent_create_params import AgentConfig
+from llama_stack_client.lib.agents.event_logger import EventLogger
+from llama_stack_client import LlamaStackClient
+
+client = LlamaStackClient(
+    base_url=f"http://localhost:8321",
+    provider_data={
+        "tavily_search_api_key": "your_TAVILY_SEARCH_API_KEY"
+    },  # Set this from the client side. No need to provide it if it has already been configured on the Llama Stack server.
+)
+
+agent = Agent(
+    client,
+    model="meta-llama/Llama-3.2-3B-Instruct",
+    instructions=(
+        "You are a web search assistant, must use websearch tool to look up the most current and precise information available. "
+    ),
+    tools=["builtin::websearch"],
+)
+
+session_id = agent.create_session("websearch-session")
+
+response = agent.create_turn(
+    messages=[
+        {"role": "user", "content": "How did the USA perform in the last Olympics?"}
+    ],
+    session_id=session_id,
+)
+for log in EventLogger().log(response):
+    log.print()
+```
+
+## Simple Example3: Using an Agent with the WolframAlpha Tool
+1. Start by registering for a WolframAlpha API key at [WolframAlpha Developer Portal](https://developer.wolframalpha.com/access).
+2. Provide the API key either when starting the Llama Stack server:
+    ```bash
+    --env WOLFRAM_ALPHA_API_KEY=${WOLFRAM_ALPHA_API_KEY}
+    ```
+    or from the client side:
+    ```python
+    client = LlamaStackClient(
+        base_url="http://localhost:8321",
+        provider_data={"wolfram_alpha_api_key": wolfram_api_key},
+    )
+    ```
+3. Configure the tools in the Agent by setting `tools=["builtin::wolfram_alpha"]`.
+4. Example user query:
+    ```python
+    response = agent.create_turn(
+        messages=[{"role": "user", "content": "Solve x^2 + 2x + 1 = 0 using WolframAlpha"}],
+        session_id=session_id,
+    )
+    ```
+```

docs/source/distributions/building_distro.md

@@ -109,8 +109,6 @@ llama stack build --list-templates
 +------------------------------+-----------------------------------------------------------------------------+
 | nvidia                       | Use NVIDIA NIM for running LLM inference                                    |
 +------------------------------+-----------------------------------------------------------------------------+
-| meta-reference-quantized-gpu | Use Meta Reference with fp8, int4 quantization for running LLM inference    |
-+------------------------------+-----------------------------------------------------------------------------+
 | cerebras                     | Use Cerebras for running LLM inference                                      |
 +------------------------------+-----------------------------------------------------------------------------+
 | ollama                       | Use (an external) Ollama server for running LLM inference                   |
@@ -176,7 +174,11 @@ distribution_spec:
     safety: inline::llama-guard
     agents: inline::meta-reference
     telemetry: inline::meta-reference
+image_name: ollama
 image_type: conda
+
+# If some providers are external, you can specify the path to the implementation
+external_providers_dir: /etc/llama-stack/providers.d
 ```
 
 ```
@@ -184,6 +186,57 @@ llama stack build --config llama_stack/templates/ollama/build.yaml
 ```
 :::
 
+:::{tab-item} Building with External Providers
+
+Llama Stack supports external providers that live outside of the main codebase. This allows you to create and maintain your own providers independently or use community-provided providers.
+
+To build a distribution with external providers, you need to:
+
+1. Configure the `external_providers_dir` in your build configuration file:
+
+```yaml
+# Example my-external-stack.yaml with external providers
+version: '2'
+distribution_spec:
+  description: Custom distro for CI tests
+  providers:
+    inference:
+    - remote::custom_ollama
+# Add more providers as needed
+image_type: container
+image_name: ci-test
+# Path to external provider implementations
+external_providers_dir: /etc/llama-stack/providers.d
+```
+
+Here's an example for a custom Ollama provider:
+
+```yaml
+adapter:
+  adapter_type: custom_ollama
+  pip_packages:
+  - ollama
+  - aiohttp
+  - llama-stack-provider-ollama # This is the provider package
+  config_class: llama_stack_ollama_provider.config.OllamaImplConfig
+  module: llama_stack_ollama_provider
+api_dependencies: []
+optional_api_dependencies: []
+```
+
+The `pip_packages` section lists the Python packages required by the provider, as well as the
+provider package itself. The package must be available on PyPI or can be provided from a local
+directory or a git repository (git must be installed on the build environment).
+
+2. Build your distribution using the config file:
+
+```
+llama stack build --config my-external-stack.yaml
+```
+
+For more information on external providers, including directory structure, provider types, and implementation requirements, see the [External Providers documentation](../providers/external.md).
+:::
+
 :::{tab-item} Building Container
 
 ```{admonition} Podman Alternative

docs/source/distributions/configuration.md

@@ -53,6 +53,13 @@ models:
   provider_id: ollama
   provider_model_id: null
 shields: []
+server:
+  port: 8321
+  auth:
+    provider_type: "kubernetes"
+    config:
+      api_server_url: "https://kubernetes.default.svc"
+      ca_cert_path: "/path/to/ca.crt"
 ```
 
 Let's break this down into the different sections. The first section specifies the set of APIs that the stack server will serve:
@@ -102,6 +109,105 @@ A Model is an instance of a "Resource" (see [Concepts](../concepts/index)) and i
 
 What's with the `provider_model_id` field? This is an identifier for the model inside the provider's model catalog. Contrast it with `model_id` which is the identifier for the same model for Llama Stack's purposes. For example, you may want to name "llama3.2:vision-11b" as "image_captioning_model" when you use it in your Stack interactions. When omitted, the server will set `provider_model_id` to be the same as `model_id`.
 
+## Server Configuration
+
+The `server` section configures the HTTP server that serves the Llama Stack APIs:
+
+```yaml
+server:
+  port: 8321  # Port to listen on (default: 8321)
+  tls_certfile: "/path/to/cert.pem"  # Optional: Path to TLS certificate for HTTPS
+  tls_keyfile: "/path/to/key.pem"    # Optional: Path to TLS key for HTTPS
+  auth:                              # Optional: Authentication configuration
+    provider_type: "kubernetes"      # Type of auth provider
+    config:                          # Provider-specific configuration
+      api_server_url: "https://kubernetes.default.svc"
+      ca_cert_path: "/path/to/ca.crt" # Optional: Path to CA certificate
+```
+
+### Authentication Configuration
+
+The `auth` section configures authentication for the server. When configured, all API requests must include a valid Bearer token in the Authorization header:
+
+```
+Authorization: Bearer <token>
+```
+
+The server supports multiple authentication providers:
+
+#### Kubernetes Provider
+
+The Kubernetes cluster must be configured to use a service account for authentication.
+
+```bash
+kubectl create namespace llama-stack
+kubectl create serviceaccount llama-stack-auth -n llama-stack
+kubectl create rolebinding llama-stack-auth-rolebinding --clusterrole=admin --serviceaccount=llama-stack:llama-stack-auth -n llama-stack
+kubectl create token llama-stack-auth -n llama-stack > llama-stack-auth-token
+```
+
+Validates tokens against the Kubernetes API server:
+```yaml
+server:
+  auth:
+    provider_type: "kubernetes"
+    config:
+      api_server_url: "https://kubernetes.default.svc"  # URL of the Kubernetes API server
+      ca_cert_path: "/path/to/ca.crt"                   # Optional: Path to CA certificate
+```
+
+The provider extracts user information from the JWT token:
+- Username from the `sub` claim becomes a role
+- Kubernetes groups become teams
+
+You can easily validate a request by running:
+
+```bash
+curl -s -L -H "Authorization: Bearer $(cat llama-stack-auth-token)" http://127.0.0.1:8321/v1/providers
+```
+
+#### Custom Provider
+Validates tokens against a custom authentication endpoint:
+```yaml
+server:
+  auth:
+    provider_type: "custom"
+    config:
+      endpoint: "https://auth.example.com/validate"  # URL of the auth endpoint
+```
+
+The custom endpoint receives a POST request with:
+```json
+{
+  "api_key": "<token>",
+  "request": {
+    "path": "/api/v1/endpoint",
+    "headers": {
+      "content-type": "application/json",
+      "user-agent": "curl/7.64.1"
+    },
+    "params": {
+      "key": ["value"]
+    }
+  }
+}
+```
+
+And must respond with:
+```json
+{
+  "access_attributes": {
+    "roles": ["admin", "user"],
+    "teams": ["ml-team", "nlp-team"],
+    "projects": ["llama-3", "project-x"],
+    "namespaces": ["research"]
+  },
+  "message": "Authentication successful"
+}
+```
+
+If no access attributes are returned, the token is used as a namespace.
+
 ## Extending to handle Safety
 
 Configuring Safety can be a little involved so it is instructive to go through an example.

docs/source/distributions/ondevice_distro/android_sdk.md

@@ -24,7 +24,7 @@ The key files in the app are `ExampleLlamaStackLocalInference.kt`, `ExampleLlama
 Add the following dependency in your `build.gradle.kts` file:
 ```
 dependencies {
- implementation("com.llama.llamastack:llama-stack-client-kotlin:0.1.4.2")
+ implementation("com.llama.llamastack:llama-stack-client-kotlin:0.2.2")
 }
 ```
 This will download jar files in your gradle cache in a directory like `~/.gradle/caches/modules-2/files-2.1/com.llama.llamastack/`
@@ -37,11 +37,7 @@ For local inferencing, it is required to include the ExecuTorch library into you
 
 Include the ExecuTorch library by:
 1. Download the `download-prebuilt-et-lib.sh` script file from the [llama-stack-client-kotlin-client-local](https://github.com/meta-llama/llama-stack-client-kotlin/tree/latest-release/llama-stack-client-kotlin-client-local/download-prebuilt-et-lib.sh) directory to your local machine.
-2. Move the script to the top level of your Android app where the app directory resides:
+2. Move the script to the top level of your Android app where the `app` directory resides.
-<p align="center">
-<img src="https://github.com/meta-llama/llama-stack-client-kotlin/blob/latest-release/doc/img/example_android_app_directory.png" style="width:300px">
-</p>
-
 3. Run `sh download-prebuilt-et-lib.sh` to create an `app/libs` directory and download the `executorch.aar` in that path. This generates an ExecuTorch library for the XNNPACK delegate.
 4. Add the `executorch.aar` dependency in your `build.gradle.kts` file:
 ```
@@ -52,6 +48,8 @@ dependencies {
 }
 ```
 
+See other dependencies for the local RAG in Android app [README](https://github.com/meta-llama/llama-stack-client-kotlin/tree/latest-release/examples/android_app#quick-start).
+
 ## Llama Stack APIs in Your Android App
 Breaking down the demo app, this section will show the core pieces that are used to initialize and run inference with Llama Stack using the Kotlin library.
 
@@ -60,7 +58,7 @@ Start a Llama Stack server on localhost. Here is an example of how you can do th
 ```
 conda create -n stack-fireworks python=3.10
 conda activate stack-fireworks
-pip install --no-cache llama-stack==0.1.4
+pip install --no-cache llama-stack==0.2.2
 llama stack build --template fireworks --image-type conda
 export FIREWORKS_API_KEY=<SOME_KEY>
 llama stack run fireworks --port 5050

docs/source/distributions/remote_hosted_distro/nvidia.md

@@ -1,88 +0,0 @@
-<!-- This file was auto-generated by distro_codegen.py, please edit source -->
-# NVIDIA Distribution
-
-The `llamastack/distribution-nvidia` distribution consists of the following provider configurations.
-
-| API | Provider(s) |
-|-----|-------------|
-| agents | `inline::meta-reference` |
-| datasetio | `inline::localfs` |
-| eval | `inline::meta-reference` |
-| inference | `remote::nvidia` |
-| post_training | `remote::nvidia` |
-| safety | `remote::nvidia` |
-| scoring | `inline::basic` |
-| telemetry | `inline::meta-reference` |
-| tool_runtime | `inline::rag-runtime` |
-| vector_io | `inline::faiss` |
-
-
-### Environment Variables
-
-The following environment variables can be configured:
-
-- `NVIDIA_API_KEY`: NVIDIA API Key (default: ``)
-- `NVIDIA_USER_ID`: NVIDIA User ID (default: `llama-stack-user`)
-- `NVIDIA_DATASET_NAMESPACE`: NVIDIA Dataset Namespace (default: `default`)
-- `NVIDIA_ACCESS_POLICIES`: NVIDIA Access Policies (default: `{}`)
-- `NVIDIA_PROJECT_ID`: NVIDIA Project ID (default: `test-project`)
-- `NVIDIA_CUSTOMIZER_URL`: NVIDIA Customizer URL (default: `https://customizer.api.nvidia.com`)
-- `NVIDIA_OUTPUT_MODEL_DIR`: NVIDIA Output Model Directory (default: `test-example-model@v1`)
-- `GUARDRAILS_SERVICE_URL`: URL for the NeMo Guardrails Service (default: `http://0.0.0.0:7331`)
-- `INFERENCE_MODEL`: Inference model (default: `Llama3.1-8B-Instruct`)
-- `SAFETY_MODEL`: Name of the model to use for safety (default: `meta/llama-3.1-8b-instruct`)
-
-### Models
-
-The following models are available by default:
-
-- `meta/llama3-8b-instruct (aliases: meta-llama/Llama-3-8B-Instruct)`
-- `meta/llama3-70b-instruct (aliases: meta-llama/Llama-3-70B-Instruct)`
-- `meta/llama-3.1-8b-instruct (aliases: meta-llama/Llama-3.1-8B-Instruct)`
-- `meta/llama-3.1-70b-instruct (aliases: meta-llama/Llama-3.1-70B-Instruct)`
-- `meta/llama-3.1-405b-instruct (aliases: meta-llama/Llama-3.1-405B-Instruct-FP8)`
-- `meta/llama-3.2-1b-instruct (aliases: meta-llama/Llama-3.2-1B-Instruct)`
-- `meta/llama-3.2-3b-instruct (aliases: meta-llama/Llama-3.2-3B-Instruct)`
-- `meta/llama-3.2-11b-vision-instruct (aliases: meta-llama/Llama-3.2-11B-Vision-Instruct)`
-- `meta/llama-3.2-90b-vision-instruct (aliases: meta-llama/Llama-3.2-90B-Vision-Instruct)`
-- `nvidia/llama-3.2-nv-embedqa-1b-v2 `
-- `nvidia/nv-embedqa-e5-v5 `
-- `nvidia/nv-embedqa-mistral-7b-v2 `
-- `snowflake/arctic-embed-l `
-
-
-### Prerequisite: API Keys
-
-Make sure you have access to a NVIDIA API Key. You can get one by visiting [https://build.nvidia.com/](https://build.nvidia.com/).
-
-
-## Running Llama Stack with NVIDIA
-
-You can do this via Conda (build code) or Docker which has a pre-built image.
-
-### Via Docker
-
-This method allows you to get started quickly without having to build the distribution code.
-
-```bash
-LLAMA_STACK_PORT=8321
-docker run \
-  -it \
-  --pull always \
-  -p $LLAMA_STACK_PORT:$LLAMA_STACK_PORT \
-  -v ./run.yaml:/root/my-run.yaml \
-  llamastack/distribution-nvidia \
-  --yaml-config /root/my-run.yaml \
-  --port $LLAMA_STACK_PORT \
-  --env NVIDIA_API_KEY=$NVIDIA_API_KEY
-```
-
-### Via Conda
-
-```bash
-llama stack build --template nvidia --image-type conda
-llama stack run ./run.yaml \
-  --port 8321 \
-  --env NVIDIA_API_KEY=$NVIDIA_API_KEY
-  --env INFERENCE_MODEL=$INFERENCE_MODEL
-```

docs/source/distributions/remote_hosted_distro/watsonx.md

@@ -0,0 +1,88 @@
+---
+orphan: true
+---
+<!-- This file was auto-generated by distro_codegen.py, please edit source -->
+# watsonx Distribution
+
+```{toctree}
+:maxdepth: 2
+:hidden:
+
+self
+```
+
+The `llamastack/distribution-watsonx` distribution consists of the following provider configurations.
+
+| API | Provider(s) |
+|-----|-------------|
+| agents | `inline::meta-reference` |
+| datasetio | `remote::huggingface`, `inline::localfs` |
+| eval | `inline::meta-reference` |
+| inference | `remote::watsonx` |
+| safety | `inline::llama-guard` |
+| scoring | `inline::basic`, `inline::llm-as-judge`, `inline::braintrust` |
+| telemetry | `inline::meta-reference` |
+| tool_runtime | `remote::brave-search`, `remote::tavily-search`, `inline::rag-runtime`, `remote::model-context-protocol` |
+| vector_io | `inline::faiss` |
+
+
+
+### Environment Variables
+
+The following environment variables can be configured:
+
+- `LLAMASTACK_PORT`: Port for the Llama Stack distribution server (default: `5001`)
+- `WATSONX_API_KEY`: watsonx API Key (default: ``)
+- `WATSONX_PROJECT_ID`: watsonx Project ID (default: ``)
+
+### Models
+
+The following models are available by default:
+
+- `meta-llama/llama-3-3-70b-instruct (aliases: meta-llama/Llama-3.3-70B-Instruct)`
+- `meta-llama/llama-2-13b-chat (aliases: meta-llama/Llama-2-13b)`
+- `meta-llama/llama-3-1-70b-instruct (aliases: meta-llama/Llama-3.1-70B-Instruct)`
+- `meta-llama/llama-3-1-8b-instruct (aliases: meta-llama/Llama-3.1-8B-Instruct)`
+- `meta-llama/llama-3-2-11b-vision-instruct (aliases: meta-llama/Llama-3.2-11B-Vision-Instruct)`
+- `meta-llama/llama-3-2-1b-instruct (aliases: meta-llama/Llama-3.2-1B-Instruct)`
+- `meta-llama/llama-3-2-3b-instruct (aliases: meta-llama/Llama-3.2-3B-Instruct)`
+- `meta-llama/llama-3-2-90b-vision-instruct (aliases: meta-llama/Llama-3.2-90B-Vision-Instruct)`
+- `meta-llama/llama-guard-3-11b-vision (aliases: meta-llama/Llama-Guard-3-11B-Vision)`
+
+
+### Prerequisite: API Keys
+
+Make sure you have access to a watsonx API Key. You can get one by referring [watsonx.ai](https://www.ibm.com/docs/en/masv-and-l/maximo-manage/continuous-delivery?topic=setup-create-watsonx-api-key).
+
+
+## Running Llama Stack with watsonx
+
+You can do this via Conda (build code), venv or Docker which has a pre-built image.
+
+### Via Docker
+
+This method allows you to get started quickly without having to build the distribution code.
+
+```bash
+LLAMA_STACK_PORT=5001
+docker run \
+  -it \
+  -p $LLAMA_STACK_PORT:$LLAMA_STACK_PORT \
+  -v ./run.yaml:/root/my-run.yaml \
+  llamastack/distribution-watsonx \
+  --yaml-config /root/my-run.yaml \
+  --port $LLAMA_STACK_PORT \
+  --env WATSONX_API_KEY=$WATSONX_API_KEY \
+  --env WATSONX_PROJECT_ID=$WATSONX_PROJECT_ID \
+  --env WATSONX_BASE_URL=$WATSONX_BASE_URL
+```
+
+### Via Conda
+
+```bash
+llama stack build --template watsonx --image-type conda
+llama stack run ./run.yaml \
+  --port $LLAMA_STACK_PORT \
+  --env WATSONX_API_KEY=$WATSONX_API_KEY \
+  --env WATSONX_PROJECT_ID=$WATSONX_PROJECT_ID
+```

docs/source/distributions/self_hosted_distro/bedrock.md

@@ -19,7 +19,7 @@ The `llamastack/distribution-bedrock` distribution consists of the following pro
 | safety | `remote::bedrock` |
 | scoring | `inline::basic`, `inline::llm-as-judge`, `inline::braintrust` |
 | telemetry | `inline::meta-reference` |
-| tool_runtime | `remote::brave-search`, `remote::tavily-search`, `inline::code-interpreter`, `inline::rag-runtime`, `remote::model-context-protocol` |
+| tool_runtime | `remote::brave-search`, `remote::tavily-search`, `inline::rag-runtime`, `remote::model-context-protocol` |
 | vector_io | `inline::faiss`, `remote::chromadb`, `remote::pgvector` |
 
 

docs/source/distributions/self_hosted_distro/cerebras.md

@@ -12,7 +12,7 @@ The `llamastack/distribution-cerebras` distribution consists of the following pr
 | safety | `inline::llama-guard` |
 | scoring | `inline::basic`, `inline::llm-as-judge`, `inline::braintrust` |
 | telemetry | `inline::meta-reference` |
-| tool_runtime | `remote::brave-search`, `remote::tavily-search`, `inline::code-interpreter`, `inline::rag-runtime` |
+| tool_runtime | `remote::brave-search`, `remote::tavily-search`, `inline::rag-runtime` |
 | vector_io | `inline::faiss`, `remote::chromadb`, `remote::pgvector` |
 
 

docs/source/distributions/self_hosted_distro/fireworks.md

@@ -22,7 +22,7 @@ The `llamastack/distribution-fireworks` distribution consists of the following p
 | safety | `inline::llama-guard` |
 | scoring | `inline::basic`, `inline::llm-as-judge`, `inline::braintrust` |
 | telemetry | `inline::meta-reference` |
-| tool_runtime | `remote::brave-search`, `remote::tavily-search`, `remote::wolfram-alpha`, `inline::code-interpreter`, `inline::rag-runtime`, `remote::model-context-protocol` |
+| tool_runtime | `remote::brave-search`, `remote::tavily-search`, `remote::wolfram-alpha`, `inline::rag-runtime`, `remote::model-context-protocol` |
 | vector_io | `inline::faiss`, `remote::chromadb`, `remote::pgvector` |
 
 

docs/source/distributions/self_hosted_distro/groq.md

@@ -22,7 +22,7 @@ The `llamastack/distribution-groq` distribution consists of the following provid
 | safety | `inline::llama-guard` |
 | scoring | `inline::basic`, `inline::llm-as-judge`, `inline::braintrust` |
 | telemetry | `inline::meta-reference` |
-| tool_runtime | `remote::brave-search`, `remote::tavily-search`, `inline::code-interpreter`, `inline::rag-runtime` |
+| tool_runtime | `remote::brave-search`, `remote::tavily-search`, `inline::rag-runtime` |
 | vector_io | `inline::faiss` |
 
 

docs/source/distributions/self_hosted_distro/meta-reference-gpu.md

@@ -22,7 +22,7 @@ The `llamastack/distribution-meta-reference-gpu` distribution consists of the fo
 | safety | `inline::llama-guard` |
 | scoring | `inline::basic`, `inline::llm-as-judge`, `inline::braintrust` |
 | telemetry | `inline::meta-reference` |
-| tool_runtime | `remote::brave-search`, `remote::tavily-search`, `inline::code-interpreter`, `inline::rag-runtime`, `remote::model-context-protocol` |
+| tool_runtime | `remote::brave-search`, `remote::tavily-search`, `inline::rag-runtime`, `remote::model-context-protocol` |
 | vector_io | `inline::faiss`, `remote::chromadb`, `remote::pgvector` |
 
 
@@ -81,6 +81,7 @@ LLAMA_STACK_PORT=8321
 docker run \
   -it \
   --pull always \
+  --gpu all \
   -p $LLAMA_STACK_PORT:$LLAMA_STACK_PORT \
   -v ~/.llama:/root/.llama \
   llamastack/distribution-meta-reference-gpu \
@@ -94,6 +95,7 @@ If you are using Llama Stack Safety / Shield APIs, use:
 docker run \
   -it \
   --pull always \
+  --gpu all \
   -p $LLAMA_STACK_PORT:$LLAMA_STACK_PORT \
   -v ~/.llama:/root/.llama \
   llamastack/distribution-meta-reference-gpu \

docs/source/distributions/self_hosted_distro/meta-reference-quantized-gpu.md

@@ -1,123 +0,0 @@
----
-orphan: true
----
-<!-- This file was auto-generated by distro_codegen.py, please edit source -->
-# Meta Reference Quantized Distribution
-
-```{toctree}
-:maxdepth: 2
-:hidden:
-
-self
-```
-
-The `llamastack/distribution-meta-reference-quantized-gpu` distribution consists of the following provider configurations:
-
-| API | Provider(s) |
-|-----|-------------|
-| agents | `inline::meta-reference` |
-| datasetio | `remote::huggingface`, `inline::localfs` |
-| eval | `inline::meta-reference` |
-| inference | `inline::meta-reference-quantized` |
-| safety | `inline::llama-guard` |
-| scoring | `inline::basic`, `inline::llm-as-judge`, `inline::braintrust` |
-| telemetry | `inline::meta-reference` |
-| tool_runtime | `remote::brave-search`, `remote::tavily-search`, `inline::code-interpreter`, `inline::rag-runtime`, `remote::model-context-protocol` |
-| vector_io | `inline::faiss`, `remote::chromadb`, `remote::pgvector` |
-
-
-The only difference vs. the `meta-reference-gpu` distribution is that it has support for more efficient inference -- with fp8, int4 quantization, etc.
-
-Note that you need access to nvidia GPUs to run this distribution. This distribution is not compatible with CPU-only machines or machines with AMD GPUs.
-
-### Environment Variables
-
-The following environment variables can be configured:
-
-- `LLAMA_STACK_PORT`: Port for the Llama Stack distribution server (default: `8321`)
-- `INFERENCE_MODEL`: Inference model loaded into the Meta Reference server (default: `meta-llama/Llama-3.2-3B-Instruct`)
-- `INFERENCE_CHECKPOINT_DIR`: Directory containing the Meta Reference model checkpoint (default: `null`)
-
-
-## Prerequisite: Downloading Models
-
-Please use `llama model list --downloaded` to check that you have llama model checkpoints downloaded in `~/.llama` before proceeding. See [installation guide](https://llama-stack.readthedocs.io/en/latest/references/llama_cli_reference/download_models.html) here to download the models. Run `llama model list` to see the available models to download, and `llama model download` to download the checkpoints.
-
-```
-$ llama model list --downloaded
-┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━┓
-┃ Model                                   ┃ Size     ┃ Modified Time       ┃
-┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━┩
-│ Llama3.2-1B-Instruct:int4-qlora-eo8     │ 1.53 GB  │ 2025-02-26 11:22:28 │
-├─────────────────────────────────────────┼──────────┼─────────────────────┤
-│ Llama3.2-1B                             │ 2.31 GB  │ 2025-02-18 21:48:52 │
-├─────────────────────────────────────────┼──────────┼─────────────────────┤
-│ Prompt-Guard-86M                        │ 0.02 GB  │ 2025-02-26 11:29:28 │
-├─────────────────────────────────────────┼──────────┼─────────────────────┤
-│ Llama3.2-3B-Instruct:int4-spinquant-eo8 │ 3.69 GB  │ 2025-02-26 11:37:41 │
-├─────────────────────────────────────────┼──────────┼─────────────────────┤
-│ Llama3.2-3B                             │ 5.99 GB  │ 2025-02-18 21:51:26 │
-├─────────────────────────────────────────┼──────────┼─────────────────────┤
-│ Llama3.1-8B                             │ 14.97 GB │ 2025-02-16 10:36:37 │
-├─────────────────────────────────────────┼──────────┼─────────────────────┤
-│ Llama3.2-1B-Instruct:int4-spinquant-eo8 │ 1.51 GB  │ 2025-02-26 11:35:02 │
-├─────────────────────────────────────────┼──────────┼─────────────────────┤
-│ Llama-Guard-3-1B                        │ 2.80 GB  │ 2025-02-26 11:20:46 │
-├─────────────────────────────────────────┼──────────┼─────────────────────┤
-│ Llama-Guard-3-1B:int4                   │ 0.43 GB  │ 2025-02-26 11:33:33 │
-└─────────────────────────────────────────┴──────────┴─────────────────────┘
-```
-
-## Running the Distribution
-
-You can do this via Conda (build code) or Docker which has a pre-built image.
-
-### Via Docker
-
-This method allows you to get started quickly without having to build the distribution code.
-
-```bash
-LLAMA_STACK_PORT=8321
-docker run \
-  -it \
-  --pull always \
-  -p $LLAMA_STACK_PORT:$LLAMA_STACK_PORT \
-  -v ~/.llama:/root/.llama \
-  llamastack/distribution-meta-reference-quantized-gpu \
-  --port $LLAMA_STACK_PORT \
-  --env INFERENCE_MODEL=meta-llama/Llama-3.2-3B-Instruct
-```
-
-If you are using Llama Stack Safety / Shield APIs, use:
-
-```bash
-docker run \
-  -it \
-  --pull always \
-  -p $LLAMA_STACK_PORT:$LLAMA_STACK_PORT \
-  -v ~/.llama:/root/.llama \
-  llamastack/distribution-meta-reference-quantized-gpu \
-  --port $LLAMA_STACK_PORT \
-  --env INFERENCE_MODEL=meta-llama/Llama-3.2-3B-Instruct \
-  --env SAFETY_MODEL=meta-llama/Llama-Guard-3-1B
-```
-
-### Via Conda
-
-Make sure you have done `uv pip install llama-stack` and have the Llama Stack CLI available.
-
-```bash
-llama stack build --template meta-reference-quantized-gpu --image-type conda
-llama stack run distributions/meta-reference-quantized-gpu/run.yaml \
-  --port $LLAMA_STACK_PORT \
-  --env INFERENCE_MODEL=meta-llama/Llama-3.2-3B-Instruct
-```
-
-If you are using Llama Stack Safety / Shield APIs, use:
-
-```bash
-llama stack run distributions/meta-reference-quantized-gpu/run-with-safety.yaml \
-  --port $LLAMA_STACK_PORT \
-  --env INFERENCE_MODEL=meta-llama/Llama-3.2-3B-Instruct \
-  --env SAFETY_MODEL=meta-llama/Llama-Guard-3-1B
-```

docs/source/distributions/self_hosted_distro/nvidia.md

@@ -6,8 +6,8 @@ The `llamastack/distribution-nvidia` distribution consists of the following prov
 | API | Provider(s) |
 |-----|-------------|
 | agents | `inline::meta-reference` |
-| datasetio | `inline::localfs` |
+| datasetio | `inline::localfs`, `remote::nvidia` |
-| eval | `inline::meta-reference` |
+| eval | `remote::nvidia` |
 | inference | `remote::nvidia` |
 | post_training | `remote::nvidia` |
 | safety | `remote::nvidia` |
@@ -22,13 +22,13 @@ The `llamastack/distribution-nvidia` distribution consists of the following prov
 The following environment variables can be configured:
 
 - `NVIDIA_API_KEY`: NVIDIA API Key (default: ``)
-- `NVIDIA_USER_ID`: NVIDIA User ID (default: `llama-stack-user`)
+- `NVIDIA_APPEND_API_VERSION`: Whether to append the API version to the base_url (default: `True`)
 - `NVIDIA_DATASET_NAMESPACE`: NVIDIA Dataset Namespace (default: `default`)
-- `NVIDIA_ACCESS_POLICIES`: NVIDIA Access Policies (default: `{}`)
 - `NVIDIA_PROJECT_ID`: NVIDIA Project ID (default: `test-project`)
 - `NVIDIA_CUSTOMIZER_URL`: NVIDIA Customizer URL (default: `https://customizer.api.nvidia.com`)
 - `NVIDIA_OUTPUT_MODEL_DIR`: NVIDIA Output Model Directory (default: `test-example-model@v1`)
 - `GUARDRAILS_SERVICE_URL`: URL for the NeMo Guardrails Service (default: `http://0.0.0.0:7331`)
+- `NVIDIA_EVALUATOR_URL`: URL for the NeMo Evaluator Service (default: `http://0.0.0.0:7331`)
 - `INFERENCE_MODEL`: Inference model (default: `Llama3.1-8B-Instruct`)
 - `SAFETY_MODEL`: Name of the model to use for safety (default: `meta/llama-3.1-8b-instruct`)
 
@@ -45,20 +45,91 @@ The following models are available by default:
 - `meta/llama-3.2-3b-instruct (aliases: meta-llama/Llama-3.2-3B-Instruct)`
 - `meta/llama-3.2-11b-vision-instruct (aliases: meta-llama/Llama-3.2-11B-Vision-Instruct)`
 - `meta/llama-3.2-90b-vision-instruct (aliases: meta-llama/Llama-3.2-90B-Vision-Instruct)`
+- `meta/llama-3.3-70b-instruct (aliases: meta-llama/Llama-3.3-70B-Instruct)`
 - `nvidia/llama-3.2-nv-embedqa-1b-v2 `
 - `nvidia/nv-embedqa-e5-v5 `
 - `nvidia/nv-embedqa-mistral-7b-v2 `
 - `snowflake/arctic-embed-l `
 
 
-### Prerequisite: API Keys
+## Prerequisites
+### NVIDIA API Keys
 
-Make sure you have access to a NVIDIA API Key. You can get one by visiting [https://build.nvidia.com/](https://build.nvidia.com/).
+Make sure you have access to a NVIDIA API Key. You can get one by visiting [https://build.nvidia.com/](https://build.nvidia.com/). Use this key for the `NVIDIA_API_KEY` environment variable.
 
+### Deploy NeMo Microservices Platform
+The NVIDIA NeMo microservices platform supports end-to-end microservice deployment of a complete AI flywheel on your Kubernetes cluster through the NeMo Microservices Helm Chart. Please reference the [NVIDIA NeMo Microservices documentation](https://docs.nvidia.com/nemo/microservices/latest/about/index.html) for platform prerequisites and instructions to install and deploy the platform.
+
+## Supported Services
+Each Llama Stack API corresponds to a specific NeMo microservice. The core microservices (Customizer, Evaluator, Guardrails) are exposed by the same endpoint. The platform components (Data Store) are each exposed by separate endpoints.
+
+### Inference: NVIDIA NIM
+NVIDIA NIM is used for running inference with registered models. There are two ways to access NVIDIA NIMs:
+  1. Hosted (default): Preview APIs hosted at https://integrate.api.nvidia.com (Requires an API key)
+  2. Self-hosted: NVIDIA NIMs that run on your own infrastructure.
+
+The deployed platform includes the NIM Proxy microservice, which is the service that provides to access your NIMs (for example, to run inference on a model). Set the `NVIDIA_BASE_URL` environment variable to use your NVIDIA NIM Proxy deployment.
+
+### Datasetio API: NeMo Data Store
+The NeMo Data Store microservice serves as the default file storage solution for the NeMo microservices platform. It exposts APIs compatible with the Hugging Face Hub client (`HfApi`), so you can use the client to interact with Data Store. The `NVIDIA_DATASETS_URL` environment variable should point to your NeMo Data Store endpoint.
+
+See the [NVIDIA Datasetio docs](/llama_stack/providers/remote/datasetio/nvidia/README.md) for supported features and example usage.
+
+### Eval API: NeMo Evaluator
+The NeMo Evaluator microservice supports evaluation of LLMs. Launching an Evaluation job with NeMo Evaluator requires an Evaluation Config (an object that contains metadata needed by the job). A Llama Stack Benchmark maps to an Evaluation Config, so registering a Benchmark creates an Evaluation Config in NeMo Evaluator. The `NVIDIA_EVALUATOR_URL` environment variable should point to your NeMo Microservices endpoint.
+
+See the [NVIDIA Eval docs](/llama_stack/providers/remote/eval/nvidia/README.md) for supported features and example usage.
+
+### Post-Training API: NeMo Customizer
+The NeMo Customizer microservice supports fine-tuning models. You can reference [this list of supported models](/llama_stack/providers/remote/post_training/nvidia/models.py) that can be fine-tuned using Llama Stack. The `NVIDIA_CUSTOMIZER_URL` environment variable should point to your NeMo Microservices endpoint.
+
+See the [NVIDIA Post-Training docs](/llama_stack/providers/remote/post_training/nvidia/README.md) for supported features and example usage.
+
+### Safety API: NeMo Guardrails
+The NeMo Guardrails microservice sits between your application and the LLM, and adds checks and content moderation to a model. The `GUARDRAILS_SERVICE_URL` environment variable should point to your NeMo Microservices endpoint.
+
+See the NVIDIA Safety docs for supported features and example usage.
+
+## Deploying models
+In order to use a registered model with the Llama Stack APIs, ensure the corresponding NIM is deployed to your environment. For example, you can use the NIM Proxy microservice to deploy `meta/llama-3.2-1b-instruct`.
+
+Note: For improved inference speeds, we need to use NIM with `fast_outlines` guided decoding system (specified in the request body). This is the default if you deployed the platform with the NeMo Microservices Helm Chart.
+```sh
+# URL to NeMo NIM Proxy service
+export NEMO_URL="http://nemo.test"
+
+curl --location "$NEMO_URL/v1/deployment/model-deployments" \
+   -H 'accept: application/json' \
+   -H 'Content-Type: application/json' \
+   -d '{
+      "name": "llama-3.2-1b-instruct",
+      "namespace": "meta",
+      "config": {
+         "model": "meta/llama-3.2-1b-instruct",
+         "nim_deployment": {
+            "image_name": "nvcr.io/nim/meta/llama-3.2-1b-instruct",
+            "image_tag": "1.8.3",
+            "pvc_size": "25Gi",
+            "gpu": 1,
+            "additional_envs": {
+               "NIM_GUIDED_DECODING_BACKEND": "fast_outlines"
+            }
+         }
+      }
+   }'
+```
+This NIM deployment should take approximately 10 minutes to go live. [See the docs](https://docs.nvidia.com/nemo/microservices/latest/get-started/tutorials/deploy-nims.html) for more information on how to deploy a NIM and verify it's available for inference.
+
+You can also remove a deployed NIM to free up GPU resources, if needed.
+```sh
+export NEMO_URL="http://nemo.test"
+
+curl -X DELETE "$NEMO_URL/v1/deployment/model-deployments/meta/llama-3.1-8b-instruct"
+```
 
 ## Running Llama Stack with NVIDIA
 
-You can do this via Conda (build code) or Docker which has a pre-built image.
+You can do this via Conda or venv (build code), or Docker which has a pre-built image.
 
 ### Via Docker
 
@@ -80,9 +151,23 @@ docker run \
 ### Via Conda
 
 ```bash
+INFERENCE_MODEL=meta-llama/Llama-3.1-8b-Instruct
 llama stack build --template nvidia --image-type conda
 llama stack run ./run.yaml \
   --port 8321 \
-  --env NVIDIA_API_KEY=$NVIDIA_API_KEY
+  --env NVIDIA_API_KEY=$NVIDIA_API_KEY \
+  --env INFERENCE_MODEL=$INFERENCE_MODEL
+```
+
+### Via venv
+
+If you've set up your local development environment, you can also build the image using your local virtual environment.
+
+```bash
+INFERENCE_MODEL=meta-llama/Llama-3.1-8b-Instruct
+llama stack build --template nvidia --image-type venv
+llama stack run ./run.yaml \
+  --port 8321 \
+  --env NVIDIA_API_KEY=$NVIDIA_API_KEY \
   --env INFERENCE_MODEL=$INFERENCE_MODEL
 ```

docs/source/distributions/self_hosted_distro/ollama.md

@@ -22,7 +22,7 @@ The `llamastack/distribution-ollama` distribution consists of the following prov
 | safety | `inline::llama-guard` |
 | scoring | `inline::basic`, `inline::llm-as-judge`, `inline::braintrust` |
 | telemetry | `inline::meta-reference` |
-| tool_runtime | `remote::brave-search`, `remote::tavily-search`, `inline::code-interpreter`, `inline::rag-runtime`, `remote::model-context-protocol`, `remote::wolfram-alpha` |
+| tool_runtime | `remote::brave-search`, `remote::tavily-search`, `inline::rag-runtime`, `remote::model-context-protocol`, `remote::wolfram-alpha` |
 | vector_io | `inline::faiss`, `remote::chromadb`, `remote::pgvector` |
 
 

docs/source/distributions/self_hosted_distro/passthrough.md

@@ -22,7 +22,7 @@ The `llamastack/distribution-passthrough` distribution consists of the following
 | safety | `inline::llama-guard` |
 | scoring | `inline::basic`, `inline::llm-as-judge`, `inline::braintrust` |
 | telemetry | `inline::meta-reference` |
-| tool_runtime | `remote::brave-search`, `remote::tavily-search`, `remote::wolfram-alpha`, `inline::code-interpreter`, `inline::rag-runtime`, `remote::model-context-protocol` |
+| tool_runtime | `remote::brave-search`, `remote::tavily-search`, `remote::wolfram-alpha`, `inline::rag-runtime`, `remote::model-context-protocol` |
 | vector_io | `inline::faiss`, `remote::chromadb`, `remote::pgvector` |
 
 

docs/source/distributions/self_hosted_distro/remote-vllm.md

@@ -21,7 +21,7 @@ The `llamastack/distribution-remote-vllm` distribution consists of the following
 | safety | `inline::llama-guard` |
 | scoring | `inline::basic`, `inline::llm-as-judge`, `inline::braintrust` |
 | telemetry | `inline::meta-reference` |
-| tool_runtime | `remote::brave-search`, `remote::tavily-search`, `inline::code-interpreter`, `inline::rag-runtime`, `remote::model-context-protocol`, `remote::wolfram-alpha` |
+| tool_runtime | `remote::brave-search`, `remote::tavily-search`, `inline::rag-runtime`, `remote::model-context-protocol`, `remote::wolfram-alpha` |
 | vector_io | `inline::faiss`, `remote::chromadb`, `remote::pgvector` |
 
 
@@ -41,10 +41,10 @@ The following environment variables can be configured:
 
 ## Setting up vLLM server
 
-In the following sections, we'll use either AMD and NVIDIA GPUs to serve as hardware accelerators for the vLLM
+In the following sections, we'll use AMD, NVIDIA or Intel GPUs to serve as hardware accelerators for the vLLM
 server, which acts as both the LLM inference provider and the safety provider. Note that vLLM also
 [supports many other hardware accelerators](https://docs.vllm.ai/en/latest/getting_started/installation.html) and
-that we only use GPUs here for demonstration purposes.
+that we only use GPUs here for demonstration purposes. Note that if you run into issues, you can include the environment variable `--env VLLM_DEBUG_LOG_API_SERVER_RESPONSE=true` (available in vLLM v0.8.3 and above) in the `docker run` command to enable log response from API server for debugging.
 
 ### Setting up vLLM server on AMD GPU
 
@@ -162,6 +162,55 @@ docker run \
     --port $SAFETY_PORT
 ```
 
+### Setting up vLLM server on Intel GPU
+
+Refer to [vLLM Documentation for XPU](https://docs.vllm.ai/en/v0.8.2/getting_started/installation/gpu.html?device=xpu) to get a vLLM endpoint. In addition to vLLM side setup which guides towards installing vLLM from sources orself-building vLLM Docker container, Intel provides prebuilt vLLM container to use on systems with Intel GPUs supported by PyTorch XPU backend:
+- [intel/vllm](https://hub.docker.com/r/intel/vllm)
+
+Here is a sample script to start a vLLM server locally via Docker using Intel provided container:
+
+```bash
+export INFERENCE_PORT=8000
+export INFERENCE_MODEL=meta-llama/Llama-3.2-1B-Instruct
+export ZE_AFFINITY_MASK=0
+
+docker run \
+    --pull always \
+    --device /dev/dri \
+    -v /dev/dri/by-path:/dev/dri/by-path \
+    -v ~/.cache/huggingface:/root/.cache/huggingface \
+    --env "HUGGING_FACE_HUB_TOKEN=$HF_TOKEN" \
+    --env ZE_AFFINITY_MASK=$ZE_AFFINITY_MASK \
+    -p $INFERENCE_PORT:$INFERENCE_PORT \
+    --ipc=host \
+    intel/vllm:xpu \
+    --gpu-memory-utilization 0.7 \
+    --model $INFERENCE_MODEL \
+    --port $INFERENCE_PORT
+```
+
+If you are using Llama Stack Safety / Shield APIs, then you will need to also run another instance of a vLLM with a corresponding safety model like `meta-llama/Llama-Guard-3-1B` using a script like:
+
+```bash
+export SAFETY_PORT=8081
+export SAFETY_MODEL=meta-llama/Llama-Guard-3-1B
+export ZE_AFFINITY_MASK=1
+
+docker run \
+    --pull always \
+    --device /dev/dri \
+    -v /dev/dri/by-path:/dev/dri/by-path \
+    -v ~/.cache/huggingface:/root/.cache/huggingface \
+    --env "HUGGING_FACE_HUB_TOKEN=$HF_TOKEN" \
+    --env ZE_AFFINITY_MASK=$ZE_AFFINITY_MASK \
+    -p $SAFETY_PORT:$SAFETY_PORT \
+    --ipc=host \
+    intel/vllm:xpu \
+    --gpu-memory-utilization 0.7 \
+    --model $SAFETY_MODEL \
+    --port $SAFETY_PORT
+```
+
 ## Running Llama Stack
 
 Now you are ready to run Llama Stack with vLLM as the inference provider. You can do this via Conda (build code) or Docker which has a pre-built image.

docs/source/distributions/self_hosted_distro/sambanova.md

@@ -19,7 +19,7 @@ The `llamastack/distribution-sambanova` distribution consists of the following p
 | inference | `remote::sambanova`, `inline::sentence-transformers` |
 | safety | `inline::llama-guard` |
 | telemetry | `inline::meta-reference` |
-| tool_runtime | `remote::brave-search`, `remote::tavily-search`, `inline::code-interpreter`, `inline::rag-runtime`, `remote::model-context-protocol`, `remote::wolfram-alpha` |
+| tool_runtime | `remote::brave-search`, `remote::tavily-search`, `inline::rag-runtime`, `remote::model-context-protocol`, `remote::wolfram-alpha` |
 | vector_io | `inline::faiss`, `remote::chromadb`, `remote::pgvector` |
 
 

docs/source/distributions/self_hosted_distro/tgi.md

@@ -23,7 +23,7 @@ The `llamastack/distribution-tgi` distribution consists of the following provide
 | safety | `inline::llama-guard` |
 | scoring | `inline::basic`, `inline::llm-as-judge`, `inline::braintrust` |
 | telemetry | `inline::meta-reference` |
-| tool_runtime | `remote::brave-search`, `remote::tavily-search`, `inline::code-interpreter`, `inline::rag-runtime`, `remote::model-context-protocol` |
+| tool_runtime | `remote::brave-search`, `remote::tavily-search`, `inline::rag-runtime`, `remote::model-context-protocol` |
 | vector_io | `inline::faiss`, `remote::chromadb`, `remote::pgvector` |
 
 

docs/source/distributions/self_hosted_distro/together.md

@@ -22,7 +22,7 @@ The `llamastack/distribution-together` distribution consists of the following pr
 | safety | `inline::llama-guard` |
 | scoring | `inline::basic`, `inline::llm-as-judge`, `inline::braintrust` |
 | telemetry | `inline::meta-reference` |
-| tool_runtime | `remote::brave-search`, `remote::tavily-search`, `inline::code-interpreter`, `inline::rag-runtime`, `remote::model-context-protocol`, `remote::wolfram-alpha` |
+| tool_runtime | `remote::brave-search`, `remote::tavily-search`, `inline::rag-runtime`, `remote::model-context-protocol`, `remote::wolfram-alpha` |
 | vector_io | `inline::faiss`, `remote::chromadb`, `remote::pgvector` |
 
 

docs/source/providers/external.md

@@ -50,9 +50,11 @@ Llama Stack supports two types of external providers:
 
 Here's a list of known external providers that you can use with Llama Stack:
 
-| Type | Name | Description | Repository |
+| Name | Description | API | Type | Repository |
-|------|------|-------------|------------|
+|------|-------------|-----|------|------------|
-| Remote | KubeFlow Training | Train models with KubeFlow | [llama-stack-provider-kft](https://github.com/opendatahub-io/llama-stack-provider-kft) |
+| KubeFlow Training | Train models with KubeFlow | Post Training | Remote | [llama-stack-provider-kft](https://github.com/opendatahub-io/llama-stack-provider-kft) |
+| KubeFlow Pipelines | Train models with KubeFlow Pipelines | Post Training | Remote | [llama-stack-provider-kfp-trainer](https://github.com/opendatahub-io/llama-stack-provider-kfp-trainer) |
+| RamaLama | Inference models with RamaLama | Inference | Remote | [ramalama-stack](https://github.com/containers/ramalama-stack) |
 
 ### Remote Provider Specification
 

docs/zero_to_hero_guide/00_Inference101.ipynb

@@ -389,5 +389,7 @@
       "pygments_lexer": "ipython3",
       "version": "3.10.15"
     }
-  }
+  },
+  "nbformat": 4,
+  "nbformat_minor": 5
 }

docs/zero_to_hero_guide/01_Local_Cloud_Inference101.ipynb

@@ -256,5 +256,7 @@
       "pygments_lexer": "ipython3",
       "version": "3.10.15"
     }
-  }
+  },
+  "nbformat": 4,
+  "nbformat_minor": 5
 }

docs/zero_to_hero_guide/02_Prompt_Engineering101.ipynb

@@ -301,5 +301,7 @@
       "pygments_lexer": "ipython3",
       "version": "3.12.2"
     }
-  }
+  },
+  "nbformat": 4,
+  "nbformat_minor": 5
 }

docs/zero_to_hero_guide/03_Image_Chat101.ipynb

@@ -200,5 +200,7 @@
       "pygments_lexer": "ipython3",
       "version": "3.12.2"
     }
-  }
+  },
+  "nbformat": 4,
+  "nbformat_minor": 5
 }

docs/zero_to_hero_guide/04_Tool_Calling101.ipynb

@@ -355,5 +355,7 @@
       "pygments_lexer": "ipython3",
       "version": "3.10.15"
     }
-  }
+  },
+  "nbformat": 4,
+  "nbformat_minor": 5
 }

docs/zero_to_hero_guide/05_Memory101.ipynb

@@ -398,5 +398,7 @@
       "pygments_lexer": "ipython3",
       "version": "3.10.15"
     }
-  }
+  },
+  "nbformat": 4,
+  "nbformat_minor": 5
 }

docs/zero_to_hero_guide/06_Safety101.ipynb

@@ -132,5 +132,7 @@
       "pygments_lexer": "ipython3",
       "version": "3.11.10"
     }
-  }
+  },
+  "nbformat": 4,
+  "nbformat_minor": 5
 }

docs/zero_to_hero_guide/07_Agents101.ipynb

@@ -188,5 +188,7 @@
       "pygments_lexer": "ipython3",
       "version": "3.10.15"
     }
-  }
+  },
+  "nbformat": 4,
+  "nbformat_minor": 5
 }

docs/zero_to_hero_guide/README.md

@@ -86,11 +86,11 @@ If you're looking for more specific topics, we have a [Zero to Hero Guide](#next
    llama stack build --template ollama --image-type conda
    ```
    **Expected Output:**
-   ```
+   ```bash
    ...
-   Build Successful! Next steps:
+   Build Successful!
-   1. Set the environment variables: LLAMA_STACK_PORT, OLLAMA_URL, INFERENCE_MODEL, SAFETY_MODEL
+   You can find the newly-built template here: ~/.llama/distributions/ollama/ollama-run.yaml
-   2. `llama stack run /Users/<username>/.llama/distributions/llamastack-ollama/ollama-run.yaml
+   You can run the new Llama Stack Distro via: llama stack run ~/.llama/distributions/ollama/ollama-run.yaml --image-type conda
    ```
 
 3. **Set the ENV variables by exporting them to the terminal**:

install.sh

@@ -0,0 +1,145 @@
+#!/usr/bin/env bash
+# Copyright (c) Meta Platforms, Inc. and affiliates.
+# All rights reserved.
+#
+# This source code is licensed under the terms described in the LICENSE file in
+# the root directory of this source tree.
+
+set -Eeuo pipefail
+
+PORT=8321
+OLLAMA_PORT=11434
+MODEL_ALIAS="llama3.2:3b"
+SERVER_IMAGE="llamastack/distribution-ollama:0.2.2"
+WAIT_TIMEOUT=300
+
+log(){ printf "\e[1;32m%s\e[0m\n" "$*"; }
+die(){ printf "\e[1;31m❌ %s\e[0m\n" "$*" >&2; exit 1; }
+
+wait_for_service() {
+  local url="$1"
+  local pattern="$2"
+  local timeout="$3"
+  local name="$4"
+  local start ts
+  log "⏳  Waiting for ${name}…"
+  start=$(date +%s)
+  while true; do
+    if curl --retry 5 --retry-delay 1 --retry-max-time "$timeout" --retry-all-errors --silent --fail "$url" 2>/dev/null | grep -q "$pattern"; then
+      break
+    fi
+    ts=$(date +%s)
+    if (( ts - start >= timeout )); then
+      return 1
+    fi
+    printf '.'
+    sleep 1
+  done
+  return 0
+}
+
+if command -v docker &> /dev/null; then
+  ENGINE="docker"
+elif command -v podman &> /dev/null; then
+  ENGINE="podman"
+else
+  die "Docker or Podman is required. Install Docker: https://docs.docker.com/get-docker/ or Podman: https://podman.io/getting-started/installation"
+fi
+
+# Explicitly set the platform for the host architecture
+HOST_ARCH="$(uname -m)"
+if [ "$HOST_ARCH" = "arm64" ]; then
+  if [ "$ENGINE" = "docker" ]; then
+    PLATFORM_OPTS=( --platform linux/amd64 )
+  else
+    PLATFORM_OPTS=( --os linux --arch amd64 )
+  fi
+else
+  PLATFORM_OPTS=()
+fi
+
+# macOS + Podman: ensure VM is running before we try to launch containers
+# If you need GPU passthrough under Podman on macOS, init the VM with libkrun:
+#   CONTAINERS_MACHINE_PROVIDER=libkrun podman machine init
+if [ "$ENGINE" = "podman" ] && [ "$(uname -s)" = "Darwin" ]; then
+  if ! podman info &>/dev/null; then
+    log "⌛️ Initializing Podman VM…"
+    podman machine init &>/dev/null || true
+    podman machine start &>/dev/null || true
+
+    log "⌛️  Waiting for Podman API…"
+    until podman info &>/dev/null; do
+      sleep 1
+    done
+    log "✅  Podman VM is up"
+  fi
+fi
+
+# Clean up any leftovers from earlier runs
+for name in ollama-server llama-stack; do
+  ids=$($ENGINE ps -aq --filter "name=^${name}$")
+  if [ -n "$ids" ]; then
+    log "⚠️   Found existing container(s) for '${name}', removing…"
+    $ENGINE rm -f "$ids" > /dev/null 2>&1
+  fi
+done
+
+###############################################################################
+# 0. Create a shared network
+###############################################################################
+if ! $ENGINE network inspect llama-net >/dev/null 2>&1; then
+  log "🌐  Creating network…"
+  $ENGINE network create llama-net >/dev/null 2>&1
+fi
+
+###############################################################################
+# 1. Ollama
+###############################################################################
+log "🦙  Starting Ollama…"
+$ENGINE run -d "${PLATFORM_OPTS[@]}" --name ollama-server \
+  --network llama-net \
+  -p "${OLLAMA_PORT}:${OLLAMA_PORT}" \
+  ollama/ollama > /dev/null 2>&1
+
+if ! wait_for_service "http://localhost:${OLLAMA_PORT}/" "Ollama" "$WAIT_TIMEOUT" "Ollama daemon"; then
+  log "❌  Ollama daemon did not become ready in ${WAIT_TIMEOUT}s; dumping container logs:"
+  $ENGINE logs --tail 200 ollama-server
+  die "Ollama startup failed"
+fi
+
+log "📦  Ensuring model is pulled: ${MODEL_ALIAS}…"
+if ! $ENGINE exec ollama-server ollama pull "${MODEL_ALIAS}" > /dev/null 2>&1; then
+  log "❌  Failed to pull model ${MODEL_ALIAS}; dumping container logs:"
+  $ENGINE logs --tail 200 ollama-server
+  die "Model pull failed"
+fi
+
+###############################################################################
+# 2. Llama‑Stack
+###############################################################################
+cmd=( run -d "${PLATFORM_OPTS[@]}" --name llama-stack \
+      --network llama-net \
+      -p "${PORT}:${PORT}" \
+      "${SERVER_IMAGE}" --port "${PORT}" \
+      --env INFERENCE_MODEL="${MODEL_ALIAS}" \
+      --env OLLAMA_URL="http://ollama-server:${OLLAMA_PORT}" )
+
+log "🦙  Starting Llama‑Stack…"
+$ENGINE "${cmd[@]}" > /dev/null 2>&1
+
+if ! wait_for_service "http://127.0.0.1:${PORT}/v1/health" "OK" "$WAIT_TIMEOUT" "Llama-Stack API"; then
+  log "❌  Llama-Stack did not become ready in ${WAIT_TIMEOUT}s; dumping container logs:"
+  $ENGINE logs --tail 200 llama-stack
+  die "Llama-Stack startup failed"
+fi
+
+###############################################################################
+# Done
+###############################################################################
+log ""
+log "🎉  Llama‑Stack is ready!"
+log "👉  API endpoint: http://localhost:${PORT}"
+log "📖 Documentation: https://llama-stack.readthedocs.io/en/latest/references/index.html"
+log "💻 To access the llama‑stack CLI, exec into the container:"
+log "   $ENGINE exec -ti llama-stack bash"
+log ""

llama_stack/apis/agents/agents.py

@@ -4,20 +4,10 @@
 # This source code is licensed under the terms described in the LICENSE file in
 # the root directory of this source tree.
 
+from collections.abc import AsyncIterator
 from datetime import datetime
 from enum import Enum
-from typing import (
+from typing import Annotated, Any, Literal, Protocol, runtime_checkable
-    Annotated,
-    Any,
-    AsyncIterator,
-    Dict,
-    List,
-    Literal,
-    Optional,
-    Protocol,
-    Union,
-    runtime_checkable,
-)
 
 from pydantic import BaseModel, ConfigDict, Field
 
@@ -38,6 +28,13 @@ from llama_stack.apis.safety import SafetyViolation
 from llama_stack.apis.tools import ToolDef
 from llama_stack.schema_utils import json_schema_type, register_schema, webmethod
 
+from .openai_responses import (
+    OpenAIResponseInputMessage,
+    OpenAIResponseInputTool,
+    OpenAIResponseObject,
+    OpenAIResponseObjectStream,
+)
+
 
 class Attachment(BaseModel):
     """An attachment to an agent turn.
@@ -72,8 +69,8 @@ class StepCommon(BaseModel):
 
     turn_id: str
     step_id: str
-    started_at: Optional[datetime] = None
+    started_at: datetime | None = None
-    completed_at: Optional[datetime] = None
+    completed_at: datetime | None = None
 
 
 class StepType(Enum):
@@ -113,8 +110,8 @@ class ToolExecutionStep(StepCommon):
     """
 
     step_type: Literal[StepType.tool_execution.value] = StepType.tool_execution.value
-    tool_calls: List[ToolCall]
+    tool_calls: list[ToolCall]
-    tool_responses: List[ToolResponse]
+    tool_responses: list[ToolResponse]
 
 
 @json_schema_type
@@ -125,7 +122,7 @@ class ShieldCallStep(StepCommon):
     """
 
     step_type: Literal[StepType.shield_call.value] = StepType.shield_call.value
-    violation: Optional[SafetyViolation]
+    violation: SafetyViolation | None
 
 
 @json_schema_type
@@ -143,12 +140,7 @@ class MemoryRetrievalStep(StepCommon):
 
 
 Step = Annotated[
-    Union[
+    InferenceStep | ToolExecutionStep | ShieldCallStep | MemoryRetrievalStep,
-        InferenceStep,
-        ToolExecutionStep,
-        ShieldCallStep,
-        MemoryRetrievalStep,
-    ],
     Field(discriminator="step_type"),
 ]
 
@@ -159,18 +151,13 @@ class Turn(BaseModel):
 
     turn_id: str
     session_id: str
-    input_messages: List[
+    input_messages: list[UserMessage | ToolResponseMessage]
-        Union[
+    steps: list[Step]
-            UserMessage,
-            ToolResponseMessage,
-        ]
-    ]
-    steps: List[Step]
     output_message: CompletionMessage
-    output_attachments: Optional[List[Attachment]] = Field(default_factory=list)
+    output_attachments: list[Attachment] | None = Field(default_factory=list)
 
     started_at: datetime
-    completed_at: Optional[datetime] = None
+    completed_at: datetime | None = None
 
 
 @json_schema_type
@@ -179,34 +166,31 @@ class Session(BaseModel):
 
     session_id: str
     session_name: str
-    turns: List[Turn]
+    turns: list[Turn]
     started_at: datetime
 
 
 class AgentToolGroupWithArgs(BaseModel):
     name: str
-    args: Dict[str, Any]
+    args: dict[str, Any]
 
 
-AgentToolGroup = Union[
+AgentToolGroup = str | AgentToolGroupWithArgs
-    str,
-    AgentToolGroupWithArgs,
-]
 register_schema(AgentToolGroup, name="AgentTool")
 
 
 class AgentConfigCommon(BaseModel):
-    sampling_params: Optional[SamplingParams] = Field(default_factory=SamplingParams)
+    sampling_params: SamplingParams | None = Field(default_factory=SamplingParams)
 
-    input_shields: Optional[List[str]] = Field(default_factory=list)
+    input_shields: list[str] | None = Field(default_factory=list)
-    output_shields: Optional[List[str]] = Field(default_factory=list)
+    output_shields: list[str] | None = Field(default_factory=list)
-    toolgroups: Optional[List[AgentToolGroup]] = Field(default_factory=list)
+    toolgroups: list[AgentToolGroup] | None = Field(default_factory=list)
-    client_tools: Optional[List[ToolDef]] = Field(default_factory=list)
+    client_tools: list[ToolDef] | None = Field(default_factory=list)
-    tool_choice: Optional[ToolChoice] = Field(default=None, deprecated="use tool_config instead")
+    tool_choice: ToolChoice | None = Field(default=None, deprecated="use tool_config instead")
-    tool_prompt_format: Optional[ToolPromptFormat] = Field(default=None, deprecated="use tool_config instead")
+    tool_prompt_format: ToolPromptFormat | None = Field(default=None, deprecated="use tool_config instead")
-    tool_config: Optional[ToolConfig] = Field(default=None)
+    tool_config: ToolConfig | None = Field(default=None)
 
-    max_infer_iters: Optional[int] = 10
+    max_infer_iters: int | None = 10
 
     def model_post_init(self, __context):
         if self.tool_config:
@@ -225,10 +209,20 @@ class AgentConfigCommon(BaseModel):
 
 @json_schema_type
 class AgentConfig(AgentConfigCommon):
+    """Configuration for an agent.
+
+    :param model: The model identifier to use for the agent
+    :param instructions: The system instructions for the agent
+    :param name: Optional name for the agent, used in telemetry and identification
+    :param enable_session_persistence: Optional flag indicating whether session data has to be persisted
+    :param response_format: Optional response format configuration
+    """
+
     model: str
     instructions: str
-    enable_session_persistence: Optional[bool] = False
+    name: str | None = None
-    response_format: Optional[ResponseFormat] = None
+    enable_session_persistence: bool | None = False
+    response_format: ResponseFormat | None = None
 
 
 @json_schema_type
@@ -240,16 +234,16 @@ class Agent(BaseModel):
 
 @json_schema_type
 class ListAgentsResponse(BaseModel):
-    data: List[Agent]
+    data: list[Agent]
 
 
 @json_schema_type
 class ListAgentSessionsResponse(BaseModel):
-    data: List[Session]
+    data: list[Session]
 
 
 class AgentConfigOverridablePerTurn(AgentConfigCommon):
-    instructions: Optional[str] = None
+    instructions: str | None = None
 
 
 class AgentTurnResponseEventType(Enum):
@@ -267,7 +261,7 @@ class AgentTurnResponseStepStartPayload(BaseModel):
     event_type: Literal[AgentTurnResponseEventType.step_start.value] = AgentTurnResponseEventType.step_start.value
     step_type: StepType
     step_id: str
-    metadata: Optional[Dict[str, Any]] = Field(default_factory=dict)
+    metadata: dict[str, Any] | None = Field(default_factory=dict)
 
 
 @json_schema_type
@@ -310,14 +304,12 @@ class AgentTurnResponseTurnAwaitingInputPayload(BaseModel):
 
 
 AgentTurnResponseEventPayload = Annotated[
-    Union[
+    AgentTurnResponseStepStartPayload
-        AgentTurnResponseStepStartPayload,
+    | AgentTurnResponseStepProgressPayload
-        AgentTurnResponseStepProgressPayload,
+    | AgentTurnResponseStepCompletePayload
-        AgentTurnResponseStepCompletePayload,
+    | AgentTurnResponseTurnStartPayload
-        AgentTurnResponseTurnStartPayload,
+    | AgentTurnResponseTurnCompletePayload
-        AgentTurnResponseTurnCompletePayload,
+    | AgentTurnResponseTurnAwaitingInputPayload,
-        AgentTurnResponseTurnAwaitingInputPayload,
-    ],
     Field(discriminator="event_type"),
 ]
 register_schema(AgentTurnResponseEventPayload, name="AgentTurnResponseEventPayload")
@@ -346,18 +338,13 @@ class AgentTurnCreateRequest(AgentConfigOverridablePerTurn):
     # TODO: figure out how we can simplify this and make why
     # ToolResponseMessage needs to be here (it is function call
     # execution from outside the system)
-    messages: List[
+    messages: list[UserMessage | ToolResponseMessage]
-        Union[
-            UserMessage,
-            ToolResponseMessage,
-        ]
-    ]
 
-    documents: Optional[List[Document]] = None
+    documents: list[Document] | None = None
-    toolgroups: Optional[List[AgentToolGroup]] = None
+    toolgroups: list[AgentToolGroup] | None = None
 
-    stream: Optional[bool] = False
+    stream: bool | None = False
-    tool_config: Optional[ToolConfig] = None
+    tool_config: ToolConfig | None = None
 
 
 @json_schema_type
@@ -365,8 +352,8 @@ class AgentTurnResumeRequest(BaseModel):
     agent_id: str
     session_id: str
     turn_id: str
-    tool_responses: List[ToolResponse]
+    tool_responses: list[ToolResponse]
-    stream: Optional[bool] = False
+    stream: bool | None = False
 
 
 @json_schema_type
@@ -412,17 +399,12 @@ class Agents(Protocol):
         self,
         agent_id: str,
         session_id: str,
-        messages: List[
+        messages: list[UserMessage | ToolResponseMessage],
-            Union[
+        stream: bool | None = False,
-                UserMessage,
+        documents: list[Document] | None = None,
-                ToolResponseMessage,
+        toolgroups: list[AgentToolGroup] | None = None,
-            ]
+        tool_config: ToolConfig | None = None,
-        ],
+    ) -> Turn | AsyncIterator[AgentTurnResponseStreamChunk]:
-        stream: Optional[bool] = False,
-        documents: Optional[List[Document]] = None,
-        toolgroups: Optional[List[AgentToolGroup]] = None,
-        tool_config: Optional[ToolConfig] = None,
-    ) -> Union[Turn, AsyncIterator[AgentTurnResponseStreamChunk]]:
         """Create a new turn for an agent.
 
         :param agent_id: The ID of the agent to create the turn for.
@@ -446,9 +428,9 @@ class Agents(Protocol):
         agent_id: str,
         session_id: str,
         turn_id: str,
-        tool_responses: List[ToolResponse],
+        tool_responses: list[ToolResponse],
-        stream: Optional[bool] = False,
+        stream: bool | None = False,
-    ) -> Union[Turn, AsyncIterator[AgentTurnResponseStreamChunk]]:
+    ) -> Turn | AsyncIterator[AgentTurnResponseStreamChunk]:
         """Resume an agent turn with executed tool call responses.
 
         When a Turn has the status `awaiting_input` due to pending input from client side tool calls, this endpoint can be used to submit the outputs from the tool calls once they are ready.
@@ -521,7 +503,7 @@ class Agents(Protocol):
         self,
         session_id: str,
         agent_id: str,
-        turn_ids: Optional[List[str]] = None,
+        turn_ids: list[str] | None = None,
     ) -> Session:
         """Retrieve an agent session by its ID.
 
@@ -583,3 +565,40 @@ class Agents(Protocol):
         :returns: A ListAgentSessionsResponse.
         """
         ...
+
+    # We situate the OpenAI Responses API in the Agents API just like we did things
+    # for Inference. The Responses API, in its intent, serves the same purpose as
+    # the Agents API above -- it is essentially a lightweight "agentic loop" with
+    # integrated tool calling.
+    #
+    # Both of these APIs are inherently stateful.
+
+    @webmethod(route="/openai/v1/responses/{id}", method="GET")
+    async def get_openai_response(
+        self,
+        id: str,
+    ) -> OpenAIResponseObject:
+        """Retrieve an OpenAI response by its ID.
+
+        :param id: The ID of the OpenAI response to retrieve.
+        :returns: An OpenAIResponseObject.
+        """
+        ...
+
+    @webmethod(route="/openai/v1/responses", method="POST")
+    async def create_openai_response(
+        self,
+        input: str | list[OpenAIResponseInputMessage],
+        model: str,
+        previous_response_id: str | None = None,
+        store: bool | None = True,
+        stream: bool | None = False,
+        temperature: float | None = None,
+        tools: list[OpenAIResponseInputTool] | None = None,
+    ) -> OpenAIResponseObject | AsyncIterator[OpenAIResponseObjectStream]:
+        """Create a new OpenAI response.
+
+        :param input: Input message(s) to create the response.
+        :param model: The underlying LLM used for completions.
+        :param previous_response_id: (Optional) if specified, the new response will be a continuation of the previous response. This can be used to easily fork-off new responses from existing responses.
+        """

llama_stack/apis/agents/openai_responses.py

@@ -0,0 +1,133 @@
+# Copyright (c) Meta Platforms, Inc. and affiliates.
+# All rights reserved.
+#
+# This source code is licensed under the terms described in the LICENSE file in
+# the root directory of this source tree.
+
+from typing import Annotated, Literal
+
+from pydantic import BaseModel, Field
+
+from llama_stack.schema_utils import json_schema_type, register_schema
+
+
+@json_schema_type
+class OpenAIResponseError(BaseModel):
+    code: str
+    message: str
+
+
+@json_schema_type
+class OpenAIResponseOutputMessageContentOutputText(BaseModel):
+    text: str
+    type: Literal["output_text"] = "output_text"
+
+
+OpenAIResponseOutputMessageContent = Annotated[
+    OpenAIResponseOutputMessageContentOutputText,
+    Field(discriminator="type"),
+]
+register_schema(OpenAIResponseOutputMessageContent, name="OpenAIResponseOutputMessageContent")
+
+
+@json_schema_type
+class OpenAIResponseOutputMessage(BaseModel):
+    id: str
+    content: list[OpenAIResponseOutputMessageContent]
+    role: Literal["assistant"] = "assistant"
+    status: str
+    type: Literal["message"] = "message"
+
+
+@json_schema_type
+class OpenAIResponseOutputMessageWebSearchToolCall(BaseModel):
+    id: str
+    status: str
+    type: Literal["web_search_call"] = "web_search_call"
+
+
+OpenAIResponseOutput = Annotated[
+    OpenAIResponseOutputMessage | OpenAIResponseOutputMessageWebSearchToolCall,
+    Field(discriminator="type"),
+]
+register_schema(OpenAIResponseOutput, name="OpenAIResponseOutput")
+
+
+@json_schema_type
+class OpenAIResponseObject(BaseModel):
+    created_at: int
+    error: OpenAIResponseError | None = None
+    id: str
+    model: str
+    object: Literal["response"] = "response"
+    output: list[OpenAIResponseOutput]
+    parallel_tool_calls: bool = False
+    previous_response_id: str | None = None
+    status: str
+    temperature: float | None = None
+    top_p: float | None = None
+    truncation: str | None = None
+    user: str | None = None
+
+
+@json_schema_type
+class OpenAIResponseObjectStreamResponseCreated(BaseModel):
+    response: OpenAIResponseObject
+    type: Literal["response.created"] = "response.created"
+
+
+@json_schema_type
+class OpenAIResponseObjectStreamResponseCompleted(BaseModel):
+    response: OpenAIResponseObject
+    type: Literal["response.completed"] = "response.completed"
+
+
+OpenAIResponseObjectStream = Annotated[
+    OpenAIResponseObjectStreamResponseCreated | OpenAIResponseObjectStreamResponseCompleted,
+    Field(discriminator="type"),
+]
+register_schema(OpenAIResponseObjectStream, name="OpenAIResponseObjectStream")
+
+
+@json_schema_type
+class OpenAIResponseInputMessageContentText(BaseModel):
+    text: str
+    type: Literal["input_text"] = "input_text"
+
+
+@json_schema_type
+class OpenAIResponseInputMessageContentImage(BaseModel):
+    detail: Literal["low"] | Literal["high"] | Literal["auto"] = "auto"
+    type: Literal["input_image"] = "input_image"
+    # TODO: handle file_id
+    image_url: str | None = None
+
+
+# TODO: handle file content types
+OpenAIResponseInputMessageContent = Annotated[
+    OpenAIResponseInputMessageContentText | OpenAIResponseInputMessageContentImage,
+    Field(discriminator="type"),
+]
+register_schema(OpenAIResponseInputMessageContent, name="OpenAIResponseInputMessageContent")
+
+
+@json_schema_type
+class OpenAIResponseInputMessage(BaseModel):
+    content: str | list[OpenAIResponseInputMessageContent]
+    role: Literal["system"] | Literal["developer"] | Literal["user"] | Literal["assistant"]
+    type: Literal["message"] | None = "message"
+
+
+@json_schema_type
+class OpenAIResponseInputToolWebSearch(BaseModel):
+    type: Literal["web_search"] | Literal["web_search_preview_2025_03_11"] = "web_search"
+    # TODO: actually use search_context_size somewhere...
+    search_context_size: str | None = Field(default="medium", pattern="^low|medium|high$")
+    # TODO: add user_location
+
+
+OpenAIResponseInputTool = Annotated[
+    OpenAIResponseInputToolWebSearch,
+    Field(discriminator="type"),
+]
+register_schema(OpenAIResponseInputTool, name="OpenAIResponseInputTool")

llama_stack/apis/batch_inference/batch_inference.py

@@ -4,7 +4,7 @@
 # This source code is licensed under the terms described in the LICENSE file in
 # the root directory of this source tree.
 
-from typing import List, Optional, Protocol, runtime_checkable
+from typing import Protocol, runtime_checkable
 
 from llama_stack.apis.common.job_types import Job
 from llama_stack.apis.inference import (
@@ -34,22 +34,22 @@ class BatchInference(Protocol):
     async def completion(
         self,
         model: str,
-        content_batch: List[InterleavedContent],
+        content_batch: list[InterleavedContent],
-        sampling_params: Optional[SamplingParams] = None,
+        sampling_params: SamplingParams | None = None,
-        response_format: Optional[ResponseFormat] = None,
+        response_format: ResponseFormat | None = None,
-        logprobs: Optional[LogProbConfig] = None,
+        logprobs: LogProbConfig | None = None,
     ) -> Job: ...
 
     @webmethod(route="/batch-inference/chat-completion", method="POST")
     async def chat_completion(
         self,
         model: str,
-        messages_batch: List[List[Message]],
+        messages_batch: list[list[Message]],
-        sampling_params: Optional[SamplingParams] = None,
+        sampling_params: SamplingParams | None = None,
         # zero-shot tool definitions as input to the model
-        tools: Optional[List[ToolDefinition]] = None,
+        tools: list[ToolDefinition] | None = None,
-        tool_choice: Optional[ToolChoice] = ToolChoice.auto,
+        tool_choice: ToolChoice | None = ToolChoice.auto,
-        tool_prompt_format: Optional[ToolPromptFormat] = None,
+        tool_prompt_format: ToolPromptFormat | None = None,
-        response_format: Optional[ResponseFormat] = None,
+        response_format: ResponseFormat | None = None,
-        logprobs: Optional[LogProbConfig] = None,
+        logprobs: LogProbConfig | None = None,
     ) -> Job: ...

llama_stack/apis/benchmarks/benchmarks.py

@@ -3,7 +3,7 @@
 #
 # This source code is licensed under the terms described in the LICENSE file in
 # the root directory of this source tree.
-from typing import Any, Dict, List, Literal, Optional, Protocol, runtime_checkable
+from typing import Any, Literal, Protocol, runtime_checkable
 
 from pydantic import BaseModel, Field
 
@@ -13,8 +13,8 @@ from llama_stack.schema_utils import json_schema_type, webmethod
 
 class CommonBenchmarkFields(BaseModel):
     dataset_id: str
-    scoring_functions: List[str]
+    scoring_functions: list[str]
-    metadata: Dict[str, Any] = Field(
+    metadata: dict[str, Any] = Field(
         default_factory=dict,
         description="Metadata for this evaluation task",
     )
@@ -35,12 +35,12 @@ class Benchmark(CommonBenchmarkFields, Resource):
 
 class BenchmarkInput(CommonBenchmarkFields, BaseModel):
     benchmark_id: str
-    provider_id: Optional[str] = None
+    provider_id: str | None = None
-    provider_benchmark_id: Optional[str] = None
+    provider_benchmark_id: str | None = None
 
 
 class ListBenchmarksResponse(BaseModel):
-    data: List[Benchmark]
+    data: list[Benchmark]
 
 
 @runtime_checkable
@@ -59,8 +59,8 @@ class Benchmarks(Protocol):
         self,
         benchmark_id: str,
         dataset_id: str,
-        scoring_functions: List[str],
+        scoring_functions: list[str],
-        provider_benchmark_id: Optional[str] = None,
+        provider_benchmark_id: str | None = None,
-        provider_id: Optional[str] = None,
+        provider_id: str | None = None,
-        metadata: Optional[Dict[str, Any]] = None,
+        metadata: dict[str, Any] | None = None,
     ) -> None: ...

llama_stack/apis/common/content_types.py

@@ -5,7 +5,7 @@
 # the root directory of this source tree.
 
 from enum import Enum
-from typing import Annotated, List, Literal, Optional, Union
+from typing import Annotated, Literal
 
 from pydantic import BaseModel, Field, model_validator
 
@@ -26,9 +26,9 @@ class _URLOrData(BaseModel):
     :param data: base64 encoded image data as string
     """
 
-    url: Optional[URL] = None
+    url: URL | None = None
     # data is a base64 encoded string, hint with contentEncoding=base64
-    data: Optional[str] = Field(contentEncoding="base64", default=None)
+    data: str | None = Field(contentEncoding="base64", default=None)
 
     @model_validator(mode="before")
     @classmethod
@@ -64,13 +64,13 @@ class TextContentItem(BaseModel):
 
 # other modalities can be added here
 InterleavedContentItem = Annotated[
-    Union[ImageContentItem, TextContentItem],
+    ImageContentItem | TextContentItem,
     Field(discriminator="type"),
 ]
 register_schema(InterleavedContentItem, name="InterleavedContentItem")
 
 # accept a single "str" as a special case since it is common
-InterleavedContent = Union[str, InterleavedContentItem, List[InterleavedContentItem]]
+InterleavedContent = str | InterleavedContentItem | list[InterleavedContentItem]
 register_schema(InterleavedContent, name="InterleavedContent")
 
 
@@ -100,13 +100,13 @@ class ToolCallDelta(BaseModel):
     # you either send an in-progress tool call so the client can stream a long
     # code generation or you send the final parsed tool call at the end of the
     # stream
-    tool_call: Union[str, ToolCall]
+    tool_call: str | ToolCall
     parse_status: ToolCallParseStatus
 
 
 # streaming completions send a stream of ContentDeltas
 ContentDelta = Annotated[
-    Union[TextDelta, ImageDelta, ToolCallDelta],
+    TextDelta | ImageDelta | ToolCallDelta,
     Field(discriminator="type"),
 ]
 register_schema(ContentDelta, name="ContentDelta")

llama_stack/apis/common/deployment_types.py

@@ -5,7 +5,7 @@
 # the root directory of this source tree.
 
 from enum import Enum
-from typing import Any, Dict, Optional
+from typing import Any
 
 from pydantic import BaseModel
 
@@ -25,6 +25,6 @@ class RestAPIMethod(Enum):
 class RestAPIExecutionConfig(BaseModel):
     url: URL
     method: RestAPIMethod
-    params: Optional[Dict[str, Any]] = None
+    params: dict[str, Any] | None = None
-    headers: Optional[Dict[str, Any]] = None
+    headers: dict[str, Any] | None = None
-    body: Optional[Dict[str, Any]] = None
+    body: dict[str, Any] | None = None

llama_stack/apis/common/responses.py

@@ -4,7 +4,7 @@
 # This source code is licensed under the terms described in the LICENSE file in
 # the root directory of this source tree.
 
-from typing import Any, Dict, List
+from typing import Any
 
 from pydantic import BaseModel
 
@@ -19,5 +19,5 @@ class PaginatedResponse(BaseModel):
     :param has_more: Whether there are more items available after this set
     """
 
-    data: List[Dict[str, Any]]
+    data: list[dict[str, Any]]
     has_more: bool

llama_stack/apis/common/training_types.py

@@ -5,7 +5,6 @@
 # the root directory of this source tree.
 
 from datetime import datetime
-from typing import Optional
 
 from pydantic import BaseModel
 
@@ -27,4 +26,4 @@ class Checkpoint(BaseModel):
     epoch: int
     post_training_job_id: str
     path: str
-    training_metrics: Optional[PostTrainingMetric] = None
+    training_metrics: PostTrainingMetric | None = None

llama_stack/apis/common/type_system.py

@@ -4,10 +4,9 @@
 # This source code is licensed under the terms described in the LICENSE file in
 # the root directory of this source tree.
 
-from typing import Literal, Union
+from typing import Annotated, Literal
 
 from pydantic import BaseModel, Field
-from typing_extensions import Annotated
 
 from llama_stack.schema_utils import json_schema_type, register_schema
 
@@ -73,18 +72,16 @@ class DialogType(BaseModel):
 
 
 ParamType = Annotated[
-    Union[
+    StringType
-        StringType,
+    | NumberType
-        NumberType,
+    | BooleanType
-        BooleanType,
+    | ArrayType
-        ArrayType,
+    | ObjectType
-        ObjectType,
+    | JsonType
-        JsonType,
+    | UnionType
-        UnionType,
+    | ChatCompletionInputType
-        ChatCompletionInputType,
+    | CompletionInputType
-        CompletionInputType,
+    | AgentTurnInputType,
-        AgentTurnInputType,
-    ],
     Field(discriminator="type"),
 ]
 register_schema(ParamType, name="ParamType")

llama_stack/apis/datasetio/datasetio.py

@@ -4,7 +4,7 @@
 # This source code is licensed under the terms described in the LICENSE file in
 # the root directory of this source tree.
 
-from typing import Any, Dict, List, Optional, Protocol, runtime_checkable
+from typing import Any, Protocol, runtime_checkable
 
 from llama_stack.apis.common.responses import PaginatedResponse
 from llama_stack.apis.datasets import Dataset
@@ -24,8 +24,8 @@ class DatasetIO(Protocol):
     async def iterrows(
         self,
         dataset_id: str,
-        start_index: Optional[int] = None,
+        start_index: int | None = None,
-        limit: Optional[int] = None,
+        limit: int | None = None,
     ) -> PaginatedResponse:
         """Get a paginated list of rows from a dataset.
 
@@ -44,4 +44,4 @@ class DatasetIO(Protocol):
         ...
 
     @webmethod(route="/datasetio/append-rows/{dataset_id:path}", method="POST")
-    async def append_rows(self, dataset_id: str, rows: List[Dict[str, Any]]) -> None: ...
+    async def append_rows(self, dataset_id: str, rows: list[dict[str, Any]]) -> None: ...

llama_stack/apis/datasets/datasets.py

@@ -5,7 +5,7 @@
 # the root directory of this source tree.
 
 from enum import Enum
-from typing import Annotated, Any, Dict, List, Literal, Optional, Protocol, Union
+from typing import Annotated, Any, Literal, Protocol
 
 from pydantic import BaseModel, Field
 
@@ -81,11 +81,11 @@ class RowsDataSource(BaseModel):
     """
 
     type: Literal["rows"] = "rows"
-    rows: List[Dict[str, Any]]
+    rows: list[dict[str, Any]]
 
 
 DataSource = Annotated[
-    Union[URIDataSource, RowsDataSource],
+    URIDataSource | RowsDataSource,
     Field(discriminator="type"),
 ]
 register_schema(DataSource, name="DataSource")
@@ -98,7 +98,7 @@ class CommonDatasetFields(BaseModel):
 
     purpose: DatasetPurpose
     source: DataSource
-    metadata: Dict[str, Any] = Field(
+    metadata: dict[str, Any] = Field(
         default_factory=dict,
         description="Any additional metadata for this dataset",
     )
@@ -122,7 +122,7 @@ class DatasetInput(CommonDatasetFields, BaseModel):
 
 
 class ListDatasetsResponse(BaseModel):
-    data: List[Dataset]
+    data: list[Dataset]
 
 
 class Datasets(Protocol):
@@ -131,8 +131,8 @@ class Datasets(Protocol):
         self,
         purpose: DatasetPurpose,
         source: DataSource,
-        metadata: Optional[Dict[str, Any]] = None,
+        metadata: dict[str, Any] | None = None,
-        dataset_id: Optional[str] = None,
+        dataset_id: str | None = None,
     ) -> Dataset:
         """
         Register a new dataset.

llama_stack/apis/datatypes.py

@@ -5,7 +5,6 @@
 # the root directory of this source tree.
 
 from enum import Enum
-from typing import Optional
 
 from pydantic import BaseModel
 
@@ -54,4 +53,4 @@ class Error(BaseModel):
     status: int
     title: str
     detail: str
-    instance: Optional[str] = None
+    instance: str | None = None

llama_stack/apis/eval/eval.py

@@ -4,10 +4,9 @@
 # This source code is licensed under the terms described in the LICENSE file in
 # the root directory of this source tree.
 
-from typing import Any, Dict, List, Literal, Optional, Protocol, Union
+from typing import Annotated, Any, Literal, Protocol
 
 from pydantic import BaseModel, Field
-from typing_extensions import Annotated
 
 from llama_stack.apis.agents import AgentConfig
 from llama_stack.apis.common.job_types import Job
@@ -29,7 +28,7 @@ class ModelCandidate(BaseModel):
     type: Literal["model"] = "model"
     model: str
     sampling_params: SamplingParams
-    system_message: Optional[SystemMessage] = None
+    system_message: SystemMessage | None = None
 
 
 @json_schema_type
@@ -43,7 +42,7 @@ class AgentCandidate(BaseModel):
     config: AgentConfig
 
 
-EvalCandidate = Annotated[Union[ModelCandidate, AgentCandidate], Field(discriminator="type")]
+EvalCandidate = Annotated[ModelCandidate | AgentCandidate, Field(discriminator="type")]
 register_schema(EvalCandidate, name="EvalCandidate")
 
 
@@ -57,11 +56,11 @@ class BenchmarkConfig(BaseModel):
     """
 
     eval_candidate: EvalCandidate
-    scoring_params: Dict[str, ScoringFnParams] = Field(
+    scoring_params: dict[str, ScoringFnParams] = Field(
         description="Map between scoring function id and parameters for each scoring function you want to run",
         default_factory=dict,
     )
-    num_examples: Optional[int] = Field(
+    num_examples: int | None = Field(
         description="Number of examples to evaluate (useful for testing), if not provided, all examples in the dataset will be evaluated",
         default=None,
     )
@@ -76,9 +75,9 @@ class EvaluateResponse(BaseModel):
     :param scores: The scores from the evaluation.
     """
 
-    generations: List[Dict[str, Any]]
+    generations: list[dict[str, Any]]
     # each key in the dict is a scoring function name
-    scores: Dict[str, ScoringResult]
+    scores: dict[str, ScoringResult]
 
 
 class Eval(Protocol):
@@ -101,8 +100,8 @@ class Eval(Protocol):
     async def evaluate_rows(
         self,
         benchmark_id: str,
-        input_rows: List[Dict[str, Any]],
+        input_rows: list[dict[str, Any]],
-        scoring_functions: List[str],
+        scoring_functions: list[str],
         benchmark_config: BenchmarkConfig,
     ) -> EvaluateResponse:
         """Evaluate a list of rows on a benchmark.

llama_stack/apis/files/files.py

@@ -4,7 +4,7 @@
 # This source code is licensed under the terms described in the LICENSE file in
 # the root directory of this source tree.
 
-from typing import List, Optional, Protocol, runtime_checkable
+from typing import Protocol, runtime_checkable
 
 from pydantic import BaseModel
 
@@ -42,7 +42,7 @@ class ListBucketResponse(BaseModel):
     :param data: List of FileResponse entries
     """
 
-    data: List[BucketResponse]
+    data: list[BucketResponse]
 
 
 @json_schema_type
@@ -74,7 +74,7 @@ class ListFileResponse(BaseModel):
     :param data: List of FileResponse entries
     """
 
-    data: List[FileResponse]
+    data: list[FileResponse]
 
 
 @runtime_checkable
@@ -102,7 +102,7 @@ class Files(Protocol):
     async def upload_content_to_session(
         self,
         upload_id: str,
-    ) -> Optional[FileResponse]:
+    ) -> FileResponse | None:
         """
         Upload file content to an existing upload session.
         On the server, request body will have the raw bytes that are uploaded.

llama_stack/apis/inference/inference.py

@@ -4,21 +4,18 @@
 # This source code is licensed under the terms described in the LICENSE file in
 # the root directory of this source tree.
 
+from collections.abc import AsyncIterator
 from enum import Enum
 from typing import (
+    Annotated,
     Any,
-    AsyncIterator,
-    Dict,
-    List,
     Literal,
-    Optional,
     Protocol,
-    Union,
     runtime_checkable,
 )
 
 from pydantic import BaseModel, Field, field_validator
-from typing_extensions import Annotated, TypedDict
+from typing_extensions import TypedDict
 
 from llama_stack.apis.common.content_types import ContentDelta, InterleavedContent, InterleavedContentItem
 from llama_stack.apis.models import Model
@@ -47,8 +44,8 @@ class GreedySamplingStrategy(BaseModel):
 @json_schema_type
 class TopPSamplingStrategy(BaseModel):
     type: Literal["top_p"] = "top_p"
-    temperature: Optional[float] = Field(..., gt=0.0)
+    temperature: float | None = Field(..., gt=0.0)
-    top_p: Optional[float] = 0.95
+    top_p: float | None = 0.95
 
 
 @json_schema_type
@@ -58,7 +55,7 @@ class TopKSamplingStrategy(BaseModel):
 
 
 SamplingStrategy = Annotated[
-    Union[GreedySamplingStrategy, TopPSamplingStrategy, TopKSamplingStrategy],
+    GreedySamplingStrategy | TopPSamplingStrategy | TopKSamplingStrategy,
     Field(discriminator="type"),
 ]
 register_schema(SamplingStrategy, name="SamplingStrategy")
@@ -79,9 +76,9 @@ class SamplingParams(BaseModel):
 
     strategy: SamplingStrategy = Field(default_factory=GreedySamplingStrategy)
 
-    max_tokens: Optional[int] = 0
+    max_tokens: int | None = 0
-    repetition_penalty: Optional[float] = 1.0
+    repetition_penalty: float | None = 1.0
-    stop: Optional[List[str]] = None
+    stop: list[str] | None = None
 
 
 class LogProbConfig(BaseModel):
@@ -90,7 +87,7 @@ class LogProbConfig(BaseModel):
     :param top_k: How many tokens (for each position) to return log probabilities for.
     """
 
-    top_k: Optional[int] = 0
+    top_k: int | None = 0
 
 
 class QuantizationType(Enum):
@@ -125,11 +122,11 @@ class Int4QuantizationConfig(BaseModel):
     """
 
     type: Literal["int4_mixed"] = "int4_mixed"
-    scheme: Optional[str] = "int4_weight_int8_dynamic_activation"
+    scheme: str | None = "int4_weight_int8_dynamic_activation"
 
 
 QuantizationConfig = Annotated[
-    Union[Bf16QuantizationConfig, Fp8QuantizationConfig, Int4QuantizationConfig],
+    Bf16QuantizationConfig | Fp8QuantizationConfig | Int4QuantizationConfig,
     Field(discriminator="type"),
 ]
 
@@ -145,7 +142,7 @@ class UserMessage(BaseModel):
 
     role: Literal["user"] = "user"
     content: InterleavedContent
-    context: Optional[InterleavedContent] = None
+    context: InterleavedContent | None = None
 
 
 @json_schema_type
@@ -190,16 +187,11 @@ class CompletionMessage(BaseModel):
     role: Literal["assistant"] = "assistant"
     content: InterleavedContent
     stop_reason: StopReason
-    tool_calls: Optional[List[ToolCall]] = Field(default_factory=list)
+    tool_calls: list[ToolCall] | None = Field(default_factory=list)
 
 
 Message = Annotated[
-    Union[
+    UserMessage | SystemMessage | ToolResponseMessage | CompletionMessage,
-        UserMessage,
-        SystemMessage,
-        ToolResponseMessage,
-        CompletionMessage,
-    ],
     Field(discriminator="role"),
 ]
 register_schema(Message, name="Message")
@@ -208,9 +200,9 @@ register_schema(Message, name="Message")
 @json_schema_type
 class ToolResponse(BaseModel):
     call_id: str
-    tool_name: Union[BuiltinTool, str]
+    tool_name: BuiltinTool | str
     content: InterleavedContent
-    metadata: Optional[Dict[str, Any]] = None
+    metadata: dict[str, Any] | None = None
 
     @field_validator("tool_name", mode="before")
     @classmethod
@@ -243,7 +235,7 @@ class TokenLogProbs(BaseModel):
     :param logprobs_by_token: Dictionary mapping tokens to their log probabilities
     """
 
-    logprobs_by_token: Dict[str, float]
+    logprobs_by_token: dict[str, float]
 
 
 class ChatCompletionResponseEventType(Enum):
@@ -271,8 +263,8 @@ class ChatCompletionResponseEvent(BaseModel):
 
     event_type: ChatCompletionResponseEventType
     delta: ContentDelta
-    logprobs: Optional[List[TokenLogProbs]] = None
+    logprobs: list[TokenLogProbs] | None = None
-    stop_reason: Optional[StopReason] = None
+    stop_reason: StopReason | None = None
 
 
 class ResponseFormatType(Enum):
@@ -295,7 +287,7 @@ class JsonSchemaResponseFormat(BaseModel):
     """
 
     type: Literal[ResponseFormatType.json_schema.value] = ResponseFormatType.json_schema.value
-    json_schema: Dict[str, Any]
+    json_schema: dict[str, Any]
 
 
 @json_schema_type
@@ -307,11 +299,11 @@ class GrammarResponseFormat(BaseModel):
     """
 
     type: Literal[ResponseFormatType.grammar.value] = ResponseFormatType.grammar.value
-    bnf: Dict[str, Any]
+    bnf: dict[str, Any]
 
 
 ResponseFormat = Annotated[
-    Union[JsonSchemaResponseFormat, GrammarResponseFormat],
+    JsonSchemaResponseFormat | GrammarResponseFormat,
     Field(discriminator="type"),
 ]
 register_schema(ResponseFormat, name="ResponseFormat")
@@ -321,10 +313,10 @@ register_schema(ResponseFormat, name="ResponseFormat")
 class CompletionRequest(BaseModel):
     model: str
     content: InterleavedContent
-    sampling_params: Optional[SamplingParams] = Field(default_factory=SamplingParams)
+    sampling_params: SamplingParams | None = Field(default_factory=SamplingParams)
-    response_format: Optional[ResponseFormat] = None
+    response_format: ResponseFormat | None = None
-    stream: Optional[bool] = False
+    stream: bool | None = False
-    logprobs: Optional[LogProbConfig] = None
+    logprobs: LogProbConfig | None = None
 
 
 @json_schema_type
@@ -338,7 +330,7 @@ class CompletionResponse(MetricResponseMixin):
 
     content: str
     stop_reason: StopReason
-    logprobs: Optional[List[TokenLogProbs]] = None
+    logprobs: list[TokenLogProbs] | None = None
 
 
 @json_schema_type
@@ -351,8 +343,8 @@ class CompletionResponseStreamChunk(MetricResponseMixin):
     """
 
     delta: str
-    stop_reason: Optional[StopReason] = None
+    stop_reason: StopReason | None = None
-    logprobs: Optional[List[TokenLogProbs]] = None
+    logprobs: list[TokenLogProbs] | None = None
 
 
 class SystemMessageBehavior(Enum):
@@ -383,9 +375,9 @@ class ToolConfig(BaseModel):
             '{{function_definitions}}' to indicate where the function definitions should be inserted.
     """
 
-    tool_choice: Optional[ToolChoice | str] = Field(default=ToolChoice.auto)
+    tool_choice: ToolChoice | str | None = Field(default=ToolChoice.auto)
-    tool_prompt_format: Optional[ToolPromptFormat] = Field(default=None)
+    tool_prompt_format: ToolPromptFormat | None = Field(default=None)
-    system_message_behavior: Optional[SystemMessageBehavior] = Field(default=SystemMessageBehavior.append)
+    system_message_behavior: SystemMessageBehavior | None = Field(default=SystemMessageBehavior.append)
 
     def model_post_init(self, __context: Any) -> None:
         if isinstance(self.tool_choice, str):
@@ -399,15 +391,15 @@ class ToolConfig(BaseModel):
 @json_schema_type
 class ChatCompletionRequest(BaseModel):
     model: str
-    messages: List[Message]
+    messages: list[Message]
-    sampling_params: Optional[SamplingParams] = Field(default_factory=SamplingParams)
+    sampling_params: SamplingParams | None = Field(default_factory=SamplingParams)
 
-    tools: Optional[List[ToolDefinition]] = Field(default_factory=list)
+    tools: list[ToolDefinition] | None = Field(default_factory=list)
-    tool_config: Optional[ToolConfig] = Field(default_factory=ToolConfig)
+    tool_config: ToolConfig | None = Field(default_factory=ToolConfig)
 
-    response_format: Optional[ResponseFormat] = None
+    response_format: ResponseFormat | None = None
-    stream: Optional[bool] = False
+    stream: bool | None = False
-    logprobs: Optional[LogProbConfig] = None
+    logprobs: LogProbConfig | None = None
 
 
 @json_schema_type
@@ -429,7 +421,7 @@ class ChatCompletionResponse(MetricResponseMixin):
     """
 
     completion_message: CompletionMessage
-    logprobs: Optional[List[TokenLogProbs]] = None
+    logprobs: list[TokenLogProbs] | None = None
 
 
 @json_schema_type
@@ -439,7 +431,7 @@ class EmbeddingsResponse(BaseModel):
     :param embeddings: List of embedding vectors, one per input content. Each embedding is a list of floats. The dimensionality of the embedding is model-specific; you can check model metadata using /models/{model_id}
     """
 
-    embeddings: List[List[float]]
+    embeddings: list[list[float]]
 
 
 @json_schema_type
@@ -451,7 +443,7 @@ class OpenAIChatCompletionContentPartTextParam(BaseModel):
 @json_schema_type
 class OpenAIImageURL(BaseModel):
     url: str
-    detail: Optional[str] = None
+    detail: str | None = None
 
 
 @json_schema_type
@@ -461,16 +453,13 @@ class OpenAIChatCompletionContentPartImageParam(BaseModel):
 
 
 OpenAIChatCompletionContentPartParam = Annotated[
-    Union[
+    OpenAIChatCompletionContentPartTextParam | OpenAIChatCompletionContentPartImageParam,
-        OpenAIChatCompletionContentPartTextParam,
-        OpenAIChatCompletionContentPartImageParam,
-    ],
     Field(discriminator="type"),
 ]
 register_schema(OpenAIChatCompletionContentPartParam, name="OpenAIChatCompletionContentPartParam")
 
 
-OpenAIChatCompletionMessageContent = Union[str, List[OpenAIChatCompletionContentPartParam]]
+OpenAIChatCompletionMessageContent = str | list[OpenAIChatCompletionContentPartParam]
 
 
 @json_schema_type
@@ -484,7 +473,7 @@ class OpenAIUserMessageParam(BaseModel):
 
     role: Literal["user"] = "user"
     content: OpenAIChatCompletionMessageContent
-    name: Optional[str] = None
+    name: str | None = None
 
 
 @json_schema_type
@@ -498,21 +487,21 @@ class OpenAISystemMessageParam(BaseModel):
 
     role: Literal["system"] = "system"
     content: OpenAIChatCompletionMessageContent
-    name: Optional[str] = None
+    name: str | None = None
 
 
 @json_schema_type
 class OpenAIChatCompletionToolCallFunction(BaseModel):
-    name: Optional[str] = None
+    name: str | None = None
-    arguments: Optional[str] = None
+    arguments: str | None = None
 
 
 @json_schema_type
 class OpenAIChatCompletionToolCall(BaseModel):
-    index: Optional[int] = None
+    index: int | None = None
-    id: Optional[str] = None
+    id: str | None = None
     type: Literal["function"] = "function"
-    function: Optional[OpenAIChatCompletionToolCallFunction] = None
+    function: OpenAIChatCompletionToolCallFunction | None = None
 
 
 @json_schema_type
@@ -526,9 +515,9 @@ class OpenAIAssistantMessageParam(BaseModel):
     """
 
     role: Literal["assistant"] = "assistant"
-    content: OpenAIChatCompletionMessageContent
+    content: OpenAIChatCompletionMessageContent | None = None
-    name: Optional[str] = None
+    name: str | None = None
-    tool_calls: Optional[List[OpenAIChatCompletionToolCall]] = Field(default_factory=list)
+    tool_calls: list[OpenAIChatCompletionToolCall] | None = None
 
 
 @json_schema_type
@@ -556,17 +545,15 @@ class OpenAIDeveloperMessageParam(BaseModel):
 
     role: Literal["developer"] = "developer"
     content: OpenAIChatCompletionMessageContent
-    name: Optional[str] = None
+    name: str | None = None
 
 
 OpenAIMessageParam = Annotated[
-    Union[
+    OpenAIUserMessageParam
-        OpenAIUserMessageParam,
+    | OpenAISystemMessageParam
-        OpenAISystemMessageParam,
+    | OpenAIAssistantMessageParam
-        OpenAIAssistantMessageParam,
+    | OpenAIToolMessageParam
-        OpenAIToolMessageParam,
+    | OpenAIDeveloperMessageParam,
-        OpenAIDeveloperMessageParam,
-    ],
     Field(discriminator="role"),
 ]
 register_schema(OpenAIMessageParam, name="OpenAIMessageParam")
@@ -580,14 +567,14 @@ class OpenAIResponseFormatText(BaseModel):
 @json_schema_type
 class OpenAIJSONSchema(TypedDict, total=False):
     name: str
-    description: Optional[str] = None
+    description: str | None = None
-    strict: Optional[bool] = None
+    strict: bool | None = None
 
     # Pydantic BaseModel cannot be used with a schema param, since it already
     # has one. And, we don't want to alias here because then have to handle
     # that alias when converting to OpenAI params. So, to support schema,
     # we use a TypedDict.
-    schema: Optional[Dict[str, Any]] = None
+    schema: dict[str, Any] | None = None
 
 
 @json_schema_type
@@ -602,11 +589,7 @@ class OpenAIResponseFormatJSONObject(BaseModel):
 
 
 OpenAIResponseFormatParam = Annotated[
-    Union[
+    OpenAIResponseFormatText | OpenAIResponseFormatJSONSchema | OpenAIResponseFormatJSONObject,
-        OpenAIResponseFormatText,
-        OpenAIResponseFormatJSONSchema,
-        OpenAIResponseFormatJSONObject,
-    ],
     Field(discriminator="type"),
 ]
 register_schema(OpenAIResponseFormatParam, name="OpenAIResponseFormatParam")
@@ -622,7 +605,7 @@ class OpenAITopLogProb(BaseModel):
     """
 
     token: str
-    bytes: Optional[List[int]] = None
+    bytes: list[int] | None = None
     logprob: float
 
 
@@ -637,9 +620,9 @@ class OpenAITokenLogProb(BaseModel):
     """
 
     token: str
-    bytes: Optional[List[int]] = None
+    bytes: list[int] | None = None
     logprob: float
-    top_logprobs: List[OpenAITopLogProb]
+    top_logprobs: list[OpenAITopLogProb]
 
 
 @json_schema_type
@@ -650,8 +633,8 @@ class OpenAIChoiceLogprobs(BaseModel):
     :param refusal: (Optional) The log probabilities for the tokens in the message
     """
 
-    content: Optional[List[OpenAITokenLogProb]] = None
+    content: list[OpenAITokenLogProb] | None = None
-    refusal: Optional[List[OpenAITokenLogProb]] = None
+    refusal: list[OpenAITokenLogProb] | None = None
 
 
 @json_schema_type
@@ -664,10 +647,10 @@ class OpenAIChoiceDelta(BaseModel):
     :param tool_calls: (Optional) The tool calls of the delta
     """
 
-    content: Optional[str] = None
+    content: str | None = None
-    refusal: Optional[str] = None
+    refusal: str | None = None
-    role: Optional[str] = None
+    role: str | None = None
-    tool_calls: Optional[List[OpenAIChatCompletionToolCall]] = None
+    tool_calls: list[OpenAIChatCompletionToolCall] | None = None
 
 
 @json_schema_type
@@ -683,7 +666,7 @@ class OpenAIChunkChoice(BaseModel):
     delta: OpenAIChoiceDelta
     finish_reason: str
     index: int
-    logprobs: Optional[OpenAIChoiceLogprobs] = None
+    logprobs: OpenAIChoiceLogprobs | None = None
 
 
 @json_schema_type
@@ -699,7 +682,7 @@ class OpenAIChoice(BaseModel):
     message: OpenAIMessageParam
     finish_reason: str
     index: int
-    logprobs: Optional[OpenAIChoiceLogprobs] = None
+    logprobs: OpenAIChoiceLogprobs | None = None
 
 
 @json_schema_type
@@ -714,7 +697,7 @@ class OpenAIChatCompletion(BaseModel):
     """
 
     id: str
-    choices: List[OpenAIChoice]
+    choices: list[OpenAIChoice]
     object: Literal["chat.completion"] = "chat.completion"
     created: int
     model: str
@@ -732,7 +715,7 @@ class OpenAIChatCompletionChunk(BaseModel):
     """
 
     id: str
-    choices: List[OpenAIChunkChoice]
+    choices: list[OpenAIChunkChoice]
     object: Literal["chat.completion.chunk"] = "chat.completion.chunk"
     created: int
     model: str
@@ -748,10 +731,10 @@ class OpenAICompletionLogprobs(BaseModel):
     :top_logprobs: (Optional) The top log probabilities for the tokens
     """
 
-    text_offset: Optional[List[int]] = None
+    text_offset: list[int] | None = None
-    token_logprobs: Optional[List[float]] = None
+    token_logprobs: list[float] | None = None
-    tokens: Optional[List[str]] = None
+    tokens: list[str] | None = None
-    top_logprobs: Optional[List[Dict[str, float]]] = None
+    top_logprobs: list[dict[str, float]] | None = None
 
 
 @json_schema_type
@@ -767,7 +750,7 @@ class OpenAICompletionChoice(BaseModel):
     finish_reason: str
     text: str
     index: int
-    logprobs: Optional[OpenAIChoiceLogprobs] = None
+    logprobs: OpenAIChoiceLogprobs | None = None
 
 
 @json_schema_type
@@ -782,7 +765,7 @@ class OpenAICompletion(BaseModel):
     """
 
     id: str
-    choices: List[OpenAICompletionChoice]
+    choices: list[OpenAICompletionChoice]
     created: int
     model: str
     object: Literal["text_completion"] = "text_completion"
@@ -818,12 +801,12 @@ class EmbeddingTaskType(Enum):
 
 @json_schema_type
 class BatchCompletionResponse(BaseModel):
-    batch: List[CompletionResponse]
+    batch: list[CompletionResponse]
 
 
 @json_schema_type
 class BatchChatCompletionResponse(BaseModel):
-    batch: List[ChatCompletionResponse]
+    batch: list[ChatCompletionResponse]
 
 
 @runtime_checkable
@@ -843,11 +826,11 @@ class Inference(Protocol):
         self,
         model_id: str,
         content: InterleavedContent,
-        sampling_params: Optional[SamplingParams] = None,
+        sampling_params: SamplingParams | None = None,
-        response_format: Optional[ResponseFormat] = None,
+        response_format: ResponseFormat | None = None,
-        stream: Optional[bool] = False,
+        stream: bool | None = False,
-        logprobs: Optional[LogProbConfig] = None,
+        logprobs: LogProbConfig | None = None,
-    ) -> Union[CompletionResponse, AsyncIterator[CompletionResponseStreamChunk]]:
+    ) -> CompletionResponse | AsyncIterator[CompletionResponseStreamChunk]:
         """Generate a completion for the given content using the specified model.
 
         :param model_id: The identifier of the model to use. The model must be registered with Llama Stack and available via the /models endpoint.
@@ -865,10 +848,10 @@ class Inference(Protocol):
     async def batch_completion(
         self,
         model_id: str,
-        content_batch: List[InterleavedContent],
+        content_batch: list[InterleavedContent],
-        sampling_params: Optional[SamplingParams] = None,
+        sampling_params: SamplingParams | None = None,
-        response_format: Optional[ResponseFormat] = None,
+        response_format: ResponseFormat | None = None,
-        logprobs: Optional[LogProbConfig] = None,
+        logprobs: LogProbConfig | None = None,
     ) -> BatchCompletionResponse:
         raise NotImplementedError("Batch completion is not implemented")
 
@@ -876,16 +859,16 @@ class Inference(Protocol):
     async def chat_completion(
         self,
         model_id: str,
-        messages: List[Message],
+        messages: list[Message],
-        sampling_params: Optional[SamplingParams] = None,
+        sampling_params: SamplingParams | None = None,
-        tools: Optional[List[ToolDefinition]] = None,
+        tools: list[ToolDefinition] | None = None,
-        tool_choice: Optional[ToolChoice] = ToolChoice.auto,
+        tool_choice: ToolChoice | None = ToolChoice.auto,
-        tool_prompt_format: Optional[ToolPromptFormat] = None,
+        tool_prompt_format: ToolPromptFormat | None = None,
-        response_format: Optional[ResponseFormat] = None,
+        response_format: ResponseFormat | None = None,
-        stream: Optional[bool] = False,
+        stream: bool | None = False,
-        logprobs: Optional[LogProbConfig] = None,
+        logprobs: LogProbConfig | None = None,
-        tool_config: Optional[ToolConfig] = None,
+        tool_config: ToolConfig | None = None,
-    ) -> Union[ChatCompletionResponse, AsyncIterator[ChatCompletionResponseStreamChunk]]:
+    ) -> ChatCompletionResponse | AsyncIterator[ChatCompletionResponseStreamChunk]:
         """Generate a chat completion for the given messages using the specified model.
 
         :param model_id: The identifier of the model to use. The model must be registered with Llama Stack and available via the /models endpoint.
@@ -916,12 +899,12 @@ class Inference(Protocol):
     async def batch_chat_completion(
         self,
         model_id: str,
-        messages_batch: List[List[Message]],
+        messages_batch: list[list[Message]],
-        sampling_params: Optional[SamplingParams] = None,
+        sampling_params: SamplingParams | None = None,
-        tools: Optional[List[ToolDefinition]] = None,
+        tools: list[ToolDefinition] | None = None,
-        tool_config: Optional[ToolConfig] = None,
+        tool_config: ToolConfig | None = None,
-        response_format: Optional[ResponseFormat] = None,
+        response_format: ResponseFormat | None = None,
-        logprobs: Optional[LogProbConfig] = None,
+        logprobs: LogProbConfig | None = None,
     ) -> BatchChatCompletionResponse:
         raise NotImplementedError("Batch chat completion is not implemented")
 
@@ -929,10 +912,10 @@ class Inference(Protocol):
     async def embeddings(
         self,
         model_id: str,
-        contents: List[str] | List[InterleavedContentItem],
+        contents: list[str] | list[InterleavedContentItem],
-        text_truncation: Optional[TextTruncation] = TextTruncation.none,
+        text_truncation: TextTruncation | None = TextTruncation.none,
-        output_dimension: Optional[int] = None,
+        output_dimension: int | None = None,
-        task_type: Optional[EmbeddingTaskType] = None,
+        task_type: EmbeddingTaskType | None = None,
     ) -> EmbeddingsResponse:
         """Generate embeddings for content pieces using the specified model.
 
@@ -950,25 +933,25 @@ class Inference(Protocol):
         self,
         # Standard OpenAI completion parameters
         model: str,
-        prompt: Union[str, List[str], List[int], List[List[int]]],
+        prompt: str | list[str] | list[int] | list[list[int]],
-        best_of: Optional[int] = None,
+        best_of: int | None = None,
-        echo: Optional[bool] = None,
+        echo: bool | None = None,
-        frequency_penalty: Optional[float] = None,
+        frequency_penalty: float | None = None,
-        logit_bias: Optional[Dict[str, float]] = None,
+        logit_bias: dict[str, float] | None = None,
-        logprobs: Optional[bool] = None,
+        logprobs: bool | None = None,
-        max_tokens: Optional[int] = None,
+        max_tokens: int | None = None,
-        n: Optional[int] = None,
+        n: int | None = None,
-        presence_penalty: Optional[float] = None,
+        presence_penalty: float | None = None,
-        seed: Optional[int] = None,
+        seed: int | None = None,
-        stop: Optional[Union[str, List[str]]] = None,
+        stop: str | list[str] | None = None,
-        stream: Optional[bool] = None,
+        stream: bool | None = None,
-        stream_options: Optional[Dict[str, Any]] = None,
+        stream_options: dict[str, Any] | None = None,
-        temperature: Optional[float] = None,
+        temperature: float | None = None,
-        top_p: Optional[float] = None,
+        top_p: float | None = None,
-        user: Optional[str] = None,
+        user: str | None = None,
         # vLLM-specific parameters
-        guided_choice: Optional[List[str]] = None,
+        guided_choice: list[str] | None = None,
-        prompt_logprobs: Optional[int] = None,
+        prompt_logprobs: int | None = None,
     ) -> OpenAICompletion:
         """Generate an OpenAI-compatible completion for the given prompt using the specified model.
 
@@ -996,29 +979,29 @@ class Inference(Protocol):
     async def openai_chat_completion(
         self,
         model: str,
-        messages: List[OpenAIMessageParam],
+        messages: list[OpenAIMessageParam],
-        frequency_penalty: Optional[float] = None,
+        frequency_penalty: float | None = None,
-        function_call: Optional[Union[str, Dict[str, Any]]] = None,
+        function_call: str | dict[str, Any] | None = None,
-        functions: Optional[List[Dict[str, Any]]] = None,
+        functions: list[dict[str, Any]] | None = None,
-        logit_bias: Optional[Dict[str, float]] = None,
+        logit_bias: dict[str, float] | None = None,
-        logprobs: Optional[bool] = None,
+        logprobs: bool | None = None,
-        max_completion_tokens: Optional[int] = None,
+        max_completion_tokens: int | None = None,
-        max_tokens: Optional[int] = None,
+        max_tokens: int | None = None,
-        n: Optional[int] = None,
+        n: int | None = None,
-        parallel_tool_calls: Optional[bool] = None,
+        parallel_tool_calls: bool | None = None,
-        presence_penalty: Optional[float] = None,
+        presence_penalty: float | None = None,
-        response_format: Optional[OpenAIResponseFormatParam] = None,
+        response_format: OpenAIResponseFormatParam | None = None,
-        seed: Optional[int] = None,
+        seed: int | None = None,
-        stop: Optional[Union[str, List[str]]] = None,
+        stop: str | list[str] | None = None,
-        stream: Optional[bool] = None,
+        stream: bool | None = None,
-        stream_options: Optional[Dict[str, Any]] = None,
+        stream_options: dict[str, Any] | None = None,
-        temperature: Optional[float] = None,
+        temperature: float | None = None,
-        tool_choice: Optional[Union[str, Dict[str, Any]]] = None,
+        tool_choice: str | dict[str, Any] | None = None,
-        tools: Optional[List[Dict[str, Any]]] = None,
+        tools: list[dict[str, Any]] | None = None,
-        top_logprobs: Optional[int] = None,
+        top_logprobs: int | None = None,
-        top_p: Optional[float] = None,
+        top_p: float | None = None,
-        user: Optional[str] = None,
+        user: str | None = None,
-    ) -> Union[OpenAIChatCompletion, AsyncIterator[OpenAIChatCompletionChunk]]:
+    ) -> OpenAIChatCompletion | AsyncIterator[OpenAIChatCompletionChunk]:
         """Generate an OpenAI-compatible chat completion for the given messages using the specified model.
 
         :param model: The identifier of the model to use. The model must be registered with Llama Stack and available via the /models endpoint.

llama_stack/apis/inspect/inspect.py

@@ -4,7 +4,7 @@
 # This source code is licensed under the terms described in the LICENSE file in
 # the root directory of this source tree.
 
-from typing import List, Protocol, runtime_checkable
+from typing import Protocol, runtime_checkable
 
 from pydantic import BaseModel
 
@@ -16,7 +16,7 @@ from llama_stack.schema_utils import json_schema_type, webmethod
 class RouteInfo(BaseModel):
     route: str
     method: str
-    provider_types: List[str]
+    provider_types: list[str]
 
 
 @json_schema_type
@@ -30,7 +30,7 @@ class VersionInfo(BaseModel):
 
 
 class ListRoutesResponse(BaseModel):
-    data: List[RouteInfo]
+    data: list[RouteInfo]
 
 
 @runtime_checkable

llama_stack/apis/models/models.py

@@ -5,7 +5,7 @@
 # the root directory of this source tree.
 
 from enum import Enum
-from typing import Any, Dict, List, Literal, Optional, Protocol, runtime_checkable
+from typing import Any, Literal, Protocol, runtime_checkable
 
 from pydantic import BaseModel, ConfigDict, Field
 
@@ -15,7 +15,7 @@ from llama_stack.schema_utils import json_schema_type, webmethod
 
 
 class CommonModelFields(BaseModel):
-    metadata: Dict[str, Any] = Field(
+    metadata: dict[str, Any] = Field(
         default_factory=dict,
         description="Any additional metadata for this model",
     )
@@ -46,14 +46,14 @@ class Model(CommonModelFields, Resource):
 
 class ModelInput(CommonModelFields):
     model_id: str
-    provider_id: Optional[str] = None
+    provider_id: str | None = None
-    provider_model_id: Optional[str] = None
+    provider_model_id: str | None = None
-    model_type: Optional[ModelType] = ModelType.llm
+    model_type: ModelType | None = ModelType.llm
     model_config = ConfigDict(protected_namespaces=())
 
 
 class ListModelsResponse(BaseModel):
-    data: List[Model]
+    data: list[Model]
 
 
 @json_schema_type
@@ -73,7 +73,7 @@ class OpenAIModel(BaseModel):
 
 
 class OpenAIListModelsResponse(BaseModel):
-    data: List[OpenAIModel]
+    data: list[OpenAIModel]
 
 
 @runtime_checkable
@@ -95,10 +95,10 @@ class Models(Protocol):
     async def register_model(
         self,
         model_id: str,
-        provider_model_id: Optional[str] = None,
+        provider_model_id: str | None = None,
-        provider_id: Optional[str] = None,
+        provider_id: str | None = None,
-        metadata: Optional[Dict[str, Any]] = None,
+        metadata: dict[str, Any] | None = None,
-        model_type: Optional[ModelType] = None,
+        model_type: ModelType | None = None,
     ) -> Model: ...
 
     @webmethod(route="/models/{model_id:path}", method="DELETE")

llama_stack/apis/post_training/post_training.py

@@ -6,10 +6,9 @@
 
 from datetime import datetime
 from enum import Enum
-from typing import Any, Dict, List, Literal, Optional, Protocol, Union
+from typing import Annotated, Any, Literal, Protocol
 
 from pydantic import BaseModel, Field
-from typing_extensions import Annotated
 
 from llama_stack.apis.common.content_types import URL
 from llama_stack.apis.common.job_types import JobStatus
@@ -36,9 +35,9 @@ class DataConfig(BaseModel):
     batch_size: int
     shuffle: bool
     data_format: DatasetFormat
-    validation_dataset_id: Optional[str] = None
+    validation_dataset_id: str | None = None
-    packed: Optional[bool] = False
+    packed: bool | None = False
-    train_on_input: Optional[bool] = False
+    train_on_input: bool | None = False
 
 
 @json_schema_type
@@ -51,10 +50,10 @@ class OptimizerConfig(BaseModel):
 
 @json_schema_type
 class EfficiencyConfig(BaseModel):
-    enable_activation_checkpointing: Optional[bool] = False
+    enable_activation_checkpointing: bool | None = False
-    enable_activation_offloading: Optional[bool] = False
+    enable_activation_offloading: bool | None = False
-    memory_efficient_fsdp_wrap: Optional[bool] = False
+    memory_efficient_fsdp_wrap: bool | None = False
-    fsdp_cpu_offload: Optional[bool] = False
+    fsdp_cpu_offload: bool | None = False
 
 
 @json_schema_type
@@ -62,23 +61,23 @@ class TrainingConfig(BaseModel):
     n_epochs: int
     max_steps_per_epoch: int = 1
     gradient_accumulation_steps: int = 1
-    max_validation_steps: Optional[int] = 1
+    max_validation_steps: int | None = 1
-    data_config: Optional[DataConfig] = None
+    data_config: DataConfig | None = None
-    optimizer_config: Optional[OptimizerConfig] = None
+    optimizer_config: OptimizerConfig | None = None
-    efficiency_config: Optional[EfficiencyConfig] = None
+    efficiency_config: EfficiencyConfig | None = None
-    dtype: Optional[str] = "bf16"
+    dtype: str | None = "bf16"
 
 
 @json_schema_type
 class LoraFinetuningConfig(BaseModel):
     type: Literal["LoRA"] = "LoRA"
-    lora_attn_modules: List[str]
+    lora_attn_modules: list[str]
     apply_lora_to_mlp: bool
     apply_lora_to_output: bool
     rank: int
     alpha: int
-    use_dora: Optional[bool] = False
+    use_dora: bool | None = False
-    quantize_base: Optional[bool] = False
+    quantize_base: bool | None = False
 
 
 @json_schema_type
@@ -88,7 +87,7 @@ class QATFinetuningConfig(BaseModel):
     group_size: int
 
 
-AlgorithmConfig = Annotated[Union[LoraFinetuningConfig, QATFinetuningConfig], Field(discriminator="type")]
+AlgorithmConfig = Annotated[LoraFinetuningConfig | QATFinetuningConfig, Field(discriminator="type")]
 register_schema(AlgorithmConfig, name="AlgorithmConfig")
 
 
@@ -97,7 +96,7 @@ class PostTrainingJobLogStream(BaseModel):
     """Stream of logs from a finetuning job."""
 
     job_uuid: str
-    log_lines: List[str]
+    log_lines: list[str]
 
 
 @json_schema_type
@@ -131,8 +130,8 @@ class PostTrainingRLHFRequest(BaseModel):
     training_config: TrainingConfig
 
     # TODO: define these
-    hyperparam_search_config: Dict[str, Any]
+    hyperparam_search_config: dict[str, Any]
-    logger_config: Dict[str, Any]
+    logger_config: dict[str, Any]
 
 
 class PostTrainingJob(BaseModel):
@@ -146,17 +145,17 @@ class PostTrainingJobStatusResponse(BaseModel):
     job_uuid: str
     status: JobStatus
 
-    scheduled_at: Optional[datetime] = None
+    scheduled_at: datetime | None = None
-    started_at: Optional[datetime] = None
+    started_at: datetime | None = None
-    completed_at: Optional[datetime] = None
+    completed_at: datetime | None = None
 
-    resources_allocated: Optional[Dict[str, Any]] = None
+    resources_allocated: dict[str, Any] | None = None
 
-    checkpoints: List[Checkpoint] = Field(default_factory=list)
+    checkpoints: list[Checkpoint] = Field(default_factory=list)
 
 
 class ListPostTrainingJobsResponse(BaseModel):
-    data: List[PostTrainingJob]
+    data: list[PostTrainingJob]
 
 
 @json_schema_type
@@ -164,7 +163,7 @@ class PostTrainingJobArtifactsResponse(BaseModel):
     """Artifacts of a finetuning job."""
 
     job_uuid: str
-    checkpoints: List[Checkpoint] = Field(default_factory=list)
+    checkpoints: list[Checkpoint] = Field(default_factory=list)
 
     # TODO(ashwin): metrics, evals
 
@@ -175,14 +174,14 @@ class PostTraining(Protocol):
         self,
         job_uuid: str,
         training_config: TrainingConfig,
-        hyperparam_search_config: Dict[str, Any],
+        hyperparam_search_config: dict[str, Any],
-        logger_config: Dict[str, Any],
+        logger_config: dict[str, Any],
-        model: Optional[str] = Field(
+        model: str | None = Field(
             default=None,
             description="Model descriptor for training if not in provider config`",
         ),
-        checkpoint_dir: Optional[str] = None,
+        checkpoint_dir: str | None = None,
-        algorithm_config: Optional[AlgorithmConfig] = None,
+        algorithm_config: AlgorithmConfig | None = None,
     ) -> PostTrainingJob: ...
 
     @webmethod(route="/post-training/preference-optimize", method="POST")
@@ -192,8 +191,8 @@ class PostTraining(Protocol):
         finetuned_model: str,
         algorithm_config: DPOAlignmentConfig,
         training_config: TrainingConfig,
-        hyperparam_search_config: Dict[str, Any],
+        hyperparam_search_config: dict[str, Any],
-        logger_config: Dict[str, Any],
+        logger_config: dict[str, Any],
     ) -> PostTrainingJob: ...
 
     @webmethod(route="/post-training/jobs", method="GET")

llama_stack/apis/providers/providers.py

@@ -4,7 +4,7 @@
 # This source code is licensed under the terms described in the LICENSE file in
 # the root directory of this source tree.
 
-from typing import Any, Dict, List, Protocol, runtime_checkable
+from typing import Any, Protocol, runtime_checkable
 
 from pydantic import BaseModel
 
@@ -17,12 +17,12 @@ class ProviderInfo(BaseModel):
     api: str
     provider_id: str
     provider_type: str
-    config: Dict[str, Any]
+    config: dict[str, Any]
     health: HealthResponse
 
 
 class ListProvidersResponse(BaseModel):
-    data: List[ProviderInfo]
+    data: list[ProviderInfo]
 
 
 @runtime_checkable

llama_stack/apis/safety/safety.py

@@ -5,7 +5,7 @@
 # the root directory of this source tree.
 
 from enum import Enum
-from typing import Any, Dict, List, Optional, Protocol, runtime_checkable
+from typing import Any, Protocol, runtime_checkable
 
 from pydantic import BaseModel, Field
 
@@ -27,16 +27,16 @@ class SafetyViolation(BaseModel):
     violation_level: ViolationLevel
 
     # what message should you convey to the user
-    user_message: Optional[str] = None
+    user_message: str | None = None
 
     # additional metadata (including specific violation codes) more for
     # debugging, telemetry
-    metadata: Dict[str, Any] = Field(default_factory=dict)
+    metadata: dict[str, Any] = Field(default_factory=dict)
 
 
 @json_schema_type
 class RunShieldResponse(BaseModel):
-    violation: Optional[SafetyViolation] = None
+    violation: SafetyViolation | None = None
 
 
 class ShieldStore(Protocol):
@@ -52,6 +52,6 @@ class Safety(Protocol):
     async def run_shield(
         self,
         shield_id: str,
-        messages: List[Message],
+        messages: list[Message],
-        params: Dict[str, Any] = None,
+        params: dict[str, Any] = None,
     ) -> RunShieldResponse: ...

llama_stack/apis/scoring/scoring.py

@@ -4,7 +4,7 @@
 # This source code is licensed under the terms described in the LICENSE file in
 # the root directory of this source tree.
 
-from typing import Any, Dict, List, Optional, Protocol, runtime_checkable
+from typing import Any, Protocol, runtime_checkable
 
 from pydantic import BaseModel
 
@@ -12,7 +12,7 @@ from llama_stack.apis.scoring_functions import ScoringFn, ScoringFnParams
 from llama_stack.schema_utils import json_schema_type, webmethod
 
 # mapping of metric to value
-ScoringResultRow = Dict[str, Any]
+ScoringResultRow = dict[str, Any]
 
 
 @json_schema_type
@@ -24,15 +24,15 @@ class ScoringResult(BaseModel):
     :param aggregated_results: Map of metric name to aggregated value
     """
 
-    score_rows: List[ScoringResultRow]
+    score_rows: list[ScoringResultRow]
     # aggregated metrics to value
-    aggregated_results: Dict[str, Any]
+    aggregated_results: dict[str, Any]
 
 
 @json_schema_type
 class ScoreBatchResponse(BaseModel):
-    dataset_id: Optional[str] = None
+    dataset_id: str | None = None
-    results: Dict[str, ScoringResult]
+    results: dict[str, ScoringResult]
 
 
 @json_schema_type
@@ -44,7 +44,7 @@ class ScoreResponse(BaseModel):
     """
 
     # each key in the dict is a scoring function name
-    results: Dict[str, ScoringResult]
+    results: dict[str, ScoringResult]
 
 
 class ScoringFunctionStore(Protocol):
@@ -59,15 +59,15 @@ class Scoring(Protocol):
     async def score_batch(
         self,
         dataset_id: str,
-        scoring_functions: Dict[str, Optional[ScoringFnParams]],
+        scoring_functions: dict[str, ScoringFnParams | None],
         save_results_dataset: bool = False,
     ) -> ScoreBatchResponse: ...
 
     @webmethod(route="/scoring/score", method="POST")
     async def score(
         self,
-        input_rows: List[Dict[str, Any]],
+        input_rows: list[dict[str, Any]],
-        scoring_functions: Dict[str, Optional[ScoringFnParams]],
+        scoring_functions: dict[str, ScoringFnParams | None],
     ) -> ScoreResponse:
         """Score a list of rows.
 

llama_stack/apis/scoring_functions/scoring_functions.py

@@ -6,18 +6,14 @@
 
 from enum import Enum
 from typing import (
+    Annotated,
     Any,
-    Dict,
-    List,
     Literal,
-    Optional,
     Protocol,
-    Union,
     runtime_checkable,
 )
 
 from pydantic import BaseModel, Field
-from typing_extensions import Annotated
 
 from llama_stack.apis.common.type_system import ParamType
 from llama_stack.apis.resource import Resource, ResourceType
@@ -46,12 +42,12 @@ class AggregationFunctionType(Enum):
 class LLMAsJudgeScoringFnParams(BaseModel):
     type: Literal[ScoringFnParamsType.llm_as_judge.value] = ScoringFnParamsType.llm_as_judge.value
     judge_model: str
-    prompt_template: Optional[str] = None
+    prompt_template: str | None = None
-    judge_score_regexes: Optional[List[str]] = Field(
+    judge_score_regexes: list[str] | None = Field(
         description="Regexes to extract the answer from generated response",
         default_factory=list,
     )
-    aggregation_functions: Optional[List[AggregationFunctionType]] = Field(
+    aggregation_functions: list[AggregationFunctionType] | None = Field(
         description="Aggregation functions to apply to the scores of each row",
         default_factory=list,
     )
@@ -60,11 +56,11 @@ class LLMAsJudgeScoringFnParams(BaseModel):
 @json_schema_type
 class RegexParserScoringFnParams(BaseModel):
     type: Literal[ScoringFnParamsType.regex_parser.value] = ScoringFnParamsType.regex_parser.value
-    parsing_regexes: Optional[List[str]] = Field(
+    parsing_regexes: list[str] | None = Field(
         description="Regex to extract the answer from generated response",
         default_factory=list,
     )
-    aggregation_functions: Optional[List[AggregationFunctionType]] = Field(
+    aggregation_functions: list[AggregationFunctionType] | None = Field(
         description="Aggregation functions to apply to the scores of each row",
         default_factory=list,
     )
@@ -73,33 +69,29 @@ class RegexParserScoringFnParams(BaseModel):
 @json_schema_type
 class BasicScoringFnParams(BaseModel):
     type: Literal[ScoringFnParamsType.basic.value] = ScoringFnParamsType.basic.value
-    aggregation_functions: Optional[List[AggregationFunctionType]] = Field(
+    aggregation_functions: list[AggregationFunctionType] | None = Field(
         description="Aggregation functions to apply to the scores of each row",
         default_factory=list,
     )
 
 
 ScoringFnParams = Annotated[
-    Union[
+    LLMAsJudgeScoringFnParams | RegexParserScoringFnParams | BasicScoringFnParams,
-        LLMAsJudgeScoringFnParams,
-        RegexParserScoringFnParams,
-        BasicScoringFnParams,
-    ],
     Field(discriminator="type"),
 ]
 register_schema(ScoringFnParams, name="ScoringFnParams")
 
 
 class CommonScoringFnFields(BaseModel):
-    description: Optional[str] = None
+    description: str | None = None
-    metadata: Dict[str, Any] = Field(
+    metadata: dict[str, Any] = Field(
         default_factory=dict,
         description="Any additional metadata for this definition",
     )
     return_type: ParamType = Field(
         description="The return type of the deterministic function",
     )
-    params: Optional[ScoringFnParams] = Field(
+    params: ScoringFnParams | None = Field(
         description="The parameters for the scoring function for benchmark eval, these can be overridden for app eval",
         default=None,
     )
@@ -120,12 +112,12 @@ class ScoringFn(CommonScoringFnFields, Resource):
 
 class ScoringFnInput(CommonScoringFnFields, BaseModel):
     scoring_fn_id: str
-    provider_id: Optional[str] = None
+    provider_id: str | None = None
-    provider_scoring_fn_id: Optional[str] = None
+    provider_scoring_fn_id: str | None = None
 
 
 class ListScoringFunctionsResponse(BaseModel):
-    data: List[ScoringFn]
+    data: list[ScoringFn]
 
 
 @runtime_checkable
@@ -142,7 +134,7 @@ class ScoringFunctions(Protocol):
         scoring_fn_id: str,
         description: str,
         return_type: ParamType,
-        provider_scoring_fn_id: Optional[str] = None,
+        provider_scoring_fn_id: str | None = None,
-        provider_id: Optional[str] = None,
+        provider_id: str | None = None,
-        params: Optional[ScoringFnParams] = None,
+        params: ScoringFnParams | None = None,
     ) -> None: ...

llama_stack/apis/shields/shields.py

@@ -4,7 +4,7 @@
 # This source code is licensed under the terms described in the LICENSE file in
 # the root directory of this source tree.
 
-from typing import Any, Dict, List, Literal, Optional, Protocol, runtime_checkable
+from typing import Any, Literal, Protocol, runtime_checkable
 
 from pydantic import BaseModel
 
@@ -14,7 +14,7 @@ from llama_stack.schema_utils import json_schema_type, webmethod
 
 
 class CommonShieldFields(BaseModel):
-    params: Optional[Dict[str, Any]] = None
+    params: dict[str, Any] | None = None
 
 
 @json_schema_type
@@ -34,12 +34,12 @@ class Shield(CommonShieldFields, Resource):
 
 class ShieldInput(CommonShieldFields):
     shield_id: str
-    provider_id: Optional[str] = None
+    provider_id: str | None = None
-    provider_shield_id: Optional[str] = None
+    provider_shield_id: str | None = None
 
 
 class ListShieldsResponse(BaseModel):
-    data: List[Shield]
+    data: list[Shield]
 
 
 @runtime_checkable
@@ -55,7 +55,7 @@ class Shields(Protocol):
     async def register_shield(
         self,
         shield_id: str,
-        provider_shield_id: Optional[str] = None,
+        provider_shield_id: str | None = None,
-        provider_id: Optional[str] = None,
+        provider_id: str | None = None,
-        params: Optional[Dict[str, Any]] = None,
+        params: dict[str, Any] | None = None,
     ) -> Shield: ...

llama_stack/apis/synthetic_data_generation/synthetic_data_generation.py

@@ -5,7 +5,7 @@
 # the root directory of this source tree.
 
 from enum import Enum
-from typing import Any, Dict, List, Optional, Protocol, Union
+from typing import Any, Protocol
 
 from pydantic import BaseModel
 
@@ -28,24 +28,24 @@ class FilteringFunction(Enum):
 class SyntheticDataGenerationRequest(BaseModel):
     """Request to generate synthetic data. A small batch of prompts and a filtering function"""
 
-    dialogs: List[Message]
+    dialogs: list[Message]
     filtering_function: FilteringFunction = FilteringFunction.none
-    model: Optional[str] = None
+    model: str | None = None
 
 
 @json_schema_type
 class SyntheticDataGenerationResponse(BaseModel):
     """Response from the synthetic data generation. Batch of (prompt, response, score) tuples that pass the threshold."""
 
-    synthetic_data: List[Dict[str, Any]]
+    synthetic_data: list[dict[str, Any]]
-    statistics: Optional[Dict[str, Any]] = None
+    statistics: dict[str, Any] | None = None
 
 
 class SyntheticDataGeneration(Protocol):
     @webmethod(route="/synthetic-data-generation/generate")
     def synthetic_data_generate(
         self,
-        dialogs: List[Message],
+        dialogs: list[Message],
         filtering_function: FilteringFunction = FilteringFunction.none,
-        model: Optional[str] = None,
+        model: str | None = None,
-    ) -> Union[SyntheticDataGenerationResponse]: ...
+    ) -> SyntheticDataGenerationResponse: ...

llama_stack/apis/telemetry/telemetry.py

@@ -7,18 +7,14 @@
 from datetime import datetime
 from enum import Enum
 from typing import (
+    Annotated,
     Any,
-    Dict,
-    List,
     Literal,
-    Optional,
     Protocol,
-    Union,
     runtime_checkable,
 )
 
 from pydantic import BaseModel, Field
-from typing_extensions import Annotated
 
 from llama_stack.models.llama.datatypes import Primitive
 from llama_stack.schema_utils import json_schema_type, register_schema, webmethod
@@ -37,11 +33,11 @@ class SpanStatus(Enum):
 class Span(BaseModel):
     span_id: str
     trace_id: str
-    parent_span_id: Optional[str] = None
+    parent_span_id: str | None = None
     name: str
     start_time: datetime
-    end_time: Optional[datetime] = None
+    end_time: datetime | None = None
-    attributes: Optional[Dict[str, Any]] = Field(default_factory=dict)
+    attributes: dict[str, Any] | None = Field(default_factory=dict)
 
     def set_attribute(self, key: str, value: Any):
         if self.attributes is None:
@@ -54,7 +50,7 @@ class Trace(BaseModel):
     trace_id: str
     root_span_id: str
     start_time: datetime
-    end_time: Optional[datetime] = None
+    end_time: datetime | None = None
 
 
 @json_schema_type
@@ -78,7 +74,7 @@ class EventCommon(BaseModel):
     trace_id: str
     span_id: str
     timestamp: datetime
-    attributes: Optional[Dict[str, Primitive]] = Field(default_factory=dict)
+    attributes: dict[str, Primitive] | None = Field(default_factory=dict)
 
 
 @json_schema_type
@@ -92,15 +88,15 @@ class UnstructuredLogEvent(EventCommon):
 class MetricEvent(EventCommon):
     type: Literal[EventType.METRIC.value] = EventType.METRIC.value
     metric: str  # this would be an enum
-    value: Union[int, float]
+    value: int | float
     unit: str
 
 
 @json_schema_type
 class MetricInResponse(BaseModel):
     metric: str
-    value: Union[int, float]
+    value: int | float
-    unit: Optional[str] = None
+    unit: str | None = None
 
 
 # This is a short term solution to allow inference API to return metrics
@@ -124,7 +120,7 @@ class MetricInResponse(BaseModel):
 
 
 class MetricResponseMixin(BaseModel):
-    metrics: Optional[List[MetricInResponse]] = None
+    metrics: list[MetricInResponse] | None = None
 
 
 @json_schema_type
@@ -137,7 +133,7 @@ class StructuredLogType(Enum):
 class SpanStartPayload(BaseModel):
     type: Literal[StructuredLogType.SPAN_START.value] = StructuredLogType.SPAN_START.value
     name: str
-    parent_span_id: Optional[str] = None
+    parent_span_id: str | None = None
 
 
 @json_schema_type
@@ -147,10 +143,7 @@ class SpanEndPayload(BaseModel):
 
 
 StructuredLogPayload = Annotated[
-    Union[
+    SpanStartPayload | SpanEndPayload,
-        SpanStartPayload,
-        SpanEndPayload,
-    ],
     Field(discriminator="type"),
 ]
 register_schema(StructuredLogPayload, name="StructuredLogPayload")
@@ -163,11 +156,7 @@ class StructuredLogEvent(EventCommon):
 
 
 Event = Annotated[
-    Union[
+    UnstructuredLogEvent | MetricEvent | StructuredLogEvent,
-        UnstructuredLogEvent,
-        MetricEvent,
-        StructuredLogEvent,
-    ],
     Field(discriminator="type"),
 ]
 register_schema(Event, name="Event")
@@ -184,7 +173,7 @@ class EvalTrace(BaseModel):
 
 @json_schema_type
 class SpanWithStatus(Span):
-    status: Optional[SpanStatus] = None
+    status: SpanStatus | None = None
 
 
 @json_schema_type
@@ -203,15 +192,15 @@ class QueryCondition(BaseModel):
 
 
 class QueryTracesResponse(BaseModel):
-    data: List[Trace]
+    data: list[Trace]
 
 
 class QuerySpansResponse(BaseModel):
-    data: List[Span]
+    data: list[Span]
 
 
 class QuerySpanTreeResponse(BaseModel):
-    data: Dict[str, SpanWithStatus]
+    data: dict[str, SpanWithStatus]
 
 
 @runtime_checkable
@@ -222,10 +211,10 @@ class Telemetry(Protocol):
     @webmethod(route="/telemetry/traces", method="POST")
     async def query_traces(
         self,
-        attribute_filters: Optional[List[QueryCondition]] = None,
+        attribute_filters: list[QueryCondition] | None = None,
-        limit: Optional[int] = 100,
+        limit: int | None = 100,
-        offset: Optional[int] = 0,
+        offset: int | None = 0,
-        order_by: Optional[List[str]] = None,
+        order_by: list[str] | None = None,
     ) -> QueryTracesResponse: ...
 
     @webmethod(route="/telemetry/traces/{trace_id:path}", method="GET")
@@ -238,23 +227,23 @@ class Telemetry(Protocol):
     async def get_span_tree(
         self,
         span_id: str,
-        attributes_to_return: Optional[List[str]] = None,
+        attributes_to_return: list[str] | None = None,
-        max_depth: Optional[int] = None,
+        max_depth: int | None = None,
     ) -> QuerySpanTreeResponse: ...
 
     @webmethod(route="/telemetry/spans", method="POST")
     async def query_spans(
         self,
-        attribute_filters: List[QueryCondition],
+        attribute_filters: list[QueryCondition],
-        attributes_to_return: List[str],
+        attributes_to_return: list[str],
-        max_depth: Optional[int] = None,
+        max_depth: int | None = None,
     ) -> QuerySpansResponse: ...
 
     @webmethod(route="/telemetry/spans/export", method="POST")
     async def save_spans_to_dataset(
         self,
-        attribute_filters: List[QueryCondition],
+        attribute_filters: list[QueryCondition],
-        attributes_to_save: List[str],
+        attributes_to_save: list[str],
         dataset_id: str,
-        max_depth: Optional[int] = None,
+        max_depth: int | None = None,
     ) -> None: ...

llama_stack/apis/tools/rag_tool.py

@@ -5,10 +5,10 @@
 # the root directory of this source tree.
 
 from enum import Enum
-from typing import Any, Dict, List, Literal, Optional, Union
+from typing import Annotated, Any, Literal
 
 from pydantic import BaseModel, Field
-from typing_extensions import Annotated, Protocol, runtime_checkable
+from typing_extensions import Protocol, runtime_checkable
 
 from llama_stack.apis.common.content_types import URL, InterleavedContent
 from llama_stack.providers.utils.telemetry.trace_protocol import trace_protocol
@@ -29,13 +29,13 @@ class RAGDocument(BaseModel):
     document_id: str
     content: InterleavedContent | URL
     mime_type: str | None = None
-    metadata: Dict[str, Any] = Field(default_factory=dict)
+    metadata: dict[str, Any] = Field(default_factory=dict)
 
 
 @json_schema_type
 class RAGQueryResult(BaseModel):
-    content: Optional[InterleavedContent] = None
+    content: InterleavedContent | None = None
-    metadata: Dict[str, Any] = Field(default_factory=dict)
+    metadata: dict[str, Any] = Field(default_factory=dict)
 
 
 @json_schema_type
@@ -59,10 +59,7 @@ class LLMRAGQueryGeneratorConfig(BaseModel):
 
 
 RAGQueryGeneratorConfig = Annotated[
-    Union[
+    DefaultRAGQueryGeneratorConfig | LLMRAGQueryGeneratorConfig,
-        DefaultRAGQueryGeneratorConfig,
-        LLMRAGQueryGeneratorConfig,
-    ],
     Field(discriminator="type"),
 ]
 register_schema(RAGQueryGeneratorConfig, name="RAGQueryGeneratorConfig")
@@ -83,7 +80,7 @@ class RAGToolRuntime(Protocol):
     @webmethod(route="/tool-runtime/rag-tool/insert", method="POST")
     async def insert(
         self,
-        documents: List[RAGDocument],
+        documents: list[RAGDocument],
         vector_db_id: str,
         chunk_size_in_tokens: int = 512,
     ) -> None:
@@ -94,8 +91,8 @@ class RAGToolRuntime(Protocol):
     async def query(
         self,
         content: InterleavedContent,
-        vector_db_ids: List[str],
+        vector_db_ids: list[str],
-        query_config: Optional[RAGQueryConfig] = None,
+        query_config: RAGQueryConfig | None = None,
     ) -> RAGQueryResult:
         """Query the RAG system for context; typically invoked by the agent"""
         ...

llama_stack/apis/tools/tools.py

@@ -5,7 +5,7 @@
 # the root directory of this source tree.
 
 from enum import Enum
-from typing import Any, Dict, List, Literal, Optional
+from typing import Any, Literal
 
 from pydantic import BaseModel, Field
 from typing_extensions import Protocol, runtime_checkable
@@ -24,7 +24,7 @@ class ToolParameter(BaseModel):
     parameter_type: str
     description: str
     required: bool = Field(default=True)
-    default: Optional[Any] = None
+    default: Any | None = None
 
 
 @json_schema_type
@@ -40,39 +40,39 @@ class Tool(Resource):
     toolgroup_id: str
     tool_host: ToolHost
     description: str
-    parameters: List[ToolParameter]
+    parameters: list[ToolParameter]
-    metadata: Optional[Dict[str, Any]] = None
+    metadata: dict[str, Any] | None = None
 
 
 @json_schema_type
 class ToolDef(BaseModel):
     name: str
-    description: Optional[str] = None
+    description: str | None = None
-    parameters: Optional[List[ToolParameter]] = None
+    parameters: list[ToolParameter] | None = None
-    metadata: Optional[Dict[str, Any]] = None
+    metadata: dict[str, Any] | None = None
 
 
 @json_schema_type
 class ToolGroupInput(BaseModel):
     toolgroup_id: str
     provider_id: str
-    args: Optional[Dict[str, Any]] = None
+    args: dict[str, Any] | None = None
-    mcp_endpoint: Optional[URL] = None
+    mcp_endpoint: URL | None = None
 
 
 @json_schema_type
 class ToolGroup(Resource):
     type: Literal[ResourceType.tool_group.value] = ResourceType.tool_group.value
-    mcp_endpoint: Optional[URL] = None
+    mcp_endpoint: URL | None = None
-    args: Optional[Dict[str, Any]] = None
+    args: dict[str, Any] | None = None
 
 
 @json_schema_type
 class ToolInvocationResult(BaseModel):
-    content: Optional[InterleavedContent] = None
+    content: InterleavedContent | None = None
-    error_message: Optional[str] = None
+    error_message: str | None = None
-    error_code: Optional[int] = None
+    error_code: int | None = None
-    metadata: Optional[Dict[str, Any]] = None
+    metadata: dict[str, Any] | None = None
 
 
 class ToolStore(Protocol):
@@ -81,11 +81,11 @@ class ToolStore(Protocol):
 
 
 class ListToolGroupsResponse(BaseModel):
-    data: List[ToolGroup]
+    data: list[ToolGroup]
 
 
 class ListToolsResponse(BaseModel):
-    data: List[Tool]
+    data: list[Tool]
 
 
 class ListToolDefsResponse(BaseModel):
@@ -100,8 +100,8 @@ class ToolGroups(Protocol):
         self,
         toolgroup_id: str,
         provider_id: str,
-        mcp_endpoint: Optional[URL] = None,
+        mcp_endpoint: URL | None = None,
-        args: Optional[Dict[str, Any]] = None,
+        args: dict[str, Any] | None = None,
     ) -> None:
         """Register a tool group"""
         ...
@@ -118,7 +118,7 @@ class ToolGroups(Protocol):
         ...
 
     @webmethod(route="/tools", method="GET")
-    async def list_tools(self, toolgroup_id: Optional[str] = None) -> ListToolsResponse:
+    async def list_tools(self, toolgroup_id: str | None = None) -> ListToolsResponse:
         """List tools with optional tool group"""
         ...
 
@@ -151,10 +151,10 @@ class ToolRuntime(Protocol):
     # TODO: This needs to be renamed once OPEN API generator name conflict issue is fixed.
     @webmethod(route="/tool-runtime/list-tools", method="GET")
     async def list_runtime_tools(
-        self, tool_group_id: Optional[str] = None, mcp_endpoint: Optional[URL] = None
+        self, tool_group_id: str | None = None, mcp_endpoint: URL | None = None
     ) -> ListToolDefsResponse: ...
 
     @webmethod(route="/tool-runtime/invoke", method="POST")
-    async def invoke_tool(self, tool_name: str, kwargs: Dict[str, Any]) -> ToolInvocationResult:
+    async def invoke_tool(self, tool_name: str, kwargs: dict[str, Any]) -> ToolInvocationResult:
         """Run a tool with the given arguments"""
         ...

llama_stack/apis/vector_dbs/vector_dbs.py

@@ -4,7 +4,7 @@
 # This source code is licensed under the terms described in the LICENSE file in
 # the root directory of this source tree.
 
-from typing import List, Literal, Optional, Protocol, runtime_checkable
+from typing import Literal, Protocol, runtime_checkable
 
 from pydantic import BaseModel
 
@@ -33,11 +33,11 @@ class VectorDBInput(BaseModel):
     vector_db_id: str
     embedding_model: str
     embedding_dimension: int
-    provider_vector_db_id: Optional[str] = None
+    provider_vector_db_id: str | None = None
 
 
 class ListVectorDBsResponse(BaseModel):
-    data: List[VectorDB]
+    data: list[VectorDB]
 
 
 @runtime_checkable
@@ -57,9 +57,9 @@ class VectorDBs(Protocol):
         self,
         vector_db_id: str,
         embedding_model: str,
-        embedding_dimension: Optional[int] = 384,
+        embedding_dimension: int | None = 384,
-        provider_id: Optional[str] = None,
+        provider_id: str | None = None,
-        provider_vector_db_id: Optional[str] = None,
+        provider_vector_db_id: str | None = None,
     ) -> VectorDB: ...
 
     @webmethod(route="/vector-dbs/{vector_db_id:path}", method="DELETE")

llama_stack/apis/vector_io/vector_io.py

@@ -8,7 +8,7 @@
 #
 # This source code is licensed under the terms described in the LICENSE file in
 # the root directory of this source tree.
-from typing import Any, Dict, List, Optional, Protocol, runtime_checkable
+from typing import Any, Protocol, runtime_checkable
 
 from pydantic import BaseModel, Field
 
@@ -20,17 +20,17 @@ from llama_stack.schema_utils import json_schema_type, webmethod
 
 class Chunk(BaseModel):
     content: InterleavedContent
-    metadata: Dict[str, Any] = Field(default_factory=dict)
+    metadata: dict[str, Any] = Field(default_factory=dict)
 
 
 @json_schema_type
 class QueryChunksResponse(BaseModel):
-    chunks: List[Chunk]
+    chunks: list[Chunk]
-    scores: List[float]
+    scores: list[float]
 
 
 class VectorDBStore(Protocol):
-    def get_vector_db(self, vector_db_id: str) -> Optional[VectorDB]: ...
+    def get_vector_db(self, vector_db_id: str) -> VectorDB | None: ...
 
 
 @runtime_checkable
@@ -44,8 +44,8 @@ class VectorIO(Protocol):
     async def insert_chunks(
         self,
         vector_db_id: str,
-        chunks: List[Chunk],
+        chunks: list[Chunk],
-        ttl_seconds: Optional[int] = None,
+        ttl_seconds: int | None = None,
     ) -> None: ...
 
     @webmethod(route="/vector-io/query", method="POST")
@@ -53,5 +53,5 @@ class VectorIO(Protocol):
         self,
         vector_db_id: str,
         query: InterleavedContent,
-        params: Optional[Dict[str, Any]] = None,
+        params: dict[str, Any] | None = None,
     ) -> QueryChunksResponse: ...

llama_stack/cli/download.py

@@ -13,7 +13,6 @@ from dataclasses import dataclass
 from datetime import datetime, timezone
 from functools import partial
 from pathlib import Path
-from typing import Dict, List, Optional
 
 import httpx
 from pydantic import BaseModel, ConfigDict
@@ -102,7 +101,7 @@ class DownloadTask:
     output_file: str
     total_size: int = 0
     downloaded_size: int = 0
-    task_id: Optional[int] = None
+    task_id: int | None = None
     retries: int = 0
     max_retries: int = 3
 
@@ -262,7 +261,7 @@ class ParallelDownloader:
             self.progress.update(task.task_id, description=f"[red]Failed: {task.output_file}[/red]")
             raise DownloadError(f"Download failed for {task.output_file}: {str(e)}") from e
 
-    def has_disk_space(self, tasks: List[DownloadTask]) -> bool:
+    def has_disk_space(self, tasks: list[DownloadTask]) -> bool:
         try:
             total_remaining_size = sum(task.total_size - task.downloaded_size for task in tasks)
             dir_path = os.path.dirname(os.path.abspath(tasks[0].output_file))
@@ -282,7 +281,7 @@ class ParallelDownloader:
         except Exception as e:
             raise DownloadError(f"Failed to check disk space: {str(e)}") from e
 
-    async def download_all(self, tasks: List[DownloadTask]) -> None:
+    async def download_all(self, tasks: list[DownloadTask]) -> None:
         if not tasks:
             raise ValueError("No download tasks provided")
 
@@ -391,20 +390,20 @@ def _meta_download(
 
 class ModelEntry(BaseModel):
     model_id: str
-    files: Dict[str, str]
+    files: dict[str, str]
 
     model_config = ConfigDict(protected_namespaces=())
 
 
 class Manifest(BaseModel):
-    models: List[ModelEntry]
+    models: list[ModelEntry]
     expires_on: datetime
 
 
 def _download_from_manifest(manifest_file: str, max_concurrent_downloads: int):
     from llama_stack.distribution.utils.model_utils import model_local_dir
 
-    with open(manifest_file, "r") as f:
+    with open(manifest_file) as f:
         d = json.load(f)
         manifest = Manifest(**d)
 
@@ -460,15 +459,17 @@ def run_download_cmd(args: argparse.Namespace, parser: argparse.ArgumentParser):
         from llama_stack.models.llama.sku_list import llama_meta_net_info, resolve_model
 
         from .model.safety_models import (
-            prompt_guard_download_info,
+            prompt_guard_download_info_map,
-            prompt_guard_model_sku,
+            prompt_guard_model_sku_map,
         )
 
-        prompt_guard = prompt_guard_model_sku()
+        prompt_guard_model_sku_map = prompt_guard_model_sku_map()
+        prompt_guard_download_info_map = prompt_guard_download_info_map()
+
         for model_id in model_ids:
-            if model_id == prompt_guard.model_id:
+            if model_id in prompt_guard_model_sku_map.keys():
-                model = prompt_guard
+                model = prompt_guard_model_sku_map[model_id]
-                info = prompt_guard_download_info()
+                info = prompt_guard_download_info_map[model_id]
             else:
                 model = resolve_model(model_id)
                 if model is None:

llama_stack/cli/model/describe.py

@@ -36,11 +36,11 @@ class ModelDescribe(Subcommand):
         )
 
     def _run_model_describe_cmd(self, args: argparse.Namespace) -> None:
-        from .safety_models import prompt_guard_model_sku
+        from .safety_models import prompt_guard_model_sku_map
 
-        prompt_guard = prompt_guard_model_sku()
+        prompt_guard_model_map = prompt_guard_model_sku_map()
-        if args.model_id == prompt_guard.model_id:
+        if args.model_id in prompt_guard_model_map.keys():
-            model = prompt_guard
+            model = prompt_guard_model_map[args.model_id]
         else:
             model = resolve_model(args.model_id)
 

llama_stack/cli/model/list.py

@@ -84,7 +84,7 @@ class ModelList(Subcommand):
         )
 
     def _run_model_list_cmd(self, args: argparse.Namespace) -> None:
-        from .safety_models import prompt_guard_model_sku
+        from .safety_models import prompt_guard_model_skus
 
         if args.downloaded:
             return _run_model_list_downloaded_cmd()
@@ -96,7 +96,7 @@ class ModelList(Subcommand):
         ]
 
         rows = []
-        for model in all_registered_models() + [prompt_guard_model_sku()]:
+        for model in all_registered_models() + prompt_guard_model_skus():
             if not args.show_all and not model.is_featured:
                 continue
 

llama_stack/cli/model/remove.py

@@ -42,11 +42,12 @@ class ModelRemove(Subcommand):
         )
 
     def _run_model_remove_cmd(self, args: argparse.Namespace) -> None:
-        from .safety_models import prompt_guard_model_sku
+        from .safety_models import prompt_guard_model_sku_map
 
-        prompt_guard = prompt_guard_model_sku()
+        prompt_guard_model_map = prompt_guard_model_sku_map()
-        if args.model == prompt_guard.model_id:
+
-            model = prompt_guard
+        if args.model in prompt_guard_model_map.keys():
+            model = prompt_guard_model_map[args.model]
         else:
             model = resolve_model(args.model)
 

llama_stack/cli/model/safety_models.py

@@ -4,7 +4,7 @@
 # This source code is licensed under the terms described in the LICENSE file in
 # the root directory of this source tree.
 
-from typing import Any, Dict
+from typing import Any
 
 from pydantic import BaseModel, ConfigDict, Field
 
@@ -15,14 +15,14 @@ from llama_stack.models.llama.sku_types import CheckpointQuantizationFormat
 class PromptGuardModel(BaseModel):
     """Make a 'fake' Model-like object for Prompt Guard. Eventually this will be removed."""
 
-    model_id: str = "Prompt-Guard-86M"
+    model_id: str
+    huggingface_repo: str
     description: str = "Prompt Guard. NOTE: this model will not be provided via `llama` CLI soon."
     is_featured: bool = False
-    huggingface_repo: str = "meta-llama/Prompt-Guard-86M"
+    max_seq_length: int = 512
-    max_seq_length: int = 2048
     is_instruct_model: bool = False
     quantization_format: CheckpointQuantizationFormat = CheckpointQuantizationFormat.bf16
-    arch_args: Dict[str, Any] = Field(default_factory=dict)
+    arch_args: dict[str, Any] = Field(default_factory=dict)
 
     def descriptor(self) -> str:
         return self.model_id
@@ -30,18 +30,35 @@ class PromptGuardModel(BaseModel):
     model_config = ConfigDict(protected_namespaces=())
 
 
-def prompt_guard_model_sku():
+def prompt_guard_model_skus():
-    return PromptGuardModel()
+    return [
+        PromptGuardModel(model_id="Prompt-Guard-86M", huggingface_repo="meta-llama/Prompt-Guard-86M"),
+        PromptGuardModel(
+            model_id="Llama-Prompt-Guard-2-86M",
+            huggingface_repo="meta-llama/Llama-Prompt-Guard-2-86M",
+        ),
+        PromptGuardModel(
+            model_id="Llama-Prompt-Guard-2-22M",
+            huggingface_repo="meta-llama/Llama-Prompt-Guard-2-22M",
+        ),
+    ]
 
 
-def prompt_guard_download_info():
+def prompt_guard_model_sku_map() -> dict[str, Any]:
-    return LlamaDownloadInfo(
+    return {model.model_id: model for model in prompt_guard_model_skus()}
-        folder="Prompt-Guard",
+
-        files=[
+
-            "model.safetensors",
+def prompt_guard_download_info_map() -> dict[str, LlamaDownloadInfo]:
-            "special_tokens_map.json",
+    return {
-            "tokenizer.json",
+        model.model_id: LlamaDownloadInfo(
-            "tokenizer_config.json",
+            folder="Prompt-Guard" if model.model_id == "Prompt-Guard-86M" else model.model_id,
-        ],
+            files=[
-        pth_size=1,
+                "model.safetensors",
-    )
+                "special_tokens_map.json",
+                "tokenizer.json",
+                "tokenizer_config.json",
+            ],
+            pth_size=1,
+        )
+        for model in prompt_guard_model_skus()
+    }

llama_stack/cli/stack/_build.py

@@ -13,13 +13,12 @@ import sys
 import textwrap
 from functools import lru_cache
 from pathlib import Path
-from typing import Dict, Optional
 
 import yaml
 from prompt_toolkit import prompt
 from prompt_toolkit.completion import WordCompleter
 from prompt_toolkit.validation import Validator
-from termcolor import cprint
+from termcolor import colored, cprint
 
 from llama_stack.cli.stack.utils import ImageType
 from llama_stack.cli.table import print_table
@@ -46,14 +45,14 @@ from llama_stack.providers.datatypes import Api
 TEMPLATES_PATH = Path(__file__).parent.parent.parent / "templates"
 
 
-@lru_cache()
+@lru_cache
-def available_templates_specs() -> Dict[str, BuildConfig]:
+def available_templates_specs() -> dict[str, BuildConfig]:
     import yaml
 
     template_specs = {}
     for p in TEMPLATES_PATH.rglob("*build.yaml"):
         template_name = p.parent.name
-        with open(p, "r") as f:
+        with open(p) as f:
             build_config = BuildConfig(**yaml.safe_load(f))
             template_specs[template_name] = build_config
     return template_specs
@@ -89,6 +88,43 @@ def run_stack_build_command(args: argparse.Namespace) -> None:
                 color="red",
             )
             sys.exit(1)
+    elif args.providers:
+        providers = dict()
+        for api_provider in args.providers.split(","):
+            if "=" not in api_provider:
+                cprint(
+                    "Could not parse `--providers`. Please ensure the list is in the format api1=provider1,api2=provider2",
+                    color="red",
+                )
+                sys.exit(1)
+            api, provider = api_provider.split("=")
+            providers_for_api = get_provider_registry().get(Api(api), None)
+            if providers_for_api is None:
+                cprint(
+                    f"{api} is not a valid API.",
+                    color="red",
+                )
+                sys.exit(1)
+            if provider in providers_for_api:
+                providers.setdefault(api, []).append(provider)
+            else:
+                cprint(
+                    f"{provider} is not a valid provider for the {api} API.",
+                    color="red",
+                )
+                sys.exit(1)
+        distribution_spec = DistributionSpec(
+            providers=providers,
+            description=",".join(args.providers),
+        )
+        if not args.image_type:
+            cprint(
+                f"Please specify a image-type (container | conda | venv) for {args.template}",
+                color="red",
+            )
+            sys.exit(1)
+
+        build_config = BuildConfig(image_type=args.image_type, distribution_spec=distribution_spec)
     elif not args.config and not args.template:
         name = prompt(
             "> Enter a name for your Llama Stack (e.g. my-local-stack): ",
@@ -99,12 +135,13 @@ def run_stack_build_command(args: argparse.Namespace) -> None:
         )
 
         image_type = prompt(
-            f"> Enter the image type you want your Llama Stack to be built as ({' or '.join(e.value for e in ImageType)}): ",
+            "> Enter the image type you want your Llama Stack to be built as (use <TAB> to see options): ",
+            completer=WordCompleter([e.value for e in ImageType]),
+            complete_while_typing=True,
             validator=Validator.from_callable(
                 lambda x: x in [e.value for e in ImageType],
-                error_message=f"Invalid image type, please enter {' or '.join(e.value for e in ImageType)}",
+                error_message="Invalid image type. Use <TAB> to see options",
             ),
-            default=ImageType.CONDA.value,
         )
 
         if image_type == ImageType.CONDA.value:
@@ -140,7 +177,7 @@ def run_stack_build_command(args: argparse.Namespace) -> None:
             if not available_providers:
                 continue
             api_provider = prompt(
-                "> Enter provider for API {}: ".format(api.value),
+                f"> Enter provider for API {api.value}: ",
                 completer=WordCompleter(available_providers),
                 complete_while_typing=True,
                 validator=Validator.from_callable(
@@ -163,7 +200,7 @@ def run_stack_build_command(args: argparse.Namespace) -> None:
 
         build_config = BuildConfig(image_type=image_type, distribution_spec=distribution_spec)
     else:
-        with open(args.config, "r") as f:
+        with open(args.config) as f:
             try:
                 build_config = BuildConfig(**yaml.safe_load(f))
             except Exception as e:
@@ -173,16 +210,9 @@ def run_stack_build_command(args: argparse.Namespace) -> None:
                 )
                 sys.exit(1)
 
-        if build_config.image_type == LlamaStackImageType.CONTAINER.value and not args.image_name:
-            cprint(
-                "Please specify --image-name when building a container from a config file",
-                color="red",
-            )
-            sys.exit(1)
-
     if args.print_deps_only:
         print(f"# Dependencies for {args.template or args.config or image_name}")
-        normal_deps, special_deps = get_provider_dependencies(build_config.distribution_spec.providers)
+        normal_deps, special_deps = get_provider_dependencies(build_config)
         normal_deps += SERVER_DEPENDENCIES
         print(f"uv pip install {' '.join(normal_deps)}")
         for special_dep in special_deps:
@@ -198,10 +228,14 @@ def run_stack_build_command(args: argparse.Namespace) -> None:
         )
 
     except (Exception, RuntimeError) as exc:
+        import traceback
+
         cprint(
             f"Error building stack: {exc}",
             color="red",
         )
+        cprint("Stack trace:", color="red")
+        traceback.print_exc()
         sys.exit(1)
     if run_config is None:
         cprint(
@@ -233,9 +267,10 @@ def _generate_run_config(
         image_name=image_name,
         apis=apis,
         providers={},
+        external_providers_dir=build_config.external_providers_dir if build_config.external_providers_dir else None,
     )
     # build providers dict
-    provider_registry = get_provider_registry()
+    provider_registry = get_provider_registry(build_config)
     for api in apis:
         run_config.providers[api] = []
         provider_types = build_config.distribution_spec.providers[api]
@@ -249,8 +284,22 @@ def _generate_run_config(
             if p.deprecation_error:
                 raise InvalidProviderError(p.deprecation_error)
 
-            config_type = instantiate_class_type(provider_registry[Api(api)][provider_type].config_class)
+            try:
-            if hasattr(config_type, "sample_run_config"):
+                config_type = instantiate_class_type(provider_registry[Api(api)][provider_type].config_class)
+            except ModuleNotFoundError:
+                # HACK ALERT:
+                # This code executes after building is done, the import cannot work since the
+                # package is either available in the venv or container - not available on the host.
+                # TODO: use a "is_external" flag in ProviderSpec to check if the provider is
+                # external
+                cprint(
+                    f"Failed to import provider {provider_type} for API {api} - assuming it's external, skipping",
+                    color="yellow",
+                )
+                # Set config_type to None to avoid UnboundLocalError
+                config_type = None
+
+            if config_type is not None and hasattr(config_type, "sample_run_config"):
                 config = config_type.sample_run_config(__distro_dir__=f"~/.llama/distributions/{image_name}")
             else:
                 config = {}
@@ -268,20 +317,25 @@ def _generate_run_config(
         to_write = json.loads(run_config.model_dump_json())
         f.write(yaml.dump(to_write, sort_keys=False))
 
-    # this path is only invoked when no template is provided
+    # Only print this message for non-container builds since it will be displayed before the
-    cprint(
+    # container is built
-        f"You can now run your stack with `llama stack run {run_config_file}`",
+    # For non-container builds, the run.yaml is generated at the very end of the build process so it
-        color="green",
+    # makes sense to display this message
-    )
+    if build_config.image_type != LlamaStackImageType.CONTAINER.value:
+        cprint(
+            f"You can now run your stack with `llama stack run {run_config_file}`",
+            color="green",
+        )
     return run_config_file
 
 
 def _run_stack_build_command_from_build_config(
     build_config: BuildConfig,
-    image_name: Optional[str] = None,
+    image_name: str | None = None,
-    template_name: Optional[str] = None,
+    template_name: str | None = None,
-    config_path: Optional[str] = None,
+    config_path: str | None = None,
 ) -> str:
+    image_name = image_name or build_config.image_name
     if build_config.image_type == LlamaStackImageType.CONTAINER.value:
         if template_name:
             image_name = f"distribution-{template_name}"
@@ -305,6 +359,13 @@ def _run_stack_build_command_from_build_config(
         build_file_path = build_dir / f"{image_name}-build.yaml"
 
     os.makedirs(build_dir, exist_ok=True)
+    run_config_file = None
+    # Generate the run.yaml so it can be included in the container image with the proper entrypoint
+    # Only do this if we're building a container image and we're not using a template
+    if build_config.image_type == LlamaStackImageType.CONTAINER.value and not template_name and config_path:
+        cprint("Generating run.yaml file", color="green")
+        run_config_file = _generate_run_config(build_config, build_dir, image_name)
+
     with open(build_file_path, "w") as f:
         to_write = json.loads(build_config.model_dump_json())
         f.write(yaml.dump(to_write, sort_keys=False))
@@ -313,7 +374,8 @@ def _run_stack_build_command_from_build_config(
         build_config,
         build_file_path,
         image_name,
-        template_or_config=template_name or config_path,
+        template_or_config=template_name or config_path or str(build_file_path),
+        run_config=run_config_file,
     )
     if return_code != 0:
         raise RuntimeError(f"Failed to build image {image_name}")
@@ -326,6 +388,11 @@ def _run_stack_build_command_from_build_config(
             shutil.copy(path, run_config_file)
 
         cprint("Build Successful!", color="green")
+        cprint("You can find the newly-built template here: " + colored(template_path, "light_blue"))
+        cprint(
+            "You can run the new Llama Stack distro via: "
+            + colored(f"llama stack run {template_path} --image-type {build_config.image_type}", "light_blue")
+        )
         return template_path
     else:
         return _generate_run_config(build_config, build_dir, image_name)

llama_stack/cli/stack/build.py

@@ -75,6 +75,12 @@ the build. If not specified, currently active environment will be used if found.
             default=False,
             help="Run the stack after building using the same image type, name, and other applicable arguments",
         )
+        self.parser.add_argument(
+            "--providers",
+            type=str,
+            default=None,
+            help="Build a config for a list of providers and only those providers. This list is formatted like: api1=provider1,api2=provider2. Where there can be multiple providers per API.",
+        )
 
     def _run_stack_build_command(self, args: argparse.Namespace) -> None:
         # always keep implementation completely silo-ed away from CLI so CLI

llama_stack/cli/stack/run.py

@@ -119,7 +119,7 @@ class StackRun(Subcommand):
 
         if not config_file.is_file():
             self.parser.error(
-                f"Config file must be a valid file path, '{config_file}’ is not a file: type={type(config_file)}"
+                f"Config file must be a valid file path, '{config_file}' is not a file: type={type(config_file)}"
             )
 
         logger.info(f"Using run configuration: {config_file}")

llama_stack/cli/table.py

@@ -4,7 +4,7 @@
 # This source code is licensed under the terms described in the LICENSE file in
 # the root directory of this source tree.
 
-from typing import Iterable
+from collections.abc import Iterable
 
 from rich.console import Console
 from rich.table import Table

llama_stack/cli/verify_download.py

@@ -9,7 +9,6 @@ import hashlib
 from dataclasses import dataclass
 from functools import partial
 from pathlib import Path
-from typing import Dict, List, Optional
 
 from rich.console import Console
 from rich.progress import Progress, SpinnerColumn, TextColumn
@@ -21,7 +20,7 @@ from llama_stack.cli.subcommand import Subcommand
 class VerificationResult:
     filename: str
     expected_hash: str
-    actual_hash: Optional[str]
+    actual_hash: str | None
     exists: bool
     matches: bool
 
@@ -60,9 +59,9 @@ def calculate_md5(filepath: Path, chunk_size: int = 8192) -> str:
     return md5_hash.hexdigest()
 
 
-def load_checksums(checklist_path: Path) -> Dict[str, str]:
+def load_checksums(checklist_path: Path) -> dict[str, str]:
     checksums = {}
-    with open(checklist_path, "r") as f:
+    with open(checklist_path) as f:
         for line in f:
             if line.strip():
                 md5sum, filepath = line.strip().split("  ", 1)
@@ -72,7 +71,7 @@ def load_checksums(checklist_path: Path) -> Dict[str, str]:
     return checksums
 
 
-def verify_files(model_dir: Path, checksums: Dict[str, str], console: Console) -> List[VerificationResult]:
+def verify_files(model_dir: Path, checksums: dict[str, str], console: Console) -> list[VerificationResult]:
     results = []
 
     with Progress(

llama_stack/distribution/access_control.py

@@ -4,7 +4,7 @@
 # This source code is licensed under the terms described in the LICENSE file in
 # the root directory of this source tree.
 
-from typing import Any, Dict, Optional
+from typing import Any
 
 from llama_stack.distribution.datatypes import AccessAttributes
 from llama_stack.log import get_logger
@@ -14,8 +14,8 @@ logger = get_logger(__name__, category="core")
 
 def check_access(
     obj_identifier: str,
-    obj_attributes: Optional[AccessAttributes],
+    obj_attributes: AccessAttributes | None,
-    user_attributes: Optional[Dict[str, Any]] = None,
+    user_attributes: dict[str, Any] | None = None,
 ) -> bool:
     """Check if the current user has access to the given object, based on access attributes.
 

llama_stack/distribution/build.py

@@ -7,16 +7,16 @@
 import importlib.resources
 import logging
 from pathlib import Path
-from typing import Dict, List
 
 from pydantic import BaseModel
 from termcolor import cprint
 
-from llama_stack.distribution.datatypes import BuildConfig, Provider
+from llama_stack.distribution.datatypes import BuildConfig
 from llama_stack.distribution.distribution import get_provider_registry
 from llama_stack.distribution.utils.exec import run_command
 from llama_stack.distribution.utils.image_types import LlamaStackImageType
 from llama_stack.providers.datatypes import Api
+from llama_stack.templates.template import DistributionTemplate
 
 log = logging.getLogger(__name__)
 
@@ -37,19 +37,23 @@ class ApiInput(BaseModel):
 
 
 def get_provider_dependencies(
-    config_providers: Dict[str, List[Provider]],
+    config: BuildConfig | DistributionTemplate,
 ) -> tuple[list[str], list[str]]:
     """Get normal and special dependencies from provider configuration."""
-    all_providers = get_provider_registry()
+    # Extract providers based on config type
+    if isinstance(config, DistributionTemplate):
+        providers = config.providers
+    elif isinstance(config, BuildConfig):
+        providers = config.distribution_spec.providers
     deps = []
-
+    registry = get_provider_registry(config)
-    for api_str, provider_or_providers in config_providers.items():
+    for api_str, provider_or_providers in providers.items():
-        providers_for_api = all_providers[Api(api_str)]
+        providers_for_api = registry[Api(api_str)]
 
         providers = provider_or_providers if isinstance(provider_or_providers, list) else [provider_or_providers]
 
         for provider in providers:
-            # Providers from BuildConfig and RunConfig are subtly different – not great
+            # Providers from BuildConfig and RunConfig are subtly different - not great
             provider_type = provider if isinstance(provider, str) else provider.provider_type
 
             if provider_type not in providers_for_api:
@@ -71,8 +75,8 @@ def get_provider_dependencies(
     return list(set(normal_deps)), list(set(special_deps))
 
 
-def print_pip_install_help(providers: Dict[str, List[Provider]]):
+def print_pip_install_help(config: BuildConfig):
-    normal_deps, special_deps = get_provider_dependencies(providers)
+    normal_deps, special_deps = get_provider_dependencies(config)
 
     cprint(
         f"Please install needed dependencies using the following commands:\n\nuv pip install {' '.join(normal_deps)}",
@@ -88,10 +92,11 @@ def build_image(
     build_file_path: Path,
     image_name: str,
     template_or_config: str,
+    run_config: str | None = None,
 ):
     container_base = build_config.distribution_spec.container_image or "python:3.10-slim"
 
-    normal_deps, special_deps = get_provider_dependencies(build_config.distribution_spec.providers)
+    normal_deps, special_deps = get_provider_dependencies(build_config)
     normal_deps += SERVER_DEPENDENCIES
 
     if build_config.image_type == LlamaStackImageType.CONTAINER.value:
@@ -103,6 +108,11 @@ def build_image(
             container_base,
             " ".join(normal_deps),
         ]
+
+        # When building from a config file (not a template), include the run config path in the
+        # build arguments
+        if run_config is not None:
+            args.append(run_config)
     elif build_config.image_type == LlamaStackImageType.CONDA.value:
         script = str(importlib.resources.files("llama_stack") / "distribution/build_conda_env.sh")
         args = [

llama_stack/distribution/build_container.sh

@@ -19,12 +19,16 @@ UV_HTTP_TIMEOUT=${UV_HTTP_TIMEOUT:-500}
 # mounting is not supported by docker buildx, so we use COPY instead
 USE_COPY_NOT_MOUNT=${USE_COPY_NOT_MOUNT:-}
 
+# Path to the run.yaml file in the container
+RUN_CONFIG_PATH=/app/run.yaml
+
+BUILD_CONTEXT_DIR=$(pwd)
+
 if [ "$#" -lt 4 ]; then
   # This only works for templates
-  echo "Usage: $0 <template_or_config> <image_name> <container_base> <pip_dependencies> [<special_pip_deps>]" >&2
+  echo "Usage: $0 <template_or_config> <image_name> <container_base> <pip_dependencies> [<run_config>] [<special_pip_deps>]" >&2
   exit 1
 fi
-
 set -euo pipefail
 
 template_or_config="$1"
@@ -35,8 +39,27 @@ container_base="$1"
 shift
 pip_dependencies="$1"
 shift
-special_pip_deps="${1:-}"
 
+# Handle optional arguments
+run_config=""
+special_pip_deps=""
+
+# Check if there are more arguments
+# The logics is becoming cumbersom, we should refactor it if we can do better
+if [ $# -gt 0 ]; then
+  # Check if the argument ends with .yaml
+  if [[ "$1" == *.yaml ]]; then
+    run_config="$1"
+    shift
+    # If there's another argument after .yaml, it must be special_pip_deps
+    if [ $# -gt 0 ]; then
+      special_pip_deps="$1"
+    fi
+  else
+    # If it's not .yaml, it must be special_pip_deps
+    special_pip_deps="$1"
+  fi
+fi
 
 # Define color codes
 RED='\033[0;31m'
@@ -72,9 +95,13 @@ if [[ $container_base == *"registry.access.redhat.com/ubi9"* ]]; then
 FROM $container_base
 WORKDIR /app
 
-RUN dnf -y update && dnf install -y iputils net-tools wget \
+# We install the Python 3.11 dev headers and build tools so that any
+# C‑extension wheels (e.g. polyleven, faiss‑cpu) can compile successfully.
+
+RUN dnf -y update && dnf install -y iputils git net-tools wget \
     vim-minimal python3.11 python3.11-pip python3.11-wheel \
-    python3.11-setuptools && ln -s /bin/pip3.11 /bin/pip && ln -s /bin/python3.11 /bin/python && dnf clean all
+    python3.11-setuptools python3.11-devel gcc make && \
+    ln -s /bin/pip3.11 /bin/pip && ln -s /bin/python3.11 /bin/python && dnf clean all
 
 ENV UV_SYSTEM_PYTHON=1
 RUN pip install uv
@@ -86,7 +113,7 @@ WORKDIR /app
 
 RUN apt-get update && apt-get install -y \
        iputils-ping net-tools iproute2 dnsutils telnet \
-       curl wget telnet \
+       curl wget telnet git\
        procps psmisc lsof \
        traceroute \
        bubblewrap \
@@ -115,6 +142,45 @@ EOF
   done
 fi
 
+# Function to get Python command
+get_python_cmd() {
+    if is_command_available python; then
+        echo "python"
+    elif is_command_available python3; then
+        echo "python3"
+    else
+        echo "Error: Neither python nor python3 is installed. Please install Python to continue." >&2
+        exit 1
+    fi
+}
+
+if [ -n "$run_config" ]; then
+  # Copy the run config to the build context since it's an absolute path
+  cp "$run_config" "$BUILD_CONTEXT_DIR/run.yaml"
+  add_to_container << EOF
+COPY run.yaml $RUN_CONFIG_PATH
+EOF
+
+  # Parse the run.yaml configuration to identify external provider directories
+  # If external providers are specified, copy their directory to the container
+  # and update the configuration to reference the new container path
+  python_cmd=$(get_python_cmd)
+  external_providers_dir=$($python_cmd -c "import yaml; config = yaml.safe_load(open('$run_config')); print(config.get('external_providers_dir') or '')")
+  if [ -n "$external_providers_dir" ]; then
+    echo "Copying external providers directory: $external_providers_dir"
+    add_to_container << EOF
+COPY $external_providers_dir /app/providers.d
+EOF
+    # Edit the run.yaml file to change the external_providers_dir to /app/providers.d
+    if [ "$(uname)" = "Darwin" ]; then
+      sed -i.bak -e 's|external_providers_dir:.*|external_providers_dir: /app/providers.d|' "$BUILD_CONTEXT_DIR/run.yaml"
+      rm -f "$BUILD_CONTEXT_DIR/run.yaml.bak"
+    else
+      sed -i 's|external_providers_dir:.*|external_providers_dir: /app/providers.d|' "$BUILD_CONTEXT_DIR/run.yaml"
+    fi
+  fi
+fi
+
 stack_mount="/app/llama-stack-source"
 client_mount="/app/llama-stack-client-source"
 
@@ -174,15 +240,16 @@ fi
 RUN pip uninstall -y uv
 EOF
 
-# if template_or_config ends with .yaml, it is not a template and we should not use the --template flag
+# If a run config is provided, we use the --config flag
-if [[ "$template_or_config" != *.yaml ]]; then
+if [[ -n "$run_config" ]]; then
+  add_to_container << EOF
+ENTRYPOINT ["python", "-m", "llama_stack.distribution.server.server", "--config", "$RUN_CONFIG_PATH"]
+EOF
+# If a template is provided (not a yaml file), we use the --template flag
+elif [[ "$template_or_config" != *.yaml ]]; then
   add_to_container << EOF
 ENTRYPOINT ["python", "-m", "llama_stack.distribution.server.server", "--template", "$template_or_config"]
 EOF
-else
-  add_to_container << EOF
-ENTRYPOINT ["python", "-m", "llama_stack.distribution.server.server"]
-EOF
 fi
 
 # Add other require item commands genearic to all containers
@@ -254,9 +321,10 @@ $CONTAINER_BINARY build \
   "${CLI_ARGS[@]}" \
   -t "$image_tag" \
   -f "$TEMP_DIR/Containerfile" \
-  "."
+  "$BUILD_CONTEXT_DIR"
 
 # clean up tmp/configs
+rm -f "$BUILD_CONTEXT_DIR/run.yaml"
 set +x
 
 echo "Success!"

llama_stack/distribution/client.py

@@ -8,7 +8,7 @@ import inspect
 import json
 from collections.abc import AsyncIterator
 from enum import Enum
-from typing import Any, Type, Union, get_args, get_origin
+from typing import Any, Union, get_args, get_origin
 
 import httpx
 from pydantic import BaseModel, parse_obj_as
@@ -27,7 +27,7 @@ async def get_client_impl(protocol, config: RemoteProviderConfig, _deps: Any):
     return impl
 
 
-def create_api_client_class(protocol) -> Type:
+def create_api_client_class(protocol) -> type:
     if protocol in _CLIENT_CLASSES:
         return _CLIENT_CLASSES[protocol]
 

llama_stack/distribution/common.sh

@@ -1,3 +1,5 @@
+#!/usr/bin/env bash
+
 # Copyright (c) Meta Platforms, Inc. and affiliates.
 # All rights reserved.
 #

llama_stack/distribution/configure.py

@@ -5,7 +5,7 @@
 # the root directory of this source tree.
 import logging
 import textwrap
-from typing import Any, Dict
+from typing import Any
 
 from llama_stack.distribution.datatypes import (
     LLAMA_STACK_RUN_CONFIG_VERSION,
@@ -24,7 +24,7 @@ from llama_stack.providers.datatypes import Api, ProviderSpec
 logger = logging.getLogger(__name__)
 
 
-def configure_single_provider(registry: Dict[str, ProviderSpec], provider: Provider) -> Provider:
+def configure_single_provider(registry: dict[str, ProviderSpec], provider: Provider) -> Provider:
     provider_spec = registry[provider.provider_type]
     config_type = instantiate_class_type(provider_spec.config_class)
     try:
@@ -120,8 +120,8 @@ def configure_api_providers(config: StackRunConfig, build_spec: DistributionSpec
 
 
 def upgrade_from_routing_table(
-    config_dict: Dict[str, Any],
+    config_dict: dict[str, Any],
-) -> Dict[str, Any]:
+) -> dict[str, Any]:
     def get_providers(entries):
         return [
             Provider(
@@ -163,7 +163,7 @@ def upgrade_from_routing_table(
     return config_dict
 
 
-def parse_and_maybe_upgrade_config(config_dict: Dict[str, Any]) -> StackRunConfig:
+def parse_and_maybe_upgrade_config(config_dict: dict[str, Any]) -> StackRunConfig:
     version = config_dict.get("version", None)
     if version == LLAMA_STACK_RUN_CONFIG_VERSION:
         return StackRunConfig(**config_dict)

llama_stack/distribution/datatypes.py

@@ -4,7 +4,8 @@
 # This source code is licensed under the terms described in the LICENSE file in
 # the root directory of this source tree.
 
-from typing import Annotated, Any, Dict, List, Optional, Union
+from enum import Enum
+from typing import Annotated, Any
 
 from pydantic import BaseModel, Field
 
@@ -29,7 +30,7 @@ LLAMA_STACK_BUILD_CONFIG_VERSION = "2"
 LLAMA_STACK_RUN_CONFIG_VERSION = "2"
 
 
-RoutingKey = Union[str, List[str]]
+RoutingKey = str | list[str]
 
 
 class AccessAttributes(BaseModel):
@@ -46,17 +47,17 @@ class AccessAttributes(BaseModel):
     """
 
     # Standard attribute categories - the minimal set we need now
-    roles: Optional[List[str]] = Field(
+    roles: list[str] | None = Field(
         default=None, description="Role-based attributes (e.g., 'admin', 'data-scientist', 'user')"
     )
 
-    teams: Optional[List[str]] = Field(default=None, description="Team-based attributes (e.g., 'ml-team', 'nlp-team')")
+    teams: list[str] | None = Field(default=None, description="Team-based attributes (e.g., 'ml-team', 'nlp-team')")
 
-    projects: Optional[List[str]] = Field(
+    projects: list[str] | None = Field(
         default=None, description="Project-based access attributes (e.g., 'llama-3', 'customer-insights')"
     )
 
-    namespaces: Optional[List[str]] = Field(
+    namespaces: list[str] | None = Field(
         default=None, description="Namespace-based access control for resource isolation"
     )
 
@@ -105,7 +106,7 @@ class ResourceWithACL(Resource):
         # ^ User must have access to the customer-insights project AND have confidential namespace
     """
 
-    access_attributes: Optional[AccessAttributes] = None
+    access_attributes: AccessAttributes | None = None
 
 
 # Use the extended Resource for all routable objects
@@ -141,41 +142,21 @@ class ToolGroupWithACL(ToolGroup, ResourceWithACL):
     pass
 
 
-RoutableObject = Union[
+RoutableObject = Model | Shield | VectorDB | Dataset | ScoringFn | Benchmark | Tool | ToolGroup
-    Model,
-    Shield,
-    VectorDB,
-    Dataset,
-    ScoringFn,
-    Benchmark,
-    Tool,
-    ToolGroup,
-]
-
 
 RoutableObjectWithProvider = Annotated[
-    Union[
+    ModelWithACL
-        ModelWithACL,
+    | ShieldWithACL
-        ShieldWithACL,
+    | VectorDBWithACL
-        VectorDBWithACL,
+    | DatasetWithACL
-        DatasetWithACL,
+    | ScoringFnWithACL
-        ScoringFnWithACL,
+    | BenchmarkWithACL
-        BenchmarkWithACL,
+    | ToolWithACL
-        ToolWithACL,
+    | ToolGroupWithACL,
-        ToolGroupWithACL,
-    ],
     Field(discriminator="type"),
 ]
 
-RoutedProtocol = Union[
+RoutedProtocol = Inference | Safety | VectorIO | DatasetIO | Scoring | Eval | ToolRuntime
-    Inference,
-    Safety,
-    VectorIO,
-    DatasetIO,
-    Scoring,
-    Eval,
-    ToolRuntime,
-]
 
 
 # Example: /inference, /safety
@@ -183,15 +164,15 @@ class AutoRoutedProviderSpec(ProviderSpec):
     provider_type: str = "router"
     config_class: str = ""
 
-    container_image: Optional[str] = None
+    container_image: str | None = None
     routing_table_api: Api
     module: str
-    provider_data_validator: Optional[str] = Field(
+    provider_data_validator: str | None = Field(
         default=None,
     )
 
     @property
-    def pip_packages(self) -> List[str]:
+    def pip_packages(self) -> list[str]:
         raise AssertionError("Should not be called on AutoRoutedProviderSpec")
 
 
@@ -199,20 +180,20 @@ class AutoRoutedProviderSpec(ProviderSpec):
 class RoutingTableProviderSpec(ProviderSpec):
     provider_type: str = "routing_table"
     config_class: str = ""
-    container_image: Optional[str] = None
+    container_image: str | None = None
 
     router_api: Api
     module: str
-    pip_packages: List[str] = Field(default_factory=list)
+    pip_packages: list[str] = Field(default_factory=list)
 
 
 class DistributionSpec(BaseModel):
-    description: Optional[str] = Field(
+    description: str | None = Field(
         default="",
         description="Description of the distribution",
     )
-    container_image: Optional[str] = None
+    container_image: str | None = None
-    providers: Dict[str, Union[str, List[str]]] = Field(
+    providers: dict[str, str | list[str]] = Field(
         default_factory=dict,
         description="""
 Provider Types for each of the APIs provided by this distribution. If you
@@ -224,21 +205,32 @@ in the runtime configuration to help route to the correct provider.""",
 class Provider(BaseModel):
     provider_id: str
     provider_type: str
-    config: Dict[str, Any]
+    config: dict[str, Any]
 
 
 class LoggingConfig(BaseModel):
-    category_levels: Dict[str, str] = Field(
+    category_levels: dict[str, str] = Field(
-        default_factory=Dict,
+        default_factory=dict,
         description="""
  Dictionary of different logging configurations for different portions (ex: core, server) of llama stack""",
     )
 
 
+class AuthProviderType(str, Enum):
+    """Supported authentication provider types."""
+
+    KUBERNETES = "kubernetes"
+    CUSTOM = "custom"
+
+
 class AuthenticationConfig(BaseModel):
-    endpoint: str = Field(
+    provider_type: AuthProviderType = Field(
         ...,
-        description="Endpoint URL to validate authentication tokens",
+        description="Type of authentication provider (e.g., 'kubernetes', 'custom')",
+    )
+    config: dict[str, str] = Field(
+        ...,
+        description="Provider-specific configuration",
     )
 
 
@@ -249,15 +241,15 @@ class ServerConfig(BaseModel):
         ge=1024,
         le=65535,
     )
-    tls_certfile: Optional[str] = Field(
+    tls_certfile: str | None = Field(
         default=None,
         description="Path to TLS certificate file for HTTPS",
     )
-    tls_keyfile: Optional[str] = Field(
+    tls_keyfile: str | None = Field(
         default=None,
         description="Path to TLS key file for HTTPS",
     )
-    auth: Optional[AuthenticationConfig] = Field(
+    auth: AuthenticationConfig | None = Field(
         default=None,
         description="Authentication configuration for the server",
     )
@@ -273,23 +265,23 @@ Reference to the distribution this package refers to. For unregistered (adhoc) p
 this could be just a hash
 """,
     )
-    container_image: Optional[str] = Field(
+    container_image: str | None = Field(
         default=None,
         description="Reference to the container image if this package refers to a container",
     )
-    apis: List[str] = Field(
+    apis: list[str] = Field(
         default_factory=list,
         description="""
 The list of APIs to serve. If not specified, all APIs specified in the provider_map will be served""",
     )
 
-    providers: Dict[str, List[Provider]] = Field(
+    providers: dict[str, list[Provider]] = Field(
         description="""
 One or more providers to use for each API. The same provider_type (e.g., meta-reference)
 can be instantiated multiple times (with different configs) if necessary.
 """,
     )
-    metadata_store: Optional[KVStoreConfig] = Field(
+    metadata_store: KVStoreConfig | None = Field(
         default=None,
         description="""
 Configuration for the persistence store used by the distribution registry. If not specified,
@@ -297,22 +289,22 @@ a default SQLite store will be used.""",
     )
 
     # registry of "resources" in the distribution
-    models: List[ModelInput] = Field(default_factory=list)
+    models: list[ModelInput] = Field(default_factory=list)
-    shields: List[ShieldInput] = Field(default_factory=list)
+    shields: list[ShieldInput] = Field(default_factory=list)
-    vector_dbs: List[VectorDBInput] = Field(default_factory=list)
+    vector_dbs: list[VectorDBInput] = Field(default_factory=list)
-    datasets: List[DatasetInput] = Field(default_factory=list)
+    datasets: list[DatasetInput] = Field(default_factory=list)
-    scoring_fns: List[ScoringFnInput] = Field(default_factory=list)
+    scoring_fns: list[ScoringFnInput] = Field(default_factory=list)
-    benchmarks: List[BenchmarkInput] = Field(default_factory=list)
+    benchmarks: list[BenchmarkInput] = Field(default_factory=list)
-    tool_groups: List[ToolGroupInput] = Field(default_factory=list)
+    tool_groups: list[ToolGroupInput] = Field(default_factory=list)
 
-    logging: Optional[LoggingConfig] = Field(default=None, description="Configuration for Llama Stack Logging")
+    logging: LoggingConfig | None = Field(default=None, description="Configuration for Llama Stack Logging")
 
     server: ServerConfig = Field(
         default_factory=ServerConfig,
         description="Configuration for the HTTP(S) server",
     )
 
-    external_providers_dir: Optional[str] = Field(
+    external_providers_dir: str | None = Field(
         default=None,
         description="Path to directory containing external provider implementations. The providers code and dependencies must be installed on the system.",
     )
@@ -326,3 +318,12 @@ class BuildConfig(BaseModel):
         default="conda",
         description="Type of package to build (conda | container | venv)",
     )
+    image_name: str | None = Field(
+        default=None,
+        description="Name of the distribution to build",
+    )
+    external_providers_dir: str | None = Field(
+        default=None,
+        description="Path to directory containing external provider implementations. The providers packages will be resolved from this directory. "
+        "pip_packages MUST contain the provider package name.",
+    )

llama_stack/distribution/distribution.py

@@ -7,12 +7,11 @@
 import glob
 import importlib
 import os
-from typing import Any, Dict, List
+from typing import Any
 
 import yaml
 from pydantic import BaseModel
 
-from llama_stack.distribution.datatypes import StackRunConfig
 from llama_stack.log import get_logger
 from llama_stack.providers.datatypes import (
     AdapterSpec,
@@ -25,7 +24,7 @@ from llama_stack.providers.datatypes import (
 logger = get_logger(name=__name__, category="core")
 
 
-def stack_apis() -> List[Api]:
+def stack_apis() -> list[Api]:
     return list(Api)
 
 
@@ -34,7 +33,7 @@ class AutoRoutedApiInfo(BaseModel):
     router_api: Api
 
 
-def builtin_automatically_routed_apis() -> List[AutoRoutedApiInfo]:
+def builtin_automatically_routed_apis() -> list[AutoRoutedApiInfo]:
     return [
         AutoRoutedApiInfo(
             routing_table_api=Api.models,
@@ -67,12 +66,12 @@ def builtin_automatically_routed_apis() -> List[AutoRoutedApiInfo]:
     ]
 
 
-def providable_apis() -> List[Api]:
+def providable_apis() -> list[Api]:
     routing_table_apis = {x.routing_table_api for x in builtin_automatically_routed_apis()}
     return [api for api in Api if api not in routing_table_apis and api != Api.inspect and api != Api.providers]
 
 
-def _load_remote_provider_spec(spec_data: Dict[str, Any], api: Api) -> ProviderSpec:
+def _load_remote_provider_spec(spec_data: dict[str, Any], api: Api) -> ProviderSpec:
     adapter = AdapterSpec(**spec_data["adapter"])
     spec = remote_provider_spec(
         api=api,
@@ -82,7 +81,7 @@ def _load_remote_provider_spec(spec_data: Dict[str, Any], api: Api) -> ProviderS
     return spec
 
 
-def _load_inline_provider_spec(spec_data: Dict[str, Any], api: Api, provider_name: str) -> ProviderSpec:
+def _load_inline_provider_spec(spec_data: dict[str, Any], api: Api, provider_name: str) -> ProviderSpec:
     spec = InlineProviderSpec(
         api=api,
         provider_type=f"inline::{provider_name}",
@@ -97,7 +96,9 @@ def _load_inline_provider_spec(spec_data: Dict[str, Any], api: Api, provider_nam
     return spec
 
 
-def get_provider_registry(config: StackRunConfig | None = None) -> Dict[Api, Dict[str, ProviderSpec]]:
+def get_provider_registry(
+    config=None,
+) -> dict[Api, dict[str, ProviderSpec]]:
     """Get the provider registry, optionally including external providers.
 
     This function loads both built-in providers and external providers from YAML files.
@@ -122,7 +123,7 @@ def get_provider_registry(config: StackRunConfig | None = None) -> Dict[Api, Dic
           llama-guard.yaml
 
     Args:
-        config: Optional StackRunConfig containing the external providers directory path
+        config: Optional object containing the external providers directory path
 
     Returns:
         A dictionary mapping APIs to their available providers
@@ -132,7 +133,7 @@ def get_provider_registry(config: StackRunConfig | None = None) -> Dict[Api, Dic
         ValueError: If any provider spec is invalid
     """
 
-    ret: Dict[Api, Dict[str, ProviderSpec]] = {}
+    ret: dict[Api, dict[str, ProviderSpec]] = {}
     for api in providable_apis():
         name = api.name.lower()
         logger.debug(f"Importing module {name}")
@@ -142,7 +143,8 @@ def get_provider_registry(config: StackRunConfig | None = None) -> Dict[Api, Dic
         except ImportError as e:
             logger.warning(f"Failed to import module {name}: {e}")
 
-    if config and config.external_providers_dir:
+    # Check if config has the external_providers_dir attribute
+    if config and hasattr(config, "external_providers_dir") and config.external_providers_dir:
         external_providers_dir = os.path.abspath(config.external_providers_dir)
         if not os.path.exists(external_providers_dir):
             raise FileNotFoundError(f"External providers directory not found: {external_providers_dir}")

llama_stack/distribution/library_client.py

@@ -12,7 +12,7 @@ import os
 from concurrent.futures import ThreadPoolExecutor
 from enum import Enum
 from pathlib import Path
-from typing import Any, Optional, TypeVar, Union, get_args, get_origin
+from typing import Any, TypeVar, Union, get_args, get_origin
 
 import httpx
 import yaml
@@ -119,8 +119,8 @@ class LlamaStackAsLibraryClient(LlamaStackClient):
         self,
         config_path_or_template_name: str,
         skip_logger_removal: bool = False,
-        custom_provider_registry: Optional[ProviderRegistry] = None,
+        custom_provider_registry: ProviderRegistry | None = None,
-        provider_data: Optional[dict[str, Any]] = None,
+        provider_data: dict[str, Any] | None = None,
     ):
         super().__init__()
         self.async_client = AsyncLlamaStackAsLibraryClient(
@@ -181,8 +181,8 @@ class AsyncLlamaStackAsLibraryClient(AsyncLlamaStackClient):
     def __init__(
         self,
         config_path_or_template_name: str,
-        custom_provider_registry: Optional[ProviderRegistry] = None,
+        custom_provider_registry: ProviderRegistry | None = None,
-        provider_data: Optional[dict[str, Any]] = None,
+        provider_data: dict[str, Any] | None = None,
     ):
         super().__init__()
         # when using the library client, we should not log to console since many
@@ -371,7 +371,7 @@ class AsyncLlamaStackAsLibraryClient(AsyncLlamaStackClient):
         )
         return await response.parse()
 
-    def _convert_body(self, path: str, method: str, body: Optional[dict] = None) -> dict:
+    def _convert_body(self, path: str, method: str, body: dict | None = None) -> dict:
         if not body:
             return {}