Release candidate 0.2.3rc5

.github/CODEOWNERS

@@ -2,4 +2,4 @@
 
 # These owners will be the default owners for everything in
 # the repo. Unless a later match takes precedence,
-* @ashwinb @yanxi0830 @hardikjshah @raghotham @ehhuang @terrytangyuan @leseb @bbrowning
+* @ashwinb @yanxi0830 @hardikjshah @dltn @raghotham @dineshyv @vladimirivic @sixianyi0721 @ehhuang @terrytangyuan @SLR722 @leseb

.github/PULL_REQUEST_TEMPLATE.md

@@ -1,8 +1,10 @@
 # What does this PR do?
-<!-- Provide a short summary of what this PR does and why. Link to relevant issues if applicable. -->
+[Provide a short summary of what this PR does and why. Link to relevant issues if applicable.]
 
-<!-- If resolving an issue, uncomment and update the line below -->
-<!-- Closes #[issue-number] -->
+[//]: # (If resolving an issue, uncomment and update the line below)
+[//]: # (Closes #[issue-number])
 
 ## Test Plan
-<!-- Describe the tests you ran to verify your changes with result summaries. *Provide clear instructions so the plan can be easily re-executed.* -->
+[Describe the tests you ran to verify your changes with result summaries. *Provide clear instructions so the plan can be easily re-executed.*]
+
+[//]: # (## Documentation)

.github/TRIAGERS.md

@@ -1,2 +1,2 @@
 # This file documents Triage members in the Llama Stack community
- @bbrowning @booxter @franciscojavierarceo @leseb
+@franciscojavierarceo @leseb

.github/actions/setup-ollama/action.yml

@@ -1,26 +0,0 @@
-name: Setup Ollama
-description: Start Ollama and cache model
-inputs:
-  models:
-    description: Comma-separated list of models to pull
-    default: "llama3.2:3b-instruct-fp16,all-minilm:latest"
-runs:
-  using: "composite"
-  steps:
-    - name: Install and start Ollama
-      shell: bash
-      run: |
-        # the ollama installer also starts the ollama service
-        curl -fsSL https://ollama.com/install.sh | sh
-
-    # 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 requested models
-      if: inputs.models != ''
-      shell: bash
-      run: |
-        for model in $(echo "${{ inputs.models }}" | tr ',' ' '); do
-          ollama pull "$model"
-        done

.github/actions/setup-runner/action.yml

@@ -1,22 +0,0 @@
-name: Setup runner
-description: Prepare a runner for the tests (install uv, python, project dependencies, etc.)
-runs:
-  using: "composite"
-  steps:
-    - name: Install uv
-      uses: astral-sh/setup-uv@6b9c6063abd6010835644d4c2e1bef4cf5cd0fca # v6.0.1
-      with:
-        python-version: "3.10"
-        activate-environment: true
-        version: 0.7.6
-
-    - name: Install dependencies
-      shell: bash
-      run: |
-        uv sync --all-groups
-        uv pip install ollama faiss-cpu
-        # always test against the latest version of the client
-        # TODO: this is not necessarily a good idea. we need to test against both published and latest
-        # to find out backwards compatibility issues.
-        uv pip install git+https://github.com/meta-llama/llama-stack-client-python.git@main
-        uv pip install -e .

.github/workflows/Dockerfile

@@ -1 +0,0 @@
-FROM localhost:5000/distribution-kvant:dev

.github/workflows/ci-playground.yaml

@@ -1,73 +0,0 @@
-name: Build and Push playground container
-run-name: Build and Push playground container
-on:
-  workflow_dispatch:
-  #schedule:
-  #  - cron: "0 10 * * *"
-  push:
-    branches:
-      - main
-      - kvant
-    tags:
-      - 'v*'
-  pull_request:
-    branches:
-      - main
-      - kvant
-env:
-  IMAGE: git.kvant.cloud/${{github.repository}}-playground
-jobs:
-  build-playground:
-    runs-on: ubuntu-latest
-    steps:
-      - name: Checkout
-        uses: actions/checkout@v4
-        with:
-          fetch-depth: 0
-
-      - name: Set current time
-        uses: https://github.com/gerred/actions/current-time@master
-        id: current_time
-
-      - name: Set up Docker Buildx
-        uses: docker/setup-buildx-action@v3
-
-      - name: Login to git.kvant.cloud registry
-        uses: docker/login-action@v3
-        with:
-          registry: git.kvant.cloud
-          username: ${{ vars.ORG_PACKAGE_WRITER_USERNAME }}
-          password: ${{ secrets.ORG_PACKAGE_WRITER_TOKEN }}
-      
-      - name: Docker meta
-        id: meta
-        uses: docker/metadata-action@v5
-        with:
-          # list of Docker images to use as base name for tags
-          images: |
-            ${{env.IMAGE}}
-          # generate Docker tags based on the following events/attributes
-          tags: |
-            type=schedule
-            type=ref,event=branch
-            type=ref,event=pr
-            type=ref,event=tag
-            type=semver,pattern={{version}}
-
-      - name: Build and push to gitea registry
-        uses: docker/build-push-action@v6
-        with:
-          push: ${{ github.event_name != 'pull_request' }}
-          tags: ${{ steps.meta.outputs.tags }}
-          labels: ${{ steps.meta.outputs.labels }}
-          context: .
-          file: llama_stack/distribution/ui/Containerfile
-          provenance: mode=max
-          sbom: true
-          build-args: |
-            BUILD_DATE=${{ steps.current_time.outputs.time }}
-          cache-from: |
-            type=registry,ref=${{ env.IMAGE }}:buildcache
-            type=registry,ref=${{ env.IMAGE }}:${{ github.ref_name }}
-            type=registry,ref=${{ env.IMAGE }}:main
-          cache-to: type=registry,ref=${{ env.IMAGE }}:buildcache,mode=max,image-manifest=true

.github/workflows/ci.yaml

@@ -1,98 +0,0 @@
-name: Build and Push container
-run-name: Build and Push container
-on:
-  workflow_dispatch:
-  #schedule:
-  #  - cron: "0 10 * * *"
-  push:
-    branches:
-      - main
-      - kvant
-    tags:
-      - 'v*'
-  pull_request:
-    branches:
-      - main
-      - kvant
-env:
-  IMAGE: git.kvant.cloud/${{github.repository}}
-jobs:
-  build:
-    runs-on: ubuntu-latest
-    services:
-      registry:
-        image: registry:2
-        ports:
-          - 5000:5000
-    steps:
-      - name: Checkout
-        uses: actions/checkout@v4
-        with:
-          fetch-depth: 0
-
-      - name: Set current time
-        uses: https://github.com/gerred/actions/current-time@master
-        id: current_time
-
-      - name: Set up Docker Buildx
-        uses: docker/setup-buildx-action@v3
-        with:
-          driver-opts: network=host
-
-      - name: Login to git.kvant.cloud registry
-        uses: docker/login-action@v3
-        with:
-          registry: git.kvant.cloud
-          username: ${{ vars.ORG_PACKAGE_WRITER_USERNAME }}
-          password: ${{ secrets.ORG_PACKAGE_WRITER_TOKEN }}
-      
-      - name: Docker meta
-        id: meta
-        uses: docker/metadata-action@v5
-        with:
-          # list of Docker images to use as base name for tags
-          images: |
-            ${{env.IMAGE}}
-          # generate Docker tags based on the following events/attributes
-          tags: |
-            type=schedule
-            type=ref,event=branch
-            type=ref,event=pr
-            type=ref,event=tag
-            type=semver,pattern={{version}}
-
-      - name: Install uv
-        uses: https://github.com/astral-sh/setup-uv@v5
-        with:
-          # Install a specific version of uv.
-          version: "0.7.8"
-            
-      - name: Build
-        env:
-          USE_COPY_NOT_MOUNT: true
-          LLAMA_STACK_DIR: .
-        run: |
-          uvx --from . llama stack build --template kvant --image-type container
-
-          # docker tag distribution-kvant:dev ${{env.IMAGE}}:kvant
-          # docker push ${{env.IMAGE}}:kvant
-
-          docker tag distribution-kvant:dev localhost:5000/distribution-kvant:dev
-          docker push localhost:5000/distribution-kvant:dev
-
-      - name: Build and push to gitea registry
-        uses: docker/build-push-action@v6
-        with:
-          push: ${{ github.event_name != 'pull_request' }}
-          tags: ${{ steps.meta.outputs.tags }}
-          labels: ${{ steps.meta.outputs.labels }}
-          context: .github/workflows
-          provenance: mode=max
-          sbom: true
-          build-args: |
-            BUILD_DATE=${{ steps.current_time.outputs.time }}
-          cache-from: |
-            type=registry,ref=${{ env.IMAGE }}:buildcache
-            type=registry,ref=${{ env.IMAGE }}:${{ github.ref_name }}
-            type=registry,ref=${{ env.IMAGE }}:main
-          cache-to: type=registry,ref=${{ env.IMAGE }}:buildcache,mode=max,image-manifest=true

.github/workflows/integration-tests.yml

@@ -24,7 +24,7 @@ jobs:
       matrix:
         # Listing tests manually since some of them currently fail
         # TODO: generate matrix list from tests/integration when fixed
-        test-type: [agents, inference, datasets, inspect, scoring, post_training, providers, tool_runtime]
+        test-type: [agents, inference, datasets, inspect, scoring, post_training, providers]
         client-type: [library, http]
       fail-fast: false # we want to run all tests regardless of failure
 
@@ -32,14 +32,30 @@ jobs:
       - name: Checkout repository
         uses: actions/checkout@11bd71901bbe5b1630ceea73d27597364c9af683 # v4.2.2
 
-      - name: Install dependencies
-        uses: ./.github/actions/setup-runner
+      - name: Install uv
+        uses: astral-sh/setup-uv@0c5e2b8115b80b4c7c5ddf6ffdd634974642d182 # v5.4.1
+        with:
+          python-version: "3.10"
 
-      - name: Setup ollama
-        uses: ./.github/actions/setup-ollama
-
-      - name: Build Llama Stack
+      - 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
+        run: |
+          # TODO: cache the model. OLLAMA_MODELS defaults to ~ollama/.ollama/models.
+          ollama pull llama3.2:3b-instruct-fp16
+
+      - name: Set Up Environment and Install Dependencies
+        run: |
+          uv sync --extra dev --extra test
+          uv pip install ollama faiss-cpu
+          # always test against the latest version of the client
+          # TODO: this is not necessarily a good idea. we need to test against both published and latest
+          # to find out backwards compatibility issues.
+          uv pip install git+https://github.com/meta-llama/llama-stack-client-python.git@main
+          uv pip install -e .
           llama stack build --template ollama --image-type venv
 
       - name: Start Llama Stack server in background
@@ -47,7 +63,8 @@ jobs:
         env:
           INFERENCE_MODEL: "meta-llama/Llama-3.2-3B-Instruct"
         run: |
-          LLAMA_STACK_LOG_FILE=server.log nohup uv run llama stack run ./llama_stack/templates/ollama/run.yaml --image-type venv &
+          source .venv/bin/activate
+          nohup uv run llama stack run ./llama_stack/templates/ollama/run.yaml --image-type venv > server.log 2>&1 &
 
       - name: Wait for Llama Stack server to be ready
         if: matrix.client-type == 'http'
@@ -75,12 +92,6 @@ jobs:
             exit 1
           fi
 
-      - name: Check Storage and Memory Available Before Tests
-        if: ${{ always() }}
-        run: |
-          free -h
-          df -h
-
       - name: Run Integration Tests
         env:
           INFERENCE_MODEL: "meta-llama/Llama-3.2-3B-Instruct"
@@ -90,27 +101,7 @@ jobs:
           else
             stack_config="http://localhost:8321"
           fi
-          uv run pytest -s -v tests/integration/${{ matrix.test-type }} --stack-config=${stack_config} \
+          uv run pytest -v tests/integration/${{ matrix.test-type }} --stack-config=${stack_config} \
             -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: Check Storage and Memory Available After Tests
-        if: ${{ always() }}
-        run: |
-          free -h
-          df -h
-
-      - name: Write ollama logs to file
-        if: ${{ always() }}
-        run: |
-          sudo journalctl -u ollama.service > ollama.log
-
-      - name: Upload all logs to artifacts
-        if: ${{ always() }}
-        uses: actions/upload-artifact@ea165f8d65b6e75b540449e92b4886f43607fa02 # v4.6.2
-        with:
-          name: logs-${{ 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@a26af69be951a213d495a4c3e4e4022e16d87065 # v5.6.0
+        uses: actions/setup-python@8d9ed9ac5c53483de85588cdf95a591a75ab9f55 # v5.5.0
         with:
           python-version: '3.11'
           cache: pip
@@ -27,9 +27,6 @@ jobs:
             .pre-commit-config.yaml
 
       - uses: pre-commit/action@2c7b3805fd2a0fd8c1884dcaebf91fc102a13ecd # v3.0.1
-        env:
-          SKIP: no-commit-to-branch
-          RUFF_OUTPUT_FORMAT: github
 
       - name: Verify if there are any diff files after pre-commit
         run: |

.github/workflows/providers-build.yml

@@ -50,8 +50,21 @@ jobs:
       - name: Checkout repository
         uses: actions/checkout@11bd71901bbe5b1630ceea73d27597364c9af683 # v4.2.2
 
-      - name: Install dependencies
-        uses: ./.github/actions/setup-runner
+      - name: Set up Python
+        uses: actions/setup-python@8d9ed9ac5c53483de85588cdf95a591a75ab9f55 # v5.5.0
+        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: Print build dependencies
         run: |
@@ -66,6 +79,7 @@ jobs:
       - name: Print dependencies in the image
         if: matrix.image-type == 'venv'
         run: |
+          source test/bin/activate
           uv pip list
 
   build-single-provider:
@@ -74,8 +88,21 @@ jobs:
       - name: Checkout repository
         uses: actions/checkout@11bd71901bbe5b1630ceea73d27597364c9af683 # v4.2.2
 
-      - name: Install dependencies
-        uses: ./.github/actions/setup-runner
+      - name: Set up Python
+        uses: actions/setup-python@8d9ed9ac5c53483de85588cdf95a591a75ab9f55 # v5.5.0
+        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: Build a single provider
         run: |
@@ -87,14 +114,27 @@ jobs:
       - name: Checkout repository
         uses: actions/checkout@11bd71901bbe5b1630ceea73d27597364c9af683 # v4.2.2
 
-      - name: Install dependencies
-        uses: ./.github/actions/setup-runner
+      - name: Set up Python
+        uses: actions/setup-python@8d9ed9ac5c53483de85588cdf95a591a75ab9f55 # v5.5.0
+        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: Build a single provider
         run: |
-          yq -i '.image_type = "container"' llama_stack/templates/starter/build.yaml
-          yq -i '.image_name = "test"' llama_stack/templates/starter/build.yaml
-          USE_COPY_NOT_MOUNT=true LLAMA_STACK_DIR=. uv run llama stack build --config llama_stack/templates/starter/build.yaml
+          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: |
@@ -105,43 +145,3 @@ jobs:
             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: Install dependencies
-        uses: ./.github/actions/setup-runner
-
-      - 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/starter/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/starter/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

@@ -23,10 +23,29 @@ jobs:
         # container and point 'uv pip install' to the correct path...
     steps:
       - name: Checkout repository
-        uses: actions/checkout@11bd71901bbe5b1630ceea73d27597364c9af683 # v4.2.2
+        uses: actions/checkout@v4
 
-      - name: Install dependencies
-        uses: ./.github/actions/setup-runner
+      - name: Install uv
+        uses: astral-sh/setup-uv@v5
+        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: Apply image type to config file
         run: |
@@ -40,32 +59,57 @@ jobs:
 
       - name: Create provider configuration
         run: |
-          mkdir -p /home/runner/.llama/providers.d/remote/inference
-          cp tests/external-provider/llama-stack-provider-ollama/custom_ollama.yaml /home/runner/.llama/providers.d/remote/inference/custom_ollama.yaml
+          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: Build distro from config file
         run: |
           USE_COPY_NOT_MOUNT=true LLAMA_STACK_DIR=. uv run llama stack build --config tests/external-provider/llama-stack-provider-ollama/custom-distro.yaml
 
+      - name: Wait for Ollama to start
+        run: |
+          echo "Waiting for Ollama..."
+          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 ci-test/bin/activate
           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 ! grep -q "remote::custom_ollama from /home/runner/.llama/providers.d/remote/inference/custom_ollama.yaml" server.log; then
-              echo "Waiting for Llama Stack server to load the provider..."
-              sleep 1
-            else
-              echo "Provider loaded"
-              exit 0
+            if curl -s http://localhost:8321/v1/health | grep -q "OK"; then
+              echo "Llama Stack server is up!"
+              if grep -q "remote::custom_ollama from /tmp/providers.d/remote/inference/custom_ollama.yaml" server.log; then
+                echo "Llama Stack server is using custom Ollama provider"
+                exit 0
+              else
+                echo "Llama Stack server is not using custom Ollama provider"
+                exit 1
+              fi
             fi
+            sleep 1
           done
-          echo "Provider failed to load"
+          echo "Llama Stack server failed to start"
           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

@@ -30,11 +30,17 @@ jobs:
           - "3.12"
           - "3.13"
     steps:
-      - name: Checkout repository
-        uses: actions/checkout@11bd71901bbe5b1630ceea73d27597364c9af683 # v4.2.2
+      - uses: actions/checkout@11bd71901bbe5b1630ceea73d27597364c9af683 # v4.2.2
 
-      - name: Install dependencies
-        uses: ./.github/actions/setup-runner
+      - name: Set up Python ${{ matrix.python }}
+        uses: actions/setup-python@8d9ed9ac5c53483de85588cdf95a591a75ab9f55 # v5.5.0
+        with:
+          python-version: ${{ matrix.python }}
+
+      - uses: astral-sh/setup-uv@0c5e2b8115b80b4c7c5ddf6ffdd634974642d182 # v5.4.1
+        with:
+          python-version: ${{ matrix.python }}
+          enable-cache: false
 
       - name: Run unit tests
         run: |

.github/workflows/update-readthedocs.yml

@@ -14,8 +14,6 @@ on:
       - 'docs/**'
       - 'pyproject.toml'
       - '.github/workflows/update-readthedocs.yml'
-    tags:
-      - '*'
   pull_request:
     branches:
       - main
@@ -37,8 +35,16 @@ jobs:
       - name: Checkout repository
         uses: actions/checkout@11bd71901bbe5b1630ceea73d27597364c9af683 # v4.2.2
 
-      - name: Install dependencies
-        uses: ./.github/actions/setup-runner
+      - name: Set up Python
+        uses: actions/setup-python@8d9ed9ac5c53483de85588cdf95a591a75ab9f55 # v5.5.0
+        with:
+          python-version: '3.11'
+
+      - name: Install the latest version of uv
+        uses: astral-sh/setup-uv@0c5e2b8115b80b4c7c5ddf6ffdd634974642d182 # v5.4.1
+
+      - name: Sync with uv
+        run: uv sync --extra docs
 
       - name: Build HTML
         run: |
@@ -55,10 +61,7 @@ jobs:
 
           response=$(curl -X POST \
             -H "Content-Type: application/json" \
-            -d "{
-              \"token\": \"$TOKEN\",
-              \"version\": \"$GITHUB_REF_NAME\"
-            }" \
+            -d "{\"token\": \"$TOKEN\"}" \
             https://readthedocs.org/api/v2/webhook/llama-stack/289768/)
 
           echo "Response: $response"

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

@@ -1,26 +0,0 @@
-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_upstream/integration-auth-tests.yml

@@ -1,132 +0,0 @@
-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: [oauth2_token]
-      fail-fast: false # we want to run all tests regardless of failure
-
-    steps:
-      - name: Checkout repository
-        uses: actions/checkout@11bd71901bbe5b1630ceea73d27597364c9af683 # v4.2.2
-
-      - name: Install dependencies
-        uses: ./.github/actions/setup-runner
-
-      - name: Build Llama Stack
-        run: |
-          llama stack build --template ollama --image-type venv
-
-      - name: Install minikube
-        if: ${{ matrix.auth-provider == 'kubernetes' }}
-        uses: medyagh/setup-minikube@cea33675329b799adccc9526aa5daccc26cd5052 # v0.0.19
-
-      - name: Start minikube
-        if: ${{ matrix.auth-provider == 'oauth2_token' }}
-        run: |
-          minikube start
-          kubectl get pods -A
-
-      - name: Configure Kube Auth
-        if: ${{ matrix.auth-provider == 'oauth2_token' }}
-        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
-          cat <<EOF | kubectl apply -f -
-          apiVersion: rbac.authorization.k8s.io/v1
-          kind: ClusterRole
-          metadata:
-            name: allow-anonymous-openid
-          rules:
-          - nonResourceURLs: ["/openid/v1/jwks"]
-            verbs: ["get"]
-          ---
-          apiVersion: rbac.authorization.k8s.io/v1
-          kind: ClusterRoleBinding
-          metadata:
-            name: allow-anonymous-openid
-          roleRef:
-            apiGroup: rbac.authorization.k8s.io
-            kind: ClusterRole
-            name: allow-anonymous-openid
-          subjects:
-          - kind: User
-            name: system:anonymous
-            apiGroup: rbac.authorization.k8s.io
-          EOF
-
-      - name: Set Kubernetes Config
-        if: ${{ matrix.auth-provider == 'oauth2_token' }}
-        run: |
-          echo "KUBERNETES_API_SERVER_URL=$(kubectl get --raw /.well-known/openid-configuration| jq -r .jwks_uri)" >> $GITHUB_ENV
-          echo "KUBERNETES_CA_CERT_PATH=$(kubectl config view --minify -o jsonpath='{.clusters[0].cluster.certificate-authority}')" >> $GITHUB_ENV
-          echo "KUBERNETES_ISSUER=$(kubectl get --raw /.well-known/openid-configuration| jq -r .issuer)" >> $GITHUB_ENV
-          echo "KUBERNETES_AUDIENCE=$(kubectl create token llama-stack-auth -n llama-stack --duration=1h | cut -d. -f2 | base64 -d | jq -r '.aud[0]')" >> $GITHUB_ENV
-
-      - name: Set Kube Auth Config and run server
-        env:
-          INFERENCE_MODEL: "meta-llama/Llama-3.2-3B-Instruct"
-        if: ${{ matrix.auth-provider == 'oauth2_token' }}
-        run: |
-          run_dir=$(mktemp -d)
-          cat <<'EOF' > $run_dir/run.yaml
-          version: '2'
-          image_name: kube
-          apis: []
-          providers: {}
-          server:
-            port: 8321
-          EOF
-          yq eval '.server.auth = {"provider_type": "${{ matrix.auth-provider }}"}' -i $run_dir/run.yaml
-          yq eval '.server.auth.config = {"tls_cafile": "${{ env.KUBERNETES_CA_CERT_PATH }}", "issuer": "${{ env.KUBERNETES_ISSUER }}", "audience": "${{ env.KUBERNETES_AUDIENCE }}"}' -i $run_dir/run.yaml
-          yq eval '.server.auth.config.jwks = {"uri": "${{ env.KUBERNETES_API_SERVER_URL }}"}' -i $run_dir/run.yaml
-          cat $run_dir/run.yaml
-
-          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

.gitignore

@@ -6,7 +6,6 @@ dev_requirements.txt
 build
 .DS_Store
 llama_stack/configs/*
-.cursor/
 xcuserdata/
 *.hmap
 .DS_Store
@@ -24,4 +23,3 @@ venv/
 pytest-report.xml
 .coverage
 .python-version
-data

.pre-commit-config.yaml

@@ -15,18 +15,6 @@ 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
@@ -53,7 +41,7 @@ repos:
         - black==24.3.0
 
 -   repo: https://github.com/astral-sh/uv-pre-commit
-    rev: 0.7.8
+    rev: 0.6.3
     hooks:
     -   id: uv-lock
     -   id: uv-export
@@ -61,7 +49,6 @@ repos:
             "--frozen",
             "--no-hashes",
             "--no-emit-project",
-            "--no-default-groups",
             "--output-file=requirements.txt"
         ]
 
@@ -89,29 +76,24 @@ repos:
       - id: distro-codegen
         name: Distribution Template Codegen
         additional_dependencies:
-          - uv==0.7.8
-        entry: uv run --group codegen ./scripts/distro_codegen.py
+          - uv==0.6.0
+        entry: uv run --extra codegen ./scripts/distro_codegen.py
         language: python
         pass_filenames: false
         require_serial: true
         files: ^llama_stack/templates/.*$|^llama_stack/providers/.*/inference/.*/models\.py$
+
+-   repo: local
+    hooks:
       - id: openapi-codegen
         name: API Spec Codegen
         additional_dependencies:
-          - uv==0.7.8
-        entry: sh -c 'uv run ./docs/openapi_generator/run_openapi_generator.sh > /dev/null'
+          - uv==0.6.2
+        entry: sh -c 'uv run --with ".[dev]" ./docs/openapi_generator/run_openapi_generator.sh > /dev/null'
         language: python
         pass_filenames: false
         require_serial: true
         files: ^llama_stack/apis/|^docs/openapi_generator/
-      - id: check-workflows-use-hashes
-        name: Check GitHub Actions use SHA-pinned actions
-        entry: ./scripts/check-workflows-use-hashes.sh
-        language: system
-        pass_filenames: false
-        require_serial: true
-        always_run: true
-        files: ^\.github/workflows/.*\.ya?ml$
 
 ci:
     autofix_commit_msg: 🎨 [pre-commit.ci] Auto format from pre-commit.com hooks

.readthedocs.yaml

@@ -5,21 +5,28 @@
 # Required
 version: 2
 
-# Build documentation in the "docs/" directory with Sphinx
-sphinx:
-  configuration: docs/source/conf.py
-
 # Set the OS, Python version and other tools you might need
 build:
   os: ubuntu-22.04
   tools:
     python: "3.12"
-  jobs:
-    pre_create_environment:
-      - asdf plugin add uv
-      - asdf install uv latest
-      - asdf global uv latest
-    create_environment:
-      - uv venv "${READTHEDOCS_VIRTUALENV_PATH}"
-    install:
-      - UV_PROJECT_ENVIRONMENT="${READTHEDOCS_VIRTUALENV_PATH}" uv sync --frozen --group docs
+    # You can also specify other tool versions:
+    # nodejs: "19"
+    # rust: "1.64"
+    # golang: "1.19"
+
+# Build documentation in the "docs/" directory with Sphinx
+sphinx:
+  configuration: docs/source/conf.py
+
+# Optionally build your docs in additional formats such as PDF and ePub
+# formats:
+#    - pdf
+#    - epub
+
+# Optional but recommended, declare the Python requirements required
+# to build your documentation
+# See https://docs.readthedocs.io/en/stable/guides/reproducible-builds.html
+python:
+   install:
+   - requirements: docs/requirements.txt

CHANGELOG.md

@@ -1,75 +1,5 @@
 # Changelog
 
-# v0.2.7
-Published on: 2025-05-16T20:38:10Z
-
-## Highlights
-
-This is a small update. But a couple highlights:
-
-* feat: function tools in OpenAI Responses by @bbrowning in https://github.com/meta-llama/llama-stack/pull/2094, getting closer to ready. Streaming is the next missing piece.
-* feat: Adding support for customizing chunk context in RAG insertion and querying by @franciscojavierarceo in https://github.com/meta-llama/llama-stack/pull/2134
-* feat: scaffolding for Llama Stack UI by @ehhuang in https://github.com/meta-llama/llama-stack/pull/2149, more to come in the coming releases.
-
-
----
-
-# v0.2.6
-Published on: 2025-05-12T18:06:52Z
-
-
-
----
-
-# v0.2.5
-Published on: 2025-05-04T20:16:49Z
-
-
-
----
-
-# v0.2.4
-Published on: 2025-04-29T17:26:01Z
-
-## Highlights
-
-* One-liner to install and run Llama Stack yay! by @reluctantfuturist in https://github.com/meta-llama/llama-stack/pull/1383
-* support for NVIDIA NeMo datastore by @raspawar in https://github.com/meta-llama/llama-stack/pull/1852
-* (yuge!) Kubernetes authentication by @leseb in https://github.com/meta-llama/llama-stack/pull/1778
-* (yuge!) OpenAI Responses API by @bbrowning in https://github.com/meta-llama/llama-stack/pull/1989
-* add api.llama provider, llama-guard-4 model by @ashwinb in https://github.com/meta-llama/llama-stack/pull/2058
-
-
----
-
-# 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
 
@@ -80,10 +10,10 @@ Published on: 2025-04-05T23:13:00Z
 # v0.2.0
 Published on: 2025-04-05T19:04:29Z
 
-## Llama 4 Support
-
-Checkout more at https://www.llama.com
-
+## Llama 4 Support
+
+Checkout more at https://www.llama.com
+
 
 
 ---
@@ -91,58 +21,58 @@ Checkout more at https://www.llama.com
 # v0.1.9
 Published on: 2025-03-29T00:52:23Z
 
-### Build and Test Agents
-* Agents: Entire document context with attachments
-* RAG: Documentation with sqlite-vec faiss comparison
-* Getting started: Fixes to getting started notebook.
-
-### Agent Evals and Model Customization
-* (**New**) Post-training: Add nemo customizer
-
-### Better Engineering
-* Moved sqlite-vec to non-blocking calls
-* Don't return a payload on file delete
-
-
+### Build and Test Agents
+* Agents: Entire document context with attachments
+* RAG: Documentation with sqlite-vec faiss comparison
+* Getting started: Fixes to getting started notebook.
+
+### Agent Evals and Model Customization
+* (**New**) Post-training: Add nemo customizer
+
+### Better Engineering
+* Moved sqlite-vec to non-blocking calls
+* Don't return a payload on file delete
+
+
 
 ---
 
 # v0.1.8
 Published on: 2025-03-24T01:28:50Z
 
-# v0.1.8 Release Notes
-
-### Build and Test Agents
-* Safety: Integrated NVIDIA as a safety provider.
-* VectorDB: Added Qdrant as an inline provider.
-* Agents: Added support for multiple tool groups in agents.
-* Agents: Simplified imports for Agents in client package
-
-
-### Agent Evals and Model Customization
-* Introduced DocVQA and IfEval benchmarks.
-
-### Deploying and Monitoring Agents
-* Introduced a Containerfile and image workflow for the Playground.
-* Implemented support for Bearer (API Key) authentication.
-* Added attribute-based access control for resources.
-* Fixes on docker deployments: use --pull always and standardized the default port to 8321
-* Deprecated: /v1/inspect/providers use /v1/providers/ instead
-
-### Better Engineering
-* Consolidated scripts under the ./scripts directory.
-* Addressed mypy violations in various modules.
-* Added Dependabot scans for Python dependencies.
-* Implemented a scheduled workflow to update the changelog automatically.
-* Enforced concurrency to reduce CI loads.
-
-
-### New Contributors
-* @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
-* @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
-
+# v0.1.8 Release Notes
+
+### Build and Test Agents
+* Safety: Integrated NVIDIA as a safety provider.
+* VectorDB: Added Qdrant as an inline provider.
+* Agents: Added support for multiple tool groups in agents.
+* Agents: Simplified imports for Agents in client package
+
+
+### Agent Evals and Model Customization
+* Introduced DocVQA and IfEval benchmarks.
+
+### Deploying and Monitoring Agents
+* Introduced a Containerfile and image workflow for the Playground.
+* Implemented support for Bearer (API Key) authentication.
+* Added attribute-based access control for resources.
+* Fixes on docker deployments: use --pull always and standardized the default port to 8321
+* Deprecated: /v1/inspect/providers use /v1/providers/ instead
+
+### Better Engineering
+* Consolidated scripts under the ./scripts directory.
+* Addressed mypy violations in various modules.
+* Added Dependabot scans for Python dependencies.
+* Implemented a scheduled workflow to update the changelog automatically.
+* Enforced concurrency to reduce CI loads.
+
+
+### New Contributors
+* @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
+* @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
+
 **Full Changelog**: https://github.com/meta-llama/llama-stack/compare/v0.1.7...v0.1.8
 
 ---
@@ -150,73 +80,73 @@ Published on: 2025-03-24T01:28:50Z
 # v0.1.7
 Published on: 2025-03-14T22:30:51Z
 
-## 0.1.7 Release Notes
-
-###  Build and Test Agents
-* Inference: ImageType is now refactored to LlamaStackImageType
-* Inference: Added tests to measure TTFT
-* Inference: Bring back usage metrics
-* 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: Deprecated ToolResponseMessage in agent.resume API
-* Added Provider API for listing and inspecting provider info
-
-### Agent Evals and Model Customization
-* Eval: Added new eval benchmarks Math 500 and BFCL v3
-* Deploy and Monitoring of Agents
-* Telemetry: Fix tracing to work across coroutines
-
-###  Better Engineering
-* Display code coverage for unit tests
-* 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
-* Added ollama inference to Integration tests CI
-* Improved documentation across examples, testing, CLI, updated providers table )
-
-
-
+## 0.1.7 Release Notes
+
+###  Build and Test Agents
+* Inference: ImageType is now refactored to LlamaStackImageType
+* Inference: Added tests to measure TTFT
+* Inference: Bring back usage metrics
+* 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: Deprecated ToolResponseMessage in agent.resume API
+* Added Provider API for listing and inspecting provider info
+
+### Agent Evals and Model Customization
+* Eval: Added new eval benchmarks Math 500 and BFCL v3
+* Deploy and Monitoring of Agents
+* Telemetry: Fix tracing to work across coroutines
+
+###  Better Engineering
+* Display code coverage for unit tests
+* 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
+* Added ollama inference to Integration tests CI
+* Improved documentation across examples, testing, CLI, updated providers table )
+
+
+
 
 ---
 
 # v0.1.6
 Published on: 2025-03-08T04:35:08Z
 
-## 0.1.6 Release Notes
-
-### Build and Test Agents
-* 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: 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: AsyncAgent Python SDK wrapper supporting async client tool calls
-* 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
-* VectorIO: MilvusDB support added
-
-### 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)
-* Eval: Documentation for eval, scoring, adding new benchmarks
-* Eval: Distribution template to run benchmarks on llama & non-llama models
-* 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.
-
-### Deploy and Monitoring of Agents
-* Better support for different log levels across all components for better monitoring
-
-### Better Engineering
-* Enhance OpenAPI spec to include Error types across all APIs
-* Moved all tests to /tests and created unit tests to run on each PR
-* Removed all dependencies on llama-models repo
-
+## 0.1.6 Release Notes
+
+### Build and Test Agents
+* 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: 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: AsyncAgent Python SDK wrapper supporting async client tool calls
+* 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
+* VectorIO: MilvusDB support added
+
+### 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)
+* Eval: Documentation for eval, scoring, adding new benchmarks
+* Eval: Distribution template to run benchmarks on llama & non-llama models
+* 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.
+
+### Deploy and Monitoring of Agents
+* Better support for different log levels across all components for better monitoring
+
+### Better Engineering
+* Enhance OpenAPI spec to include Error types across all APIs
+* Moved all tests to /tests and created unit tests to run on each PR
+* Removed all dependencies on llama-models repo
+
 
 ---
 
 # v0.1.5.1
 Published on: 2025-02-28T22:37:44Z
 
-## 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
-
+## 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
+
 **Full Changelog**: https://github.com/meta-llama/llama-stack/compare/v0.1.5...v0.1.5.1
 
 ---
@@ -224,176 +154,176 @@ Published on: 2025-02-28T22:37:44Z
 # v0.1.5
 Published on: 2025-02-28T18:14:01Z
 
-## 0.1.5 Release Notes
-###  Build Agents
-* Inference: Support more non-llama models (openai, anthropic, gemini)
-* 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
-* RAG: Improved system prompt for RAG and no more need for hard-coded rag-tool calling
-* Embeddings: Added support for Nemo retriever embedding models
-* Tools: Added support for MCP tools in Ollama Distribution
-* Distributions: Added new Groq distribution
-
-### Customize Models
-* Save post-trained checkpoint in SafeTensor format to allow Ollama inference provider to use the post-trained model
-
-### Monitor agents
-* More comprehensive logging of agent steps including client tools
-* Telemetry inputs/outputs are now structured and queryable
-* Ability to retrieve agents session, turn, step by ids
-
-### Better Engineering
-* 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
-* Completed text /chat-completion and /completion tests
-
+## 0.1.5 Release Notes
+###  Build Agents
+* Inference: Support more non-llama models (openai, anthropic, gemini)
+* 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
+* RAG: Improved system prompt for RAG and no more need for hard-coded rag-tool calling
+* Embeddings: Added support for Nemo retriever embedding models
+* Tools: Added support for MCP tools in Ollama Distribution
+* Distributions: Added new Groq distribution
+
+### Customize Models
+* Save post-trained checkpoint in SafeTensor format to allow Ollama inference provider to use the post-trained model
+
+### Monitor agents
+* More comprehensive logging of agent steps including client tools
+* Telemetry inputs/outputs are now structured and queryable
+* Ability to retrieve agents session, turn, step by ids
+
+### Better Engineering
+* 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
+* Completed text /chat-completion and /completion tests
+
 
 ---
 
 # v0.1.4
 Published on: 2025-02-25T00:02:43Z
 
-## v0.1.4 Release Notes
-Here are the key changes coming as part of this release:
-
-### Build and Test Agents
-* Inference: Added support for non-llama 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: 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 support for logging for tool execution metadata
-* 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
-* VectorIO: Improved performance of sqlite-vec using chunked writes
-### Agent Evals and Model Customization
-* Deprecated api /eval-tasks. Use /eval/benchmark  instead
-* Added CPU training support for TorchTune
-### Deploy and Monitoring of Agents
-* Consistent view of client and server tool calls in telemetry
-### Better Engineering
-* Made tests more data-driven for consistent evaluation
-* Fixed documentation links and improved API reference generation
-* Various small fixes for build scripts and system reliability
-
-
+## v0.1.4 Release Notes
+Here are the key changes coming as part of this release:
+
+### Build and Test Agents
+* Inference: Added support for non-llama 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: 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 support for logging for tool execution metadata
+* 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
+* VectorIO: Improved performance of sqlite-vec using chunked writes
+### Agent Evals and Model Customization
+* Deprecated api /eval-tasks. Use /eval/benchmark  instead
+* Added CPU training support for TorchTune
+### Deploy and Monitoring of Agents
+* Consistent view of client and server tool calls in telemetry
+### Better Engineering
+* Made tests more data-driven for consistent evaluation
+* Fixed documentation links and improved API reference generation
+* Various small fixes for build scripts and system reliability
+
+
 
 ---
 
 # v0.1.3
 Published on: 2025-02-14T20:24:32Z
 
-## v0.1.3 Release
-
-Here are some key changes that are coming as part of this release.
-
-### Build and Test Agents
-Streamlined the initial development experience
-- Added support for  llama stack run --image-type venv
-- Enhanced vector store options with new sqlite-vec provider and improved Qdrant integration
-- vLLM improvements for tool calling and logprobs
-- Better handling of sporadic code_interpreter tool calls
-
-### Agent Evals
-Better benchmarking and Agent performance assessment
-- Renamed eval API /eval-task to /benchmarks
-- Improved documentation and notebooks for RAG and evals
-
-### Deploy and Monitoring of Agents
-Improved production readiness
-- Added usage metrics collection for chat completions
-- CLI improvements for provider information
-- Improved error handling and system reliability
-- Better model endpoint handling and accessibility
-- Improved signal handling on distro server
-
-### Better Engineering
-Infrastructure and code quality improvements
-- Faster text-based chat completion tests
-- Improved testing for non-streaming agent apis
-- Standardized import formatting with ruff linter
-- Added conventional commits standard
-- Fixed documentation parsing issues
-
+## v0.1.3 Release
+
+Here are some key changes that are coming as part of this release.
+
+### Build and Test Agents
+Streamlined the initial development experience
+- Added support for  llama stack run --image-type venv
+- Enhanced vector store options with new sqlite-vec provider and improved Qdrant integration
+- vLLM improvements for tool calling and logprobs
+- Better handling of sporadic code_interpreter tool calls
+
+### Agent Evals
+Better benchmarking and Agent performance assessment
+- Renamed eval API /eval-task to /benchmarks
+- Improved documentation and notebooks for RAG and evals
+
+### Deploy and Monitoring of Agents
+Improved production readiness
+- Added usage metrics collection for chat completions
+- CLI improvements for provider information
+- Improved error handling and system reliability
+- Better model endpoint handling and accessibility
+- Improved signal handling on distro server
+
+### Better Engineering
+Infrastructure and code quality improvements
+- Faster text-based chat completion tests
+- Improved testing for non-streaming agent apis
+- Standardized import formatting with ruff linter
+- Added conventional commits standard
+- Fixed documentation parsing issues
+
 
 ---
 
 # v0.1.2
 Published on: 2025-02-07T22:06:49Z
 
-# TL;DR
-- 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)
-- Added automated rebuilds for ReadTheDocs
-- Llama Stack server supports HTTPS
-- Added system prompt overrides support
-- Several bug fixes and improvements to documentation (check out Kubernetes deployment guide by @terrytangyuan )
-
+# TL;DR
+- 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)
+- Added automated rebuilds for ReadTheDocs
+- Llama Stack server supports HTTPS
+- Added system prompt overrides support
+- 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.
-
-## 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.
-
-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.
-
-## 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.
-
-There are example standalone apps in llama-stack-apps.
-
-
-## Key Features of this release
-
-- **Unified API Layer**
-  - Inference: Run LLM models
-  - RAG: Store and retrieve knowledge for RAG
-  - Agents: Build multi-step agentic workflows
-  - Tools: Register tools that can be called by the agent
-  - Safety: Apply content filtering and safety policies
-  - Evaluation: Test model and agent quality
-  - Telemetry: Collect and analyze usage data and complex agentic traces
-  - Post Training ( Coming Soon ): Fine tune models for specific use cases
-
-- **Rich Provider Ecosystem**
-  - Local Development: Meta's Reference, Ollama
-  - Cloud: Fireworks, Together, Nvidia, AWS Bedrock, Groq, Cerebras
-  - On-premises: Nvidia NIM, vLLM, TGI, Dell-TGI
-  - On-device: iOS and Android support
-
-- **Built for Production**
-  - Pre-packaged distributions for common deployment scenarios
-  - Backwards compatibility across model versions
-  - Comprehensive evaluation capabilities
-  - Full observability and monitoring
-
-- **Multiple developer interfaces**
-  - CLI: Command line interface
-  - Python SDK
-  - Swift iOS SDK
-  - Kotlin Android SDK
-
-- **Sample llama stack applications**
-  - Python
-  - iOS
-  - Android
-
-
+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
+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.
+
+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
+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.
+
+
+## Key Features of this release
+
+- **Unified API Layer**
+  - Inference: Run LLM models
+  - RAG: Store and retrieve knowledge for RAG
+  - Agents: Build multi-step agentic workflows
+  - Tools: Register tools that can be called by the agent
+  - Safety: Apply content filtering and safety policies
+  - Evaluation: Test model and agent quality
+  - Telemetry: Collect and analyze usage data and complex agentic traces
+  - Post Training ( Coming Soon ): Fine tune models for specific use cases
+
+- **Rich Provider Ecosystem**
+  - Local Development: Meta's Reference, Ollama
+  - Cloud: Fireworks, Together, Nvidia, AWS Bedrock, Groq, Cerebras
+  - On-premises: Nvidia NIM, vLLM, TGI, Dell-TGI
+  - On-device: iOS and Android support
+
+- **Built for Production**
+  - Pre-packaged distributions for common deployment scenarios
+  - Backwards compatibility across model versions
+  - Comprehensive evaluation capabilities
+  - Full observability and monitoring
+
+- **Multiple developer interfaces**
+  - CLI: Command line interface
+  - Python SDK
+  - Swift iOS SDK
+  - Kotlin Android SDK
+
+- **Sample llama stack applications**
+  - Python
+  - iOS
+  - Android
+
+
 
 ---
 
@@ -407,8 +337,8 @@ 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
 
 ---
@@ -444,39 +374,39 @@ Published on: 2024-11-22T00:36:09Z
 # v0.0.53
 Published on: 2024-11-20T22:18:00Z
 
-🚀  Initial Release Notes for Llama Stack!
-
-### Added
-- Resource-oriented design for models, shields, memory banks, datasets and eval tasks
-- Persistence for registered objects with distribution
-- Ability to persist memory banks created for FAISS
-- PostgreSQL KVStore implementation
-- Environment variable placeholder support in run.yaml files
-- Comprehensive Zero-to-Hero notebooks and quickstart guides
-- Support for quantized models in Ollama
-- Vision models support for Together, Fireworks, Meta-Reference, and Ollama, and vLLM
-- Bedrock distribution with safety shields support
-- Evals API with task registration and scoring functions
-- MMLU and SimpleQA benchmark scoring functions
-- Huggingface dataset provider integration for benchmarks
-- Support for custom dataset registration from local paths
-- Benchmark evaluation CLI tools with visualization tables
-- RAG evaluation scoring functions and metrics
-- Local persistence for datasets and eval tasks
-
-### Changed
-- Split safety into distinct providers (llama-guard, prompt-guard, code-scanner)
-- Changed provider naming convention (`impls` → `inline`, `adapters` → `remote`)
-- Updated API signatures for dataset and eval task registration
-- Restructured folder organization for providers
-- Enhanced Docker build configuration
-- Added version prefixing for REST API routes
-- Enhanced evaluation task registration workflow
-- Improved benchmark evaluation output formatting
-- Restructured evals folder organization for better modularity
-
-### Removed
-- `llama stack configure` command
-
+🚀  Initial Release Notes for Llama Stack!
+
+### Added
+- Resource-oriented design for models, shields, memory banks, datasets and eval tasks
+- Persistence for registered objects with distribution
+- Ability to persist memory banks created for FAISS
+- PostgreSQL KVStore implementation
+- Environment variable placeholder support in run.yaml files
+- Comprehensive Zero-to-Hero notebooks and quickstart guides
+- Support for quantized models in Ollama
+- Vision models support for Together, Fireworks, Meta-Reference, and Ollama, and vLLM
+- Bedrock distribution with safety shields support
+- Evals API with task registration and scoring functions
+- MMLU and SimpleQA benchmark scoring functions
+- Huggingface dataset provider integration for benchmarks
+- Support for custom dataset registration from local paths
+- Benchmark evaluation CLI tools with visualization tables
+- RAG evaluation scoring functions and metrics
+- Local persistence for datasets and eval tasks
+
+### Changed
+- Split safety into distinct providers (llama-guard, prompt-guard, code-scanner)
+- Changed provider naming convention (`impls` → `inline`, `adapters` → `remote`)
+- Updated API signatures for dataset and eval task registration
+- Restructured folder organization for providers
+- Enhanced Docker build configuration
+- Added version prefixing for REST API routes
+- Enhanced evaluation task registration workflow
+- Improved benchmark evaluation output formatting
+- Restructured evals folder organization for better modularity
+
+### Removed
+- `llama stack configure` command
+
 
 ---

CONTRIBUTING.md

@@ -110,9 +110,25 @@ uv run pre-commit run --all-files
 > [!CAUTION]
 > Before pushing your changes, make sure that the pre-commit hooks have passed successfully.
 
-## Running tests
+## Running unit tests
 
-You can find the Llama Stack testing documentation here [here](tests/README.md).
+You can run the unit tests by running:
+
+```bash
+source .venv/bin/activate
+./scripts/unit-tests.sh
+```
+
+If you'd like to run for a non-default version of Python (currently 3.10), pass `PYTHON_VERSION` variable as follows:
+
+```
+source .venv/bin/activate
+PYTHON_VERSION=3.13 ./scripts/unit-tests.sh
+```
+
+## Running integration tests
+
+You can run integration tests following the instructions [here](tests/integration/README.md).
 
 ## Adding a new dependency to the project
 
@@ -125,20 +141,11 @@ 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.
-* 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`.
+* 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.
+* 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 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.
-* Don't use unicode characters in the codebase. ASCII-only is preferred for compatibility or
-  readability reasons.
+* 4 spaces for indentation rather than tabs
 
 ## Common Tasks
 
@@ -167,11 +174,14 @@ If you have made changes to a provider's configuration in any form (introducing
 If you are making changes to the documentation at [https://llama-stack.readthedocs.io/en/latest/](https://llama-stack.readthedocs.io/en/latest/), you can use the following command to build the documentation and preview your changes. You will need [Sphinx](https://www.sphinx-doc.org/en/master/) and the readthedocs theme.
 
 ```bash
+cd docs
+uv sync --extra docs
+
 # This rebuilds the documentation pages.
-uv run --group docs make -C docs/ html
+uv run make html
 
 # This will start a local server (usually at http://127.0.0.1:8000) that automatically rebuilds and refreshes when you make changes to the documentation.
-uv run --group docs sphinx-autobuild docs/source docs/build/html --write-all
+uv run sphinx-autobuild source build/html --write-all
 ```
 
 ### Update API Documentation
@@ -179,7 +189,7 @@ uv run --group docs sphinx-autobuild docs/source docs/build/html --write-all
 If you modify or add new API endpoints, update the API documentation accordingly. You can do this by running the following command:
 
 ```bash
-uv run ./docs/openapi_generator/run_openapi_generator.sh
+uv run --with ".[dev]" ./docs/openapi_generator/run_openapi_generator.sh
 ```
 
 The generated API documentation will be available in `docs/_static/`. Make sure to review the changes before committing.

MANIFEST.in

@@ -1,4 +1,5 @@
 include pyproject.toml
+include llama_stack/templates/dependencies.json
 include llama_stack/models/llama/llama3/tokenizer.model
 include llama_stack/models/llama/llama4/tokenizer.model
 include llama_stack/distribution/*.sh

README.md

@@ -7,7 +7,7 @@
 [![Unit Tests](https://github.com/meta-llama/llama-stack/actions/workflows/unit-tests.yml/badge.svg?branch=main)](https://github.com/meta-llama/llama-stack/actions/workflows/unit-tests.yml?query=branch%3Amain)
 [![Integration Tests](https://github.com/meta-llama/llama-stack/actions/workflows/integration-tests.yml/badge.svg?branch=main)](https://github.com/meta-llama/llama-stack/actions/workflows/integration-tests.yml?query=branch%3Amain)
 
-[**Quick Start**](https://llama-stack.readthedocs.io/en/latest/getting_started/index.html) | [**Documentation**](https://llama-stack.readthedocs.io/en/latest/index.html) | [**Colab Notebook**](./docs/getting_started.ipynb) | [**Discord**](https://discord.gg/llama-stack)
+[**Quick Start**](https://llama-stack.readthedocs.io/en/latest/getting_started/index.html) | [**Documentation**](https://llama-stack.readthedocs.io/en/latest/index.html) | [**Colab Notebook**](./docs/getting_started.ipynb)
 
 ### ✨🎉 Llama 4 Support  🎉✨
 We released [Version 0.2.0](https://github.com/meta-llama/llama-stack/releases/tag/v0.2.0) with support for the Llama 4 herd of models released by Meta.
@@ -70,13 +70,6 @@ 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
 
@@ -107,29 +100,26 @@ By reducing friction and complexity, Llama Stack empowers developers to focus on
 ### API Providers
 Here is a list of the various API providers and available distributions that can help developers get started easily with Llama Stack.
 
-| **API Provider Builder** |    **Environments**    | **Agents** | **Inference** | **Memory** | **Safety** | **Telemetry** | **Post Training** |
-|:------------------------:|:----------------------:|:----------:|:-------------:|:----------:|:----------:|:-------------:|:-----------------:|
-|      Meta Reference      |      Single Node       |     ✅      |       ✅       |     ✅      |     ✅      |       ✅       |               |
-|        SambaNova         |         Hosted         |            |       ✅       |            |     ✅      |               |                  |
-|         Cerebras         |         Hosted         |            |       ✅       |            |            |               |                  |
-|        Fireworks         |         Hosted         |     ✅      |       ✅       |     ✅      |            |               |                |
-|       AWS Bedrock        |         Hosted         |            |       ✅       |            |     ✅      |               |                |
-|         Together         |         Hosted         |     ✅      |       ✅       |            |     ✅      |               |                |
-|           Groq           |         Hosted         |            |       ✅       |            |            |               |                 |
-|          Ollama          |      Single Node       |            |       ✅       |            |            |               |                 |
-|           TGI            | Hosted and Single Node |            |       ✅       |            |            |               |                 |
-|        NVIDIA NIM        | Hosted and Single Node |            |       ✅       |            |            |               |                 |
-|          Chroma          |      Single Node       |            |               |     ✅      |            |               |                 |
-|        PG Vector         |      Single Node       |            |               |     ✅      |            |               |                 |
-|    PyTorch ExecuTorch    |     On-device iOS      |     ✅      |       ✅       |            |            |               |                |
-|           vLLM           | Hosted and Single Node |            |       ✅       |            |            |               |                 |
-|          OpenAI          |         Hosted         |            |       ✅       |            |            |               |                 |
-|        Anthropic         |         Hosted         |            |       ✅       |            |            |               |                 |
-|          Gemini          |         Hosted         |            |       ✅       |            |            |               |                 |
-|          watsonx         |         Hosted         |            |       ✅       |            |            |               |                 |
-|        HuggingFace       |       Single Node      |            |                |            |            |               |       ✅        |
-|         TorchTune        |       Single Node      |            |                |            |            |               |       ✅        |
-|       NVIDIA NEMO        |         Hosted         |            |                |            |            |               |       ✅        |
+| **API Provider Builder** |    **Environments**    | **Agents** | **Inference** | **Memory** | **Safety** | **Telemetry** |
+|:------------------------:|:----------------------:|:----------:|:-------------:|:----------:|:----------:|:-------------:|
+|      Meta Reference      |      Single Node       |     ✅      |       ✅       |     ✅      |     ✅      |       ✅       |
+|        SambaNova         |         Hosted         |            |       ✅       |            |            |               |
+|         Cerebras         |         Hosted         |            |       ✅       |            |            |               |
+|        Fireworks         |         Hosted         |     ✅      |       ✅       |     ✅      |            |               |
+|       AWS Bedrock        |         Hosted         |            |       ✅       |            |     ✅      |               |
+|         Together         |         Hosted         |     ✅      |       ✅       |            |     ✅      |               |
+|           Groq           |         Hosted         |            |       ✅       |            |            |               |
+|          Ollama          |      Single Node       |            |       ✅       |            |            |               |
+|           TGI            | Hosted and Single Node |            |       ✅       |            |            |               |
+|        NVIDIA NIM        | Hosted and Single Node |            |       ✅       |            |            |               |
+|          Chroma          |      Single Node       |            |               |     ✅      |            |               |
+|        PG Vector         |      Single Node       |            |               |     ✅      |            |               |
+|    PyTorch ExecuTorch    |     On-device iOS      |     ✅      |       ✅       |            |            |               |
+|           vLLM           | Hosted and Single Node |            |       ✅       |            |            |               |
+|          OpenAI          |         Hosted         |            |       ✅       |            |            |               |
+|        Anthropic         |         Hosted         |            |       ✅       |            |            |               |
+|          Gemini          |         Hosted         |            |       ✅       |            |            |               |
+|          watsonx         |         Hosted         |            |       ✅       |            |            |               |
 
 
 ### Distributions

docs/_static/css/my_theme.css

@@ -27,9 +27,3 @@ 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/getting_started.ipynb

@@ -1050,6 +1050,8 @@
             "text/html": [
               "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #800080; text-decoration-color: #800080; font-weight: bold\">ToolGroup</span><span style=\"font-weight: bold\">(</span>\n",
               "<span style=\"color: #7fbf7f; text-decoration-color: #7fbf7f\">│   </span><span style=\"color: #808000; text-decoration-color: #808000\">identifier</span>=<span style=\"color: #008000; text-decoration-color: #008000\">'builtin::code_interpreter'</span>,\n",
+              "<span style=\"color: #7fbf7f; text-decoration-color: #7fbf7f\">│   </span><span style=\"color: #808000; text-decoration-color: #808000\">provider_id</span>=<span style=\"color: #008000; text-decoration-color: #008000\">'code-interpreter'</span>,\n",
+              "<span style=\"color: #7fbf7f; text-decoration-color: #7fbf7f\">│   </span><span style=\"color: #808000; text-decoration-color: #808000\">provider_resource_id</span>=<span style=\"color: #008000; text-decoration-color: #008000\">'builtin::code_interpreter'</span>,\n",
               "<span style=\"color: #7fbf7f; text-decoration-color: #7fbf7f\">│   </span><span style=\"color: #808000; text-decoration-color: #808000\">type</span>=<span style=\"color: #008000; text-decoration-color: #008000\">'tool_group'</span>,\n",
               "<span style=\"color: #7fbf7f; text-decoration-color: #7fbf7f\">│   </span><span style=\"color: #808000; text-decoration-color: #808000\">args</span>=<span style=\"color: #800080; text-decoration-color: #800080; font-style: italic\">None</span>,\n",
               "<span style=\"color: #7fbf7f; text-decoration-color: #7fbf7f\">│   </span><span style=\"color: #808000; text-decoration-color: #808000\">mcp_endpoint</span>=<span style=\"color: #800080; text-decoration-color: #800080; font-style: italic\">None</span>\n",
@@ -1059,6 +1061,7 @@
             "text/plain": [
               "\u001b[1;35mToolGroup\u001b[0m\u001b[1m(\u001b[0m\n",
               "\u001b[2;32m│   \u001b[0m\u001b[33midentifier\u001b[0m=\u001b[32m'builtin::code_interpreter'\u001b[0m,\n",
+              "\u001b[2;32m│   \u001b[0m\u001b[33mprovider_id\u001b[0m=\u001b[32m'code-interpreter'\u001b[0m,\n",
               "\u001b[2;32m│   \u001b[0m\u001b[33mprovider_resource_id\u001b[0m=\u001b[32m'builtin::code_interpreter'\u001b[0m,\n",
               "\u001b[2;32m│   \u001b[0m\u001b[33mtype\u001b[0m=\u001b[32m'tool_group'\u001b[0m,\n",
               "\u001b[2;32m│   \u001b[0m\u001b[33margs\u001b[0m=\u001b[3;35mNone\u001b[0m,\n",

docs/make.bat

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

docs/notebooks/Llama_Stack_RAG_Lifecycle.ipynb

@@ -840,6 +840,7 @@
     "    \"memory_optimizations.rst\",\n",
     "    \"chat.rst\",\n",
     "    \"llama3.rst\",\n",
+    "    \"datasets.rst\",\n",
     "    \"qat_finetune.rst\",\n",
     "    \"lora_finetune.rst\",\n",
     "]\n",
@@ -1585,6 +1586,7 @@
     "    \"memory_optimizations.rst\",\n",
     "    \"chat.rst\",\n",
     "    \"llama3.rst\",\n",
+    "    \"datasets.rst\",\n",
     "    \"qat_finetune.rst\",\n",
     "    \"lora_finetune.rst\",\n",
     "]\n",

docs/openapi_generator/generate.py

@@ -44,7 +44,7 @@ def main(output_dir: str):
     if return_type_errors:
         print("\nAPI Method Return Type Validation Errors:\n")
         for error in return_type_errors:
-            print(error, file=sys.stderr)
+            print(error)
         sys.exit(1)
     now = str(datetime.now())
     print(

docs/openapi_generator/pyopenapi/generator.py

@@ -6,7 +6,6 @@
 
 import hashlib
 import ipaddress
-import types
 import typing
 from dataclasses import make_dataclass
 from typing import Any, Dict, Set, Union
@@ -180,7 +179,7 @@ class ContentBuilder:
         "Creates the content subtree for a request or response."
 
         def is_iterator_type(t):
-            return "StreamChunk" in str(t) or "OpenAIResponseObjectStream" in str(t)
+            return "StreamChunk" in str(t)
 
         def get_media_type(t):
             if is_generic_list(t):
@@ -190,7 +189,7 @@ class ContentBuilder:
             else:
                 return "application/json"
 
-        if typing.get_origin(payload_type) in (typing.Union, types.UnionType):
+        if typing.get_origin(payload_type) is typing.Union:
             media_types = []
             item_types = []
             for x in typing.get_args(payload_type):
@@ -759,7 +758,7 @@ class Generator:
         )
 
         return Operation(
-            tags=[getattr(op.defining_class, "API_NAMESPACE", op.defining_class.__name__)],
+            tags=[op.defining_class.__name__],
             summary=None,
             # summary=doc_string.short_description,
             description=description,
@@ -805,8 +804,6 @@ class Generator:
         operation_tags: List[Tag] = []
         for cls in endpoint_classes:
             doc_string = parse_type(cls)
-            if hasattr(cls, "API_NAMESPACE") and cls.API_NAMESPACE != cls.__name__:
-                continue
             operation_tags.append(
                 Tag(
                     name=cls.__name__,

docs/openapi_generator/pyopenapi/utility.py

@@ -174,64 +174,14 @@ def _validate_list_parameters_contain_data(method) -> str | None:
         return "does not have a mandatory data attribute containing the list of objects"
 
 
-def _validate_has_ellipsis(method) -> str | None:
-    source = inspect.getsource(method)
-    if "..." not in source and not "NotImplementedError" in source:
-        return "does not contain ellipsis (...) in its implementation"
-
-def _validate_has_return_in_docstring(method) -> str | None:
-    source = inspect.getsource(method)
-    return_type = method.__annotations__.get('return')
-    if return_type is not None and return_type != type(None) and ":returns:" not in source:
-        return "does not have a ':returns:' in its docstring"
-
-def _validate_has_params_in_docstring(method) -> str | None:
-    source = inspect.getsource(method)
-    sig = inspect.signature(method)
-    # Only check if the method has more than one parameter
-    if len(sig.parameters) > 1 and ":param" not in source:
-        return "does not have a ':param' in its docstring"
-
-def _validate_has_no_return_none_in_docstring(method) -> str | None:
-    source = inspect.getsource(method)
-    return_type = method.__annotations__.get('return')
-    if return_type is None and ":returns: None" in source:
-        return "has a ':returns: None' in its docstring which is redundant for None-returning functions"
-
-def _validate_docstring_lines_end_with_dot(method) -> str | None:
-    docstring = inspect.getdoc(method)
-    if docstring is None:
-        return None
-
-    lines = docstring.split('\n')
-    for line in lines:
-        line = line.strip()
-        if line and not any(line.endswith(char) for char in '.:{}[]()",'):
-            return f"docstring line '{line}' does not end with a valid character: . : {{ }} [ ] ( ) , \""
-
 _VALIDATORS = {
     "GET": [
         _validate_api_method_return_type,
         _validate_list_parameters_contain_data,
         _validate_api_method_doesnt_return_list,
-        _validate_has_ellipsis,
-        _validate_has_return_in_docstring,
-        _validate_has_params_in_docstring,
-        _validate_docstring_lines_end_with_dot,
     ],
     "DELETE": [
         _validate_api_delete_method_returns_none,
-        _validate_has_ellipsis,
-        _validate_has_return_in_docstring,
-        _validate_has_params_in_docstring,
-        _validate_has_no_return_none_in_docstring
-    ],
-    "POST": [
-        _validate_has_ellipsis,
-        _validate_has_return_in_docstring,
-        _validate_has_params_in_docstring,
-        _validate_has_no_return_none_in_docstring,
-        _validate_docstring_lines_end_with_dot,
     ],
 }
 

docs/readme.md

@@ -3,10 +3,10 @@
 Here's a collection of comprehensive guides, examples, and resources for building AI applications with Llama Stack. For the complete documentation, visit our [ReadTheDocs page](https://llama-stack.readthedocs.io/en/latest/index.html).
 
 ## Render locally
-
-From the llama-stack root directory, run the following command to render the docs locally:
 ```bash
-uv run --group docs sphinx-autobuild docs/source docs/build/html --write-all
+pip install -r requirements.txt
+cd docs
+python -m sphinx_autobuild source _build
 ```
 You can open up the docs in your browser at http://localhost:8000
 

docs/requirements.txt

@@ -0,0 +1,16 @@
+sphinx==8.1.3
+myst-parser
+linkify
+-e git+https://github.com/pytorch/pytorch_sphinx_theme.git#egg=pytorch_sphinx_theme
+sphinx-rtd-theme>=1.0.0
+sphinx_autobuild
+sphinx-copybutton
+sphinx-design
+sphinx-pdj-theme
+sphinx_rtd_dark_mode
+sphinx-tabs
+sphinxcontrib-openapi
+sphinxcontrib-redoc
+sphinxcontrib-mermaid
+sphinxcontrib-video
+tomli

docs/source/building_applications/rag.md

@@ -51,37 +51,11 @@ chunks = [
         "mime_type": "text/plain",
         "metadata": {
             "document_id": "doc1",
-            "author": "Jane Doe",
         },
     },
 ]
 client.vector_io.insert(vector_db_id=vector_db_id, chunks=chunks)
 ```
-
-#### Using Precomputed Embeddings
-If you decide to precompute embeddings for your documents, you can insert them directly into the vector database by
-including the embedding vectors in the chunk data. This is useful if you have a separate embedding service or if you
-want to customize the ingestion process.
-```python
-chunks_with_embeddings = [
-    {
-        "content": "First chunk of text",
-        "mime_type": "text/plain",
-        "embedding": [0.1, 0.2, 0.3, ...],  # Your precomputed embedding vector
-        "metadata": {"document_id": "doc1", "section": "introduction"},
-    },
-    {
-        "content": "Second chunk of text",
-        "mime_type": "text/plain",
-        "embedding": [0.2, 0.3, 0.4, ...],  # Your precomputed embedding vector
-        "metadata": {"document_id": "doc1", "section": "methodology"},
-    },
-]
-client.vector_io.insert(vector_db_id=vector_db_id, chunks=chunks_with_embeddings)
-```
-When providing precomputed embeddings, ensure the embedding dimension matches the embedding_dimension specified when
-registering the vector database.
-
 ### Retrieval
 You can query the vector database to retrieve documents based on their embeddings.
 ```python
@@ -124,17 +98,6 @@ results = client.tool_runtime.rag_tool.query(
 )
 ```
 
-You can configure how the RAG tool adds metadata to the context if you find it useful for your application. Simply add:
-```python
-# Query documents
-results = client.tool_runtime.rag_tool.query(
-    vector_db_ids=[vector_db_id],
-    content="What do you know about...",
-    query_config={
-        "chunk_template": "Result {index}\nContent: {chunk.content}\nMetadata: {metadata}\n",
-    },
-)
-```
 ### Building RAG-Enhanced Agents
 
 One of the most powerful patterns is combining agents with RAG capabilities. Here's a complete example:
@@ -152,12 +115,6 @@ agent = Agent(
             "name": "builtin::rag/knowledge_search",
             "args": {
                 "vector_db_ids": [vector_db_id],
-                # Defaults
-                "query_config": {
-                    "chunk_size_in_tokens": 512,
-                    "chunk_overlap_in_tokens": 0,
-                    "chunk_template": "Result {index}\nContent: {chunk.content}\nMetadata: {metadata}\n",
-                },
             },
         }
     ],

docs/source/building_applications/tools.md

@@ -43,6 +43,27 @@ The tool requires an API key which can be provided either in the configuration o
 
 > **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
 
@@ -81,7 +102,7 @@ Features:
 - Context retrieval with token limits
 
 
-> **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.
+> **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.
 
 ## Model Context Protocol (MCP) Tools
 
@@ -165,6 +186,34 @@ all_tools = client.tools.list_tools()
 group_tools = client.tools.list_tools(toolgroup_id="search_tools")
 ```
 
+## Simple Example: Using an Agent with the Code-Interpreter Tool
+
+```python
+from llama_stack_client import Agent
+
+# Instantiate the AI agent with the given configuration
+agent = Agent(
+    client,
+    name="code-interpreter",
+    description="A code interpreter agent for executing Python code snippets",
+    instructions="""
+    You are a highly reliable, concise, and precise assistant.
+    Always show the generated code, never generate your own code, and never anticipate results.
+    """,
+    model="meta-llama/Llama-3.2-3B-Instruct",
+    tools=["builtin::code_interpreter"],
+    max_infer_iters=5,
+)
+
+# Start a session
+session_id = agent.create_session("tool_session")
+
+# Send a query to the AI agent for code execution
+response = agent.create_turn(
+    messages=[{"role": "user", "content": "Run this code: print(3 ** 4 - 5 * 2)"}],
+    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

docs/source/conf.py

@@ -22,11 +22,7 @@ from docutils import nodes
 # Read version from pyproject.toml
 with Path(__file__).parent.parent.parent.joinpath("pyproject.toml").open("rb") as f:
     pypi_url = "https://pypi.org/pypi/llama-stack/json"
-    headers = {
-        'User-Agent': 'pip/23.0.1 (python 3.11)',  # Mimic pip's user agent
-        'Accept': 'application/json'
-    }
-    version_tag = json.loads(requests.get(pypi_url, headers=headers).text)["info"]["version"]
+    version_tag = json.loads(requests.get(pypi_url).text)["info"]["version"]
     print(f"{version_tag=}")
 
     # generate the full link including text and url here
@@ -57,6 +53,14 @@ myst_enable_extensions = ["colon_fence"]
 
 html_theme = "sphinx_rtd_theme"
 html_use_relative_paths = True
+
+# html_theme = "sphinx_pdj_theme"
+# html_theme_path = [sphinx_pdj_theme.get_html_theme_path()]
+
+# html_theme = "pytorch_sphinx_theme"
+# html_theme_path = [pytorch_sphinx_theme.get_html_theme_path()]
+
+
 templates_path = ["_templates"]
 exclude_patterns = ["_build", "Thumbs.db", ".DS_Store"]
 
@@ -106,8 +110,6 @@ html_theme_options = {
     "canonical_url": "https://github.com/meta-llama/llama-stack",
     "collapse_navigation": False,
     # "style_nav_header_background": "#c3c9d4",
-    'display_version': True,
-    'version_selector': True,
 }
 
 default_dark_mode = False

docs/source/contributing/new_api_provider.md

@@ -6,7 +6,7 @@ This guide will walk you through the process of adding a new API provider to Lla
 - Begin by reviewing the [core concepts](../concepts/index.md) of Llama Stack and choose the API your provider belongs to (Inference, Safety, VectorIO, etc.)
 - Determine the provider type ({repopath}`Remote::llama_stack/providers/remote` or {repopath}`Inline::llama_stack/providers/inline`). Remote providers make requests to external services, while inline providers execute implementation locally.
 - Add your provider to the appropriate {repopath}`Registry::llama_stack/providers/registry/`. Specify pip dependencies necessary.
-- Update any distribution {repopath}`Templates::llama_stack/templates/` `build.yaml` and `run.yaml` files if they should include your provider by default. Run {repopath}`./scripts/distro_codegen.py` if necessary. Note that `distro_codegen.py` will fail if the new provider causes any distribution template to attempt to import provider-specific dependencies. This usually means the distribution's `get_distribution_template()` code path should only import any necessary Config or model alias definitions from each provider and not the provider's actual implementation.
+- Update any distribution {repopath}`Templates::llama_stack/templates/` build.yaml and run.yaml files if they should include your provider by default. Run {repopath}`./scripts/distro_codegen.py` if necessary. Note that `distro_codegen.py` will fail if the new provider causes any distribution template to attempt to import provider-specific dependencies. This usually means the distribution's `get_distribution_template()` code path should only import any necessary Config or model alias definitions from each provider and not the provider's actual implementation.
 
 
 Here are some example PRs to help you get started:
@@ -33,7 +33,6 @@ Note that each provider's `sample_run_config()` method (in the configuration cla
 
 Unit tests are located in {repopath}`tests/unit`. Provider-specific unit tests are located in {repopath}`tests/unit/providers`. These tests are all run automatically as part of the CI process.
 
-Consult {repopath}`tests/unit/README.md` for more details on how to run the tests manually.
 
 ### 3. Additional end-to-end testing
 

docs/source/distributions/building_distro.md

@@ -178,7 +178,7 @@ image_name: ollama
 image_type: conda
 
 # If some providers are external, you can specify the path to the implementation
-external_providers_dir: ~/.llama/providers.d
+external_providers_dir: /etc/llama-stack/providers.d
 ```
 
 ```
@@ -206,7 +206,7 @@ distribution_spec:
 image_type: container
 image_name: ci-test
 # Path to external provider implementations
-external_providers_dir: ~/.llama/providers.d
+external_providers_dir: /etc/llama-stack/providers.d
 ```
 
 Here's an example for a custom Ollama provider:
@@ -271,7 +271,7 @@ Now, let's start the Llama Stack Distribution Server. You will need the YAML con
 
 ```
 llama stack run -h
-usage: llama stack run [-h] [--port PORT] [--image-name IMAGE_NAME] [--env KEY=VALUE] [--tls-keyfile TLS_KEYFILE] [--tls-certfile TLS_CERTFILE]
+usage: llama stack run [-h] [--port PORT] [--image-name IMAGE_NAME] [--disable-ipv6] [--env KEY=VALUE] [--tls-keyfile TLS_KEYFILE] [--tls-certfile TLS_CERTFILE]
                        [--image-type {conda,container,venv}]
                        config
 
@@ -285,6 +285,7 @@ options:
   --port PORT           Port to run the server on. It can also be passed via the env var LLAMA_STACK_PORT. (default: 8321)
   --image-name IMAGE_NAME
                         Name of the image to run. Defaults to the current environment (default: None)
+  --disable-ipv6        Disable IPv6 support (default: False)
   --env KEY=VALUE       Environment variables to pass to the server in KEY=VALUE format. Can be specified multiple times. (default: [])
   --tls-keyfile TLS_KEYFILE
                         Path to TLS key file for HTTPS (default: None)
@@ -338,48 +339,6 @@ INFO:     Application startup complete.
 INFO:     Uvicorn running on http://['::', '0.0.0.0']:8321 (Press CTRL+C to quit)
 INFO:     2401:db00:35c:2d2b:face:0:c9:0:54678 - "GET /models/list HTTP/1.1" 200 OK
 ```
-### Listing Distributions
-Using the list command, you can view all existing Llama Stack distributions, including stacks built from templates, from scratch, or using custom configuration files.
-
-```
-llama stack list -h
-usage: llama stack list [-h]
-
-list the build stacks
-
-options:
-  -h, --help  show this help message and exit
-```
-
-Example Usage
-
-```
-llama stack list
-```
-
-### Removing a Distribution
-Use the remove command to delete a distribution you've previously built.
-
-```
-llama stack rm -h
-usage: llama stack rm [-h] [--all] [name]
-
-Remove the build stack
-
-positional arguments:
-  name        Name of the stack to delete (default: None)
-
-options:
-  -h, --help  show this help message and exit
-  --all, -a   Delete all stacks (use with caution) (default: False)
-```
-
-Example
-```
-llama stack rm llamastack-test
-```
-
-To keep your environment organized and avoid clutter, consider using `llama stack list` to review old or unused distributions and `llama stack rm <name>` to delete them when they’re no longer needed.
 
 ### Troubleshooting
 

docs/source/distributions/configuration.md

@@ -53,13 +53,6 @@ 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:
@@ -109,227 +102,6 @@ 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
-```
-
-### 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:
-
-#### OAuth 2.0/OpenID Connect Provider with Kubernetes
-
-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
-```
-
-Make sure the `kube-apiserver` runs with `--anonymous-auth=true` to allow unauthenticated requests
-and that the correct RoleBinding is created to allow the service account to access the necessary
-resources. If that is not the case, you can create a RoleBinding for the service account to access
-the necessary resources:
-
-```yaml
-# allow-anonymous-openid.yaml
-apiVersion: rbac.authorization.k8s.io/v1
-kind: ClusterRole
-metadata:
-  name: allow-anonymous-openid
-rules:
-- nonResourceURLs: ["/openid/v1/jwks"]
-  verbs: ["get"]
----
-apiVersion: rbac.authorization.k8s.io/v1
-kind: ClusterRoleBinding
-metadata:
-  name: allow-anonymous-openid
-roleRef:
-  apiGroup: rbac.authorization.k8s.io
-  kind: ClusterRole
-  name: allow-anonymous-openid
-subjects:
-- kind: User
-  name: system:anonymous
-  apiGroup: rbac.authorization.k8s.io
-```
-
-And then apply the configuration:
-```bash
-kubectl apply -f allow-anonymous-openid.yaml
-```
-
-Validates tokens against the Kubernetes API server through the OIDC provider:
-```yaml
-server:
-  auth:
-    provider_type: "oauth2_token"
-    config:
-      jwks:
-        uri: "https://kubernetes.default.svc"
-        key_recheck_period: 3600
-      tls_cafile: "/path/to/ca.crt"
-      issuer: "https://kubernetes.default.svc"
-      audience: "https://kubernetes.default.svc"
-```
-
-To find your cluster's audience, run:
-```bash
-kubectl create token default --duration=1h | cut -d. -f2 | base64 -d | jq .aud
-```
-
-For the issuer, you can use the OIDC provider's URL:
-```bash
-kubectl get --raw /.well-known/openid-configuration| jq .issuer
-```
-
-For the tls_cafile, you can use the CA certificate of the OIDC provider:
-```bash
-kubectl config view --minify -o jsonpath='{.clusters[0].cluster.certificate-authority}'
-```
-
-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.
-
-### Quota Configuration
-
-The `quota` section allows you to enable server-side request throttling for both
-authenticated and anonymous clients. This is useful for preventing abuse, enforcing
-fairness across tenants, and controlling infrastructure costs without requiring
-client-side rate limiting or external proxies.
-
-Quotas are disabled by default. When enabled, each client is tracked using either:
-
-* Their authenticated `client_id` (derived from the Bearer token), or
-* Their IP address (fallback for anonymous requests)
-
-Quota state is stored in a SQLite-backed key-value store, and rate limits are applied
-within a configurable time window (currently only `day` is supported).
-
-#### Example
-
-```yaml
-server:
-  quota:
-    kvstore:
-      type: sqlite
-      db_path: ./quotas.db
-    anonymous_max_requests: 100
-    authenticated_max_requests: 1000
-    period: day
-```
-
-#### Configuration Options
-
-| Field                        | Description                                                                |
-| ---------------------------- | -------------------------------------------------------------------------- |
-| `kvstore`                    | Required. Backend storage config for tracking request counts.              |
-| `kvstore.type`               | Must be `"sqlite"` for now. Other backends may be supported in the future. |
-| `kvstore.db_path`            | File path to the SQLite database.                                          |
-| `anonymous_max_requests`     | Max requests per period for unauthenticated clients.                       |
-| `authenticated_max_requests` | Max requests per period for authenticated clients.                         |
-| `period`                     | Time window for quota enforcement. Only `"day"` is supported.              |
-
-> Note: if `authenticated_max_requests` is set but no authentication provider is
-configured, the server will fall back to applying `anonymous_max_requests` to all
-clients.
-
-#### Example with Authentication Enabled
-
-```yaml
-server:
-  port: 8321
-  auth:
-    provider_type: custom
-    config:
-      endpoint: https://auth.example.com/validate
-  quota:
-    kvstore:
-      type: sqlite
-      db_path: ./quotas.db
-    anonymous_max_requests: 100
-    authenticated_max_requests: 1000
-    period: day
-```
-
-If a client exceeds their limit, the server responds with:
-
-```http
-HTTP/1.1 429 Too Many Requests
-Content-Type: application/json
-
-{
-  "error": {
-    "message": "Quota exceeded"
-  }
-}
-```
-
 ## Extending to handle Safety
 
 Configuring Safety can be a little involved so it is instructive to go through an example.

docs/source/distributions/kubernetes_deployment.md

@@ -172,7 +172,7 @@ spec:
       - name: llama-stack
         image: localhost/llama-stack-run-k8s:latest
         imagePullPolicy: IfNotPresent
-        command: ["python", "-m", "llama_stack.distribution.server.server", "--config", "/app/config.yaml"]
+        command: ["python", "-m", "llama_stack.distribution.server.server", "--yaml-config", "/app/config.yaml"]
         ports:
           - containerPort: 5000
         volumeMounts:

docs/source/distributions/remote_hosted_distro/watsonx.md

@@ -18,11 +18,11 @@ The `llamastack/distribution-watsonx` distribution consists of the following pro
 | agents | `inline::meta-reference` |
 | datasetio | `remote::huggingface`, `inline::localfs` |
 | eval | `inline::meta-reference` |
-| inference | `remote::watsonx`, `inline::sentence-transformers` |
+| 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` |
+| tool_runtime | `remote::brave-search`, `remote::tavily-search`, `inline::code-interpreter`, `inline::rag-runtime`, `remote::model-context-protocol` |
 | vector_io | `inline::faiss` |
 
 
@@ -70,7 +70,7 @@ docker run \
   -p $LLAMA_STACK_PORT:$LLAMA_STACK_PORT \
   -v ./run.yaml:/root/my-run.yaml \
   llamastack/distribution-watsonx \
-  --config /root/my-run.yaml \
+  --yaml-config /root/my-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::rag-runtime`, `remote::model-context-protocol` |
+| 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` |
 
 

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::rag-runtime` |
+| tool_runtime | `remote::brave-search`, `remote::tavily-search`, `inline::code-interpreter`, `inline::rag-runtime` |
 | vector_io | `inline::faiss`, `remote::chromadb`, `remote::pgvector` |
 
 
@@ -52,7 +52,7 @@ docker run \
   -p $LLAMA_STACK_PORT:$LLAMA_STACK_PORT \
   -v ./run.yaml:/root/my-run.yaml \
   llamastack/distribution-cerebras \
-  --config /root/my-run.yaml \
+  --yaml-config /root/my-run.yaml \
   --port $LLAMA_STACK_PORT \
   --env CEREBRAS_API_KEY=$CEREBRAS_API_KEY
 ```

docs/source/distributions/self_hosted_distro/dell.md

@@ -23,7 +23,7 @@ The `llamastack/distribution-dell` 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::rag-runtime` |
+| tool_runtime | `remote::brave-search`, `remote::tavily-search`, `inline::code-interpreter`, `inline::rag-runtime` |
 | vector_io | `inline::faiss`, `remote::chromadb`, `remote::pgvector` |
 
 
@@ -155,7 +155,7 @@ docker run \
   -v $HOME/.llama:/root/.llama \
   -v ./llama_stack/templates/tgi/run-with-safety.yaml:/root/my-run.yaml \
   llamastack/distribution-dell \
-  --config /root/my-run.yaml \
+  --yaml-config /root/my-run.yaml \
   --port $LLAMA_STACK_PORT \
   --env INFERENCE_MODEL=$INFERENCE_MODEL \
   --env DEH_URL=$DEH_URL \

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::rag-runtime`, `remote::model-context-protocol` |
+| tool_runtime | `remote::brave-search`, `remote::tavily-search`, `remote::wolfram-alpha`, `inline::code-interpreter`, `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::rag-runtime` |
+| tool_runtime | `remote::brave-search`, `remote::tavily-search`, `inline::code-interpreter`, `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::rag-runtime`, `remote::model-context-protocol` |
+| 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` |
 
 

docs/source/distributions/self_hosted_distro/nvidia.md

@@ -6,7 +6,7 @@ The `llamastack/distribution-nvidia` distribution consists of the following prov
 | API | Provider(s) |
 |-----|-------------|
 | agents | `inline::meta-reference` |
-| datasetio | `inline::localfs`, `remote::nvidia` |
+| datasetio | `inline::localfs` |
 | eval | `remote::nvidia` |
 | inference | `remote::nvidia` |
 | post_training | `remote::nvidia` |
@@ -143,7 +143,7 @@ docker run \
   -p $LLAMA_STACK_PORT:$LLAMA_STACK_PORT \
   -v ./run.yaml:/root/my-run.yaml \
   llamastack/distribution-nvidia \
-  --config /root/my-run.yaml \
+  --yaml-config /root/my-run.yaml \
   --port $LLAMA_STACK_PORT \
   --env NVIDIA_API_KEY=$NVIDIA_API_KEY
 ```

docs/source/distributions/self_hosted_distro/ollama.md

@@ -19,11 +19,10 @@ The `llamastack/distribution-ollama` distribution consists of the following prov
 | datasetio | `remote::huggingface`, `inline::localfs` |
 | eval | `inline::meta-reference` |
 | inference | `remote::ollama` |
-| post_training | `inline::huggingface` |
 | 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`, `remote::wolfram-alpha` |
+| tool_runtime | `remote::brave-search`, `remote::tavily-search`, `inline::code-interpreter`, `inline::rag-runtime`, `remote::model-context-protocol`, `remote::wolfram-alpha` |
 | vector_io | `inline::faiss`, `remote::chromadb`, `remote::pgvector` |
 
 
@@ -98,7 +97,7 @@ docker run \
   -v ~/.llama:/root/.llama \
   -v ./llama_stack/templates/ollama/run-with-safety.yaml:/root/my-run.yaml \
   llamastack/distribution-ollama \
-  --config /root/my-run.yaml \
+  --yaml-config /root/my-run.yaml \
   --port $LLAMA_STACK_PORT \
   --env INFERENCE_MODEL=$INFERENCE_MODEL \
   --env SAFETY_MODEL=$SAFETY_MODEL \

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::rag-runtime`, `remote::model-context-protocol` |
+| tool_runtime | `remote::brave-search`, `remote::tavily-search`, `remote::wolfram-alpha`, `inline::code-interpreter`, `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::rag-runtime`, `remote::model-context-protocol`, `remote::wolfram-alpha` |
+| tool_runtime | `remote::brave-search`, `remote::tavily-search`, `inline::code-interpreter`, `inline::rag-runtime`, `remote::model-context-protocol`, `remote::wolfram-alpha` |
 | vector_io | `inline::faiss`, `remote::chromadb`, `remote::pgvector` |
 
 
@@ -233,7 +233,7 @@ docker run \
   -p $LLAMA_STACK_PORT:$LLAMA_STACK_PORT \
   -v ./llama_stack/templates/remote-vllm/run.yaml:/root/my-run.yaml \
   llamastack/distribution-remote-vllm \
-  --config /root/my-run.yaml \
+  --yaml-config /root/my-run.yaml \
   --port $LLAMA_STACK_PORT \
   --env INFERENCE_MODEL=$INFERENCE_MODEL \
   --env VLLM_URL=http://host.docker.internal:$INFERENCE_PORT/v1
@@ -255,7 +255,7 @@ docker run \
   -v ~/.llama:/root/.llama \
   -v ./llama_stack/templates/remote-vllm/run-with-safety.yaml:/root/my-run.yaml \
   llamastack/distribution-remote-vllm \
-  --config /root/my-run.yaml \
+  --yaml-config /root/my-run.yaml \
   --port $LLAMA_STACK_PORT \
   --env INFERENCE_MODEL=$INFERENCE_MODEL \
   --env VLLM_URL=http://host.docker.internal:$INFERENCE_PORT/v1 \

docs/source/distributions/self_hosted_distro/sambanova.md

@@ -16,10 +16,10 @@ The `llamastack/distribution-sambanova` distribution consists of the following p
 | API | Provider(s) |
 |-----|-------------|
 | agents | `inline::meta-reference` |
-| inference | `remote::sambanova`, `inline::sentence-transformers` |
-| safety | `remote::sambanova` |
+| inference | `remote::sambanova` |
+| safety | `inline::llama-guard` |
 | telemetry | `inline::meta-reference` |
-| tool_runtime | `remote::brave-search`, `remote::tavily-search`, `inline::rag-runtime`, `remote::model-context-protocol`, `remote::wolfram-alpha` |
+| tool_runtime | `remote::brave-search`, `remote::tavily-search`, `inline::code-interpreter`, `inline::rag-runtime` |
 | vector_io | `inline::faiss`, `remote::chromadb`, `remote::pgvector` |
 
 
@@ -28,64 +28,53 @@ The `llamastack/distribution-sambanova` distribution consists of the following p
 The following environment variables can be configured:
 
 - `LLAMASTACK_PORT`: Port for the Llama Stack distribution server (default: `8321`)
-- `SAMBANOVA_API_KEY`: SambaNova API Key (default: ``)
+- `SAMBANOVA_API_KEY`: SambaNova.AI API Key (default: ``)
 
 ### Models
 
 The following models are available by default:
 
-- `sambanova/Meta-Llama-3.1-8B-Instruct (aliases: meta-llama/Llama-3.1-8B-Instruct)`
-- `sambanova/Meta-Llama-3.1-405B-Instruct (aliases: meta-llama/Llama-3.1-405B-Instruct-FP8)`
-- `sambanova/Meta-Llama-3.2-1B-Instruct (aliases: meta-llama/Llama-3.2-1B-Instruct)`
-- `sambanova/Meta-Llama-3.2-3B-Instruct (aliases: meta-llama/Llama-3.2-3B-Instruct)`
-- `sambanova/Meta-Llama-3.3-70B-Instruct (aliases: meta-llama/Llama-3.3-70B-Instruct)`
-- `sambanova/Llama-3.2-11B-Vision-Instruct (aliases: meta-llama/Llama-3.2-11B-Vision-Instruct)`
-- `sambanova/Llama-3.2-90B-Vision-Instruct (aliases: meta-llama/Llama-3.2-90B-Vision-Instruct)`
-- `sambanova/Llama-4-Scout-17B-16E-Instruct (aliases: meta-llama/Llama-4-Scout-17B-16E-Instruct)`
-- `sambanova/Llama-4-Maverick-17B-128E-Instruct (aliases: meta-llama/Llama-4-Maverick-17B-128E-Instruct)`
-- `sambanova/Meta-Llama-Guard-3-8B (aliases: meta-llama/Llama-Guard-3-8B)`
+- `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-3.3-70B-Instruct (aliases: meta-llama/Llama-3.3-70B-Instruct)`
+- `Llama-3.2-11B-Vision-Instruct (aliases: meta-llama/Llama-3.2-11B-Vision-Instruct)`
+- `Llama-3.2-90B-Vision-Instruct (aliases: meta-llama/Llama-3.2-90B-Vision-Instruct)`
+- `Meta-Llama-Guard-3-8B (aliases: meta-llama/Llama-Guard-3-8B)`
+- `Llama-4-Scout-17B-16E-Instruct (aliases: meta-llama/Llama-4-Scout-17B-16E-Instruct)`
 
 
 ### Prerequisite: API Keys
 
-Make sure you have access to a SambaNova API Key. You can get one by visiting [SambaNova.ai](http://cloud.sambanova.ai?utm_source=llamastack&utm_medium=external&utm_campaign=cloud_signup).
+Make sure you have access to a SambaNova API Key. You can get one by visiting [SambaNova.ai](https://sambanova.ai/).
 
 
 ## Running Llama Stack with SambaNova
 
 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
-llama stack build --template sambanova --image-type container
 docker run \
   -it \
+  --pull always \
   -p $LLAMA_STACK_PORT:$LLAMA_STACK_PORT \
-  -v ~/.llama:/root/.llama \
-  distribution-sambanova \
+  llamastack/distribution-sambanova \
   --port $LLAMA_STACK_PORT \
   --env SAMBANOVA_API_KEY=$SAMBANOVA_API_KEY
 ```
 
-
-### Via Venv
-
-```bash
-llama stack build --template sambanova --image-type venv
-llama stack run --image-type venv ~/.llama/distributions/sambanova/sambanova-run.yaml \
-  --port $LLAMA_STACK_PORT \
-  --env SAMBANOVA_API_KEY=$SAMBANOVA_API_KEY
-```
-
-
 ### Via Conda
 
 ```bash
 llama stack build --template sambanova --image-type conda
-llama stack run --image-type conda ~/.llama/distributions/sambanova/sambanova-run.yaml \
+llama stack run ./run.yaml \
   --port $LLAMA_STACK_PORT \
   --env SAMBANOVA_API_KEY=$SAMBANOVA_API_KEY
 ```

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::rag-runtime`, `remote::model-context-protocol` |
+| 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` |
 
 
@@ -117,7 +117,7 @@ docker run \
   -v ~/.llama:/root/.llama \
   -v ./llama_stack/templates/tgi/run-with-safety.yaml:/root/my-run.yaml \
   llamastack/distribution-tgi \
-  --config /root/my-run.yaml \
+  --yaml-config /root/my-run.yaml \
   --port $LLAMA_STACK_PORT \
   --env INFERENCE_MODEL=$INFERENCE_MODEL \
   --env TGI_URL=http://host.docker.internal:$INFERENCE_PORT \

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::rag-runtime`, `remote::model-context-protocol`, `remote::wolfram-alpha` |
+| tool_runtime | `remote::brave-search`, `remote::tavily-search`, `inline::code-interpreter`, `inline::rag-runtime`, `remote::model-context-protocol`, `remote::wolfram-alpha` |
 | vector_io | `inline::faiss`, `remote::chromadb`, `remote::pgvector` |
 
 

docs/source/getting_started/detailed_tutorial.md

@@ -42,7 +42,7 @@ powershell -ExecutionPolicy ByPass -c "irm https://astral.sh/uv/install.ps1 | ie
 Setup your virtual environment.
 
 ```bash
-uv sync --python 3.10
+uv venv --python 3.10
 source .venv/bin/activate
 ```
 ## Step 2:  Run Llama Stack
@@ -445,6 +445,7 @@ from llama_stack_client import LlamaStackClient
 from llama_stack_client import Agent, AgentEventLogger
 from llama_stack_client.types import Document
 import uuid
+from termcolor import cprint
 
 client = LlamaStackClient(base_url="http://localhost:8321")
 
@@ -462,6 +463,7 @@ urls = [
     "memory_optimizations.rst",
     "chat.rst",
     "llama3.rst",
+    "datasets.rst",
     "qat_finetune.rst",
     "lora_finetune.rst",
 ]

docs/source/providers/external.md

@@ -10,7 +10,7 @@ Llama Stack supports external providers that live outside of the main codebase.
 To enable external providers, you need to configure the `external_providers_dir` in your Llama Stack configuration. This directory should contain your external provider specifications:
 
 ```yaml
-external_providers_dir: ~/.llama/providers.d/
+external_providers_dir: /etc/llama-stack/providers.d/
 ```
 
 ## Directory Structure
@@ -53,9 +53,7 @@ Here's a list of known external providers that you can use with Llama Stack:
 | Name | Description | API | Type | Repository |
 |------|-------------|-----|------|------------|
 | 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 | Inline **and** 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) |
-| TrustyAI LM-Eval | Evaluate models with TrustyAI LM-Eval | Eval | Remote | [llama-stack-provider-lmeval](https://github.com/trustyai-explainability/llama-stack-provider-lmeval) |
 
 ### Remote Provider Specification
 
@@ -182,7 +180,7 @@ dependencies = ["llama-stack", "pydantic", "ollama", "aiohttp"]
 3. Create the provider specification:
 
 ```yaml
-# ~/.llama/providers.d/remote/inference/custom_ollama.yaml
+# /etc/llama-stack/providers.d/remote/inference/custom_ollama.yaml
 adapter:
   adapter_type: custom_ollama
   pip_packages: ["ollama", "aiohttp"]
@@ -201,7 +199,7 @@ uv pip install -e .
 5. Configure Llama Stack to use external providers:
 
 ```yaml
-external_providers_dir: ~/.llama/providers.d/
+external_providers_dir: /etc/llama-stack/providers.d/
 ```
 
 The provider will now be available in Llama Stack with the type `remote::custom_ollama`.

docs/source/providers/index.md

@@ -30,18 +30,6 @@ Runs inference with an LLM.
 ## Post Training
 Fine-tunes a model.
 
-#### Post Training Providers
-The following providers are available for Post Training:
-
-```{toctree}
-:maxdepth: 1
-
-external
-post_training/huggingface
-post_training/torchtune
-post_training/nvidia_nemo
-```
-
 ## Safety
 Applies safety policies to the output at a Systems (not only model) level.
 

docs/source/providers/post_training/huggingface.md

@@ -1,122 +0,0 @@
----
-orphan: true
----
-# HuggingFace SFTTrainer
-
-[HuggingFace SFTTrainer](https://huggingface.co/docs/trl/en/sft_trainer) is an inline post training provider for Llama Stack. It allows you to run supervised fine tuning on a variety of models using many datasets
-
-## Features
-
-- Simple access through the post_training API
-- Fully integrated with Llama Stack
-- GPU support, CPU support, and MPS support (MacOS Metal Performance Shaders)
-
-## Usage
-
-To use the HF SFTTrainer in your Llama Stack project, follow these steps:
-
-1. Configure your Llama Stack project to use this provider.
-2. Kick off a SFT job using the Llama Stack post_training API.
-
-## Setup
-
-You can access the HuggingFace trainer via the `ollama` distribution:
-
-```bash
-llama stack build --template ollama --image-type venv
-llama stack run --image-type venv ~/.llama/distributions/ollama/ollama-run.yaml
-```
-
-## Run Training
-
-You can access the provider and the `supervised_fine_tune` method via the post_training API:
-
-```python
-import time
-import uuid
-
-
-from llama_stack_client.types import (
-    post_training_supervised_fine_tune_params,
-    algorithm_config_param,
-)
-
-
-def create_http_client():
-    from llama_stack_client import LlamaStackClient
-
-    return LlamaStackClient(base_url="http://localhost:8321")
-
-
-client = create_http_client()
-
-# Example Dataset
-client.datasets.register(
-    purpose="post-training/messages",
-    source={
-        "type": "uri",
-        "uri": "huggingface://datasets/llamastack/simpleqa?split=train",
-    },
-    dataset_id="simpleqa",
-)
-
-training_config = post_training_supervised_fine_tune_params.TrainingConfig(
-    data_config=post_training_supervised_fine_tune_params.TrainingConfigDataConfig(
-        batch_size=32,
-        data_format="instruct",
-        dataset_id="simpleqa",
-        shuffle=True,
-    ),
-    gradient_accumulation_steps=1,
-    max_steps_per_epoch=0,
-    max_validation_steps=1,
-    n_epochs=4,
-)
-
-algorithm_config = algorithm_config_param.LoraFinetuningConfig(  # this config is also currently mandatory but should not be
-    alpha=1,
-    apply_lora_to_mlp=True,
-    apply_lora_to_output=False,
-    lora_attn_modules=["q_proj"],
-    rank=1,
-    type="LoRA",
-)
-
-job_uuid = f"test-job{uuid.uuid4()}"
-
-# Example Model
-training_model = "ibm-granite/granite-3.3-8b-instruct"
-
-start_time = time.time()
-response = client.post_training.supervised_fine_tune(
-    job_uuid=job_uuid,
-    logger_config={},
-    model=training_model,
-    hyperparam_search_config={},
-    training_config=training_config,
-    algorithm_config=algorithm_config,
-    checkpoint_dir="output",
-)
-print("Job: ", job_uuid)
-
-
-# Wait for the job to complete!
-while True:
-    status = client.post_training.job.status(job_uuid=job_uuid)
-    if not status:
-        print("Job not found")
-        break
-
-    print(status)
-    if status.status == "completed":
-        break
-
-    print("Waiting for job to complete...")
-    time.sleep(5)
-
-end_time = time.time()
-print("Job completed in", end_time - start_time, "seconds!")
-
-print("Artifacts:")
-print(client.post_training.job.artifacts(job_uuid=job_uuid))
-```

docs/source/providers/post_training/nvidia_nemo.md

@@ -1,163 +0,0 @@
----
-orphan: true
----
-# NVIDIA NEMO
-
-[NVIDIA NEMO](https://developer.nvidia.com/nemo-framework) is a remote post training provider for Llama Stack. It provides enterprise-grade fine-tuning capabilities through NVIDIA's NeMo Customizer service.
-
-## Features
-
-- Enterprise-grade fine-tuning capabilities
-- Support for LoRA and SFT fine-tuning
-- Integration with NVIDIA's NeMo Customizer service
-- Support for various NVIDIA-optimized models
-- Efficient training with NVIDIA hardware acceleration
-
-## Usage
-
-To use NVIDIA NEMO in your Llama Stack project, follow these steps:
-
-1. Configure your Llama Stack project to use this provider.
-2. Set up your NVIDIA API credentials.
-3. Kick off a fine-tuning job using the Llama Stack post_training API.
-
-## Setup
-
-You'll need to set the following environment variables:
-
-```bash
-export NVIDIA_API_KEY="your-api-key"
-export NVIDIA_DATASET_NAMESPACE="default"
-export NVIDIA_CUSTOMIZER_URL="your-customizer-url"
-export NVIDIA_PROJECT_ID="your-project-id"
-export NVIDIA_OUTPUT_MODEL_DIR="your-output-model-dir"
-```
-
-## Run Training
-
-You can access the provider and the `supervised_fine_tune` method via the post_training API:
-
-```python
-import time
-import uuid
-
-from llama_stack_client.types import (
-    post_training_supervised_fine_tune_params,
-    algorithm_config_param,
-)
-
-
-def create_http_client():
-    from llama_stack_client import LlamaStackClient
-
-    return LlamaStackClient(base_url="http://localhost:8321")
-
-
-client = create_http_client()
-
-# Example Dataset
-client.datasets.register(
-    purpose="post-training/messages",
-    source={
-        "type": "uri",
-        "uri": "huggingface://datasets/llamastack/simpleqa?split=train",
-    },
-    dataset_id="simpleqa",
-)
-
-training_config = post_training_supervised_fine_tune_params.TrainingConfig(
-    data_config=post_training_supervised_fine_tune_params.TrainingConfigDataConfig(
-        batch_size=8,  # Default batch size for NEMO
-        data_format="instruct",
-        dataset_id="simpleqa",
-        shuffle=True,
-    ),
-    n_epochs=50,  # Default epochs for NEMO
-    optimizer_config=post_training_supervised_fine_tune_params.TrainingConfigOptimizerConfig(
-        lr=0.0001,  # Default learning rate
-        weight_decay=0.01,  # NEMO-specific parameter
-    ),
-    # NEMO-specific parameters
-    log_every_n_steps=None,
-    val_check_interval=0.25,
-    sequence_packing_enabled=False,
-    hidden_dropout=None,
-    attention_dropout=None,
-    ffn_dropout=None,
-)
-
-algorithm_config = algorithm_config_param.LoraFinetuningConfig(
-    alpha=16,  # Default alpha for NEMO
-    type="LoRA",
-)
-
-job_uuid = f"test-job{uuid.uuid4()}"
-
-# Example Model - must be a supported NEMO model
-training_model = "meta/llama-3.1-8b-instruct"
-
-start_time = time.time()
-response = client.post_training.supervised_fine_tune(
-    job_uuid=job_uuid,
-    logger_config={},
-    model=training_model,
-    hyperparam_search_config={},
-    training_config=training_config,
-    algorithm_config=algorithm_config,
-    checkpoint_dir="output",
-)
-print("Job: ", job_uuid)
-
-# Wait for the job to complete!
-while True:
-    status = client.post_training.job.status(job_uuid=job_uuid)
-    if not status:
-        print("Job not found")
-        break
-
-    print(status)
-    if status.status == "completed":
-        break
-
-    print("Waiting for job to complete...")
-    time.sleep(5)
-
-end_time = time.time()
-print("Job completed in", end_time - start_time, "seconds!")
-
-print("Artifacts:")
-print(client.post_training.job.artifacts(job_uuid=job_uuid))
-```
-
-## Supported Models
-
-Currently supports the following models:
-- meta/llama-3.1-8b-instruct
-- meta/llama-3.2-1b-instruct
-
-## Supported Parameters
-
-### TrainingConfig
-- n_epochs (default: 50)
-- data_config
-- optimizer_config
-- log_every_n_steps
-- val_check_interval (default: 0.25)
-- sequence_packing_enabled (default: False)
-- hidden_dropout (0.0-1.0)
-- attention_dropout (0.0-1.0)
-- ffn_dropout (0.0-1.0)
-
-### DataConfig
-- dataset_id
-- batch_size (default: 8)
-
-### OptimizerConfig
-- lr (default: 0.0001)
-- weight_decay (default: 0.01)
-
-### LoRA Config
-- alpha (default: 16)
-- type (must be "LoRA")
-
-Note: Some parameters from the standard Llama Stack API are not supported and will be ignored with a warning.

docs/source/providers/post_training/torchtune.md

@@ -1,125 +0,0 @@
----
-orphan: true
----
-# TorchTune
-
-[TorchTune](https://github.com/pytorch/torchtune) is an inline post training provider for Llama Stack. It provides a simple and efficient way to fine-tune language models using PyTorch.
-
-## Features
-
-- Simple access through the post_training API
-- Fully integrated with Llama Stack
-- GPU support and single device capabilities.
-- Support for LoRA
-
-## Usage
-
-To use TorchTune in your Llama Stack project, follow these steps:
-
-1. Configure your Llama Stack project to use this provider.
-2. Kick off a fine-tuning job using the Llama Stack post_training API.
-
-## Setup
-
-You can access the TorchTune trainer by writing your own yaml pointing to the provider:
-
-```yaml
-post_training:
-  - provider_id: torchtune
-    provider_type: inline::torchtune
-    config: {}
-```
-
-you can then build and run your own stack with this provider.
-
-## Run Training
-
-You can access the provider and the `supervised_fine_tune` method via the post_training API:
-
-```python
-import time
-import uuid
-
-from llama_stack_client.types import (
-    post_training_supervised_fine_tune_params,
-    algorithm_config_param,
-)
-
-
-def create_http_client():
-    from llama_stack_client import LlamaStackClient
-
-    return LlamaStackClient(base_url="http://localhost:8321")
-
-
-client = create_http_client()
-
-# Example Dataset
-client.datasets.register(
-    purpose="post-training/messages",
-    source={
-        "type": "uri",
-        "uri": "huggingface://datasets/llamastack/simpleqa?split=train",
-    },
-    dataset_id="simpleqa",
-)
-
-training_config = post_training_supervised_fine_tune_params.TrainingConfig(
-    data_config=post_training_supervised_fine_tune_params.TrainingConfigDataConfig(
-        batch_size=32,
-        data_format="instruct",
-        dataset_id="simpleqa",
-        shuffle=True,
-    ),
-    gradient_accumulation_steps=1,
-    max_steps_per_epoch=0,
-    max_validation_steps=1,
-    n_epochs=4,
-)
-
-algorithm_config = algorithm_config_param.LoraFinetuningConfig(
-    alpha=1,
-    apply_lora_to_mlp=True,
-    apply_lora_to_output=False,
-    lora_attn_modules=["q_proj"],
-    rank=1,
-    type="LoRA",
-)
-
-job_uuid = f"test-job{uuid.uuid4()}"
-
-# Example Model
-training_model = "meta-llama/Llama-2-7b-hf"
-
-start_time = time.time()
-response = client.post_training.supervised_fine_tune(
-    job_uuid=job_uuid,
-    logger_config={},
-    model=training_model,
-    hyperparam_search_config={},
-    training_config=training_config,
-    algorithm_config=algorithm_config,
-    checkpoint_dir="output",
-)
-print("Job: ", job_uuid)
-
-# Wait for the job to complete!
-while True:
-    status = client.post_training.job.status(job_uuid=job_uuid)
-    if not status:
-        print("Job not found")
-        break
-
-    print(status)
-    if status.status == "completed":
-        break
-
-    print("Waiting for job to complete...")
-    time.sleep(5)
-
-end_time = time.time()
-print("Job completed in", end_time - start_time, "seconds!")
-
-print("Artifacts:")
-print(client.post_training.job.artifacts(job_uuid=job_uuid))
-```

docs/source/providers/vector_io/milvus.md

@@ -1,107 +0,0 @@
----
-orphan: true
----
-# Milvus
-
-[Milvus](https://milvus.io/) is an inline and remote vector database provider for Llama Stack. It
-allows you to store and query vectors directly within a Milvus database.
-That means you're not limited to storing vectors in memory or in a separate service.
-
-## Features
-
-- Easy to use
-- Fully integrated with Llama Stack
-
-## Usage
-
-To use Milvus in your Llama Stack project, follow these steps:
-
-1. Install the necessary dependencies.
-2. Configure your Llama Stack project to use Milvus.
-3. Start storing and querying vectors.
-
-## Installation
-
-You can install Milvus using pymilvus:
-
-```bash
-pip install pymilvus
-```
-
-## Configuration
-
-In Llama Stack, Milvus can be configured in two ways:
-- **Inline (Local) Configuration** - Uses Milvus-Lite for local storage
-- **Remote Configuration** - Connects to a remote Milvus server
-
-### Inline (Local) Configuration
-
-The simplest method is local configuration, which requires setting `db_path`, a path for locally storing Milvus-Lite files:
-
-```yaml
-vector_io:
-  - provider_id: milvus
-    provider_type: inline::milvus
-    config:
-      db_path: ~/.llama/distributions/together/milvus_store.db
-```
-
-### Remote Configuration
-
-Remote configuration is suitable for larger data storage requirements:
-
-#### Standard Remote Connection
-
-```yaml
-vector_io:
-  - provider_id: milvus
-    provider_type: remote::milvus
-    config:
-      uri: "http://<host>:<port>"
-      token: "<user>:<password>"
-```
-
-#### TLS-Enabled Remote Connection (One-way TLS)
-
-For connections to Milvus instances with one-way TLS enabled:
-
-```yaml
-vector_io:
-  - provider_id: milvus
-    provider_type: remote::milvus
-    config:
-      uri: "https://<host>:<port>"
-      token: "<user>:<password>"
-      secure: True
-      server_pem_path: "/path/to/server.pem"
-```
-
-#### Mutual TLS (mTLS) Remote Connection
-
-For connections to Milvus instances with mutual TLS (mTLS) enabled:
-
-```yaml
-vector_io:
-  - provider_id: milvus
-    provider_type: remote::milvus
-    config:
-      uri: "https://<host>:<port>"
-      token: "<user>:<password>"
-      secure: True
-      ca_pem_path: "/path/to/ca.pem"
-      client_pem_path: "/path/to/client.pem"
-      client_key_path: "/path/to/client.key"
-```
-
-#### Key Parameters for TLS Configuration
-
-- **`secure`**: Enables TLS encryption when set to `true`. Defaults to `false`.
-- **`server_pem_path`**: Path to the **server certificate** for verifying the server’s identity (used in one-way TLS).
-- **`ca_pem_path`**: Path to the **Certificate Authority (CA) certificate** for validating the server certificate (required in mTLS).
-- **`client_pem_path`**: Path to the **client certificate** file (required for mTLS).
-- **`client_key_path`**: Path to the **client private key** file (required for mTLS).
-
-## Documentation
-See the [Milvus documentation](https://milvus.io/docs/install-overview.md) for more details about Milvus in general.
-
-For more details on TLS configuration, refer to the [TLS setup guide](https://milvus.io/docs/tls.md).

docs/source/providers/vector_io/mivus.md

@@ -0,0 +1,31 @@
+---
+orphan: true
+---
+# Milvus
+
+[Milvus](https://milvus.io/) is an inline and remote vector database provider for Llama Stack. It
+allows you to store and query vectors directly within a Milvus database.
+That means you're not limited to storing vectors in memory or in a separate service.
+
+## Features
+
+- Easy to use
+- Fully integrated with Llama Stack
+
+## Usage
+
+To use Milvus in your Llama Stack project, follow these steps:
+
+1. Install the necessary dependencies.
+2. Configure your Llama Stack project to use Milvus.
+3. Start storing and querying vectors.
+
+## Installation
+
+You can install Milvus using pymilvus:
+
+```bash
+pip install pymilvus
+```
+## Documentation
+See the [Milvus documentation](https://milvus.io/docs/install-overview.md) for more details about Milvus in general.

docs/source/providers/vector_io/sqlite-vec.md

@@ -66,25 +66,6 @@ To use sqlite-vec in your Llama Stack project, follow these steps:
 2. Configure your Llama Stack project to use SQLite-Vec.
 3. Start storing and querying vectors.
 
-## Supported Search Modes
-
-The sqlite-vec provider supports both vector-based and keyword-based (full-text) search modes.
-
-When using the RAGTool interface, you can specify the desired search behavior via the `mode` parameter in
-`RAGQueryConfig`. For example:
-
-```python
-from llama_stack.apis.tool_runtime.rag import RAGQueryConfig
-
-query_config = RAGQueryConfig(max_chunks=6, mode="vector")
-
-results = client.tool_runtime.rag_tool.query(
-    vector_db_ids=[vector_db_id],
-    content="what is torchtune",
-    query_config=query_config,
-)
-```
-
 ## Installation
 
 You can install SQLite-Vec using pip:

docs/source/references/llama_stack_client_cli_reference.md

@@ -253,6 +253,8 @@ llama-stack-client toolgroups list
 +---------------------------+------------------+------+---------------+
 | identifier                | provider_id      | args | mcp_endpoint  |
 +===========================+==================+======+===============+
+| builtin::code_interpreter | code-interpreter | None | None          |
++---------------------------+------------------+------+---------------+
 | builtin::rag              | rag-runtime      | None | None          |
 +---------------------------+------------------+------+---------------+
 | builtin::websearch        | tavily-search    | None | None          |

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!
-   You can find the newly-built template here: ~/.llama/distributions/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
+   Build Successful! Next steps:
+   1. Set the environment variables: LLAMA_STACK_PORT, OLLAMA_URL, INFERENCE_MODEL, SAFETY_MODEL
+   2. `llama stack run /Users/<username>/.llama/distributions/llamastack-ollama/ollama-run.yaml
    ```
 
 3. **Set the ENV variables by exporting them to the terminal**:

install.sh

@@ -1,206 +0,0 @@
-#!/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
-}
-
-usage() {
-    cat << EOF
-📚 Llama-Stack Deployment Script
-
-Description:
-    This script sets up and deploys Llama-Stack with Ollama integration in containers.
-    It handles both Docker and Podman runtimes and includes automatic platform detection.
-
-Usage:
-    $(basename "$0") [OPTIONS]
-
-Options:
-    -p, --port PORT            Server port for Llama-Stack (default: ${PORT})
-    -o, --ollama-port PORT     Ollama service port (default: ${OLLAMA_PORT})
-    -m, --model MODEL          Model alias to use (default: ${MODEL_ALIAS})
-    -i, --image IMAGE          Server image (default: ${SERVER_IMAGE})
-    -t, --timeout SECONDS      Service wait timeout in seconds (default: ${WAIT_TIMEOUT})
-    -h, --help                 Show this help message
-
-For more information:
-    Documentation: https://llama-stack.readthedocs.io/
-    GitHub: https://github.com/meta-llama/llama-stack
-
-Report issues:
-    https://github.com/meta-llama/llama-stack/issues
-EOF
-}
-
-# Parse command line arguments
-while [[ $# -gt 0 ]]; do
-    case $1 in
-        -h|--help)
-            usage
-            exit 0
-            ;;
-        -p|--port)
-            PORT="$2"
-            shift 2
-            ;;
-        -o|--ollama-port)
-            OLLAMA_PORT="$2"
-            shift 2
-            ;;
-        -m|--model)
-            MODEL_ALIAS="$2"
-            shift 2
-            ;;
-        -i|--image)
-            SERVER_IMAGE="$2"
-            shift 2
-            ;;
-        -t|--timeout)
-            WAIT_TIMEOUT="$2"
-            shift 2
-            ;;
-        *)
-            die "Unknown option: $1"
-            ;;
-    esac
-done
-
-if command -v docker &> /dev/null; then
-  ENGINE="docker"
-elif command -v podman &> /dev/null; then
-  ENGINE="podman"
-else
-  die "Docker or Podman is required. Install Docker: https://docs.docker.com/get-docker/ or Podman: https://podman.io/getting-started/installation"
-fi
-
-# Explicitly set the platform for the host architecture
-HOST_ARCH="$(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 ""

kvant_build_local.sh

@@ -1,6 +0,0 @@
-#!/usr/bin/env bash
-
-export USE_COPY_NOT_MOUNT=true
-export LLAMA_STACK_DIR=.
-
-uvx --from . llama stack build --template kvant --image-type container  --image-name kvant

kvant_start_local.sh

@@ -1,17 +0,0 @@
-#!/usr/bin/env bash
-
-export LLAMA_STACK_PORT=8321
-# VLLM_API_TOKEN= env file
-# KEYCLOAK_CLIENT_SECRET= env file
-
-
-docker run -it \
-  -p $LLAMA_STACK_PORT:$LLAMA_STACK_PORT \
-  -v $(pwd)/data:/root/.llama \
-  --mount type=bind,source="$(pwd)"/llama_stack/templates/kvant/run.yaml,target=/root/.llama/config.yaml,readonly \
-  --entrypoint python \
-  --env-file ./.env \
-  distribution-kvant:dev \
-  -m llama_stack.distribution.server.server  --config /root/.llama/config.yaml \
-  --port $LLAMA_STACK_PORT \
-

llama_stack/apis/agents/agents.py

@@ -4,16 +4,24 @@
 # This source code is licensed under the terms described in the LICENSE file in
 # the root directory of this source tree.
 
-import sys
-from collections.abc import AsyncIterator
 from datetime import datetime
 from enum import Enum
-from typing import Annotated, Any, Literal, Protocol, runtime_checkable
+from typing import (
+    Annotated,
+    Any,
+    AsyncIterator,
+    Dict,
+    List,
+    Literal,
+    Optional,
+    Protocol,
+    Union,
+    runtime_checkable,
+)
 
 from pydantic import BaseModel, ConfigDict, Field
 
 from llama_stack.apis.common.content_types import URL, ContentDelta, InterleavedContent
-from llama_stack.apis.common.responses import Order, PaginatedResponse
 from llama_stack.apis.inference import (
     CompletionMessage,
     ResponseFormat,
@@ -30,23 +38,6 @@ 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 (
-    ListOpenAIResponseInputItem,
-    ListOpenAIResponseObject,
-    OpenAIResponseInput,
-    OpenAIResponseInputTool,
-    OpenAIResponseObject,
-    OpenAIResponseObjectStream,
-)
-
-# TODO: use enum.StrEnum when we drop support for python 3.10
-if sys.version_info >= (3, 11):
-    from enum import StrEnum
-else:
-
-    class StrEnum(str, Enum):
-        """Backport of StrEnum for Python 3.10 and below."""
-
 
 class Attachment(BaseModel):
     """An attachment to an agent turn.
@@ -81,11 +72,11 @@ class StepCommon(BaseModel):
 
     turn_id: str
     step_id: str
-    started_at: datetime | None = None
-    completed_at: datetime | None = None
+    started_at: Optional[datetime] = None
+    completed_at: Optional[datetime] = None
 
 
-class StepType(StrEnum):
+class StepType(Enum):
     """Type of the step in an agent turn.
 
     :cvar inference: The step is an inference step that calls an LLM.
@@ -109,7 +100,7 @@ class InferenceStep(StepCommon):
 
     model_config = ConfigDict(protected_namespaces=())
 
-    step_type: Literal[StepType.inference] = StepType.inference
+    step_type: Literal[StepType.inference.value] = StepType.inference.value
     model_response: CompletionMessage
 
 
@@ -121,9 +112,9 @@ class ToolExecutionStep(StepCommon):
     :param tool_responses: The tool responses from the tool calls.
     """
 
-    step_type: Literal[StepType.tool_execution] = StepType.tool_execution
-    tool_calls: list[ToolCall]
-    tool_responses: list[ToolResponse]
+    step_type: Literal[StepType.tool_execution.value] = StepType.tool_execution.value
+    tool_calls: List[ToolCall]
+    tool_responses: List[ToolResponse]
 
 
 @json_schema_type
@@ -133,8 +124,8 @@ class ShieldCallStep(StepCommon):
     :param violation: The violation from the shield call.
     """
 
-    step_type: Literal[StepType.shield_call] = StepType.shield_call
-    violation: SafetyViolation | None
+    step_type: Literal[StepType.shield_call.value] = StepType.shield_call.value
+    violation: Optional[SafetyViolation]
 
 
 @json_schema_type
@@ -145,14 +136,19 @@ class MemoryRetrievalStep(StepCommon):
     :param inserted_context: The context retrieved from the vector databases.
     """
 
-    step_type: Literal[StepType.memory_retrieval] = StepType.memory_retrieval
+    step_type: Literal[StepType.memory_retrieval.value] = StepType.memory_retrieval.value
     # TODO: should this be List[str]?
     vector_db_ids: str
     inserted_context: InterleavedContent
 
 
 Step = Annotated[
-    InferenceStep | ToolExecutionStep | ShieldCallStep | MemoryRetrievalStep,
+    Union[
+        InferenceStep,
+        ToolExecutionStep,
+        ShieldCallStep,
+        MemoryRetrievalStep,
+    ],
     Field(discriminator="step_type"),
 ]
 
@@ -163,13 +159,18 @@ class Turn(BaseModel):
 
     turn_id: str
     session_id: str
-    input_messages: list[UserMessage | ToolResponseMessage]
-    steps: list[Step]
+    input_messages: List[
+        Union[
+            UserMessage,
+            ToolResponseMessage,
+        ]
+    ]
+    steps: List[Step]
     output_message: CompletionMessage
-    output_attachments: list[Attachment] | None = Field(default_factory=lambda: [])
+    output_attachments: Optional[List[Attachment]] = Field(default_factory=list)
 
     started_at: datetime
-    completed_at: datetime | None = None
+    completed_at: Optional[datetime] = None
 
 
 @json_schema_type
@@ -178,31 +179,34 @@ 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 = str | AgentToolGroupWithArgs
+AgentToolGroup = Union[
+    str,
+    AgentToolGroupWithArgs,
+]
 register_schema(AgentToolGroup, name="AgentTool")
 
 
 class AgentConfigCommon(BaseModel):
-    sampling_params: SamplingParams | None = Field(default_factory=SamplingParams)
+    sampling_params: Optional[SamplingParams] = Field(default_factory=SamplingParams)
 
-    input_shields: list[str] | None = Field(default_factory=lambda: [])
-    output_shields: list[str] | None = Field(default_factory=lambda: [])
-    toolgroups: list[AgentToolGroup] | None = Field(default_factory=lambda: [])
-    client_tools: list[ToolDef] | None = Field(default_factory=lambda: [])
-    tool_choice: ToolChoice | None = Field(default=None, deprecated="use tool_config instead")
-    tool_prompt_format: ToolPromptFormat | None = Field(default=None, deprecated="use tool_config instead")
-    tool_config: ToolConfig | None = Field(default=None)
+    input_shields: Optional[List[str]] = Field(default_factory=list)
+    output_shields: Optional[List[str]] = Field(default_factory=list)
+    toolgroups: Optional[List[AgentToolGroup]] = Field(default_factory=list)
+    client_tools: Optional[List[ToolDef]] = Field(default_factory=list)
+    tool_choice: Optional[ToolChoice] = Field(default=None, deprecated="use tool_config instead")
+    tool_prompt_format: Optional[ToolPromptFormat] = Field(default=None, deprecated="use tool_config instead")
+    tool_config: Optional[ToolConfig] = Field(default=None)
 
-    max_infer_iters: int | None = 10
+    max_infer_iters: Optional[int] = 10
 
     def model_post_init(self, __context):
         if self.tool_config:
@@ -232,9 +236,9 @@ class AgentConfig(AgentConfigCommon):
 
     model: str
     instructions: str
-    name: str | None = None
-    enable_session_persistence: bool | None = False
-    response_format: ResponseFormat | None = None
+    name: Optional[str] = None
+    enable_session_persistence: Optional[bool] = False
+    response_format: Optional[ResponseFormat] = None
 
 
 @json_schema_type
@@ -244,11 +248,21 @@ class Agent(BaseModel):
     created_at: datetime
 
 
+@json_schema_type
+class ListAgentsResponse(BaseModel):
+    data: List[Agent]
+
+
+@json_schema_type
+class ListAgentSessionsResponse(BaseModel):
+    data: List[Session]
+
+
 class AgentConfigOverridablePerTurn(AgentConfigCommon):
-    instructions: str | None = None
+    instructions: Optional[str] = None
 
 
-class AgentTurnResponseEventType(StrEnum):
+class AgentTurnResponseEventType(Enum):
     step_start = "step_start"
     step_complete = "step_complete"
     step_progress = "step_progress"
@@ -260,15 +274,15 @@ class AgentTurnResponseEventType(StrEnum):
 
 @json_schema_type
 class AgentTurnResponseStepStartPayload(BaseModel):
-    event_type: Literal[AgentTurnResponseEventType.step_start] = AgentTurnResponseEventType.step_start
+    event_type: Literal[AgentTurnResponseEventType.step_start.value] = AgentTurnResponseEventType.step_start.value
     step_type: StepType
     step_id: str
-    metadata: dict[str, Any] | None = Field(default_factory=lambda: {})
+    metadata: Optional[Dict[str, Any]] = Field(default_factory=dict)
 
 
 @json_schema_type
 class AgentTurnResponseStepCompletePayload(BaseModel):
-    event_type: Literal[AgentTurnResponseEventType.step_complete] = AgentTurnResponseEventType.step_complete
+    event_type: Literal[AgentTurnResponseEventType.step_complete.value] = AgentTurnResponseEventType.step_complete.value
     step_type: StepType
     step_id: str
     step_details: Step
@@ -278,7 +292,7 @@ class AgentTurnResponseStepCompletePayload(BaseModel):
 class AgentTurnResponseStepProgressPayload(BaseModel):
     model_config = ConfigDict(protected_namespaces=())
 
-    event_type: Literal[AgentTurnResponseEventType.step_progress] = AgentTurnResponseEventType.step_progress
+    event_type: Literal[AgentTurnResponseEventType.step_progress.value] = AgentTurnResponseEventType.step_progress.value
     step_type: StepType
     step_id: str
 
@@ -287,29 +301,33 @@ class AgentTurnResponseStepProgressPayload(BaseModel):
 
 @json_schema_type
 class AgentTurnResponseTurnStartPayload(BaseModel):
-    event_type: Literal[AgentTurnResponseEventType.turn_start] = AgentTurnResponseEventType.turn_start
+    event_type: Literal[AgentTurnResponseEventType.turn_start.value] = AgentTurnResponseEventType.turn_start.value
     turn_id: str
 
 
 @json_schema_type
 class AgentTurnResponseTurnCompletePayload(BaseModel):
-    event_type: Literal[AgentTurnResponseEventType.turn_complete] = AgentTurnResponseEventType.turn_complete
+    event_type: Literal[AgentTurnResponseEventType.turn_complete.value] = AgentTurnResponseEventType.turn_complete.value
     turn: Turn
 
 
 @json_schema_type
 class AgentTurnResponseTurnAwaitingInputPayload(BaseModel):
-    event_type: Literal[AgentTurnResponseEventType.turn_awaiting_input] = AgentTurnResponseEventType.turn_awaiting_input
+    event_type: Literal[AgentTurnResponseEventType.turn_awaiting_input.value] = (
+        AgentTurnResponseEventType.turn_awaiting_input.value
+    )
     turn: Turn
 
 
 AgentTurnResponseEventPayload = Annotated[
-    AgentTurnResponseStepStartPayload
-    | AgentTurnResponseStepProgressPayload
-    | AgentTurnResponseStepCompletePayload
-    | AgentTurnResponseTurnStartPayload
-    | AgentTurnResponseTurnCompletePayload
-    | AgentTurnResponseTurnAwaitingInputPayload,
+    Union[
+        AgentTurnResponseStepStartPayload,
+        AgentTurnResponseStepProgressPayload,
+        AgentTurnResponseStepCompletePayload,
+        AgentTurnResponseTurnStartPayload,
+        AgentTurnResponseTurnCompletePayload,
+        AgentTurnResponseTurnAwaitingInputPayload,
+    ],
     Field(discriminator="event_type"),
 ]
 register_schema(AgentTurnResponseEventPayload, name="AgentTurnResponseEventPayload")
@@ -338,13 +356,18 @@ 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[UserMessage | ToolResponseMessage]
+    messages: List[
+        Union[
+            UserMessage,
+            ToolResponseMessage,
+        ]
+    ]
 
-    documents: list[Document] | None = None
-    toolgroups: list[AgentToolGroup] | None = Field(default_factory=lambda: [])
+    documents: Optional[List[Document]] = None
+    toolgroups: Optional[List[AgentToolGroup]] = None
 
-    stream: bool | None = False
-    tool_config: ToolConfig | None = None
+    stream: Optional[bool] = False
+    tool_config: Optional[ToolConfig] = None
 
 
 @json_schema_type
@@ -352,8 +375,8 @@ class AgentTurnResumeRequest(BaseModel):
     agent_id: str
     session_id: str
     turn_id: str
-    tool_responses: list[ToolResponse]
-    stream: bool | None = False
+    tool_responses: List[ToolResponse]
+    stream: Optional[bool] = False
 
 
 @json_schema_type
@@ -399,12 +422,17 @@ class Agents(Protocol):
         self,
         agent_id: str,
         session_id: str,
-        messages: list[UserMessage | ToolResponseMessage],
-        stream: bool | None = False,
-        documents: list[Document] | None = None,
-        toolgroups: list[AgentToolGroup] | None = None,
-        tool_config: ToolConfig | None = None,
-    ) -> Turn | AsyncIterator[AgentTurnResponseStreamChunk]:
+        messages: List[
+            Union[
+                UserMessage,
+                ToolResponseMessage,
+            ]
+        ],
+        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.
@@ -415,9 +443,8 @@ class Agents(Protocol):
         :param toolgroups: (Optional) List of toolgroups to create the turn with, will be used in addition to the agent's config toolgroups for the request.
         :param tool_config: (Optional) The tool configuration to create the turn with, will be used to override the agent's tool_config.
         :returns: If stream=False, returns a Turn object.
-                  If stream=True, returns an SSE event stream of AgentTurnResponseStreamChunk.
+                  If stream=True, returns an SSE event stream of AgentTurnResponseStreamChunk
         """
-        ...
 
     @webmethod(
         route="/agents/{agent_id}/session/{session_id}/turn/{turn_id}/resume",
@@ -429,9 +456,9 @@ class Agents(Protocol):
         agent_id: str,
         session_id: str,
         turn_id: str,
-        tool_responses: list[ToolResponse],
-        stream: bool | None = False,
-    ) -> Turn | AsyncIterator[AgentTurnResponseStreamChunk]:
+        tool_responses: List[ToolResponse],
+        stream: Optional[bool] = False,
+    ) -> Union[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.
@@ -504,14 +531,13 @@ class Agents(Protocol):
         self,
         session_id: str,
         agent_id: str,
-        turn_ids: list[str] | None = None,
+        turn_ids: Optional[List[str]] = None,
     ) -> Session:
         """Retrieve an agent session by its ID.
 
         :param session_id: The ID of the session to get.
         :param agent_id: The ID of the agent to get the session for.
         :param turn_ids: (Optional) List of turn IDs to filter the session by.
-        :returns: A Session.
         """
         ...
 
@@ -521,7 +547,7 @@ class Agents(Protocol):
         session_id: str,
         agent_id: str,
     ) -> None:
-        """Delete an agent session by its ID and its associated turns.
+        """Delete an agent session by its ID.
 
         :param session_id: The ID of the session to delete.
         :param agent_id: The ID of the agent to delete the session for.
@@ -533,19 +559,17 @@ class Agents(Protocol):
         self,
         agent_id: str,
     ) -> None:
-        """Delete an agent by its ID and its associated sessions and turns.
+        """Delete an agent by its ID.
 
         :param agent_id: The ID of the agent to delete.
         """
         ...
 
     @webmethod(route="/agents", method="GET")
-    async def list_agents(self, start_index: int | None = None, limit: int | None = None) -> PaginatedResponse:
+    async def list_agents(self) -> ListAgentsResponse:
         """List all agents.
 
-        :param start_index: The index to start the pagination from.
-        :param limit: The number of agents to return.
-        :returns: A PaginatedResponse.
+        :returns: A ListAgentsResponse.
         """
         ...
 
@@ -562,94 +586,10 @@ class Agents(Protocol):
     async def list_agent_sessions(
         self,
         agent_id: str,
-        start_index: int | None = None,
-        limit: int | None = None,
-    ) -> PaginatedResponse:
+    ) -> ListAgentSessionsResponse:
         """List all session(s) of a given agent.
 
         :param agent_id: The ID of the agent to list sessions for.
-        :param start_index: The index to start the pagination from.
-        :param limit: The number of sessions to return.
-        :returns: A PaginatedResponse.
-        """
-        ...
-
-    # 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/{response_id}", method="GET")
-    async def get_openai_response(
-        self,
-        response_id: str,
-    ) -> OpenAIResponseObject:
-        """Retrieve an OpenAI response by its ID.
-
-        :param response_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[OpenAIResponseInput],
-        model: str,
-        instructions: str | None = None,
-        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.
-        :returns: An OpenAIResponseObject.
-        """
-        ...
-
-    @webmethod(route="/openai/v1/responses", method="GET")
-    async def list_openai_responses(
-        self,
-        after: str | None = None,
-        limit: int | None = 50,
-        model: str | None = None,
-        order: Order | None = Order.desc,
-    ) -> ListOpenAIResponseObject:
-        """List all OpenAI responses.
-
-        :param after: The ID of the last response to return.
-        :param limit: The number of responses to return.
-        :param model: The model to filter responses by.
-        :param order: The order to sort responses by when sorted by created_at ('asc' or 'desc').
-        :returns: A ListOpenAIResponseObject.
-        """
-        ...
-
-    @webmethod(route="/openai/v1/responses/{response_id}/input_items", method="GET")
-    async def list_openai_response_input_items(
-        self,
-        response_id: str,
-        after: str | None = None,
-        before: str | None = None,
-        include: list[str] | None = None,
-        limit: int | None = 20,
-        order: Order | None = Order.desc,
-    ) -> ListOpenAIResponseInputItem:
-        """List input items for a given OpenAI response.
-
-        :param response_id: The ID of the response to retrieve input items for.
-        :param after: An item ID to list items after, used for pagination.
-        :param before: An item ID to list items before, used for pagination.
-        :param include: Additional fields to include in the response.
-        :param limit: A limit on the number of objects to be returned. Limit can range between 1 and 100, and the default is 20.
-        :param order: The order to return the input items in. Default is desc.
-        :returns: An ListOpenAIResponseInputItem.
+        :returns: A ListAgentSessionsResponse.
         """
         ...

llama_stack/apis/agents/openai_responses.py

@@ -1,279 +0,0 @@
-# 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, Any, Literal
-
-from pydantic import BaseModel, Field
-
-from llama_stack.schema_utils import json_schema_type, register_schema
-
-# NOTE(ashwin): this file is literally a copy of the OpenAI responses API schema. We should probably
-# take their YAML and generate this file automatically. Their YAML is available.
-
-
-@json_schema_type
-class OpenAIResponseError(BaseModel):
-    code: str
-    message: str
-
-
-@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 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 OpenAIResponseMessage(BaseModel):
-    """
-    Corresponds to the various Message types in the Responses API.
-    They are all under one type because the Responses API gives them all
-    the same "type" value, and there is no way to tell them apart in certain
-    scenarios.
-    """
-
-    content: str | list[OpenAIResponseInputMessageContent] | list[OpenAIResponseOutputMessageContent]
-    role: Literal["system"] | Literal["developer"] | Literal["user"] | Literal["assistant"]
-    type: Literal["message"] = "message"
-
-    # The fields below are not used in all scenarios, but are required in others.
-    id: str | None = None
-    status: str | None = None
-
-
-@json_schema_type
-class OpenAIResponseOutputMessageWebSearchToolCall(BaseModel):
-    id: str
-    status: str
-    type: Literal["web_search_call"] = "web_search_call"
-
-
-@json_schema_type
-class OpenAIResponseOutputMessageFunctionToolCall(BaseModel):
-    call_id: str
-    name: str
-    arguments: str
-    type: Literal["function_call"] = "function_call"
-    id: str | None = None
-    status: str | None = None
-
-
-@json_schema_type
-class OpenAIResponseOutputMessageMCPCall(BaseModel):
-    id: str
-    type: Literal["mcp_call"] = "mcp_call"
-    arguments: str
-    name: str
-    server_label: str
-    error: str | None = None
-    output: str | None = None
-
-
-class MCPListToolsTool(BaseModel):
-    input_schema: dict[str, Any]
-    name: str
-    description: str | None = None
-
-
-@json_schema_type
-class OpenAIResponseOutputMessageMCPListTools(BaseModel):
-    id: str
-    type: Literal["mcp_list_tools"] = "mcp_list_tools"
-    server_label: str
-    tools: list[MCPListToolsTool]
-
-
-OpenAIResponseOutput = Annotated[
-    OpenAIResponseMessage
-    | OpenAIResponseOutputMessageWebSearchToolCall
-    | OpenAIResponseOutputMessageFunctionToolCall
-    | OpenAIResponseOutputMessageMCPCall
-    | OpenAIResponseOutputMessageMCPListTools,
-    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 OpenAIResponseObjectStreamResponseOutputTextDelta(BaseModel):
-    content_index: int
-    delta: str
-    item_id: str
-    output_index: int
-    sequence_number: int
-    type: Literal["response.output_text.delta"] = "response.output_text.delta"
-
-
-@json_schema_type
-class OpenAIResponseObjectStreamResponseCompleted(BaseModel):
-    response: OpenAIResponseObject
-    type: Literal["response.completed"] = "response.completed"
-
-
-OpenAIResponseObjectStream = Annotated[
-    OpenAIResponseObjectStreamResponseCreated
-    | OpenAIResponseObjectStreamResponseOutputTextDelta
-    | OpenAIResponseObjectStreamResponseCompleted,
-    Field(discriminator="type"),
-]
-register_schema(OpenAIResponseObjectStream, name="OpenAIResponseObjectStream")
-
-
-@json_schema_type
-class OpenAIResponseInputFunctionToolCallOutput(BaseModel):
-    """
-    This represents the output of a function call that gets passed back to the model.
-    """
-
-    call_id: str
-    output: str
-    type: Literal["function_call_output"] = "function_call_output"
-    id: str | None = None
-    status: str | None = None
-
-
-OpenAIResponseInput = Annotated[
-    # Responses API allows output messages to be passed in as input
-    OpenAIResponseOutputMessageWebSearchToolCall
-    | OpenAIResponseOutputMessageFunctionToolCall
-    | OpenAIResponseInputFunctionToolCallOutput
-    |
-    # Fallback to the generic message type as a last resort
-    OpenAIResponseMessage,
-    Field(union_mode="left_to_right"),
-]
-register_schema(OpenAIResponseInput, name="OpenAIResponseInput")
-
-
-@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
-
-
-@json_schema_type
-class OpenAIResponseInputToolFunction(BaseModel):
-    type: Literal["function"] = "function"
-    name: str
-    description: str | None = None
-    parameters: dict[str, Any] | None
-    strict: bool | None = None
-
-
-class FileSearchRankingOptions(BaseModel):
-    ranker: str | None = None
-    score_threshold: float | None = Field(default=0.0, ge=0.0, le=1.0)
-
-
-@json_schema_type
-class OpenAIResponseInputToolFileSearch(BaseModel):
-    type: Literal["file_search"] = "file_search"
-    vector_store_id: list[str]
-    ranking_options: FileSearchRankingOptions | None = None
-    # TODO: add filters
-
-
-class ApprovalFilter(BaseModel):
-    always: list[str] | None = None
-    never: list[str] | None = None
-
-
-class AllowedToolsFilter(BaseModel):
-    tool_names: list[str] | None = None
-
-
-@json_schema_type
-class OpenAIResponseInputToolMCP(BaseModel):
-    type: Literal["mcp"] = "mcp"
-    server_label: str
-    server_url: str
-    headers: dict[str, Any] | None = None
-
-    require_approval: Literal["always"] | Literal["never"] | ApprovalFilter = "never"
-    allowed_tools: list[str] | AllowedToolsFilter | None = None
-
-
-OpenAIResponseInputTool = Annotated[
-    OpenAIResponseInputToolWebSearch
-    | OpenAIResponseInputToolFileSearch
-    | OpenAIResponseInputToolFunction
-    | OpenAIResponseInputToolMCP,
-    Field(discriminator="type"),
-]
-register_schema(OpenAIResponseInputTool, name="OpenAIResponseInputTool")
-
-
-class ListOpenAIResponseInputItem(BaseModel):
-    data: list[OpenAIResponseInput]
-    object: Literal["list"] = "list"
-
-
-@json_schema_type
-class OpenAIResponseObjectWithInput(OpenAIResponseObject):
-    input: list[OpenAIResponseInput]
-
-
-@json_schema_type
-class ListOpenAIResponseObject(BaseModel):
-    data: list[OpenAIResponseObjectWithInput]
-    has_more: bool
-    first_id: str
-    last_id: str
-    object: Literal["list"] = "list"

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 Protocol, runtime_checkable
+from typing import List, Optional, Protocol, runtime_checkable
 
 from llama_stack.apis.common.job_types import Job
 from llama_stack.apis.inference import (
@@ -34,45 +34,22 @@ class BatchInference(Protocol):
     async def completion(
         self,
         model: str,
-        content_batch: list[InterleavedContent],
-        sampling_params: SamplingParams | None = None,
-        response_format: ResponseFormat | None = None,
-        logprobs: LogProbConfig | None = None,
-    ) -> Job:
-        """Generate completions for a batch of content.
-
-        :param model: The model to use for the completion.
-        :param content_batch: The content to complete.
-        :param sampling_params: The sampling parameters to use for the completion.
-        :param response_format: The response format to use for the completion.
-        :param logprobs: The logprobs to use for the completion.
-        :returns: A job for the completion.
-        """
-        ...
+        content_batch: List[InterleavedContent],
+        sampling_params: Optional[SamplingParams] = None,
+        response_format: Optional[ResponseFormat] = None,
+        logprobs: Optional[LogProbConfig] = None,
+    ) -> Job: ...
 
     @webmethod(route="/batch-inference/chat-completion", method="POST")
     async def chat_completion(
         self,
         model: str,
-        messages_batch: list[list[Message]],
-        sampling_params: SamplingParams | None = None,
+        messages_batch: List[List[Message]],
+        sampling_params: Optional[SamplingParams] = None,
         # zero-shot tool definitions as input to the model
-        tools: list[ToolDefinition] | None = None,
-        tool_choice: ToolChoice | None = ToolChoice.auto,
-        tool_prompt_format: ToolPromptFormat | None = None,
-        response_format: ResponseFormat | None = None,
-        logprobs: LogProbConfig | None = None,
-    ) -> Job:
-        """Generate chat completions for a batch of messages.
-
-        :param model: The model to use for the chat completion.
-        :param messages_batch: The messages to complete.
-        :param sampling_params: The sampling parameters to use for the completion.
-        :param tools: The tools to use for the chat completion.
-        :param tool_choice: The tool choice to use for the chat completion.
-        :param tool_prompt_format: The tool prompt format to use for the chat completion.
-        :param response_format: The response format to use for the chat completion.
-        :param logprobs: The logprobs to use for the chat completion.
-        :returns: A job for the chat completion.
-        """
-        ...
+        tools: Optional[List[ToolDefinition]] = None,
+        tool_choice: Optional[ToolChoice] = ToolChoice.auto,
+        tool_prompt_format: Optional[ToolPromptFormat] = None,
+        response_format: Optional[ResponseFormat] = None,
+        logprobs: Optional[LogProbConfig] = 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, Literal, Protocol, runtime_checkable
+from typing import Any, Dict, List, Literal, Optional, 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]
-    metadata: dict[str, Any] = Field(
+    scoring_functions: List[str]
+    metadata: Dict[str, Any] = Field(
         default_factory=dict,
         description="Metadata for this evaluation task",
     )
@@ -22,66 +22,45 @@ class CommonBenchmarkFields(BaseModel):
 
 @json_schema_type
 class Benchmark(CommonBenchmarkFields, Resource):
-    type: Literal[ResourceType.benchmark] = ResourceType.benchmark
+    type: Literal[ResourceType.benchmark.value] = ResourceType.benchmark.value
 
     @property
     def benchmark_id(self) -> str:
         return self.identifier
 
     @property
-    def provider_benchmark_id(self) -> str | None:
+    def provider_benchmark_id(self) -> str:
         return self.provider_resource_id
 
 
 class BenchmarkInput(CommonBenchmarkFields, BaseModel):
     benchmark_id: str
-    provider_id: str | None = None
-    provider_benchmark_id: str | None = None
+    provider_id: Optional[str] = None
+    provider_benchmark_id: Optional[str] = None
 
 
 class ListBenchmarksResponse(BaseModel):
-    data: list[Benchmark]
+    data: List[Benchmark]
 
 
 @runtime_checkable
 class Benchmarks(Protocol):
     @webmethod(route="/eval/benchmarks", method="GET")
-    async def list_benchmarks(self) -> ListBenchmarksResponse:
-        """List all benchmarks.
-
-        :returns: A ListBenchmarksResponse.
-        """
-        ...
+    async def list_benchmarks(self) -> ListBenchmarksResponse: ...
 
     @webmethod(route="/eval/benchmarks/{benchmark_id}", method="GET")
     async def get_benchmark(
         self,
         benchmark_id: str,
-    ) -> Benchmark:
-        """Get a benchmark by its ID.
-
-        :param benchmark_id: The ID of the benchmark to get.
-        :returns: A Benchmark.
-        """
-        ...
+    ) -> Benchmark: ...
 
     @webmethod(route="/eval/benchmarks", method="POST")
     async def register_benchmark(
         self,
         benchmark_id: str,
         dataset_id: str,
-        scoring_functions: list[str],
-        provider_benchmark_id: str | None = None,
-        provider_id: str | None = None,
-        metadata: dict[str, Any] | None = None,
-    ) -> None:
-        """Register a benchmark.
-
-        :param benchmark_id: The ID of the benchmark to register.
-        :param dataset_id: The ID of the dataset to use for the benchmark.
-        :param scoring_functions: The scoring functions to use for the benchmark.
-        :param provider_benchmark_id: The ID of the provider benchmark to use for the benchmark.
-        :param provider_id: The ID of the provider to use for the benchmark.
-        :param metadata: The metadata to use for the benchmark.
-        """
-        ...
+        scoring_functions: List[str],
+        provider_benchmark_id: Optional[str] = None,
+        provider_id: Optional[str] = None,
+        metadata: Optional[Dict[str, Any]] = 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, Literal
+from typing import Annotated, List, Literal, Optional, Union
 
 from pydantic import BaseModel, Field, model_validator
 
@@ -26,9 +26,9 @@ class _URLOrData(BaseModel):
     :param data: base64 encoded image data as string
     """
 
-    url: URL | None = None
+    url: Optional[URL] = None
     # data is a base64 encoded string, hint with contentEncoding=base64
-    data: str | None = Field(default=None, json_schema_extra={"contentEncoding": "base64"})
+    data: Optional[str] = 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[
-    ImageContentItem | TextContentItem,
+    Union[ImageContentItem, TextContentItem],
     Field(discriminator="type"),
 ]
 register_schema(InterleavedContentItem, name="InterleavedContentItem")
 
 # accept a single "str" as a special case since it is common
-InterleavedContent = str | InterleavedContentItem | list[InterleavedContentItem]
+InterleavedContent = Union[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: str | ToolCall
+    tool_call: Union[str, ToolCall]
     parse_status: ToolCallParseStatus
 
 
 # streaming completions send a stream of ContentDeltas
 ContentDelta = Annotated[
-    TextDelta | ImageDelta | ToolCallDelta,
+    Union[TextDelta, ImageDelta, ToolCallDelta],
     Field(discriminator="type"),
 ]
 register_schema(ContentDelta, name="ContentDelta")

llama_stack/apis/common/deployment_types.py

@@ -0,0 +1,30 @@
+# 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 enum import Enum
+from typing import Any, Dict, Optional
+
+from pydantic import BaseModel
+
+from llama_stack.apis.common.content_types import URL
+from llama_stack.schema_utils import json_schema_type
+
+
+@json_schema_type
+class RestAPIMethod(Enum):
+    GET = "GET"
+    POST = "POST"
+    PUT = "PUT"
+    DELETE = "DELETE"
+
+
+@json_schema_type
+class RestAPIExecutionConfig(BaseModel):
+    url: URL
+    method: RestAPIMethod
+    params: Optional[Dict[str, Any]] = None
+    headers: Optional[Dict[str, Any]] = None
+    body: Optional[Dict[str, Any]] = None

llama_stack/apis/common/responses.py

@@ -4,19 +4,13 @@
 # This source code is licensed under the terms described in the LICENSE file in
 # the root directory of this source tree.
 
-from enum import Enum
-from typing import Any
+from typing import Any, Dict, List
 
 from pydantic import BaseModel
 
 from llama_stack.schema_utils import json_schema_type
 
 
-class Order(Enum):
-    asc = "asc"
-    desc = "desc"
-
-
 @json_schema_type
 class PaginatedResponse(BaseModel):
     """A generic paginated response that follows a simple format.
@@ -25,5 +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

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

llama_stack/apis/common/type_system.py

@@ -4,9 +4,10 @@
 # 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 typing import Literal, Union
 
 from pydantic import BaseModel, Field
+from typing_extensions import Annotated
 
 from llama_stack.schema_utils import json_schema_type, register_schema
 
@@ -72,16 +73,18 @@ class DialogType(BaseModel):
 
 
 ParamType = Annotated[
-    StringType
-    | NumberType
-    | BooleanType
-    | ArrayType
-    | ObjectType
-    | JsonType
-    | UnionType
-    | ChatCompletionInputType
-    | CompletionInputType
-    | AgentTurnInputType,
+    Union[
+        StringType,
+        NumberType,
+        BooleanType,
+        ArrayType,
+        ObjectType,
+        JsonType,
+        UnionType,
+        ChatCompletionInputType,
+        CompletionInputType,
+        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, Protocol, runtime_checkable
+from typing import Any, Dict, List, Optional, 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: int | None = None,
-        limit: int | None = None,
+        start_index: Optional[int] = None,
+        limit: Optional[int] = None,
     ) -> PaginatedResponse:
         """Get a paginated list of rows from a dataset.
 
@@ -34,21 +34,14 @@ class DatasetIO(Protocol):
         - limit: Number of items to return. If None or -1, returns all items.
 
         The response includes:
-        - data: List of items for the current page.
-        - has_more: Whether there are more items available after this set.
+        - data: List of items for the current page
+        - has_more: Whether there are more items available after this set
 
         :param dataset_id: The ID of the dataset to get the rows from.
         :param start_index: Index into dataset for the first row to get. Get all rows if None.
         :param limit: The number of rows to get.
-        :returns: A PaginatedResponse.
         """
         ...
 
     @webmethod(route="/datasetio/append-rows/{dataset_id:path}", method="POST")
-    async def append_rows(self, dataset_id: str, rows: list[dict[str, Any]]) -> None:
-        """Append rows to a dataset.
-
-        :param dataset_id: The ID of the dataset to append the rows to.
-        :param rows: The rows to append to the dataset.
-        """
-        ...
+    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, Literal, Protocol
+from typing import Annotated, Any, Dict, List, Literal, Optional, Protocol, Union
 
 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[
-    URIDataSource | RowsDataSource,
+    Union[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",
     )
@@ -106,14 +106,14 @@ class CommonDatasetFields(BaseModel):
 
 @json_schema_type
 class Dataset(CommonDatasetFields, Resource):
-    type: Literal[ResourceType.dataset] = ResourceType.dataset
+    type: Literal[ResourceType.dataset.value] = ResourceType.dataset.value
 
     @property
     def dataset_id(self) -> str:
         return self.identifier
 
     @property
-    def provider_dataset_id(self) -> str | None:
+    def provider_dataset_id(self) -> str:
         return self.provider_resource_id
 
 
@@ -122,7 +122,7 @@ class DatasetInput(CommonDatasetFields, BaseModel):
 
 
 class ListDatasetsResponse(BaseModel):
-    data: list[Dataset]
+    data: List[Dataset]
 
 
 class Datasets(Protocol):
@@ -131,14 +131,13 @@ class Datasets(Protocol):
         self,
         purpose: DatasetPurpose,
         source: DataSource,
-        metadata: dict[str, Any] | None = None,
-        dataset_id: str | None = None,
+        metadata: Optional[Dict[str, Any]] = None,
+        dataset_id: Optional[str] = None,
     ) -> Dataset:
         """
         Register a new dataset.
 
-        :param purpose: The purpose of the dataset.
-        One of:
+        :param purpose: The purpose of the dataset. One of
             - "post-training/messages": The dataset contains a messages column with list of messages for post-training.
                 {
                     "messages": [
@@ -189,9 +188,8 @@ class Datasets(Protocol):
                ]
            }
         :param metadata: The metadata for the dataset.
-           - E.g. {"description": "My dataset"}.
+           - E.g. {"description": "My dataset"}
         :param dataset_id: The ID of the dataset. If not provided, an ID will be generated.
-        :returns: A Dataset.
         """
         ...
 
@@ -199,29 +197,13 @@ class Datasets(Protocol):
     async def get_dataset(
         self,
         dataset_id: str,
-    ) -> Dataset:
-        """Get a dataset by its ID.
-
-        :param dataset_id: The ID of the dataset to get.
-        :returns: A Dataset.
-        """
-        ...
+    ) -> Dataset: ...
 
     @webmethod(route="/datasets", method="GET")
-    async def list_datasets(self) -> ListDatasetsResponse:
-        """List all datasets.
-
-        :returns: A ListDatasetsResponse.
-        """
-        ...
+    async def list_datasets(self) -> ListDatasetsResponse: ...
 
     @webmethod(route="/datasets/{dataset_id:path}", method="DELETE")
     async def unregister_dataset(
         self,
         dataset_id: str,
-    ) -> None:
-        """Unregister a dataset by its ID.
-
-        :param dataset_id: The ID of the dataset to unregister.
-        """
-        ...
+    ) -> None: ...

llama_stack/apis/datatypes.py

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

llama_stack/apis/eval/eval.py

@@ -4,9 +4,10 @@
 # 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, Literal, Protocol
+from typing import Any, Dict, List, Literal, Optional, Protocol, Union
 
 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
@@ -28,7 +29,7 @@ class ModelCandidate(BaseModel):
     type: Literal["model"] = "model"
     model: str
     sampling_params: SamplingParams
-    system_message: SystemMessage | None = None
+    system_message: Optional[SystemMessage] = None
 
 
 @json_schema_type
@@ -42,7 +43,7 @@ class AgentCandidate(BaseModel):
     config: AgentConfig
 
 
-EvalCandidate = Annotated[ModelCandidate | AgentCandidate, Field(discriminator="type")]
+EvalCandidate = Annotated[Union[ModelCandidate, AgentCandidate], Field(discriminator="type")]
 register_schema(EvalCandidate, name="EvalCandidate")
 
 
@@ -56,11 +57,11 @@ 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: int | None = Field(
+    num_examples: Optional[int] = Field(
         description="Number of examples to evaluate (useful for testing), if not provided, all examples in the dataset will be evaluated",
         default=None,
     )
@@ -75,9 +76,9 @@ 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):
@@ -93,16 +94,15 @@ class Eval(Protocol):
 
         :param benchmark_id: The ID of the benchmark to run the evaluation on.
         :param benchmark_config: The configuration for the benchmark.
-        :returns: The job that was created to run the evaluation.
+        :return: The job that was created to run the evaluation.
         """
-        ...
 
     @webmethod(route="/eval/benchmarks/{benchmark_id}/evaluations", method="POST")
     async def evaluate_rows(
         self,
         benchmark_id: str,
-        input_rows: list[dict[str, Any]],
-        scoring_functions: list[str],
+        input_rows: List[Dict[str, Any]],
+        scoring_functions: List[str],
         benchmark_config: BenchmarkConfig,
     ) -> EvaluateResponse:
         """Evaluate a list of rows on a benchmark.
@@ -111,9 +111,8 @@ class Eval(Protocol):
         :param input_rows: The rows to evaluate.
         :param scoring_functions: The scoring functions to use for the evaluation.
         :param benchmark_config: The configuration for the benchmark.
-        :returns: EvaluateResponse object containing generations and scores.
+        :return: EvaluateResponse object containing generations and scores
         """
-        ...
 
     @webmethod(route="/eval/benchmarks/{benchmark_id}/jobs/{job_id}", method="GET")
     async def job_status(self, benchmark_id: str, job_id: str) -> Job:
@@ -121,7 +120,7 @@ class Eval(Protocol):
 
         :param benchmark_id: The ID of the benchmark to run the evaluation on.
         :param job_id: The ID of the job to get the status of.
-        :returns: The status of the evaluation job.
+        :return: The status of the evaluationjob.
         """
         ...
 
@@ -140,6 +139,5 @@ class Eval(Protocol):
 
         :param benchmark_id: The ID of the benchmark to run the evaluation on.
         :param job_id: The ID of the job to get the result of.
-        :returns: The result of the job.
+        :return: The result of the job.
         """
-        ...

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 Protocol, runtime_checkable
+from typing import List, Optional, 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
@@ -91,11 +91,10 @@ class Files(Protocol):
         """
         Create a new upload session for a file identified by a bucket and key.
 
-        :param bucket: Bucket under which the file is stored (valid chars: a-zA-Z0-9_-).
-        :param key: Key under which the file is stored (valid chars: a-zA-Z0-9_-/.).
-        :param mime_type: MIME type of the file.
-        :param size: File size in bytes.
-        :returns: A FileUploadResponse.
+        :param bucket: Bucket under which the file is stored (valid chars: a-zA-Z0-9_-)
+        :param key: Key under which the file is stored (valid chars: a-zA-Z0-9_-/.)
+        :param mime_type: MIME type of the file
+        :param size: File size in bytes
         """
         ...
 
@@ -103,13 +102,12 @@ class Files(Protocol):
     async def upload_content_to_session(
         self,
         upload_id: str,
-    ) -> FileResponse | None:
+    ) -> Optional[FileResponse]:
         """
         Upload file content to an existing upload session.
         On the server, request body will have the raw bytes that are uploaded.
 
-        :param upload_id: ID of the upload session.
-        :returns: A FileResponse or None if the upload is not complete.
+        :param upload_id: ID of the upload session
         """
         ...
 
@@ -119,10 +117,9 @@ class Files(Protocol):
         upload_id: str,
     ) -> FileUploadResponse:
         """
-        Returns information about an existsing upload session.
+        Returns information about an existsing upload session
 
-        :param upload_id: ID of the upload session.
-        :returns: A FileUploadResponse.
+        :param upload_id: ID of the upload session
         """
         ...
 
@@ -133,9 +130,6 @@ class Files(Protocol):
     ) -> ListBucketResponse:
         """
         List all buckets.
-
-        :param bucket: Bucket name (valid chars: a-zA-Z0-9_-).
-        :returns: A ListBucketResponse.
         """
         ...
 
@@ -147,8 +141,7 @@ class Files(Protocol):
         """
         List all files in a bucket.
 
-        :param bucket: Bucket name (valid chars: a-zA-Z0-9_-).
-        :returns: A ListFileResponse.
+        :param bucket: Bucket name (valid chars: a-zA-Z0-9_-)
         """
         ...
 
@@ -161,9 +154,8 @@ class Files(Protocol):
         """
         Get a file info identified by a bucket and key.
 
-        :param bucket: Bucket name (valid chars: a-zA-Z0-9_-).
-        :param key: Key under which the file is stored (valid chars: a-zA-Z0-9_-/.).
-        :returns: A FileResponse.
+        :param bucket: Bucket name (valid chars: a-zA-Z0-9_-)
+        :param key: Key under which the file is stored (valid chars: a-zA-Z0-9_-/.)
         """
         ...
 
@@ -176,7 +168,7 @@ class Files(Protocol):
         """
         Delete a file identified by a bucket and key.
 
-        :param bucket: Bucket name (valid chars: a-zA-Z0-9_-).
-        :param key: Key under which the file is stored (valid chars: a-zA-Z0-9_-/.).
+        :param bucket: Bucket name (valid chars: a-zA-Z0-9_-)
+        :param key: Key under which the file is stored (valid chars: a-zA-Z0-9_-/.)
         """
         ...

llama_stack/apis/inference/inference.py

@@ -4,22 +4,23 @@
 # This source code is licensed under the terms described in the LICENSE file in
 # the root directory of this source tree.
 
-import sys
-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 TypedDict
+from typing_extensions import Annotated, TypedDict
 
 from llama_stack.apis.common.content_types import ContentDelta, InterleavedContent, InterleavedContentItem
-from llama_stack.apis.common.responses import Order
 from llama_stack.apis.models import Model
 from llama_stack.apis.telemetry.telemetry import MetricResponseMixin
 from llama_stack.models.llama.datatypes import (
@@ -37,16 +38,6 @@ register_schema(ToolCall)
 register_schema(ToolParamDefinition)
 register_schema(ToolDefinition)
 
-# TODO: use enum.StrEnum when we drop support for python 3.10
-if sys.version_info >= (3, 11):
-    from enum import StrEnum
-else:
-
-    class StrEnum(str, Enum):
-        """Backport of StrEnum for Python 3.10 and below."""
-
-        pass
-
 
 @json_schema_type
 class GreedySamplingStrategy(BaseModel):
@@ -56,8 +47,8 @@ class GreedySamplingStrategy(BaseModel):
 @json_schema_type
 class TopPSamplingStrategy(BaseModel):
     type: Literal["top_p"] = "top_p"
-    temperature: float | None = Field(..., gt=0.0)
-    top_p: float | None = 0.95
+    temperature: Optional[float] = Field(..., gt=0.0)
+    top_p: Optional[float] = 0.95
 
 
 @json_schema_type
@@ -67,7 +58,7 @@ class TopKSamplingStrategy(BaseModel):
 
 
 SamplingStrategy = Annotated[
-    GreedySamplingStrategy | TopPSamplingStrategy | TopKSamplingStrategy,
+    Union[GreedySamplingStrategy, TopPSamplingStrategy, TopKSamplingStrategy],
     Field(discriminator="type"),
 ]
 register_schema(SamplingStrategy, name="SamplingStrategy")
@@ -88,9 +79,9 @@ class SamplingParams(BaseModel):
 
     strategy: SamplingStrategy = Field(default_factory=GreedySamplingStrategy)
 
-    max_tokens: int | None = 0
-    repetition_penalty: float | None = 1.0
-    stop: list[str] | None = None
+    max_tokens: Optional[int] = 0
+    repetition_penalty: Optional[float] = 1.0
+    stop: Optional[List[str]] = None
 
 
 class LogProbConfig(BaseModel):
@@ -99,7 +90,7 @@ class LogProbConfig(BaseModel):
     :param top_k: How many tokens (for each position) to return log probabilities for.
     """
 
-    top_k: int | None = 0
+    top_k: Optional[int] = 0
 
 
 class QuantizationType(Enum):
@@ -134,11 +125,11 @@ class Int4QuantizationConfig(BaseModel):
     """
 
     type: Literal["int4_mixed"] = "int4_mixed"
-    scheme: str | None = "int4_weight_int8_dynamic_activation"
+    scheme: Optional[str] = "int4_weight_int8_dynamic_activation"
 
 
 QuantizationConfig = Annotated[
-    Bf16QuantizationConfig | Fp8QuantizationConfig | Int4QuantizationConfig,
+    Union[Bf16QuantizationConfig, Fp8QuantizationConfig, Int4QuantizationConfig],
     Field(discriminator="type"),
 ]
 
@@ -154,7 +145,7 @@ class UserMessage(BaseModel):
 
     role: Literal["user"] = "user"
     content: InterleavedContent
-    context: InterleavedContent | None = None
+    context: Optional[InterleavedContent] = None
 
 
 @json_schema_type
@@ -199,11 +190,16 @@ class CompletionMessage(BaseModel):
     role: Literal["assistant"] = "assistant"
     content: InterleavedContent
     stop_reason: StopReason
-    tool_calls: list[ToolCall] | None = Field(default_factory=lambda: [])
+    tool_calls: Optional[List[ToolCall]] = Field(default_factory=list)
 
 
 Message = Annotated[
-    UserMessage | SystemMessage | ToolResponseMessage | CompletionMessage,
+    Union[
+        UserMessage,
+        SystemMessage,
+        ToolResponseMessage,
+        CompletionMessage,
+    ],
     Field(discriminator="role"),
 ]
 register_schema(Message, name="Message")
@@ -212,9 +208,9 @@ register_schema(Message, name="Message")
 @json_schema_type
 class ToolResponse(BaseModel):
     call_id: str
-    tool_name: BuiltinTool | str
+    tool_name: Union[BuiltinTool, str]
     content: InterleavedContent
-    metadata: dict[str, Any] | None = None
+    metadata: Optional[Dict[str, Any]] = None
 
     @field_validator("tool_name", mode="before")
     @classmethod
@@ -247,7 +243,7 @@ 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):
@@ -275,11 +271,11 @@ class ChatCompletionResponseEvent(BaseModel):
 
     event_type: ChatCompletionResponseEventType
     delta: ContentDelta
-    logprobs: list[TokenLogProbs] | None = None
-    stop_reason: StopReason | None = None
+    logprobs: Optional[List[TokenLogProbs]] = None
+    stop_reason: Optional[StopReason] = None
 
 
-class ResponseFormatType(StrEnum):
+class ResponseFormatType(Enum):
     """Types of formats for structured (guided) decoding.
 
     :cvar json_schema: Response should conform to a JSON schema. In a Python SDK, this is often a `pydantic` model.
@@ -298,8 +294,8 @@ class JsonSchemaResponseFormat(BaseModel):
     :param json_schema: The JSON schema the response should conform to. In a Python SDK, this is often a `pydantic` model.
     """
 
-    type: Literal[ResponseFormatType.json_schema] = ResponseFormatType.json_schema
-    json_schema: dict[str, Any]
+    type: Literal[ResponseFormatType.json_schema.value] = ResponseFormatType.json_schema.value
+    json_schema: Dict[str, Any]
 
 
 @json_schema_type
@@ -310,12 +306,12 @@ class GrammarResponseFormat(BaseModel):
     :param bnf: The BNF grammar specification the response should conform to
     """
 
-    type: Literal[ResponseFormatType.grammar] = ResponseFormatType.grammar
-    bnf: dict[str, Any]
+    type: Literal[ResponseFormatType.grammar.value] = ResponseFormatType.grammar.value
+    bnf: Dict[str, Any]
 
 
 ResponseFormat = Annotated[
-    JsonSchemaResponseFormat | GrammarResponseFormat,
+    Union[JsonSchemaResponseFormat, GrammarResponseFormat],
     Field(discriminator="type"),
 ]
 register_schema(ResponseFormat, name="ResponseFormat")
@@ -325,10 +321,10 @@ register_schema(ResponseFormat, name="ResponseFormat")
 class CompletionRequest(BaseModel):
     model: str
     content: InterleavedContent
-    sampling_params: SamplingParams | None = Field(default_factory=SamplingParams)
-    response_format: ResponseFormat | None = None
-    stream: bool | None = False
-    logprobs: LogProbConfig | None = None
+    sampling_params: Optional[SamplingParams] = Field(default_factory=SamplingParams)
+    response_format: Optional[ResponseFormat] = None
+    stream: Optional[bool] = False
+    logprobs: Optional[LogProbConfig] = None
 
 
 @json_schema_type
@@ -342,7 +338,7 @@ class CompletionResponse(MetricResponseMixin):
 
     content: str
     stop_reason: StopReason
-    logprobs: list[TokenLogProbs] | None = None
+    logprobs: Optional[List[TokenLogProbs]] = None
 
 
 @json_schema_type
@@ -355,8 +351,8 @@ class CompletionResponseStreamChunk(MetricResponseMixin):
     """
 
     delta: str
-    stop_reason: StopReason | None = None
-    logprobs: list[TokenLogProbs] | None = None
+    stop_reason: Optional[StopReason] = None
+    logprobs: Optional[List[TokenLogProbs]] = None
 
 
 class SystemMessageBehavior(Enum):
@@ -387,9 +383,9 @@ class ToolConfig(BaseModel):
             '{{function_definitions}}' to indicate where the function definitions should be inserted.
     """
 
-    tool_choice: ToolChoice | str | None = Field(default=ToolChoice.auto)
-    tool_prompt_format: ToolPromptFormat | None = Field(default=None)
-    system_message_behavior: SystemMessageBehavior | None = Field(default=SystemMessageBehavior.append)
+    tool_choice: Optional[ToolChoice | str] = Field(default=ToolChoice.auto)
+    tool_prompt_format: Optional[ToolPromptFormat] = Field(default=None)
+    system_message_behavior: Optional[SystemMessageBehavior] = Field(default=SystemMessageBehavior.append)
 
     def model_post_init(self, __context: Any) -> None:
         if isinstance(self.tool_choice, str):
@@ -403,15 +399,15 @@ class ToolConfig(BaseModel):
 @json_schema_type
 class ChatCompletionRequest(BaseModel):
     model: str
-    messages: list[Message]
-    sampling_params: SamplingParams | None = Field(default_factory=SamplingParams)
+    messages: List[Message]
+    sampling_params: Optional[SamplingParams] = Field(default_factory=SamplingParams)
 
-    tools: list[ToolDefinition] | None = Field(default_factory=lambda: [])
-    tool_config: ToolConfig | None = Field(default_factory=ToolConfig)
+    tools: Optional[List[ToolDefinition]] = Field(default_factory=list)
+    tool_config: Optional[ToolConfig] = Field(default_factory=ToolConfig)
 
-    response_format: ResponseFormat | None = None
-    stream: bool | None = False
-    logprobs: LogProbConfig | None = None
+    response_format: Optional[ResponseFormat] = None
+    stream: Optional[bool] = False
+    logprobs: Optional[LogProbConfig] = None
 
 
 @json_schema_type
@@ -433,7 +429,7 @@ class ChatCompletionResponse(MetricResponseMixin):
     """
 
     completion_message: CompletionMessage
-    logprobs: list[TokenLogProbs] | None = None
+    logprobs: Optional[List[TokenLogProbs]] = None
 
 
 @json_schema_type
@@ -443,7 +439,7 @@ 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
@@ -455,7 +451,7 @@ class OpenAIChatCompletionContentPartTextParam(BaseModel):
 @json_schema_type
 class OpenAIImageURL(BaseModel):
     url: str
-    detail: str | None = None
+    detail: Optional[str] = None
 
 
 @json_schema_type
@@ -465,13 +461,16 @@ class OpenAIChatCompletionContentPartImageParam(BaseModel):
 
 
 OpenAIChatCompletionContentPartParam = Annotated[
-    OpenAIChatCompletionContentPartTextParam | OpenAIChatCompletionContentPartImageParam,
+    Union[
+        OpenAIChatCompletionContentPartTextParam,
+        OpenAIChatCompletionContentPartImageParam,
+    ],
     Field(discriminator="type"),
 ]
 register_schema(OpenAIChatCompletionContentPartParam, name="OpenAIChatCompletionContentPartParam")
 
 
-OpenAIChatCompletionMessageContent = str | list[OpenAIChatCompletionContentPartParam]
+OpenAIChatCompletionMessageContent = Union[str, List[OpenAIChatCompletionContentPartParam]]
 
 
 @json_schema_type
@@ -485,7 +484,7 @@ class OpenAIUserMessageParam(BaseModel):
 
     role: Literal["user"] = "user"
     content: OpenAIChatCompletionMessageContent
-    name: str | None = None
+    name: Optional[str] = None
 
 
 @json_schema_type
@@ -499,21 +498,21 @@ class OpenAISystemMessageParam(BaseModel):
 
     role: Literal["system"] = "system"
     content: OpenAIChatCompletionMessageContent
-    name: str | None = None
+    name: Optional[str] = None
 
 
 @json_schema_type
 class OpenAIChatCompletionToolCallFunction(BaseModel):
-    name: str | None = None
-    arguments: str | None = None
+    name: Optional[str] = None
+    arguments: Optional[str] = None
 
 
 @json_schema_type
 class OpenAIChatCompletionToolCall(BaseModel):
-    index: int | None = None
-    id: str | None = None
+    index: Optional[int] = None
+    id: Optional[str] = None
     type: Literal["function"] = "function"
-    function: OpenAIChatCompletionToolCallFunction | None = None
+    function: Optional[OpenAIChatCompletionToolCallFunction] = None
 
 
 @json_schema_type
@@ -527,9 +526,9 @@ class OpenAIAssistantMessageParam(BaseModel):
     """
 
     role: Literal["assistant"] = "assistant"
-    content: OpenAIChatCompletionMessageContent | None = None
-    name: str | None = None
-    tool_calls: list[OpenAIChatCompletionToolCall] | None = None
+    content: Optional[OpenAIChatCompletionMessageContent] = None
+    name: Optional[str] = None
+    tool_calls: Optional[List[OpenAIChatCompletionToolCall]] = None
 
 
 @json_schema_type
@@ -557,15 +556,17 @@ class OpenAIDeveloperMessageParam(BaseModel):
 
     role: Literal["developer"] = "developer"
     content: OpenAIChatCompletionMessageContent
-    name: str | None = None
+    name: Optional[str] = None
 
 
 OpenAIMessageParam = Annotated[
-    OpenAIUserMessageParam
-    | OpenAISystemMessageParam
-    | OpenAIAssistantMessageParam
-    | OpenAIToolMessageParam
-    | OpenAIDeveloperMessageParam,
+    Union[
+        OpenAIUserMessageParam,
+        OpenAISystemMessageParam,
+        OpenAIAssistantMessageParam,
+        OpenAIToolMessageParam,
+        OpenAIDeveloperMessageParam,
+    ],
     Field(discriminator="role"),
 ]
 register_schema(OpenAIMessageParam, name="OpenAIMessageParam")
@@ -579,14 +580,14 @@ class OpenAIResponseFormatText(BaseModel):
 @json_schema_type
 class OpenAIJSONSchema(TypedDict, total=False):
     name: str
-    description: str | None
-    strict: bool | None
+    description: Optional[str] = None
+    strict: Optional[bool] = 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: dict[str, Any] | None
+    schema: Optional[Dict[str, Any]] = None
 
 
 @json_schema_type
@@ -601,7 +602,11 @@ class OpenAIResponseFormatJSONObject(BaseModel):
 
 
 OpenAIResponseFormatParam = Annotated[
-    OpenAIResponseFormatText | OpenAIResponseFormatJSONSchema | OpenAIResponseFormatJSONObject,
+    Union[
+        OpenAIResponseFormatText,
+        OpenAIResponseFormatJSONSchema,
+        OpenAIResponseFormatJSONObject,
+    ],
     Field(discriminator="type"),
 ]
 register_schema(OpenAIResponseFormatParam, name="OpenAIResponseFormatParam")
@@ -617,7 +622,7 @@ class OpenAITopLogProb(BaseModel):
     """
 
     token: str
-    bytes: list[int] | None = None
+    bytes: Optional[List[int]] = None
     logprob: float
 
 
@@ -632,9 +637,9 @@ class OpenAITokenLogProb(BaseModel):
     """
 
     token: str
-    bytes: list[int] | None = None
+    bytes: Optional[List[int]] = None
     logprob: float
-    top_logprobs: list[OpenAITopLogProb]
+    top_logprobs: List[OpenAITopLogProb]
 
 
 @json_schema_type
@@ -645,8 +650,8 @@ class OpenAIChoiceLogprobs(BaseModel):
     :param refusal: (Optional) The log probabilities for the tokens in the message
     """
 
-    content: list[OpenAITokenLogProb] | None = None
-    refusal: list[OpenAITokenLogProb] | None = None
+    content: Optional[List[OpenAITokenLogProb]] = None
+    refusal: Optional[List[OpenAITokenLogProb]] = None
 
 
 @json_schema_type
@@ -659,10 +664,10 @@ class OpenAIChoiceDelta(BaseModel):
     :param tool_calls: (Optional) The tool calls of the delta
     """
 
-    content: str | None = None
-    refusal: str | None = None
-    role: str | None = None
-    tool_calls: list[OpenAIChatCompletionToolCall] | None = None
+    content: Optional[str] = None
+    refusal: Optional[str] = None
+    role: Optional[str] = None
+    tool_calls: Optional[List[OpenAIChatCompletionToolCall]] = None
 
 
 @json_schema_type
@@ -678,7 +683,7 @@ class OpenAIChunkChoice(BaseModel):
     delta: OpenAIChoiceDelta
     finish_reason: str
     index: int
-    logprobs: OpenAIChoiceLogprobs | None = None
+    logprobs: Optional[OpenAIChoiceLogprobs] = None
 
 
 @json_schema_type
@@ -694,7 +699,7 @@ class OpenAIChoice(BaseModel):
     message: OpenAIMessageParam
     finish_reason: str
     index: int
-    logprobs: OpenAIChoiceLogprobs | None = None
+    logprobs: Optional[OpenAIChoiceLogprobs] = None
 
 
 @json_schema_type
@@ -709,7 +714,7 @@ class OpenAIChatCompletion(BaseModel):
     """
 
     id: str
-    choices: list[OpenAIChoice]
+    choices: List[OpenAIChoice]
     object: Literal["chat.completion"] = "chat.completion"
     created: int
     model: str
@@ -727,7 +732,7 @@ class OpenAIChatCompletionChunk(BaseModel):
     """
 
     id: str
-    choices: list[OpenAIChunkChoice]
+    choices: List[OpenAIChunkChoice]
     object: Literal["chat.completion.chunk"] = "chat.completion.chunk"
     created: int
     model: str
@@ -743,10 +748,10 @@ class OpenAICompletionLogprobs(BaseModel):
     :top_logprobs: (Optional) The top log probabilities for the tokens
     """
 
-    text_offset: list[int] | None = None
-    token_logprobs: list[float] | None = None
-    tokens: list[str] | None = None
-    top_logprobs: list[dict[str, float]] | None = None
+    text_offset: Optional[List[int]] = None
+    token_logprobs: Optional[List[float]] = None
+    tokens: Optional[List[str]] = None
+    top_logprobs: Optional[List[Dict[str, float]]] = None
 
 
 @json_schema_type
@@ -762,7 +767,7 @@ class OpenAICompletionChoice(BaseModel):
     finish_reason: str
     text: str
     index: int
-    logprobs: OpenAIChoiceLogprobs | None = None
+    logprobs: Optional[OpenAIChoiceLogprobs] = None
 
 
 @json_schema_type
@@ -777,54 +782,12 @@ class OpenAICompletion(BaseModel):
     """
 
     id: str
-    choices: list[OpenAICompletionChoice]
+    choices: List[OpenAICompletionChoice]
     created: int
     model: str
     object: Literal["text_completion"] = "text_completion"
 
 
-@json_schema_type
-class OpenAIEmbeddingData(BaseModel):
-    """A single embedding data object from an OpenAI-compatible embeddings response.
-
-    :param object: The object type, which will be "embedding"
-    :param embedding: The embedding vector as a list of floats (when encoding_format="float") or as a base64-encoded string (when encoding_format="base64")
-    :param index: The index of the embedding in the input list
-    """
-
-    object: Literal["embedding"] = "embedding"
-    embedding: list[float] | str
-    index: int
-
-
-@json_schema_type
-class OpenAIEmbeddingUsage(BaseModel):
-    """Usage information for an OpenAI-compatible embeddings response.
-
-    :param prompt_tokens: The number of tokens in the input
-    :param total_tokens: The total number of tokens used
-    """
-
-    prompt_tokens: int
-    total_tokens: int
-
-
-@json_schema_type
-class OpenAIEmbeddingsResponse(BaseModel):
-    """Response from an OpenAI-compatible embeddings request.
-
-    :param object: The object type, which will be "list"
-    :param data: List of embedding data objects
-    :param model: The model that was used to generate the embeddings
-    :param usage: Usage information
-    """
-
-    object: Literal["list"] = "list"
-    data: list[OpenAIEmbeddingData]
-    model: str
-    usage: OpenAIEmbeddingUsage
-
-
 class ModelStore(Protocol):
     async def get_model(self, identifier: str) -> Model: ...
 
@@ -855,35 +818,23 @@ class EmbeddingTaskType(Enum):
 
 @json_schema_type
 class BatchCompletionResponse(BaseModel):
-    batch: list[CompletionResponse]
+    batch: List[CompletionResponse]
 
 
 @json_schema_type
 class BatchChatCompletionResponse(BaseModel):
-    batch: list[ChatCompletionResponse]
-
-
-class OpenAICompletionWithInputMessages(OpenAIChatCompletion):
-    input_messages: list[OpenAIMessageParam]
-
-
-@json_schema_type
-class ListOpenAIChatCompletionResponse(BaseModel):
-    data: list[OpenAICompletionWithInputMessages]
-    has_more: bool
-    first_id: str
-    last_id: str
-    object: Literal["list"] = "list"
+    batch: List[ChatCompletionResponse]
 
 
 @runtime_checkable
 @trace_protocol
-class InferenceProvider(Protocol):
-    """
-    This protocol defines the interface that should be implemented by all inference providers.
-    """
+class Inference(Protocol):
+    """Llama Stack Inference API for generating completions, chat completions, and embeddings.
 
-    API_NAMESPACE: str = "Inference"
+    This API provides the raw interface to the underlying models. Two kinds of models are supported:
+    - LLM models: these models generate "raw" and "chat" (conversational) completions.
+    - Embedding models: these models generate embeddings to be used for semantic search.
+    """
 
     model_store: ModelStore | None = None
 
@@ -892,21 +843,21 @@ class InferenceProvider(Protocol):
         self,
         model_id: str,
         content: InterleavedContent,
-        sampling_params: SamplingParams | None = None,
-        response_format: ResponseFormat | None = None,
-        stream: bool | None = False,
-        logprobs: LogProbConfig | None = None,
-    ) -> CompletionResponse | AsyncIterator[CompletionResponseStreamChunk]:
+        sampling_params: Optional[SamplingParams] = None,
+        response_format: Optional[ResponseFormat] = None,
+        stream: Optional[bool] = False,
+        logprobs: Optional[LogProbConfig] = None,
+    ) -> Union[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.
-        :param content: The content to generate a completion for.
-        :param sampling_params: (Optional) Parameters to control the sampling strategy.
-        :param response_format: (Optional) Grammar specification for guided (structured) decoding.
+        :param content: The content to generate a completion for
+        :param sampling_params: (Optional) Parameters to control the sampling strategy
+        :param response_format: (Optional) Grammar specification for guided (structured) decoding
         :param stream: (Optional) If True, generate an SSE event stream of the response. Defaults to False.
         :param logprobs: (Optional) If specified, log probabilities for each token position will be returned.
         :returns: If stream=False, returns a CompletionResponse with the full completion.
-                 If stream=True, returns an SSE event stream of CompletionResponseStreamChunk.
+                 If stream=True, returns an SSE event stream of CompletionResponseStreamChunk
         """
         ...
 
@@ -914,42 +865,33 @@ class InferenceProvider(Protocol):
     async def batch_completion(
         self,
         model_id: str,
-        content_batch: list[InterleavedContent],
-        sampling_params: SamplingParams | None = None,
-        response_format: ResponseFormat | None = None,
-        logprobs: LogProbConfig | None = None,
+        content_batch: List[InterleavedContent],
+        sampling_params: Optional[SamplingParams] = None,
+        response_format: Optional[ResponseFormat] = None,
+        logprobs: Optional[LogProbConfig] = None,
     ) -> BatchCompletionResponse:
-        """Generate completions for a batch of 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.
-        :param content_batch: The content to generate completions for.
-        :param sampling_params: (Optional) Parameters to control the sampling strategy.
-        :param response_format: (Optional) Grammar specification for guided (structured) decoding.
-        :param logprobs: (Optional) If specified, log probabilities for each token position will be returned.
-        :returns: A BatchCompletionResponse with the full completions.
-        """
         raise NotImplementedError("Batch completion is not implemented")
 
     @webmethod(route="/inference/chat-completion", method="POST")
     async def chat_completion(
         self,
         model_id: str,
-        messages: list[Message],
-        sampling_params: SamplingParams | None = None,
-        tools: list[ToolDefinition] | None = None,
-        tool_choice: ToolChoice | None = ToolChoice.auto,
-        tool_prompt_format: ToolPromptFormat | None = None,
-        response_format: ResponseFormat | None = None,
-        stream: bool | None = False,
-        logprobs: LogProbConfig | None = None,
-        tool_config: ToolConfig | None = None,
-    ) -> ChatCompletionResponse | AsyncIterator[ChatCompletionResponseStreamChunk]:
+        messages: List[Message],
+        sampling_params: Optional[SamplingParams] = None,
+        tools: Optional[List[ToolDefinition]] = None,
+        tool_choice: Optional[ToolChoice] = ToolChoice.auto,
+        tool_prompt_format: Optional[ToolPromptFormat] = None,
+        response_format: Optional[ResponseFormat] = None,
+        stream: Optional[bool] = False,
+        logprobs: Optional[LogProbConfig] = None,
+        tool_config: Optional[ToolConfig] = None,
+    ) -> Union[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.
-        :param messages: List of messages in the conversation.
-        :param sampling_params: Parameters to control the sampling strategy.
-        :param tools: (Optional) List of tool definitions available to the model.
+        :param messages: List of messages in the conversation
+        :param sampling_params: Parameters to control the sampling strategy
+        :param tools: (Optional) List of tool definitions available to the model
         :param tool_choice: (Optional) Whether tool use is required or automatic. Defaults to ToolChoice.auto.
             .. deprecated::
                Use tool_config instead.
@@ -966,7 +908,7 @@ class InferenceProvider(Protocol):
         :param logprobs: (Optional) If specified, log probabilities for each token position will be returned.
         :param tool_config: (Optional) Configuration for tool use.
         :returns: If stream=False, returns a ChatCompletionResponse with the full completion.
-                 If stream=True, returns an SSE event stream of ChatCompletionResponseStreamChunk.
+                 If stream=True, returns an SSE event stream of ChatCompletionResponseStreamChunk
         """
         ...
 
@@ -974,34 +916,23 @@ class InferenceProvider(Protocol):
     async def batch_chat_completion(
         self,
         model_id: str,
-        messages_batch: list[list[Message]],
-        sampling_params: SamplingParams | None = None,
-        tools: list[ToolDefinition] | None = None,
-        tool_config: ToolConfig | None = None,
-        response_format: ResponseFormat | None = None,
-        logprobs: LogProbConfig | None = None,
+        messages_batch: List[List[Message]],
+        sampling_params: Optional[SamplingParams] = None,
+        tools: Optional[List[ToolDefinition]] = None,
+        tool_config: Optional[ToolConfig] = None,
+        response_format: Optional[ResponseFormat] = None,
+        logprobs: Optional[LogProbConfig] = None,
     ) -> BatchChatCompletionResponse:
-        """Generate chat completions for a batch of 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.
-        :param messages_batch: The messages to generate completions for.
-        :param sampling_params: (Optional) Parameters to control the sampling strategy.
-        :param tools: (Optional) List of tool definitions available to the model.
-        :param tool_config: (Optional) Configuration for tool use.
-        :param response_format: (Optional) Grammar specification for guided (structured) decoding.
-        :param logprobs: (Optional) If specified, log probabilities for each token position will be returned.
-        :returns: A BatchChatCompletionResponse with the full completions.
-        """
         raise NotImplementedError("Batch chat completion is not implemented")
 
     @webmethod(route="/inference/embeddings", method="POST")
     async def embeddings(
         self,
         model_id: str,
-        contents: list[str] | list[InterleavedContentItem],
-        text_truncation: TextTruncation | None = TextTruncation.none,
-        output_dimension: int | None = None,
-        task_type: EmbeddingTaskType | None = None,
+        contents: List[str] | List[InterleavedContentItem],
+        text_truncation: Optional[TextTruncation] = TextTruncation.none,
+        output_dimension: Optional[int] = None,
+        task_type: Optional[EmbeddingTaskType] = None,
     ) -> EmbeddingsResponse:
         """Generate embeddings for content pieces using the specified model.
 
@@ -1010,7 +941,7 @@ class InferenceProvider(Protocol):
         :param output_dimension: (Optional) Output dimensionality for the embeddings. Only supported by Matryoshka models.
         :param text_truncation: (Optional) Config for how to truncate text for embedding when text is longer than the model's max sequence length.
         :param task_type: (Optional) How is the embedding being used? This is only supported by asymmetric embedding models.
-        :returns: An array of embeddings, one for each 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}.
+        :returns: An array of embeddings, one for each 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}
         """
         ...
 
@@ -1019,46 +950,45 @@ class InferenceProvider(Protocol):
         self,
         # Standard OpenAI completion parameters
         model: str,
-        prompt: str | list[str] | list[int] | list[list[int]],
-        best_of: int | None = None,
-        echo: bool | None = None,
-        frequency_penalty: float | None = None,
-        logit_bias: dict[str, float] | None = None,
-        logprobs: bool | None = None,
-        max_tokens: int | None = None,
-        n: int | None = None,
-        presence_penalty: float | None = None,
-        seed: int | None = None,
-        stop: str | list[str] | None = None,
-        stream: bool | None = None,
-        stream_options: dict[str, Any] | None = None,
-        temperature: float | None = None,
-        top_p: float | None = None,
-        user: str | None = None,
+        prompt: Union[str, List[str], List[int], List[List[int]]],
+        best_of: Optional[int] = None,
+        echo: Optional[bool] = None,
+        frequency_penalty: Optional[float] = None,
+        logit_bias: Optional[Dict[str, float]] = None,
+        logprobs: Optional[bool] = None,
+        max_tokens: Optional[int] = None,
+        n: Optional[int] = None,
+        presence_penalty: Optional[float] = None,
+        seed: Optional[int] = None,
+        stop: Optional[Union[str, List[str]]] = None,
+        stream: Optional[bool] = None,
+        stream_options: Optional[Dict[str, Any]] = None,
+        temperature: Optional[float] = None,
+        top_p: Optional[float] = None,
+        user: Optional[str] = None,
         # vLLM-specific parameters
-        guided_choice: list[str] | None = None,
-        prompt_logprobs: int | None = None,
+        guided_choice: Optional[List[str]] = None,
+        prompt_logprobs: Optional[int] = None,
     ) -> OpenAICompletion:
         """Generate an OpenAI-compatible completion for the given prompt 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.
-        :param prompt: The prompt to generate a completion for.
-        :param best_of: (Optional) The number of completions to generate.
-        :param echo: (Optional) Whether to echo the prompt.
-        :param frequency_penalty: (Optional) The penalty for repeated tokens.
-        :param logit_bias: (Optional) The logit bias to use.
-        :param logprobs: (Optional) The log probabilities to use.
-        :param max_tokens: (Optional) The maximum number of tokens to generate.
-        :param n: (Optional) The number of completions to generate.
-        :param presence_penalty: (Optional) The penalty for repeated tokens.
-        :param seed: (Optional) The seed to use.
-        :param stop: (Optional) The stop tokens to use.
-        :param stream: (Optional) Whether to stream the response.
-        :param stream_options: (Optional) The stream options to use.
-        :param temperature: (Optional) The temperature to use.
-        :param top_p: (Optional) The top p to use.
-        :param user: (Optional) The user to use.
-        :returns: An OpenAICompletion.
+        :param prompt: The prompt to generate a completion for
+        :param best_of: (Optional) The number of completions to generate
+        :param echo: (Optional) Whether to echo the prompt
+        :param frequency_penalty: (Optional) The penalty for repeated tokens
+        :param logit_bias: (Optional) The logit bias to use
+        :param logprobs: (Optional) The log probabilities to use
+        :param max_tokens: (Optional) The maximum number of tokens to generate
+        :param n: (Optional) The number of completions to generate
+        :param presence_penalty: (Optional) The penalty for repeated tokens
+        :param seed: (Optional) The seed to use
+        :param stop: (Optional) The stop tokens to use
+        :param stream: (Optional) Whether to stream the response
+        :param stream_options: (Optional) The stream options to use
+        :param temperature: (Optional) The temperature to use
+        :param top_p: (Optional) The top p to use
+        :param user: (Optional) The user to use
         """
         ...
 
@@ -1066,110 +996,53 @@ class InferenceProvider(Protocol):
     async def openai_chat_completion(
         self,
         model: str,
-        messages: list[OpenAIMessageParam],
-        frequency_penalty: float | None = None,
-        function_call: str | dict[str, Any] | None = None,
-        functions: list[dict[str, Any]] | None = None,
-        logit_bias: dict[str, float] | None = None,
-        logprobs: bool | None = None,
-        max_completion_tokens: int | None = None,
-        max_tokens: int | None = None,
-        n: int | None = None,
-        parallel_tool_calls: bool | None = None,
-        presence_penalty: float | None = None,
-        response_format: OpenAIResponseFormatParam | None = None,
-        seed: int | None = None,
-        stop: str | list[str] | None = None,
-        stream: bool | None = None,
-        stream_options: dict[str, Any] | None = None,
-        temperature: float | None = None,
-        tool_choice: str | dict[str, Any] | None = None,
-        tools: list[dict[str, Any]] | None = None,
-        top_logprobs: int | None = None,
-        top_p: float | None = None,
-        user: str | None = None,
-    ) -> OpenAIChatCompletion | AsyncIterator[OpenAIChatCompletionChunk]:
+        messages: List[OpenAIMessageParam],
+        frequency_penalty: Optional[float] = None,
+        function_call: Optional[Union[str, Dict[str, Any]]] = None,
+        functions: Optional[List[Dict[str, Any]]] = None,
+        logit_bias: Optional[Dict[str, float]] = None,
+        logprobs: Optional[bool] = None,
+        max_completion_tokens: Optional[int] = None,
+        max_tokens: Optional[int] = None,
+        n: Optional[int] = None,
+        parallel_tool_calls: Optional[bool] = None,
+        presence_penalty: Optional[float] = None,
+        response_format: Optional[OpenAIResponseFormatParam] = None,
+        seed: Optional[int] = None,
+        stop: Optional[Union[str, List[str]]] = None,
+        stream: Optional[bool] = None,
+        stream_options: Optional[Dict[str, Any]] = None,
+        temperature: Optional[float] = None,
+        tool_choice: Optional[Union[str, Dict[str, Any]]] = None,
+        tools: Optional[List[Dict[str, Any]]] = None,
+        top_logprobs: Optional[int] = None,
+        top_p: Optional[float] = None,
+        user: Optional[str] = None,
+    ) -> Union[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.
-        :param messages: List of messages in the conversation.
-        :param frequency_penalty: (Optional) The penalty for repeated tokens.
-        :param function_call: (Optional) The function call to use.
-        :param functions: (Optional) List of functions to use.
-        :param logit_bias: (Optional) The logit bias to use.
-        :param logprobs: (Optional) The log probabilities to use.
-        :param max_completion_tokens: (Optional) The maximum number of tokens to generate.
-        :param max_tokens: (Optional) The maximum number of tokens to generate.
-        :param n: (Optional) The number of completions to generate.
-        :param parallel_tool_calls: (Optional) Whether to parallelize tool calls.
-        :param presence_penalty: (Optional) The penalty for repeated tokens.
-        :param response_format: (Optional) The response format to use.
-        :param seed: (Optional) The seed to use.
-        :param stop: (Optional) The stop tokens to use.
-        :param stream: (Optional) Whether to stream the response.
-        :param stream_options: (Optional) The stream options to use.
-        :param temperature: (Optional) The temperature to use.
-        :param tool_choice: (Optional) The tool choice to use.
-        :param tools: (Optional) The tools to use.
-        :param top_logprobs: (Optional) The top log probabilities to use.
-        :param top_p: (Optional) The top p to use.
-        :param user: (Optional) The user to use.
-        :returns: An OpenAIChatCompletion.
+        :param messages: List of messages in the conversation
+        :param frequency_penalty: (Optional) The penalty for repeated tokens
+        :param function_call: (Optional) The function call to use
+        :param functions: (Optional) List of functions to use
+        :param logit_bias: (Optional) The logit bias to use
+        :param logprobs: (Optional) The log probabilities to use
+        :param max_completion_tokens: (Optional) The maximum number of tokens to generate
+        :param max_tokens: (Optional) The maximum number of tokens to generate
+        :param n: (Optional) The number of completions to generate
+        :param parallel_tool_calls: (Optional) Whether to parallelize tool calls
+        :param presence_penalty: (Optional) The penalty for repeated tokens
+        :param response_format: (Optional) The response format to use
+        :param seed: (Optional) The seed to use
+        :param stop: (Optional) The stop tokens to use
+        :param stream: (Optional) Whether to stream the response
+        :param stream_options: (Optional) The stream options to use
+        :param temperature: (Optional) The temperature to use
+        :param tool_choice: (Optional) The tool choice to use
+        :param tools: (Optional) The tools to use
+        :param top_logprobs: (Optional) The top log probabilities to use
+        :param top_p: (Optional) The top p to use
+        :param user: (Optional) The user to use
         """
         ...
-
-    @webmethod(route="/openai/v1/embeddings", method="POST")
-    async def openai_embeddings(
-        self,
-        model: str,
-        input: str | list[str],
-        encoding_format: str | None = "float",
-        dimensions: int | None = None,
-        user: str | None = None,
-    ) -> OpenAIEmbeddingsResponse:
-        """Generate OpenAI-compatible embeddings for the given input using the specified model.
-
-        :param model: The identifier of the model to use. The model must be an embedding model registered with Llama Stack and available via the /models endpoint.
-        :param input: Input text to embed, encoded as a string or array of strings. To embed multiple inputs in a single request, pass an array of strings.
-        :param encoding_format: (Optional) The format to return the embeddings in. Can be either "float" or "base64". Defaults to "float".
-        :param dimensions: (Optional) The number of dimensions the resulting output embeddings should have. Only supported in text-embedding-3 and later models.
-        :param user: (Optional) A unique identifier representing your end-user, which can help OpenAI to monitor and detect abuse.
-        :returns: An OpenAIEmbeddingsResponse containing the embeddings.
-        """
-        ...
-
-
-class Inference(InferenceProvider):
-    """Llama Stack Inference API for generating completions, chat completions, and embeddings.
-
-    This API provides the raw interface to the underlying models. Two kinds of models are supported:
-    - LLM models: these models generate "raw" and "chat" (conversational) completions.
-    - Embedding models: these models generate embeddings to be used for semantic search.
-    """
-
-    @webmethod(route="/openai/v1/chat/completions", method="GET")
-    async def list_chat_completions(
-        self,
-        after: str | None = None,
-        limit: int | None = 20,
-        model: str | None = None,
-        order: Order | None = Order.desc,
-    ) -> ListOpenAIChatCompletionResponse:
-        """List all chat completions.
-
-        :param after: The ID of the last chat completion to return.
-        :param limit: The maximum number of chat completions to return.
-        :param model: The model to filter by.
-        :param order: The order to sort the chat completions by: "asc" or "desc". Defaults to "desc".
-        :returns: A ListOpenAIChatCompletionResponse.
-        """
-        raise NotImplementedError("List chat completions is not implemented")
-
-    @webmethod(route="/openai/v1/chat/completions/{completion_id}", method="GET")
-    async def get_chat_completion(self, completion_id: str) -> OpenAICompletionWithInputMessages:
-        """Describe a chat completion by its ID.
-
-        :param completion_id: ID of the chat completion.
-        :returns: A OpenAICompletionWithInputMessages.
-        """
-        raise NotImplementedError("Get chat completion is not implemented")

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 Protocol, runtime_checkable
+from typing import List, 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
@@ -30,31 +30,16 @@ class VersionInfo(BaseModel):
 
 
 class ListRoutesResponse(BaseModel):
-    data: list[RouteInfo]
+    data: List[RouteInfo]
 
 
 @runtime_checkable
 class Inspect(Protocol):
     @webmethod(route="/inspect/routes", method="GET")
-    async def list_routes(self) -> ListRoutesResponse:
-        """List all routes.
-
-        :returns: A ListRoutesResponse.
-        """
-        ...
+    async def list_routes(self) -> ListRoutesResponse: ...
 
     @webmethod(route="/health", method="GET")
-    async def health(self) -> HealthInfo:
-        """Get the health of the service.
-
-        :returns: A HealthInfo.
-        """
-        ...
+    async def health(self) -> HealthInfo: ...
 
     @webmethod(route="/version", method="GET")
-    async def version(self) -> VersionInfo:
-        """Get the version of the service.
-
-        :returns: A VersionInfo.
-        """
-        ...
+    async def version(self) -> VersionInfo: ...

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, Literal, Protocol, runtime_checkable
+from typing import Any, Dict, List, Literal, Optional, 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",
     )
@@ -29,14 +29,14 @@ class ModelType(str, Enum):
 
 @json_schema_type
 class Model(CommonModelFields, Resource):
-    type: Literal[ResourceType.model] = ResourceType.model
+    type: Literal[ResourceType.model.value] = ResourceType.model.value
 
     @property
     def model_id(self) -> str:
         return self.identifier
 
     @property
-    def provider_model_id(self) -> str | None:
+    def provider_model_id(self) -> str:
         return self.provider_resource_id
 
     model_config = ConfigDict(protected_namespaces=())
@@ -46,14 +46,14 @@ class Model(CommonModelFields, Resource):
 
 class ModelInput(CommonModelFields):
     model_id: str
-    provider_id: str | None = None
-    provider_model_id: str | None = None
-    model_type: ModelType | None = ModelType.llm
+    provider_id: Optional[str] = None
+    provider_model_id: Optional[str] = None
+    model_type: Optional[ModelType] = ModelType.llm
     model_config = ConfigDict(protected_namespaces=())
 
 
 class ListModelsResponse(BaseModel):
-    data: list[Model]
+    data: List[Model]
 
 
 @json_schema_type
@@ -73,67 +73,36 @@ class OpenAIModel(BaseModel):
 
 
 class OpenAIListModelsResponse(BaseModel):
-    data: list[OpenAIModel]
+    data: List[OpenAIModel]
 
 
 @runtime_checkable
 @trace_protocol
 class Models(Protocol):
     @webmethod(route="/models", method="GET")
-    async def list_models(self) -> ListModelsResponse:
-        """List all models.
-
-        :returns: A ListModelsResponse.
-        """
-        ...
+    async def list_models(self) -> ListModelsResponse: ...
 
     @webmethod(route="/openai/v1/models", method="GET")
-    async def openai_list_models(self) -> OpenAIListModelsResponse:
-        """List models using the OpenAI API.
-
-        :returns: A OpenAIListModelsResponse.
-        """
-        ...
+    async def openai_list_models(self) -> OpenAIListModelsResponse: ...
 
     @webmethod(route="/models/{model_id:path}", method="GET")
     async def get_model(
         self,
         model_id: str,
-    ) -> Model:
-        """Get a model by its identifier.
-
-        :param model_id: The identifier of the model to get.
-        :returns: A Model.
-        """
-        ...
+    ) -> Model: ...
 
     @webmethod(route="/models", method="POST")
     async def register_model(
         self,
         model_id: str,
-        provider_model_id: str | None = None,
-        provider_id: str | None = None,
-        metadata: dict[str, Any] | None = None,
-        model_type: ModelType | None = None,
-    ) -> Model:
-        """Register a model.
-
-        :param model_id: The identifier of the model to register.
-        :param provider_model_id: The identifier of the model in the provider.
-        :param provider_id: The identifier of the provider.
-        :param metadata: Any additional metadata for this model.
-        :param model_type: The type of model to register.
-        :returns: A Model.
-        """
-        ...
+        provider_model_id: Optional[str] = None,
+        provider_id: Optional[str] = None,
+        metadata: Optional[Dict[str, Any]] = None,
+        model_type: Optional[ModelType] = None,
+    ) -> Model: ...
 
     @webmethod(route="/models/{model_id:path}", method="DELETE")
     async def unregister_model(
         self,
         model_id: str,
-    ) -> None:
-        """Unregister a model.
-
-        :param model_id: The identifier of the model to unregister.
-        """
-        ...
+    ) -> None: ...

llama_stack/apis/post_training/post_training.py

@@ -6,9 +6,10 @@
 
 from datetime import datetime
 from enum import Enum
-from typing import Annotated, Any, Literal, Protocol
+from typing import Any, Dict, List, Literal, Optional, Protocol, Union
 
 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
@@ -35,9 +36,9 @@ class DataConfig(BaseModel):
     batch_size: int
     shuffle: bool
     data_format: DatasetFormat
-    validation_dataset_id: str | None = None
-    packed: bool | None = False
-    train_on_input: bool | None = False
+    validation_dataset_id: Optional[str] = None
+    packed: Optional[bool] = False
+    train_on_input: Optional[bool] = False
 
 
 @json_schema_type
@@ -50,10 +51,10 @@ class OptimizerConfig(BaseModel):
 
 @json_schema_type
 class EfficiencyConfig(BaseModel):
-    enable_activation_checkpointing: bool | None = False
-    enable_activation_offloading: bool | None = False
-    memory_efficient_fsdp_wrap: bool | None = False
-    fsdp_cpu_offload: bool | None = False
+    enable_activation_checkpointing: Optional[bool] = False
+    enable_activation_offloading: Optional[bool] = False
+    memory_efficient_fsdp_wrap: Optional[bool] = False
+    fsdp_cpu_offload: Optional[bool] = False
 
 
 @json_schema_type
@@ -61,23 +62,23 @@ class TrainingConfig(BaseModel):
     n_epochs: int
     max_steps_per_epoch: int = 1
     gradient_accumulation_steps: int = 1
-    max_validation_steps: int | None = 1
-    data_config: DataConfig | None = None
-    optimizer_config: OptimizerConfig | None = None
-    efficiency_config: EfficiencyConfig | None = None
-    dtype: str | None = "bf16"
+    max_validation_steps: Optional[int] = 1
+    data_config: Optional[DataConfig] = None
+    optimizer_config: Optional[OptimizerConfig] = None
+    efficiency_config: Optional[EfficiencyConfig] = None
+    dtype: Optional[str] = "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: bool | None = False
-    quantize_base: bool | None = False
+    use_dora: Optional[bool] = False
+    quantize_base: Optional[bool] = False
 
 
 @json_schema_type
@@ -87,7 +88,7 @@ class QATFinetuningConfig(BaseModel):
     group_size: int
 
 
-AlgorithmConfig = Annotated[LoraFinetuningConfig | QATFinetuningConfig, Field(discriminator="type")]
+AlgorithmConfig = Annotated[Union[LoraFinetuningConfig, QATFinetuningConfig], Field(discriminator="type")]
 register_schema(AlgorithmConfig, name="AlgorithmConfig")
 
 
@@ -96,7 +97,7 @@ class PostTrainingJobLogStream(BaseModel):
     """Stream of logs from a finetuning job."""
 
     job_uuid: str
-    log_lines: list[str]
+    log_lines: List[str]
 
 
 @json_schema_type
@@ -130,8 +131,8 @@ class PostTrainingRLHFRequest(BaseModel):
     training_config: TrainingConfig
 
     # TODO: define these
-    hyperparam_search_config: dict[str, Any]
-    logger_config: dict[str, Any]
+    hyperparam_search_config: Dict[str, Any]
+    logger_config: Dict[str, Any]
 
 
 class PostTrainingJob(BaseModel):
@@ -145,17 +146,17 @@ class PostTrainingJobStatusResponse(BaseModel):
     job_uuid: str
     status: JobStatus
 
-    scheduled_at: datetime | None = None
-    started_at: datetime | None = None
-    completed_at: datetime | None = None
+    scheduled_at: Optional[datetime] = None
+    started_at: Optional[datetime] = None
+    completed_at: Optional[datetime] = None
 
-    resources_allocated: dict[str, Any] | None = None
+    resources_allocated: Optional[Dict[str, Any]] = 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
@@ -163,7 +164,7 @@ 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
 
@@ -174,27 +175,15 @@ class PostTraining(Protocol):
         self,
         job_uuid: str,
         training_config: TrainingConfig,
-        hyperparam_search_config: dict[str, Any],
-        logger_config: dict[str, Any],
-        model: str | None = Field(
+        hyperparam_search_config: Dict[str, Any],
+        logger_config: Dict[str, Any],
+        model: Optional[str] = Field(
             default=None,
             description="Model descriptor for training if not in provider config`",
         ),
-        checkpoint_dir: str | None = None,
-        algorithm_config: AlgorithmConfig | None = None,
-    ) -> PostTrainingJob:
-        """Run supervised fine-tuning of a model.
-
-        :param job_uuid: The UUID of the job to create.
-        :param training_config: The training configuration.
-        :param hyperparam_search_config: The hyperparam search configuration.
-        :param logger_config: The logger configuration.
-        :param model: The model to fine-tune.
-        :param checkpoint_dir: The directory to save checkpoint(s) to.
-        :param algorithm_config: The algorithm configuration.
-        :returns: A PostTrainingJob.
-        """
-        ...
+        checkpoint_dir: Optional[str] = None,
+        algorithm_config: Optional[AlgorithmConfig] = None,
+    ) -> PostTrainingJob: ...
 
     @webmethod(route="/post-training/preference-optimize", method="POST")
     async def preference_optimize(
@@ -203,51 +192,18 @@ class PostTraining(Protocol):
         finetuned_model: str,
         algorithm_config: DPOAlignmentConfig,
         training_config: TrainingConfig,
-        hyperparam_search_config: dict[str, Any],
-        logger_config: dict[str, Any],
-    ) -> PostTrainingJob:
-        """Run preference optimization of a model.
-
-        :param job_uuid: The UUID of the job to create.
-        :param finetuned_model: The model to fine-tune.
-        :param algorithm_config: The algorithm configuration.
-        :param training_config: The training configuration.
-        :param hyperparam_search_config: The hyperparam search configuration.
-        :param logger_config: The logger configuration.
-        :returns: A PostTrainingJob.
-        """
-        ...
+        hyperparam_search_config: Dict[str, Any],
+        logger_config: Dict[str, Any],
+    ) -> PostTrainingJob: ...
 
     @webmethod(route="/post-training/jobs", method="GET")
-    async def get_training_jobs(self) -> ListPostTrainingJobsResponse:
-        """Get all training jobs.
-
-        :returns: A ListPostTrainingJobsResponse.
-        """
-        ...
+    async def get_training_jobs(self) -> ListPostTrainingJobsResponse: ...
 
     @webmethod(route="/post-training/job/status", method="GET")
-    async def get_training_job_status(self, job_uuid: str) -> PostTrainingJobStatusResponse:
-        """Get the status of a training job.
-
-        :param job_uuid: The UUID of the job to get the status of.
-        :returns: A PostTrainingJobStatusResponse.
-        """
-        ...
+    async def get_training_job_status(self, job_uuid: str) -> PostTrainingJobStatusResponse: ...
 
     @webmethod(route="/post-training/job/cancel", method="POST")
-    async def cancel_training_job(self, job_uuid: str) -> None:
-        """Cancel a training job.
-
-        :param job_uuid: The UUID of the job to cancel.
-        """
-        ...
+    async def cancel_training_job(self, job_uuid: str) -> None: ...
 
     @webmethod(route="/post-training/job/artifacts", method="GET")
-    async def get_training_job_artifacts(self, job_uuid: str) -> PostTrainingJobArtifactsResponse:
-        """Get the artifacts of a training job.
-
-        :param job_uuid: The UUID of the job to get the artifacts of.
-        :returns: A PostTrainingJobArtifactsResponse.
-        """
-        ...
+    async def get_training_job_artifacts(self, job_uuid: str) -> PostTrainingJobArtifactsResponse: ...

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, Protocol, runtime_checkable
+from typing import Any, Dict, List, 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
@@ -32,18 +32,7 @@ class Providers(Protocol):
     """
 
     @webmethod(route="/providers", method="GET")
-    async def list_providers(self) -> ListProvidersResponse:
-        """List all available providers.
-
-        :returns: A ListProvidersResponse containing information about all providers.
-        """
-        ...
+    async def list_providers(self) -> ListProvidersResponse: ...
 
     @webmethod(route="/providers/{provider_id}", method="GET")
-    async def inspect_provider(self, provider_id: str) -> ProviderInfo:
-        """Get detailed information about a specific provider.
-
-        :param provider_id: The ID of the provider to inspect.
-        :returns: A ProviderInfo object containing the provider's details.
-        """
-        ...
+    async def inspect_provider(self, provider_id: str) -> ProviderInfo: ...

llama_stack/apis/resource.py

@@ -4,23 +4,12 @@
 # This source code is licensed under the terms described in the LICENSE file in
 # the root directory of this source tree.
 
-import sys
 from enum import Enum
 
 from pydantic import BaseModel, Field
 
-# TODO: use enum.StrEnum when we drop support for python 3.10
-if sys.version_info >= (3, 11):
-    from enum import StrEnum
-else:
 
-    class StrEnum(str, Enum):
-        """Backport of StrEnum for Python 3.10 and below."""
-
-        pass
-
-
-class ResourceType(StrEnum):
+class ResourceType(Enum):
     model = "model"
     shield = "shield"
     vector_db = "vector_db"
@@ -36,9 +25,9 @@ class Resource(BaseModel):
 
     identifier: str = Field(description="Unique identifier for this resource in llama stack")
 
-    provider_resource_id: str | None = Field(
-        default=None,
+    provider_resource_id: str = Field(
         description="Unique identifier for this resource in the provider",
+        default=None,
     )
 
     provider_id: str = Field(description="ID of the provider that owns this resource")

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, Protocol, runtime_checkable
+from typing import Any, Dict, List, Optional, 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: str | None = None
+    user_message: Optional[str] = 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: SafetyViolation | None = None
+    violation: Optional[SafetyViolation] = None
 
 
 class ShieldStore(Protocol):
@@ -52,14 +52,6 @@ class Safety(Protocol):
     async def run_shield(
         self,
         shield_id: str,
-        messages: list[Message],
-        params: dict[str, Any],
-    ) -> RunShieldResponse:
-        """Run a shield.
-
-        :param shield_id: The identifier of the shield to run.
-        :param messages: The messages to run the shield on.
-        :param params: The parameters of the shield.
-        :returns: A RunShieldResponse.
-        """
-        ...
+        messages: List[Message],
+        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, Protocol, runtime_checkable
+from typing import Any, Dict, List, Optional, 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: str | None = None
-    results: dict[str, ScoringResult]
+    dataset_id: Optional[str] = None
+    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):
@@ -59,28 +59,20 @@ class Scoring(Protocol):
     async def score_batch(
         self,
         dataset_id: str,
-        scoring_functions: dict[str, ScoringFnParams | None],
+        scoring_functions: Dict[str, Optional[ScoringFnParams]],
         save_results_dataset: bool = False,
-    ) -> ScoreBatchResponse:
-        """Score a batch of rows.
-
-        :param dataset_id: The ID of the dataset to score.
-        :param scoring_functions: The scoring functions to use for the scoring.
-        :param save_results_dataset: Whether to save the results to a dataset.
-        :returns: A ScoreBatchResponse.
-        """
-        ...
+    ) -> ScoreBatchResponse: ...
 
     @webmethod(route="/scoring/score", method="POST")
     async def score(
         self,
-        input_rows: list[dict[str, Any]],
-        scoring_functions: dict[str, ScoringFnParams | None],
+        input_rows: List[Dict[str, Any]],
+        scoring_functions: Dict[str, Optional[ScoringFnParams]],
     ) -> ScoreResponse:
         """Score a list of rows.
 
         :param input_rows: The rows to score.
         :param scoring_functions: The scoring functions to use for the scoring.
-        :returns: A ScoreResponse object containing rows and aggregated results.
+        :return: ScoreResponse object containing rows and aggregated results
         """
         ...

llama_stack/apis/scoring_functions/scoring_functions.py

@@ -4,44 +4,37 @@
 # This source code is licensed under the terms described in the LICENSE file in
 # the root directory of this source tree.
 
-# TODO: use enum.StrEnum when we drop support for python 3.10
-import sys
 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
 from llama_stack.schema_utils import json_schema_type, register_schema, webmethod
 
-if sys.version_info >= (3, 11):
-    from enum import StrEnum
-else:
-
-    class StrEnum(str, Enum):
-        """Backport of StrEnum for Python 3.10 and below."""
-
-        pass
-
 
 # Perhaps more structure can be imposed on these functions. Maybe they could be associated
 # with standard metrics so they can be rolled up?
 @json_schema_type
-class ScoringFnParamsType(StrEnum):
+class ScoringFnParamsType(Enum):
     llm_as_judge = "llm_as_judge"
     regex_parser = "regex_parser"
     basic = "basic"
 
 
 @json_schema_type
-class AggregationFunctionType(StrEnum):
+class AggregationFunctionType(Enum):
     average = "average"
     weighted_average = "weighted_average"
     median = "median"
@@ -51,58 +44,62 @@ class AggregationFunctionType(StrEnum):
 
 @json_schema_type
 class LLMAsJudgeScoringFnParams(BaseModel):
-    type: Literal[ScoringFnParamsType.llm_as_judge] = ScoringFnParamsType.llm_as_judge
+    type: Literal[ScoringFnParamsType.llm_as_judge.value] = ScoringFnParamsType.llm_as_judge.value
     judge_model: str
-    prompt_template: str | None = None
-    judge_score_regexes: list[str] = Field(
+    prompt_template: Optional[str] = None
+    judge_score_regexes: Optional[List[str]] = Field(
         description="Regexes to extract the answer from generated response",
-        default_factory=lambda: [],
+        default_factory=list,
     )
-    aggregation_functions: list[AggregationFunctionType] = Field(
+    aggregation_functions: Optional[List[AggregationFunctionType]] = Field(
         description="Aggregation functions to apply to the scores of each row",
-        default_factory=lambda: [],
+        default_factory=list,
     )
 
 
 @json_schema_type
 class RegexParserScoringFnParams(BaseModel):
-    type: Literal[ScoringFnParamsType.regex_parser] = ScoringFnParamsType.regex_parser
-    parsing_regexes: list[str] = Field(
+    type: Literal[ScoringFnParamsType.regex_parser.value] = ScoringFnParamsType.regex_parser.value
+    parsing_regexes: Optional[List[str]] = Field(
         description="Regex to extract the answer from generated response",
-        default_factory=lambda: [],
+        default_factory=list,
     )
-    aggregation_functions: list[AggregationFunctionType] = Field(
+    aggregation_functions: Optional[List[AggregationFunctionType]] = Field(
         description="Aggregation functions to apply to the scores of each row",
-        default_factory=lambda: [],
+        default_factory=list,
     )
 
 
 @json_schema_type
 class BasicScoringFnParams(BaseModel):
-    type: Literal[ScoringFnParamsType.basic] = ScoringFnParamsType.basic
-    aggregation_functions: list[AggregationFunctionType] = Field(
+    type: Literal[ScoringFnParamsType.basic.value] = ScoringFnParamsType.basic.value
+    aggregation_functions: Optional[List[AggregationFunctionType]] = Field(
         description="Aggregation functions to apply to the scores of each row",
         default_factory=list,
     )
 
 
 ScoringFnParams = Annotated[
-    LLMAsJudgeScoringFnParams | RegexParserScoringFnParams | BasicScoringFnParams,
+    Union[
+        LLMAsJudgeScoringFnParams,
+        RegexParserScoringFnParams,
+        BasicScoringFnParams,
+    ],
     Field(discriminator="type"),
 ]
 register_schema(ScoringFnParams, name="ScoringFnParams")
 
 
 class CommonScoringFnFields(BaseModel):
-    description: str | None = None
-    metadata: dict[str, Any] = Field(
+    description: Optional[str] = None
+    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: ScoringFnParams | None = Field(
+    params: Optional[ScoringFnParams] = Field(
         description="The parameters for the scoring function for benchmark eval, these can be overridden for app eval",
         default=None,
     )
@@ -110,45 +107,34 @@ class CommonScoringFnFields(BaseModel):
 
 @json_schema_type
 class ScoringFn(CommonScoringFnFields, Resource):
-    type: Literal[ResourceType.scoring_function] = ResourceType.scoring_function
+    type: Literal[ResourceType.scoring_function.value] = ResourceType.scoring_function.value
 
     @property
     def scoring_fn_id(self) -> str:
         return self.identifier
 
     @property
-    def provider_scoring_fn_id(self) -> str | None:
+    def provider_scoring_fn_id(self) -> str:
         return self.provider_resource_id
 
 
 class ScoringFnInput(CommonScoringFnFields, BaseModel):
     scoring_fn_id: str
-    provider_id: str | None = None
-    provider_scoring_fn_id: str | None = None
+    provider_id: Optional[str] = None
+    provider_scoring_fn_id: Optional[str] = None
 
 
 class ListScoringFunctionsResponse(BaseModel):
-    data: list[ScoringFn]
+    data: List[ScoringFn]
 
 
 @runtime_checkable
 class ScoringFunctions(Protocol):
     @webmethod(route="/scoring-functions", method="GET")
-    async def list_scoring_functions(self) -> ListScoringFunctionsResponse:
-        """List all scoring functions.
-
-        :returns: A ListScoringFunctionsResponse.
-        """
-        ...
+    async def list_scoring_functions(self) -> ListScoringFunctionsResponse: ...
 
     @webmethod(route="/scoring-functions/{scoring_fn_id:path}", method="GET")
-    async def get_scoring_function(self, scoring_fn_id: str, /) -> ScoringFn:
-        """Get a scoring function by its ID.
-
-        :param scoring_fn_id: The ID of the scoring function to get.
-        :returns: A ScoringFn.
-        """
-        ...
+    async def get_scoring_function(self, scoring_fn_id: str, /) -> ScoringFn: ...
 
     @webmethod(route="/scoring-functions", method="POST")
     async def register_scoring_function(
@@ -156,17 +142,7 @@ class ScoringFunctions(Protocol):
         scoring_fn_id: str,
         description: str,
         return_type: ParamType,
-        provider_scoring_fn_id: str | None = None,
-        provider_id: str | None = None,
-        params: ScoringFnParams | None = None,
-    ) -> None:
-        """Register a scoring function.
-
-        :param scoring_fn_id: The ID of the scoring function to register.
-        :param description: The description of the scoring function.
-        :param return_type: The return type of the scoring function.
-        :param provider_scoring_fn_id: The ID of the provider scoring function to use for the scoring function.
-        :param provider_id: The ID of the provider to use for the scoring function.
-        :param params: The parameters for the scoring function for benchmark eval, these can be overridden for app eval.
-        """
-        ...
+        provider_scoring_fn_id: Optional[str] = None,
+        provider_id: Optional[str] = None,
+        params: Optional[ScoringFnParams] = 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, Literal, Protocol, runtime_checkable
+from typing import Any, Dict, List, Literal, Optional, Protocol, runtime_checkable
 
 from pydantic import BaseModel
 
@@ -14,68 +14,48 @@ from llama_stack.schema_utils import json_schema_type, webmethod
 
 
 class CommonShieldFields(BaseModel):
-    params: dict[str, Any] | None = None
+    params: Optional[Dict[str, Any]] = None
 
 
 @json_schema_type
 class Shield(CommonShieldFields, Resource):
     """A safety shield resource that can be used to check content"""
 
-    type: Literal[ResourceType.shield] = ResourceType.shield
+    type: Literal[ResourceType.shield.value] = ResourceType.shield.value
 
     @property
     def shield_id(self) -> str:
         return self.identifier
 
     @property
-    def provider_shield_id(self) -> str | None:
+    def provider_shield_id(self) -> str:
         return self.provider_resource_id
 
 
 class ShieldInput(CommonShieldFields):
     shield_id: str
-    provider_id: str | None = None
-    provider_shield_id: str | None = None
+    provider_id: Optional[str] = None
+    provider_shield_id: Optional[str] = None
 
 
 class ListShieldsResponse(BaseModel):
-    data: list[Shield]
+    data: List[Shield]
 
 
 @runtime_checkable
 @trace_protocol
 class Shields(Protocol):
     @webmethod(route="/shields", method="GET")
-    async def list_shields(self) -> ListShieldsResponse:
-        """List all shields.
-
-        :returns: A ListShieldsResponse.
-        """
-        ...
+    async def list_shields(self) -> ListShieldsResponse: ...
 
     @webmethod(route="/shields/{identifier:path}", method="GET")
-    async def get_shield(self, identifier: str) -> Shield:
-        """Get a shield by its identifier.
-
-        :param identifier: The identifier of the shield to get.
-        :returns: A Shield.
-        """
-        ...
+    async def get_shield(self, identifier: str) -> Shield: ...
 
     @webmethod(route="/shields", method="POST")
     async def register_shield(
         self,
         shield_id: str,
-        provider_shield_id: str | None = None,
-        provider_id: str | None = None,
-        params: dict[str, Any] | None = None,
-    ) -> Shield:
-        """Register a shield.
-
-        :param shield_id: The identifier of the shield to register.
-        :param provider_shield_id: The identifier of the shield in the provider.
-        :param provider_id: The identifier of the provider.
-        :param params: The parameters of the shield.
-        :returns: A Shield.
-        """
-        ...
+        provider_shield_id: Optional[str] = None,
+        provider_id: Optional[str] = None,
+        params: Optional[Dict[str, Any]] = None,
+    ) -> Shield: ...