Resolved merge conflicts

.github/workflows/integration-tests.yml

@@ -0,0 +1,80 @@
+name: Integration tests
+
+on:
+  pull_request:
+  push:
+    branches: [main]
+
+jobs:
+  ollama:
+    runs-on: ubuntu-latest
+
+    steps:
+      - name: Checkout repository
+        uses: actions/checkout@v4
+
+      - name: Install uv
+        uses: astral-sh/setup-uv@v5
+        with:
+          python-version: "3.10"
+
+      - name: Install Ollama
+        run: |
+          curl -fsSL https://ollama.com/install.sh | sh
+
+      - name: Pull Ollama image
+        run: |
+          ollama pull llama3.2:3b-instruct-fp16
+
+      - name: Start Ollama in background
+        run: |
+          nohup ollama run llama3.2:3b-instruct-fp16 > ollama.log 2>&1 &
+
+      - name: Set Up Environment and Install Dependencies
+        run: |
+          uv sync --extra dev --extra test
+          uv pip install ollama faiss-cpu
+          uv pip install -e .
+
+      - name: Wait for Ollama to start
+        run: |
+          echo "Waiting for Ollama..."
+          for i in {1..30}; do
+            if curl -s http://localhost:11434 | grep -q "Ollama is running"; then
+              echo "Ollama is running!"
+              exit 0
+            fi
+            sleep 1
+          done
+          echo "Ollama failed to start"
+          ollama ps
+          ollama.log
+          exit 1
+
+      - name: Start Llama Stack server in background
+        env:
+          INFERENCE_MODEL: "meta-llama/Llama-3.2-3B-Instruct"
+        run: |
+          source .venv/bin/activate
+          # TODO: use "llama stack run"
+          nohup uv run python -m llama_stack.distribution.server.server --yaml-config ./llama_stack/templates/ollama/run.yaml > server.log 2>&1 &
+
+      - name: Wait for Llama Stack server to be ready
+        run: |
+          echo "Waiting for Llama Stack server..."
+          for i in {1..30}; do
+            if curl -s http://localhost:8321/v1/health | grep -q "OK"; then
+              echo " Llama Stack server is up!"
+              exit 0
+            fi
+            sleep 1
+          done
+          echo " Llama Stack server failed to start"
+          cat server.log
+          exit 1
+
+      - name: Run Inference Integration Tests
+        env:
+          INFERENCE_MODEL: "meta-llama/Llama-3.2-3B-Instruct"
+        run: |
+          uv run pytest -v tests/integration/inference --stack-config=ollama --text-model="meta-llama/Llama-3.2-3B-Instruct" --embedding-model=all-MiniLM-L6-v2

.github/workflows/providers-build.yml

@@ -0,0 +1,76 @@
+name: Test Llama Stack Build
+
+on:
+  push:
+    branches:
+      - main
+    paths:
+      - 'llama_stack/cli/stack/build.py'
+      - 'llama_stack/cli/stack/_build.py'
+      - 'llama_stack/distribution/build.*'
+      - 'llama_stack/distribution/*.sh'
+      - '.github/workflows/providers-build.yml'
+  pull_request:
+    paths:
+      - 'llama_stack/cli/stack/build.py'
+      - 'llama_stack/cli/stack/_build.py'
+      - 'llama_stack/distribution/build.*'
+      - 'llama_stack/distribution/*.sh'
+      - '.github/workflows/providers-build.yml'
+
+jobs:
+  generate-matrix:
+    runs-on: ubuntu-latest
+    outputs:
+      templates: ${{ steps.set-matrix.outputs.templates }}
+    steps:
+      - name: Checkout repository
+        uses: actions/checkout@v4
+
+      - name: Generate Template List
+        id: set-matrix
+        run: |
+          templates=$(ls llama_stack/templates/*/*build.yaml | awk -F'/' '{print $(NF-1)}' | jq -R -s -c 'split("\n")[:-1]')
+          echo "templates=$templates" >> "$GITHUB_OUTPUT"
+
+  build:
+    needs: generate-matrix
+    runs-on: ubuntu-latest
+    strategy:
+      matrix:
+        template: ${{ fromJson(needs.generate-matrix.outputs.templates) }}
+        image-type: [venv, container]
+
+    steps:
+      - name: Checkout repository
+        uses: actions/checkout@v4
+
+      - name: Set up Python
+        uses: actions/setup-python@v5
+        with:
+          python-version: '3.10'
+
+      - name: Install uv
+        uses: astral-sh/setup-uv@v5
+        with:
+          python-version: "3.10"
+
+      - name: Install LlamaStack
+        run: |
+          uv venv
+          source .venv/bin/activate
+          uv pip install -e .
+
+      - name: Print build dependencies
+        run: |
+          uv run llama stack build --template ${{ matrix.template }} --image-type ${{ matrix.image-type }} --image-name test --print-deps-only
+
+      - name: Run Llama Stack Build
+        run: |
+          uv run llama stack build --template ${{ matrix.template }} --image-type ${{ matrix.image-type }} --image-name test
+
+      - name: Print dependencies in the image
+        if: matrix.image-type == 'venv'
+        run: |
+          source test/bin/activate
+          uv pip list

.github/workflows/stale_bot.yml

@@ -0,0 +1,45 @@
+name: Close stale issues and PRs
+
+on:
+  schedule:
+    - cron: '0 0 * * *' # every day at midnight
+
+env:
+  LC_ALL: en_US.UTF-8
+
+defaults:
+  run:
+    shell: bash
+
+permissions:
+  contents: read
+
+jobs:
+  stale:
+    permissions:
+      issues: write
+      pull-requests: write
+    runs-on: ubuntu-latest
+    steps:
+      - name: Stale Action
+        uses: actions/stale@v9
+        with:
+          stale-issue-label: 'stale'
+          stale-issue-message: >
+            This issue has been automatically marked as stale because it has not had activity within 60 days.
+            It will be automatically closed if no further activity occurs within 30 days.
+          close-issue-message: >
+            This issue has been automatically closed due to inactivity.
+            Please feel free to reopen if you feel it is still relevant!
+          days-before-issue-stale: 60
+          days-before-issue-close: 30
+          stale-pr-label: 'stale'
+          stale-pr-message: >
+            This pull request has been automatically marked as stale because it has not had activity within 60 days.
+            It will be automatically closed if no further activity occurs within 30 days.
+          close-pr-message: >
+            This pull request has been automatically closed due to inactivity.
+            Please feel free to reopen if you intend to continue working on it!
+          days-before-pr-stale: 60
+          days-before-pr-close: 30
+          operations-per-run: 300

.github/workflows/unit-tests.yml

@@ -33,7 +33,7 @@ jobs:
 
       - name: Run unit tests
         run: |
-          uv run --python ${{ matrix.python }} --with-editable . --with-editable ".[dev]" --with-editable ".[unit]" pytest --cov=llama_stack -s -v tests/unit/ --junitxml=pytest-report-${{ matrix.python }}.xml --cov-report=html:htmlcov-${{ matrix.python }}
+          PYTHON_VERSION=${{ matrix.python }} ./scripts/unit-tests.sh --cov=llama_stack --junitxml=pytest-report-${{ matrix.python }}.xml --cov-report=html:htmlcov-${{ matrix.python }}
 
       - name: Upload test results
         if: always()

.pre-commit-config.yaml

@@ -8,6 +8,7 @@ repos:
     rev: v5.0.0  # Latest stable version
     hooks:
     -   id: check-merge-conflict
+        args: ['--assume-in-merge']
     -   id: trailing-whitespace
         exclude: '\.py$'  # Exclude Python files as Ruff already handles them
     -   id: check-added-large-files
@@ -82,6 +83,17 @@ repos:
         require_serial: true
         files: ^llama_stack/templates/.*$|^llama_stack/providers/.*/inference/.*/models\.py$
 
+-   repo: local
+    hooks:
+      - id: openapi-codegen
+        name: API Spec Codegen
+        additional_dependencies:
+          - uv==0.6.2
+        entry: sh -c 'uv run --with ".[dev]" ./docs/openapi_generator/run_openapi_generator.sh > /dev/null 2>&1'
+        language: python
+        pass_filenames: false
+        require_serial: true
+
 ci:
     autofix_commit_msg: 🎨 [pre-commit.ci] Auto format from pre-commit.com hooks
     autoupdate_commit_msg: ⬆ [pre-commit.ci] pre-commit autoupdate

CONTRIBUTING.md

@@ -108,6 +108,22 @@ uv run pre-commit run --all-files
 > [!CAUTION]
 > Before pushing your changes, make sure that the pre-commit hooks have passed successfully.
 
+## Running unit tests
+
+You can run the unit tests by running:
+
+```bash
+source .venv/bin/activate
+./scripts/unit-tests.sh
+```
+
+If you'd like to run for a non-default version of Python (currently 3.10), pass `PYTHON_VERSION` variable as follows:
+
+```
+source .venv/bin/activate
+PYTHON_VERSION=3.13 ./scripts/unit-tests.sh
+```
+
 ## Adding a new dependency to the project
 
 To add a new dependency to the project, you can use the `uv` command. For example, to add `foo` to the project, you can run:

README.md

@@ -51,6 +51,10 @@ Here is a list of the various API providers and available distributions that can
 |        PG Vector         |      Single Node       |            |               |     ✅      |            |               |
 |    PyTorch ExecuTorch    |     On-device iOS      |     ✅      |       ✅       |            |            |               |
 |           vLLM           | Hosted and Single Node |            |       ✅       |            |            |               |
+|          OpenAI          |         Hosted         |            |       ✅       |            |            |               |
+|        Anthropic         |         Hosted         |            |       ✅       |            |            |               |
+|          Gemini          |         Hosted         |            |       ✅       |            |            |               |
+
 
 ### Distributions
 

distributions/dependencies.json

@@ -30,6 +30,7 @@
     "sentencepiece",
     "tqdm",
     "transformers",
+    "tree_sitter",
     "uvicorn"
   ],
   "cerebras": [
@@ -62,6 +63,7 @@
     "sentencepiece",
     "tqdm",
     "transformers",
+    "tree_sitter",
     "uvicorn",
     "sentence-transformers --no-deps",
     "torch torchvision --index-url https://download.pytorch.org/whl/cpu"
@@ -97,6 +99,7 @@
     "sqlite-vec",
     "tqdm",
     "transformers",
+    "tree_sitter",
     "uvicorn",
     "sentence-transformers --no-deps",
     "torch torchvision --index-url https://download.pytorch.org/whl/cpu"
@@ -132,6 +135,7 @@
     "sentencepiece",
     "tqdm",
     "transformers",
+    "tree_sitter",
     "uvicorn",
     "sentence-transformers --no-deps",
     "torch torchvision --index-url https://download.pytorch.org/whl/cpu"
@@ -168,6 +172,7 @@
     "sqlite-vec",
     "tqdm",
     "transformers",
+    "tree_sitter",
     "uvicorn",
     "sentence-transformers --no-deps",
     "torch torchvision --index-url https://download.pytorch.org/whl/cpu"
@@ -203,6 +208,7 @@
     "sentencepiece",
     "tqdm",
     "transformers",
+    "tree_sitter",
     "uvicorn",
     "sentence-transformers --no-deps",
     "torch torchvision --index-url https://download.pytorch.org/whl/cpu"
@@ -236,6 +242,7 @@
     "sentencepiece",
     "tqdm",
     "transformers",
+    "tree_sitter",
     "uvicorn"
   ],
   "hf-endpoint": [
@@ -270,6 +277,7 @@
     "sentencepiece",
     "tqdm",
     "transformers",
+    "tree_sitter",
     "uvicorn"
   ],
   "hf-serverless": [
@@ -304,6 +312,7 @@
     "sentencepiece",
     "tqdm",
     "transformers",
+    "tree_sitter",
     "uvicorn",
     "sentence-transformers --no-deps",
     "torch torchvision --index-url https://download.pytorch.org/whl/cpu"
@@ -344,6 +353,7 @@
     "torchvision",
     "tqdm",
     "transformers",
+    "tree_sitter",
     "uvicorn",
     "zmq"
   ],
@@ -385,6 +395,7 @@
     "torchvision",
     "tqdm",
     "transformers",
+    "tree_sitter",
     "uvicorn",
     "zmq"
   ],
@@ -414,6 +425,7 @@
     "sentencepiece",
     "tqdm",
     "transformers",
+    "tree_sitter",
     "uvicorn"
   ],
   "ollama": [
@@ -448,6 +460,7 @@
     "sentencepiece",
     "tqdm",
     "transformers",
+    "tree_sitter",
     "uvicorn"
   ],
   "open-benchmark": [
@@ -482,8 +495,44 @@
     "together",
     "tqdm",
     "transformers",
+    "tree_sitter",
     "uvicorn"
   ],
+  "passthrough": [
+    "aiosqlite",
+    "autoevals",
+    "blobfile",
+    "chardet",
+    "chromadb-client",
+    "datasets",
+    "faiss-cpu",
+    "fastapi",
+    "fire",
+    "httpx",
+    "matplotlib",
+    "mcp",
+    "nltk",
+    "numpy",
+    "openai",
+    "opentelemetry-exporter-otlp-proto-http",
+    "opentelemetry-sdk",
+    "pandas",
+    "pillow",
+    "psycopg2-binary",
+    "pymongo",
+    "pypdf",
+    "redis",
+    "requests",
+    "scikit-learn",
+    "scipy",
+    "sentencepiece",
+    "tqdm",
+    "transformers",
+    "tree_sitter",
+    "uvicorn",
+    "sentence-transformers --no-deps",
+    "torch torchvision --index-url https://download.pytorch.org/whl/cpu"
+  ],
   "remote-vllm": [
     "aiosqlite",
     "autoevals",
@@ -514,6 +563,7 @@
     "sentencepiece",
     "tqdm",
     "transformers",
+    "tree_sitter",
     "uvicorn",
     "sentence-transformers --no-deps",
     "torch torchvision --index-url https://download.pytorch.org/whl/cpu"
@@ -579,6 +629,7 @@
     "sentencepiece",
     "tqdm",
     "transformers",
+    "tree_sitter",
     "uvicorn",
     "sentence-transformers --no-deps",
     "torch torchvision --index-url https://download.pytorch.org/whl/cpu"
@@ -614,6 +665,7 @@
     "together",
     "tqdm",
     "transformers",
+    "tree_sitter",
     "uvicorn",
     "sentence-transformers --no-deps",
     "torch torchvision --index-url https://download.pytorch.org/whl/cpu"
@@ -648,6 +700,7 @@
     "sentencepiece",
     "tqdm",
     "transformers",
+    "tree_sitter",
     "uvicorn",
     "vllm",
     "sentence-transformers --no-deps",

docs/_static/llama-stack-spec.html

@@ -2151,6 +2151,48 @@
                 }
             }
         },
+        "/v1/providers/{provider_id}": {
+            "get": {
+                "responses": {
+                    "200": {
+                        "description": "OK",
+                        "content": {
+                            "application/json": {
+                                "schema": {
+                                    "$ref": "#/components/schemas/ProviderInfo"
+                                }
+                            }
+                        }
+                    },
+                    "400": {
+                        "$ref": "#/components/responses/BadRequest400"
+                    },
+                    "429": {
+                        "$ref": "#/components/responses/TooManyRequests429"
+                    },
+                    "500": {
+                        "$ref": "#/components/responses/InternalServerError500"
+                    },
+                    "default": {
+                        "$ref": "#/components/responses/DefaultError"
+                    }
+                },
+                "tags": [
+                    "Providers"
+                ],
+                "description": "",
+                "parameters": [
+                    {
+                        "name": "provider_id",
+                        "in": "path",
+                        "required": true,
+                        "schema": {
+                            "type": "string"
+                        }
+                    }
+                ]
+            }
+        },
         "/v1/tool-runtime/invoke": {
             "post": {
                 "responses": {
@@ -2642,7 +2684,7 @@
                 }
             }
         },
-        "/v1/inspect/providers": {
+        "/v1/providers": {
             "get": {
                 "responses": {
                     "200": {
@@ -7912,6 +7954,53 @@
                 ],
                 "title": "InsertChunksRequest"
             },
+            "ProviderInfo": {
+                "type": "object",
+                "properties": {
+                    "api": {
+                        "type": "string"
+                    },
+                    "provider_id": {
+                        "type": "string"
+                    },
+                    "provider_type": {
+                        "type": "string"
+                    },
+                    "config": {
+                        "type": "object",
+                        "additionalProperties": {
+                            "oneOf": [
+                                {
+                                    "type": "null"
+                                },
+                                {
+                                    "type": "boolean"
+                                },
+                                {
+                                    "type": "number"
+                                },
+                                {
+                                    "type": "string"
+                                },
+                                {
+                                    "type": "array"
+                                },
+                                {
+                                    "type": "object"
+                                }
+                            ]
+                        }
+                    }
+                },
+                "additionalProperties": false,
+                "required": [
+                    "api",
+                    "provider_id",
+                    "provider_type",
+                    "config"
+                ],
+                "title": "ProviderInfo"
+            },
             "InvokeToolRequest": {
                 "type": "object",
                 "properties": {
@@ -8124,27 +8213,6 @@
                 ],
                 "title": "ListModelsResponse"
             },
-            "ProviderInfo": {
-                "type": "object",
-                "properties": {
-                    "api": {
-                        "type": "string"
-                    },
-                    "provider_id": {
-                        "type": "string"
-                    },
-                    "provider_type": {
-                        "type": "string"
-                    }
-                },
-                "additionalProperties": false,
-                "required": [
-                    "api",
-                    "provider_id",
-                    "provider_type"
-                ],
-                "title": "ProviderInfo"
-            },
             "ListProvidersResponse": {
                 "type": "object",
                 "properties": {
@@ -10145,6 +10213,10 @@
         {
             "name": "PostTraining (Coming Soon)"
         },
+        {
+            "name": "Providers",
+            "x-displayName": "Providers API for inspecting, listing, and modifying providers and their configurations."
+        },
         {
             "name": "Safety"
         },
@@ -10191,6 +10263,7 @@
                 "Inspect",
                 "Models",
                 "PostTraining (Coming Soon)",
+                "Providers",
                 "Safety",
                 "Scoring",
                 "ScoringFunctions",

docs/_static/llama-stack-spec.yaml

@@ -1444,6 +1444,34 @@ paths:
             schema:
               $ref: '#/components/schemas/InsertChunksRequest'
         required: true
+  /v1/providers/{provider_id}:
+    get:
+      responses:
+        '200':
+          description: OK
+          content:
+            application/json:
+              schema:
+                $ref: '#/components/schemas/ProviderInfo'
+        '400':
+          $ref: '#/components/responses/BadRequest400'
+        '429':
+          $ref: >-
+            #/components/responses/TooManyRequests429
+        '500':
+          $ref: >-
+            #/components/responses/InternalServerError500
+        default:
+          $ref: '#/components/responses/DefaultError'
+      tags:
+        - Providers
+      description: ''
+      parameters:
+        - name: provider_id
+          in: path
+          required: true
+          schema:
+            type: string
   /v1/tool-runtime/invoke:
     post:
       responses:
@@ -1782,7 +1810,7 @@ paths:
             schema:
               $ref: '#/components/schemas/RegisterModelRequest'
         required: true
-  /v1/inspect/providers:
+  /v1/providers:
     get:
       responses:
         '200':
@@ -5409,6 +5437,32 @@ components:
         - vector_db_id
         - chunks
       title: InsertChunksRequest
+    ProviderInfo:
+      type: object
+      properties:
+        api:
+          type: string
+        provider_id:
+          type: string
+        provider_type:
+          type: string
+        config:
+          type: object
+          additionalProperties:
+            oneOf:
+              - type: 'null'
+              - type: boolean
+              - type: number
+              - type: string
+              - type: array
+              - type: object
+      additionalProperties: false
+      required:
+        - api
+        - provider_id
+        - provider_type
+        - config
+      title: ProviderInfo
     InvokeToolRequest:
       type: object
       properties:
@@ -5544,21 +5598,6 @@ components:
       required:
         - data
       title: ListModelsResponse
-    ProviderInfo:
-      type: object
-      properties:
-        api:
-          type: string
-        provider_id:
-          type: string
-        provider_type:
-          type: string
-      additionalProperties: false
-      required:
-        - api
-        - provider_id
-        - provider_type
-      title: ProviderInfo
     ListProvidersResponse:
       type: object
       properties:
@@ -6832,6 +6871,9 @@ tags:
   - name: Inspect
   - name: Models
   - name: PostTraining (Coming Soon)
+  - name: Providers
+    x-displayName: >-
+      Providers API for inspecting, listing, and modifying providers and their configurations.
   - name: Safety
   - name: Scoring
   - name: ScoringFunctions
@@ -6856,6 +6898,7 @@ x-tagGroups:
       - Inspect
       - Models
       - PostTraining (Coming Soon)
+      - Providers
       - Safety
       - Scoring
       - ScoringFunctions

docs/source/distributions/self_hosted_distro/passthrough.md

@@ -0,0 +1,42 @@
+---
+orphan: true
+---
+<!-- This file was auto-generated by distro_codegen.py, please edit source -->
+# Passthrough Distribution
+
+```{toctree}
+:maxdepth: 2
+:hidden:
+
+self
+```
+
+The `llamastack/distribution-passthrough` distribution consists of the following provider configurations.
+
+| API | Provider(s) |
+|-----|-------------|
+| agents | `inline::meta-reference` |
+| datasetio | `remote::huggingface`, `inline::localfs` |
+| eval | `inline::meta-reference` |
+| inference | `remote::passthrough`, `inline::sentence-transformers` |
+| safety | `inline::llama-guard` |
+| scoring | `inline::basic`, `inline::llm-as-judge`, `inline::braintrust` |
+| telemetry | `inline::meta-reference` |
+| tool_runtime | `remote::brave-search`, `remote::tavily-search`, `remote::wolfram-alpha`, `inline::code-interpreter`, `inline::rag-runtime`, `remote::model-context-protocol` |
+| vector_io | `inline::faiss`, `remote::chromadb`, `remote::pgvector` |
+
+
+### Environment Variables
+
+The following environment variables can be configured:
+
+- `LLAMA_STACK_PORT`: Port for the Llama Stack distribution server (default: `5001`)
+- `PASSTHROUGH_API_KEY`: Passthrough API Key (default: ``)
+- `PASSTHROUGH_URL`: Passthrough URL (default: ``)
+
+### Models
+
+The following models are available by default:
+
+- `llama3.1-8b-instruct `
+- `llama3.2-11b-vision-instruct `

docs/source/index.md

@@ -61,6 +61,10 @@ A number of "adapters" are available for some popular Inference and Vector Store
 |  Groq  |  Hosted  |
 |  SambaNova  |  Hosted  |
 | PyTorch ExecuTorch | On-device iOS, Android |
+|  OpenAI  |  Hosted  |
+|  Anthropic  |  Hosted  |
+|  Gemini  |  Hosted  |
+
 
 **Vector IO API**
 |  **Provider** |  **Environments** |

llama_stack/apis/datatypes.py

@@ -14,6 +14,7 @@ from llama_stack.schema_utils import json_schema_type
 
 @json_schema_type
 class Api(Enum):
+    providers = "providers"
     inference = "inference"
     safety = "safety"
     agents = "agents"

llama_stack/apis/inspect/inspect.py

@@ -11,13 +11,6 @@ from pydantic import BaseModel
 from llama_stack.schema_utils import json_schema_type, webmethod
 
 
-@json_schema_type
-class ProviderInfo(BaseModel):
-    api: str
-    provider_id: str
-    provider_type: str
-
-
 @json_schema_type
 class RouteInfo(BaseModel):
     route: str
@@ -32,14 +25,21 @@ class HealthInfo(BaseModel):
 
 
 @json_schema_type
-class VersionInfo(BaseModel):
+class ProviderInfo(BaseModel):
-    version: str
+    api: str
+    provider_id: str
+    provider_type: str
 
 
 class ListProvidersResponse(BaseModel):
     data: List[ProviderInfo]
 
 
+@json_schema_type
+class VersionInfo(BaseModel):
+    version: str
+
+
 class ListRoutesResponse(BaseModel):
     data: List[RouteInfo]
 

llama_stack/apis/providers/__init__.py

@@ -0,0 +1,7 @@
+# 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 .providers import *  # noqa: F401 F403

llama_stack/apis/providers/providers.py

@@ -0,0 +1,36 @@
+# 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 Any, Dict, List, Protocol, runtime_checkable
+
+from pydantic import BaseModel
+
+from llama_stack.schema_utils import json_schema_type, webmethod
+
+
+@json_schema_type
+class ProviderInfo(BaseModel):
+    api: str
+    provider_id: str
+    provider_type: str
+    config: Dict[str, Any]
+
+
+class ListProvidersResponse(BaseModel):
+    data: List[ProviderInfo]
+
+
+@runtime_checkable
+class Providers(Protocol):
+    """
+    Providers API for inspecting, listing, and modifying providers and their configurations.
+    """
+
+    @webmethod(route="/providers", method="GET")
+    async def list_providers(self) -> ListProvidersResponse: ...
+
+    @webmethod(route="/providers/{provider_id}", method="GET")
+    async def inspect_provider(self, provider_id: str) -> ProviderInfo: ...

llama_stack/cli/download.py

@@ -10,7 +10,7 @@ import json
 import os
 import shutil
 from dataclasses import dataclass
-from datetime import datetime
+from datetime import datetime, timezone
 from functools import partial
 from pathlib import Path
 from typing import Dict, List, Optional
@@ -404,7 +404,7 @@ def _download_from_manifest(manifest_file: str, max_concurrent_downloads: int):
         d = json.load(f)
         manifest = Manifest(**d)
 
-    if datetime.now() > manifest.expires_on:
+    if datetime.now(timezone.utc) > manifest.expires_on:
         raise ValueError(f"Manifest URLs have expired on {manifest.expires_on}")
 
     console = Console()

llama_stack/cli/model/prompt_format.py

@@ -41,8 +41,14 @@ class ModelPromptFormat(Subcommand):
             "-m",
             "--model-name",
             type=str,
-            default="llama3_1",
+            help="Example: Llama3.1-8B or Llama3.2-11B-Vision, etc\n"
-            help="Model Family (llama3_1, llama3_X, etc.)",
+            "(Run `llama model list` to see a list of valid model names)",
+        )
+        self.parser.add_argument(
+            "-l",
+            "--list",
+            action="store_true",
+            help="List all available models",
         )
         self.parser.add_argument(
             "-l",
@@ -60,7 +66,6 @@ class ModelPromptFormat(Subcommand):
         ]
 
         model_list = [m.value for m in supported_model_ids]
-        model_str = "\n".join(model_list)
 
         if args.list:
             headers = ["Model(s)"]
@@ -81,10 +86,16 @@ class ModelPromptFormat(Subcommand):
         try:
             model_id = CoreModelId(args.model_name)
         except ValueError:
-            self.parser.error(f"{args.model_name} is not a valid Model. Choose one from --\n{model_str}")
+            self.parser.error(
+                f"{args.model_name} is not a valid Model. Choose one from the list of valid models. "
+                f"Run `llama model list` to see the valid model names."
+            )
 
         if model_id not in supported_model_ids:
-            self.parser.error(f"{model_id} is not a valid Model. Choose one from --\n {model_str}")
+            self.parser.error(
+                f"{model_id} is not a valid Model. Choose one from the list of valid models. "
+                f"Run `llama model list` to see the valid model names."
+            )
 
         llama_3_1_file = ROOT_DIR / "models" / "llama" / "llama3_1" / "prompt_format.md"
         llama_3_2_text_file = ROOT_DIR / "models" / "llama" / "llama3_2" / "text_prompt_format.md"

llama_stack/distribution/configure.py

@@ -62,7 +62,7 @@ def configure_api_providers(config: StackRunConfig, build_spec: DistributionSpec
     if config.apis:
         apis_to_serve = config.apis
     else:
-        apis_to_serve = [a.value for a in Api if a not in (Api.telemetry, Api.inspect)]
+        apis_to_serve = [a.value for a in Api if a not in (Api.telemetry, Api.inspect, Api.providers)]
 
     for api_str in apis_to_serve:
         api = Api(api_str)

llama_stack/distribution/datatypes.py

@@ -117,6 +117,14 @@ class Provider(BaseModel):
     config: Dict[str, Any]
 
 
+class LoggingConfig(BaseModel):
+    category_levels: Dict[str, str] = Field(
+        default_factory=Dict,
+        description="""
+ Dictionary of different logging configurations for different portions (ex: core, server) of llama stack""",
+    )
+
+
 class ServerConfig(BaseModel):
     port: int = Field(
         default=8321,
@@ -176,6 +184,8 @@ a default SQLite store will be used.""",
     benchmarks: List[BenchmarkInput] = Field(default_factory=list)
     tool_groups: List[ToolGroupInput] = Field(default_factory=list)
 
+    logging: Optional[LoggingConfig] = Field(default=None, description="Configuration for Llama Stack Logging")
+
     server: ServerConfig = Field(
         default_factory=ServerConfig,
         description="Configuration for the HTTP(S) server",

llama_stack/distribution/distribution.py

@@ -56,7 +56,7 @@ def builtin_automatically_routed_apis() -> List[AutoRoutedApiInfo]:
 
 def providable_apis() -> List[Api]:
     routing_table_apis = {x.routing_table_api for x in builtin_automatically_routed_apis()}
-    return [api for api in Api if api not in routing_table_apis and api != Api.inspect]
+    return [api for api in Api if api not in routing_table_apis and api != Api.inspect and api != Api.providers]
 
 
 def get_provider_registry() -> Dict[Api, Dict[str, ProviderSpec]]:

llama_stack/distribution/providers.py

@@ -0,0 +1,59 @@
+# 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 pydantic import BaseModel
+
+from llama_stack.apis.providers import ListProvidersResponse, ProviderInfo, Providers
+
+from .datatypes import StackRunConfig
+from .stack import redact_sensitive_fields
+
+
+class ProviderImplConfig(BaseModel):
+    run_config: StackRunConfig
+
+
+async def get_provider_impl(config, deps):
+    impl = ProviderImpl(config, deps)
+    await impl.initialize()
+    return impl
+
+
+class ProviderImpl(Providers):
+    def __init__(self, config, deps):
+        self.config = config
+        self.deps = deps
+
+    async def initialize(self) -> None:
+        pass
+
+    async def list_providers(self) -> ListProvidersResponse:
+        run_config = self.config.run_config
+        safe_config = StackRunConfig(**redact_sensitive_fields(run_config.model_dump()))
+        ret = []
+        for api, providers in safe_config.providers.items():
+            ret.extend(
+                [
+                    ProviderInfo(
+                        api=api,
+                        provider_id=p.provider_id,
+                        provider_type=p.provider_type,
+                        config=p.config,
+                    )
+                    for p in providers
+                ]
+            )
+
+        return ListProvidersResponse(data=ret)
+
+    async def inspect_provider(self, provider_id: str) -> ProviderInfo:
+        all_providers = await self.list_providers()
+        for p in all_providers.data:
+            if p.provider_id == provider_id:
+                return p
+
+        raise ValueError(f"Provider {provider_id} not found")

llama_stack/distribution/resolver.py

@@ -16,6 +16,7 @@ from llama_stack.apis.inference import Inference
 from llama_stack.apis.inspect import Inspect
 from llama_stack.apis.models import Models
 from llama_stack.apis.post_training import PostTraining
+from llama_stack.apis.providers import Providers as ProvidersAPI
 from llama_stack.apis.safety import Safety
 from llama_stack.apis.scoring import Scoring
 from llama_stack.apis.scoring_functions import ScoringFunctions
@@ -59,6 +60,7 @@ class InvalidProviderError(Exception):
 
 def api_protocol_map() -> Dict[Api, Any]:
     return {
+        Api.providers: ProvidersAPI,
         Api.agents: Agents,
         Api.inference: Inference,
         Api.inspect: Inspect,
@@ -247,6 +249,25 @@ def sort_providers_by_deps(
         )
     )
 
+    sorted_providers.append(
+        (
+            "providers",
+            ProviderWithSpec(
+                provider_id="__builtin__",
+                provider_type="__builtin__",
+                config={"run_config": run_config.model_dump()},
+                spec=InlineProviderSpec(
+                    api=Api.providers,
+                    provider_type="__builtin__",
+                    config_class="llama_stack.distribution.providers.ProviderImplConfig",
+                    module="llama_stack.distribution.providers",
+                    api_dependencies=apis,
+                    deps__=[x.value for x in apis],
+                ),
+            ),
+        )
+    )
+
     logger.debug(f"Resolved {len(sorted_providers)} providers")
     for api_str, provider in sorted_providers:
         logger.debug(f" {api_str} => {provider.provider_id}")

llama_stack/distribution/server/server.py

@@ -25,7 +25,7 @@ from fastapi.responses import JSONResponse, StreamingResponse
 from pydantic import BaseModel, ValidationError
 from typing_extensions import Annotated
 
-from llama_stack.distribution.datatypes import StackRunConfig
+from llama_stack.distribution.datatypes import LoggingConfig, StackRunConfig
 from llama_stack.distribution.distribution import builtin_automatically_routed_apis
 from llama_stack.distribution.request_headers import (
     PROVIDER_DATA_VAR,
@@ -306,34 +306,42 @@ def main():
 
     args = parser.parse_args()
 
-    if args.env:
+    log_line = ""
-        for env_pair in args.env:
-            try:
-                key, value = validate_env_pair(env_pair)
-                logger.info(f"Setting CLI environment variable {key} => {value}")
-                os.environ[key] = value
-            except ValueError as e:
-                logger.error(f"Error: {str(e)}")
-                sys.exit(1)
-
     if args.yaml_config:
         # if the user provided a config file, use it, even if template was specified
         config_file = Path(args.yaml_config)
         if not config_file.exists():
             raise ValueError(f"Config file {config_file} does not exist")
-        logger.info(f"Using config file: {config_file}")
+        log_line = f"Using config file: {config_file}"
     elif args.template:
         config_file = Path(REPO_ROOT) / "llama_stack" / "templates" / args.template / "run.yaml"
         if not config_file.exists():
             raise ValueError(f"Template {args.template} does not exist")
-        logger.info(f"Using template {args.template} config file: {config_file}")
+        log_line = f"Using template {args.template} config file: {config_file}"
     else:
         raise ValueError("Either --yaml-config or --template must be provided")
 
+    logger_config = None
     with open(config_file, "r") as fp:
-        config = replace_env_vars(yaml.safe_load(fp))
+        config_contents = yaml.safe_load(fp)
+        if isinstance(config_contents, dict) and (cfg := config_contents.get("logging_config")):
+            logger_config = LoggingConfig(**cfg)
+        logger = get_logger(name=__name__, category="server", config=logger_config)
+        if args.env:
+            for env_pair in args.env:
+                try:
+                    key, value = validate_env_pair(env_pair)
+                    logger.info(f"Setting CLI environment variable {key} => {value}")
+                    os.environ[key] = value
+                except ValueError as e:
+                    logger.error(f"Error: {str(e)}")
+                    sys.exit(1)
+        config = replace_env_vars(config_contents)
         config = StackRunConfig(**config)
 
+    # now that the logger is initialized, print the line about which type of config we are using.
+    logger.info(log_line)
+
     logger.info("Run configuration:")
     safe_config = redact_sensitive_fields(config.model_dump())
     logger.info(yaml.dump(safe_config, indent=2))
@@ -368,6 +376,7 @@ def main():
         apis_to_serve.add(inf.routing_table_api.value)
 
     apis_to_serve.add("inspect")
+    apis_to_serve.add("providers")
     for api_str in apis_to_serve:
         api = Api(api_str)
 

llama_stack/distribution/stack.py

@@ -23,6 +23,7 @@ from llama_stack.apis.inference import Inference
 from llama_stack.apis.inspect import Inspect
 from llama_stack.apis.models import Models
 from llama_stack.apis.post_training import PostTraining
+from llama_stack.apis.providers import Providers
 from llama_stack.apis.safety import Safety
 from llama_stack.apis.scoring import Scoring
 from llama_stack.apis.scoring_functions import ScoringFunctions
@@ -44,6 +45,7 @@ logger = get_logger(name=__name__, category="core")
 
 
 class LlamaStack(
+    Providers,
     VectorDBs,
     Inference,
     BatchInference,

llama_stack/log.py

@@ -7,13 +7,15 @@
 import logging
 import os
 from logging.config import dictConfig
-from typing import Dict
+from typing import Dict, Optional
 
 from rich.console import Console
 from rich.errors import MarkupError
 from rich.logging import RichHandler
 from termcolor import cprint
 
+from .distribution.datatypes import LoggingConfig
+
 # Default log level
 DEFAULT_LOG_LEVEL = logging.INFO
 
@@ -34,6 +36,56 @@ CATEGORIES = [
 _category_levels: Dict[str, int] = {category: DEFAULT_LOG_LEVEL for category in CATEGORIES}
 
 
+def config_to_category_levels(category: str, level: str):
+    """
+    Helper function to be called either by environment parsing or yaml parsing to go from a list of categories and levels to a dictionary ready to be
+    used by the logger dictConfig.
+
+    Parameters:
+        category (str): logging category to apply the level to
+        level (str): logging level to be used in the category
+
+    Returns:
+        Dict[str, int]: A dictionary mapping categories to their log levels.
+    """
+
+    category_levels: Dict[str, int] = {}
+    level_value = logging._nameToLevel.get(str(level).upper())
+    if level_value is None:
+        logging.warning(f"Unknown log level '{level}' for category '{category}'. Falling back to default 'INFO'.")
+        return category_levels
+
+    if category == "all":
+        # Apply the log level to all categories and the root logger
+        for cat in CATEGORIES:
+            category_levels[cat] = level_value
+        # Set the root logger's level to the specified level
+        category_levels["root"] = level_value
+    elif category in CATEGORIES:
+        category_levels[category] = level_value
+        logging.info(f"Setting '{category}' category to level '{level}'.")
+    else:
+        logging.warning(f"Unknown logging category: {category}. No changes made.")
+    return category_levels
+
+
+def parse_yaml_config(yaml_config: LoggingConfig) -> Dict[str, int]:
+    """
+    Helper function to parse a yaml logging configuration found in the run.yaml
+
+    Parameters:
+        yaml_config (Logging): the logger config object found in the run.yaml
+
+    Returns:
+        Dict[str, int]: A dictionary mapping categories to their log levels.
+    """
+    category_levels = {}
+    for category, level in yaml_config.category_levels.items():
+        category_levels.update(config_to_category_levels(category=category, level=level))
+
+    return category_levels
+
+
 def parse_environment_config(env_config: str) -> Dict[str, int]:
     """
     Parse the LLAMA_STACK_LOGGING environment variable and return a dictionary of category log levels.
@@ -53,25 +105,7 @@ def parse_environment_config(env_config: str) -> Dict[str, int]:
             category, level = pair.split("=", 1)
             category = category.strip().lower()
             level = level.strip().upper()  # Convert to uppercase for logging._nameToLevel
-
+            category_levels.update(config_to_category_levels(category=category, level=level))
-            level_value = logging._nameToLevel.get(level)
-            if level_value is None:
-                logging.warning(
-                    f"Unknown log level '{level}' for category '{category}'. Falling back to default 'INFO'."
-                )
-                continue
-
-            if category == "all":
-                # Apply the log level to all categories and the root logger
-                for cat in CATEGORIES:
-                    category_levels[cat] = level_value
-                # Set the root logger's level to the specified level
-                category_levels["root"] = level_value
-            elif category in CATEGORIES:
-                category_levels[category] = level_value
-                logging.info(f"Setting '{category}' category to level '{level}'.")
-            else:
-                logging.warning(f"Unknown logging category: {category}. No changes made.")
 
         except ValueError:
             logging.warning(f"Invalid logging configuration: '{pair}'. Expected format: 'category=level'.")
@@ -176,7 +210,9 @@ def setup_logging(category_levels: Dict[str, int], log_file: str | None) -> None
             logger.setLevel(root_level)
 
 
-def get_logger(name: str, category: str = "uncategorized") -> logging.LoggerAdapter:
+def get_logger(
+    name: str, category: str = "uncategorized", config: Optional[LoggingConfig] | None = None
+) -> logging.LoggerAdapter:
     """
     Returns a logger with the specified name and category.
     If no category is provided, defaults to 'uncategorized'.
@@ -184,10 +220,14 @@ def get_logger(name: str, category: str = "uncategorized") -> logging.LoggerAdap
     Parameters:
         name (str): The name of the logger (e.g., module or filename).
         category (str): The category of the logger (default 'uncategorized').
+        config (Logging): optional yaml config to override the existing logger configuration
 
     Returns:
         logging.LoggerAdapter: Configured logger with category support.
     """
+    if config:
+        _category_levels.update(parse_yaml_config(config))
+
     logger = logging.getLogger(name)
     logger.setLevel(_category_levels.get(category, DEFAULT_LOG_LEVEL))
     return logging.LoggerAdapter(logger, {"category": category})

llama_stack/models/llama/llama3/prompt_templates/system_prompts.py

@@ -34,7 +34,9 @@ class SystemDefaultGenerator(PromptTemplateGeneratorBase):
         )
         return PromptTemplate(
             template_str.lstrip("\n"),
-            {"today": datetime.now().strftime("%d %B %Y")},
+            {
+                "today": datetime.now().strftime("%d %B %Y")  # noqa: DTZ005 - we don't care about timezones here since we are displaying the date
+            },
         )
 
     def data_examples(self) -> List[Any]:

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

@@ -11,7 +11,7 @@ import re
 import secrets
 import string
 import uuid
-from datetime import datetime
+from datetime import datetime, timezone
 from typing import AsyncGenerator, List, Optional, Union
 from urllib.parse import urlparse
 
@@ -239,7 +239,7 @@ class ChatAgent(ShieldRunnerMixin):
             in_progress_tool_call_step = await self.storage.get_in_progress_tool_call_step(
                 request.session_id, request.turn_id
             )
-            now = datetime.now().astimezone().isoformat()
+            now = datetime.now(timezone.utc).isoformat()
             tool_execution_step = ToolExecutionStep(
                 step_id=(in_progress_tool_call_step.step_id if in_progress_tool_call_step else str(uuid.uuid4())),
                 turn_id=request.turn_id,
@@ -264,7 +264,7 @@ class ChatAgent(ShieldRunnerMixin):
             start_time = last_turn.started_at
         else:
             messages.extend(request.messages)
-            start_time = datetime.now().astimezone().isoformat()
+            start_time = datetime.now(timezone.utc).isoformat()
             input_messages = request.messages
 
         output_message = None
@@ -295,7 +295,7 @@ class ChatAgent(ShieldRunnerMixin):
             input_messages=input_messages,
             output_message=output_message,
             started_at=start_time,
-            completed_at=datetime.now().astimezone().isoformat(),
+            completed_at=datetime.now(timezone.utc).isoformat(),
             steps=steps,
         )
         await self.storage.add_turn_to_session(request.session_id, turn)
@@ -386,7 +386,7 @@ class ChatAgent(ShieldRunnerMixin):
                 return
 
             step_id = str(uuid.uuid4())
-            shield_call_start_time = datetime.now().astimezone().isoformat()
+            shield_call_start_time = datetime.now(timezone.utc).isoformat()
             try:
                 yield AgentTurnResponseStreamChunk(
                     event=AgentTurnResponseEvent(
@@ -410,7 +410,7 @@ class ChatAgent(ShieldRunnerMixin):
                                 turn_id=turn_id,
                                 violation=e.violation,
                                 started_at=shield_call_start_time,
-                                completed_at=datetime.now().astimezone().isoformat(),
+                                completed_at=datetime.now(timezone.utc).isoformat(),
                             ),
                         )
                     )
@@ -433,7 +433,7 @@ class ChatAgent(ShieldRunnerMixin):
                             turn_id=turn_id,
                             violation=None,
                             started_at=shield_call_start_time,
-                            completed_at=datetime.now().astimezone().isoformat(),
+                            completed_at=datetime.now(timezone.utc).isoformat(),
                         ),
                     )
                 )
@@ -472,7 +472,7 @@ class ChatAgent(ShieldRunnerMixin):
             client_tools[tool.name] = tool
         while True:
             step_id = str(uuid.uuid4())
-            inference_start_time = datetime.now().astimezone().isoformat()
+            inference_start_time = datetime.now(timezone.utc).isoformat()
             yield AgentTurnResponseStreamChunk(
                 event=AgentTurnResponseEvent(
                     payload=AgentTurnResponseStepStartPayload(
@@ -582,7 +582,7 @@ class ChatAgent(ShieldRunnerMixin):
                             turn_id=turn_id,
                             model_response=copy.deepcopy(message),
                             started_at=inference_start_time,
-                            completed_at=datetime.now().astimezone().isoformat(),
+                            completed_at=datetime.now(timezone.utc).isoformat(),
                         ),
                     )
                 )
@@ -653,7 +653,7 @@ class ChatAgent(ShieldRunnerMixin):
                             turn_id=turn_id,
                             tool_calls=[tool_call],
                             tool_responses=[],
-                            started_at=datetime.now().astimezone().isoformat(),
+                            started_at=datetime.now(timezone.utc).isoformat(),
                         ),
                     )
                     yield message
@@ -670,7 +670,7 @@ class ChatAgent(ShieldRunnerMixin):
                         "input": message.model_dump_json(),
                     },
                 ) as span:
-                    tool_execution_start_time = datetime.now().astimezone().isoformat()
+                    tool_execution_start_time = datetime.now(timezone.utc).isoformat()
                     tool_call = message.tool_calls[0]
                     tool_result = await self.execute_tool_call_maybe(
                         session_id,
@@ -708,7 +708,7 @@ class ChatAgent(ShieldRunnerMixin):
                                     )
                                 ],
                                 started_at=tool_execution_start_time,
-                                completed_at=datetime.now().astimezone().isoformat(),
+                                completed_at=datetime.now(timezone.utc).isoformat(),
                             ),
                         )
                     )

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

@@ -7,7 +7,7 @@
 import json
 import logging
 import uuid
-from datetime import datetime
+from datetime import datetime, timezone
 from typing import List, Optional
 
 from pydantic import BaseModel
@@ -36,7 +36,7 @@ class AgentPersistence:
         session_info = AgentSessionInfo(
             session_id=session_id,
             session_name=name,
-            started_at=datetime.now(),
+            started_at=datetime.now(timezone.utc),
         )
         await self.kvstore.set(
             key=f"session:{self.agent_id}:{session_id}",

llama_stack/providers/inline/eval/meta_reference/eval.py

@@ -12,7 +12,7 @@ from llama_stack.apis.agents import Agents, StepType
 from llama_stack.apis.benchmarks import Benchmark
 from llama_stack.apis.datasetio import DatasetIO
 from llama_stack.apis.datasets import Datasets
-from llama_stack.apis.inference import Inference, UserMessage
+from llama_stack.apis.inference import Inference, SystemMessage, UserMessage
 from llama_stack.apis.scoring import Scoring
 from llama_stack.distribution.datatypes import Api
 from llama_stack.providers.datatypes import BenchmarksProtocolPrivate
@@ -118,7 +118,7 @@ class MetaReferenceEvalImpl(
         for i, x in tqdm(enumerate(input_rows)):
             assert ColumnName.chat_completion_input.value in x, "Invalid input row"
             input_messages = json.loads(x[ColumnName.chat_completion_input.value])
-            input_messages = [UserMessage(**x) for x in input_messages]
+            input_messages = [UserMessage(**x) for x in input_messages if x["role"] == "user"]
 
             # NOTE: only single-turn agent generation is supported. Create a new session for each input row
             session_create_response = await self.agents_api.create_agent_session(agent_id, f"session-{i}")
@@ -168,10 +168,11 @@ class MetaReferenceEvalImpl(
                 generations.append({ColumnName.generated_answer.value: response.completion_message.content})
             elif ColumnName.chat_completion_input.value in x:
                 chat_completion_input_json = json.loads(x[ColumnName.chat_completion_input.value])
-                input_messages = [UserMessage(**x) for x in chat_completion_input_json]
+                input_messages = [UserMessage(**x) for x in chat_completion_input_json if x["role"] == "user"]
                 messages = []
                 if candidate.system_message:
                     messages.append(candidate.system_message)
+                messages += [SystemMessage(**x) for x in chat_completion_input_json if x["role"] == "system"]
                 messages += input_messages
                 response = await self.inference_api.chat_completion(
                     model_id=candidate.model,

llama_stack/providers/inline/post_training/torchtune/post_training.py

@@ -3,7 +3,7 @@
 #
 # This source code is licensed under the terms described in the LICENSE file in
 # the root directory of this source tree.
-from datetime import datetime
+from datetime import datetime, timezone
 from typing import Any, Dict, Optional
 
 from llama_stack.apis.datasetio import DatasetIO
@@ -64,7 +64,7 @@ class TorchtunePostTrainingImpl:
         job_status_response = PostTrainingJobStatusResponse(
             job_uuid=job_uuid,
             status=JobStatus.scheduled,
-            scheduled_at=datetime.now(),
+            scheduled_at=datetime.now(timezone.utc),
         )
         self.jobs[job_uuid] = job_status_response
 
@@ -84,7 +84,7 @@ class TorchtunePostTrainingImpl:
                 )
 
                 job_status_response.status = JobStatus.in_progress
-                job_status_response.started_at = datetime.now()
+                job_status_response.started_at = datetime.now(timezone.utc)
 
                 await recipe.setup()
                 resources_allocated, checkpoints = await recipe.train()
@@ -93,7 +93,7 @@ class TorchtunePostTrainingImpl:
                 job_status_response.resources_allocated = resources_allocated
                 job_status_response.checkpoints = checkpoints
                 job_status_response.status = JobStatus.completed
-                job_status_response.completed_at = datetime.now()
+                job_status_response.completed_at = datetime.now(timezone.utc)
 
             except Exception:
                 job_status_response.status = JobStatus.failed

llama_stack/providers/inline/post_training/torchtune/recipes/lora_finetuning_single_device.py

@@ -8,7 +8,7 @@ import gc
 import logging
 import os
 import time
-from datetime import datetime
+from datetime import datetime, timezone
 from functools import partial
 from pathlib import Path
 from typing import Any, Dict, List, Optional, Tuple
@@ -532,7 +532,7 @@ class LoraFinetuningSingleDevice:
             checkpoint_path = await self.save_checkpoint(epoch=curr_epoch)
             checkpoint = Checkpoint(
                 identifier=f"{self.model_id}-sft-{curr_epoch}",
-                created_at=datetime.now(),
+                created_at=datetime.now(timezone.utc),
                 epoch=curr_epoch,
                 post_training_job_id=self.job_uuid,
                 path=checkpoint_path,

llama_stack/providers/inline/scoring/basic/scoring.py

@@ -22,12 +22,19 @@ from llama_stack.providers.utils.common.data_schema_validator import (
 )
 
 from .config import BasicScoringConfig
+from .scoring_fn.bfcl_scoring_fn import BFCLScoringFn
 from .scoring_fn.equality_scoring_fn import EqualityScoringFn
 from .scoring_fn.regex_parser_math_response_scoring_fn import RegexParserMathResponseScoringFn
 from .scoring_fn.regex_parser_scoring_fn import RegexParserScoringFn
 from .scoring_fn.subset_of_scoring_fn import SubsetOfScoringFn
 
-FIXED_FNS = [EqualityScoringFn, SubsetOfScoringFn, RegexParserScoringFn, RegexParserMathResponseScoringFn]
+FIXED_FNS = [
+    EqualityScoringFn,
+    SubsetOfScoringFn,
+    RegexParserScoringFn,
+    RegexParserMathResponseScoringFn,
+    BFCLScoringFn,
+]
 
 
 class BasicScoringImpl(

llama_stack/providers/inline/scoring/basic/scoring_fn/bfcl_scoring_fn.py

@@ -0,0 +1,93 @@
+# 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 json
+import re
+from typing import Any, Dict, Optional
+
+from llama_stack.apis.scoring import ScoringResultRow
+from llama_stack.apis.scoring_functions import ScoringFnParams
+from llama_stack.providers.utils.scoring.base_scoring_fn import RegisteredBaseScoringFn
+
+from ..utils.bfcl.ast_parser import decode_ast
+from ..utils.bfcl.checker import ast_checker, is_empty_output
+from .fn_defs.bfcl import bfcl
+
+
+def postprocess(x: Dict[str, Any], test_category: str) -> Dict[str, Any]:
+    contain_func_call = False
+    error = None
+    error_type = None
+    checker_result = {}
+    try:
+        prediction = decode_ast(x["generated_answer"], x["language"]) or ""
+        contain_func_call = True
+        # if not is_function_calling_format_output(prediction):
+        if is_empty_output(prediction):
+            contain_func_call = False
+            error = "Did not output in the specified format. Note: the model_result is wrapped in a string to ensure json serializability."
+            error_type = "ast_decoder:decoder_wrong_output_format"
+        else:
+            checker_result = ast_checker(
+                json.loads(x["function"]),
+                prediction,
+                json.loads(x["ground_truth"]),
+                x["language"],
+                test_category=test_category,
+                model_name="",
+            )
+    except Exception as e:
+        prediction = ""
+        error = f"Invalid syntax. Failed to decode AST. {str(e)}"
+        error_type = "ast_decoder:decoder_failed"
+    return {
+        "prediction": prediction,
+        "contain_func_call": contain_func_call,
+        "valid": checker_result.get("valid", False),
+        "error": error or checker_result.get("error", ""),
+        "error_type": error_type or checker_result.get("error_type", ""),
+    }
+
+
+def gen_valid(x: Dict[str, Any]) -> Dict[str, float]:
+    return {"valid": x["valid"]}
+
+
+def gen_relevance_acc(x: Dict[str, Any]) -> Dict[str, float]:
+    # This function serves for both relevance and irrelevance tests, which share the exact opposite logic.
+    # If `test_category` is "irrelevance", the model is expected to output no function call.
+    # No function call means either the AST decoding fails (a error message is generated) or the decoded AST does not contain any function call (such as a empty list, `[]`).
+    # If `test_category` is "relevance", the model is expected to output to a function call, and empty list doesn't count as a function call.
+    acc = not x["contain_func_call"] if "irrelevance" in x["id"] else x["contain_func_call"]
+    return {"valid": float(acc)}
+
+
+class BFCLScoringFn(RegisteredBaseScoringFn):
+    """
+    A scoring_fn for BFCL
+    """
+
+    def __init__(self, *args, **kwargs) -> None:
+        super().__init__(*args, **kwargs)
+        self.supported_fn_defs_registry = {
+            bfcl.identifier: bfcl,
+        }
+
+    async def score_row(
+        self,
+        input_row: Dict[str, Any],
+        scoring_fn_identifier: Optional[str] = "bfcl",
+        scoring_params: Optional[ScoringFnParams] = None,
+    ) -> ScoringResultRow:
+        test_category = re.sub(r"_[0-9_-]+$", "", input_row["id"])
+        score_result = postprocess(input_row, test_category)
+        if test_category in {"irrelevance", "live_relevance", "live_irrelevance"}:
+            score = gen_relevance_acc(score_result)["valid"]
+        else:
+            score = gen_valid(score_result)["valid"]
+        return {
+            "score": float(score),
+        }

llama_stack/providers/inline/scoring/basic/scoring_fn/fn_defs/bfcl.py

@@ -0,0 +1,21 @@
+# 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 llama_stack.apis.common.type_system import NumberType
+from llama_stack.apis.scoring_functions import (
+    AggregationFunctionType,
+    BasicScoringFnParams,
+    ScoringFn,
+)
+
+bfcl = ScoringFn(
+    identifier="basic::bfcl",
+    description="BFCL complex scoring",
+    return_type=NumberType(),
+    provider_id="basic",
+    provider_resource_id="bfcl",
+    params=BasicScoringFnParams(aggregation_functions=[AggregationFunctionType.accuracy]),
+)

llama_stack/providers/inline/scoring/basic/utils/bfcl/__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/providers/inline/scoring/basic/utils/bfcl/ast_parser.py

@@ -0,0 +1,296 @@
+# ruff: noqa
+# 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 ast
+
+from .tree_sitter import get_parser
+
+
+def parse_java_function_call(source_code):
+    if not source_code.endswith(";"):
+        source_code += ";"  # Necessary for the parser not to register an error
+    parser = get_parser("java")
+    tree = parser.parse(bytes(source_code, "utf8"))
+    root_node = tree.root_node
+
+    if root_node.has_error:
+        raise Exception("Error parsing java the source code.")
+
+    def get_text(node):
+        """Returns the text represented by the node."""
+        return source_code[node.start_byte : node.end_byte]
+
+    def traverse_node(node, nested=False):
+        if node.type == "string_literal":
+            if nested:
+                return get_text(node)
+            # Strip surrounding quotes from string literals
+            return get_text(node)[1:-1]
+        elif node.type == "character_literal":
+            if nested:
+                return get_text(node)
+            # Strip surrounding single quotes from character literals
+            return get_text(node)[1:-1]
+        """Traverse the node to collect texts for complex structures."""
+        if node.type in [
+            "identifier",
+            "class_literal",
+            "type_identifier",
+            "method_invocation",
+        ]:
+            return get_text(node)
+        elif node.type == "array_creation_expression":
+            # Handle array creation expression specifically
+            type_node = node.child_by_field_name("type")
+            value_node = node.child_by_field_name("value")
+            type_text = traverse_node(type_node, True)
+            value_text = traverse_node(value_node, True)
+            return f"new {type_text}[]{value_text}"
+        elif node.type == "object_creation_expression":
+            # Handle object creation expression specifically
+            type_node = node.child_by_field_name("type")
+            arguments_node = node.child_by_field_name("arguments")
+            type_text = traverse_node(type_node, True)
+            if arguments_node:
+                # Process each argument carefully, avoiding unnecessary punctuation
+                argument_texts = []
+                for child in arguments_node.children:
+                    if child.type not in [
+                        ",",
+                        "(",
+                        ")",
+                    ]:  # Exclude commas and parentheses
+                        argument_text = traverse_node(child, True)
+                        argument_texts.append(argument_text)
+                arguments_text = ", ".join(argument_texts)
+                return f"new {type_text}({arguments_text})"
+            else:
+                return f"new {type_text}()"
+        elif node.type == "set":
+            # Handling sets specifically
+            items = [traverse_node(n, True) for n in node.children if n.type not in [",", "set"]]
+            return "{" + ", ".join(items) + "}"
+
+        elif node.child_count > 0:
+            return "".join(traverse_node(child, True) for child in node.children)
+        else:
+            return get_text(node)
+
+    def extract_arguments(args_node):
+        arguments = {}
+        for child in args_node.children:
+            if child.type == "assignment_expression":
+                # For named parameters
+                name_node, value_node = child.children[0], child.children[2]
+                name = get_text(name_node)
+                value = traverse_node(value_node)
+                if name in arguments:
+                    if not isinstance(arguments[name], list):
+                        arguments[name] = [arguments[name]]
+                    arguments[name].append(value)
+                else:
+                    arguments[name] = value
+                # arguments.append({'name': name, 'value': value})
+            elif child.type in ["identifier", "class_literal", "set"]:
+                # For unnamed parameters and handling sets
+                value = traverse_node(child)
+                if None in arguments:
+                    if not isinstance(arguments[None], list):
+                        arguments[None] = [arguments[None]]
+                    arguments[None].append(value)
+                else:
+                    arguments[None] = value
+        return arguments
+
+    def traverse(node):
+        if node.type == "method_invocation":
+            # Extract the function name and its arguments
+            method_name = get_text(node.child_by_field_name("name"))
+            class_name_node = node.child_by_field_name("object")
+            if class_name_node:
+                class_name = get_text(class_name_node)
+                function_name = f"{class_name}.{method_name}"
+            else:
+                function_name = method_name
+            arguments_node = node.child_by_field_name("arguments")
+            if arguments_node:
+                arguments = extract_arguments(arguments_node)
+                for key, value in arguments.items():
+                    if isinstance(value, list):
+                        raise Exception("Error: Multiple arguments with the same name are not supported.")
+                return [{function_name: arguments}]
+
+        else:
+            for child in node.children:
+                result = traverse(child)
+                if result:
+                    return result
+
+    result = traverse(root_node)
+    return result if result else {}
+
+
+def parse_javascript_function_call(source_code):
+    if not source_code.endswith(";"):
+        source_code += ";"  # Necessary for the parser not to register an error
+    parser = get_parser("javascript")
+    # Parse the source code
+    tree = parser.parse(bytes(source_code, "utf8"))
+    root_node = tree.root_node
+    if root_node.has_error:
+        raise Exception("Error js parsing the source code.")
+
+    # Function to recursively extract argument details
+    def extract_arguments(node):
+        args = {}
+        for child in node.children:
+            if child.type == "assignment_expression":
+                # Extract left (name) and right (value) parts of the assignment
+                name = child.children[0].text.decode("utf-8")
+                value = child.children[2].text.decode("utf-8")
+                if (value.startswith('"') and value.endswith('"')) or (value.startswith("'") and value.endswith("'")):
+                    value = value[1:-1]  # Trim the quotation marks
+                if name in args:
+                    if not isinstance(args[name], list):
+                        args[name] = [args[name]]
+                    args[name].append(value)
+                else:
+                    args[name] = value
+
+            elif child.type == "identifier" or child.type == "true":
+                # Handle non-named arguments and boolean values
+                value = child.text.decode("utf-8")
+                if None in args:
+                    if not isinstance(args[None], list):
+                        args[None] = [args[None]]
+                    args[None].append(value)
+                else:
+                    args[None] = value
+        return args
+
+    # Find the function call and extract its name and arguments
+    if root_node.type == "program":
+        for child in root_node.children:
+            if child.type == "expression_statement":
+                for sub_child in child.children:
+                    if sub_child.type == "call_expression":
+                        function_name = sub_child.children[0].text.decode("utf8")
+                        arguments_node = sub_child.children[1]
+                        parameters = extract_arguments(arguments_node)
+                        for key, value in parameters.items():
+                            if isinstance(value, list):
+                                raise Exception("Error: Multiple arguments with the same name are not supported.")
+                        result = [{function_name: parameters}]
+                        return result
+
+
+def ast_parse(input_str, language="Python"):
+    if language == "Python":
+        cleaned_input = input_str.strip("[]'")
+        parsed = ast.parse(cleaned_input, mode="eval")
+        extracted = []
+        if isinstance(parsed.body, ast.Call):
+            extracted.append(resolve_ast_call(parsed.body))
+        else:
+            for elem in parsed.body.elts:
+                extracted.append(resolve_ast_call(elem))
+        return extracted
+    elif language == "Java":
+        return parse_java_function_call(input_str[1:-1])  # Remove the [ and ] from the string
+    elif language == "JavaScript":
+        return parse_javascript_function_call(input_str[1:-1])
+    else:
+        raise NotImplementedError(f"Unsupported language: {language}")
+
+
+def resolve_ast_call(elem):
+    # Handle nested attributes for deeply nested module paths
+    func_parts = []
+    func_part = elem.func
+    while isinstance(func_part, ast.Attribute):
+        func_parts.append(func_part.attr)
+        func_part = func_part.value
+    if isinstance(func_part, ast.Name):
+        func_parts.append(func_part.id)
+    func_name = ".".join(reversed(func_parts))
+    args_dict = {}
+    # Parse when args are simply passed as an unnamed dictionary arg
+    for arg in elem.args:
+        if isinstance(arg, ast.Dict):
+            for key, value in zip(arg.keys, arg.values):
+                if isinstance(key, ast.Constant):
+                    arg_name = key.value
+                output = resolve_ast_by_type(value)
+                args_dict[arg_name] = output
+    for arg in elem.keywords:
+        output = resolve_ast_by_type(arg.value)
+        args_dict[arg.arg] = output
+    return {func_name: args_dict}
+
+
+def resolve_ast_by_type(value):
+    if isinstance(value, ast.Constant):
+        if value.value is Ellipsis:
+            output = "..."
+        else:
+            output = value.value
+    elif isinstance(value, ast.UnaryOp):
+        output = -value.operand.value
+    elif isinstance(value, ast.List):
+        output = [resolve_ast_by_type(v) for v in value.elts]
+    elif isinstance(value, ast.Dict):
+        output = {resolve_ast_by_type(k): resolve_ast_by_type(v) for k, v in zip(value.keys, value.values)}
+    elif isinstance(value, ast.NameConstant):  # Added this condition to handle boolean values
+        output = value.value
+    elif isinstance(value, ast.BinOp):  # Added this condition to handle function calls as arguments
+        output = eval(ast.unparse(value))
+    elif isinstance(value, ast.Name):
+        output = value.id
+    elif isinstance(value, ast.Call):
+        if len(value.keywords) == 0:
+            output = ast.unparse(value)
+        else:
+            output = resolve_ast_call(value)
+    elif isinstance(value, ast.Tuple):
+        output = tuple(resolve_ast_by_type(v) for v in value.elts)
+    elif isinstance(value, ast.Lambda):
+        output = eval(ast.unparse(value.body[0].value))
+    elif isinstance(value, ast.Ellipsis):
+        output = "..."
+    elif isinstance(value, ast.Subscript):
+        try:
+            output = ast.unparse(value.body[0].value)
+        except:
+            output = ast.unparse(value.value) + "[" + ast.unparse(value.slice) + "]"
+    else:
+        raise Exception(f"Unsupported AST type: {type(value)}")
+    return output
+
+
+def decode_ast(result, language="Python"):
+    func = result
+    func = func.replace("\n", "")  # remove new line characters
+    if not func.startswith("["):
+        func = "[" + func
+    if not func.endswith("]"):
+        func = func + "]"
+    decoded_output = ast_parse(func, language)
+    return decoded_output
+
+
+def decode_execute(result):
+    func = result
+    func = func.replace("\n", "")  # remove new line characters
+    if not func.startswith("["):
+        func = "[" + func
+    if not func.endswith("]"):
+        func = func + "]"
+    decode_output = ast_parse(func)
+    execution_list = []
+    for function_call in decode_output:
+        for key, value in function_call.items():
+            execution_list.append(f"{key}({','.join([f'{k}={repr(v)}' for k, v in value.items()])})")
+    return execution_list

llama_stack/providers/inline/scoring/basic/utils/bfcl/checker.py

@@ -0,0 +1,989 @@
+# ruff: noqa
+# 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 json
+import re
+import time
+from typing import Any
+
+# Comment out for now until we actually use the rest checker in evals
+# import requests  # Do not remove this import even though it seems to be unused. It's used in the executable_checker_rest function.
+
+
+class NoAPIKeyError(Exception):
+    def __init__(self):
+        self.message = "❗️Please fill in the API keys in the function_credential_config.json file. If you do not provide the API keys, the executable test category results will be inaccurate."
+        super().__init__(self.message)
+
+
+REAL_TIME_MATCH_ALLOWED_DIFFERENCE = 0.2
+
+
+JAVA_TYPE_CONVERSION = {
+    "byte": int,
+    "short": int,
+    "integer": int,
+    "float": float,
+    "double": float,
+    "long": int,
+    "boolean": bool,
+    "char": str,
+    "Array": list,
+    "ArrayList": list,
+    "Set": set,
+    "HashMap": dict,
+    "Hashtable": dict,
+    "Queue": list,  # this can be `queue.Queue` as well, for simplicity we check with list
+    "Stack": list,
+    "String": str,
+    "any": str,
+}
+
+JS_TYPE_CONVERSION = {
+    "String": str,
+    "integer": int,
+    "float": float,
+    "Bigint": int,
+    "Boolean": bool,
+    "dict": dict,
+    "array": list,
+    "any": str,
+}
+
+# We switch to conditional import for the following two imports to avoid unnecessary installations.
+# User doesn't need to setup the tree-sitter packages if they are not running the test for that language.
+# from js_type_converter import js_type_converter
+# from java_type_converter import java_type_converter
+
+PYTHON_TYPE_MAPPING = {
+    "string": str,
+    "integer": int,
+    "float": float,
+    "boolean": bool,
+    "array": list,
+    "tuple": list,
+    "dict": dict,
+    "any": str,
+}
+
+# This is the list of types that we need to recursively check its values
+PYTHON_NESTED_TYPE_CHECK_LIST = ["array", "tuple"]
+
+
+NESTED_CONVERSION_TYPE_LIST = ["Array", "ArrayList", "array"]
+
+
+#### Helper functions for AST ####
+def find_description(func_descriptions, name):
+    if type(func_descriptions) == list:
+        for func_description in func_descriptions:
+            if func_description["name"] == name:
+                return func_description
+        return None
+    else:
+        # it is a dict, there is only one function
+        return func_descriptions
+
+
+def get_possible_answer_type(possible_answer: list):
+    for answer in possible_answer:
+        if answer != "":  # Optional parameter
+            return type(answer)
+    return None
+
+
+def type_checker(
+    param: str,
+    value,
+    possible_answer: list,
+    expected_type_description: str,
+    expected_type_converted,
+    nested_type_converted,
+):
+    # NOTE: This type checker only supports nested type checking for one level deep.
+    # We didn't implement recursive type checking for nested types, as it's not needed for the current use case and it's very complex.
+
+    result: Any = {
+        "valid": True,
+        "error": [],
+        "is_variable": False,
+        "error_type": "type_error:simple",
+    }
+
+    is_variable = False
+    # check for the case where a variable is used instead of a actual value.
+    # use the type in possible_answer as the expected type
+    possible_answer_type = get_possible_answer_type(possible_answer)
+    # if possible_answer only contains optional parameters, we can't determine the type
+    if possible_answer_type != None:
+        # we are being precise here.
+        # in fact, possible_answer_type should always be string, as that's how we treat varibale in possible_answer
+        if possible_answer_type != expected_type_converted:
+            is_variable = True
+
+    # value is the same type as in function description
+    if type(value) == expected_type_converted:
+        # We don't need to do recursive check for simple types
+        if nested_type_converted == None:
+            result["is_variable"] = is_variable
+            return result
+        else:
+            for possible_answer_item in possible_answer:
+                flag = True  # Each parameter should match to at least one possible answer type.
+                # Here, we assume that each item should be the same type. We could also relax it.
+                if type(possible_answer_item) == list:
+                    for value_item in value:
+                        checker_result = type_checker(
+                            param,
+                            value_item,
+                            possible_answer_item,
+                            str(nested_type_converted),
+                            nested_type_converted,
+                            None,
+                        )
+                        if not checker_result["valid"]:
+                            flag = False
+                            break
+
+                if flag:
+                    return {"valid": True, "error": [], "is_variable": is_variable}
+
+            result["valid"] = False
+            result["error"] = [
+                f"Nested type checking failed for parameter {repr(param)}. Expected outer type {expected_type_description} with inner type {str(nested_type_converted)}. Parameter value: {repr(value)}."
+            ]
+            result["error_type"] = "type_error:nested"
+
+    # value is not as expected, check for the case where a variable is used instead of a actual value
+    # use the type in possible_answer as the expected type
+    possible_answer_type = get_possible_answer_type(possible_answer)
+    # if possible_answer only contains optional parameters, we can't determine the type
+    if possible_answer_type != None:
+        # we are being precise here.
+        # in fact, possible_answer_type should always be string, as that's how we treat varibale in possible_answer
+        if type(value) == possible_answer_type:
+            result["is_variable"] = True
+            return result
+
+    result["valid"] = False
+    result["error"].append(
+        f"Incorrect type for parameter {repr(param)}. Expected type {expected_type_description}, got {type(value).__name__}. Parameter value: {repr(value)}."
+    )
+    result["error_type"] = "type_error:simple"
+    return result
+
+
+def standardize_string(input_string: str):
+    # This function standardizes the string by removing all the spaces, ",./-_*^" punctuation, and converting it to lowercase
+    # It will also convert all the single quotes to double quotes
+    # This is used to compare the model output with the possible answers
+    # We don't want to punish model for answer like April 1, 2024 vs April 1,2024, vs April 1 2024
+    regex_string = r"[ \,\.\/\-\_\*\^]"
+    return re.sub(regex_string, "", input_string).lower().replace("'", '"')
+
+
+def string_checker(param: str, model_output: str, possible_answer: list):
+    standardize_possible_answer = []
+    standardize_model_output = standardize_string(model_output)
+    for i in range(len(possible_answer)):
+        if type(possible_answer[i]) == str:
+            standardize_possible_answer.append(standardize_string(possible_answer[i]))
+
+    if standardize_model_output not in standardize_possible_answer:
+        return {
+            "valid": False,
+            "error": [
+                f"Invalid value for parameter {repr(param)}: {repr(model_output)}. Expected one of {possible_answer}. Case insensitive."
+            ],
+            "error_type": "value_error:string",
+        }
+
+    return {"valid": True, "error": []}
+
+
+def list_checker(param: str, model_output: list, possible_answer: list):
+    # Convert the tuple to a list
+
+    standardize_model_output = list(model_output)
+
+    # If the element in the list is a string, we need to standardize it
+    for i in range(len(standardize_model_output)):
+        if type(standardize_model_output[i]) == str:
+            standardize_model_output[i] = standardize_string(model_output[i])
+
+    standardize_possible_answer: Any = []
+    # We also need to standardize the possible answers
+    for i in range(len(possible_answer)):
+        standardize_possible_answer.append([])
+        for j in range(len(possible_answer[i])):
+            if type(possible_answer[i][j]) == str:
+                standardize_possible_answer[i].append(standardize_string(possible_answer[i][j]))
+            else:
+                standardize_possible_answer[i].append(possible_answer[i][j])
+
+    if standardize_model_output not in standardize_possible_answer:
+        return {
+            "valid": False,
+            "error": [
+                f"Invalid value for parameter {repr(param)}: {repr(model_output)}. Expected one of {possible_answer}."
+            ],
+            "error_type": "value_error:list/tuple",
+        }
+
+    return {"valid": True, "error": []}
+
+
+def dict_checker(param: str, model_output: dict, possible_answers: list):
+    # This function works for simple dictionaries, but not dictionaries with nested dictionaries.
+    # The current dataset only contains simple dictionaries, so this is sufficient.
+
+    result = {"valid": False, "error": [], "error_type": "dict_checker:unclear"}
+    for i in range(len(possible_answers)):
+        if possible_answers[i] == "":
+            continue
+
+        result = {"valid": False, "error": [], "error_type": "dict_checker:unclear"}
+
+        flag = True
+
+        possible_answer = possible_answers[i]
+        # possible_anwer is a single dictionary
+
+        for key, value in model_output.items():
+            if key not in possible_answer:
+                result["valid"] = False
+                result["error"].append(f"Unexpected dict key parameter: '{key}'.")  # type: ignore[attr-defined]
+                result["error_type"] = "value_error:dict_key"
+                flag = False
+                break
+
+            standardize_value = value
+            # If the value is a string, we need to standardize it
+            if type(value) == str:
+                standardize_value = standardize_string(value)
+
+            # We also need to standardize the possible answers if they are string
+            standardize_possible_answer = []
+            for i in range(len(possible_answer[key])):
+                if type(possible_answer[key][i]) == str:
+                    standardize_possible_answer.append(standardize_string(possible_answer[key][i]))
+                else:
+                    standardize_possible_answer.append(possible_answer[key][i])
+
+            if standardize_value not in standardize_possible_answer:
+                result["valid"] = False
+                result["error"].append(  # type: ignore[attr-defined]
+                    f"Invalid value for parameter {repr(key)}: {repr(value)}. Expected one of {standardize_possible_answer}."
+                )
+                result["error_type"] = "value_error:dict_value"
+                flag = False
+                break
+
+        for key, value in possible_answer.items():
+            if key not in model_output and "" not in value:
+                result["valid"] = False
+                result["error"].append(f"Missing dict key parameter: '{key}'.")  # type: ignore[attr-defined]
+                result["error_type"] = "value_error:dict_key"
+                flag = False
+                break
+
+        if flag:
+            return {"valid": True, "error": []}
+
+    return result
+
+
+def list_dict_checker(param: str, model_output: list, possible_answers: list):
+    # This function takes in a list of dictionaries and checks if each dictionary is valid
+    # The order of the dictionaries in the list must match the order of the possible answers
+
+    result = {"valid": False, "error": [], "error_type": "list_dict_checker:unclear"}
+
+    for answer_index in range(len(possible_answers)):
+        flag = True  # True means so far, all dictionaries are valid
+
+        # Only proceed if the number of dictionaries in the list matches the number of dictionaries in the possible answers
+        if len(model_output) != len(possible_answers[answer_index]):
+            result["valid"] = False
+            result["error"] = ["Wrong number of dictionaries in the list."]
+            result["error_type"] = "value_error:list_dict_count"
+            flag = False
+            continue
+
+        for dict_index in range(len(model_output)):
+            result = dict_checker(
+                param,
+                model_output[dict_index],
+                [possible_answers[answer_index][dict_index]],
+            )
+            if not result["valid"]:
+                flag = False
+                break
+        if flag:
+            return {"valid": True, "error": []}
+
+    return result
+
+
+def simple_function_checker(
+    func_description: dict,
+    model_output: dict,
+    possible_answer: dict,
+    language: str,
+    model_name: str,
+):
+    possible_answer = list(possible_answer.values())[0]
+    # Extract function name and parameters details
+    func_name = func_description["name"]
+    param_details = func_description["parameters"]["properties"]
+    required_params = func_description["parameters"]["required"]
+
+    # Initialize a result dictionary
+    result = {
+        "valid": True,
+        "error": [],
+        "error_type": "simple_function_checker:unclear",
+    }
+
+    # Check if function name matches
+    if func_name not in model_output:
+        result["valid"] = False
+        result["error"].append(  # type: ignore[attr-defined]
+            f"Function name {repr(func_name)} not found in model output."
+        )
+        result["error_type"] = "simple_function_checker:wrong_func_name"
+        return result
+
+    model_params = model_output[func_name]
+
+    # Check for required parameters in model output
+    for param in required_params:
+        if param not in model_params:
+            result["valid"] = False
+            result["error"].append(f"Missing required parameter: {repr(param)}.")  # type: ignore[attr-defined]
+            result["error_type"] = "simple_function_checker:missing_required"
+            return result
+
+    # Validate types and values for each parameter in model output
+    for param, value in model_params.items():
+        if param not in param_details or param not in possible_answer:
+            result["valid"] = False
+            result["error"].append(f"Unexpected parameter: {repr(param)}.")  # type: ignore[attr-defined]
+            result["error_type"] = "simple_function_checker:unexpected_param"
+            return result
+
+        full_param_details = param_details[param]
+        expected_type_description = full_param_details["type"]  # This is a string
+        is_variable = False
+        nested_type_converted = None
+
+        if language == "Java":
+            from evals.utils.bfcl.java_type_converter import java_type_converter
+
+            expected_type_converted = JAVA_TYPE_CONVERSION[expected_type_description]
+
+            if expected_type_description in JAVA_TYPE_CONVERSION:
+                if type(value) != str:
+                    result["valid"] = False
+                    result["error"].append(  # type: ignore[attr-defined]
+                        f"Incorrect type for parameter {repr(param)}. Expected type String, got {type(value).__name__}. Parameter value: {repr(value)}."
+                    )
+                    result["error_type"] = "type_error:java"
+                    return result
+
+                if expected_type_description in NESTED_CONVERSION_TYPE_LIST:
+                    nested_type = param_details[param]["items"]["type"]
+                    nested_type_converted = JAVA_TYPE_CONVERSION[nested_type]
+                    value = java_type_converter(value, expected_type_description, nested_type)
+                else:
+                    value = java_type_converter(value, expected_type_description)
+
+        elif language == "JavaScript":
+            from evals.utils.bfcl.js_type_converter import js_type_converter
+
+            expected_type_converted = JS_TYPE_CONVERSION[expected_type_description]
+
+            if expected_type_description in JS_TYPE_CONVERSION:
+                if type(value) != str:
+                    result["valid"] = False
+                    result["error"].append(  # type: ignore[attr-defined]
+                        f"Incorrect type for parameter {repr(param)}. Expected type String, got {type(value).__name__}. Parameter value: {repr(value)}."
+                    )
+                    result["error_type"] = "type_error:js"
+                    return result
+
+                if expected_type_description in NESTED_CONVERSION_TYPE_LIST:
+                    nested_type = param_details[param]["items"]["type"]
+                    nested_type_converted = JS_TYPE_CONVERSION[nested_type]
+                    value = js_type_converter(value, expected_type_description, nested_type)
+                else:
+                    value = js_type_converter(value, expected_type_description)
+
+        elif language == "Python":
+            expected_type_converted = PYTHON_TYPE_MAPPING[expected_type_description]
+            if expected_type_description in PYTHON_NESTED_TYPE_CHECK_LIST:
+                nested_type = param_details[param]["items"]["type"]
+                nested_type_converted = PYTHON_TYPE_MAPPING[nested_type]
+
+        # We convert all tuple value to list when the expected type is tuple.
+        # The conversion is necessary because any tuple in the possible answer would become a list after being processed through json.dump() and json.load().
+        # This does introduce some false positive (eg, when the model provides a list value instead of tuple). We hope to find a better solution in the future.
+        if expected_type_description == "tuple" and type(value) == tuple:
+            value = list(value)
+
+        # Allow python auto conversion from int to float
+        if language == "Python" and expected_type_description == "float" and type(value) == int:
+            value = float(value)
+
+        # Type checking
+        # In fact, we only check for Python here.
+        # Type check for other languages are handled by the type converter, and so their value (after conversion) is always correct.
+        type_check_result = type_checker(
+            param,
+            value,
+            possible_answer[param],
+            expected_type_description,
+            expected_type_converted,
+            nested_type_converted,
+        )
+        is_variable = type_check_result["is_variable"]
+        if not type_check_result["valid"]:
+            return type_check_result
+
+        # It doesn't make sense to special handle dictionaries and list of dictionaries if the value is a variable.
+        # We can just treat the variable as a string and use the normal flow.
+        if not is_variable:
+            # Special handle for dictionaries
+            if expected_type_converted == dict:
+                result = dict_checker(param, value, possible_answer[param])
+                if not result["valid"]:
+                    return result
+                continue
+
+            # Special handle for list of dictionaries
+            elif expected_type_converted == list and nested_type_converted == dict:
+                result = list_dict_checker(param, value, possible_answer[param])
+                if not result["valid"]:
+                    return result
+                continue
+
+            # Special handle for strings
+            elif expected_type_converted == str:
+                # We don't check for case sensitivity for string, as long as it's not a variable
+                result = string_checker(param, value, possible_answer[param])
+                if not result["valid"]:
+                    return result
+                continue
+
+            elif expected_type_converted == list:
+                result = list_checker(param, value, possible_answer[param])
+                if not result["valid"]:
+                    return result
+                continue
+
+        # Check if the value is within the possible answers
+        if value not in possible_answer[param]:
+            result["valid"] = False
+            result["error"].append(  # type: ignore[attr-defined]
+                f"Invalid value for parameter {repr(param)}: {repr(value)}. Expected one of {possible_answer[param]}."
+            )
+            result["error_type"] = "value_error:others"
+            return result
+
+    # Check for optional parameters not provided but allowed
+    for param in possible_answer:
+        if param not in model_params and "" not in possible_answer[param]:
+            result["valid"] = False
+            result["error"].append(  # type: ignore[attr-defined]
+                f"Optional parameter {repr(param)} not provided and not marked as optional."
+            )
+            result["error_type"] = "simple_function_checker:missing_optional"
+            return result
+
+    return result
+
+
+def parallel_function_checker_enforce_order(
+    func_descriptions: list,
+    model_output: list,
+    possible_answers: dict,
+    language: str,
+    model_name: str,
+):
+    if len(model_output) != len(possible_answers):
+        return {
+            "valid": False,
+            "error": ["Wrong number of functions."],
+            "error_type": "parallel_function_checker_enforce_order:wrong_count",
+        }
+
+    func_name_list = list(possible_answers.keys())
+    possible_answers_list = []
+
+    for key, value in possible_answers.items():
+        possible_answers_list.append({key: value})
+
+    for i in range(len(possible_answers_list)):
+        func_description = find_description(func_descriptions, func_name_list[i])
+
+        result = simple_function_checker(
+            func_description,
+            model_output[i],
+            possible_answers_list[i],
+            language,
+            model_name,
+        )
+        if not result["valid"]:
+            return result
+
+    return {"valid": True, "error": []}
+
+
+def parallel_function_checker_no_order(
+    func_descriptions: list,
+    model_output: list,
+    possible_answers: list,
+    language: str,
+    model_name: str,
+):
+    if len(model_output) != len(possible_answers):
+        return {
+            "valid": False,
+            "error": ["Wrong number of functions."],
+            "error_type": "parallel_function_checker_no_order:wrong_count",
+        }
+
+    matched_indices = []
+
+    # We go throught the possible answers one by one, and eliminate the model output that matches the possible answer
+    # It must be this way because we need ground truth to fetch the correct function description
+    for i in range(len(possible_answers)):
+        # possible_answers[i] is a dictionary with only one key
+        func_name_expected = list(possible_answers[i].keys())[0]
+        func_description = find_description(func_descriptions, func_name_expected)
+
+        all_errors = []
+
+        for index in range(len(model_output)):
+            if index in matched_indices:
+                continue
+
+            result = simple_function_checker(
+                func_description,
+                model_output[index],
+                possible_answers[i],
+                language,
+                model_name,
+            )
+
+            if result["valid"]:
+                matched_indices.append(index)
+                break
+            else:
+                all_errors.append(
+                    {
+                        f"Model Result Index {index}": {
+                            "sub_error": result["error"],
+                            "sub_error_type": result["error_type"],
+                            "model_output_item": model_output[index],
+                            "possible_answer_item": possible_answers[i],
+                        }
+                    }
+                )
+
+        if not result["valid"]:
+            considered_indices = [i for i in range(len(model_output)) if i not in matched_indices]
+            all_errors.insert(
+                0,
+                f"Could not find a matching function among index {considered_indices} of model output for index {i} of possible answers.",  # type: ignore[arg-type]
+            )
+            return {
+                "valid": False,
+                "error": all_errors,
+                "error_type": "parallel_function_checker_no_order:cannot_find_match",
+            }
+
+    return {"valid": True, "error": []}
+
+
+def multiple_function_checker(
+    func_descriptions: list,
+    model_output: list,
+    possible_answers: list,
+    language: str,
+    model_name: str,
+):
+    if len(model_output) != len(possible_answers):
+        return {
+            "valid": False,
+            "error": ["Wrong number of functions."],
+            "error_type": "multiple_function_checker:wrong_count",
+        }
+
+    # possible_answers is a list of only one dictionary with only one key
+    func_name_expected = list(possible_answers[0].keys())[0]
+    func_description = find_description(func_descriptions, func_name_expected)
+    return simple_function_checker(
+        func_description,
+        model_output[0],
+        possible_answers[0],
+        language,
+        model_name,
+    )
+
+
+def patten_matcher(exec_output, expected_result, function_call, is_sanity_check):
+    result = {"valid": True, "error": [], "error_type": "executable_checker:unclear"}
+
+    if type(exec_output) != type(expected_result):
+        return {
+            "valid": False,
+            "error": [
+                f"Wrong execution result type for {repr(function_call)}. Expected type: {type(expected_result)}, but got: {type(exec_output)}."
+            ],
+            "error_type": "executable_checker:wrong_result_type",
+            "model_executed_output": exec_output,
+        }
+    if type(exec_output) == dict:
+        # We loose the requirement for the sanity check as the expected result used in the sanity check might not be the most up-to-date one.
+        # This happens when the key is a timestamp or a random number.
+        if is_sanity_check:
+            if len(exec_output) != len(expected_result):
+                return {
+                    "valid": False,
+                    "error": [
+                        f"Wrong execution result pattern for {repr(function_call)}. Expect type Dict, but wrong number of elements in the output. Expected length: {len(expected_result)}, but got: {len(exec_output)}."
+                    ],
+                    "error_type": "executable_checker:wrong_result_type:dict_length",
+                    "model_executed_output": exec_output,
+                }
+            else:
+                return result
+
+        for key, value in expected_result.items():
+            if key not in exec_output:
+                return {
+                    "valid": False,
+                    "error": [
+                        f"Wrong execution result pattern for {repr(function_call)}. Expect type Dict, but key {repr(key)} not found in the model output."
+                    ],
+                    "error_type": "executable_checker:wrong_result_type:dict_key_not_found",
+                    "model_executed_output": exec_output,
+                }
+        for key, value in exec_output.items():
+            if key not in expected_result:
+                return {
+                    "valid": False,
+                    "error": [
+                        f"Wrong execution result pattern for {repr(function_call)}. Expect type Dict, but key {repr(key)} not expected in the model output."
+                    ],
+                    "error_type": "executable_checker:wrong_result_type:dict_extra_key",
+                    "model_executed_output": exec_output,
+                }
+    if type(exec_output) == list:
+        if len(exec_output) != len(expected_result):
+            return {
+                "valid": False,
+                "error": [
+                    f"Wrong execution result pattern for {repr(function_call)}. Expect type list, but wrong number of elements in the output. Expected length: {len(expected_result)}, but got: {len(exec_output)}."
+                ],
+                "error_type": "executable_checker:wrong_result_type:list_length",
+                "model_executed_output": exec_output,
+            }
+    return result
+
+
+#### Helper functions for Exec ####
+def executable_checker_simple(
+    function_call: str,
+    expected_result,
+    expected_result_type: str,
+    is_sanity_check=False,
+):
+    result = {"valid": True, "error": [], "error_type": "executable_checker:unclear"}
+
+    exec_dict: Any = {}
+
+    try:
+        exec(
+            "from executable_python_function import *" + "\nresult=" + function_call,
+            exec_dict,
+        )
+        exec_output = exec_dict["result"]
+    except NoAPIKeyError as e:
+        raise e
+    except Exception as e:
+        result["valid"] = False
+        result["error"].append(  # type: ignore[attr-defined]
+            f"Error in execution: {repr(function_call)}. Error: {str(e)}"
+        )
+        result["error_type"] = "executable_checker:execution_error"
+        return result
+
+    # We need to special handle the case where the execution result is a tuple and convert it to a list
+    # Because when json is stored, the tuple is converted to a list, and so the expected result is a list when loaded from json
+    if isinstance(exec_output, tuple):
+        exec_output = list(exec_output)
+
+    if expected_result_type == "exact_match":
+        if exec_output != expected_result:
+            result["valid"] = False
+            result["error"].append(  # type: ignore[attr-defined]
+                f"Wrong execution result for {repr(function_call)}. Expected: {expected_result}, but got: {exec_output}."
+            )
+            result["error_type"] = "executable_checker:wrong_result"
+            result["model_executed_output"] = exec_output
+            return result
+
+    elif expected_result_type == "real_time_match":
+        # Allow for 5% difference
+        if (type(expected_result) == float or type(expected_result) == int) and (
+            type(exec_output) == float or type(exec_output) == int
+        ):
+            if not (
+                expected_result * (1 - REAL_TIME_MATCH_ALLOWED_DIFFERENCE)
+                <= exec_output
+                <= expected_result * (1 + REAL_TIME_MATCH_ALLOWED_DIFFERENCE)
+            ):
+                result["valid"] = False
+                result["error"].append(  # type: ignore[attr-defined]
+                    f"Wrong execution result for {repr(function_call)}. Expected: {expected_result}, but got: {exec_output}. {REAL_TIME_MATCH_ALLOWED_DIFFERENCE * 100}% difference allowed."
+                )
+                result["error_type"] = "executable_checker:wrong_result_real_time"
+                result["model_executed_output"] = exec_output
+                return result
+        else:
+            result["valid"] = False
+            result["error"].append(  # type: ignore[attr-defined]
+                f"Wrong execution result for {repr(function_call)}. Expected: {expected_result}, but got: {exec_output}. Type needs to be float or int for real time match criteria."
+            )
+            result["error_type"] = "executable_checker:wrong_result_real_time"
+            result["model_executed_output"] = exec_output
+            return result
+
+    else:
+        # structural match
+        pattern_match_result = patten_matcher(exec_output, expected_result, function_call, is_sanity_check)
+        if not pattern_match_result["valid"]:
+            return pattern_match_result
+
+    return result
+
+
+def executable_checker_parallel_no_order(
+    decoded_result: list, expected_exec_result: list, expected_exec_result_type: list
+):
+    if len(decoded_result) != len(expected_exec_result):
+        return {
+            "valid": False,
+            "error": [
+                f"Wrong number of functions provided. Expected {len(expected_exec_result)}, but got {len(decoded_result)}."
+            ],
+            "error_type": "value_error:exec_result_count",
+        }
+
+    matched_indices = []
+    for i in range(len(expected_exec_result)):
+        all_errors = []
+        for index in range(len(decoded_result)):
+            if index in matched_indices:
+                continue
+
+            result = executable_checker_simple(
+                decoded_result[index],
+                expected_exec_result[i],
+                expected_exec_result_type[i],
+                False,
+            )
+
+            if result["valid"]:
+                matched_indices.append(index)
+                break
+            else:
+                all_errors.append(
+                    {
+                        f"Model Result Index {index}": {
+                            "sub_error": result["error"],
+                            "sub_error_type": result["error_type"],
+                            "model_executed_output": (
+                                result["model_executed_output"] if "model_executed_output" in result else None
+                            ),
+                        }
+                    }
+                )
+
+        if not result["valid"]:
+            considered_indices = [i for i in range(len(decoded_result)) if i not in matched_indices]
+            all_errors.insert(
+                0,
+                f"Could not find a matching function among index {considered_indices} of model output for index {i} of possible answers.",  # type: ignore[arg-type]
+            )
+            return {
+                "valid": False,
+                "error": all_errors,
+                "error_type": "executable_checker:cannot_find_match",
+            }
+
+    return {"valid": True, "error": [], "error_type": "executable_checker:unclear"}
+
+
+#### Main function ####
+def executable_checker_rest(func_call, idx):
+    # Move this here for now to avoid needing to read this file / fix paths to be relative to dataset_dir. Fix when it's actually needed / used.
+    EVAL_GROUND_TRUTH_PATH = "/mnt/wsfuse/fair_llm_v2/datasets/eval/bfcl/rest-eval-response_v5.jsonl"  # Ground truth file for v5 for rest execution
+    with open(EVAL_GROUND_TRUTH_PATH, "r") as f:
+        EVAL_GROUND_TRUTH = f.readlines()
+    if "https://geocode.maps.co" in func_call:
+        time.sleep(2)
+    if "requests_get" in func_call:
+        func_call = func_call.replace("requests_get", "requests.get")
+    try:
+        response = eval(func_call)
+    except Exception as e:
+        return {
+            "valid": False,
+            "error": [f"Execution failed. {str(e)}"],
+            "error_type": "executable_checker_rest:execution_error",
+        }
+
+    try:
+        if response.status_code == 200:
+            eval_GT_json = json.loads(EVAL_GROUND_TRUTH[idx])
+            try:
+                if isinstance(eval_GT_json, dict):
+                    if isinstance(response.json(), dict):
+                        if set(eval_GT_json.keys()) == set(response.json().keys()):
+                            return {"valid": True, "error": [], "error_type": ""}
+                        return {
+                            "valid": False,
+                            "error": ["Key inconsistency"],
+                            "error_type": "executable_checker_rest:wrong_key",
+                        }
+                    return {
+                        "valid": False,
+                        "error": [f"Expected dictionary, but got {type(response.json())}"],
+                        "error_type": "executable_checker_rest:wrong_type",
+                    }
+
+                elif isinstance(eval_GT_json, list):
+                    if isinstance(response.json(), list):
+                        if len(eval_GT_json) != len(response.json()):
+                            return {
+                                "valid": False,
+                                "error": [f"Response list length inconsistency."],
+                                "error_type": "value_error:exec_result_rest_count",
+                            }
+
+                        else:
+                            for i in range(len(eval_GT_json)):
+                                if set(eval_GT_json[i].keys()) != set(response.json()[i].keys()):
+                                    return {
+                                        "valid": False,
+                                        "error": [f"Key inconsistency"],
+                                        "error_type": "executable_checker_rest:wrong_key",
+                                    }
+
+                            return {"valid": True, "error": []}
+                    else:
+                        return {
+                            "valid": False,
+                            "error": [f"Expected list, but got {type(response.json())}"],
+                            "error_type": "executable_checker_rest:wrong_type",
+                        }
+                return {
+                    "valid": False,
+                    "error": [f"Expected dict or list, but got {type(response.json())}"],
+                    "error_type": "executable_checker_rest:wrong_type",
+                }
+            except Exception as e:
+                return {
+                    "valid": False,
+                    "error": [
+                        f"Error in execution and type checking. Status code: {response.status_code}. Error: {str(e)}"
+                    ],
+                    "error_type": "executable_checker_rest:response_format_error",
+                }
+        else:
+            return {
+                "valid": False,
+                "error": [f"Execution result status code is not 200, got {response.status_code}"],
+                "error_type": "executable_checker_rest:wrong_status_code",
+            }
+    except Exception as e:
+        return {
+            "valid": False,
+            "error": [f"Cannot get status code of the response. Error: {str(e)}"],
+            "error_type": "executable_checker_rest:cannot_get_status_code",
+        }
+
+
+def ast_checker(func_description, model_output, possible_answer, language, test_category, model_name):
+    if "parallel" in test_category:
+        return parallel_function_checker_no_order(func_description, model_output, possible_answer, language, model_name)
+
+    elif "multiple" in test_category:
+        return multiple_function_checker(func_description, model_output, possible_answer, language, model_name)
+
+    else:
+        if len(model_output) != 1:
+            return {
+                "valid": False,
+                "error": ["Wrong number of functions."],
+                "error_type": "simple_function_checker:wrong_count",
+            }
+
+        return simple_function_checker(
+            func_description[0],
+            model_output[0],
+            possible_answer[0],
+            language,
+            model_name,
+        )
+
+
+def exec_checker(decoded_result: list, func_description: dict, test_category: str):
+    if "multiple" in test_category or "parallel" in test_category:
+        return executable_checker_parallel_no_order(
+            decoded_result,
+            func_description["execution_result"],
+            func_description["execution_result_type"],
+        )
+
+    else:
+        if len(decoded_result) != 1:
+            return {
+                "valid": False,
+                "error": ["Wrong number of functions."],
+                "error_type": "simple_exec_checker:wrong_count",
+            }
+        return executable_checker_simple(
+            decoded_result[0],
+            func_description["execution_result"][0],
+            func_description["execution_result_type"][0],
+            False,
+        )
+
+
+def is_empty_output(decoded_output):
+    # This function is a patch to the ast decoder for relevance detection
+    # Sometimes the ast decoder will parse successfully, but the input doens't really have a function call
+    # [], [{}], and anything that is not in function calling format is considered empty (and thus should be marked as correct)
+    if not is_function_calling_format_output(decoded_output):
+        return True
+    if len(decoded_output) == 0:
+        return True
+    if len(decoded_output) == 1 and len(decoded_output[0]) == 0:
+        return True
+
+
+def is_function_calling_format_output(decoded_output):
+    # Ensure the output is a list of dictionaries
+    if type(decoded_output) == list:
+        for item in decoded_output:
+            if type(item) != dict:
+                return False
+        return True
+    return False

llama_stack/providers/inline/scoring/basic/utils/bfcl/tree_sitter.py

@@ -0,0 +1,40 @@
+# 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.
+
+"""
+Tree-sitter changes its API with unfortunate frequency. Modules that need it should
+import it from here so that we can centrally manage things as necessary.
+"""
+
+# These currently work with tree-sitter 0.23.0
+# NOTE: Don't import tree-sitter or any of the language modules in the main module
+# because not all environments have them. Import lazily inside functions where needed.
+
+import importlib
+import typing
+
+if typing.TYPE_CHECKING:
+    import tree_sitter
+
+
+def get_language(language: str) -> "tree_sitter.Language":
+    import tree_sitter
+
+    language_module_name = f"tree_sitter_{language}"
+    try:
+        language_module = importlib.import_module(language_module_name)
+    except ModuleNotFoundError as exc:
+        raise ValueError(
+            f"Language {language} is not found. Please install the tree-sitter-{language} package."
+        ) from exc
+    return tree_sitter.Language(language_module.language())
+
+
+def get_parser(language: str, **kwargs) -> "tree_sitter.Parser":
+    import tree_sitter
+
+    lang = get_language(language)
+    return tree_sitter.Parser(lang, **kwargs)

llama_stack/providers/inline/telemetry/meta_reference/console_span_processor.py

@@ -5,7 +5,7 @@
 # the root directory of this source tree.
 
 import json
-from datetime import datetime
+from datetime import datetime, timezone
 
 from opentelemetry.sdk.trace import ReadableSpan
 from opentelemetry.sdk.trace.export import SpanProcessor
@@ -34,7 +34,7 @@ class ConsoleSpanProcessor(SpanProcessor):
         if span.attributes and span.attributes.get("__autotraced__"):
             return
 
-        timestamp = datetime.utcfromtimestamp(span.start_time / 1e9).strftime("%H:%M:%S.%f")[:-3]
+        timestamp = datetime.fromtimestamp(span.start_time / 1e9, tz=timezone.utc).strftime("%H:%M:%S.%f")[:-3]
 
         print(
             f"{COLORS['dim']}{timestamp}{COLORS['reset']} "
@@ -46,7 +46,7 @@ class ConsoleSpanProcessor(SpanProcessor):
         if span.attributes and span.attributes.get("__autotraced__"):
             return
 
-        timestamp = datetime.utcfromtimestamp(span.end_time / 1e9).strftime("%H:%M:%S.%f")[:-3]
+        timestamp = datetime.fromtimestamp(span.end_time / 1e9, tz=timezone.utc).strftime("%H:%M:%S.%f")[:-3]
 
         span_context = (
             f"{COLORS['dim']}{timestamp}{COLORS['reset']} "
@@ -74,7 +74,7 @@ class ConsoleSpanProcessor(SpanProcessor):
                 print(f"    {COLORS['dim']}{key}: {str_value}{COLORS['reset']}")
 
         for event in span.events:
-            event_time = datetime.utcfromtimestamp(event.timestamp / 1e9).strftime("%H:%M:%S.%f")[:-3]
+            event_time = datetime.fromtimestamp(event.timestamp / 1e9, tz=timezone.utc).strftime("%H:%M:%S.%f")[:-3]
 
             severity = event.attributes.get("severity", "info")
             message = event.attributes.get("message", event.name)

llama_stack/providers/inline/telemetry/meta_reference/sqlite_span_processor.py

@@ -8,7 +8,7 @@ import json
 import os
 import sqlite3
 import threading
-from datetime import datetime
+from datetime import datetime, timezone
 
 from opentelemetry.sdk.trace import SpanProcessor
 from opentelemetry.trace import Span
@@ -124,8 +124,8 @@ class SQLiteSpanProcessor(SpanProcessor):
                     trace_id,
                     service_name,
                     (span_id if not parent_span_id else None),
-                    datetime.fromtimestamp(span.start_time / 1e9).isoformat(),
+                    datetime.fromtimestamp(span.start_time / 1e9, timezone.utc).isoformat(),
-                    datetime.fromtimestamp(span.end_time / 1e9).isoformat(),
+                    datetime.fromtimestamp(span.end_time / 1e9, timezone.utc).isoformat(),
                 ),
             )
 
@@ -143,8 +143,8 @@ class SQLiteSpanProcessor(SpanProcessor):
                     trace_id,
                     parent_span_id,
                     span.name,
-                    datetime.fromtimestamp(span.start_time / 1e9).isoformat(),
+                    datetime.fromtimestamp(span.start_time / 1e9, timezone.utc).isoformat(),
-                    datetime.fromtimestamp(span.end_time / 1e9).isoformat(),
+                    datetime.fromtimestamp(span.end_time / 1e9, timezone.utc).isoformat(),
                     json.dumps(dict(span.attributes)),
                     span.status.status_code.name,
                     span.kind.name,
@@ -161,7 +161,7 @@ class SQLiteSpanProcessor(SpanProcessor):
                     (
                         span_id,
                         event.name,
-                        datetime.fromtimestamp(event.timestamp / 1e9).isoformat(),
+                        datetime.fromtimestamp(event.timestamp / 1e9, timezone.utc).isoformat(),
                         json.dumps(dict(event.attributes)),
                     ),
                 )

llama_stack/providers/inline/tool_runtime/code_interpreter/code_execution.py

@@ -168,7 +168,7 @@ def process_matplotlib_response(response, matplotlib_dump_dir: str):
     image_paths = []
     for i, img in enumerate(images):
         # create new directory for each day to better organize data:
-        dump_dname = datetime.today().strftime("%Y-%m-%d")
+        dump_dname = datetime.today().strftime("%Y-%m-%d")  # noqa: DTZ002 - we don't care about timezones here since we are displaying the date
         dump_dpath = Path(matplotlib_dump_dir, dump_dname)
         dump_dpath.mkdir(parents=True, exist_ok=True)
         # save image into a file

llama_stack/providers/registry/eval.py

@@ -14,7 +14,7 @@ def available_providers() -> List[ProviderSpec]:
         InlineProviderSpec(
             api=Api.eval,
             provider_type="inline::meta-reference",
-            pip_packages=[],
+            pip_packages=["tree_sitter"],
             module="llama_stack.providers.inline.eval.meta_reference",
             config_class="llama_stack.providers.inline.eval.meta_reference.MetaReferenceEvalConfig",
             api_dependencies=[

llama_stack/providers/tests/safety/conftest.py

@@ -102,3 +102,4 @@ def pytest_generate_tests(metafunc):
             get_provider_fixture_overrides(metafunc.config, available_fixtures) or DEFAULT_PROVIDER_COMBINATIONS
         )
         metafunc.parametrize("safety_stack", combinations, indirect=True)
+

llama_stack/providers/utils/common/data_schema_validator.py

@@ -23,6 +23,10 @@ class ColumnName(Enum):
     generated_answer = "generated_answer"
     context = "context"
     dialog = "dialog"
+    function = "function"
+    language = "language"
+    id = "id"
+    ground_truth = "ground_truth"
 
 
 VALID_SCHEMAS_FOR_SCORING = [
@@ -37,6 +41,15 @@ VALID_SCHEMAS_FOR_SCORING = [
         ColumnName.generated_answer.value: StringType(),
         ColumnName.context.value: StringType(),
     },
+    {
+        ColumnName.input_query.value: StringType(),
+        ColumnName.expected_answer.value: StringType(),
+        ColumnName.generated_answer.value: StringType(),
+        ColumnName.function.value: StringType(),
+        ColumnName.language.value: StringType(),
+        ColumnName.id.value: StringType(),
+        ColumnName.ground_truth.value: StringType(),
+    },
 ]
 
 VALID_SCHEMAS_FOR_EVAL = [
@@ -50,6 +63,15 @@ VALID_SCHEMAS_FOR_EVAL = [
         ColumnName.expected_answer.value: StringType(),
         ColumnName.completion_input.value: CompletionInputType(),
     },
+    {
+        ColumnName.input_query.value: StringType(),
+        ColumnName.expected_answer.value: StringType(),
+        ColumnName.generated_answer.value: StringType(),
+        ColumnName.function.value: StringType(),
+        ColumnName.language.value: StringType(),
+        ColumnName.id.value: StringType(),
+        ColumnName.ground_truth.value: StringType(),
+    },
 ]
 
 

llama_stack/providers/utils/telemetry/tracing.py

@@ -11,7 +11,7 @@ import logging
 import queue
 import threading
 import uuid
-from datetime import datetime
+from datetime import datetime, timezone
 from functools import wraps
 from typing import Any, Callable, Dict, List, Optional
 
@@ -86,7 +86,7 @@ class TraceContext:
             span_id=generate_short_uuid(),
             trace_id=self.trace_id,
             name=name,
-            start_time=datetime.now(),
+            start_time=datetime.now(timezone.utc),
             parent_span_id=current_span.span_id if current_span else None,
             attributes=attributes,
         )
@@ -203,7 +203,7 @@ class TelemetryHandler(logging.Handler):
             UnstructuredLogEvent(
                 trace_id=span.trace_id,
                 span_id=span.span_id,
-                timestamp=datetime.now(),
+                timestamp=datetime.now(timezone.utc),
                 message=self.format(record),
                 severity=severity(record.levelname),
             )

llama_stack/templates/nvidia/run.yaml

@@ -26,11 +26,18 @@ providers:
         namespace: null
         db_path: ${env.SQLITE_STORE_DIR:~/.llama/distributions/nvidia}/faiss_store.db
   safety:
+<<<<<<< HEAD
   - provider_id: nvidia
     provider_type: remote::nvidia
     config:
       guardrails_service_url: ${env.GUARDRAILS_SERVICE_URL:http://localhost:7331}
       config_id: self-check
+=======
+  - provider_id: llama-guard
+    provider_type: inline::llama-guard
+    config:
+      excluded_categories: []
+>>>>>>> upstream/main
   agents:
   - provider_id: meta-reference
     provider_type: inline::meta-reference
@@ -55,6 +62,16 @@ providers:
         namespace: null
         db_path: ${env.SQLITE_STORE_DIR:~/.llama/distributions/nvidia}/meta_reference_eval.db
   datasetio:
+<<<<<<< HEAD
+=======
+  - provider_id: huggingface
+    provider_type: remote::huggingface
+    config:
+      kvstore:
+        type: sqlite
+        namespace: null
+        db_path: ${env.SQLITE_STORE_DIR:~/.llama/distributions/nvidia}/huggingface_datasetio.db
+>>>>>>> upstream/main
   - provider_id: localfs
     provider_type: inline::localfs
     config:

llama_stack/templates/open-benchmark/__init__.py

@@ -3,5 +3,8 @@
 #
 # This source code is licensed under the terms described in the LICENSE file in
 # the root directory of this source tree.
+<<<<<<<< HEAD:llama_stack/templates/open-benchmark/__init__.py
 
 from .open_benchmark import get_distribution_template  # noqa: F401
+========
+>>>>>>>> upstream/main:llama_stack/providers/inline/scoring/basic/utils/bfcl/__init__.py

llama_stack/templates/open-benchmark/open_benchmark.py

@@ -226,6 +226,25 @@ def get_distribution_template() -> DistributionTemplate:
                 "chat_completion_input": {"type": "string"},
             },
         ),
+<<<<<<< HEAD
+=======
+        DatasetInput(
+            dataset_id="bfcl",
+            provider_id="huggingface",
+            url=URL(uri="https://huggingface.co/datasets/llamastack/bfcl_v3"),
+            metadata={
+                "path": "llamastack/bfcl_v3",
+                "split": "train",
+            },
+            dataset_schema={
+                "function": {"type": "string"},
+                "language": {"type": "string"},
+                "ground_truth": {"type": "string"},
+                "id": {"type": "string"},
+                "chat_completion_input": {"type": "string"},
+            },
+        ),
+>>>>>>> upstream/main
     ]
 
     default_benchmarks = [
@@ -249,6 +268,14 @@ def get_distribution_template() -> DistributionTemplate:
             dataset_id="math_500",
             scoring_functions=["basic::regex_parser_math_response"],
         ),
+<<<<<<< HEAD
+=======
+        BenchmarkInput(
+            benchmark_id="meta-reference-bfcl",
+            dataset_id="bfcl",
+            scoring_functions=["basic::bfcl"],
+        ),
+>>>>>>> upstream/main
     ]
     return DistributionTemplate(
         name=name,

llama_stack/templates/open-benchmark/run.yaml

@@ -216,6 +216,27 @@ datasets:
     split: test
   dataset_id: math_500
   provider_id: huggingface
+<<<<<<< HEAD
+=======
+- dataset_schema:
+    function:
+      type: string
+    language:
+      type: string
+    ground_truth:
+      type: string
+    id:
+      type: string
+    chat_completion_input:
+      type: string
+  url:
+    uri: https://huggingface.co/datasets/llamastack/bfcl_v3
+  metadata:
+    path: llamastack/bfcl_v3
+    split: train
+  dataset_id: bfcl
+  provider_id: huggingface
+>>>>>>> upstream/main
 scoring_fns: []
 benchmarks:
 - dataset_id: simpleqa
@@ -238,6 +259,14 @@ benchmarks:
   - basic::regex_parser_math_response
   metadata: {}
   benchmark_id: meta-reference-math-500
+<<<<<<< HEAD
+=======
+- dataset_id: bfcl
+  scoring_functions:
+  - basic::bfcl
+  metadata: {}
+  benchmark_id: meta-reference-bfcl
+>>>>>>> upstream/main
 tool_groups:
 - toolgroup_id: builtin::websearch
   provider_id: tavily-search

llama_stack/templates/passthrough/__init__.py

@@ -0,0 +1,7 @@
+# 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 .passthrough import get_distribution_template  # noqa: F401

llama_stack/templates/passthrough/build.yaml

@@ -1,9 +1,10 @@
 version: '2'
 distribution_spec:
-  description: Use for running LLM inference with the endpoint that compatible with Llama Stack API
+  description: Use Passthrough hosted llama-stack endpoint for LLM inference
   providers:
     inference:
     - remote::passthrough
+    - inline::sentence-transformers
     vector_io:
     - inline::faiss
     - remote::chromadb
@@ -26,6 +27,7 @@ distribution_spec:
     tool_runtime:
     - remote::brave-search
     - remote::tavily-search
+    - remote::wolfram-alpha
     - inline::code-interpreter
     - inline::rag-runtime
     - remote::model-context-protocol

llama_stack/templates/passthrough/doc_template.md

@@ -0,0 +1,35 @@
+---
+orphan: true
+---
+# Passthrough Distribution
+
+```{toctree}
+:maxdepth: 2
+:hidden:
+
+self
+```
+
+The `llamastack/distribution-{{ name }}` distribution consists of the following provider configurations.
+
+{{ providers_table }}
+
+{% if run_config_env_vars %}
+### Environment Variables
+
+The following environment variables can be configured:
+
+{% for var, (default_value, description) in run_config_env_vars.items() %}
+- `{{ var }}`: {{ description }} (default: `{{ default_value }}`)
+{% endfor %}
+{% endif %}
+
+{% if default_models %}
+### Models
+
+The following models are available by default:
+
+{% for model in default_models %}
+- `{{ model.model_id }} {{ model.doc_string }}`
+{% endfor %}
+{% endif %}

llama_stack/templates/passthrough/passthrough.py

@@ -0,0 +1,201 @@
+# 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 pathlib import Path
+
+from llama_stack.apis.models.models import ModelType
+from llama_stack.distribution.datatypes import (
+    ModelInput,
+    Provider,
+    ShieldInput,
+    ToolGroupInput,
+)
+from llama_stack.providers.inline.inference.sentence_transformers import (
+    SentenceTransformersInferenceConfig,
+)
+from llama_stack.providers.inline.vector_io.faiss.config import FaissVectorIOConfig
+from llama_stack.providers.remote.inference.passthrough.config import (
+    PassthroughImplConfig,
+)
+from llama_stack.providers.utils.inference.model_registry import ProviderModelEntry
+from llama_stack.templates.template import (
+    DistributionTemplate,
+    RunConfigSettings,
+)
+
+
+def get_distribution_template() -> DistributionTemplate:
+    providers = {
+        "inference": ["remote::passthrough", "inline::sentence-transformers"],
+        "vector_io": ["inline::faiss", "remote::chromadb", "remote::pgvector"],
+        "safety": ["inline::llama-guard"],
+        "agents": ["inline::meta-reference"],
+        "telemetry": ["inline::meta-reference"],
+        "eval": ["inline::meta-reference"],
+        "datasetio": ["remote::huggingface", "inline::localfs"],
+        "scoring": ["inline::basic", "inline::llm-as-judge", "inline::braintrust"],
+        "tool_runtime": [
+            "remote::brave-search",
+            "remote::tavily-search",
+            "remote::wolfram-alpha",
+            "inline::code-interpreter",
+            "inline::rag-runtime",
+            "remote::model-context-protocol",
+        ],
+    }
+
+    name = "passthrough"
+
+    inference_provider = Provider(
+        provider_id="passthrough",
+        provider_type="remote::passthrough",
+        config=PassthroughImplConfig.sample_run_config(),
+    )
+    embedding_provider = Provider(
+        provider_id="sentence-transformers",
+        provider_type="inline::sentence-transformers",
+        config=SentenceTransformersInferenceConfig.sample_run_config(),
+    )
+    vector_io_provider = Provider(
+        provider_id="faiss",
+        provider_type="inline::faiss",
+        config=FaissVectorIOConfig.sample_run_config(f"~/.llama/distributions/{name}"),
+    )
+
+    default_models = [
+        ModelInput(
+            metadata={},
+            model_id="meta-llama/Llama-3.1-8B-Instruct",
+            provider_id="passthrough",
+            provider_model_id="llama3.1-8b-instruct",
+            model_type=ModelType.llm,
+        ),
+        ModelInput(
+            metadata={},
+            model_id="meta-llama/Llama-3.2-11B-Vision-Instruct",
+            provider_id="passthrough",
+            provider_model_id="llama3.2-11b-vision-instruct",
+            model_type=ModelType.llm,
+        ),
+    ]
+
+    embedding_model = ModelInput(
+        model_id="all-MiniLM-L6-v2",
+        provider_id="sentence-transformers",
+        model_type=ModelType.embedding,
+        metadata={
+            "embedding_dimension": 384,
+        },
+    )
+    default_tool_groups = [
+        ToolGroupInput(
+            toolgroup_id="builtin::websearch",
+            provider_id="tavily-search",
+        ),
+        ToolGroupInput(
+            toolgroup_id="builtin::wolfram_alpha",
+            provider_id="wolfram-alpha",
+        ),
+        ToolGroupInput(
+            toolgroup_id="builtin::rag",
+            provider_id="rag-runtime",
+        ),
+        ToolGroupInput(
+            toolgroup_id="builtin::code_interpreter",
+            provider_id="code-interpreter",
+        ),
+    ]
+
+    return DistributionTemplate(
+        name=name,
+        distro_type="self_hosted",
+        description="Use Passthrough hosted llama-stack endpoint for LLM inference",
+        container_image=None,
+        template_path=Path(__file__).parent / "doc_template.md",
+        providers=providers,
+        available_models_by_provider={
+            "passthrough": [
+                ProviderModelEntry(
+                    provider_model_id="llama3.1-8b-instruct",
+                    model_type=ModelType.llm,
+                ),
+                ProviderModelEntry(
+                    provider_model_id="llama3.2-11b-vision-instruct",
+                    model_type=ModelType.llm,
+                ),
+            ],
+        },
+        run_configs={
+            "run.yaml": RunConfigSettings(
+                provider_overrides={
+                    "inference": [inference_provider, embedding_provider],
+                    "vector_io": [vector_io_provider],
+                },
+                default_models=default_models + [embedding_model],
+                default_shields=[ShieldInput(shield_id="meta-llama/Llama-Guard-3-8B")],
+                default_tool_groups=default_tool_groups,
+            ),
+            "run-with-safety.yaml": RunConfigSettings(
+                provider_overrides={
+                    "inference": [
+                        inference_provider,
+                        embedding_provider,
+                    ],
+                    "vector_io": [vector_io_provider],
+                    "safety": [
+                        Provider(
+                            provider_id="llama-guard",
+                            provider_type="inline::llama-guard",
+                            config={},
+                        ),
+                        Provider(
+                            provider_id="llama-guard-vision",
+                            provider_type="inline::llama-guard",
+                            config={},
+                        ),
+                        Provider(
+                            provider_id="code-scanner",
+                            provider_type="inline::code-scanner",
+                            config={},
+                        ),
+                    ],
+                },
+                default_models=[
+                    *default_models,
+                    embedding_model,
+                ],
+                default_shields=[
+                    ShieldInput(
+                        shield_id="meta-llama/Llama-Guard-3-8B",
+                        provider_id="llama-guard",
+                    ),
+                    ShieldInput(
+                        shield_id="meta-llama/Llama-Guard-3-11B-Vision",
+                        provider_id="llama-guard-vision",
+                    ),
+                    ShieldInput(
+                        shield_id="CodeScanner",
+                        provider_id="code-scanner",
+                    ),
+                ],
+                default_tool_groups=default_tool_groups,
+            ),
+        },
+        run_config_env_vars={
+            "LLAMA_STACK_PORT": (
+                "5001",
+                "Port for the Llama Stack distribution server",
+            ),
+            "PASSTHROUGH_API_KEY": (
+                "",
+                "Passthrough API Key",
+            ),
+            "PASSTHROUGH_URL": (
+                "",
+                "Passthrough URL",
+            ),
+        },
+    )

llama_stack/templates/passthrough/run-with-safety.yaml

@@ -0,0 +1,154 @@
+version: '2'
+image_name: passthrough
+apis:
+- agents
+- datasetio
+- eval
+- inference
+- safety
+- scoring
+- telemetry
+- tool_runtime
+- vector_io
+providers:
+  inference:
+  - provider_id: passthrough
+    provider_type: remote::passthrough
+    config:
+      url: ${env.PASSTHROUGH_URL}
+      api_key: ${env.PASSTHROUGH_API_KEY}
+  - provider_id: sentence-transformers
+    provider_type: inline::sentence-transformers
+    config: {}
+  vector_io:
+  - provider_id: faiss
+    provider_type: inline::faiss
+    config:
+      kvstore:
+        type: sqlite
+        namespace: null
+        db_path: ${env.SQLITE_STORE_DIR:~/.llama/distributions/passthrough}/faiss_store.db
+  safety:
+  - provider_id: llama-guard
+    provider_type: inline::llama-guard
+    config: {}
+  - provider_id: llama-guard-vision
+    provider_type: inline::llama-guard
+    config: {}
+  - provider_id: code-scanner
+    provider_type: inline::code-scanner
+    config: {}
+  agents:
+  - provider_id: meta-reference
+    provider_type: inline::meta-reference
+    config:
+      persistence_store:
+        type: sqlite
+        namespace: null
+        db_path: ${env.SQLITE_STORE_DIR:~/.llama/distributions/passthrough}/agents_store.db
+  telemetry:
+  - provider_id: meta-reference
+    provider_type: inline::meta-reference
+    config:
+      service_name: ${env.OTEL_SERVICE_NAME:llama-stack}
+      sinks: ${env.TELEMETRY_SINKS:console,sqlite}
+      sqlite_db_path: ${env.SQLITE_DB_PATH:~/.llama/distributions/passthrough/trace_store.db}
+  eval:
+  - provider_id: meta-reference
+    provider_type: inline::meta-reference
+    config:
+      kvstore:
+        type: sqlite
+        namespace: null
+        db_path: ${env.SQLITE_STORE_DIR:~/.llama/distributions/passthrough}/meta_reference_eval.db
+  datasetio:
+  - provider_id: huggingface
+    provider_type: remote::huggingface
+    config:
+      kvstore:
+        type: sqlite
+        namespace: null
+        db_path: ${env.SQLITE_STORE_DIR:~/.llama/distributions/passthrough}/huggingface_datasetio.db
+  - provider_id: localfs
+    provider_type: inline::localfs
+    config:
+      kvstore:
+        type: sqlite
+        namespace: null
+        db_path: ${env.SQLITE_STORE_DIR:~/.llama/distributions/passthrough}/localfs_datasetio.db
+  scoring:
+  - provider_id: basic
+    provider_type: inline::basic
+    config: {}
+  - provider_id: llm-as-judge
+    provider_type: inline::llm-as-judge
+    config: {}
+  - provider_id: braintrust
+    provider_type: inline::braintrust
+    config:
+      openai_api_key: ${env.OPENAI_API_KEY:}
+  tool_runtime:
+  - provider_id: brave-search
+    provider_type: remote::brave-search
+    config:
+      api_key: ${env.BRAVE_SEARCH_API_KEY:}
+      max_results: 3
+  - provider_id: tavily-search
+    provider_type: remote::tavily-search
+    config:
+      api_key: ${env.TAVILY_SEARCH_API_KEY:}
+      max_results: 3
+  - provider_id: wolfram-alpha
+    provider_type: remote::wolfram-alpha
+    config:
+      api_key: ${env.WOLFRAM_ALPHA_API_KEY:}
+  - provider_id: code-interpreter
+    provider_type: inline::code-interpreter
+    config: {}
+  - provider_id: rag-runtime
+    provider_type: inline::rag-runtime
+    config: {}
+  - provider_id: model-context-protocol
+    provider_type: remote::model-context-protocol
+    config: {}
+metadata_store:
+  type: sqlite
+  db_path: ${env.SQLITE_STORE_DIR:~/.llama/distributions/passthrough}/registry.db
+models:
+- metadata: {}
+  model_id: meta-llama/Llama-3.1-8B-Instruct
+  provider_id: passthrough
+  provider_model_id: llama3.1-8b-instruct
+  model_type: llm
+- metadata: {}
+  model_id: meta-llama/Llama-3.2-11B-Vision-Instruct
+  provider_id: passthrough
+  provider_model_id: llama3.2-11b-vision-instruct
+  model_type: llm
+- metadata:
+    embedding_dimension: 384
+  model_id: all-MiniLM-L6-v2
+  provider_id: sentence-transformers
+  model_type: embedding
+shields:
+- shield_id: meta-llama/Llama-Guard-3-8B
+  provider_id: llama-guard
+- shield_id: meta-llama/Llama-Guard-3-11B-Vision
+  provider_id: llama-guard-vision
+- shield_id: CodeScanner
+  provider_id: code-scanner
+vector_dbs: []
+datasets: []
+scoring_fns: []
+benchmarks: []
+tool_groups:
+- toolgroup_id: builtin::websearch
+  provider_id: tavily-search
+- toolgroup_id: builtin::wolfram_alpha
+  provider_id: wolfram-alpha
+- toolgroup_id: builtin::rag
+  provider_id: rag-runtime
+- toolgroup_id: builtin::code_interpreter
+  provider_id: code-interpreter
+server:
+  port: 8321

llama_stack/templates/passthrough/run.yaml

@@ -31,7 +31,8 @@ providers:
   safety:
   - provider_id: llama-guard
     provider_type: inline::llama-guard
-    config: {}
+    config:
+      excluded_categories: []
   agents:
   - provider_id: meta-reference
     provider_type: inline::meta-reference
@@ -50,14 +51,26 @@ providers:
   eval:
   - provider_id: meta-reference
     provider_type: inline::meta-reference
-    config: {}
+    config:
+      kvstore:
+        type: sqlite
+        namespace: null
+        db_path: ${env.SQLITE_STORE_DIR:~/.llama/distributions/passthrough}/meta_reference_eval.db
   datasetio:
   - provider_id: huggingface
     provider_type: remote::huggingface
-    config: {}
+    config:
+      kvstore:
+        type: sqlite
+        namespace: null
+        db_path: ${env.SQLITE_STORE_DIR:~/.llama/distributions/passthrough}/huggingface_datasetio.db
   - provider_id: localfs
     provider_type: inline::localfs
-    config: {}
+    config:
+      kvstore:
+        type: sqlite
+        namespace: null
+        db_path: ${env.SQLITE_STORE_DIR:~/.llama/distributions/passthrough}/localfs_datasetio.db
   scoring:
   - provider_id: basic
     provider_type: inline::basic
@@ -80,6 +93,10 @@ providers:
     config:
       api_key: ${env.TAVILY_SEARCH_API_KEY:}
       max_results: 3
+  - provider_id: wolfram-alpha
+    provider_type: remote::wolfram-alpha
+    config:
+      api_key: ${env.WOLFRAM_ALPHA_API_KEY:}
   - provider_id: code-interpreter
     provider_type: inline::code-interpreter
     config: {}
@@ -91,7 +108,7 @@ providers:
     config: {}
 metadata_store:
   type: sqlite
-  db_path: ${env.SQLITE_STORE_DIR:~/.llama/distributions/meta-llama}/registry.db
+  db_path: ${env.SQLITE_STORE_DIR:~/.llama/distributions/passthrough}/registry.db
 models:
 - metadata: {}
   model_id: meta-llama/Llama-3.1-8B-Instruct
@@ -103,15 +120,22 @@ models:
   provider_id: passthrough
   provider_model_id: llama3.2-11b-vision-instruct
   model_type: llm
+- metadata:
+    embedding_dimension: 384
+  model_id: all-MiniLM-L6-v2
+  provider_id: sentence-transformers
+  model_type: embedding
 shields:
 - shield_id: meta-llama/Llama-Guard-3-8B
 vector_dbs: []
 datasets: []
 scoring_fns: []
-eval_tasks: []
+benchmarks: []
 tool_groups:
 - toolgroup_id: builtin::websearch
   provider_id: tavily-search
+- toolgroup_id: builtin::wolfram_alpha
+  provider_id: wolfram-alpha
 - toolgroup_id: builtin::rag
   provider_id: rag-runtime
 - toolgroup_id: builtin::code_interpreter

pyproject.toml

@@ -124,14 +124,15 @@ exclude = [
 
 [tool.ruff.lint]
 select = [
-    "B",  # flake8-bugbear
+    "B",   # flake8-bugbear
-    "B9", # flake8-bugbear subset
+    "B9",  # flake8-bugbear subset
-    "C",  # comprehensions
+    "C",   # comprehensions
-    "E",  # pycodestyle
+    "E",   # pycodestyle
-    "F",  # Pyflakes
+    "F",   # Pyflakes
-    "N",  # Naming
+    "N",   # Naming
-    "W",  # Warnings
+    "W",   # Warnings
-    "I",  # isort
+    "I",   # isort
+    "DTZ", # datetime rules
 ]
 ignore = [
     # The following ignores are desired by the project maintainers.
@@ -145,6 +146,10 @@ ignore = [
     "C901", # Complexity of the function is too high
 ]
 
+# Ignore the following errors for the following files
+[tool.ruff.lint.per-file-ignores]
+"tests/**/*.py" = ["DTZ"] # Ignore datetime rules for tests
+
 [tool.mypy]
 mypy_path = ["llama_stack"]
 packages = ["llama_stack"]
@@ -170,6 +175,10 @@ exclude = [
     "^llama_stack/apis/inspect/inspect\\.py$",
     "^llama_stack/apis/models/models\\.py$",
     "^llama_stack/apis/post_training/post_training\\.py$",
+<<<<<<< HEAD
+=======
+    "^llama_stack/apis/providers/providers\\.py$",
+>>>>>>> upstream/main
     "^llama_stack/apis/resource\\.py$",
     "^llama_stack/apis/safety/safety\\.py$",
     "^llama_stack/apis/scoring/scoring\\.py$",

requirements.txt

@@ -12,7 +12,7 @@ distro==1.9.0
 exceptiongroup==1.2.2 ; python_full_version < '3.11'
 filelock==3.17.0
 fire==0.7.0
-fsspec==2025.2.0
+fsspec==2024.12.0
 h11==0.14.0
 httpcore==1.0.7
 httpx==0.28.1

scripts/unit-tests.sh

@@ -0,0 +1,19 @@
+#!/bin/sh
+
+# 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.
+
+PYTHON_VERSION=${PYTHON_VERSION:-3.10}
+
+command -v uv >/dev/null 2>&1 || { echo >&2 "uv is required but it's not installed. Exiting."; exit 1; }
+
+uv python find $PYTHON_VERSION
+FOUND_PYTHON=$?
+if [ $FOUND_PYTHON -ne 0 ]; then
+     uv python install $PYTHON_VERSION
+fi
+
+uv run --python $PYTHON_VERSION --with-editable . --with-editable ".[dev]" --with-editable ".[unit]" pytest -s -v tests/unit/ $@

tests/integration/agents/test_agents.py

@@ -10,8 +10,7 @@ from uuid import uuid4
 import pytest
 from llama_stack_client.lib.agents.agent import Agent
 from llama_stack_client.lib.agents.event_logger import EventLogger
-from llama_stack_client.types.agents.turn_create_params import Document as AgentDocument
+from llama_stack_client.types.agents.turn_create_params import Document
-from llama_stack_client.types.memory_insert_params import Document
 from llama_stack_client.types.shared_params.agent_config import AgentConfig, ToolConfig
 
 from llama_stack.apis.agents.agents import (
@@ -242,7 +241,7 @@ def test_code_interpreter_for_attachments(llama_stack_client_with_mocked_inferen
 
     codex_agent = Agent(llama_stack_client_with_mocked_inference, **agent_config)
     session_id = codex_agent.create_session(f"test-session-{uuid4()}")
-    inflation_doc = AgentDocument(
+    inflation_doc = Document(
         content="https://raw.githubusercontent.com/meta-llama/llama-stack-apps/main/examples/resources/inflation.csv",
         mime_type="text/csv",
     )

tests/integration/datasetio/test_datasetio.py

@@ -9,11 +9,31 @@ import mimetypes
 import os
 from pathlib import Path
 
+<<<<<<< HEAD
+=======
+import pytest
+
+>>>>>>> upstream/main
 # How to run this test:
 #
 # LLAMA_STACK_CONFIG="template-name" pytest -v tests/integration/datasetio
 
 
+<<<<<<< HEAD
+=======
+@pytest.fixture
+def dataset_for_test(llama_stack_client):
+    dataset_id = "test_dataset"
+    register_dataset(llama_stack_client, dataset_id=dataset_id)
+    yield
+    # Teardown - this always runs, even if the test fails
+    try:
+        llama_stack_client.datasets.unregister(dataset_id)
+    except Exception as e:
+        print(f"Warning: Failed to unregister test_dataset: {e}")
+
+
+>>>>>>> upstream/main
 def data_url_from_file(file_path: str) -> str:
     if not os.path.exists(file_path):
         raise FileNotFoundError(f"File not found: {file_path}")
@@ -80,8 +100,12 @@ def test_register_unregister_dataset(llama_stack_client):
     assert len(response) == 0
 
 
+<<<<<<< HEAD
 def test_get_rows_paginated(llama_stack_client):
     register_dataset(llama_stack_client)
+=======
+def test_get_rows_paginated(llama_stack_client, dataset_for_test):
+>>>>>>> upstream/main
     response = llama_stack_client.datasetio.get_rows_paginated(
         dataset_id="test_dataset",
         rows_in_page=3,

tests/integration/fixtures/common.py

@@ -52,6 +52,8 @@ def llama_stack_client_with_mocked_inference(llama_stack_client, request):
 
     If --record-responses is passed, it will call the real APIs and record the responses.
     """
+    # TODO: will rework this to be more stable
+    return llama_stack_client
     if not isinstance(llama_stack_client, LlamaStackAsLibraryClient):
         logging.warning(
             "llama_stack_client_with_mocked_inference is not supported for this client, returning original client without mocking"

tests/integration/inference/test_vision_inference.py

@@ -36,7 +36,7 @@ def test_image_chat_completion_non_streaming(client_with_models, vision_model_id
                 "type": "image",
                 "image": {
                     "url": {
-                        "uri": "https://raw.githubusercontent.com/meta-llama/llama-stack/main/tests/api/inference/dog.png"
+                        "uri": "https://raw.githubusercontent.com/meta-llama/llama-stack/main/tests/integration/inference/dog.png"
                     },
                 },
             },
@@ -65,7 +65,7 @@ def test_image_chat_completion_streaming(client_with_models, vision_model_id):
                 "type": "image",
                 "image": {
                     "url": {
-                        "uri": "https://raw.githubusercontent.com/meta-llama/llama-stack/main/tests/api/inference/dog.png"
+                        "uri": "https://raw.githubusercontent.com/meta-llama/llama-stack/main/tests/integration/inference/dog.png"
                     },
                 },
             },

tests/integration/providers/__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.

tests/integration/providers/test_providers.py

@@ -0,0 +1,17 @@
+# 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 pytest
+from llama_stack_client import LlamaStackClient
+
+from llama_stack import LlamaStackAsLibraryClient
+
+
+class TestProviders:
+    @pytest.mark.asyncio
+    def test_list(self, llama_stack_client: LlamaStackAsLibraryClient | LlamaStackClient):
+        provider_list = llama_stack_client.providers.list()
+        assert provider_list is not None

tests/integration/scoring/test_scoring.py

@@ -10,6 +10,19 @@ import pytest
 from ..datasetio.test_datasetio import register_dataset
 
 
+@pytest.fixture
+def rag_dataset_for_test(llama_stack_client):
+    dataset_id = "test_dataset"
+    register_dataset(llama_stack_client, for_rag=True, dataset_id=dataset_id)
+    yield  # This is where the test function will run
+
+    # Teardown - this always runs, even if the test fails
+    try:
+        llama_stack_client.datasets.unregister(dataset_id)
+    except Exception as e:
+        print(f"Warning: Failed to unregister test_dataset: {e}")
+
+
 @pytest.fixture
 def sample_judge_prompt_template():
     return "Output a number response in the following format: Score: <number>, where <number> is the number between 0 and 9."
@@ -79,9 +92,7 @@ def test_scoring_functions_register(
     # TODO: add unregister api for scoring functions
 
 
-def test_scoring_score(llama_stack_client):
+def test_scoring_score(llama_stack_client, rag_dataset_for_test):
-    register_dataset(llama_stack_client, for_rag=True)
-
     # scoring individual rows
     rows = llama_stack_client.datasetio.get_rows_paginated(
         dataset_id="test_dataset",
@@ -115,9 +126,9 @@ def test_scoring_score(llama_stack_client):
         assert len(response.results[x].score_rows) == 5
 
 
-def test_scoring_score_with_params_llm_as_judge(llama_stack_client, sample_judge_prompt_template, judge_model_id):
+def test_scoring_score_with_params_llm_as_judge(
-    register_dataset(llama_stack_client, for_rag=True)
+    llama_stack_client, sample_judge_prompt_template, judge_model_id, rag_dataset_for_test
-
+):
     # scoring individual rows
     rows = llama_stack_client.datasetio.get_rows_paginated(
         dataset_id="test_dataset",
@@ -167,9 +178,8 @@ def test_scoring_score_with_params_llm_as_judge(llama_stack_client, sample_judge
     ],
 )
 def test_scoring_score_with_aggregation_functions(
-    llama_stack_client, sample_judge_prompt_template, judge_model_id, provider_id
+    llama_stack_client, sample_judge_prompt_template, judge_model_id, provider_id, rag_dataset_for_test
 ):
-    register_dataset(llama_stack_client, for_rag=True)
     rows = llama_stack_client.datasetio.get_rows_paginated(
         dataset_id="test_dataset",
         rows_in_page=3,

uv.lock

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 [[package]]
 name = "frozenlist"
 version = "1.5.0"
+<<<<<<< HEAD
 source = { registry = "https://pypi.org/simple" }
 sdist = { url = "https://files.pythonhosted.org/packages/8f/ed/0f4cec13a93c02c47ec32d81d11c0c1efbadf4a471e3f3ce7cad366cbbd3/frozenlist-1.5.0.tar.gz", hash = "sha256:81d5af29e61b9c8348e876d442253723928dce6433e0e76cd925cd83f1b4b817", size = 39930 }
 wheels = [
@@ -770,10 +771,86 @@ wheels = [
 [[package]]
 name = "fsspec"
 version = "2025.2.0"
+=======
+>>>>>>> upstream/main
 source = { registry = "https://pypi.org/simple" }
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