Merge branch 'main' into dead_code_removal

.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 @reluctantfuturist @mattf @slekkala1
+* @ashwinb @yanxi0830 @hardikjshah @raghotham @ehhuang @terrytangyuan @leseb @bbrowning @reluctantfuturist @mattf @slekkala1 @franciscojavierarceo

.github/TRIAGERS.md

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

.github/workflows/README.md

@@ -12,6 +12,7 @@ Llama Stack uses GitHub Actions for Continuous Integration (CI). Below is a tabl
 | Integration Tests (Replay) | [integration-tests.yml](integration-tests.yml) | Run the integration test suites from tests/integration in replay mode |
 | Vector IO Integration Tests | [integration-vector-io-tests.yml](integration-vector-io-tests.yml) | Run the integration test suite with various VectorIO providers |
 | Pre-commit | [pre-commit.yml](pre-commit.yml) | Run pre-commit checks |
+| Pre-commit Bot | [precommit-trigger.yml](precommit-trigger.yml) | Pre-commit bot for PR |
 | Test Llama Stack Build | [providers-build.yml](providers-build.yml) | Test llama stack build |
 | Python Package Build Test | [python-build-test.yml](python-build-test.yml) | Test building the llama-stack PyPI project |
 | Integration Tests (Record) | [record-integration-tests.yml](record-integration-tests.yml) | Run the integration test suite from tests/integration |

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

@@ -84,6 +84,8 @@ jobs:
           yq eval '.server.auth.provider_config.jwks.token = "${{ env.TOKEN }}"' -i $run_dir/run.yaml
           cat $run_dir/run.yaml
 
+          # avoid line breaks in the server log, especially because we grep it below.
+          export COLUMNS=1984
           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

.github/workflows/integration-tests.yml

@@ -42,18 +42,27 @@ jobs:
 
   run-replay-mode-tests:
     runs-on: ubuntu-latest
-    name: ${{ format('Integration Tests ({0}, {1}, {2}, client={3}, {4})', matrix.client-type, matrix.setup, matrix.python-version, matrix.client-version, matrix.suite) }}
+    name: ${{ format('Integration Tests ({0}, {1}, {2}, client={3}, {4})', matrix.client-type, matrix.config.setup, matrix.python-version, matrix.client-version, matrix.config.suite) }}
 
     strategy:
       fail-fast: false
       matrix:
         client-type: [library, server]
-        # Use vllm on weekly schedule, otherwise use test-setup input (defaults to ollama)
-        setup: ${{ (github.event.schedule == '1 0 * * 0') && fromJSON('["vllm"]') || fromJSON(format('["{0}"]', github.event.inputs.test-setup || 'ollama')) }}
         # Use Python 3.13 only on nightly schedule (daily latest client test), otherwise use 3.12
         python-version: ${{ github.event.schedule == '0 0 * * *' && fromJSON('["3.12", "3.13"]') || fromJSON('["3.12"]') }}
         client-version: ${{ (github.event.schedule == '0 0 * * *' || github.event.inputs.test-all-client-versions == 'true') && fromJSON('["published", "latest"]') || fromJSON('["latest"]') }}
-        suite: [base, vision]
+        # Define (setup, suite) pairs - they are always matched and cannot be independent
+        # Weekly schedule (Sun 1 AM): vllm+base
+        # Input test-setup=ollama-vision: ollama-vision+vision
+        # Default (including test-setup=ollama): both ollama+base and ollama-vision+vision
+        config: >-
+          ${{
+            github.event.schedule == '1 0 * * 0'
+              && fromJSON('[{"setup": "vllm", "suite": "base"}]')
+            || github.event.inputs.test-setup == 'ollama-vision'
+              && fromJSON('[{"setup": "ollama-vision", "suite": "vision"}]')
+            || fromJSON('[{"setup": "ollama", "suite": "base"}, {"setup": "ollama-vision", "suite": "vision"}]')
+          }}
 
     steps:
       - name: Checkout repository
@@ -64,14 +73,14 @@ jobs:
         with:
           python-version: ${{ matrix.python-version }}
           client-version: ${{ matrix.client-version }}
-          setup: ${{ matrix.setup }}
-          suite: ${{ matrix.suite }}
+          setup: ${{ matrix.config.setup }}
+          suite: ${{ matrix.config.suite }}
           inference-mode: 'replay'
 
       - name: Run tests
         uses: ./.github/actions/run-and-record-tests
         with:
           stack-config: ${{ matrix.client-type == 'library' && 'ci-tests' || 'server:ci-tests' }}
-          setup: ${{ matrix.setup }}
+          setup: ${{ matrix.config.setup }}
           inference-mode: 'replay'
-          suite: ${{ matrix.suite }}
+          suite: ${{ matrix.config.suite }}

.github/workflows/precommit-trigger.yml

@@ -0,0 +1,227 @@
+name: Pre-commit Bot
+
+run-name: Pre-commit bot for PR #${{ github.event.issue.number }}
+
+on:
+  issue_comment:
+    types: [created]
+
+jobs:
+  pre-commit:
+    # Only run on pull request comments
+    if: github.event.issue.pull_request && contains(github.event.comment.body, '@github-actions run precommit')
+    runs-on: ubuntu-latest
+    permissions:
+      contents: write
+      pull-requests: write
+
+    steps:
+      - name: Check comment author and get PR details
+        id: check_author
+        uses: actions/github-script@ed597411d8f924073f98dfc5c65a23a2325f34cd # v8.0.0
+        with:
+          github-token: ${{ secrets.GITHUB_TOKEN }}
+          script: |
+            // Get PR details
+            const pr = await github.rest.pulls.get({
+              owner: context.repo.owner,
+              repo: context.repo.repo,
+              pull_number: context.issue.number
+            });
+
+            // Check if commenter has write access or is the PR author
+            const commenter = context.payload.comment.user.login;
+            const prAuthor = pr.data.user.login;
+
+            let hasPermission = false;
+
+            // Check if commenter is PR author
+            if (commenter === prAuthor) {
+              hasPermission = true;
+              console.log(`Comment author ${commenter} is the PR author`);
+            } else {
+              // Check if commenter has write/admin access
+              try {
+                const permission = await github.rest.repos.getCollaboratorPermissionLevel({
+                  owner: context.repo.owner,
+                  repo: context.repo.repo,
+                  username: commenter
+                });
+
+                const level = permission.data.permission;
+                hasPermission = ['write', 'admin', 'maintain'].includes(level);
+                console.log(`Comment author ${commenter} has permission: ${level}`);
+              } catch (error) {
+                console.log(`Could not check permissions for ${commenter}: ${error.message}`);
+              }
+            }
+
+            if (!hasPermission) {
+              await github.rest.issues.createComment({
+                owner: context.repo.owner,
+                repo: context.repo.repo,
+                issue_number: context.issue.number,
+                body: `❌ @${commenter} You don't have permission to trigger pre-commit. Only PR authors or repository collaborators can run this command.`
+              });
+              core.setFailed(`User ${commenter} does not have permission`);
+              return;
+            }
+
+            // Save PR info for later steps
+            core.setOutput('pr_number', context.issue.number);
+            core.setOutput('pr_head_ref', pr.data.head.ref);
+            core.setOutput('pr_head_sha', pr.data.head.sha);
+            core.setOutput('pr_head_repo', pr.data.head.repo.full_name);
+            core.setOutput('pr_base_ref', pr.data.base.ref);
+            core.setOutput('is_fork', pr.data.head.repo.full_name !== context.payload.repository.full_name);
+            core.setOutput('authorized', 'true');
+
+      - name: React to comment
+        if: steps.check_author.outputs.authorized == 'true'
+        uses: actions/github-script@ed597411d8f924073f98dfc5c65a23a2325f34cd # v8.0.0
+        with:
+          github-token: ${{ secrets.GITHUB_TOKEN }}
+          script: |
+            await github.rest.reactions.createForIssueComment({
+              owner: context.repo.owner,
+              repo: context.repo.repo,
+              comment_id: context.payload.comment.id,
+              content: 'rocket'
+            });
+
+      - name: Comment starting
+        if: steps.check_author.outputs.authorized == 'true'
+        uses: actions/github-script@ed597411d8f924073f98dfc5c65a23a2325f34cd # v8.0.0
+        with:
+          github-token: ${{ secrets.GITHUB_TOKEN }}
+          script: |
+            await github.rest.issues.createComment({
+              owner: context.repo.owner,
+              repo: context.repo.repo,
+              issue_number: ${{ steps.check_author.outputs.pr_number }},
+              body: `⏳ Running pre-commit hooks on PR #${{ steps.check_author.outputs.pr_number }}...`
+            });
+
+      - name: Checkout PR branch (same-repo)
+        if: steps.check_author.outputs.authorized == 'true' && steps.check_author.outputs.is_fork == 'false'
+        uses: actions/checkout@08c6903cd8c0fde910a37f88322edcfb5dd907a8 # v5.0.0
+        with:
+          ref: ${{ steps.check_author.outputs.pr_head_ref }}
+          fetch-depth: 0
+          token: ${{ secrets.GITHUB_TOKEN }}
+
+      - name: Checkout PR branch (fork)
+        if: steps.check_author.outputs.authorized == 'true' && steps.check_author.outputs.is_fork == 'true'
+        uses: actions/checkout@08c6903cd8c0fde910a37f88322edcfb5dd907a8 # v5.0.0
+        with:
+          repository: ${{ steps.check_author.outputs.pr_head_repo }}
+          ref: ${{ steps.check_author.outputs.pr_head_ref }}
+          fetch-depth: 0
+          token: ${{ secrets.GITHUB_TOKEN }}
+
+      - name: Verify checkout
+        if: steps.check_author.outputs.authorized == 'true'
+        run: |
+          echo "Current SHA: $(git rev-parse HEAD)"
+          echo "Expected SHA: ${{ steps.check_author.outputs.pr_head_sha }}"
+          if [[ "$(git rev-parse HEAD)" != "${{ steps.check_author.outputs.pr_head_sha }}" ]]; then
+            echo "::error::Checked out SHA does not match expected SHA"
+            exit 1
+          fi
+
+      - name: Set up Python
+        if: steps.check_author.outputs.authorized == 'true'
+        uses: actions/setup-python@e797f83bcb11b83ae66e0230d6156d7c80228e7c # v6.0.0
+        with:
+          python-version: '3.12'
+          cache: pip
+          cache-dependency-path: |
+            **/requirements*.txt
+            .pre-commit-config.yaml
+
+      - name: Set up Node.js
+        if: steps.check_author.outputs.authorized == 'true'
+        uses: actions/setup-node@a0853c24544627f65ddf259abe73b1d18a591444 # v5.0.0
+        with:
+          node-version: '20'
+          cache: 'npm'
+          cache-dependency-path: 'llama_stack/ui/'
+
+      - name: Install npm dependencies
+        if: steps.check_author.outputs.authorized == 'true'
+        run: npm ci
+        working-directory: llama_stack/ui
+
+      - name: Run pre-commit
+        if: steps.check_author.outputs.authorized == 'true'
+        id: precommit
+        uses: pre-commit/action@2c7b3805fd2a0fd8c1884dcaebf91fc102a13ecd # v3.0.1
+        continue-on-error: true
+        env:
+          SKIP: no-commit-to-branch
+          RUFF_OUTPUT_FORMAT: github
+
+      - name: Check for changes
+        if: steps.check_author.outputs.authorized == 'true'
+        id: changes
+        run: |
+          if ! git diff --exit-code || [ -n "$(git ls-files --others --exclude-standard)" ]; then
+            echo "has_changes=true" >> $GITHUB_OUTPUT
+            echo "Changes detected after pre-commit"
+          else
+            echo "has_changes=false" >> $GITHUB_OUTPUT
+            echo "No changes after pre-commit"
+          fi
+
+      - name: Commit and push changes
+        if: steps.check_author.outputs.authorized == 'true' && steps.changes.outputs.has_changes == 'true'
+        run: |
+          git config --local user.email "github-actions[bot]@users.noreply.github.com"
+          git config --local user.name "github-actions[bot]"
+
+          git add -A
+          git commit -m "style: apply pre-commit fixes
+
+          🤖 Applied by @github-actions bot via pre-commit workflow"
+
+          # Push changes
+          git push origin HEAD:${{ steps.check_author.outputs.pr_head_ref }}
+
+      - name: Comment success with changes
+        if: steps.check_author.outputs.authorized == 'true' && steps.changes.outputs.has_changes == 'true'
+        uses: actions/github-script@ed597411d8f924073f98dfc5c65a23a2325f34cd # v8.0.0
+        with:
+          github-token: ${{ secrets.GITHUB_TOKEN }}
+          script: |
+            await github.rest.issues.createComment({
+              owner: context.repo.owner,
+              repo: context.repo.repo,
+              issue_number: ${{ steps.check_author.outputs.pr_number }},
+              body: `✅ Pre-commit hooks completed successfully!\n\n🔧 Changes have been committed and pushed to the PR branch.`
+            });
+
+      - name: Comment success without changes
+        if: steps.check_author.outputs.authorized == 'true' && steps.changes.outputs.has_changes == 'false' && steps.precommit.outcome == 'success'
+        uses: actions/github-script@ed597411d8f924073f98dfc5c65a23a2325f34cd # v8.0.0
+        with:
+          github-token: ${{ secrets.GITHUB_TOKEN }}
+          script: |
+            await github.rest.issues.createComment({
+              owner: context.repo.owner,
+              repo: context.repo.repo,
+              issue_number: ${{ steps.check_author.outputs.pr_number }},
+              body: `✅ Pre-commit hooks passed!\n\n✨ No changes needed - your code is already formatted correctly.`
+            });
+
+      - name: Comment failure
+        if: failure()
+        uses: actions/github-script@ed597411d8f924073f98dfc5c65a23a2325f34cd # v8.0.0
+        with:
+          github-token: ${{ secrets.GITHUB_TOKEN }}
+          script: |
+            await github.rest.issues.createComment({
+              owner: context.repo.owner,
+              repo: context.repo.repo,
+              issue_number: ${{ steps.check_author.outputs.pr_number }},
+              body: `❌ Pre-commit workflow failed!\n\nPlease check the [workflow logs](https://github.com/${context.repo.owner}/${context.repo.repo}/actions/runs/${context.runId}) for details.`
+            });

.github/workflows/providers-build.yml

@@ -112,7 +112,7 @@ jobs:
           fi
           entrypoint=$(docker inspect --format '{{ .Config.Entrypoint }}' $IMAGE_ID)
           echo "Entrypoint: $entrypoint"
-          if [ "$entrypoint" != "[python -m llama_stack.core.server.server /app/run.yaml]" ]; then
+          if [ "$entrypoint" != "[llama stack run /app/run.yaml]" ]; then
             echo "Entrypoint is not correct"
             exit 1
           fi
@@ -150,7 +150,7 @@ jobs:
           fi
           entrypoint=$(docker inspect --format '{{ .Config.Entrypoint }}' $IMAGE_ID)
           echo "Entrypoint: $entrypoint"
-          if [ "$entrypoint" != "[python -m llama_stack.core.server.server /app/run.yaml]" ]; then
+          if [ "$entrypoint" != "[llama stack run /app/run.yaml]" ]; then
             echo "Entrypoint is not correct"
             exit 1
           fi

.github/workflows/python-build-test.yml

@@ -24,7 +24,7 @@ jobs:
       uses: actions/checkout@08c6903cd8c0fde910a37f88322edcfb5dd907a8 # v5.0.0
 
     - name: Install uv
-      uses: astral-sh/setup-uv@b75a909f75acd358c2196fb9a5f1299a9a8868a4 # v6.7.0
+      uses: astral-sh/setup-uv@d0cc045d04ccac9d8b7881df0226f9e82c39688e # v6.8.0
       with:
         python-version: ${{ matrix.python-version }}
         activate-environment: true

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://llamastack.github.io/latest/getting_started/index.html) | [**Documentation**](https://llamastack.github.io/latest/index.html) | [**Colab Notebook**](./docs/getting_started.ipynb) | [**Discord**](https://discord.gg/llama-stack)
+[**Quick Start**](https://llamastack.github.io/docs/getting_started/quickstart) | [**Documentation**](https://llamastack.github.io/docs) | [**Colab Notebook**](./docs/getting_started.ipynb) | [**Discord**](https://discord.gg/llama-stack)
 
 
 ### ✨🎉 Llama 4 Support  🎉✨

docs/docs/building_applications/telemetry.mdx

@@ -187,21 +187,21 @@ Configure telemetry behavior using environment variables:
 - **`OTEL_SERVICE_NAME`**: Service name for telemetry (default: empty string)
 - **`TELEMETRY_SINKS`**: Comma-separated list of sinks (default: `console,sqlite`)
 
-## Visualization with Jaeger
+### Quick Setup: Complete Telemetry Stack
 
-The `otel_trace` sink works with any service compatible with the OpenTelemetry collector. Traces and metrics use separate endpoints but can share the same collector.
-
-### Starting Jaeger
-
-Start a Jaeger instance with OTLP HTTP endpoint at 4318 and the Jaeger UI at 16686:
+Use the automated setup script to launch the complete telemetry stack (Jaeger, OpenTelemetry Collector, Prometheus, and Grafana):
 
 ```bash
-docker run --pull always --rm --name jaeger \
-  -p 16686:16686 -p 4318:4318 \
-  jaegertracing/jaeger:2.1.0
+./scripts/telemetry/setup_telemetry.sh
 ```
 
-Once running, you can visualize traces by navigating to [http://localhost:16686/](http://localhost:16686/).
+This sets up:
+- **Jaeger UI**: http://localhost:16686 (traces visualization)
+- **Prometheus**: http://localhost:9090 (metrics)
+- **Grafana**: http://localhost:3000 (dashboards with auto-configured data sources)
+- **OTEL Collector**: http://localhost:4318 (OTLP endpoint)
+
+Once running, you can visualize traces by navigating to [Grafana](http://localhost:3000/) and login with login `admin` and password `admin`.
 
 ## Querying Metrics
 

docs/docs/concepts/apis/external.mdx

@@ -152,7 +152,6 @@ __all__ = ["WeatherAPI", "available_providers"]
 from typing import Protocol
 
 from llama_stack.providers.datatypes import (
-    AdapterSpec,
     Api,
     ProviderSpec,
     RemoteProviderSpec,
@@ -166,12 +165,10 @@ def available_providers() -> list[ProviderSpec]:
             api=Api.weather,
             provider_type="remote::kaze",
             config_class="llama_stack_provider_kaze.KazeProviderConfig",
-            adapter=AdapterSpec(
-                adapter_type="kaze",
-                module="llama_stack_provider_kaze",
-                pip_packages=["llama_stack_provider_kaze"],
-                config_class="llama_stack_provider_kaze.KazeProviderConfig",
-            ),
+            adapter_type="kaze",
+            module="llama_stack_provider_kaze",
+            pip_packages=["llama_stack_provider_kaze"],
+            config_class="llama_stack_provider_kaze.KazeProviderConfig",
         ),
     ]
 
@@ -325,11 +322,10 @@ class WeatherKazeAdapter(WeatherProvider):
 
 ```yaml
 # ~/.llama/providers.d/remote/weather/kaze.yaml
-adapter:
-  adapter_type: kaze
-  pip_packages: ["llama_stack_provider_kaze"]
-  config_class: llama_stack_provider_kaze.config.KazeProviderConfig
-  module: llama_stack_provider_kaze
+adapter_type: kaze
+pip_packages: ["llama_stack_provider_kaze"]
+config_class: llama_stack_provider_kaze.config.KazeProviderConfig
+module: llama_stack_provider_kaze
 optional_api_dependencies: []
 ```
 
@@ -361,7 +357,7 @@ server:
 8. Run the server:
 
 ```bash
-python -m llama_stack.core.server.server --yaml-config ~/.llama/run-byoa.yaml
+llama stack run ~/.llama/run-byoa.yaml
 ```
 
 9. Test the API:

docs/docs/deploying/kubernetes_deployment.mdx

@@ -170,7 +170,7 @@ spec:
       - name: llama-stack
         image: localhost/llama-stack-run-k8s:latest
         imagePullPolicy: IfNotPresent
-        command: ["python", "-m", "llama_stack.core.server.server", "--config", "/app/config.yaml"]
+        command: ["llama", "stack", "run", "/app/config.yaml"]
         ports:
           - containerPort: 5000
         volumeMounts:

docs/docs/distributions/configuration.mdx

@@ -509,16 +509,16 @@ server:
     provider_config:
       type: "github_token"
       github_api_base_url: "https://api.github.com"
-  access_policy:
-  - permit:
-      principal: user-1
-      actions: [create, read, delete]
-    description: user-1 has full access to all resources
-  - permit:
-      principal: user-2
-      actions: [read]
-      resource: model::model-1
-    description: user-2 has read access to model-1 only
+    access_policy:
+    - permit:
+        principal: user-1
+        actions: [create, read, delete]
+      description: user-1 has full access to all resources
+    - permit:
+        principal: user-2
+        actions: [read]
+        resource: model::model-1
+      description: user-2 has read access to model-1 only
 ```
 
 Similarly, the following restricts access to particular kubernetes

docs/docs/distributions/k8s/stack-k8s.yaml.template

@@ -52,7 +52,7 @@ spec:
           value: "${SAFETY_MODEL}"
         - name: TAVILY_SEARCH_API_KEY
           value: "${TAVILY_SEARCH_API_KEY}"
-        command: ["python", "-m", "llama_stack.core.server.server", "/etc/config/stack_run_config.yaml", "--port", "8321"]
+        command: ["llama", "stack", "run", "/etc/config/stack_run_config.yaml", "--port", "8321"]
         ports:
           - containerPort: 8321
         volumeMounts:

docs/docs/providers/external/external-providers-guide.mdx

@@ -11,38 +11,6 @@ an example entry in your build.yaml should look like:
   module: ramalama_stack
 ```
 
-Additionally you can configure the `external_providers_dir` in your Llama Stack configuration. This method is in the process of being deprecated in favor of the `module` method. If using this method, the external provider directory should contain your external provider specifications:
-
-```yaml
-external_providers_dir: ~/.llama/providers.d/
-```
-
-## Directory Structure
-
-The external providers directory should follow this structure:
-
-```
-providers.d/
-  remote/
-    inference/
-      custom_ollama.yaml
-      vllm.yaml
-    vector_io/
-      qdrant.yaml
-    safety/
-      llama-guard.yaml
-  inline/
-    inference/
-      custom_ollama.yaml
-      vllm.yaml
-    vector_io/
-      qdrant.yaml
-    safety/
-      llama-guard.yaml
-```
-
-Each YAML file in these directories defines a provider specification for that particular API.
-
 ## Provider Types
 
 Llama Stack supports two types of external providers:
@@ -50,30 +18,37 @@ Llama Stack supports two types of external providers:
 1. **Remote Providers**: Providers that communicate with external services (e.g., cloud APIs)
 2. **Inline Providers**: Providers that run locally within the Llama Stack process
 
+
+### Provider Specification (Common between inline and remote providers)
+
+- `provider_type`: The type of the provider to be installed (remote or inline). eg. `remote::ollama`
+- `api`: The API for this provider, eg. `inference`
+- `config_class`: The full path to the configuration class
+- `module`: The Python module containing the provider implementation
+- `optional_api_dependencies`: List of optional Llama Stack APIs that this provider can use
+- `api_dependencies`:  List of Llama Stack APIs that this provider depends on
+- `provider_data_validator`: Optional validator for provider data.
+- `pip_packages`: List of Python packages required by the provider
+
 ### Remote Provider Specification
 
 Remote providers are used when you need to communicate with external services. Here's an example for a custom Ollama provider:
 
 ```yaml
-adapter:
-  adapter_type: custom_ollama
-  pip_packages:
-  - ollama
-  - aiohttp
-  config_class: llama_stack_ollama_provider.config.OllamaImplConfig
-  module: llama_stack_ollama_provider
+adapter_type: custom_ollama
+provider_type: "remote::ollama"
+pip_packages:
+- ollama
+- aiohttp
+config_class: llama_stack_ollama_provider.config.OllamaImplConfig
+module: llama_stack_ollama_provider
 api_dependencies: []
 optional_api_dependencies: []
 ```
 
-#### Adapter Configuration
+#### Remote Provider Configuration
 
-The `adapter` section defines how to load and configure the provider:
-
-- `adapter_type`: A unique identifier for this adapter
-- `pip_packages`: List of Python packages required by the provider
-- `config_class`: The full path to the configuration class
-- `module`: The Python module containing the provider implementation
+- `adapter_type`: A unique identifier for this adapter, eg. `ollama`
 
 ### Inline Provider Specification
 
@@ -81,6 +56,7 @@ Inline providers run locally within the Llama Stack process. Here's an example f
 
 ```yaml
 module: llama_stack_vector_provider
+provider_type: inline::llama_stack_vector_provider
 config_class: llama_stack_vector_provider.config.VectorStoreConfig
 pip_packages:
   - faiss-cpu
@@ -95,12 +71,6 @@ container_image: custom-vector-store:latest  # optional
 
 #### Inline Provider Fields
 
-- `module`: The Python module containing the provider implementation
-- `config_class`: The full path to the configuration class
-- `pip_packages`: List of Python packages required by the provider
-- `api_dependencies`: List of Llama Stack APIs that this provider depends on
-- `optional_api_dependencies`: List of optional Llama Stack APIs that this provider can use
-- `provider_data_validator`: Optional validator for provider data
 - `container_image`: Optional container image to use instead of pip packages
 
 ## Required Fields
@@ -113,20 +83,17 @@ All providers must contain a `get_provider_spec` function in their `provider` mo
 from llama_stack.providers.datatypes import (
     ProviderSpec,
     Api,
-    AdapterSpec,
-    remote_provider_spec,
+    RemoteProviderSpec,
 )
 
 
 def get_provider_spec() -> ProviderSpec:
-    return remote_provider_spec(
+    return RemoteProviderSpec(
         api=Api.inference,
-        adapter=AdapterSpec(
-            adapter_type="ramalama",
-            pip_packages=["ramalama>=0.8.5", "pymilvus"],
-            config_class="ramalama_stack.config.RamalamaImplConfig",
-            module="ramalama_stack",
-        ),
+        adapter_type="ramalama",
+        pip_packages=["ramalama>=0.8.5", "pymilvus"],
+        config_class="ramalama_stack.config.RamalamaImplConfig",
+        module="ramalama_stack",
     )
 ```
 
@@ -197,18 +164,16 @@ information. Execute the test for the Provider type you are developing.
 If your external provider isn't being loaded:
 
 1. Check that `module` points to a published pip package with a top level `provider` module including `get_provider_spec`.
-1. Check that the `external_providers_dir` path is correct and accessible.
 2. Verify that the YAML files are properly formatted.
 3. Ensure all required Python packages are installed.
 4. Check the Llama Stack server logs for any error messages - turn on debug logging to get more
    information using `LLAMA_STACK_LOGGING=all=debug`.
-5. Verify that the provider package is installed in your Python environment if using `external_providers_dir`.
 
 ## Examples
 
-### Example using `external_providers_dir`: Custom Ollama Provider
+### How to create an external provider module
 
-Here's a complete example of creating and using a custom Ollama provider:
+If you are creating a new external provider called `llama-stack-provider-ollama` here is how you would set up the package properly:
 
 1. First, create the provider package:
 
@@ -230,33 +195,28 @@ requires-python = ">=3.12"
 dependencies = ["llama-stack", "pydantic", "ollama", "aiohttp"]
 ```
 
-3. Create the provider specification:
-
-```yaml
-# ~/.llama/providers.d/remote/inference/custom_ollama.yaml
-adapter:
-  adapter_type: custom_ollama
-  pip_packages: ["ollama", "aiohttp"]
-  config_class: llama_stack_provider_ollama.config.OllamaImplConfig
-  module: llama_stack_provider_ollama
-api_dependencies: []
-optional_api_dependencies: []
-```
-
-4. Install the provider:
+3. Install the provider:
 
 ```bash
 uv pip install -e .
 ```
 
-5. Configure Llama Stack to use external providers:
+4. Edit `provider.py`
 
-```yaml
-external_providers_dir: ~/.llama/providers.d/
+provider.py must be updated to contain `get_provider_spec`. This is used by llama stack to install the provider.
+
+```python
+def get_provider_spec() -> ProviderSpec:
+    return RemoteProviderSpec(
+        api=Api.inference,
+        adapter_type="llama-stack-provider-ollama",
+        pip_packages=["ollama", "aiohttp"],
+        config_class="llama_stack_provider_ollama.config.OllamaImplConfig",
+        module="llama_stack_provider_ollama",
+    )
 ```
 
-The provider will now be available in Llama Stack with the type `remote::custom_ollama`.
-
+5. Implement the provider as outlined above with `get_provider_impl` or `get_adapter_impl`, etc.
 
 ### Example using `module`: ramalama-stack
 
@@ -275,7 +235,6 @@ distribution_spec:
       module: ramalama_stack==0.3.0a0
 image_type: venv
 image_name: null
-external_providers_dir: null
 additional_pip_packages:
 - aiosqlite
 - sqlalchemy[asyncio]

docs/docs/providers/files/index.mdx

@@ -1,4 +1,7 @@
 ---
+description: "Files
+
+    This API is used to upload documents that can be used with other Llama Stack APIs."
 sidebar_label: Files
 title: Files
 ---
@@ -7,4 +10,8 @@ title: Files
 
 ## Overview
 
+Files
+
+    This API is used to upload documents that can be used with other Llama Stack APIs.
+
 This section contains documentation for all available providers for the **files** API.

docs/docs/providers/inference/index.mdx

@@ -1,5 +1,7 @@
 ---
-description: "Llama Stack Inference API for generating completions, chat completions, and embeddings.
+description: "Inference
+
+    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.
@@ -12,7 +14,9 @@ title: Inference
 
 ## Overview
 
-Llama Stack Inference API for generating completions, chat completions, and embeddings.
+Inference
+
+    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.

docs/docs/providers/inference/remote_anthropic.mdx

@@ -14,6 +14,7 @@ Anthropic inference provider for accessing Claude models and Anthropic's AI serv
 
 | Field | Type | Required | Default | Description |
 |-------|------|----------|---------|-------------|
+| `allowed_models` | `list[str \| None` | No |  | List of models that should be registered with the model registry. If None, all models are allowed. |
 | `api_key` | `str \| None` | No |  | API key for Anthropic models |
 
 ## Sample Configuration

docs/docs/providers/inference/remote_azure.mdx

@@ -21,6 +21,7 @@ https://learn.microsoft.com/en-us/azure/ai-foundry/openai/overview
 
 | Field | Type | Required | Default | Description |
 |-------|------|----------|---------|-------------|
+| `allowed_models` | `list[str \| None` | No |  | List of models that should be registered with the model registry. If None, all models are allowed. |
 | `api_key` | `<class 'pydantic.types.SecretStr'>` | No |  | Azure API key for Azure |
 | `api_base` | `<class 'pydantic.networks.HttpUrl'>` | No |  | Azure API base for Azure (e.g., https://your-resource-name.openai.azure.com) |
 | `api_version` | `str \| None` | No |  | Azure API version for Azure (e.g., 2024-12-01-preview) |

docs/docs/providers/inference/remote_bedrock.mdx

@@ -14,6 +14,7 @@ AWS Bedrock inference provider for accessing various AI models through AWS's man
 
 | Field | Type | Required | Default | Description |
 |-------|------|----------|---------|-------------|
+| `allowed_models` | `list[str \| None` | No |  | List of models that should be registered with the model registry. If None, all models are allowed. |
 | `aws_access_key_id` | `str \| None` | No |  | The AWS access key to use. Default use environment variable: AWS_ACCESS_KEY_ID |
 | `aws_secret_access_key` | `str \| None` | No |  | The AWS secret access key to use. Default use environment variable: AWS_SECRET_ACCESS_KEY |
 | `aws_session_token` | `str \| None` | No |  | The AWS session token to use. Default use environment variable: AWS_SESSION_TOKEN |

docs/docs/providers/inference/remote_cerebras.mdx

@@ -14,6 +14,7 @@ Cerebras inference provider for running models on Cerebras Cloud platform.
 
 | Field | Type | Required | Default | Description |
 |-------|------|----------|---------|-------------|
+| `allowed_models` | `list[str \| None` | No |  | List of models that should be registered with the model registry. If None, all models are allowed. |
 | `base_url` | `<class 'str'>` | No | https://api.cerebras.ai | Base URL for the Cerebras API |
 | `api_key` | `<class 'pydantic.types.SecretStr'>` | No |  | Cerebras API Key |
 

docs/docs/providers/inference/remote_databricks.mdx

@@ -14,7 +14,8 @@ Databricks inference provider for running models on Databricks' unified analytic
 
 | Field | Type | Required | Default | Description |
 |-------|------|----------|---------|-------------|
-| `url` | `<class 'str'>` | No |  | The URL for the Databricks model serving endpoint |
+| `allowed_models` | `list[str \| None` | No |  | List of models that should be registered with the model registry. If None, all models are allowed. |
+| `url` | `str \| None` | No |  | The URL for the Databricks model serving endpoint |
 | `api_token` | `<class 'pydantic.types.SecretStr'>` | No |  | The Databricks API token |
 
 ## Sample Configuration

docs/docs/providers/inference/remote_gemini.mdx

@@ -14,6 +14,7 @@ Google Gemini inference provider for accessing Gemini models and Google's AI ser
 
 | Field | Type | Required | Default | Description |
 |-------|------|----------|---------|-------------|
+| `allowed_models` | `list[str \| None` | No |  | List of models that should be registered with the model registry. If None, all models are allowed. |
 | `api_key` | `str \| None` | No |  | API key for Gemini models |
 
 ## Sample Configuration

docs/docs/providers/inference/remote_groq.mdx

@@ -14,6 +14,7 @@ Groq inference provider for ultra-fast inference using Groq's LPU technology.
 
 | Field | Type | Required | Default | Description |
 |-------|------|----------|---------|-------------|
+| `allowed_models` | `list[str \| None` | No |  | List of models that should be registered with the model registry. If None, all models are allowed. |
 | `api_key` | `str \| None` | No |  | The Groq API key |
 | `url` | `<class 'str'>` | No | https://api.groq.com | The URL for the Groq AI server |
 

docs/docs/providers/inference/remote_llama-openai-compat.mdx

@@ -14,6 +14,7 @@ Llama OpenAI-compatible provider for using Llama models with OpenAI API format.
 
 | Field | Type | Required | Default | Description |
 |-------|------|----------|---------|-------------|
+| `allowed_models` | `list[str \| None` | No |  | List of models that should be registered with the model registry. If None, all models are allowed. |
 | `api_key` | `str \| None` | No |  | The Llama API key |
 | `openai_compat_api_base` | `<class 'str'>` | No | https://api.llama.com/compat/v1/ | The URL for the Llama API server |
 

docs/docs/providers/inference/remote_nvidia.mdx

@@ -14,6 +14,7 @@ NVIDIA inference provider for accessing NVIDIA NIM models and AI services.
 
 | Field | Type | Required | Default | Description |
 |-------|------|----------|---------|-------------|
+| `allowed_models` | `list[str \| None` | No |  | List of models that should be registered with the model registry. If None, all models are allowed. |
 | `url` | `<class 'str'>` | No | https://integrate.api.nvidia.com | A base url for accessing the NVIDIA NIM |
 | `api_key` | `pydantic.types.SecretStr \| None` | No |  | The NVIDIA API key, only needed of using the hosted service |
 | `timeout` | `<class 'int'>` | No | 60 | Timeout for the HTTP requests |

docs/docs/providers/inference/remote_ollama.mdx

@@ -14,6 +14,7 @@ Ollama inference provider for running local models through the Ollama runtime.
 
 | Field | Type | Required | Default | Description |
 |-------|------|----------|---------|-------------|
+| `allowed_models` | `list[str \| None` | No |  | List of models that should be registered with the model registry. If None, all models are allowed. |
 | `url` | `<class 'str'>` | No | http://localhost:11434 |  |
 | `refresh_models` | `<class 'bool'>` | No | False | Whether to refresh models periodically |
 

docs/docs/providers/inference/remote_openai.mdx

@@ -14,6 +14,7 @@ OpenAI inference provider for accessing GPT models and other OpenAI services.
 
 | Field | Type | Required | Default | Description |
 |-------|------|----------|---------|-------------|
+| `allowed_models` | `list[str \| None` | No |  | List of models that should be registered with the model registry. If None, all models are allowed. |
 | `api_key` | `str \| None` | No |  | API key for OpenAI models |
 | `base_url` | `<class 'str'>` | No | https://api.openai.com/v1 | Base URL for OpenAI API |
 

docs/docs/providers/inference/remote_passthrough.mdx

@@ -14,6 +14,7 @@ Passthrough inference provider for connecting to any external inference service
 
 | Field | Type | Required | Default | Description |
 |-------|------|----------|---------|-------------|
+| `allowed_models` | `list[str \| None` | No |  | List of models that should be registered with the model registry. If None, all models are allowed. |
 | `url` | `<class 'str'>` | No |  | The URL for the passthrough endpoint |
 | `api_key` | `pydantic.types.SecretStr \| None` | No |  | API Key for the passthrouth endpoint |
 

docs/docs/providers/inference/remote_runpod.mdx

@@ -14,6 +14,7 @@ RunPod inference provider for running models on RunPod's cloud GPU platform.
 
 | Field | Type | Required | Default | Description |
 |-------|------|----------|---------|-------------|
+| `allowed_models` | `list[str \| None` | No |  | List of models that should be registered with the model registry. If None, all models are allowed. |
 | `url` | `str \| None` | No |  | The URL for the Runpod model serving endpoint |
 | `api_token` | `str \| None` | No |  | The API token |
 

docs/docs/providers/inference/remote_sambanova.mdx

@@ -14,6 +14,7 @@ SambaNova inference provider for running models on SambaNova's dataflow architec
 
 | Field | Type | Required | Default | Description |
 |-------|------|----------|---------|-------------|
+| `allowed_models` | `list[str \| None` | No |  | List of models that should be registered with the model registry. If None, all models are allowed. |
 | `url` | `<class 'str'>` | No | https://api.sambanova.ai/v1 | The URL for the SambaNova AI server |
 | `api_key` | `pydantic.types.SecretStr \| None` | No |  | The SambaNova cloud API Key |
 

docs/docs/providers/inference/remote_tgi.mdx

@@ -14,6 +14,7 @@ Text Generation Inference (TGI) provider for HuggingFace model serving.
 
 | Field | Type | Required | Default | Description |
 |-------|------|----------|---------|-------------|
+| `allowed_models` | `list[str \| None` | No |  | List of models that should be registered with the model registry. If None, all models are allowed. |
 | `url` | `<class 'str'>` | No |  | The URL for the TGI serving endpoint |
 
 ## Sample Configuration

docs/docs/providers/inference/remote_vertexai.mdx

@@ -53,6 +53,7 @@ Available Models:
 
 | Field | Type | Required | Default | Description |
 |-------|------|----------|---------|-------------|
+| `allowed_models` | `list[str \| None` | No |  | List of models that should be registered with the model registry. If None, all models are allowed. |
 | `project` | `<class 'str'>` | No |  | Google Cloud project ID for Vertex AI |
 | `location` | `<class 'str'>` | No | us-central1 | Google Cloud location for Vertex AI |
 

docs/docs/providers/inference/remote_vllm.mdx

@@ -14,6 +14,7 @@ Remote vLLM inference provider for connecting to vLLM servers.
 
 | Field | Type | Required | Default | Description |
 |-------|------|----------|---------|-------------|
+| `allowed_models` | `list[str \| None` | No |  | List of models that should be registered with the model registry. If None, all models are allowed. |
 | `url` | `str \| None` | No |  | The URL for the vLLM model serving endpoint |
 | `max_tokens` | `<class 'int'>` | No | 4096 | Maximum number of tokens to generate. |
 | `api_token` | `str \| None` | No | fake | The API token |

docs/docs/providers/inference/remote_watsonx.mdx

@@ -14,6 +14,7 @@ IBM WatsonX inference provider for accessing AI models on IBM's WatsonX platform
 
 | Field | Type | Required | Default | Description |
 |-------|------|----------|---------|-------------|
+| `allowed_models` | `list[str \| None` | No |  | List of models that should be registered with the model registry. If None, all models are allowed. |
 | `url` | `<class 'str'>` | No | https://us-south.ml.cloud.ibm.com | A base url for accessing the watsonx.ai |
 | `api_key` | `pydantic.types.SecretStr \| None` | No |  | The watsonx API key |
 | `project_id` | `str \| None` | No |  | The Project ID key |

docs/docs/providers/safety/index.mdx

@@ -1,4 +1,7 @@
 ---
+description: "Safety
+
+    OpenAI-compatible Moderations API."
 sidebar_label: Safety
 title: Safety
 ---
@@ -7,4 +10,8 @@ title: Safety
 
 ## Overview
 
+Safety
+
+    OpenAI-compatible Moderations API.
+
 This section contains documentation for all available providers for the **safety** API.

docs/docs/providers/safety/remote_bedrock.mdx

@@ -14,6 +14,7 @@ AWS Bedrock safety provider for content moderation using AWS's safety services.
 
 | Field | Type | Required | Default | Description |
 |-------|------|----------|---------|-------------|
+| `allowed_models` | `list[str \| None` | No |  | List of models that should be registered with the model registry. If None, all models are allowed. |
 | `aws_access_key_id` | `str \| None` | No |  | The AWS access key to use. Default use environment variable: AWS_ACCESS_KEY_ID |
 | `aws_secret_access_key` | `str \| None` | No |  | The AWS secret access key to use. Default use environment variable: AWS_SECRET_ACCESS_KEY |
 | `aws_session_token` | `str \| None` | No |  | The AWS session token to use. Default use environment variable: AWS_SESSION_TOKEN |

docs/openapi_generator/pyopenapi/generator.py

@@ -50,6 +50,7 @@ from .specification import (
     Document,
     Example,
     ExampleRef,
+    ExtraBodyParameter,
     MediaType,
     Operation,
     Parameter,
@@ -677,6 +678,27 @@ class Generator:
         # parameters passed anywhere
         parameters = path_parameters + query_parameters
 
+        # Build extra body parameters documentation
+        extra_body_parameters = []
+        for param_name, param_type, description in op.extra_body_params:
+            if is_type_optional(param_type):
+                inner_type: type = unwrap_optional_type(param_type)
+                required = False
+            else:
+                inner_type = param_type
+                required = True
+
+            # Use description from ExtraBodyField if available, otherwise from docstring
+            param_description = description or doc_params.get(param_name)
+
+            extra_body_param = ExtraBodyParameter(
+                name=param_name,
+                schema=self.schema_builder.classdef_to_ref(inner_type),
+                description=param_description,
+                required=required,
+            )
+            extra_body_parameters.append(extra_body_param)
+
         webmethod = getattr(op.func_ref, "__webmethod__", None)
         raw_bytes_request_body = False
         if webmethod:
@@ -898,6 +920,7 @@ class Generator:
             deprecated=getattr(op.webmethod, "deprecated", False)
             or "DEPRECATED" in op.func_name,
             security=[] if op.public else None,
+            extraBodyParameters=extra_body_parameters if extra_body_parameters else None,
         )
 
     def _get_api_stability_priority(self, api_level: str) -> int:

docs/openapi_generator/pyopenapi/operations.py

@@ -23,6 +23,8 @@ from fastapi import UploadFile
 from fastapi.params import File, Form
 from typing import Annotated
 
+from llama_stack.schema_utils import ExtraBodyField
+
 
 def split_prefix(
     s: str, sep: str, prefix: Union[str, Iterable[str]]
@@ -89,6 +91,7 @@ class EndpointOperation:
     :param query_params: Parameters of the operation signature that are passed in the query string as `key=value` pairs.
     :param request_params: The parameter that corresponds to the data transmitted in the request body.
     :param multipart_params: Parameters that indicate multipart/form-data request body.
+    :param extra_body_params: Parameters that arrive via extra_body and are documented but not in SDK.
     :param event_type: The Python type of the data that is transmitted out-of-band (e.g. via websockets) while the operation is in progress.
     :param response_type: The Python type of the data that is transmitted in the response body.
     :param http_method: The HTTP method used to invoke the endpoint such as POST, GET or PUT.
@@ -106,6 +109,7 @@ class EndpointOperation:
     query_params: List[OperationParameter]
     request_params: Optional[OperationParameter]
     multipart_params: List[OperationParameter]
+    extra_body_params: List[tuple[str, type, str | None]]
     event_type: Optional[type]
     response_type: type
     http_method: HTTPMethod
@@ -265,6 +269,7 @@ def get_endpoint_operations(
             query_params = []
             request_params = []
             multipart_params = []
+            extra_body_params = []
 
             for param_name, parameter in signature.parameters.items():
                 param_type = _get_annotation_type(parameter.annotation, func_ref)
@@ -279,6 +284,13 @@ def get_endpoint_operations(
                         f"parameter '{param_name}' in function '{func_name}' has no type annotation"
                     )
 
+                # Check if this is an extra_body parameter
+                is_extra_body, extra_body_desc = _is_extra_body_param(param_type)
+                if is_extra_body:
+                    # Store in a separate list for documentation
+                    extra_body_params.append((param_name, param_type, extra_body_desc))
+                    continue  # Skip adding to request_params
+
                 is_multipart = _is_multipart_param(param_type)
 
                 if prefix in ["get", "delete"]:
@@ -351,6 +363,7 @@ def get_endpoint_operations(
                 query_params=query_params,
                 request_params=request_params,
                 multipart_params=multipart_params,
+                extra_body_params=extra_body_params,
                 event_type=event_type,
                 response_type=response_type,
                 http_method=http_method,
@@ -429,3 +442,22 @@ def _is_multipart_param(param_type: type) -> bool:
             if isinstance(annotation, (File, Form)):
                 return True
     return False
+
+
+def _is_extra_body_param(param_type: type) -> tuple[bool, str | None]:
+    """
+    Check if parameter is marked as coming from extra_body.
+
+    Returns:
+        (is_extra_body, description): Tuple of boolean and optional description
+    """
+    origin = get_origin(param_type)
+    if origin is Annotated:
+        args = get_args(param_type)
+        for annotation in args[1:]:
+            if isinstance(annotation, ExtraBodyField):
+                return True, annotation.description
+            # Also check by type name for cases where import matters
+            if type(annotation).__name__ == 'ExtraBodyField':
+                return True, getattr(annotation, 'description', None)
+    return False, None

docs/openapi_generator/pyopenapi/specification.py

@@ -106,6 +106,15 @@ class Parameter:
     example: Optional[Any] = None
 
 
+@dataclass
+class ExtraBodyParameter:
+    """Represents a parameter that arrives via extra_body in the request."""
+    name: str
+    schema: SchemaOrRef
+    description: Optional[str] = None
+    required: Optional[bool] = None
+
+
 @dataclass
 class Operation:
     responses: Dict[str, Union[Response, ResponseRef]]
@@ -118,6 +127,7 @@ class Operation:
     callbacks: Optional[Dict[str, "Callback"]] = None
     security: Optional[List["SecurityRequirement"]] = None
     deprecated: Optional[bool] = None
+    extraBodyParameters: Optional[List[ExtraBodyParameter]] = None
 
 
 @dataclass

docs/openapi_generator/pyopenapi/utility.py

@@ -52,6 +52,17 @@ class Specification:
                     if display_name:
                         tag["x-displayName"] = display_name
 
+            # Handle operations to rename extraBodyParameters -> x-llama-stack-extra-body-params
+            paths = json_doc.get("paths", {})
+            for path_item in paths.values():
+                if isinstance(path_item, dict):
+                    for method in ["get", "post", "put", "delete", "patch"]:
+                        operation = path_item.get(method)
+                        if operation and isinstance(operation, dict):
+                            extra_body_params = operation.pop("extraBodyParameters", None)
+                            if extra_body_params:
+                                operation["x-llama-stack-extra-body-params"] = extra_body_params
+
         return json_doc
 
     def get_json_string(self, pretty_print: bool = False) -> str:

docs/static/deprecated-llama-stack-spec.html

@@ -1443,8 +1443,8 @@
                 "tags": [
                     "Inference"
                 ],
-                "summary": "List all chat completions.",
-                "description": "List all chat completions.",
+                "summary": "List chat completions.",
+                "description": "List chat completions.",
                 "parameters": [
                     {
                         "name": "after",
@@ -1520,8 +1520,8 @@
                 "tags": [
                     "Inference"
                 ],
-                "summary": "Generate an OpenAI-compatible chat completion for the given messages using the specified model.",
-                "description": "Generate an OpenAI-compatible chat completion for the given messages using the specified model.",
+                "summary": "Create chat completions.",
+                "description": "Create chat completions.\nGenerate an OpenAI-compatible chat completion for the given messages using the specified model.",
                 "parameters": [],
                 "requestBody": {
                     "content": {
@@ -1565,8 +1565,8 @@
                 "tags": [
                     "Inference"
                 ],
-                "summary": "Describe a chat completion by its ID.",
-                "description": "Describe a chat completion by its ID.",
+                "summary": "Get chat completion.",
+                "description": "Get chat completion.\nDescribe a chat completion by its ID.",
                 "parameters": [
                     {
                         "name": "completion_id",
@@ -1610,8 +1610,8 @@
                 "tags": [
                     "Inference"
                 ],
-                "summary": "Generate an OpenAI-compatible completion for the given prompt using the specified model.",
-                "description": "Generate an OpenAI-compatible completion for the given prompt using the specified model.",
+                "summary": "Create completion.",
+                "description": "Create completion.\nGenerate an OpenAI-compatible completion for the given prompt using the specified model.",
                 "parameters": [],
                 "requestBody": {
                     "content": {
@@ -1655,8 +1655,8 @@
                 "tags": [
                     "Inference"
                 ],
-                "summary": "Generate OpenAI-compatible embeddings for the given input using the specified model.",
-                "description": "Generate OpenAI-compatible embeddings for the given input using the specified model.",
+                "summary": "Create embeddings.",
+                "description": "Create embeddings.\nGenerate OpenAI-compatible embeddings for the given input using the specified model.",
                 "parameters": [],
                 "requestBody": {
                     "content": {
@@ -1700,8 +1700,8 @@
                 "tags": [
                     "Files"
                 ],
-                "summary": "Returns a list of files that belong to the user's organization.",
-                "description": "Returns a list of files that belong to the user's organization.",
+                "summary": "List files.",
+                "description": "List files.\nReturns a list of files that belong to the user's organization.",
                 "parameters": [
                     {
                         "name": "after",
@@ -1770,8 +1770,8 @@
                 "tags": [
                     "Files"
                 ],
-                "summary": "Upload a file that can be used across various endpoints.",
-                "description": "Upload a file that can be used across various endpoints.\nThe file upload should be a multipart form request with:\n- file: The File object (not file name) to be uploaded.\n- purpose: The intended purpose of the uploaded file.\n- expires_after: Optional form values describing expiration for the file.",
+                "summary": "Upload file.",
+                "description": "Upload file.\nUpload a file that can be used across various endpoints.\n\nThe file upload should be a multipart form request with:\n- file: The File object (not file name) to be uploaded.\n- purpose: The intended purpose of the uploaded file.\n- expires_after: Optional form values describing expiration for the file.",
                 "parameters": [],
                 "requestBody": {
                     "content": {
@@ -1831,8 +1831,8 @@
                 "tags": [
                     "Files"
                 ],
-                "summary": "Returns information about a specific file.",
-                "description": "Returns information about a specific file.",
+                "summary": "Retrieve file.",
+                "description": "Retrieve file.\nReturns information about a specific file.",
                 "parameters": [
                     {
                         "name": "file_id",
@@ -1874,8 +1874,8 @@
                 "tags": [
                     "Files"
                 ],
-                "summary": "Delete a file.",
-                "description": "Delete a file.",
+                "summary": "Delete file.",
+                "description": "Delete file.",
                 "parameters": [
                     {
                         "name": "file_id",
@@ -1919,8 +1919,8 @@
                 "tags": [
                     "Files"
                 ],
-                "summary": "Returns the contents of the specified file.",
-                "description": "Returns the contents of the specified file.",
+                "summary": "Retrieve file content.",
+                "description": "Retrieve file content.\nReturns the contents of the specified file.",
                 "parameters": [
                     {
                         "name": "file_id",
@@ -1999,8 +1999,8 @@
                 "tags": [
                     "Safety"
                 ],
-                "summary": "Classifies if text and/or image inputs are potentially harmful.",
-                "description": "Classifies if text and/or image inputs are potentially harmful.",
+                "summary": "Create moderation.",
+                "description": "Create moderation.\nClassifies if text and/or image inputs are potentially harmful.",
                 "parameters": [],
                 "requestBody": {
                     "content": {
@@ -2044,8 +2044,8 @@
                 "tags": [
                     "Agents"
                 ],
-                "summary": "List all OpenAI responses.",
-                "description": "List all OpenAI responses.",
+                "summary": "List all responses.",
+                "description": "List all responses.",
                 "parameters": [
                     {
                         "name": "after",
@@ -2119,8 +2119,8 @@
                 "tags": [
                     "Agents"
                 ],
-                "summary": "Create a new OpenAI response.",
-                "description": "Create a new OpenAI response.",
+                "summary": "Create a model response.",
+                "description": "Create a model response.",
                 "parameters": [],
                 "requestBody": {
                     "content": {
@@ -2132,7 +2132,27 @@
                     },
                     "required": true
                 },
-                "deprecated": true
+                "deprecated": true,
+                "x-llama-stack-extra-body-params": [
+                    {
+                        "name": "shields",
+                        "schema": {
+                            "type": "array",
+                            "items": {
+                                "oneOf": [
+                                    {
+                                        "type": "string"
+                                    },
+                                    {
+                                        "$ref": "#/components/schemas/ResponseShieldSpec"
+                                    }
+                                ]
+                            }
+                        },
+                        "description": "List of shields to apply during response generation. Shields provide safety and content moderation.",
+                        "required": false
+                    }
+                ]
             }
         },
         "/v1/openai/v1/responses/{response_id}": {
@@ -2164,8 +2184,8 @@
                 "tags": [
                     "Agents"
                 ],
-                "summary": "Retrieve an OpenAI response by its ID.",
-                "description": "Retrieve an OpenAI response by its ID.",
+                "summary": "Get a model response.",
+                "description": "Get a model response.",
                 "parameters": [
                     {
                         "name": "response_id",
@@ -2207,8 +2227,8 @@
                 "tags": [
                     "Agents"
                 ],
-                "summary": "Delete an OpenAI response by its ID.",
-                "description": "Delete an OpenAI response by its ID.",
+                "summary": "Delete a response.",
+                "description": "Delete a response.",
                 "parameters": [
                     {
                         "name": "response_id",
@@ -2252,8 +2272,8 @@
                 "tags": [
                     "Agents"
                 ],
-                "summary": "List input items for a given OpenAI response.",
-                "description": "List input items for a given OpenAI response.",
+                "summary": "List input items.",
+                "description": "List input items.",
                 "parameters": [
                     {
                         "name": "response_id",
@@ -9521,6 +9541,21 @@
                 "title": "OpenAIResponseText",
                 "description": "Text response configuration for OpenAI responses."
             },
+            "ResponseShieldSpec": {
+                "type": "object",
+                "properties": {
+                    "type": {
+                        "type": "string",
+                        "description": "The type/identifier of the shield."
+                    }
+                },
+                "additionalProperties": false,
+                "required": [
+                    "type"
+                ],
+                "title": "ResponseShieldSpec",
+                "description": "Specification for a shield to apply during response generation."
+            },
             "OpenAIResponseInputTool": {
                 "oneOf": [
                     {
@@ -13331,12 +13366,13 @@
         },
         {
             "name": "Files",
-            "description": ""
+            "description": "This API is used to upload documents that can be used with other Llama Stack APIs.",
+            "x-displayName": "Files"
         },
         {
             "name": "Inference",
-            "description": "This API provides the raw interface to the underlying models. Two kinds of models are supported:\n- LLM models: these models generate \"raw\" and \"chat\" (conversational) completions.\n- Embedding models: these models generate embeddings to be used for semantic search.",
-            "x-displayName": "Llama Stack Inference API for generating completions, chat completions, and embeddings."
+            "description": "Llama Stack Inference API for generating completions, chat completions, and embeddings.\n\nThis API provides the raw interface to the underlying models. Two kinds of models are supported:\n- LLM models: these models generate \"raw\" and \"chat\" (conversational) completions.\n- Embedding models: these models generate embeddings to be used for semantic search.",
+            "x-displayName": "Inference"
         },
         {
             "name": "Models",
@@ -13348,7 +13384,8 @@
         },
         {
             "name": "Safety",
-            "description": ""
+            "description": "OpenAI-compatible Moderations API.",
+            "x-displayName": "Safety"
         },
         {
             "name": "Telemetry",

docs/static/deprecated-llama-stack-spec.yaml

@@ -1033,8 +1033,8 @@ paths:
           $ref: '#/components/responses/DefaultError'
       tags:
         - Inference
-      summary: List all chat completions.
-      description: List all chat completions.
+      summary: List chat completions.
+      description: List chat completions.
       parameters:
         - name: after
           in: query
@@ -1087,10 +1087,10 @@ paths:
           $ref: '#/components/responses/DefaultError'
       tags:
         - Inference
-      summary: >-
-        Generate an OpenAI-compatible chat completion for the given messages using
-        the specified model.
+      summary: Create chat completions.
       description: >-
+        Create chat completions.
+
         Generate an OpenAI-compatible chat completion for the given messages using
         the specified model.
       parameters: []
@@ -1122,8 +1122,11 @@ paths:
           $ref: '#/components/responses/DefaultError'
       tags:
         - Inference
-      summary: Describe a chat completion by its ID.
-      description: Describe a chat completion by its ID.
+      summary: Get chat completion.
+      description: >-
+        Get chat completion.
+
+        Describe a chat completion by its ID.
       parameters:
         - name: completion_id
           in: path
@@ -1153,10 +1156,10 @@ paths:
           $ref: '#/components/responses/DefaultError'
       tags:
         - Inference
-      summary: >-
-        Generate an OpenAI-compatible completion for the given prompt using the specified
-        model.
+      summary: Create completion.
       description: >-
+        Create completion.
+
         Generate an OpenAI-compatible completion for the given prompt using the specified
         model.
       parameters: []
@@ -1189,10 +1192,10 @@ paths:
           $ref: '#/components/responses/DefaultError'
       tags:
         - Inference
-      summary: >-
-        Generate OpenAI-compatible embeddings for the given input using the specified
-        model.
+      summary: Create embeddings.
       description: >-
+        Create embeddings.
+
         Generate OpenAI-compatible embeddings for the given input using the specified
         model.
       parameters: []
@@ -1225,9 +1228,10 @@ paths:
           $ref: '#/components/responses/DefaultError'
       tags:
         - Files
-      summary: >-
-        Returns a list of files that belong to the user's organization.
+      summary: List files.
       description: >-
+        List files.
+
         Returns a list of files that belong to the user's organization.
       parameters:
         - name: after
@@ -1285,11 +1289,13 @@ paths:
           $ref: '#/components/responses/DefaultError'
       tags:
         - Files
-      summary: >-
-        Upload a file that can be used across various endpoints.
+      summary: Upload file.
       description: >-
+        Upload file.
+
         Upload a file that can be used across various endpoints.
 
+
         The file upload should be a multipart form request with:
 
         - file: The File object (not file name) to be uploaded.
@@ -1338,9 +1344,10 @@ paths:
           $ref: '#/components/responses/DefaultError'
       tags:
         - Files
-      summary: >-
-        Returns information about a specific file.
+      summary: Retrieve file.
       description: >-
+        Retrieve file.
+
         Returns information about a specific file.
       parameters:
         - name: file_id
@@ -1372,8 +1379,8 @@ paths:
           $ref: '#/components/responses/DefaultError'
       tags:
         - Files
-      summary: Delete a file.
-      description: Delete a file.
+      summary: Delete file.
+      description: Delete file.
       parameters:
         - name: file_id
           in: path
@@ -1405,9 +1412,10 @@ paths:
           $ref: '#/components/responses/DefaultError'
       tags:
         - Files
-      summary: >-
-        Returns the contents of the specified file.
+      summary: Retrieve file content.
       description: >-
+        Retrieve file content.
+
         Returns the contents of the specified file.
       parameters:
         - name: file_id
@@ -1464,9 +1472,10 @@ paths:
           $ref: '#/components/responses/DefaultError'
       tags:
         - Safety
-      summary: >-
-        Classifies if text and/or image inputs are potentially harmful.
+      summary: Create moderation.
       description: >-
+        Create moderation.
+
         Classifies if text and/or image inputs are potentially harmful.
       parameters: []
       requestBody:
@@ -1497,8 +1506,8 @@ paths:
           $ref: '#/components/responses/DefaultError'
       tags:
         - Agents
-      summary: List all OpenAI responses.
-      description: List all OpenAI responses.
+      summary: List all responses.
+      description: List all responses.
       parameters:
         - name: after
           in: query
@@ -1549,8 +1558,8 @@ paths:
           $ref: '#/components/responses/DefaultError'
       tags:
         - Agents
-      summary: Create a new OpenAI response.
-      description: Create a new OpenAI response.
+      summary: Create a model response.
+      description: Create a model response.
       parameters: []
       requestBody:
         content:
@@ -1559,6 +1568,18 @@ paths:
               $ref: '#/components/schemas/CreateOpenaiResponseRequest'
         required: true
       deprecated: true
+      x-llama-stack-extra-body-params:
+        - name: shields
+          schema:
+            type: array
+            items:
+              oneOf:
+                - type: string
+                - $ref: '#/components/schemas/ResponseShieldSpec'
+          description: >-
+            List of shields to apply during response generation. Shields provide safety
+            and content moderation.
+          required: false
   /v1/openai/v1/responses/{response_id}:
     get:
       responses:
@@ -1580,8 +1601,8 @@ paths:
           $ref: '#/components/responses/DefaultError'
       tags:
         - Agents
-      summary: Retrieve an OpenAI response by its ID.
-      description: Retrieve an OpenAI response by its ID.
+      summary: Get a model response.
+      description: Get a model response.
       parameters:
         - name: response_id
           in: path
@@ -1611,8 +1632,8 @@ paths:
           $ref: '#/components/responses/DefaultError'
       tags:
         - Agents
-      summary: Delete an OpenAI response by its ID.
-      description: Delete an OpenAI response by its ID.
+      summary: Delete a response.
+      description: Delete a response.
       parameters:
         - name: response_id
           in: path
@@ -1642,10 +1663,8 @@ paths:
           $ref: '#/components/responses/DefaultError'
       tags:
         - Agents
-      summary: >-
-        List input items for a given OpenAI response.
-      description: >-
-        List input items for a given OpenAI response.
+      summary: List input items.
+      description: List input items.
       parameters:
         - name: response_id
           in: path
@@ -7076,6 +7095,18 @@ components:
       title: OpenAIResponseText
       description: >-
         Text response configuration for OpenAI responses.
+    ResponseShieldSpec:
+      type: object
+      properties:
+        type:
+          type: string
+          description: The type/identifier of the shield.
+      additionalProperties: false
+      required:
+        - type
+      title: ResponseShieldSpec
+      description: >-
+        Specification for a shield to apply during response generation.
     OpenAIResponseInputTool:
       oneOf:
         - $ref: '#/components/schemas/OpenAIResponseInputToolWebSearch'
@@ -9987,9 +10018,16 @@ tags:
     x-displayName: >-
       Llama Stack Evaluation API for running evaluations on model and agent candidates.
   - name: Files
-    description: ''
+    description: >-
+      This API is used to upload documents that can be used with other Llama Stack
+      APIs.
+    x-displayName: Files
   - name: Inference
     description: >-
+      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:
 
@@ -9997,15 +10035,14 @@ tags:
 
       - Embedding models: these models generate embeddings to be used for semantic
       search.
-    x-displayName: >-
-      Llama Stack Inference API for generating completions, chat completions, and
-      embeddings.
+    x-displayName: Inference
   - name: Models
     description: ''
   - name: PostTraining (Coming Soon)
     description: ''
   - name: Safety
-    description: ''
+    description: OpenAI-compatible Moderations API.
+    x-displayName: Safety
   - name: Telemetry
     description: ''
   - name: VectorIO

llama_stack/apis/agents/agents.py

@@ -28,7 +28,7 @@ from llama_stack.apis.inference import (
 from llama_stack.apis.safety import SafetyViolation
 from llama_stack.apis.tools import ToolDef
 from llama_stack.apis.version import LLAMA_STACK_API_V1, LLAMA_STACK_API_V1ALPHA
-from llama_stack.schema_utils import json_schema_type, register_schema, webmethod
+from llama_stack.schema_utils import ExtraBodyField, json_schema_type, register_schema, webmethod
 
 from .openai_responses import (
     ListOpenAIResponseInputItem,
@@ -42,6 +42,20 @@ from .openai_responses import (
 )
 
 
+@json_schema_type
+class ResponseShieldSpec(BaseModel):
+    """Specification for a shield to apply during response generation.
+
+    :param type: The type/identifier of the shield.
+    """
+
+    type: str
+    # TODO: more fields to be added for shield configuration
+
+
+ResponseShield = str | ResponseShieldSpec
+
+
 class Attachment(BaseModel):
     """An attachment to an agent turn.
 
@@ -783,7 +797,7 @@ class Agents(Protocol):
         self,
         response_id: str,
     ) -> OpenAIResponseObject:
-        """Retrieve an OpenAI response by its ID.
+        """Get a model response.
 
         :param response_id: The ID of the OpenAI response to retrieve.
         :returns: An OpenAIResponseObject.
@@ -805,13 +819,20 @@ class Agents(Protocol):
         tools: list[OpenAIResponseInputTool] | None = None,
         include: list[str] | None = None,
         max_infer_iters: int | None = 10,  # this is an extension to the OpenAI API
+        shields: Annotated[
+            list[ResponseShield] | None,
+            ExtraBodyField(
+                "List of shields to apply during response generation. Shields provide safety and content moderation."
+            ),
+        ] = None,
     ) -> OpenAIResponseObject | AsyncIterator[OpenAIResponseObjectStream]:
-        """Create a new OpenAI response.
+        """Create a model 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.
         :param include: (Optional) Additional fields to include in the response.
+        :param shields: (Optional) List of shields to apply during response generation. Can be shield IDs (strings) or shield specifications.
         :returns: An OpenAIResponseObject.
         """
         ...
@@ -825,7 +846,7 @@ class Agents(Protocol):
         model: str | None = None,
         order: Order | None = Order.desc,
     ) -> ListOpenAIResponseObject:
-        """List all OpenAI responses.
+        """List all responses.
 
         :param after: The ID of the last response to return.
         :param limit: The number of responses to return.
@@ -848,7 +869,7 @@ class Agents(Protocol):
         limit: int | None = 20,
         order: Order | None = Order.desc,
     ) -> ListOpenAIResponseInputItem:
-        """List input items for a given OpenAI response.
+        """List input items.
 
         :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.
@@ -863,7 +884,7 @@ class Agents(Protocol):
     @webmethod(route="/openai/v1/responses/{response_id}", method="DELETE", level=LLAMA_STACK_API_V1, deprecated=True)
     @webmethod(route="/responses/{response_id}", method="DELETE", level=LLAMA_STACK_API_V1)
     async def delete_openai_response(self, response_id: str) -> OpenAIDeleteResponseObject:
-        """Delete an OpenAI response by its ID.
+        """Delete a response.
 
         :param response_id: The ID of the OpenAI response to delete.
         :returns: An OpenAIDeleteResponseObject

llama_stack/apis/agents/openai_responses.py

@@ -888,6 +888,10 @@ class OpenAIResponseObjectWithInput(OpenAIResponseObject):
 
     input: list[OpenAIResponseInput]
 
+    def to_response_object(self) -> OpenAIResponseObject:
+        """Convert to OpenAIResponseObject by excluding input field."""
+        return OpenAIResponseObject(**{k: v for k, v in self.model_dump().items() if k != "input"})
+
 
 @json_schema_type
 class ListOpenAIResponseObject(BaseModel):

llama_stack/apis/conversations/__init__.py

@@ -0,0 +1,31 @@
+# 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 .conversations import (
+    Conversation,
+    ConversationCreateRequest,
+    ConversationDeletedResource,
+    ConversationItem,
+    ConversationItemCreateRequest,
+    ConversationItemDeletedResource,
+    ConversationItemList,
+    Conversations,
+    ConversationUpdateRequest,
+    Metadata,
+)
+
+__all__ = [
+    "Conversation",
+    "ConversationCreateRequest",
+    "ConversationDeletedResource",
+    "ConversationItem",
+    "ConversationItemCreateRequest",
+    "ConversationItemDeletedResource",
+    "ConversationItemList",
+    "Conversations",
+    "ConversationUpdateRequest",
+    "Metadata",
+]

llama_stack/apis/conversations/conversations.py

@@ -0,0 +1,260 @@
+# Copyright (c) Meta Platforms, Inc. and affiliates.
+# All rights reserved.
+#
+# This source code is licensed under the terms described in the LICENSE file in
+# the root directory of this source tree.
+
+from typing import Annotated, Literal, Protocol, runtime_checkable
+
+from openai import NOT_GIVEN
+from openai._types import NotGiven
+from openai.types.responses.response_includable import ResponseIncludable
+from pydantic import BaseModel, Field
+
+from llama_stack.apis.agents.openai_responses import (
+    OpenAIResponseMessage,
+    OpenAIResponseOutputMessageFileSearchToolCall,
+    OpenAIResponseOutputMessageFunctionToolCall,
+    OpenAIResponseOutputMessageMCPCall,
+    OpenAIResponseOutputMessageMCPListTools,
+    OpenAIResponseOutputMessageWebSearchToolCall,
+)
+from llama_stack.apis.version import LLAMA_STACK_API_V1
+from llama_stack.providers.utils.telemetry.trace_protocol import trace_protocol
+from llama_stack.schema_utils import json_schema_type, register_schema, webmethod
+
+Metadata = dict[str, str]
+
+
+@json_schema_type
+class Conversation(BaseModel):
+    """OpenAI-compatible conversation object."""
+
+    id: str = Field(..., description="The unique ID of the conversation.")
+    object: Literal["conversation"] = Field(
+        default="conversation", description="The object type, which is always conversation."
+    )
+    created_at: int = Field(
+        ..., description="The time at which the conversation was created, measured in seconds since the Unix epoch."
+    )
+    metadata: Metadata | None = Field(
+        default=None,
+        description="Set of 16 key-value pairs that can be attached to an object. This can be useful for storing additional information about the object in a structured format, and querying for objects via API or the dashboard.",
+    )
+    items: list[dict] | None = Field(
+        default=None,
+        description="Initial items to include in the conversation context. You may add up to 20 items at a time.",
+    )
+
+
+@json_schema_type
+class ConversationMessage(BaseModel):
+    """OpenAI-compatible message item for conversations."""
+
+    id: str = Field(..., description="unique identifier for this message")
+    content: list[dict] = Field(..., description="message content")
+    role: str = Field(..., description="message role")
+    status: str = Field(..., description="message status")
+    type: Literal["message"] = "message"
+    object: Literal["message"] = "message"
+
+
+ConversationItem = Annotated[
+    OpenAIResponseMessage
+    | OpenAIResponseOutputMessageFunctionToolCall
+    | OpenAIResponseOutputMessageFileSearchToolCall
+    | OpenAIResponseOutputMessageWebSearchToolCall
+    | OpenAIResponseOutputMessageMCPCall
+    | OpenAIResponseOutputMessageMCPListTools,
+    Field(discriminator="type"),
+]
+register_schema(ConversationItem, name="ConversationItem")
+
+# Using OpenAI types directly caused issues but some notes for reference:
+# Note that ConversationItem is a Annotated Union of the types below:
+# from openai.types.responses import *
+# from openai.types.responses.response_item import *
+# from openai.types.conversations import ConversationItem
+# f = [
+#     ResponseFunctionToolCallItem,
+#     ResponseFunctionToolCallOutputItem,
+#     ResponseFileSearchToolCall,
+#     ResponseFunctionWebSearch,
+#     ImageGenerationCall,
+#     ResponseComputerToolCall,
+#     ResponseComputerToolCallOutputItem,
+#     ResponseReasoningItem,
+#     ResponseCodeInterpreterToolCall,
+#     LocalShellCall,
+#     LocalShellCallOutput,
+#     McpListTools,
+#     McpApprovalRequest,
+#     McpApprovalResponse,
+#     McpCall,
+#     ResponseCustomToolCall,
+#     ResponseCustomToolCallOutput
+# ]
+
+
+@json_schema_type
+class ConversationCreateRequest(BaseModel):
+    """Request body for creating a conversation."""
+
+    items: list[ConversationItem] | None = Field(
+        default=[],
+        description="Initial items to include in the conversation context. You may add up to 20 items at a time.",
+        max_length=20,
+    )
+    metadata: Metadata | None = Field(
+        default={},
+        description="Set of 16 key-value pairs that can be attached to an object. Useful for storing additional information",
+        max_length=16,
+    )
+
+
+@json_schema_type
+class ConversationUpdateRequest(BaseModel):
+    """Request body for updating a conversation."""
+
+    metadata: Metadata = Field(
+        ...,
+        description="Set of 16 key-value pairs that can be attached to an object. This can be useful for storing additional information about the object in a structured format, and querying for objects via API or the dashboard. Keys are strings with a maximum length of 64 characters. Values are strings with a maximum length of 512 characters.",
+    )
+
+
+@json_schema_type
+class ConversationDeletedResource(BaseModel):
+    """Response for deleted conversation."""
+
+    id: str = Field(..., description="The deleted conversation identifier")
+    object: str = Field(default="conversation.deleted", description="Object type")
+    deleted: bool = Field(default=True, description="Whether the object was deleted")
+
+
+@json_schema_type
+class ConversationItemCreateRequest(BaseModel):
+    """Request body for creating conversation items."""
+
+    items: list[ConversationItem] = Field(
+        ...,
+        description="Items to include in the conversation context. You may add up to 20 items at a time.",
+        max_length=20,
+    )
+
+
+@json_schema_type
+class ConversationItemList(BaseModel):
+    """List of conversation items with pagination."""
+
+    object: str = Field(default="list", description="Object type")
+    data: list[ConversationItem] = Field(..., description="List of conversation items")
+    first_id: str | None = Field(default=None, description="The ID of the first item in the list")
+    last_id: str | None = Field(default=None, description="The ID of the last item in the list")
+    has_more: bool = Field(default=False, description="Whether there are more items available")
+
+
+@json_schema_type
+class ConversationItemDeletedResource(BaseModel):
+    """Response for deleted conversation item."""
+
+    id: str = Field(..., description="The deleted item identifier")
+    object: str = Field(default="conversation.item.deleted", description="Object type")
+    deleted: bool = Field(default=True, description="Whether the object was deleted")
+
+
+@runtime_checkable
+@trace_protocol
+class Conversations(Protocol):
+    """Protocol for conversation management operations."""
+
+    @webmethod(route="/conversations", method="POST", level=LLAMA_STACK_API_V1)
+    async def create_conversation(
+        self, items: list[ConversationItem] | None = None, metadata: Metadata | None = None
+    ) -> Conversation:
+        """Create a conversation.
+
+        :param items: Initial items to include in the conversation context.
+        :param metadata: Set of key-value pairs that can be attached to an object.
+        :returns: The created conversation object.
+        """
+        ...
+
+    @webmethod(route="/conversations/{conversation_id}", method="GET", level=LLAMA_STACK_API_V1)
+    async def get_conversation(self, conversation_id: str) -> Conversation:
+        """Get a conversation with the given ID.
+
+        :param conversation_id: The conversation identifier.
+        :returns: The conversation object.
+        """
+        ...
+
+    @webmethod(route="/conversations/{conversation_id}", method="POST", level=LLAMA_STACK_API_V1)
+    async def update_conversation(self, conversation_id: str, metadata: Metadata) -> Conversation:
+        """Update a conversation's metadata with the given ID.
+
+        :param conversation_id: The conversation identifier.
+        :param metadata: Set of key-value pairs that can be attached to an object.
+        :returns: The updated conversation object.
+        """
+        ...
+
+    @webmethod(route="/conversations/{conversation_id}", method="DELETE", level=LLAMA_STACK_API_V1)
+    async def openai_delete_conversation(self, conversation_id: str) -> ConversationDeletedResource:
+        """Delete a conversation with the given ID.
+
+        :param conversation_id: The conversation identifier.
+        :returns: The deleted conversation resource.
+        """
+        ...
+
+    @webmethod(route="/conversations/{conversation_id}/items", method="POST", level=LLAMA_STACK_API_V1)
+    async def add_items(self, conversation_id: str, items: list[ConversationItem]) -> ConversationItemList:
+        """Create items in the conversation.
+
+        :param conversation_id: The conversation identifier.
+        :param items: Items to include in the conversation context.
+        :returns: List of created items.
+        """
+        ...
+
+    @webmethod(route="/conversations/{conversation_id}/items/{item_id}", method="GET", level=LLAMA_STACK_API_V1)
+    async def retrieve(self, conversation_id: str, item_id: str) -> ConversationItem:
+        """Retrieve a conversation item.
+
+        :param conversation_id: The conversation identifier.
+        :param item_id: The item identifier.
+        :returns: The conversation item.
+        """
+        ...
+
+    @webmethod(route="/conversations/{conversation_id}/items", method="GET", level=LLAMA_STACK_API_V1)
+    async def list(
+        self,
+        conversation_id: str,
+        after: str | NotGiven = NOT_GIVEN,
+        include: list[ResponseIncludable] | NotGiven = NOT_GIVEN,
+        limit: int | NotGiven = NOT_GIVEN,
+        order: Literal["asc", "desc"] | NotGiven = NOT_GIVEN,
+    ) -> ConversationItemList:
+        """List items in the conversation.
+
+        :param conversation_id: The conversation identifier.
+        :param after: An item ID to list items after, used in pagination.
+        :param include: Specify additional output data to include in the response.
+        :param limit: A limit on the number of objects to be returned (1-100, default 20).
+        :param order: The order to return items in (asc or desc, default desc).
+        :returns: List of conversation items.
+        """
+        ...
+
+    @webmethod(route="/conversations/{conversation_id}/items/{item_id}", method="DELETE", level=LLAMA_STACK_API_V1)
+    async def openai_delete_conversation_item(
+        self, conversation_id: str, item_id: str
+    ) -> ConversationItemDeletedResource:
+        """Delete a conversation item.
+
+        :param conversation_id: The conversation identifier.
+        :param item_id: The item identifier.
+        :returns: The deleted item resource.
+        """
+        ...

llama_stack/apis/datatypes.py

@@ -129,6 +129,7 @@ class Api(Enum, metaclass=DynamicApiMeta):
     tool_groups = "tool_groups"
     files = "files"
     prompts = "prompts"
+    conversations = "conversations"
 
     # built-in API
     inspect = "inspect"

llama_stack/apis/files/files.py

@@ -104,6 +104,11 @@ class OpenAIFileDeleteResponse(BaseModel):
 @runtime_checkable
 @trace_protocol
 class Files(Protocol):
+    """Files
+
+    This API is used to upload documents that can be used with other Llama Stack APIs.
+    """
+
     # OpenAI Files API Endpoints
     @webmethod(route="/openai/v1/files", method="POST", level=LLAMA_STACK_API_V1, deprecated=True)
     @webmethod(route="/files", method="POST", level=LLAMA_STACK_API_V1)
@@ -113,7 +118,8 @@ class Files(Protocol):
         purpose: Annotated[OpenAIFilePurpose, Form()],
         expires_after: Annotated[ExpiresAfter | None, Form()] = None,
     ) -> OpenAIFileObject:
-        """
+        """Upload file.
+
         Upload a file that can be used across various endpoints.
 
         The file upload should be a multipart form request with:
@@ -137,7 +143,8 @@ class Files(Protocol):
         order: Order | None = Order.desc,
         purpose: OpenAIFilePurpose | None = None,
     ) -> ListOpenAIFileResponse:
-        """
+        """List files.
+
         Returns a list of files that belong to the user's organization.
 
         :param after: A cursor for use in pagination. `after` is an object ID that defines your place in the list. For instance, if you make a list request and receive 100 objects, ending with obj_foo, your subsequent call can include after=obj_foo in order to fetch the next page of the list.
@@ -154,7 +161,8 @@ class Files(Protocol):
         self,
         file_id: str,
     ) -> OpenAIFileObject:
-        """
+        """Retrieve file.
+
         Returns information about a specific file.
 
         :param file_id: The ID of the file to use for this request.
@@ -168,8 +176,7 @@ class Files(Protocol):
         self,
         file_id: str,
     ) -> OpenAIFileDeleteResponse:
-        """
-        Delete a file.
+        """Delete file.
 
         :param file_id: The ID of the file to use for this request.
         :returns: An OpenAIFileDeleteResponse indicating successful deletion.
@@ -182,7 +189,8 @@ class Files(Protocol):
         self,
         file_id: str,
     ) -> Response:
-        """
+        """Retrieve file content.
+
         Returns the contents of the specified file.
 
         :param file_id: The ID of the file to use for this request.

llama_stack/apis/inference/inference.py

@@ -982,45 +982,6 @@ class InferenceProvider(Protocol):
 
     model_store: ModelStore | None = None
 
-    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]:
-        """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 tool_choice: (Optional) Whether tool use is required or automatic. Defaults to ToolChoice.auto.
-            .. deprecated::
-               Use tool_config instead.
-        :param tool_prompt_format: (Optional) Instructs the model how to format tool calls. By default, Llama Stack will attempt to use a format that is best adapted to the model.
-            - `ToolPromptFormat.json`: The tool calls are formatted as a JSON object.
-            - `ToolPromptFormat.function_tag`: The tool calls are enclosed in a <function=function_name> tag.
-            - `ToolPromptFormat.python_list`: The tool calls are output as Python syntax -- a list of function calls.
-            .. deprecated::
-               Use tool_config instead.
-        :param response_format: (Optional) Grammar specification for guided (structured) decoding. There are two options:
-            - `ResponseFormat.json_schema`: The grammar is a JSON schema. Most providers support this format.
-            - `ResponseFormat.grammar`: The grammar is a BNF grammar. This format is more flexible, but not all providers support it.
-        :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.
-        :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.
-        """
-        ...
-
     @webmethod(route="/inference/rerank", method="POST", level=LLAMA_STACK_API_V1ALPHA)
     async def rerank(
         self,
@@ -1081,7 +1042,9 @@ class InferenceProvider(Protocol):
         # for fill-in-the-middle type completion
         suffix: str | None = None,
     ) -> OpenAICompletion:
-        """Generate an OpenAI-compatible completion for the given prompt using the specified model.
+        """Create completion.
+
+        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.
@@ -1138,7 +1101,9 @@ class InferenceProvider(Protocol):
         top_p: float | None = None,
         user: str | None = None,
     ) -> OpenAIChatCompletion | AsyncIterator[OpenAIChatCompletionChunk]:
-        """Generate an OpenAI-compatible chat completion for the given messages using the specified model.
+        """Create chat completions.
+
+        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.
@@ -1182,7 +1147,9 @@ class InferenceProvider(Protocol):
         dimensions: int | None = None,
         user: str | None = None,
     ) -> OpenAIEmbeddingsResponse:
-        """Generate OpenAI-compatible embeddings for the given input using the specified model.
+        """Create embeddings.
+
+        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.
@@ -1195,7 +1162,9 @@ class InferenceProvider(Protocol):
 
 
 class Inference(InferenceProvider):
-    """Llama Stack Inference API for generating completions, chat completions, and embeddings.
+    """Inference
+
+    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.
@@ -1216,7 +1185,7 @@ class Inference(InferenceProvider):
         model: str | None = None,
         order: Order | None = Order.desc,
     ) -> ListOpenAIChatCompletionResponse:
-        """List all chat completions.
+        """List chat completions.
 
         :param after: The ID of the last chat completion to return.
         :param limit: The maximum number of chat completions to return.
@@ -1237,10 +1206,11 @@ class Inference(InferenceProvider):
         method="GET",
         level=LLAMA_STACK_API_V1,
     )
-    async def get_chat_completion(
-        self, completion_id: str
-    ) -> OpenAICompletionWithInputMessages:
-        """Describe a chat completion by its ID.
+    @webmethod(route="/chat/completions/{completion_id}", method="GET", level=LLAMA_STACK_API_V1)
+    async def get_chat_completion(self, completion_id: str) -> OpenAICompletionWithInputMessages:
+        """Get chat completion.
+
+        Describe a chat completion by its ID.
 
         :param completion_id: ID of the chat completion.
         :returns: A OpenAICompletionWithInputMessages.

llama_stack/apis/inspect/inspect.py

@@ -58,9 +58,16 @@ class ListRoutesResponse(BaseModel):
 
 @runtime_checkable
 class Inspect(Protocol):
+    """Inspect
+
+    APIs for inspecting the Llama Stack service, including health status, available API routes with methods and implementing providers.
+    """
+
     @webmethod(route="/inspect/routes", method="GET", level=LLAMA_STACK_API_V1)
     async def list_routes(self) -> ListRoutesResponse:
-        """List all available API routes with their methods and implementing providers.
+        """List routes.
+
+        List all available API routes with their methods and implementing providers.
 
         :returns: Response containing information about all available routes.
         """
@@ -68,7 +75,9 @@ class Inspect(Protocol):
 
     @webmethod(route="/health", method="GET", level=LLAMA_STACK_API_V1)
     async def health(self) -> HealthInfo:
-        """Get the current health status of the service.
+        """Get health status.
+
+        Get the current health status of the service.
 
         :returns: Health information indicating if the service is operational.
         """
@@ -76,7 +85,9 @@ class Inspect(Protocol):
 
     @webmethod(route="/version", method="GET", level=LLAMA_STACK_API_V1)
     async def version(self) -> VersionInfo:
-        """Get the version of the service.
+        """Get version.
+
+        Get the version of the service.
 
         :returns: Version information containing the service version number.
         """

llama_stack/apis/models/models.py

@@ -124,7 +124,9 @@ class Models(Protocol):
         self,
         model_id: str,
     ) -> Model:
-        """Get a model by its identifier.
+        """Get model.
+
+        Get a model by its identifier.
 
         :param model_id: The identifier of the model to get.
         :returns: A Model.
@@ -140,7 +142,9 @@ class Models(Protocol):
         metadata: dict[str, Any] | None = None,
         model_type: ModelType | None = None,
     ) -> Model:
-        """Register a model.
+        """Register 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.
@@ -156,7 +160,9 @@ class Models(Protocol):
         self,
         model_id: str,
     ) -> None:
-        """Unregister a model.
+        """Unregister model.
+
+        Unregister a model.
 
         :param model_id: The identifier of the model to unregister.
         """

llama_stack/apis/prompts/prompts.py

@@ -94,7 +94,9 @@ class ListPromptsResponse(BaseModel):
 @runtime_checkable
 @trace_protocol
 class Prompts(Protocol):
-    """Protocol for prompt management operations."""
+    """Prompts
+
+    Protocol for prompt management operations."""
 
     @webmethod(route="/prompts", method="GET", level=LLAMA_STACK_API_V1)
     async def list_prompts(self) -> ListPromptsResponse:
@@ -109,7 +111,9 @@ class Prompts(Protocol):
         self,
         prompt_id: str,
     ) -> ListPromptsResponse:
-        """List all versions of a specific prompt.
+        """List prompt versions.
+
+        List all versions of a specific prompt.
 
         :param prompt_id: The identifier of the prompt to list versions for.
         :returns: A ListPromptsResponse containing all versions of the prompt.
@@ -122,7 +126,9 @@ class Prompts(Protocol):
         prompt_id: str,
         version: int | None = None,
     ) -> Prompt:
-        """Get a prompt by its identifier and optional version.
+        """Get prompt.
+
+        Get a prompt by its identifier and optional version.
 
         :param prompt_id: The identifier of the prompt to get.
         :param version: The version of the prompt to get (defaults to latest).
@@ -136,7 +142,9 @@ class Prompts(Protocol):
         prompt: str,
         variables: list[str] | None = None,
     ) -> Prompt:
-        """Create a new prompt.
+        """Create prompt.
+
+        Create a new prompt.
 
         :param prompt: The prompt text content with variable placeholders.
         :param variables: List of variable names that can be used in the prompt template.
@@ -153,7 +161,9 @@ class Prompts(Protocol):
         variables: list[str] | None = None,
         set_as_default: bool = True,
     ) -> Prompt:
-        """Update an existing prompt (increments version).
+        """Update prompt.
+
+        Update an existing prompt (increments version).
 
         :param prompt_id: The identifier of the prompt to update.
         :param prompt: The updated prompt text content.
@@ -169,7 +179,9 @@ class Prompts(Protocol):
         self,
         prompt_id: str,
     ) -> None:
-        """Delete a prompt.
+        """Delete prompt.
+
+        Delete a prompt.
 
         :param prompt_id: The identifier of the prompt to delete.
         """
@@ -181,7 +193,9 @@ class Prompts(Protocol):
         prompt_id: str,
         version: int,
     ) -> Prompt:
-        """Set which version of a prompt should be the default in get_prompt (latest).
+        """Set prompt version.
+
+        Set which version of a prompt should be the default in get_prompt (latest).
 
         :param prompt_id: The identifier of the prompt.
         :param version: The version to set as default.

llama_stack/apis/providers/providers.py

@@ -42,13 +42,16 @@ class ListProvidersResponse(BaseModel):
 
 @runtime_checkable
 class Providers(Protocol):
-    """
+    """Providers
+
     Providers API for inspecting, listing, and modifying providers and their configurations.
     """
 
     @webmethod(route="/providers", method="GET", level=LLAMA_STACK_API_V1)
     async def list_providers(self) -> ListProvidersResponse:
-        """List all available providers.
+        """List providers.
+
+        List all available providers.
 
         :returns: A ListProvidersResponse containing information about all providers.
         """
@@ -56,7 +59,9 @@ class Providers(Protocol):
 
     @webmethod(route="/providers/{provider_id}", method="GET", level=LLAMA_STACK_API_V1)
     async def inspect_provider(self, provider_id: str) -> ProviderInfo:
-        """Get detailed information about a specific provider.
+        """Get provider.
+
+        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.

llama_stack/apis/safety/safety.py

@@ -96,6 +96,11 @@ class ShieldStore(Protocol):
 @runtime_checkable
 @trace_protocol
 class Safety(Protocol):
+    """Safety
+
+    OpenAI-compatible Moderations API.
+    """
+
     shield_store: ShieldStore
 
     @webmethod(route="/safety/run-shield", method="POST", level=LLAMA_STACK_API_V1)
@@ -105,7 +110,9 @@ class Safety(Protocol):
         messages: list[Message],
         params: dict[str, Any],
     ) -> RunShieldResponse:
-        """Run a shield.
+        """Run shield.
+
+        Run a shield.
 
         :param shield_id: The identifier of the shield to run.
         :param messages: The messages to run the shield on.
@@ -117,7 +124,9 @@ class Safety(Protocol):
     @webmethod(route="/openai/v1/moderations", method="POST", level=LLAMA_STACK_API_V1, deprecated=True)
     @webmethod(route="/moderations", method="POST", level=LLAMA_STACK_API_V1)
     async def run_moderation(self, input: str | list[str], model: str) -> ModerationObject:
-        """Classifies if text and/or image inputs are potentially harmful.
+        """Create moderation.
+
+        Classifies if text and/or image inputs are potentially harmful.
         :param input: Input (or inputs) to classify.
         Can be a single string, an array of strings, or an array of multi-modal input objects similar to other models.
         :param model: The content moderation model you would like to use.

llama_stack/cli/stack/run.py

@@ -6,11 +6,18 @@
 
 import argparse
 import os
+import ssl
 import subprocess
 from pathlib import Path
 
+import uvicorn
+import yaml
+
 from llama_stack.cli.stack.utils import ImageType
 from llama_stack.cli.subcommand import Subcommand
+from llama_stack.core.datatypes import LoggingConfig, StackRunConfig
+from llama_stack.core.stack import cast_image_name_to_string, replace_env_vars, validate_env_pair
+from llama_stack.core.utils.config_resolution import Mode, resolve_config_or_distro
 from llama_stack.log import get_logger
 
 REPO_ROOT = Path(__file__).parent.parent.parent.parent
@@ -146,23 +153,7 @@ class StackRun(Subcommand):
         # using the current environment packages.
         if not image_type and not image_name:
             logger.info("No image type or image name provided. Assuming environment packages.")
-            from llama_stack.core.server.server import main as server_main
-
-            # Build the server args from the current args passed to the CLI
-            server_args = argparse.Namespace()
-            for arg in vars(args):
-                # If this is a function, avoid passing it
-                # "args" contains:
-                # func=<bound method StackRun._run_stack_run_cmd of <llama_stack.cli.stack.run.StackRun object at 0x10484b010>>
-                if callable(getattr(args, arg)):
-                    continue
-                if arg == "config":
-                    server_args.config = str(config_file)
-                else:
-                    setattr(server_args, arg, getattr(args, arg))
-
-            # Run the server
-            server_main(server_args)
+            self._uvicorn_run(config_file, args)
         else:
             run_args = formulate_run_args(image_type, image_name)
 
@@ -184,6 +175,76 @@ class StackRun(Subcommand):
 
             run_command(run_args)
 
+    def _uvicorn_run(self, config_file: Path | None, args: argparse.Namespace) -> None:
+        if not config_file:
+            self.parser.error("Config file is required")
+
+        # Set environment variables if provided
+        if args.env:
+            for env_pair in args.env:
+                try:
+                    key, value = validate_env_pair(env_pair)
+                    logger.info(f"Setting environment variable {key} => {value}")
+                    os.environ[key] = value
+                except ValueError as e:
+                    logger.error(f"Error: {str(e)}")
+                    self.parser.error(f"Invalid environment variable format: {env_pair}")
+
+        config_file = resolve_config_or_distro(str(config_file), Mode.RUN)
+        with open(config_file) as fp:
+            config_contents = yaml.safe_load(fp)
+            if isinstance(config_contents, dict) and (cfg := config_contents.get("logging_config")):
+                logger_config = LoggingConfig(**cfg)
+            else:
+                logger_config = None
+            config = StackRunConfig(**cast_image_name_to_string(replace_env_vars(config_contents)))
+
+        port = args.port or config.server.port
+        host = config.server.host or ["::", "0.0.0.0"]
+
+        # Set the config file in environment so create_app can find it
+        os.environ["LLAMA_STACK_CONFIG"] = str(config_file)
+
+        uvicorn_config = {
+            "factory": True,
+            "host": host,
+            "port": port,
+            "lifespan": "on",
+            "log_level": logger.getEffectiveLevel(),
+            "log_config": logger_config,
+        }
+
+        keyfile = config.server.tls_keyfile
+        certfile = config.server.tls_certfile
+        if keyfile and certfile:
+            uvicorn_config["ssl_keyfile"] = config.server.tls_keyfile
+            uvicorn_config["ssl_certfile"] = config.server.tls_certfile
+            if config.server.tls_cafile:
+                uvicorn_config["ssl_ca_certs"] = config.server.tls_cafile
+                uvicorn_config["ssl_cert_reqs"] = ssl.CERT_REQUIRED
+
+            logger.info(
+                f"HTTPS enabled with certificates:\n  Key: {keyfile}\n  Cert: {certfile}\n  CA: {config.server.tls_cafile}"
+            )
+        else:
+            logger.info(f"HTTPS enabled with certificates:\n  Key: {keyfile}\n  Cert: {certfile}")
+
+        logger.info(f"Listening on {host}:{port}")
+
+        # We need to catch KeyboardInterrupt because uvicorn's signal handling
+        # re-raises SIGINT signals using signal.raise_signal(), which Python
+        # converts to KeyboardInterrupt. Without this catch, we'd get a confusing
+        # stack trace when using Ctrl+C or kill -2 (SIGINT).
+        # SIGTERM (kill -15) works fine without this because Python doesn't
+        # have a default handler for it.
+        #
+        # Another approach would be to ignore SIGINT entirely - let uvicorn handle it through its own
+        # signal handling but this is quite intrusive and not worth the effort.
+        try:
+            uvicorn.run("llama_stack.core.server.server:create_app", **uvicorn_config)
+        except (KeyboardInterrupt, SystemExit):
+            logger.info("Received interrupt signal, shutting down gracefully...")
+
     def _start_ui_development_server(self, stack_server_port: int):
         logger.info("Attempting to start UI development server...")
         # Check if npm is available

llama_stack/core/build_container.sh

@@ -324,14 +324,14 @@ fi
 RUN pip uninstall -y uv
 EOF
 
-# If a run config is provided, we use the --config flag
+# If a run config is provided, we use the llama stack CLI
 if [[ -n "$run_config" ]]; then
   add_to_container << EOF
-ENTRYPOINT ["python", "-m", "llama_stack.core.server.server", "$RUN_CONFIG_PATH"]
+ENTRYPOINT ["llama", "stack", "run", "$RUN_CONFIG_PATH"]
 EOF
 elif [[ "$distro_or_config" != *.yaml ]]; then
   add_to_container << EOF
-ENTRYPOINT ["python", "-m", "llama_stack.core.server.server", "$distro_or_config"]
+ENTRYPOINT ["llama", "stack", "run", "$distro_or_config"]
 EOF
 fi
 

llama_stack/core/conversations/__init__.py

@@ -0,0 +1,5 @@
+# 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.

llama_stack/core/conversations/conversations.py

@@ -0,0 +1,306 @@
+# 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.
+
+import os
+import secrets
+import time
+from typing import Any
+
+from openai import NOT_GIVEN
+from pydantic import BaseModel, TypeAdapter
+
+from llama_stack.apis.conversations.conversations import (
+    Conversation,
+    ConversationDeletedResource,
+    ConversationItem,
+    ConversationItemDeletedResource,
+    ConversationItemList,
+    Conversations,
+    Metadata,
+)
+from llama_stack.core.datatypes import AccessRule
+from llama_stack.core.utils.config_dirs import DISTRIBS_BASE_DIR
+from llama_stack.log import get_logger
+from llama_stack.providers.utils.sqlstore.api import ColumnDefinition, ColumnType
+from llama_stack.providers.utils.sqlstore.authorized_sqlstore import AuthorizedSqlStore
+from llama_stack.providers.utils.sqlstore.sqlstore import (
+    SqliteSqlStoreConfig,
+    SqlStoreConfig,
+    sqlstore_impl,
+)
+
+logger = get_logger(name=__name__, category="openai::conversations")
+
+
+class ConversationServiceConfig(BaseModel):
+    """Configuration for the built-in conversation service.
+
+    :param conversations_store: SQL store configuration for conversations (defaults to SQLite)
+    :param policy: Access control rules
+    """
+
+    conversations_store: SqlStoreConfig = SqliteSqlStoreConfig(
+        db_path=(DISTRIBS_BASE_DIR / "conversations.db").as_posix()
+    )
+    policy: list[AccessRule] = []
+
+
+async def get_provider_impl(config: ConversationServiceConfig, deps: dict[Any, Any]):
+    """Get the conversation service implementation."""
+    impl = ConversationServiceImpl(config, deps)
+    await impl.initialize()
+    return impl
+
+
+class ConversationServiceImpl(Conversations):
+    """Built-in conversation service implementation using AuthorizedSqlStore."""
+
+    def __init__(self, config: ConversationServiceConfig, deps: dict[Any, Any]):
+        self.config = config
+        self.deps = deps
+        self.policy = config.policy
+
+        base_sql_store = sqlstore_impl(config.conversations_store)
+        self.sql_store = AuthorizedSqlStore(base_sql_store, self.policy)
+
+    async def initialize(self) -> None:
+        """Initialize the store and create tables."""
+        if isinstance(self.config.conversations_store, SqliteSqlStoreConfig):
+            os.makedirs(os.path.dirname(self.config.conversations_store.db_path), exist_ok=True)
+
+        await self.sql_store.create_table(
+            "openai_conversations",
+            {
+                "id": ColumnDefinition(type=ColumnType.STRING, primary_key=True),
+                "created_at": ColumnType.INTEGER,
+                "items": ColumnType.JSON,
+                "metadata": ColumnType.JSON,
+            },
+        )
+
+        await self.sql_store.create_table(
+            "conversation_items",
+            {
+                "id": ColumnDefinition(type=ColumnType.STRING, primary_key=True),
+                "conversation_id": ColumnType.STRING,
+                "created_at": ColumnType.INTEGER,
+                "item_data": ColumnType.JSON,
+            },
+        )
+
+    async def create_conversation(
+        self, items: list[ConversationItem] | None = None, metadata: Metadata | None = None
+    ) -> Conversation:
+        """Create a conversation."""
+        random_bytes = secrets.token_bytes(24)
+        conversation_id = f"conv_{random_bytes.hex()}"
+        created_at = int(time.time())
+
+        record_data = {
+            "id": conversation_id,
+            "created_at": created_at,
+            "items": [],
+            "metadata": metadata,
+        }
+
+        await self.sql_store.insert(
+            table="openai_conversations",
+            data=record_data,
+        )
+
+        if items:
+            item_records = []
+            for item in items:
+                item_dict = item.model_dump()
+                item_id = self._get_or_generate_item_id(item, item_dict)
+
+                item_record = {
+                    "id": item_id,
+                    "conversation_id": conversation_id,
+                    "created_at": created_at,
+                    "item_data": item_dict,
+                }
+
+                item_records.append(item_record)
+
+            await self.sql_store.insert(table="conversation_items", data=item_records)
+
+        conversation = Conversation(
+            id=conversation_id,
+            created_at=created_at,
+            metadata=metadata,
+            object="conversation",
+        )
+
+        logger.info(f"Created conversation {conversation_id}")
+        return conversation
+
+    async def get_conversation(self, conversation_id: str) -> Conversation:
+        """Get a conversation with the given ID."""
+        record = await self.sql_store.fetch_one(table="openai_conversations", where={"id": conversation_id})
+
+        if record is None:
+            raise ValueError(f"Conversation {conversation_id} not found")
+
+        return Conversation(
+            id=record["id"], created_at=record["created_at"], metadata=record.get("metadata"), object="conversation"
+        )
+
+    async def update_conversation(self, conversation_id: str, metadata: Metadata) -> Conversation:
+        """Update a conversation's metadata with the given ID"""
+        await self.sql_store.update(
+            table="openai_conversations", data={"metadata": metadata}, where={"id": conversation_id}
+        )
+
+        return await self.get_conversation(conversation_id)
+
+    async def openai_delete_conversation(self, conversation_id: str) -> ConversationDeletedResource:
+        """Delete a conversation with the given ID."""
+        await self.sql_store.delete(table="openai_conversations", where={"id": conversation_id})
+
+        logger.info(f"Deleted conversation {conversation_id}")
+        return ConversationDeletedResource(id=conversation_id)
+
+    def _validate_conversation_id(self, conversation_id: str) -> None:
+        """Validate conversation ID format."""
+        if not conversation_id.startswith("conv_"):
+            raise ValueError(
+                f"Invalid 'conversation_id': '{conversation_id}'. Expected an ID that begins with 'conv_'."
+            )
+
+    def _get_or_generate_item_id(self, item: ConversationItem, item_dict: dict) -> str:
+        """Get existing item ID or generate one if missing."""
+        if item.id is None:
+            random_bytes = secrets.token_bytes(24)
+            if item.type == "message":
+                item_id = f"msg_{random_bytes.hex()}"
+            else:
+                item_id = f"item_{random_bytes.hex()}"
+            item_dict["id"] = item_id
+            return item_id
+        return item.id
+
+    async def _get_validated_conversation(self, conversation_id: str) -> Conversation:
+        """Validate conversation ID and return the conversation if it exists."""
+        self._validate_conversation_id(conversation_id)
+        return await self.get_conversation(conversation_id)
+
+    async def add_items(self, conversation_id: str, items: list[ConversationItem]) -> ConversationItemList:
+        """Create (add) items to a conversation."""
+        await self._get_validated_conversation(conversation_id)
+
+        created_items = []
+        created_at = int(time.time())
+
+        for item in items:
+            item_dict = item.model_dump()
+            item_id = self._get_or_generate_item_id(item, item_dict)
+
+            item_record = {
+                "id": item_id,
+                "conversation_id": conversation_id,
+                "created_at": created_at,
+                "item_data": item_dict,
+            }
+
+            # TODO: Add support for upsert in sql_store, this will fail first if ID exists and then update
+            try:
+                await self.sql_store.insert(table="conversation_items", data=item_record)
+            except Exception:
+                # If insert fails due to ID conflict, update existing record
+                await self.sql_store.update(
+                    table="conversation_items",
+                    data={"created_at": created_at, "item_data": item_dict},
+                    where={"id": item_id},
+                )
+
+            created_items.append(item_dict)
+
+        logger.info(f"Created {len(created_items)} items in conversation {conversation_id}")
+
+        # Convert created items (dicts) to proper ConversationItem types
+        adapter: TypeAdapter[ConversationItem] = TypeAdapter(ConversationItem)
+        response_items: list[ConversationItem] = [adapter.validate_python(item_dict) for item_dict in created_items]
+
+        return ConversationItemList(
+            data=response_items,
+            first_id=created_items[0]["id"] if created_items else None,
+            last_id=created_items[-1]["id"] if created_items else None,
+            has_more=False,
+        )
+
+    async def retrieve(self, conversation_id: str, item_id: str) -> ConversationItem:
+        """Retrieve a conversation item."""
+        if not conversation_id:
+            raise ValueError(f"Expected a non-empty value for `conversation_id` but received {conversation_id!r}")
+        if not item_id:
+            raise ValueError(f"Expected a non-empty value for `item_id` but received {item_id!r}")
+
+        # Get item from conversation_items table
+        record = await self.sql_store.fetch_one(
+            table="conversation_items", where={"id": item_id, "conversation_id": conversation_id}
+        )
+
+        if record is None:
+            raise ValueError(f"Item {item_id} not found in conversation {conversation_id}")
+
+        adapter: TypeAdapter[ConversationItem] = TypeAdapter(ConversationItem)
+        return adapter.validate_python(record["item_data"])
+
+    async def list(self, conversation_id: str, after=NOT_GIVEN, include=NOT_GIVEN, limit=NOT_GIVEN, order=NOT_GIVEN):
+        """List items in the conversation."""
+        result = await self.sql_store.fetch_all(table="conversation_items", where={"conversation_id": conversation_id})
+        records = result.data
+
+        if order != NOT_GIVEN and order == "asc":
+            records.sort(key=lambda x: x["created_at"])
+        else:
+            records.sort(key=lambda x: x["created_at"], reverse=True)
+
+        actual_limit = 20
+        if limit != NOT_GIVEN and isinstance(limit, int):
+            actual_limit = limit
+
+        records = records[:actual_limit]
+        items = [record["item_data"] for record in records]
+
+        adapter: TypeAdapter[ConversationItem] = TypeAdapter(ConversationItem)
+        response_items: list[ConversationItem] = [adapter.validate_python(item) for item in items]
+
+        first_id = response_items[0].id if response_items else None
+        last_id = response_items[-1].id if response_items else None
+
+        return ConversationItemList(
+            data=response_items,
+            first_id=first_id,
+            last_id=last_id,
+            has_more=False,
+        )
+
+    async def openai_delete_conversation_item(
+        self, conversation_id: str, item_id: str
+    ) -> ConversationItemDeletedResource:
+        """Delete a conversation item."""
+        if not conversation_id:
+            raise ValueError(f"Expected a non-empty value for `conversation_id` but received {conversation_id!r}")
+        if not item_id:
+            raise ValueError(f"Expected a non-empty value for `item_id` but received {item_id!r}")
+
+        _ = await self._get_validated_conversation(conversation_id)
+
+        record = await self.sql_store.fetch_one(
+            table="conversation_items", where={"id": item_id, "conversation_id": conversation_id}
+        )
+
+        if record is None:
+            raise ValueError(f"Item {item_id} not found in conversation {conversation_id}")
+
+        await self.sql_store.delete(
+            table="conversation_items", where={"id": item_id, "conversation_id": conversation_id}
+        )
+
+        logger.info(f"Deleted item {item_id} from conversation {conversation_id}")
+        return ConversationItemDeletedResource(id=item_id)

llama_stack/core/datatypes.py

@@ -475,6 +475,13 @@ InferenceStoreConfig (with queue tuning parameters) or a SqlStoreConfig (depreca
 If not specified, a default SQLite store will be used.""",
     )
 
+    conversations_store: SqlStoreConfig | None = Field(
+        default=None,
+        description="""
+Configuration for the persistence store used by the conversations API.
+If not specified, a default SQLite store will be used.""",
+    )
+
     # registry of "resources" in the distribution
     models: list[ModelInput] = Field(default_factory=list)
     shields: list[ShieldInput] = Field(default_factory=list)

llama_stack/core/distribution.py

@@ -25,7 +25,7 @@ from llama_stack.providers.datatypes import (
 logger = get_logger(name=__name__, category="core")
 
 
-INTERNAL_APIS = {Api.inspect, Api.providers, Api.prompts}
+INTERNAL_APIS = {Api.inspect, Api.providers, Api.prompts, Api.conversations}
 
 
 def stack_apis() -> list[Api]:
@@ -243,6 +243,7 @@ def get_external_providers_from_module(
                     spec = module.get_provider_spec()
                 else:
                     # pass in a partially filled out provider spec to satisfy the registry -- knowing we will be overwriting it later upon build and run
+                    # in the case we are building we CANNOT import this module of course because it has not been installed.
                     spec = ProviderSpec(
                         api=Api(provider_api),
                         provider_type=provider.provider_type,
@@ -251,9 +252,20 @@ def get_external_providers_from_module(
                         config_class="",
                     )
                 provider_type = provider.provider_type
-                # in the case we are building we CANNOT import this module of course because it has not been installed.
-                # return a partially filled out spec that the build script will populate.
-                registry[Api(provider_api)][provider_type] = spec
+                if isinstance(spec, list):
+                    # optionally allow people to pass inline and remote provider specs as a returned list.
+                    # with the old method, users could pass in directories of specs using overlapping code
+                    # we want to ensure we preserve that flexibility in this method.
+                    logger.info(
+                        f"Detected a list of external provider specs from {provider.module} adding all to the registry"
+                    )
+                    for provider_spec in spec:
+                        if provider_spec.provider_type != provider.provider_type:
+                            continue
+                        logger.info(f"Adding {provider.provider_type} to registry")
+                        registry[Api(provider_api)][provider.provider_type] = provider_spec
+                else:
+                    registry[Api(provider_api)][provider_type] = spec
             except ModuleNotFoundError as exc:
                 raise ValueError(
                     "get_provider_spec not found. If specifying an external provider via `module` in the Provider spec, the Provider must have the `provider.get_provider_spec` module available"

llama_stack/core/library_client.py

@@ -374,6 +374,10 @@ class AsyncLlamaStackAsLibraryClient(AsyncLlamaStackClient):
         body = options.params or {}
         body |= options.json_data or {}
 
+        # Merge extra_json parameters (extra_body from SDK is converted to extra_json)
+        if hasattr(options, "extra_json") and options.extra_json:
+            body |= options.extra_json
+
         matched_func, path_params, route_path, webmethod = find_matching_route(options.method, path, self.route_impls)
         body |= path_params
 

llama_stack/core/resolver.py

@@ -10,6 +10,7 @@ from typing import Any
 from llama_stack.apis.agents import Agents
 from llama_stack.apis.batches import Batches
 from llama_stack.apis.benchmarks import Benchmarks
+from llama_stack.apis.conversations import Conversations
 from llama_stack.apis.datasetio import DatasetIO
 from llama_stack.apis.datasets import Datasets
 from llama_stack.apis.datatypes import ExternalApiSpec
@@ -96,6 +97,7 @@ def api_protocol_map(external_apis: dict[Api, ExternalApiSpec] | None = None) ->
         Api.tool_runtime: ToolRuntime,
         Api.files: Files,
         Api.prompts: Prompts,
+        Api.conversations: Conversations,
     }
 
     if external_apis:

llama_stack/core/routers/inference.py

@@ -19,7 +19,6 @@ from llama_stack.apis.inference import (
     CompletionMessage,
     Inference,
     ListOpenAIChatCompletionResponse,
-    LogProbConfig,
     Message,
     OpenAIAssistantMessageParam,
     OpenAIChatCompletion,
@@ -34,12 +33,7 @@ from llama_stack.apis.inference import (
     OpenAIMessageParam,
     OpenAIResponseFormatParam,
     Order,
-    ResponseFormat,
-    SamplingParams,
     StopReason,
-    ToolChoice,
-    ToolConfig,
-    ToolDefinition,
     ToolPromptFormat,
 )
 from llama_stack.apis.models import Model, ModelType
@@ -193,102 +187,6 @@ class InferenceRouter(Inference):
             raise ModelTypeError(model_id, model.model_type, expected_model_type)
         return model
 
-    async def chat_completion(
-        self,
-        model_id: str,
-        messages: list[Message],
-        sampling_params: SamplingParams | None = None,
-        response_format: ResponseFormat | None = None,
-        tools: list[ToolDefinition] | None = None,
-        tool_choice: ToolChoice | None = None,
-        tool_prompt_format: ToolPromptFormat | None = None,
-        stream: bool | None = False,
-        logprobs: LogProbConfig | None = None,
-        tool_config: ToolConfig | None = None,
-    ) -> ChatCompletionResponse | AsyncIterator[ChatCompletionResponseStreamChunk]:
-        logger.debug(
-            f"InferenceRouter.chat_completion: {model_id=}, {stream=}, {messages=}, {tools=}, {tool_config=}, {response_format=}",
-        )
-        if sampling_params is None:
-            sampling_params = SamplingParams()
-        model = await self._get_model(model_id, ModelType.llm)
-        if tool_config:
-            if tool_choice and tool_choice != tool_config.tool_choice:
-                raise ValueError("tool_choice and tool_config.tool_choice must match")
-            if (
-                tool_prompt_format
-                and tool_prompt_format != tool_config.tool_prompt_format
-            ):
-                raise ValueError(
-                    "tool_prompt_format and tool_config.tool_prompt_format must match"
-                )
-        else:
-            params = {}
-            if tool_choice:
-                params["tool_choice"] = tool_choice
-            if tool_prompt_format:
-                params["tool_prompt_format"] = tool_prompt_format
-            tool_config = ToolConfig(**params)
-
-        tools = tools or []
-        if tool_config.tool_choice == ToolChoice.none:
-            tools = []
-        elif tool_config.tool_choice == ToolChoice.auto:
-            pass
-        elif tool_config.tool_choice == ToolChoice.required:
-            pass
-        else:
-            # verify tool_choice is one of the tools
-            tool_names = [
-                t.tool_name if isinstance(t.tool_name, str) else t.tool_name.value
-                for t in tools
-            ]
-            if tool_config.tool_choice not in tool_names:
-                raise ValueError(
-                    f"Tool choice {tool_config.tool_choice} is not one of the tools: {tool_names}"
-                )
-
-        params = dict(
-            model_id=model_id,
-            messages=messages,
-            sampling_params=sampling_params,
-            tools=tools,
-            tool_choice=tool_choice,
-            tool_prompt_format=tool_prompt_format,
-            response_format=response_format,
-            stream=stream,
-            logprobs=logprobs,
-            tool_config=tool_config,
-        )
-        provider = await self.routing_table.get_provider_impl(model_id)
-        prompt_tokens = await self._count_tokens(
-            messages, tool_config.tool_prompt_format
-        )
-
-        if stream:
-            response_stream = await provider.chat_completion(**params)
-            return self.stream_tokens_and_compute_metrics(
-                response=response_stream,
-                prompt_tokens=prompt_tokens,
-                model=model,
-                tool_prompt_format=tool_config.tool_prompt_format,
-            )
-
-        response = await provider.chat_completion(**params)
-        metrics = await self.count_tokens_and_compute_metrics(
-            response=response,
-            prompt_tokens=prompt_tokens,
-            model=model,
-            tool_prompt_format=tool_config.tool_prompt_format,
-        )
-        # these metrics will show up in the client response.
-        response.metrics = (
-            metrics
-            if not hasattr(response, "metrics") or response.metrics is None
-            else response.metrics + metrics
-        )
-        return response
-
     async def openai_completion(
         self,
         model: str,

llama_stack/core/server/server.py

@@ -4,7 +4,6 @@
 # This source code is licensed under the terms described in the LICENSE file in
 # the root directory of this source tree.
 
-import argparse
 import asyncio
 import concurrent.futures
 import functools
@@ -12,7 +11,6 @@ import inspect
 import json
 import logging  # allow-direct-logging
 import os
-import ssl
 import sys
 import traceback
 import warnings
@@ -35,7 +33,6 @@ from pydantic import BaseModel, ValidationError
 
 from llama_stack.apis.common.errors import ConflictError, ResourceNotFoundError
 from llama_stack.apis.common.responses import PaginatedResponse
-from llama_stack.cli.utils import add_config_distro_args, get_config_from_args
 from llama_stack.core.access_control.access_control import AccessDeniedError
 from llama_stack.core.datatypes import (
     AuthenticationRequiredError,
@@ -55,7 +52,6 @@ from llama_stack.core.stack import (
     Stack,
     cast_image_name_to_string,
     replace_env_vars,
-    validate_env_pair,
 )
 from llama_stack.core.utils.config import redact_sensitive_fields
 from llama_stack.core.utils.config_resolution import Mode, resolve_config_or_distro
@@ -333,23 +329,18 @@ class ClientVersionMiddleware:
         return await self.app(scope, receive, send)
 
 
-def create_app(
-    config_file: str | None = None,
-    env_vars: list[str] | None = None,
-) -> StackApp:
+def create_app() -> StackApp:
     """Create and configure the FastAPI application.
 
-    Args:
-        config_file: Path to config file. If None, uses LLAMA_STACK_CONFIG env var or default resolution.
-        env_vars: List of environment variables in KEY=value format.
-        disable_version_check: Whether to disable version checking. If None, uses LLAMA_STACK_DISABLE_VERSION_CHECK env var.
+    This factory function reads configuration from environment variables:
+    - LLAMA_STACK_CONFIG: Path to config file (required)
 
     Returns:
         Configured StackApp instance.
     """
-    config_file = config_file or os.getenv("LLAMA_STACK_CONFIG")
+    config_file = os.getenv("LLAMA_STACK_CONFIG")
     if config_file is None:
-        raise ValueError("No config file provided and LLAMA_STACK_CONFIG env var is not set")
+        raise ValueError("LLAMA_STACK_CONFIG environment variable is required")
 
     config_file = resolve_config_or_distro(config_file, Mode.RUN)
 
@@ -361,16 +352,6 @@ def create_app(
             logger_config = LoggingConfig(**cfg)
         logger = get_logger(name=__name__, category="core::server", config=logger_config)
 
-        if env_vars:
-            for env_pair in env_vars:
-                try:
-                    key, value = validate_env_pair(env_pair)
-                    logger.info(f"Setting environment variable {key} => {value}")
-                    os.environ[key] = value
-                except ValueError as e:
-                    logger.error(f"Error: {str(e)}")
-                    raise ValueError(f"Invalid environment variable format: {env_pair}") from e
-
         config = replace_env_vars(config_contents)
         config = StackRunConfig(**cast_image_name_to_string(config))
 
@@ -451,6 +432,7 @@ def create_app(
     apis_to_serve.add("inspect")
     apis_to_serve.add("providers")
     apis_to_serve.add("prompts")
+    apis_to_serve.add("conversations")
     for api_str in apis_to_serve:
         api = Api(api_str)
 
@@ -493,101 +475,6 @@ def create_app(
     return app
 
 
-def main(args: argparse.Namespace | None = None):
-    """Start the LlamaStack server."""
-    parser = argparse.ArgumentParser(description="Start the LlamaStack server.")
-
-    add_config_distro_args(parser)
-    parser.add_argument(
-        "--port",
-        type=int,
-        default=int(os.getenv("LLAMA_STACK_PORT", 8321)),
-        help="Port to listen on",
-    )
-    parser.add_argument(
-        "--env",
-        action="append",
-        help="Environment variables in KEY=value format. Can be specified multiple times.",
-    )
-
-    # Determine whether the server args are being passed by the "run" command, if this is the case
-    # the args will be passed as a Namespace object to the main function, otherwise they will be
-    # parsed from the command line
-    if args is None:
-        args = parser.parse_args()
-
-    config_or_distro = get_config_from_args(args)
-
-    try:
-        app = create_app(
-            config_file=config_or_distro,
-            env_vars=args.env,
-        )
-    except Exception as e:
-        logger.error(f"Error creating app: {str(e)}")
-        sys.exit(1)
-
-    config_file = resolve_config_or_distro(config_or_distro, Mode.RUN)
-    with open(config_file) as fp:
-        config_contents = yaml.safe_load(fp)
-        if isinstance(config_contents, dict) and (cfg := config_contents.get("logging_config")):
-            logger_config = LoggingConfig(**cfg)
-        else:
-            logger_config = None
-        config = StackRunConfig(**cast_image_name_to_string(replace_env_vars(config_contents)))
-
-    import uvicorn
-
-    # Configure SSL if certificates are provided
-    port = args.port or config.server.port
-
-    ssl_config = None
-    keyfile = config.server.tls_keyfile
-    certfile = config.server.tls_certfile
-
-    if keyfile and certfile:
-        ssl_config = {
-            "ssl_keyfile": keyfile,
-            "ssl_certfile": certfile,
-        }
-        if config.server.tls_cafile:
-            ssl_config["ssl_ca_certs"] = config.server.tls_cafile
-            ssl_config["ssl_cert_reqs"] = ssl.CERT_REQUIRED
-            logger.info(
-                f"HTTPS enabled with certificates:\n  Key: {keyfile}\n  Cert: {certfile}\n  CA: {config.server.tls_cafile}"
-            )
-        else:
-            logger.info(f"HTTPS enabled with certificates:\n  Key: {keyfile}\n  Cert: {certfile}")
-
-    listen_host = config.server.host or ["::", "0.0.0.0"]
-    logger.info(f"Listening on {listen_host}:{port}")
-
-    uvicorn_config = {
-        "app": app,
-        "host": listen_host,
-        "port": port,
-        "lifespan": "on",
-        "log_level": logger.getEffectiveLevel(),
-        "log_config": logger_config,
-    }
-    if ssl_config:
-        uvicorn_config.update(ssl_config)
-
-    # We need to catch KeyboardInterrupt because uvicorn's signal handling
-    # re-raises SIGINT signals using signal.raise_signal(), which Python
-    # converts to KeyboardInterrupt. Without this catch, we'd get a confusing
-    # stack trace when using Ctrl+C or kill -2 (SIGINT).
-    # SIGTERM (kill -15) works fine without this because Python doesn't
-    # have a default handler for it.
-    #
-    # Another approach would be to ignore SIGINT entirely - let uvicorn handle it through its own
-    # signal handling but this is quite intrusive and not worth the effort.
-    try:
-        asyncio.run(uvicorn.Server(uvicorn.Config(**uvicorn_config)).serve())
-    except (KeyboardInterrupt, SystemExit):
-        logger.info("Received interrupt signal, shutting down gracefully...")
-
-
 def _log_run_config(run_config: StackRunConfig):
     """Logs the run config with redacted fields and disabled providers removed."""
     logger.info("Run configuration:")
@@ -614,7 +501,3 @@ def remove_disabled_providers(obj):
         return [item for item in (remove_disabled_providers(i) for i in obj) if item is not None]
     else:
         return obj
-
-
-if __name__ == "__main__":
-    main()

llama_stack/core/stack.py

@@ -15,6 +15,7 @@ import yaml
 
 from llama_stack.apis.agents import Agents
 from llama_stack.apis.benchmarks import Benchmarks
+from llama_stack.apis.conversations import Conversations
 from llama_stack.apis.datasetio import DatasetIO
 from llama_stack.apis.datasets import Datasets
 from llama_stack.apis.eval import Eval
@@ -34,6 +35,7 @@ from llama_stack.apis.telemetry import Telemetry
 from llama_stack.apis.tools import RAGToolRuntime, ToolGroups, ToolRuntime
 from llama_stack.apis.vector_dbs import VectorDBs
 from llama_stack.apis.vector_io import VectorIO
+from llama_stack.core.conversations.conversations import ConversationServiceConfig, ConversationServiceImpl
 from llama_stack.core.datatypes import Provider, StackRunConfig
 from llama_stack.core.distribution import get_provider_registry
 from llama_stack.core.inspect import DistributionInspectConfig, DistributionInspectImpl
@@ -73,6 +75,7 @@ class LlamaStack(
     RAGToolRuntime,
     Files,
     Prompts,
+    Conversations,
 ):
     pass
 
@@ -312,6 +315,12 @@ def add_internal_implementations(impls: dict[Api, Any], run_config: StackRunConf
     )
     impls[Api.prompts] = prompts_impl
 
+    conversations_impl = ConversationServiceImpl(
+        ConversationServiceConfig(run_config=run_config),
+        deps=impls,
+    )
+    impls[Api.conversations] = conversations_impl
+
 
 class Stack:
     def __init__(self, run_config: StackRunConfig, provider_registry: ProviderRegistry | None = None):
@@ -342,6 +351,8 @@ class Stack:
 
         if Api.prompts in impls:
             await impls[Api.prompts].initialize()
+        if Api.conversations in impls:
+            await impls[Api.conversations].initialize()
 
         await register_resources(self.run_config, impls)
 

llama_stack/core/start_stack.sh

@@ -116,7 +116,7 @@ if [[ "$env_type" == "venv" ]]; then
         yaml_config_arg=""
     fi
 
-    $PYTHON_BINARY -m llama_stack.core.server.server \
+    llama stack run \
     $yaml_config_arg \
     --port "$port" \
     $env_vars \

llama_stack/log.py

@@ -128,7 +128,7 @@ def strip_rich_markup(text):
 
 class CustomRichHandler(RichHandler):
     def __init__(self, *args, **kwargs):
-        kwargs["console"] = Console(width=150)
+        kwargs["console"] = Console()
         super().__init__(*args, **kwargs)
 
     def emit(self, record):

llama_stack/models/llama/tokenizer_utils.py

@@ -9,7 +9,7 @@ from pathlib import Path
 
 from llama_stack.log import get_logger
 
-logger = get_logger(__name__, "tokenizer_utils")
+logger = get_logger(__name__, "models")
 
 
 def load_bpe_file(model_path: Path) -> dict[bytes, int]:

llama_stack/providers/inline/agents/meta_reference/agents.py

@@ -329,6 +329,7 @@ class MetaReferenceAgentsImpl(Agents):
         tools: list[OpenAIResponseInputTool] | None = None,
         include: list[str] | None = None,
         max_infer_iters: int | None = 10,
+        shields: list | None = None,
     ) -> OpenAIResponseObject:
         return await self.openai_responses_impl.create_openai_response(
             input,
@@ -342,6 +343,7 @@ class MetaReferenceAgentsImpl(Agents):
             tools,
             include,
             max_infer_iters,
+            shields,
         )
 
     async def list_openai_responses(

llama_stack/providers/inline/agents/meta_reference/responses/openai_responses.py

@@ -8,7 +8,7 @@ import time
 import uuid
 from collections.abc import AsyncIterator
 
-from pydantic import BaseModel
+from pydantic import BaseModel, TypeAdapter
 
 from llama_stack.apis.agents import Order
 from llama_stack.apis.agents.openai_responses import (
@@ -26,12 +26,16 @@ from llama_stack.apis.agents.openai_responses import (
 )
 from llama_stack.apis.inference import (
     Inference,
+    OpenAIMessageParam,
     OpenAISystemMessageParam,
 )
 from llama_stack.apis.tools import ToolGroups, ToolRuntime
 from llama_stack.apis.vector_io import VectorIO
 from llama_stack.log import get_logger
-from llama_stack.providers.utils.responses.responses_store import ResponsesStore
+from llama_stack.providers.utils.responses.responses_store import (
+    ResponsesStore,
+    _OpenAIResponseObjectWithInputAndMessages,
+)
 
 from .streaming import StreamingResponseOrchestrator
 from .tool_executor import ToolExecutor
@@ -72,26 +76,48 @@ class OpenAIResponsesImpl:
     async def _prepend_previous_response(
         self,
         input: str | list[OpenAIResponseInput],
-        previous_response_id: str | None = None,
+        previous_response: _OpenAIResponseObjectWithInputAndMessages,
     ):
+        new_input_items = previous_response.input.copy()
+        new_input_items.extend(previous_response.output)
+
+        if isinstance(input, str):
+            new_input_items.append(OpenAIResponseMessage(content=input, role="user"))
+        else:
+            new_input_items.extend(input)
+
+        return new_input_items
+
+    async def _process_input_with_previous_response(
+        self,
+        input: str | list[OpenAIResponseInput],
+        previous_response_id: str | None,
+    ) -> tuple[str | list[OpenAIResponseInput], list[OpenAIMessageParam]]:
+        """Process input with optional previous response context.
+
+        Returns:
+            tuple: (all_input for storage, messages for chat completion)
+        """
         if previous_response_id:
-            previous_response_with_input = await self.responses_store.get_response_object(previous_response_id)
+            previous_response: _OpenAIResponseObjectWithInputAndMessages = (
+                await self.responses_store.get_response_object(previous_response_id)
+            )
+            all_input = await self._prepend_previous_response(input, previous_response)
 
-            # previous response input items
-            new_input_items = previous_response_with_input.input
-
-            # previous response output items
-            new_input_items.extend(previous_response_with_input.output)
-
-            # new input items from the current request
-            if isinstance(input, str):
-                new_input_items.append(OpenAIResponseMessage(content=input, role="user"))
+            if previous_response.messages:
+                # Use stored messages directly and convert only new input
+                message_adapter = TypeAdapter(list[OpenAIMessageParam])
+                messages = message_adapter.validate_python(previous_response.messages)
+                new_messages = await convert_response_input_to_chat_messages(input)
+                messages.extend(new_messages)
             else:
-                new_input_items.extend(input)
+                # Backward compatibility: reconstruct from inputs
+                messages = await convert_response_input_to_chat_messages(all_input)
+        else:
+            all_input = input
+            messages = await convert_response_input_to_chat_messages(input)
 
-            input = new_input_items
-
-        return input
+        return all_input, messages
 
     async def _prepend_instructions(self, messages, instructions):
         if instructions:
@@ -102,7 +128,7 @@ class OpenAIResponsesImpl:
         response_id: str,
     ) -> OpenAIResponseObject:
         response_with_input = await self.responses_store.get_response_object(response_id)
-        return OpenAIResponseObject(**{k: v for k, v in response_with_input.model_dump().items() if k != "input"})
+        return response_with_input.to_response_object()
 
     async def list_openai_responses(
         self,
@@ -138,6 +164,7 @@ class OpenAIResponsesImpl:
         self,
         response: OpenAIResponseObject,
         input: str | list[OpenAIResponseInput],
+        messages: list[OpenAIMessageParam],
     ) -> None:
         new_input_id = f"msg_{uuid.uuid4()}"
         if isinstance(input, str):
@@ -165,6 +192,7 @@ class OpenAIResponsesImpl:
         await self.responses_store.store_response_object(
             response_object=response,
             input=input_items_data,
+            messages=messages,
         )
 
     async def create_openai_response(
@@ -180,10 +208,15 @@ class OpenAIResponsesImpl:
         tools: list[OpenAIResponseInputTool] | None = None,
         include: list[str] | None = None,
         max_infer_iters: int | None = 10,
+        shields: list | None = None,
     ):
         stream = bool(stream)
         text = OpenAIResponseText(format=OpenAIResponseTextFormat(type="text")) if text is None else text
 
+        # Shields parameter received via extra_body - not yet implemented
+        if shields is not None:
+            raise NotImplementedError("Shields parameter is not yet implemented in the meta-reference provider")
+
         stream_gen = self._create_streaming_response(
             input=input,
             model=model,
@@ -224,8 +257,7 @@ class OpenAIResponsesImpl:
         max_infer_iters: int | None = 10,
     ) -> AsyncIterator[OpenAIResponseObjectStream]:
         # Input preprocessing
-        input = await self._prepend_previous_response(input, previous_response_id)
-        messages = await convert_response_input_to_chat_messages(input)
+        all_input, messages = await self._process_input_with_previous_response(input, previous_response_id)
         await self._prepend_instructions(messages, instructions)
 
         # Structured outputs
@@ -265,7 +297,8 @@ class OpenAIResponsesImpl:
         if store and final_response:
             await self._store_response(
                 response=final_response,
-                input=input,
+                input=all_input,
+                messages=orchestrator.final_messages,
             )
 
     async def delete_openai_response(self, response_id: str) -> OpenAIDeleteResponseObject:

llama_stack/providers/inline/agents/meta_reference/responses/streaming.py

@@ -43,6 +43,7 @@ from llama_stack.apis.inference import (
     OpenAIChatCompletion,
     OpenAIChatCompletionToolCall,
     OpenAIChoice,
+    OpenAIMessageParam,
 )
 from llama_stack.log import get_logger
 
@@ -94,6 +95,8 @@ class StreamingResponseOrchestrator:
         self.sequence_number = 0
         # Store MCP tool mapping that gets built during tool processing
         self.mcp_tool_to_server: dict[str, OpenAIResponseInputToolMCP] = {}
+        # Track final messages after all tool executions
+        self.final_messages: list[OpenAIMessageParam] = []
 
     async def create_response(self) -> AsyncIterator[OpenAIResponseObjectStream]:
         # Initialize output messages
@@ -183,6 +186,8 @@ class StreamingResponseOrchestrator:
 
             messages = next_turn_messages
 
+        self.final_messages = messages.copy() + [current_response.choices[0].message]
+
         # Create final response
         final_response = OpenAIResponseObject(
             created_at=self.created_at,

llama_stack/providers/inline/inference/meta_reference/inference.py

@@ -5,37 +5,17 @@
 # the root directory of this source tree.
 
 import asyncio
-import os
-import sys
-from collections.abc import AsyncGenerator
+from collections.abc import AsyncIterator
+from typing import Any
 
-from pydantic import BaseModel
-from termcolor import cprint
-
-from llama_stack.apis.common.content_types import (
-    TextDelta,
-    ToolCallDelta,
-    ToolCallParseStatus,
-)
 from llama_stack.apis.inference import (
-    ChatCompletionRequest,
-    ChatCompletionResponse,
-    ChatCompletionResponseEvent,
-    ChatCompletionResponseEventType,
-    ChatCompletionResponseStreamChunk,
-    CompletionMessage,
     InferenceProvider,
-    LogProbConfig,
-    Message,
-    ResponseFormat,
-    SamplingParams,
-    StopReason,
-    TokenLogProbs,
-    ToolChoice,
-    ToolConfig,
-    ToolDefinition,
-    ToolPromptFormat,
-    UserMessage,
+)
+from llama_stack.apis.inference.inference import (
+    OpenAIChatCompletion,
+    OpenAIChatCompletionChunk,
+    OpenAIMessageParam,
+    OpenAIResponseFormatParam,
 )
 from llama_stack.apis.models import Model, ModelType
 from llama_stack.log import get_logger
@@ -53,13 +33,6 @@ from llama_stack.providers.utils.inference.model_registry import (
     ModelRegistryHelper,
     build_hf_repo_model_entry,
 )
-from llama_stack.providers.utils.inference.openai_compat import (
-    OpenAIChatCompletionToLlamaStackMixin,
-)
-from llama_stack.providers.utils.inference.prompt_adapter import (
-    chat_completion_request_to_messages,
-    convert_request_to_raw,
-)
 
 from .config import MetaReferenceInferenceConfig
 from .generators import LlamaGenerator
@@ -76,7 +49,6 @@ def llama_builder_fn(config: MetaReferenceInferenceConfig, model_id: str, llama_
 
 
 class MetaReferenceInferenceImpl(
-    OpenAIChatCompletionToLlamaStackMixin,
     SentenceTransformerEmbeddingMixin,
     InferenceProvider,
     ModelsProtocolPrivate,
@@ -161,10 +133,10 @@ class MetaReferenceInferenceImpl(
         self.llama_model = llama_model
 
         log.info("Warming up...")
-        await self.chat_completion(
-            model_id=model_id,
-            messages=[UserMessage(content="Hi how are you?")],
-            sampling_params=SamplingParams(max_tokens=20),
+        await self.openai_chat_completion(
+            model=model_id,
+            messages=[{"role": "user", "content": "Hi how are you?"}],
+            max_tokens=20,
         )
         log.info("Warmed up!")
 
@@ -176,242 +148,30 @@ class MetaReferenceInferenceImpl(
         elif request.model != self.model_id:
             raise RuntimeError(f"Model mismatch: request model: {request.model} != loaded model: {self.model_id}")
 
-    async def chat_completion(
+    async def openai_chat_completion(
         self,
-        model_id: str,
-        messages: list[Message],
-        sampling_params: SamplingParams | None = None,
-        response_format: ResponseFormat | None = None,
-        tools: list[ToolDefinition] | None = None,
-        tool_choice: ToolChoice | None = ToolChoice.auto,
-        tool_prompt_format: ToolPromptFormat | None = None,
-        stream: bool | None = False,
-        logprobs: LogProbConfig | None = None,
-        tool_config: ToolConfig | None = None,
-    ) -> AsyncGenerator:
-        if sampling_params is None:
-            sampling_params = SamplingParams()
-        if logprobs:
-            assert logprobs.top_k == 1, f"Unexpected top_k={logprobs.top_k}"
-
-        # wrapper request to make it easier to pass around (internal only, not exposed to API)
-        request = ChatCompletionRequest(
-            model=model_id,
-            messages=messages,
-            sampling_params=sampling_params,
-            tools=tools or [],
-            response_format=response_format,
-            stream=stream,
-            logprobs=logprobs,
-            tool_config=tool_config or ToolConfig(),
-        )
-        self.check_model(request)
-
-        # augment and rewrite messages depending on the model
-        request.messages = chat_completion_request_to_messages(request, self.llama_model.core_model_id.value)
-        # download media and convert to raw content so we can send it to the model
-        request = await convert_request_to_raw(request)
-
-        if self.config.create_distributed_process_group:
-            if SEMAPHORE.locked():
-                raise RuntimeError("Only one concurrent request is supported")
-
-        if request.stream:
-            return self._stream_chat_completion(request)
-        else:
-            results = await self._nonstream_chat_completion([request])
-            return results[0]
-
-    async def _nonstream_chat_completion(
-        self, request_batch: list[ChatCompletionRequest]
-    ) -> list[ChatCompletionResponse]:
-        tokenizer = self.generator.formatter.tokenizer
-
-        first_request = request_batch[0]
-
-        class ItemState(BaseModel):
-            tokens: list[int] = []
-            logprobs: list[TokenLogProbs] = []
-            stop_reason: StopReason | None = None
-            finished: bool = False
-
-        def impl():
-            states = [ItemState() for _ in request_batch]
-
-            for token_results in self.generator.chat_completion(request_batch):
-                first = token_results[0]
-                if not first.finished and not first.ignore_token:
-                    if os.environ.get("LLAMA_MODELS_DEBUG", "0") in ("1", "2"):
-                        cprint(first.text, color="cyan", end="", file=sys.stderr)
-                    if os.environ.get("LLAMA_MODELS_DEBUG", "0") == "2":
-                        cprint(f"<{first.token}>", color="magenta", end="", file=sys.stderr)
-
-                for result in token_results:
-                    idx = result.batch_idx
-                    state = states[idx]
-                    if state.finished or result.ignore_token:
-                        continue
-
-                    state.finished = result.finished
-                    if first_request.logprobs:
-                        state.logprobs.append(TokenLogProbs(logprobs_by_token={result.text: result.logprobs[0]}))
-
-                    state.tokens.append(result.token)
-                    if result.token == tokenizer.eot_id:
-                        state.stop_reason = StopReason.end_of_turn
-                    elif result.token == tokenizer.eom_id:
-                        state.stop_reason = StopReason.end_of_message
-
-            results = []
-            for state in states:
-                if state.stop_reason is None:
-                    state.stop_reason = StopReason.out_of_tokens
-
-                raw_message = self.generator.formatter.decode_assistant_message(state.tokens, state.stop_reason)
-                results.append(
-                    ChatCompletionResponse(
-                        completion_message=CompletionMessage(
-                            content=raw_message.content,
-                            stop_reason=raw_message.stop_reason,
-                            tool_calls=raw_message.tool_calls,
-                        ),
-                        logprobs=state.logprobs if first_request.logprobs else None,
-                    )
-                )
-
-            return results
-
-        if self.config.create_distributed_process_group:
-            async with SEMAPHORE:
-                return impl()
-        else:
-            return impl()
-
-    async def _stream_chat_completion(self, request: ChatCompletionRequest) -> AsyncGenerator:
-        tokenizer = self.generator.formatter.tokenizer
-
-        def impl():
-            yield ChatCompletionResponseStreamChunk(
-                event=ChatCompletionResponseEvent(
-                    event_type=ChatCompletionResponseEventType.start,
-                    delta=TextDelta(text=""),
-                )
-            )
-
-            tokens = []
-            logprobs = []
-            stop_reason = None
-            ipython = False
-
-            for token_results in self.generator.chat_completion([request]):
-                token_result = token_results[0]
-                if os.environ.get("LLAMA_MODELS_DEBUG", "0") == "1":
-                    cprint(token_result.text, color="cyan", end="", file=sys.stderr)
-                if os.environ.get("LLAMA_MODELS_DEBUG", "0") == "2":
-                    cprint(f"<{token_result.token}>", color="magenta", end="", file=sys.stderr)
-
-                if token_result.token == tokenizer.eot_id:
-                    stop_reason = StopReason.end_of_turn
-                    text = ""
-                elif token_result.token == tokenizer.eom_id:
-                    stop_reason = StopReason.end_of_message
-                    text = ""
-                else:
-                    text = token_result.text
-
-                if request.logprobs:
-                    assert len(token_result.logprobs) == 1
-
-                    logprobs.append(TokenLogProbs(logprobs_by_token={token_result.text: token_result.logprobs[0]}))
-
-                tokens.append(token_result.token)
-
-                if not ipython and token_result.text.startswith("<|python_tag|>"):
-                    ipython = True
-                    yield ChatCompletionResponseStreamChunk(
-                        event=ChatCompletionResponseEvent(
-                            event_type=ChatCompletionResponseEventType.progress,
-                            delta=ToolCallDelta(
-                                tool_call="",
-                                parse_status=ToolCallParseStatus.started,
-                            ),
-                        )
-                    )
-                    continue
-
-                if token_result.token == tokenizer.eot_id:
-                    stop_reason = StopReason.end_of_turn
-                    text = ""
-                elif token_result.token == tokenizer.eom_id:
-                    stop_reason = StopReason.end_of_message
-                    text = ""
-                else:
-                    text = token_result.text
-
-                if ipython:
-                    delta = ToolCallDelta(
-                        tool_call=text,
-                        parse_status=ToolCallParseStatus.in_progress,
-                    )
-                else:
-                    delta = TextDelta(text=text)
-
-                if stop_reason is None:
-                    if request.logprobs:
-                        assert len(token_result.logprobs) == 1
-
-                        logprobs.append(TokenLogProbs(logprobs_by_token={token_result.text: token_result.logprobs[0]}))
-                    yield ChatCompletionResponseStreamChunk(
-                        event=ChatCompletionResponseEvent(
-                            event_type=ChatCompletionResponseEventType.progress,
-                            delta=delta,
-                            stop_reason=stop_reason,
-                            logprobs=logprobs if request.logprobs else None,
-                        )
-                    )
-
-            if stop_reason is None:
-                stop_reason = StopReason.out_of_tokens
-
-            message = self.generator.formatter.decode_assistant_message(tokens, stop_reason)
-
-            parsed_tool_calls = len(message.tool_calls) > 0
-            if ipython and not parsed_tool_calls:
-                yield ChatCompletionResponseStreamChunk(
-                    event=ChatCompletionResponseEvent(
-                        event_type=ChatCompletionResponseEventType.progress,
-                        delta=ToolCallDelta(
-                            tool_call="",
-                            parse_status=ToolCallParseStatus.failed,
-                        ),
-                        stop_reason=stop_reason,
-                    )
-                )
-
-            for tool_call in message.tool_calls:
-                yield ChatCompletionResponseStreamChunk(
-                    event=ChatCompletionResponseEvent(
-                        event_type=ChatCompletionResponseEventType.progress,
-                        delta=ToolCallDelta(
-                            tool_call=tool_call,
-                            parse_status=ToolCallParseStatus.succeeded,
-                        ),
-                        stop_reason=stop_reason,
-                    )
-                )
-
-            yield ChatCompletionResponseStreamChunk(
-                event=ChatCompletionResponseEvent(
-                    event_type=ChatCompletionResponseEventType.complete,
-                    delta=TextDelta(text=""),
-                    stop_reason=stop_reason,
-                )
-            )
-
-        if self.config.create_distributed_process_group:
-            async with SEMAPHORE:
-                for x in impl():
-                    yield x
-        else:
-            for x in impl():
-                yield x
+        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]:
+        raise NotImplementedError("OpenAI chat completion not supported by meta-reference inference provider")

llama_stack/providers/inline/inference/sentence_transformers/sentence_transformers.py

@@ -4,21 +4,19 @@
 # This source code is licensed under the terms described in the LICENSE file in
 # the root directory of this source tree.
 
-from collections.abc import AsyncGenerator
+from collections.abc import AsyncIterator
 from typing import Any
 
 from llama_stack.apis.inference import (
     InferenceProvider,
-    LogProbConfig,
-    Message,
-    ResponseFormat,
-    SamplingParams,
-    ToolChoice,
-    ToolConfig,
-    ToolDefinition,
-    ToolPromptFormat,
 )
-from llama_stack.apis.inference.inference import OpenAICompletion
+from llama_stack.apis.inference.inference import (
+    OpenAIChatCompletion,
+    OpenAIChatCompletionChunk,
+    OpenAICompletion,
+    OpenAIMessageParam,
+    OpenAIResponseFormatParam,
+)
 from llama_stack.apis.models import ModelType
 from llama_stack.log import get_logger
 from llama_stack.providers.datatypes import Model, ModelsProtocolPrivate
@@ -69,21 +67,6 @@ class SentenceTransformersInferenceImpl(
     async def unregister_model(self, model_id: str) -> None:
         pass
 
-    async def chat_completion(
-        self,
-        model_id: str,
-        messages: list[Message],
-        sampling_params: SamplingParams | None = None,
-        response_format: ResponseFormat | None = None,
-        tools: list[ToolDefinition] | None = None,
-        tool_choice: ToolChoice | None = ToolChoice.auto,
-        tool_prompt_format: ToolPromptFormat | None = None,
-        stream: bool | None = False,
-        logprobs: LogProbConfig | None = None,
-        tool_config: ToolConfig | None = None,
-    ) -> AsyncGenerator:
-        raise ValueError("Sentence transformers don't support chat completion")
-
     async def openai_completion(
         self,
         # Standard OpenAI completion parameters
@@ -110,6 +93,32 @@ class SentenceTransformersInferenceImpl(
         # for fill-in-the-middle type completion
         suffix: str | None = None,
     ) -> OpenAICompletion:
-        raise NotImplementedError(
-            "OpenAI completion not supported by sentence transformers provider"
-        )
+        raise NotImplementedError("OpenAI completion not supported by sentence transformers provider")
+
+    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]:
+        raise NotImplementedError("OpenAI chat completion not supported by sentence transformers provider")

llama_stack/providers/registry/inference.py

@@ -52,9 +52,7 @@ def available_providers() -> list[ProviderSpec]:
             api=Api.inference,
             adapter_type="cerebras",
             provider_type="remote::cerebras",
-            pip_packages=[
-                "cerebras_cloud_sdk",
-            ],
+            pip_packages=[],
             module="llama_stack.providers.remote.inference.cerebras",
             config_class="llama_stack.providers.remote.inference.cerebras.CerebrasImplConfig",
             description="Cerebras inference provider for running models on Cerebras Cloud platform.",
@@ -169,7 +167,7 @@ def available_providers() -> list[ProviderSpec]:
             api=Api.inference,
             adapter_type="openai",
             provider_type="remote::openai",
-            pip_packages=["litellm"],
+            pip_packages=[],
             module="llama_stack.providers.remote.inference.openai",
             config_class="llama_stack.providers.remote.inference.openai.OpenAIConfig",
             provider_data_validator="llama_stack.providers.remote.inference.openai.config.OpenAIProviderDataValidator",
@@ -179,7 +177,7 @@ def available_providers() -> list[ProviderSpec]:
             api=Api.inference,
             adapter_type="anthropic",
             provider_type="remote::anthropic",
-            pip_packages=["litellm"],
+            pip_packages=["anthropic"],
             module="llama_stack.providers.remote.inference.anthropic",
             config_class="llama_stack.providers.remote.inference.anthropic.AnthropicConfig",
             provider_data_validator="llama_stack.providers.remote.inference.anthropic.config.AnthropicProviderDataValidator",
@@ -189,9 +187,7 @@ def available_providers() -> list[ProviderSpec]:
             api=Api.inference,
             adapter_type="gemini",
             provider_type="remote::gemini",
-            pip_packages=[
-                "litellm",
-            ],
+            pip_packages=[],
             module="llama_stack.providers.remote.inference.gemini",
             config_class="llama_stack.providers.remote.inference.gemini.GeminiConfig",
             provider_data_validator="llama_stack.providers.remote.inference.gemini.config.GeminiProviderDataValidator",
@@ -202,7 +198,6 @@ def available_providers() -> list[ProviderSpec]:
             adapter_type="vertexai",
             provider_type="remote::vertexai",
             pip_packages=[
-                "litellm",
                 "google-cloud-aiplatform",
             ],
             module="llama_stack.providers.remote.inference.vertexai",
@@ -233,9 +228,7 @@ Available Models:
             api=Api.inference,
             adapter_type="groq",
             provider_type="remote::groq",
-            pip_packages=[
-                "litellm",
-            ],
+            pip_packages=[],
             module="llama_stack.providers.remote.inference.groq",
             config_class="llama_stack.providers.remote.inference.groq.GroqConfig",
             provider_data_validator="llama_stack.providers.remote.inference.groq.config.GroqProviderDataValidator",
@@ -245,7 +238,7 @@ Available Models:
             api=Api.inference,
             adapter_type="llama-openai-compat",
             provider_type="remote::llama-openai-compat",
-            pip_packages=["litellm"],
+            pip_packages=[],
             module="llama_stack.providers.remote.inference.llama_openai_compat",
             config_class="llama_stack.providers.remote.inference.llama_openai_compat.config.LlamaCompatConfig",
             provider_data_validator="llama_stack.providers.remote.inference.llama_openai_compat.config.LlamaProviderDataValidator",
@@ -255,9 +248,7 @@ Available Models:
             api=Api.inference,
             adapter_type="sambanova",
             provider_type="remote::sambanova",
-            pip_packages=[
-                "litellm",
-            ],
+            pip_packages=[],
             module="llama_stack.providers.remote.inference.sambanova",
             config_class="llama_stack.providers.remote.inference.sambanova.SambaNovaImplConfig",
             provider_data_validator="llama_stack.providers.remote.inference.sambanova.config.SambaNovaProviderDataValidator",
@@ -287,7 +278,7 @@ Available Models:
             api=Api.inference,
             provider_type="remote::azure",
             adapter_type="azure",
-            pip_packages=["litellm"],
+            pip_packages=[],
             module="llama_stack.providers.remote.inference.azure",
             config_class="llama_stack.providers.remote.inference.azure.AzureConfig",
             provider_data_validator="llama_stack.providers.remote.inference.azure.config.AzureProviderDataValidator",

llama_stack/providers/registry/vector_io.py

@@ -500,7 +500,7 @@ See [PGVector's documentation](https://github.com/pgvector/pgvector) for more de
             api=Api.vector_io,
             adapter_type="weaviate",
             provider_type="remote::weaviate",
-            pip_packages=["weaviate-client"],
+            pip_packages=["weaviate-client>=4.16.5"],
             module="llama_stack.providers.remote.vector_io.weaviate",
             config_class="llama_stack.providers.remote.vector_io.weaviate.WeaviateVectorIOConfig",
             provider_data_validator="llama_stack.providers.remote.vector_io.weaviate.WeaviateRequestProviderData",

llama_stack/providers/remote/inference/anthropic/__init__.py

@@ -10,6 +10,6 @@ from .config import AnthropicConfig
 async def get_adapter_impl(config: AnthropicConfig, _deps):
     from .anthropic import AnthropicInferenceAdapter
 
-    impl = AnthropicInferenceAdapter(config)
+    impl = AnthropicInferenceAdapter(config=config)
     await impl.initialize()
     return impl

llama_stack/providers/remote/inference/anthropic/anthropic.py

@@ -4,13 +4,19 @@
 # This source code is licensed under the terms described in the LICENSE file in
 # the root directory of this source tree.
 
-from llama_stack.providers.utils.inference.litellm_openai_mixin import LiteLLMOpenAIMixin
+from collections.abc import Iterable
+
+from anthropic import AsyncAnthropic
+
 from llama_stack.providers.utils.inference.openai_mixin import OpenAIMixin
 
 from .config import AnthropicConfig
 
 
-class AnthropicInferenceAdapter(OpenAIMixin, LiteLLMOpenAIMixin):
+class AnthropicInferenceAdapter(OpenAIMixin):
+    config: AnthropicConfig
+
+    provider_data_api_key_field: str = "anthropic_api_key"
     # source: https://docs.claude.com/en/docs/build-with-claude/embeddings
     # TODO: add support for voyageai, which is where these models are hosted
     # embedding_model_metadata = {
@@ -23,22 +29,11 @@ class AnthropicInferenceAdapter(OpenAIMixin, LiteLLMOpenAIMixin):
     #     "voyage-multimodal-3": {"embedding_dimension": 1024, "context_length": 32000},
     # }
 
-    def __init__(self, config: AnthropicConfig) -> None:
-        LiteLLMOpenAIMixin.__init__(
-            self,
-            litellm_provider_name="anthropic",
-            api_key_from_config=config.api_key,
-            provider_data_api_key_field="anthropic_api_key",
-        )
-        self.config = config
-
-    async def initialize(self) -> None:
-        await super().initialize()
-
-    async def shutdown(self) -> None:
-        await super().shutdown()
-
-    get_api_key = LiteLLMOpenAIMixin.get_api_key
+    def get_api_key(self) -> str:
+        return self.config.api_key or ""
 
     def get_base_url(self):
         return "https://api.anthropic.com/v1"
+
+    async def list_provider_model_ids(self) -> Iterable[str]:
+        return [m.id async for m in AsyncAnthropic(api_key=self.get_api_key()).models.list()]

llama_stack/providers/remote/inference/anthropic/config.py

@@ -8,6 +8,7 @@ from typing import Any
 
 from pydantic import BaseModel, Field
 
+from llama_stack.providers.utils.inference.model_registry import RemoteInferenceProviderConfig
 from llama_stack.schema_utils import json_schema_type
 
 
@@ -19,7 +20,7 @@ class AnthropicProviderDataValidator(BaseModel):
 
 
 @json_schema_type
-class AnthropicConfig(BaseModel):
+class AnthropicConfig(RemoteInferenceProviderConfig):
     api_key: str | None = Field(
         default=None,
         description="API key for Anthropic models",

llama_stack/providers/remote/inference/azure/__init__.py

@@ -10,6 +10,6 @@ from .config import AzureConfig
 async def get_adapter_impl(config: AzureConfig, _deps):
     from .azure import AzureInferenceAdapter
 
-    impl = AzureInferenceAdapter(config)
+    impl = AzureInferenceAdapter(config=config)
     await impl.initialize()
     return impl

llama_stack/providers/remote/inference/azure/azure.py

@@ -4,31 +4,20 @@
 # 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
 from urllib.parse import urljoin
 
-from llama_stack.apis.inference import ChatCompletionRequest
-from llama_stack.providers.utils.inference.litellm_openai_mixin import (
-    LiteLLMOpenAIMixin,
-)
 from llama_stack.providers.utils.inference.openai_mixin import OpenAIMixin
 
 from .config import AzureConfig
 
 
-class AzureInferenceAdapter(OpenAIMixin, LiteLLMOpenAIMixin):
-    def __init__(self, config: AzureConfig) -> None:
-        LiteLLMOpenAIMixin.__init__(
-            self,
-            litellm_provider_name="azure",
-            api_key_from_config=config.api_key.get_secret_value(),
-            provider_data_api_key_field="azure_api_key",
-            openai_compat_api_base=str(config.api_base),
-        )
-        self.config = config
+class AzureInferenceAdapter(OpenAIMixin):
+    config: AzureConfig
 
-    # Delegate the client data handling get_api_key method to LiteLLMOpenAIMixin
-    get_api_key = LiteLLMOpenAIMixin.get_api_key
+    provider_data_api_key_field: str = "azure_api_key"
+
+    def get_api_key(self) -> str:
+        return self.config.api_key.get_secret_value()
 
     def get_base_url(self) -> str:
         """
@@ -37,26 +26,3 @@ class AzureInferenceAdapter(OpenAIMixin, LiteLLMOpenAIMixin):
         Returns the Azure API base URL from the configuration.
         """
         return urljoin(str(self.config.api_base), "/openai/v1")
-
-    async def _get_params(self, request: ChatCompletionRequest) -> dict[str, Any]:
-        # Get base parameters from parent
-        params = await super()._get_params(request)
-
-        # Add Azure specific parameters
-        provider_data = self.get_request_provider_data()
-        if provider_data:
-            if getattr(provider_data, "azure_api_key", None):
-                params["api_key"] = provider_data.azure_api_key
-            if getattr(provider_data, "azure_api_base", None):
-                params["api_base"] = provider_data.azure_api_base
-            if getattr(provider_data, "azure_api_version", None):
-                params["api_version"] = provider_data.azure_api_version
-            if getattr(provider_data, "azure_api_type", None):
-                params["api_type"] = provider_data.azure_api_type
-        else:
-            params["api_key"] = self.config.api_key.get_secret_value()
-            params["api_base"] = str(self.config.api_base)
-            params["api_version"] = self.config.api_version
-            params["api_type"] = self.config.api_type
-
-        return params

llama_stack/providers/remote/inference/azure/config.py

@@ -9,6 +9,7 @@ from typing import Any
 
 from pydantic import BaseModel, Field, HttpUrl, SecretStr
 
+from llama_stack.providers.utils.inference.model_registry import RemoteInferenceProviderConfig
 from llama_stack.schema_utils import json_schema_type
 
 
@@ -30,7 +31,7 @@ class AzureProviderDataValidator(BaseModel):
 
 
 @json_schema_type
-class AzureConfig(BaseModel):
+class AzureConfig(RemoteInferenceProviderConfig):
     api_key: SecretStr = Field(
         description="Azure API key for Azure",
     )

llama_stack/providers/remote/inference/bedrock/bedrock.py

@@ -5,39 +5,30 @@
 # the root directory of this source tree.
 
 import json
-from collections.abc import AsyncGenerator, AsyncIterator
+from collections.abc import AsyncIterator
 from typing import Any
 
 from botocore.client import BaseClient
 
 from llama_stack.apis.inference import (
     ChatCompletionRequest,
-    ChatCompletionResponse,
-    ChatCompletionResponseStreamChunk,
     Inference,
-    LogProbConfig,
-    Message,
     OpenAIEmbeddingsResponse,
-    ResponseFormat,
-    SamplingParams,
-    ToolChoice,
-    ToolConfig,
-    ToolDefinition,
-    ToolPromptFormat,
 )
-from llama_stack.apis.inference.inference import OpenAICompletion
+from llama_stack.apis.inference.inference import (
+    OpenAIChatCompletion,
+    OpenAIChatCompletionChunk,
+    OpenAICompletion,
+    OpenAIMessageParam,
+    OpenAIResponseFormatParam,
+)
 from llama_stack.providers.remote.inference.bedrock.config import BedrockConfig
 from llama_stack.providers.utils.bedrock.client import create_bedrock_client
 from llama_stack.providers.utils.inference.model_registry import (
     ModelRegistryHelper,
 )
 from llama_stack.providers.utils.inference.openai_compat import (
-    OpenAIChatCompletionToLlamaStackMixin,
-    OpenAICompatCompletionChoice,
-    OpenAICompatCompletionResponse,
     get_sampling_strategy_options,
-    process_chat_completion_response,
-    process_chat_completion_stream_response,
 )
 from llama_stack.providers.utils.inference.prompt_adapter import (
     chat_completion_request_to_prompt,
@@ -86,7 +77,6 @@ def _to_inference_profile_id(model_id: str, region: str = None) -> str:
 class BedrockInferenceAdapter(
     ModelRegistryHelper,
     Inference,
-    OpenAIChatCompletionToLlamaStackMixin,
 ):
     def __init__(self, config: BedrockConfig) -> None:
         ModelRegistryHelper.__init__(self, model_entries=MODEL_ENTRIES)
@@ -106,71 +96,6 @@ class BedrockInferenceAdapter(
         if self._client is not None:
             self._client.close()
 
-    async def chat_completion(
-        self,
-        model_id: str,
-        messages: list[Message],
-        sampling_params: SamplingParams | None = None,
-        response_format: ResponseFormat | None = None,
-        tools: list[ToolDefinition] | None = None,
-        tool_choice: ToolChoice | None = ToolChoice.auto,
-        tool_prompt_format: ToolPromptFormat | None = None,
-        stream: bool | None = False,
-        logprobs: LogProbConfig | None = None,
-        tool_config: ToolConfig | None = None,
-    ) -> ChatCompletionResponse | AsyncIterator[ChatCompletionResponseStreamChunk]:
-        if sampling_params is None:
-            sampling_params = SamplingParams()
-        model = await self.model_store.get_model(model_id)
-        request = ChatCompletionRequest(
-            model=model.provider_resource_id,
-            messages=messages,
-            sampling_params=sampling_params,
-            tools=tools or [],
-            response_format=response_format,
-            stream=stream,
-            logprobs=logprobs,
-            tool_config=tool_config,
-        )
-
-        if stream:
-            return self._stream_chat_completion(request)
-        else:
-            return await self._nonstream_chat_completion(request)
-
-    async def _nonstream_chat_completion(self, request: ChatCompletionRequest) -> ChatCompletionResponse:
-        params = await self._get_params_for_chat_completion(request)
-        res = self.client.invoke_model(**params)
-        chunk = next(res["body"])
-        result = json.loads(chunk.decode("utf-8"))
-
-        choice = OpenAICompatCompletionChoice(
-            finish_reason=result["stop_reason"],
-            text=result["generation"],
-        )
-
-        response = OpenAICompatCompletionResponse(choices=[choice])
-        return process_chat_completion_response(response, request)
-
-    async def _stream_chat_completion(self, request: ChatCompletionRequest) -> AsyncGenerator:
-        params = await self._get_params_for_chat_completion(request)
-        res = self.client.invoke_model_with_response_stream(**params)
-        event_stream = res["body"]
-
-        async def _generate_and_convert_to_openai_compat():
-            for chunk in event_stream:
-                chunk = chunk["chunk"]["bytes"]
-                result = json.loads(chunk.decode("utf-8"))
-                choice = OpenAICompatCompletionChoice(
-                    finish_reason=result["stop_reason"],
-                    text=result["generation"],
-                )
-                yield OpenAICompatCompletionResponse(choices=[choice])
-
-        stream = _generate_and_convert_to_openai_compat()
-        async for chunk in process_chat_completion_stream_response(stream, request):
-            yield chunk
-
     async def _get_params_for_chat_completion(self, request: ChatCompletionRequest) -> dict:
         bedrock_model = request.model
 
@@ -235,3 +160,31 @@ class BedrockInferenceAdapter(
         suffix: str | None = None,
     ) -> OpenAICompletion:
         raise NotImplementedError("OpenAI completion not supported by the Bedrock provider")
+
+    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]:
+        raise NotImplementedError("OpenAI chat completion not supported by the Bedrock provider")

llama_stack/providers/remote/inference/cerebras/__init__.py

@@ -12,7 +12,7 @@ async def get_adapter_impl(config: CerebrasImplConfig, _deps):
 
     assert isinstance(config, CerebrasImplConfig), f"Unexpected config type: {type(config)}"
 
-    impl = CerebrasInferenceAdapter(config)
+    impl = CerebrasInferenceAdapter(config=config)
 
     await impl.initialize()
 

llama_stack/providers/remote/inference/cerebras/cerebras.py

@@ -4,53 +4,16 @@
 # This source code is licensed under the terms described in the LICENSE file in
 # the root directory of this source tree.
 
-from collections.abc import AsyncGenerator
 from urllib.parse import urljoin
 
-from cerebras.cloud.sdk import AsyncCerebras
-
-from llama_stack.apis.inference import (
-    ChatCompletionRequest,
-    ChatCompletionResponse,
-    Inference,
-    LogProbConfig,
-    Message,
-    OpenAIEmbeddingsResponse,
-    ResponseFormat,
-    SamplingParams,
-    ToolChoice,
-    ToolConfig,
-    ToolDefinition,
-    ToolPromptFormat,
-    TopKSamplingStrategy,
-)
-from llama_stack.providers.utils.inference.model_registry import ModelRegistryHelper
-from llama_stack.providers.utils.inference.openai_compat import (
-    get_sampling_options,
-    process_chat_completion_response,
-    process_chat_completion_stream_response,
-)
+from llama_stack.apis.inference import OpenAIEmbeddingsResponse
 from llama_stack.providers.utils.inference.openai_mixin import OpenAIMixin
-from llama_stack.providers.utils.inference.prompt_adapter import (
-    chat_completion_request_to_prompt,
-)
 
 from .config import CerebrasImplConfig
 
 
-class CerebrasInferenceAdapter(
-    OpenAIMixin,
-    ModelRegistryHelper,
-    Inference,
-):
-    def __init__(self, config: CerebrasImplConfig) -> None:
-        self.config = config
-
-        # TODO: make this use provider data, etc. like other providers
-        self._cerebras_client = AsyncCerebras(
-            base_url=self.config.base_url,
-            api_key=self.config.api_key.get_secret_value(),
-        )
+class CerebrasInferenceAdapter(OpenAIMixin):
+    config: CerebrasImplConfig
 
     def get_api_key(self) -> str:
         return self.config.api_key.get_secret_value()
@@ -58,86 +21,6 @@ class CerebrasInferenceAdapter(
     def get_base_url(self) -> str:
         return urljoin(self.config.base_url, "v1")
 
-    async def initialize(self) -> None:
-        return
-
-    async def shutdown(self) -> None:
-        pass
-
-    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,
-    ) -> AsyncGenerator:
-        if sampling_params is None:
-            sampling_params = SamplingParams()
-        model = await self.model_store.get_model(model_id)
-        request = ChatCompletionRequest(
-            model=model.provider_resource_id,
-            messages=messages,
-            sampling_params=sampling_params,
-            tools=tools or [],
-            tool_choice=tool_choice,
-            tool_prompt_format=tool_prompt_format,
-            response_format=response_format,
-            stream=stream,
-            logprobs=logprobs,
-            tool_config=tool_config,
-        )
-
-        if stream:
-            return self._stream_chat_completion(request)
-        else:
-            return await self._nonstream_chat_completion(request)
-
-    async def _nonstream_chat_completion(
-        self, request: ChatCompletionRequest
-    ) -> ChatCompletionResponse:
-        params = await self._get_params(request)
-
-        r = await self._cerebras_client.completions.create(**params)
-
-        return process_chat_completion_response(r, request)
-
-    async def _stream_chat_completion(
-        self, request: ChatCompletionRequest
-    ) -> AsyncGenerator:
-        params = await self._get_params(request)
-
-        stream = await self._cerebras_client.completions.create(**params)
-
-        async for chunk in process_chat_completion_stream_response(stream, request):
-            yield chunk
-
-    async def _get_params(self, request: ChatCompletionRequest) -> dict:
-        if request.sampling_params and isinstance(
-            request.sampling_params.strategy, TopKSamplingStrategy
-        ):
-            raise ValueError("`top_k` not supported by Cerebras")
-
-        prompt = ""
-        if isinstance(request, ChatCompletionRequest):
-            prompt = await chat_completion_request_to_prompt(
-                request, self.get_llama_model(request.model)
-            )
-        else:
-            raise ValueError(f"Unknown request type {type(request)}")
-
-        return {
-            "model": request.model,
-            "prompt": prompt,
-            "stream": request.stream,
-            **get_sampling_options(request.sampling_params),
-        }
-
     async def openai_embeddings(
         self,
         model: str,

llama_stack/providers/remote/inference/cerebras/config.py

@@ -7,21 +7,22 @@
 import os
 from typing import Any
 
-from pydantic import BaseModel, Field, SecretStr
+from pydantic import Field, SecretStr
 
+from llama_stack.providers.utils.inference.model_registry import RemoteInferenceProviderConfig
 from llama_stack.schema_utils import json_schema_type
 
 DEFAULT_BASE_URL = "https://api.cerebras.ai"
 
 
 @json_schema_type
-class CerebrasImplConfig(BaseModel):
+class CerebrasImplConfig(RemoteInferenceProviderConfig):
     base_url: str = Field(
         default=os.environ.get("CEREBRAS_BASE_URL", DEFAULT_BASE_URL),
         description="Base URL for the Cerebras API",
     )
     api_key: SecretStr = Field(
-        default=SecretStr(os.environ.get("CEREBRAS_API_KEY")),
+        default=SecretStr(os.environ.get("CEREBRAS_API_KEY")),  # type: ignore[arg-type]
         description="Cerebras API Key",
     )
 

llama_stack/providers/remote/inference/databricks/__init__.py

@@ -11,6 +11,6 @@ async def get_adapter_impl(config: DatabricksImplConfig, _deps):
     from .databricks import DatabricksInferenceAdapter
 
     assert isinstance(config, DatabricksImplConfig), f"Unexpected config type: {type(config)}"
-    impl = DatabricksInferenceAdapter(config)
+    impl = DatabricksInferenceAdapter(config=config)
     await impl.initialize()
     return impl

llama_stack/providers/remote/inference/databricks/config.py

@@ -6,19 +6,20 @@
 
 from typing import Any
 
-from pydantic import BaseModel, Field, SecretStr
+from pydantic import Field, SecretStr
 
+from llama_stack.providers.utils.inference.model_registry import RemoteInferenceProviderConfig
 from llama_stack.schema_utils import json_schema_type
 
 
 @json_schema_type
-class DatabricksImplConfig(BaseModel):
-    url: str = Field(
+class DatabricksImplConfig(RemoteInferenceProviderConfig):
+    url: str | None = Field(
         default=None,
         description="The URL for the Databricks model serving endpoint",
     )
     api_token: SecretStr = Field(
-        default=SecretStr(None),
+        default=SecretStr(None),  # type: ignore[arg-type]
         description="The Databricks API token",
     )
 

llama_stack/providers/remote/inference/databricks/databricks.py

@@ -4,27 +4,12 @@
 # This source code is licensed under the terms described in the LICENSE file in
 # the root directory of this source tree.
 
-from collections.abc import AsyncIterator
+from collections.abc import Iterable
 from typing import Any
 
 from databricks.sdk import WorkspaceClient
 
-from llama_stack.apis.inference import (
-    ChatCompletionResponse,
-    ChatCompletionResponseStreamChunk,
-    Inference,
-    LogProbConfig,
-    Message,
-    Model,
-    OpenAICompletion,
-    ResponseFormat,
-    SamplingParams,
-    ToolChoice,
-    ToolConfig,
-    ToolDefinition,
-    ToolPromptFormat,
-)
-from llama_stack.apis.models import ModelType
+from llama_stack.apis.inference import OpenAICompletion
 from llama_stack.log import get_logger
 from llama_stack.providers.utils.inference.openai_mixin import OpenAIMixin
 
@@ -33,30 +18,31 @@ from .config import DatabricksImplConfig
 logger = get_logger(name=__name__, category="inference::databricks")
 
 
-class DatabricksInferenceAdapter(
-    OpenAIMixin,
-    Inference,
-):
+class DatabricksInferenceAdapter(OpenAIMixin):
+    config: DatabricksImplConfig
+
     # source: https://docs.databricks.com/aws/en/machine-learning/foundation-model-apis/supported-models
-    embedding_model_metadata = {
+    embedding_model_metadata: dict[str, dict[str, int]] = {
         "databricks-gte-large-en": {"embedding_dimension": 1024, "context_length": 8192},
         "databricks-bge-large-en": {"embedding_dimension": 1024, "context_length": 512},
     }
 
-    def __init__(self, config: DatabricksImplConfig) -> None:
-        self.config = config
-
     def get_api_key(self) -> str:
         return self.config.api_token.get_secret_value()
 
     def get_base_url(self) -> str:
         return f"{self.config.url}/serving-endpoints"
 
-    async def initialize(self) -> None:
-        return
+    async def list_provider_model_ids(self) -> Iterable[str]:
+        return [
+            endpoint.name
+            for endpoint in WorkspaceClient(
+                host=self.config.url, token=self.get_api_key()
+            ).serving_endpoints.list()  # TODO: this is not async
+        ]
 
-    async def shutdown(self) -> None:
-        pass
+    async def should_refresh_models(self) -> bool:
+        return False
 
     async def openai_completion(
         self,
@@ -82,47 +68,3 @@ class DatabricksInferenceAdapter(
         suffix: str | None = None,
     ) -> OpenAICompletion:
         raise NotImplementedError()
-
-    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]:
-        raise NotImplementedError()
-
-    async def list_models(self) -> list[Model] | None:
-        self._model_cache = {}  # from OpenAIMixin
-        ws_client = WorkspaceClient(host=self.config.url, token=self.get_api_key())  # TODO: this is not async
-        endpoints = ws_client.serving_endpoints.list()
-        for endpoint in endpoints:
-            model = Model(
-                provider_id=self.__provider_id__,
-                provider_resource_id=endpoint.name,
-                identifier=endpoint.name,
-            )
-            if endpoint.task == "llm/v1/chat":
-                model.model_type = ModelType.llm  # this is redundant, but informative
-            elif endpoint.task == "llm/v1/embeddings":
-                if endpoint.name not in self.embedding_model_metadata:
-                    logger.warning(f"No metadata information available for embedding model {endpoint.name}, skipping.")
-                    continue
-                model.model_type = ModelType.embedding
-                model.metadata = self.embedding_model_metadata[endpoint.name]
-            else:
-                logger.warning(f"Unknown model type, skipping: {endpoint}")
-                continue
-
-            self._model_cache[endpoint.name] = model
-
-        return list(self._model_cache.values())
-
-    async def should_refresh_models(self) -> bool:
-        return False

llama_stack/providers/remote/inference/fireworks/__init__.py

@@ -17,6 +17,6 @@ async def get_adapter_impl(config: FireworksImplConfig, _deps):
     from .fireworks import FireworksInferenceAdapter
 
     assert isinstance(config, FireworksImplConfig), f"Unexpected config type: {type(config)}"
-    impl = FireworksInferenceAdapter(config)
+    impl = FireworksInferenceAdapter(config=config)
     await impl.initialize()
     return impl

llama_stack/providers/remote/inference/fireworks/fireworks.py

@@ -4,195 +4,27 @@
 # This source code is licensed under the terms described in the LICENSE file in
 # the root directory of this source tree.
 
-from collections.abc import AsyncGenerator
 
-from fireworks.client import Fireworks
-
-from llama_stack.apis.inference import (
-    ChatCompletionRequest,
-    ChatCompletionResponse,
-    Inference,
-    LogProbConfig,
-    Message,
-    ResponseFormat,
-    ResponseFormatType,
-    SamplingParams,
-    ToolChoice,
-    ToolConfig,
-    ToolDefinition,
-    ToolPromptFormat,
-)
-from llama_stack.core.request_headers import NeedsRequestProviderData
 from llama_stack.log import get_logger
-from llama_stack.providers.utils.inference.model_registry import (
-    ModelRegistryHelper,
-)
-from llama_stack.providers.utils.inference.openai_compat import (
-    convert_message_to_openai_dict,
-    get_sampling_options,
-    process_chat_completion_response,
-    process_chat_completion_stream_response,
-)
 from llama_stack.providers.utils.inference.openai_mixin import OpenAIMixin
-from llama_stack.providers.utils.inference.prompt_adapter import (
-    chat_completion_request_to_prompt,
-    request_has_media,
-)
 
 from .config import FireworksImplConfig
 
 logger = get_logger(name=__name__, category="inference::fireworks")
 
 
-class FireworksInferenceAdapter(OpenAIMixin, ModelRegistryHelper, Inference, NeedsRequestProviderData):
-    embedding_model_metadata = {
+class FireworksInferenceAdapter(OpenAIMixin):
+    config: FireworksImplConfig
+
+    embedding_model_metadata: dict[str, dict[str, int]] = {
         "nomic-ai/nomic-embed-text-v1.5": {"embedding_dimension": 768, "context_length": 8192},
         "accounts/fireworks/models/qwen3-embedding-8b": {"embedding_dimension": 4096, "context_length": 40960},
     }
 
-    def __init__(self, config: FireworksImplConfig) -> None:
-        ModelRegistryHelper.__init__(self)
-        self.config = config
-        self.allowed_models = config.allowed_models
-
-    async def initialize(self) -> None:
-        pass
-
-    async def shutdown(self) -> None:
-        pass
+    provider_data_api_key_field: str = "fireworks_api_key"
 
     def get_api_key(self) -> str:
-        config_api_key = self.config.api_key.get_secret_value() if self.config.api_key else None
-        if config_api_key:
-            return config_api_key
-        else:
-            provider_data = self.get_request_provider_data()
-            if provider_data is None or not provider_data.fireworks_api_key:
-                raise ValueError(
-                    'Pass Fireworks API Key in the header X-LlamaStack-Provider-Data as { "fireworks_api_key": <your api key>}'
-                )
-            return provider_data.fireworks_api_key
+        return self.config.api_key.get_secret_value() if self.config.api_key else None  # type: ignore[return-value]
 
     def get_base_url(self) -> str:
         return "https://api.fireworks.ai/inference/v1"
-
-    def _get_client(self) -> Fireworks:
-        fireworks_api_key = self.get_api_key()
-        return Fireworks(api_key=fireworks_api_key)
-
-    def _preprocess_prompt_for_fireworks(self, prompt: str) -> str:
-        """Remove BOS token as Fireworks automatically prepends it"""
-        if prompt.startswith("<|begin_of_text|>"):
-            return prompt[len("<|begin_of_text|>") :]
-        return prompt
-
-    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,
-    ) -> AsyncGenerator:
-        if sampling_params is None:
-            sampling_params = SamplingParams()
-        model = await self.model_store.get_model(model_id)
-        request = ChatCompletionRequest(
-            model=model.provider_resource_id,
-            messages=messages,
-            sampling_params=sampling_params,
-            tools=tools or [],
-            response_format=response_format,
-            stream=stream,
-            logprobs=logprobs,
-            tool_config=tool_config,
-        )
-
-        if stream:
-            return self._stream_chat_completion(request)
-        else:
-            return await self._nonstream_chat_completion(request)
-
-    async def _nonstream_chat_completion(self, request: ChatCompletionRequest) -> ChatCompletionResponse:
-        params = await self._get_params(request)
-        if "messages" in params:
-            r = await self._get_client().chat.completions.acreate(**params)
-        else:
-            r = await self._get_client().completion.acreate(**params)
-        return process_chat_completion_response(r, request)
-
-    async def _stream_chat_completion(self, request: ChatCompletionRequest) -> AsyncGenerator:
-        params = await self._get_params(request)
-
-        async def _to_async_generator():
-            if "messages" in params:
-                stream = self._get_client().chat.completions.acreate(**params)
-            else:
-                stream = self._get_client().completion.acreate(**params)
-            async for chunk in stream:
-                yield chunk
-
-        stream = _to_async_generator()
-        async for chunk in process_chat_completion_stream_response(stream, request):
-            yield chunk
-
-    def _build_options(
-        self,
-        sampling_params: SamplingParams | None,
-        fmt: ResponseFormat | None,
-        logprobs: LogProbConfig | None,
-    ) -> dict:
-        options = get_sampling_options(sampling_params)
-        options.setdefault("max_tokens", 512)
-
-        if fmt:
-            if fmt.type == ResponseFormatType.json_schema.value:
-                options["response_format"] = {
-                    "type": "json_object",
-                    "schema": fmt.json_schema,
-                }
-            elif fmt.type == ResponseFormatType.grammar.value:
-                options["response_format"] = {
-                    "type": "grammar",
-                    "grammar": fmt.bnf,
-                }
-            else:
-                raise ValueError(f"Unknown response format {fmt.type}")
-
-        if logprobs and logprobs.top_k:
-            options["logprobs"] = logprobs.top_k
-            if options["logprobs"] <= 0 or options["logprobs"] >= 5:
-                raise ValueError("Required range: 0 < top_k < 5")
-
-        return options
-
-    async def _get_params(self, request: ChatCompletionRequest) -> dict:
-        input_dict = {}
-        media_present = request_has_media(request)
-
-        llama_model = self.get_llama_model(request.model)
-        # TODO: tools are never added to the request, so we need to add them here
-        if media_present or not llama_model:
-            input_dict["messages"] = [await convert_message_to_openai_dict(m, download=True) for m in request.messages]
-        else:
-            input_dict["prompt"] = await chat_completion_request_to_prompt(request, llama_model)
-
-        # Fireworks always prepends with BOS
-        if "prompt" in input_dict:
-            if input_dict["prompt"].startswith("<|begin_of_text|>"):
-                input_dict["prompt"] = input_dict["prompt"][len("<|begin_of_text|>") :]
-
-        params = {
-            "model": request.model,
-            **input_dict,
-            "stream": bool(request.stream),
-            **self._build_options(request.sampling_params, request.response_format, request.logprobs),
-        }
-        logger.debug(f"params to fireworks: {params}")
-
-        return params

llama_stack/providers/remote/inference/gemini/__init__.py

@@ -10,6 +10,6 @@ from .config import GeminiConfig
 async def get_adapter_impl(config: GeminiConfig, _deps):
     from .gemini import GeminiInferenceAdapter
 
-    impl = GeminiInferenceAdapter(config)
+    impl = GeminiInferenceAdapter(config=config)
     await impl.initialize()
     return impl

llama_stack/providers/remote/inference/gemini/config.py

@@ -8,6 +8,7 @@ from typing import Any
 
 from pydantic import BaseModel, Field
 
+from llama_stack.providers.utils.inference.model_registry import RemoteInferenceProviderConfig
 from llama_stack.schema_utils import json_schema_type
 
 
@@ -19,7 +20,7 @@ class GeminiProviderDataValidator(BaseModel):
 
 
 @json_schema_type
-class GeminiConfig(BaseModel):
+class GeminiConfig(RemoteInferenceProviderConfig):
     api_key: str | None = Field(
         default=None,
         description="API key for Gemini models",

llama_stack/providers/remote/inference/gemini/gemini.py

@@ -4,33 +4,21 @@
 # This source code is licensed under the terms described in the LICENSE file in
 # the root directory of this source tree.
 
-from llama_stack.providers.utils.inference.litellm_openai_mixin import LiteLLMOpenAIMixin
 from llama_stack.providers.utils.inference.openai_mixin import OpenAIMixin
 
 from .config import GeminiConfig
 
 
-class GeminiInferenceAdapter(OpenAIMixin, LiteLLMOpenAIMixin):
-    embedding_model_metadata = {
+class GeminiInferenceAdapter(OpenAIMixin):
+    config: GeminiConfig
+
+    provider_data_api_key_field: str = "gemini_api_key"
+    embedding_model_metadata: dict[str, dict[str, int]] = {
         "text-embedding-004": {"embedding_dimension": 768, "context_length": 2048},
     }
 
-    def __init__(self, config: GeminiConfig) -> None:
-        LiteLLMOpenAIMixin.__init__(
-            self,
-            litellm_provider_name="gemini",
-            api_key_from_config=config.api_key,
-            provider_data_api_key_field="gemini_api_key",
-        )
-        self.config = config
-
-    get_api_key = LiteLLMOpenAIMixin.get_api_key
+    def get_api_key(self) -> str:
+        return self.config.api_key or ""
 
     def get_base_url(self):
         return "https://generativelanguage.googleapis.com/v1beta/openai/"
-
-    async def initialize(self) -> None:
-        await super().initialize()
-
-    async def shutdown(self) -> None:
-        await super().shutdown()

llama_stack/providers/remote/inference/groq/__init__.py

@@ -11,5 +11,5 @@ async def get_adapter_impl(config: GroqConfig, _deps):
     # import dynamically so the import is used only when it is needed
     from .groq import GroqInferenceAdapter
 
-    adapter = GroqInferenceAdapter(config)
+    adapter = GroqInferenceAdapter(config=config)
     return adapter