feat: small ollama package

.github/PULL_REQUEST_TEMPLATE.md

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

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

@@ -13,7 +13,7 @@ runs:
     - name: Install dependencies
       shell: bash
       run: |
-        uv sync --all-extras
+        uv sync --all-groups
         uv pip install ollama faiss-cpu
         # always test against the latest version of the client
         # TODO: this is not necessarily a good idea. we need to test against both published and latest

.pre-commit-config.yaml

@@ -53,7 +53,7 @@ repos:
         - black==24.3.0
 
 -   repo: https://github.com/astral-sh/uv-pre-commit
-    rev: 0.6.3
+    rev: 0.7.8
     hooks:
     -   id: uv-lock
     -   id: uv-export
@@ -61,6 +61,7 @@ repos:
             "--frozen",
             "--no-hashes",
             "--no-emit-project",
+            "--no-default-groups",
             "--output-file=requirements.txt"
         ]
 
@@ -88,8 +89,8 @@ repos:
       - id: distro-codegen
         name: Distribution Template Codegen
         additional_dependencies:
-          - uv==0.6.0
-        entry: uv run --extra codegen ./scripts/distro_codegen.py
+          - uv==0.7.8
+        entry: uv run --group codegen ./scripts/distro_codegen.py
         language: python
         pass_filenames: false
         require_serial: true
@@ -97,8 +98,8 @@ repos:
       - 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'
+          - uv==0.7.8
+        entry: sh -c 'uv run ./docs/openapi_generator/run_openapi_generator.sh > /dev/null'
         language: python
         pass_filenames: false
         require_serial: true

.readthedocs.yaml

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

CONTRIBUTING.md

@@ -168,10 +168,10 @@ If you are making changes to the documentation at [https://llama-stack.readthedo
 
 ```bash
 # This rebuilds the documentation pages.
-uv run --with ".[docs]" make -C docs/ html
+uv run --group docs make -C docs/ html
 
 # This will start a local server (usually at http://127.0.0.1:8000) that automatically rebuilds and refreshes when you make changes to the documentation.
-uv run --with ".[docs]" sphinx-autobuild docs/source docs/build/html --write-all
+uv run --group docs sphinx-autobuild docs/source docs/build/html --write-all
 ```
 
 ### Update API Documentation
@@ -179,7 +179,7 @@ uv run --with ".[docs]" sphinx-autobuild docs/source docs/build/html --write-all
 If you modify or add new API endpoints, update the API documentation accordingly. You can do this by running the following command:
 
 ```bash
-uv run --with ".[dev]" ./docs/openapi_generator/run_openapi_generator.sh
+uv run ./docs/openapi_generator/run_openapi_generator.sh
 ```
 
 The generated API documentation will be available in `docs/_static/`. Make sure to review the changes before committing.

MANIFEST.in

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

README.md

@@ -107,26 +107,29 @@ By reducing friction and complexity, Llama Stack empowers developers to focus on
 ### API Providers
 Here is a list of the various API providers and available distributions that can help developers get started easily with Llama Stack.
 
-| **API Provider Builder** |    **Environments**    | **Agents** | **Inference** | **Memory** | **Safety** | **Telemetry** |
-|:------------------------:|:----------------------:|:----------:|:-------------:|:----------:|:----------:|:-------------:|
-|      Meta Reference      |      Single Node       |     ✅      |       ✅       |     ✅      |     ✅      |       ✅       |
-|        SambaNova         |         Hosted         |            |       ✅       |            |     ✅      |               |
-|         Cerebras         |         Hosted         |            |       ✅       |            |            |               |
-|        Fireworks         |         Hosted         |     ✅      |       ✅       |     ✅      |            |               |
-|       AWS Bedrock        |         Hosted         |            |       ✅       |            |     ✅      |               |
-|         Together         |         Hosted         |     ✅      |       ✅       |            |     ✅      |               |
-|           Groq           |         Hosted         |            |       ✅       |            |            |               |
-|          Ollama          |      Single Node       |            |       ✅       |            |            |               |
-|           TGI            | Hosted and Single Node |            |       ✅       |            |            |               |
-|        NVIDIA NIM        | Hosted and Single Node |            |       ✅       |            |            |               |
-|          Chroma          |      Single Node       |            |               |     ✅      |            |               |
-|        PG Vector         |      Single Node       |            |               |     ✅      |            |               |
-|    PyTorch ExecuTorch    |     On-device iOS      |     ✅      |       ✅       |            |            |               |
-|           vLLM           | Hosted and Single Node |            |       ✅       |            |            |               |
-|          OpenAI          |         Hosted         |            |       ✅       |            |            |               |
-|        Anthropic         |         Hosted         |            |       ✅       |            |            |               |
-|          Gemini          |         Hosted         |            |       ✅       |            |            |               |
-|          watsonx         |         Hosted         |            |       ✅       |            |            |               |
+| **API Provider Builder** |    **Environments**    | **Agents** | **Inference** | **Memory** | **Safety** | **Telemetry** | **Post Training** |
+|:------------------------:|:----------------------:|:----------:|:-------------:|:----------:|:----------:|:-------------:|:-----------------:|
+|      Meta Reference      |      Single Node       |     ✅      |       ✅       |     ✅      |     ✅      |       ✅       |               |
+|        SambaNova         |         Hosted         |            |       ✅       |            |     ✅      |               |                  |
+|         Cerebras         |         Hosted         |            |       ✅       |            |            |               |                  |
+|        Fireworks         |         Hosted         |     ✅      |       ✅       |     ✅      |            |               |                |
+|       AWS Bedrock        |         Hosted         |            |       ✅       |            |     ✅      |               |                |
+|         Together         |         Hosted         |     ✅      |       ✅       |            |     ✅      |               |                |
+|           Groq           |         Hosted         |            |       ✅       |            |            |               |                 |
+|          Ollama          |      Single Node       |            |       ✅       |            |            |               |                 |
+|           TGI            | Hosted and Single Node |            |       ✅       |            |            |               |                 |
+|        NVIDIA NIM        | Hosted and Single Node |            |       ✅       |            |            |               |                 |
+|          Chroma          |      Single Node       |            |               |     ✅      |            |               |                 |
+|        PG Vector         |      Single Node       |            |               |     ✅      |            |               |                 |
+|    PyTorch ExecuTorch    |     On-device iOS      |     ✅      |       ✅       |            |            |               |                |
+|           vLLM           | Hosted and Single Node |            |       ✅       |            |            |               |                 |
+|          OpenAI          |         Hosted         |            |       ✅       |            |            |               |                 |
+|        Anthropic         |         Hosted         |            |       ✅       |            |            |               |                 |
+|          Gemini          |         Hosted         |            |       ✅       |            |            |               |                 |
+|          watsonx         |         Hosted         |            |       ✅       |            |            |               |                 |
+|        HuggingFace       |       Single Node      |            |                |            |            |               |       ✅        |
+|         TorchTune        |       Single Node      |            |                |            |            |               |       ✅        |
+|       NVIDIA NEMO        |         Hosted         |            |                |            |            |               |       ✅        |
 
 
 ### Distributions

docs/_static/llama-stack-spec.html

@@ -7540,6 +7540,9 @@
                     {
                         "$ref": "#/components/schemas/OpenAIResponseObjectStreamResponseCreated"
                     },
+                    {
+                        "$ref": "#/components/schemas/OpenAIResponseObjectStreamResponseOutputTextDelta"
+                    },
                     {
                         "$ref": "#/components/schemas/OpenAIResponseObjectStreamResponseCompleted"
                     }
@@ -7548,6 +7551,7 @@
                     "propertyName": "type",
                     "mapping": {
                         "response.created": "#/components/schemas/OpenAIResponseObjectStreamResponseCreated",
+                        "response.output_text.delta": "#/components/schemas/OpenAIResponseObjectStreamResponseOutputTextDelta",
                         "response.completed": "#/components/schemas/OpenAIResponseObjectStreamResponseCompleted"
                     }
                 }
@@ -7590,6 +7594,41 @@
                 ],
                 "title": "OpenAIResponseObjectStreamResponseCreated"
             },
+            "OpenAIResponseObjectStreamResponseOutputTextDelta": {
+                "type": "object",
+                "properties": {
+                    "content_index": {
+                        "type": "integer"
+                    },
+                    "delta": {
+                        "type": "string"
+                    },
+                    "item_id": {
+                        "type": "string"
+                    },
+                    "output_index": {
+                        "type": "integer"
+                    },
+                    "sequence_number": {
+                        "type": "integer"
+                    },
+                    "type": {
+                        "type": "string",
+                        "const": "response.output_text.delta",
+                        "default": "response.output_text.delta"
+                    }
+                },
+                "additionalProperties": false,
+                "required": [
+                    "content_index",
+                    "delta",
+                    "item_id",
+                    "output_index",
+                    "sequence_number",
+                    "type"
+                ],
+                "title": "OpenAIResponseObjectStreamResponseOutputTextDelta"
+            },
             "CreateUploadSessionRequest": {
                 "type": "object",
                 "properties": {
@@ -9555,9 +9594,6 @@
                     "toolgroup_id": {
                         "type": "string"
                     },
-                    "tool_host": {
-                        "$ref": "#/components/schemas/ToolHost"
-                    },
                     "description": {
                         "type": "string"
                     },
@@ -9599,21 +9635,11 @@
                     "provider_id",
                     "type",
                     "toolgroup_id",
-                    "tool_host",
                     "description",
                     "parameters"
                 ],
                 "title": "Tool"
             },
-            "ToolHost": {
-                "type": "string",
-                "enum": [
-                    "distribution",
-                    "client",
-                    "model_context_protocol"
-                ],
-                "title": "ToolHost"
-            },
             "ToolGroup": {
                 "type": "object",
                 "properties": {

docs/_static/llama-stack-spec.yaml

@@ -5294,11 +5294,13 @@ components:
     OpenAIResponseObjectStream:
       oneOf:
         - $ref: '#/components/schemas/OpenAIResponseObjectStreamResponseCreated'
+        - $ref: '#/components/schemas/OpenAIResponseObjectStreamResponseOutputTextDelta'
         - $ref: '#/components/schemas/OpenAIResponseObjectStreamResponseCompleted'
       discriminator:
         propertyName: type
         mapping:
           response.created: '#/components/schemas/OpenAIResponseObjectStreamResponseCreated'
+          response.output_text.delta: '#/components/schemas/OpenAIResponseObjectStreamResponseOutputTextDelta'
           response.completed: '#/components/schemas/OpenAIResponseObjectStreamResponseCompleted'
     "OpenAIResponseObjectStreamResponseCompleted":
       type: object
@@ -5330,6 +5332,33 @@ components:
         - type
       title: >-
         OpenAIResponseObjectStreamResponseCreated
+    "OpenAIResponseObjectStreamResponseOutputTextDelta":
+      type: object
+      properties:
+        content_index:
+          type: integer
+        delta:
+          type: string
+        item_id:
+          type: string
+        output_index:
+          type: integer
+        sequence_number:
+          type: integer
+        type:
+          type: string
+          const: response.output_text.delta
+          default: response.output_text.delta
+      additionalProperties: false
+      required:
+        - content_index
+        - delta
+        - item_id
+        - output_index
+        - sequence_number
+        - type
+      title: >-
+        OpenAIResponseObjectStreamResponseOutputTextDelta
     CreateUploadSessionRequest:
       type: object
       properties:
@@ -6713,8 +6742,6 @@ components:
           default: tool
         toolgroup_id:
           type: string
-        tool_host:
-          $ref: '#/components/schemas/ToolHost'
         description:
           type: string
         parameters:
@@ -6737,17 +6764,9 @@ components:
         - provider_id
         - type
         - toolgroup_id
-        - tool_host
         - description
         - parameters
       title: Tool
-    ToolHost:
-      type: string
-      enum:
-        - distribution
-        - client
-        - model_context_protocol
-      title: ToolHost
     ToolGroup:
       type: object
       properties:

docs/readme.md

@@ -6,7 +6,7 @@ Here's a collection of comprehensive guides, examples, and resources for buildin
 
 From the llama-stack root directory, run the following command to render the docs locally:
 ```bash
-uv run --with ".[docs]" sphinx-autobuild docs/source docs/build/html --write-all
+uv run --group docs sphinx-autobuild docs/source docs/build/html --write-all
 ```
 You can open up the docs in your browser at http://localhost:8000
 

docs/source/providers/index.md

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

docs/source/providers/post_training/huggingface.md

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

docs/source/providers/post_training/nvidia_nemo.md

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

docs/source/providers/post_training/torchtune.md

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

llama_stack/apis/agents/openai_responses.py

@@ -149,6 +149,16 @@ class OpenAIResponseObjectStreamResponseCreated(BaseModel):
     type: Literal["response.created"] = "response.created"
 
 
+@json_schema_type
+class OpenAIResponseObjectStreamResponseOutputTextDelta(BaseModel):
+    content_index: int
+    delta: str
+    item_id: str
+    output_index: int
+    sequence_number: int
+    type: Literal["response.output_text.delta"] = "response.output_text.delta"
+
+
 @json_schema_type
 class OpenAIResponseObjectStreamResponseCompleted(BaseModel):
     response: OpenAIResponseObject
@@ -156,7 +166,9 @@ class OpenAIResponseObjectStreamResponseCompleted(BaseModel):
 
 
 OpenAIResponseObjectStream = Annotated[
-    OpenAIResponseObjectStreamResponseCreated | OpenAIResponseObjectStreamResponseCompleted,
+    OpenAIResponseObjectStreamResponseCreated
+    | OpenAIResponseObjectStreamResponseOutputTextDelta
+    | OpenAIResponseObjectStreamResponseCompleted,
     Field(discriminator="type"),
 ]
 register_schema(OpenAIResponseObjectStream, name="OpenAIResponseObjectStream")

llama_stack/apis/common/deployment_types.py

@@ -1,30 +0,0 @@
-# Copyright (c) Meta Platforms, Inc. and affiliates.
-# All rights reserved.
-#
-# This source code is licensed under the terms described in the LICENSE file in
-# the root directory of this source tree.
-
-from enum import Enum
-from typing import Any
-
-from pydantic import BaseModel
-
-from llama_stack.apis.common.content_types import URL
-from llama_stack.schema_utils import json_schema_type
-
-
-@json_schema_type
-class RestAPIMethod(Enum):
-    GET = "GET"
-    POST = "POST"
-    PUT = "PUT"
-    DELETE = "DELETE"
-
-
-@json_schema_type
-class RestAPIExecutionConfig(BaseModel):
-    url: URL
-    method: RestAPIMethod
-    params: dict[str, Any] | None = None
-    headers: dict[str, Any] | None = None
-    body: dict[str, Any] | None = None

llama_stack/apis/tools/tools.py

@@ -27,18 +27,10 @@ class ToolParameter(BaseModel):
     default: Any | None = None
 
 
-@json_schema_type
-class ToolHost(Enum):
-    distribution = "distribution"
-    client = "client"
-    model_context_protocol = "model_context_protocol"
-
-
 @json_schema_type
 class Tool(Resource):
     type: Literal[ResourceType.tool] = ResourceType.tool
     toolgroup_id: str
-    tool_host: ToolHost
     description: str
     parameters: list[ToolParameter]
     metadata: dict[str, Any] | None = None

llama_stack/cli/stack/_build.py

@@ -267,8 +267,8 @@ def run_stack_build_command(args: argparse.Namespace) -> None:
     if args.run:
         config_dict = yaml.safe_load(run_config.read_text())
         config = parse_and_maybe_upgrade_config(config_dict)
-        if not os.path.exists(config.external_providers_dir):
-            os.makedirs(config.external_providers_dir, exist_ok=True)
+        if config.external_providers_dir and not config.external_providers_dir.exists():
+            config.external_providers_dir.mkdir(exist_ok=True)
         run_args = formulate_run_args(args.image_type, args.image_name, config, args.template)
         run_args.extend([str(os.getenv("LLAMA_STACK_PORT", 8321)), "--config", run_config])
         run_command(run_args)

llama_stack/distribution/build_container.sh

@@ -125,7 +125,6 @@ RUN apt-get update && apt-get install -y \
        curl wget telnet git\
        procps psmisc lsof \
        traceroute \
-       bubblewrap \
        gcc \
        && rm -rf /var/lib/apt/lists/*
 

llama_stack/distribution/inspect.py

@@ -16,7 +16,7 @@ from llama_stack.apis.inspect import (
     VersionInfo,
 )
 from llama_stack.distribution.datatypes import StackRunConfig
-from llama_stack.distribution.server.endpoints import get_all_api_endpoints
+from llama_stack.distribution.server.routes import get_all_api_routes
 from llama_stack.providers.datatypes import HealthStatus
 
 
@@ -42,15 +42,15 @@ class DistributionInspectImpl(Inspect):
         run_config: StackRunConfig = self.config.run_config
 
         ret = []
-        all_endpoints = get_all_api_endpoints()
+        all_endpoints = get_all_api_routes()
         for api, endpoints in all_endpoints.items():
             # Always include provider and inspect APIs, filter others based on run config
             if api.value in ["providers", "inspect"]:
                 ret.extend(
                     [
                         RouteInfo(
-                            route=e.route,
-                            method=e.method,
+                            route=e.path,
+                            method=next(iter([m for m in e.methods if m != "HEAD"])),
                             provider_types=[],  # These APIs don't have "real" providers - they're internal to the stack
                         )
                         for e in endpoints
@@ -62,8 +62,8 @@ class DistributionInspectImpl(Inspect):
                     ret.extend(
                         [
                             RouteInfo(
-                                route=e.route,
-                                method=e.method,
+                                route=e.path,
+                                method=next(iter([m for m in e.methods if m != "HEAD"])),
                                 provider_types=[p.provider_type for p in providers],
                             )
                             for e in endpoints

llama_stack/distribution/library_client.py

@@ -37,10 +37,7 @@ from llama_stack.distribution.request_headers import (
     request_provider_data_context,
 )
 from llama_stack.distribution.resolver import ProviderRegistry
-from llama_stack.distribution.server.endpoints import (
-    find_matching_endpoint,
-    initialize_endpoint_impls,
-)
+from llama_stack.distribution.server.routes import find_matching_route, initialize_route_impls
 from llama_stack.distribution.stack import (
     construct_stack,
     get_stack_run_config_from_template,
@@ -208,7 +205,7 @@ class AsyncLlamaStackAsLibraryClient(AsyncLlamaStackClient):
 
     async def initialize(self) -> bool:
         try:
-            self.endpoint_impls = None
+            self.route_impls = None
             self.impls = await construct_stack(self.config, self.custom_provider_registry)
         except ModuleNotFoundError as _e:
             cprint(_e.msg, color="red", file=sys.stderr)
@@ -254,7 +251,7 @@ class AsyncLlamaStackAsLibraryClient(AsyncLlamaStackClient):
             safe_config = redact_sensitive_fields(self.config.model_dump())
             console.print(yaml.dump(safe_config, indent=2))
 
-        self.endpoint_impls = initialize_endpoint_impls(self.impls)
+        self.route_impls = initialize_route_impls(self.impls)
         return True
 
     async def request(
@@ -265,7 +262,7 @@ class AsyncLlamaStackAsLibraryClient(AsyncLlamaStackClient):
         stream=False,
         stream_cls=None,
     ):
-        if not self.endpoint_impls:
+        if not self.route_impls:
             raise ValueError("Client not initialized")
 
         # Create headers with provider data if available
@@ -296,11 +293,14 @@ class AsyncLlamaStackAsLibraryClient(AsyncLlamaStackClient):
         cast_to: Any,
         options: Any,
     ):
+        if self.route_impls is None:
+            raise ValueError("Client not initialized")
+
         path = options.url
         body = options.params or {}
         body |= options.json_data or {}
 
-        matched_func, path_params, route = find_matching_endpoint(options.method, path, self.endpoint_impls)
+        matched_func, path_params, route = find_matching_route(options.method, path, self.route_impls)
         body |= path_params
         body = self._convert_body(path, options.method, body)
         await start_trace(route, {"__location__": "library_client"})
@@ -342,10 +342,13 @@ class AsyncLlamaStackAsLibraryClient(AsyncLlamaStackClient):
         options: Any,
         stream_cls: Any,
     ):
+        if self.route_impls is None:
+            raise ValueError("Client not initialized")
+
         path = options.url
         body = options.params or {}
         body |= options.json_data or {}
-        func, path_params, route = find_matching_endpoint(options.method, path, self.endpoint_impls)
+        func, path_params, route = find_matching_route(options.method, path, self.route_impls)
         body |= path_params
 
         body = self._convert_body(path, options.method, body)
@@ -397,7 +400,10 @@ class AsyncLlamaStackAsLibraryClient(AsyncLlamaStackClient):
         if not body:
             return {}
 
-        func, _, _ = find_matching_endpoint(method, path, self.endpoint_impls)
+        if self.route_impls is None:
+            raise ValueError("Client not initialized")
+
+        func, _, _ = find_matching_route(method, path, self.route_impls)
         sig = inspect.signature(func)
 
         # Strip NOT_GIVENs to use the defaults in signature

llama_stack/distribution/resolver.py

@@ -47,7 +47,7 @@ from llama_stack.providers.datatypes import (
     RemoteProviderSpec,
     ScoringFunctionsProtocolPrivate,
     ShieldsProtocolPrivate,
-    ToolsProtocolPrivate,
+    ToolGroupsProtocolPrivate,
     VectorDBsProtocolPrivate,
 )
 
@@ -93,7 +93,7 @@ def api_protocol_map_for_compliance_check() -> dict[Api, Any]:
 def additional_protocols_map() -> dict[Api, Any]:
     return {
         Api.inference: (ModelsProtocolPrivate, Models, Api.models),
-        Api.tool_groups: (ToolsProtocolPrivate, ToolGroups, Api.tool_groups),
+        Api.tool_groups: (ToolGroupsProtocolPrivate, ToolGroups, Api.tool_groups),
         Api.vector_io: (VectorDBsProtocolPrivate, VectorDBs, Api.vector_dbs),
         Api.safety: (ShieldsProtocolPrivate, Shields, Api.shields),
         Api.datasetio: (DatasetsProtocolPrivate, Datasets, Api.datasets),

llama_stack/distribution/routers/tool_runtime.py

@@ -11,7 +11,7 @@ from llama_stack.apis.common.content_types import (
     InterleavedContent,
 )
 from llama_stack.apis.tools import (
-    ListToolDefsResponse,
+    ListToolsResponse,
     RAGDocument,
     RAGQueryConfig,
     RAGQueryResult,
@@ -19,7 +19,8 @@ from llama_stack.apis.tools import (
     ToolRuntime,
 )
 from llama_stack.log import get_logger
-from llama_stack.providers.datatypes import RoutingTable
+
+from ..routing_tables.toolgroups import ToolGroupsRoutingTable
 
 logger = get_logger(name=__name__, category="core")
 
@@ -28,7 +29,7 @@ class ToolRuntimeRouter(ToolRuntime):
     class RagToolImpl(RAGToolRuntime):
         def __init__(
             self,
-            routing_table: RoutingTable,
+            routing_table: ToolGroupsRoutingTable,
         ) -> None:
             logger.debug("Initializing ToolRuntimeRouter.RagToolImpl")
             self.routing_table = routing_table
@@ -59,7 +60,7 @@ class ToolRuntimeRouter(ToolRuntime):
 
     def __init__(
         self,
-        routing_table: RoutingTable,
+        routing_table: ToolGroupsRoutingTable,
     ) -> None:
         logger.debug("Initializing ToolRuntimeRouter")
         self.routing_table = routing_table
@@ -86,6 +87,6 @@ class ToolRuntimeRouter(ToolRuntime):
 
     async def list_runtime_tools(
         self, tool_group_id: str | None = None, mcp_endpoint: URL | None = None
-    ) -> ListToolDefsResponse:
+    ) -> ListToolsResponse:
         logger.debug(f"ToolRuntimeRouter.list_runtime_tools: {tool_group_id}")
-        return await self.routing_table.get_provider_impl(tool_group_id).list_tools(tool_group_id, mcp_endpoint)
+        return await self.routing_table.list_tools(tool_group_id)

llama_stack/distribution/routing_tables/common.py

@@ -46,7 +46,7 @@ async def register_object_with_provider(obj: RoutableObject, p: Any) -> Routable
     elif api == Api.eval:
         return await p.register_benchmark(obj)
     elif api == Api.tool_runtime:
-        return await p.register_tool(obj)
+        return await p.register_toolgroup(obj)
     else:
         raise ValueError(f"Unknown API {api} for registering object with provider")
 
@@ -60,7 +60,7 @@ async def unregister_object_from_provider(obj: RoutableObject, p: Any) -> None:
     elif api == Api.datasetio:
         return await p.unregister_dataset(obj.identifier)
     elif api == Api.tool_runtime:
-        return await p.unregister_tool(obj.identifier)
+        return await p.unregister_toolgroup(obj.identifier)
     else:
         raise ValueError(f"Unregister not supported for {api}")
 
@@ -136,7 +136,7 @@ class CommonRoutingTableImpl(RoutingTable):
             elif isinstance(self, BenchmarksRoutingTable):
                 return ("Eval", "benchmark")
             elif isinstance(self, ToolGroupsRoutingTable):
-                return ("Tools", "tool")
+                return ("ToolGroups", "tool_group")
             else:
                 raise ValueError("Unknown routing table type")
 

llama_stack/distribution/routing_tables/toolgroups.py

@@ -7,11 +7,8 @@
 from typing import Any
 
 from llama_stack.apis.common.content_types import URL
-from llama_stack.apis.tools import ListToolGroupsResponse, ListToolsResponse, Tool, ToolGroup, ToolGroups, ToolHost
-from llama_stack.distribution.datatypes import (
-    ToolGroupWithACL,
-    ToolWithACL,
-)
+from llama_stack.apis.tools import ListToolGroupsResponse, ListToolsResponse, Tool, ToolGroup, ToolGroups
+from llama_stack.distribution.datatypes import ToolGroupWithACL
 from llama_stack.log import get_logger
 
 from .common import CommonRoutingTableImpl
@@ -19,12 +16,70 @@ from .common import CommonRoutingTableImpl
 logger = get_logger(name=__name__, category="core")
 
 
+def parse_toolgroup_from_toolgroup_name_pair(toolgroup_name_with_maybe_tool_name: str) -> str | None:
+    # handle the funny case like "builtin::rag/knowledge_search"
+    parts = toolgroup_name_with_maybe_tool_name.split("/")
+    if len(parts) == 2:
+        return parts[0]
+    else:
+        return None
+
+
 class ToolGroupsRoutingTable(CommonRoutingTableImpl, ToolGroups):
-    async def list_tools(self, toolgroup_id: str | None = None) -> ListToolsResponse:
-        tools = await self.get_all_with_type("tool")
+    toolgroups_to_tools: dict[str, list[Tool]] = {}
+    tool_to_toolgroup: dict[str, str] = {}
+
+    # overridden
+    def get_provider_impl(self, routing_key: str, provider_id: str | None = None) -> Any:
+        # we don't index tools in the registry anymore, but only keep a cache of them by toolgroup_id
+        # TODO: we may want to invalidate the cache (for a given toolgroup_id) every once in a while?
+
+        toolgroup_id = parse_toolgroup_from_toolgroup_name_pair(routing_key)
         if toolgroup_id:
-            tools = [tool for tool in tools if tool.toolgroup_id == toolgroup_id]
-        return ListToolsResponse(data=tools)
+            routing_key = toolgroup_id
+
+        if routing_key in self.tool_to_toolgroup:
+            routing_key = self.tool_to_toolgroup[routing_key]
+        return super().get_provider_impl(routing_key, provider_id)
+
+    async def list_tools(self, toolgroup_id: str | None = None) -> ListToolsResponse:
+        if toolgroup_id:
+            if group_id := parse_toolgroup_from_toolgroup_name_pair(toolgroup_id):
+                toolgroup_id = group_id
+            toolgroups = [await self.get_tool_group(toolgroup_id)]
+        else:
+            toolgroups = await self.get_all_with_type("tool_group")
+
+        all_tools = []
+        for toolgroup in toolgroups:
+            if toolgroup.identifier not in self.toolgroups_to_tools:
+                await self._index_tools(toolgroup)
+            all_tools.extend(self.toolgroups_to_tools[toolgroup.identifier])
+
+        return ListToolsResponse(data=all_tools)
+
+    async def _index_tools(self, toolgroup: ToolGroup):
+        provider_impl = super().get_provider_impl(toolgroup.identifier, toolgroup.provider_id)
+        tooldefs_response = await provider_impl.list_runtime_tools(toolgroup.identifier, toolgroup.mcp_endpoint)
+
+        # TODO: kill this Tool vs ToolDef distinction
+        tooldefs = tooldefs_response.data
+        tools = []
+        for t in tooldefs:
+            tools.append(
+                Tool(
+                    identifier=t.name,
+                    toolgroup_id=toolgroup.identifier,
+                    description=t.description or "",
+                    parameters=t.parameters or [],
+                    metadata=t.metadata,
+                    provider_id=toolgroup.provider_id,
+                )
+            )
+
+        self.toolgroups_to_tools[toolgroup.identifier] = tools
+        for tool in tools:
+            self.tool_to_toolgroup[tool.identifier] = toolgroup.identifier
 
     async def list_tool_groups(self) -> ListToolGroupsResponse:
         return ListToolGroupsResponse(data=await self.get_all_with_type("tool_group"))
@@ -36,7 +91,13 @@ class ToolGroupsRoutingTable(CommonRoutingTableImpl, ToolGroups):
         return tool_group
 
     async def get_tool(self, tool_name: str) -> Tool:
-        return await self.get_object_by_identifier("tool", tool_name)
+        if tool_name in self.tool_to_toolgroup:
+            toolgroup_id = self.tool_to_toolgroup[tool_name]
+            tools = self.toolgroups_to_tools[toolgroup_id]
+            for tool in tools:
+                if tool.identifier == tool_name:
+                    return tool
+        raise ValueError(f"Tool '{tool_name}' not found")
 
     async def register_tool_group(
         self,
@@ -45,53 +106,26 @@ class ToolGroupsRoutingTable(CommonRoutingTableImpl, ToolGroups):
         mcp_endpoint: URL | None = None,
         args: dict[str, Any] | None = None,
     ) -> None:
-        tools = []
-        tool_defs = await self.impls_by_provider_id[provider_id].list_runtime_tools(toolgroup_id, mcp_endpoint)
-        tool_host = ToolHost.model_context_protocol if mcp_endpoint else ToolHost.distribution
-
-        for tool_def in tool_defs.data:
-            tools.append(
-                ToolWithACL(
-                    identifier=tool_def.name,
-                    toolgroup_id=toolgroup_id,
-                    description=tool_def.description or "",
-                    parameters=tool_def.parameters or [],
-                    provider_id=provider_id,
-                    provider_resource_id=tool_def.name,
-                    metadata=tool_def.metadata,
-                    tool_host=tool_host,
-                )
-            )
-        for tool in tools:
-            existing_tool = await self.get_tool(tool.identifier)
-            # Compare existing and new object if one exists
-            if existing_tool:
-                existing_dict = existing_tool.model_dump()
-                new_dict = tool.model_dump()
-
-                if existing_dict != new_dict:
-                    raise ValueError(
-                        f"Object {tool.identifier} already exists in registry. Please use a different identifier."
-                    )
-            await self.register_object(tool)
-
-        await self.dist_registry.register(
-            ToolGroupWithACL(
+        toolgroup = ToolGroupWithACL(
             identifier=toolgroup_id,
             provider_id=provider_id,
             provider_resource_id=toolgroup_id,
             mcp_endpoint=mcp_endpoint,
             args=args,
         )
-        )
+        await self.register_object(toolgroup)
+
+        # ideally, indexing of the tools should not be necessary because anyone using
+        # the tools should first list the tools and then use them. but there are assumptions
+        # baked in some of the code and tests right now.
+        if not toolgroup.mcp_endpoint:
+            await self._index_tools(toolgroup)
+        return toolgroup
 
     async def unregister_toolgroup(self, toolgroup_id: str) -> None:
         tool_group = await self.get_tool_group(toolgroup_id)
         if tool_group is None:
             raise ValueError(f"Tool group {toolgroup_id} not found")
-        tools = await self.list_tools(toolgroup_id)
-        for tool in getattr(tools, "data", []):
-            await self.unregister_object(tool)
         await self.unregister_object(tool_group)
 
     async def shutdown(self) -> None:

llama_stack/distribution/server/routes.py

@@ -6,20 +6,23 @@
 
 import inspect
 import re
+from collections.abc import Callable
+from typing import Any
 
-from pydantic import BaseModel
+from aiohttp import hdrs
+from starlette.routing import Route
 
 from llama_stack.apis.tools import RAGToolRuntime, SpecialToolGroup
 from llama_stack.apis.version import LLAMA_STACK_API_VERSION
 from llama_stack.distribution.resolver import api_protocol_map
 from llama_stack.providers.datatypes import Api
 
-
-class ApiEndpoint(BaseModel):
-    route: str
-    method: str
-    name: str
-    descriptive_name: str | None = None
+EndpointFunc = Callable[..., Any]
+PathParams = dict[str, str]
+RouteInfo = tuple[EndpointFunc, str]
+PathImpl = dict[str, RouteInfo]
+RouteImpls = dict[str, PathImpl]
+RouteMatch = tuple[EndpointFunc, PathParams, str]
 
 
 def toolgroup_protocol_map():
@@ -28,13 +31,13 @@ def toolgroup_protocol_map():
     }
 
 
-def get_all_api_endpoints() -> dict[Api, list[ApiEndpoint]]:
+def get_all_api_routes() -> dict[Api, list[Route]]:
     apis = {}
 
     protocols = api_protocol_map()
     toolgroup_protocols = toolgroup_protocol_map()
     for api, protocol in protocols.items():
-        endpoints = []
+        routes = []
         protocol_methods = inspect.getmembers(protocol, predicate=inspect.isfunction)
 
         # HACK ALERT
@@ -51,26 +54,28 @@ def get_all_api_endpoints() -> dict[Api, list[ApiEndpoint]]:
             if not hasattr(method, "__webmethod__"):
                 continue
 
-            webmethod = method.__webmethod__
-            route = f"/{LLAMA_STACK_API_VERSION}/{webmethod.route.lstrip('/')}"
-            if webmethod.method == "GET":
-                method = "get"
-            elif webmethod.method == "DELETE":
-                method = "delete"
+            # The __webmethod__ attribute is dynamically added by the @webmethod decorator
+            # mypy doesn't know about this dynamic attribute, so we ignore the attr-defined error
+            webmethod = method.__webmethod__  # type: ignore[attr-defined]
+            path = f"/{LLAMA_STACK_API_VERSION}/{webmethod.route.lstrip('/')}"
+            if webmethod.method == hdrs.METH_GET:
+                http_method = hdrs.METH_GET
+            elif webmethod.method == hdrs.METH_DELETE:
+                http_method = hdrs.METH_DELETE
             else:
-                method = "post"
-            endpoints.append(
-                ApiEndpoint(route=route, method=method, name=name, descriptive_name=webmethod.descriptive_name)
-            )
+                http_method = hdrs.METH_POST
+            routes.append(
+                Route(path=path, methods=[http_method], name=name, endpoint=None)
+            )  # setting endpoint to None since don't use a Router object
 
-        apis[api] = endpoints
+        apis[api] = routes
 
     return apis
 
 
-def initialize_endpoint_impls(impls):
-    endpoints = get_all_api_endpoints()
-    endpoint_impls = {}
+def initialize_route_impls(impls: dict[Api, Any]) -> RouteImpls:
+    routes = get_all_api_routes()
+    route_impls: RouteImpls = {}
 
     def _convert_path_to_regex(path: str) -> str:
         # Convert {param} to named capture groups
@@ -83,29 +88,34 @@ def initialize_endpoint_impls(impls):
 
         return f"^{pattern}$"
 
-    for api, api_endpoints in endpoints.items():
+    for api, api_routes in routes.items():
         if api not in impls:
             continue
-        for endpoint in api_endpoints:
+        for route in api_routes:
             impl = impls[api]
-            func = getattr(impl, endpoint.name)
-            if endpoint.method not in endpoint_impls:
-                endpoint_impls[endpoint.method] = {}
-            endpoint_impls[endpoint.method][_convert_path_to_regex(endpoint.route)] = (
+            func = getattr(impl, route.name)
+            # Get the first (and typically only) method from the set, filtering out HEAD
+            available_methods = [m for m in route.methods if m != "HEAD"]
+            if not available_methods:
+                continue  # Skip if only HEAD method is available
+            method = available_methods[0].lower()
+            if method not in route_impls:
+                route_impls[method] = {}
+            route_impls[method][_convert_path_to_regex(route.path)] = (
                 func,
-                endpoint.descriptive_name or endpoint.route,
+                route.path,
             )
 
-    return endpoint_impls
+    return route_impls
 
 
-def find_matching_endpoint(method, path, endpoint_impls):
+def find_matching_route(method: str, path: str, route_impls: RouteImpls) -> RouteMatch:
     """Find the matching endpoint implementation for a given method and path.
 
     Args:
         method: HTTP method (GET, POST, etc.)
         path: URL path to match against
-        endpoint_impls: A dictionary of endpoint implementations
+        route_impls: A dictionary of endpoint implementations
 
     Returns:
         A tuple of (endpoint_function, path_params, descriptive_name)
@@ -113,7 +123,7 @@ def find_matching_endpoint(method, path, endpoint_impls):
     Raises:
         ValueError: If no matching endpoint is found
     """
-    impls = endpoint_impls.get(method.lower())
+    impls = route_impls.get(method.lower())
     if not impls:
         raise ValueError(f"No endpoint found for {path}")
 

llama_stack/distribution/server/server.py

@@ -6,6 +6,7 @@
 
 import argparse
 import asyncio
+import functools
 import inspect
 import json
 import os
@@ -13,6 +14,7 @@ import ssl
 import sys
 import traceback
 import warnings
+from collections.abc import Callable
 from contextlib import asynccontextmanager
 from importlib.metadata import version as parse_version
 from pathlib import Path
@@ -20,6 +22,7 @@ from typing import Annotated, Any
 
 import rich.pretty
 import yaml
+from aiohttp import hdrs
 from fastapi import Body, FastAPI, HTTPException, Request
 from fastapi import Path as FastapiPath
 from fastapi.exceptions import RequestValidationError
@@ -35,9 +38,10 @@ from llama_stack.distribution.request_headers import (
     request_provider_data_context,
 )
 from llama_stack.distribution.resolver import InvalidProviderError
-from llama_stack.distribution.server.endpoints import (
-    find_matching_endpoint,
-    initialize_endpoint_impls,
+from llama_stack.distribution.server.routes import (
+    find_matching_route,
+    get_all_api_routes,
+    initialize_route_impls,
 )
 from llama_stack.distribution.stack import (
     construct_stack,
@@ -60,7 +64,6 @@ from llama_stack.providers.utils.telemetry.tracing import (
 )
 
 from .auth import AuthenticationMiddleware
-from .endpoints import get_all_api_endpoints
 from .quota import QuotaMiddleware
 
 REPO_ROOT = Path(__file__).parent.parent.parent.parent
@@ -209,8 +212,9 @@ async def log_request_pre_validation(request: Request):
             logger.warning(f"Could not read or log request body for {request.method} {request.url.path}: {e}")
 
 
-def create_dynamic_typed_route(func: Any, method: str, route: str):
-    async def endpoint(request: Request, **kwargs):
+def create_dynamic_typed_route(func: Any, method: str, route: str) -> Callable:
+    @functools.wraps(func)
+    async def route_handler(request: Request, **kwargs):
         # Get auth attributes from the request scope
         user_attributes = request.scope.get("user_attributes", {})
 
@@ -250,9 +254,9 @@ def create_dynamic_typed_route(func: Any, method: str, route: str):
             for param in new_params[1:]
         ]
 
-    endpoint.__signature__ = sig.replace(parameters=new_params)
+    route_handler.__signature__ = sig.replace(parameters=new_params)
 
-    return endpoint
+    return route_handler
 
 
 class TracingMiddleware:
@@ -274,14 +278,14 @@ class TracingMiddleware:
             logger.debug(f"Bypassing custom routing for FastAPI built-in path: {path}")
             return await self.app(scope, receive, send)
 
-        if not hasattr(self, "endpoint_impls"):
-            self.endpoint_impls = initialize_endpoint_impls(self.impls)
+        if not hasattr(self, "route_impls"):
+            self.route_impls = initialize_route_impls(self.impls)
 
         try:
-            _, _, trace_path = find_matching_endpoint(scope.get("method", "GET"), path, self.endpoint_impls)
+            _, _, trace_path = find_matching_route(scope.get("method", hdrs.METH_GET), path, self.route_impls)
         except ValueError:
             # If no matching endpoint is found, pass through to FastAPI
-            logger.debug(f"No matching endpoint found for path: {path}, falling back to FastAPI")
+            logger.debug(f"No matching route found for path: {path}, falling back to FastAPI")
             return await self.app(scope, receive, send)
 
         trace_attributes = {"__location__": "server", "raw_path": path}
@@ -423,7 +427,7 @@ def main(args: argparse.Namespace | None = None):
 
     logger.info("Run configuration:")
     safe_config = redact_sensitive_fields(config.model_dump())
-    logger.info(yaml.dump(safe_config, indent=2))
+    logger.info(yaml.dump(safe_config, indent=2, default_style=None))
 
     app = FastAPI(
         lifespan=lifespan,
@@ -490,7 +494,7 @@ def main(args: argparse.Namespace | None = None):
     else:
         setup_logger(TelemetryAdapter(TelemetryConfig(), {}))
 
-    all_endpoints = get_all_api_endpoints()
+    all_routes = get_all_api_routes()
 
     if config.apis:
         apis_to_serve = set(config.apis)
@@ -508,24 +512,29 @@ def main(args: argparse.Namespace | None = None):
     for api_str in apis_to_serve:
         api = Api(api_str)
 
-        endpoints = all_endpoints[api]
+        routes = all_routes[api]
         impl = impls[api]
 
-        for endpoint in endpoints:
-            if not hasattr(impl, endpoint.name):
+        for route in routes:
+            if not hasattr(impl, route.name):
                 # ideally this should be a typing violation already
-                raise ValueError(f"Could not find method {endpoint.name} on {impl}!!")
+                raise ValueError(f"Could not find method {route.name} on {impl}!")
 
-            impl_method = getattr(impl, endpoint.name)
-            logger.debug(f"{endpoint.method.upper()} {endpoint.route}")
+            impl_method = getattr(impl, route.name)
+            # Filter out HEAD method since it's automatically handled by FastAPI for GET routes
+            available_methods = [m for m in route.methods if m != "HEAD"]
+            if not available_methods:
+                raise ValueError(f"No methods found for {route.name} on {impl}")
+            method = available_methods[0]
+            logger.debug(f"{method} {route.path}")
 
             with warnings.catch_warnings():
                 warnings.filterwarnings("ignore", category=UserWarning, module="pydantic._internal._fields")
-                getattr(app, endpoint.method)(endpoint.route, response_model=None)(
+                getattr(app, method.lower())(route.path, response_model=None)(
                     create_dynamic_typed_route(
                         impl_method,
-                        endpoint.method,
-                        endpoint.route,
+                        method.lower(),
+                        route.path,
                     )
                 )
 

llama_stack/distribution/store/registry.py

@@ -36,7 +36,7 @@ class DistributionRegistry(Protocol):
 
 
 REGISTER_PREFIX = "distributions:registry"
-KEY_VERSION = "v8"
+KEY_VERSION = "v9"
 KEY_FORMAT = f"{REGISTER_PREFIX}:{KEY_VERSION}::" + "{type}:{identifier}"
 
 

llama_stack/providers/datatypes.py

@@ -16,7 +16,7 @@ from llama_stack.apis.datatypes import Api
 from llama_stack.apis.models import Model
 from llama_stack.apis.scoring_functions import ScoringFn
 from llama_stack.apis.shields import Shield
-from llama_stack.apis.tools import Tool
+from llama_stack.apis.tools import ToolGroup
 from llama_stack.apis.vector_dbs import VectorDB
 from llama_stack.schema_utils import json_schema_type
 
@@ -74,10 +74,10 @@ class BenchmarksProtocolPrivate(Protocol):
     async def register_benchmark(self, benchmark: Benchmark) -> None: ...
 
 
-class ToolsProtocolPrivate(Protocol):
-    async def register_tool(self, tool: Tool) -> None: ...
+class ToolGroupsProtocolPrivate(Protocol):
+    async def register_toolgroup(self, toolgroup: ToolGroup) -> None: ...
 
-    async def unregister_tool(self, tool_id: str) -> None: ...
+    async def unregister_toolgroup(self, toolgroup_id: str) -> None: ...
 
 
 @json_schema_type

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

@@ -5,6 +5,7 @@
 # the root directory of this source tree.
 
 import json
+import time
 import uuid
 from collections.abc import AsyncIterator
 from typing import Any, cast
@@ -29,10 +30,12 @@ from llama_stack.apis.agents.openai_responses import (
     OpenAIResponseObjectStream,
     OpenAIResponseObjectStreamResponseCompleted,
     OpenAIResponseObjectStreamResponseCreated,
+    OpenAIResponseObjectStreamResponseOutputTextDelta,
     OpenAIResponseOutput,
     OpenAIResponseOutputMessageContent,
     OpenAIResponseOutputMessageContentOutputText,
     OpenAIResponseOutputMessageFunctionToolCall,
+    OpenAIResponseOutputMessageMCPListTools,
     OpenAIResponseOutputMessageWebSearchToolCall,
 )
 from llama_stack.apis.inference.inference import (
@@ -255,110 +258,14 @@ class OpenAIResponsesImpl:
         """
         return await self.responses_store.list_response_input_items(response_id, after, before, include, limit, order)
 
-    async def create_openai_response(
+    async def _process_response_choices(
         self,
-        input: str | list[OpenAIResponseInput],
-        model: str,
-        instructions: str | None = None,
-        previous_response_id: str | None = None,
-        store: bool | None = True,
-        stream: bool | None = False,
-        temperature: float | None = None,
-        tools: list[OpenAIResponseInputTool] | None = None,
-    ):
+        chat_response: OpenAIChatCompletion,
+        ctx: ChatCompletionContext,
+        tools: list[OpenAIResponseInputTool] | None,
+    ) -> list[OpenAIResponseOutput]:
+        """Handle tool execution and response message creation."""
         output_messages: list[OpenAIResponseOutput] = []
-
-        stream = False if stream is None else stream
-
-        # Huge TODO: we need to run this in a loop, until morale improves
-
-        # Create context to run "chat completion"
-        input = await self._prepend_previous_response(input, previous_response_id)
-        messages = await _convert_response_input_to_chat_messages(input)
-        await self._prepend_instructions(messages, instructions)
-        chat_tools, mcp_tool_to_server, mcp_list_message = (
-            await self._convert_response_tools_to_chat_tools(tools) if tools else (None, {}, None)
-        )
-        if mcp_list_message:
-            output_messages.append(mcp_list_message)
-
-        ctx = ChatCompletionContext(
-            model=model,
-            messages=messages,
-            tools=chat_tools,
-            mcp_tool_to_server=mcp_tool_to_server,
-            stream=stream,
-            temperature=temperature,
-        )
-
-        # Run inference
-        chat_response = await self.inference_api.openai_chat_completion(
-            model=model,
-            messages=messages,
-            tools=chat_tools,
-            stream=stream,
-            temperature=temperature,
-        )
-
-        # Collect output
-        if stream:
-            # TODO: refactor this into a separate method that handles streaming
-            chat_response_id = ""
-            chat_response_content = []
-            chat_response_tool_calls: dict[int, OpenAIChatCompletionToolCall] = {}
-            # TODO: these chunk_ fields are hacky and only take the last chunk into account
-            chunk_created = 0
-            chunk_model = ""
-            chunk_finish_reason = ""
-            async for chunk in chat_response:
-                chat_response_id = chunk.id
-                chunk_created = chunk.created
-                chunk_model = chunk.model
-                for chunk_choice in chunk.choices:
-                    # TODO: this only works for text content
-                    chat_response_content.append(chunk_choice.delta.content or "")
-                    if chunk_choice.finish_reason:
-                        chunk_finish_reason = chunk_choice.finish_reason
-
-                    # Aggregate tool call arguments across chunks, using their index as the aggregation key
-                    if chunk_choice.delta.tool_calls:
-                        for tool_call in chunk_choice.delta.tool_calls:
-                            response_tool_call = chat_response_tool_calls.get(tool_call.index, None)
-                            if response_tool_call:
-                                response_tool_call.function.arguments += tool_call.function.arguments
-                            else:
-                                tool_call_dict: dict[str, Any] = tool_call.model_dump()
-                                # Ensure we don't have any empty type field in the tool call dict.
-                                # The OpenAI client used by providers often returns a type=None here.
-                                tool_call_dict.pop("type", None)
-                                response_tool_call = OpenAIChatCompletionToolCall(**tool_call_dict)
-                            chat_response_tool_calls[tool_call.index] = response_tool_call
-
-            # Convert the dict of tool calls by index to a list of tool calls to pass back in our response
-            if chat_response_tool_calls:
-                tool_calls = [chat_response_tool_calls[i] for i in sorted(chat_response_tool_calls.keys())]
-            else:
-                tool_calls = None
-            assistant_message = OpenAIAssistantMessageParam(
-                content="".join(chat_response_content),
-                tool_calls=tool_calls,
-            )
-            chat_response = OpenAIChatCompletion(
-                id=chat_response_id,
-                choices=[
-                    OpenAIChoice(
-                        message=assistant_message,
-                        finish_reason=chunk_finish_reason,
-                        index=0,
-                    )
-                ],
-                created=chunk_created,
-                model=chunk_model,
-            )
-        else:
-            # dump and reload to map to our pydantic types
-            chat_response = OpenAIChatCompletion(**chat_response.model_dump())
-
         # Execute tool calls if any
         for choice in chat_response.choices:
             if choice.message.tool_calls and tools:
@@ -380,19 +287,13 @@ class OpenAIResponsesImpl:
             else:
                 output_messages.append(await _convert_chat_choice_to_response_message(choice))
 
-        # Create response object
-        response = OpenAIResponseObject(
-            created_at=chat_response.created,
-            id=f"resp-{uuid.uuid4()}",
-            model=model,
-            object="response",
-            status="completed",
-            output=output_messages,
-        )
-        logger.debug(f"OpenAI Responses response: {response}")
+        return output_messages
 
-        # Store response if requested
-        if store:
+    async def _store_response(
+        self,
+        response: OpenAIResponseObject,
+        input: str | list[OpenAIResponseInput],
+    ) -> None:
         new_input_id = f"msg_{uuid.uuid4()}"
         if isinstance(input, str):
             # synthesize a message from the input string
@@ -421,17 +322,233 @@ class OpenAIResponsesImpl:
             input=input_items_data,
         )
 
+    async def create_openai_response(
+        self,
+        input: str | list[OpenAIResponseInput],
+        model: str,
+        instructions: str | None = None,
+        previous_response_id: str | None = None,
+        store: bool | None = True,
+        stream: bool | None = False,
+        temperature: float | None = None,
+        tools: list[OpenAIResponseInputTool] | None = None,
+    ):
+        stream = False if stream is None else stream
+
+        output_messages: list[OpenAIResponseOutput] = []
+
+        # Input preprocessing
+        input = await self._prepend_previous_response(input, previous_response_id)
+        messages = await _convert_response_input_to_chat_messages(input)
+        await self._prepend_instructions(messages, instructions)
+
+        # Tool setup
+        chat_tools, mcp_tool_to_server, mcp_list_message = (
+            await self._convert_response_tools_to_chat_tools(tools) if tools else (None, {}, None)
+        )
+        if mcp_list_message:
+            output_messages.append(mcp_list_message)
+
+        ctx = ChatCompletionContext(
+            model=model,
+            messages=messages,
+            tools=chat_tools,
+            mcp_tool_to_server=mcp_tool_to_server,
+            stream=stream,
+            temperature=temperature,
+        )
+
+        inference_result = await self.inference_api.openai_chat_completion(
+            model=model,
+            messages=messages,
+            tools=chat_tools,
+            stream=stream,
+            temperature=temperature,
+        )
+
         if stream:
+            return self._create_streaming_response(
+                inference_result=inference_result,
+                ctx=ctx,
+                output_messages=output_messages,
+                input=input,
+                model=model,
+                store=store,
+                tools=tools,
+            )
+        else:
+            return await self._create_non_streaming_response(
+                inference_result=inference_result,
+                ctx=ctx,
+                output_messages=output_messages,
+                input=input,
+                model=model,
+                store=store,
+                tools=tools,
+            )
 
-            async def async_response() -> AsyncIterator[OpenAIResponseObjectStream]:
-                # TODO: response created should actually get emitted much earlier in the process
-                yield OpenAIResponseObjectStreamResponseCreated(response=response)
-                yield OpenAIResponseObjectStreamResponseCompleted(response=response)
+    async def _create_non_streaming_response(
+        self,
+        inference_result: Any,
+        ctx: ChatCompletionContext,
+        output_messages: list[OpenAIResponseOutput],
+        input: str | list[OpenAIResponseInput],
+        model: str,
+        store: bool | None,
+        tools: list[OpenAIResponseInputTool] | None,
+    ) -> OpenAIResponseObject:
+        chat_response = OpenAIChatCompletion(**inference_result.model_dump())
 
-            return async_response()
+        # Process response choices (tool execution and message creation)
+        output_messages.extend(
+            await self._process_response_choices(
+                chat_response=chat_response,
+                ctx=ctx,
+                tools=tools,
+            )
+        )
+
+        response = OpenAIResponseObject(
+            created_at=chat_response.created,
+            id=f"resp-{uuid.uuid4()}",
+            model=model,
+            object="response",
+            status="completed",
+            output=output_messages,
+        )
+        logger.debug(f"OpenAI Responses response: {response}")
+
+        # Store response if requested
+        if store:
+            await self._store_response(
+                response=response,
+                input=input,
+            )
 
         return response
 
+    async def _create_streaming_response(
+        self,
+        inference_result: Any,
+        ctx: ChatCompletionContext,
+        output_messages: list[OpenAIResponseOutput],
+        input: str | list[OpenAIResponseInput],
+        model: str,
+        store: bool | None,
+        tools: list[OpenAIResponseInputTool] | None,
+    ) -> AsyncIterator[OpenAIResponseObjectStream]:
+        # Create initial response and emit response.created immediately
+        response_id = f"resp-{uuid.uuid4()}"
+        created_at = int(time.time())
+
+        initial_response = OpenAIResponseObject(
+            created_at=created_at,
+            id=response_id,
+            model=model,
+            object="response",
+            status="in_progress",
+            output=output_messages.copy(),
+        )
+
+        # Emit response.created immediately
+        yield OpenAIResponseObjectStreamResponseCreated(response=initial_response)
+
+        # For streaming, inference_result is an async iterator of chunks
+        # Stream chunks and emit delta events as they arrive
+        chat_response_id = ""
+        chat_response_content = []
+        chat_response_tool_calls: dict[int, OpenAIChatCompletionToolCall] = {}
+        chunk_created = 0
+        chunk_model = ""
+        chunk_finish_reason = ""
+        sequence_number = 0
+
+        # Create a placeholder message item for delta events
+        message_item_id = f"msg_{uuid.uuid4()}"
+
+        async for chunk in inference_result:
+            chat_response_id = chunk.id
+            chunk_created = chunk.created
+            chunk_model = chunk.model
+            for chunk_choice in chunk.choices:
+                # Emit incremental text content as delta events
+                if chunk_choice.delta.content:
+                    sequence_number += 1
+                    yield OpenAIResponseObjectStreamResponseOutputTextDelta(
+                        content_index=0,
+                        delta=chunk_choice.delta.content,
+                        item_id=message_item_id,
+                        output_index=0,
+                        sequence_number=sequence_number,
+                    )
+
+                # Collect content for final response
+                chat_response_content.append(chunk_choice.delta.content or "")
+                if chunk_choice.finish_reason:
+                    chunk_finish_reason = chunk_choice.finish_reason
+
+                # Aggregate tool call arguments across chunks, using their index as the aggregation key
+                if chunk_choice.delta.tool_calls:
+                    for tool_call in chunk_choice.delta.tool_calls:
+                        response_tool_call = chat_response_tool_calls.get(tool_call.index, None)
+                        if response_tool_call:
+                            response_tool_call.function.arguments += tool_call.function.arguments
+                        else:
+                            tool_call_dict: dict[str, Any] = tool_call.model_dump()
+                            tool_call_dict.pop("type", None)
+                            response_tool_call = OpenAIChatCompletionToolCall(**tool_call_dict)
+                        chat_response_tool_calls[tool_call.index] = response_tool_call
+
+        # Convert collected chunks to complete response
+        if chat_response_tool_calls:
+            tool_calls = [chat_response_tool_calls[i] for i in sorted(chat_response_tool_calls.keys())]
+        else:
+            tool_calls = None
+        assistant_message = OpenAIAssistantMessageParam(
+            content="".join(chat_response_content),
+            tool_calls=tool_calls,
+        )
+        chat_response_obj = OpenAIChatCompletion(
+            id=chat_response_id,
+            choices=[
+                OpenAIChoice(
+                    message=assistant_message,
+                    finish_reason=chunk_finish_reason,
+                    index=0,
+                )
+            ],
+            created=chunk_created,
+            model=chunk_model,
+        )
+
+        # Process response choices (tool execution and message creation)
+        output_messages.extend(
+            await self._process_response_choices(
+                chat_response=chat_response_obj,
+                ctx=ctx,
+                tools=tools,
+            )
+        )
+
+        # Create final response
+        final_response = OpenAIResponseObject(
+            created_at=created_at,
+            id=response_id,
+            model=model,
+            object="response",
+            status="completed",
+            output=output_messages,
+        )
+
+        if store:
+            await self._store_response(
+                response=final_response,
+                input=input,
+            )
+
+        # Emit response.completed
+        yield OpenAIResponseObjectStreamResponseCompleted(response=final_response)
+
     async def _convert_response_tools_to_chat_tools(
         self, tools: list[OpenAIResponseInputTool]
     ) -> tuple[
@@ -441,7 +558,6 @@ class OpenAIResponsesImpl:
     ]:
         from llama_stack.apis.agents.openai_responses import (
             MCPListToolsTool,
-            OpenAIResponseOutputMessageMCPListTools,
         )
         from llama_stack.apis.tools.tools import Tool
 

llama_stack/providers/inline/safety/prompt_guard/prompt_guard.py

@@ -75,6 +75,8 @@ class PromptGuardShield:
         self.temperature = temperature
         self.threshold = threshold
 
+        self.device = "cpu"
+        if torch.cuda.is_available():
             self.device = "cuda"
 
         # load model and tokenizer

llama_stack/providers/inline/tool_runtime/rag/memory.py

@@ -25,14 +25,14 @@ from llama_stack.apis.tools import (
     RAGQueryConfig,
     RAGQueryResult,
     RAGToolRuntime,
-    Tool,
     ToolDef,
+    ToolGroup,
     ToolInvocationResult,
     ToolParameter,
     ToolRuntime,
 )
 from llama_stack.apis.vector_io import QueryChunksResponse, VectorIO
-from llama_stack.providers.datatypes import ToolsProtocolPrivate
+from llama_stack.providers.datatypes import ToolGroupsProtocolPrivate
 from llama_stack.providers.utils.inference.prompt_adapter import interleaved_content_as_str
 from llama_stack.providers.utils.memory.vector_store import (
     content_from_doc,
@@ -49,7 +49,7 @@ def make_random_string(length: int = 8):
     return "".join(secrets.choice(string.ascii_letters + string.digits) for _ in range(length))
 
 
-class MemoryToolRuntimeImpl(ToolsProtocolPrivate, ToolRuntime, RAGToolRuntime):
+class MemoryToolRuntimeImpl(ToolGroupsProtocolPrivate, ToolRuntime, RAGToolRuntime):
     def __init__(
         self,
         config: RagToolRuntimeConfig,
@@ -66,10 +66,10 @@ class MemoryToolRuntimeImpl(ToolsProtocolPrivate, ToolRuntime, RAGToolRuntime):
     async def shutdown(self):
         pass
 
-    async def register_tool(self, tool: Tool) -> None:
+    async def register_toolgroup(self, toolgroup: ToolGroup) -> None:
         pass
 
-    async def unregister_tool(self, tool_id: str) -> None:
+    async def unregister_toolgroup(self, toolgroup_id: str) -> None:
         return
 
     async def insert(

llama_stack/providers/registry/agents.py

@@ -19,10 +19,10 @@ def available_providers() -> list[ProviderSpec]:
             api=Api.agents,
             provider_type="inline::meta-reference",
             pip_packages=[
-                "matplotlib",
-                "pillow",
-                "pandas",
-                "scikit-learn",
+                # "matplotlib",
+                # "pillow",
+                # "pandas",
+                # "scikit-learn",
             ]
             + kvstore_dependencies(),
             module="llama_stack.providers.inline.agents.meta_reference",

llama_stack/providers/registry/eval.py

@@ -13,7 +13,7 @@ def available_providers() -> list[ProviderSpec]:
         InlineProviderSpec(
             api=Api.eval,
             provider_type="inline::meta-reference",
-            pip_packages=["tree_sitter", "pythainlp", "langdetect", "emoji", "nltk"],
+            # pip_packages=["tree_sitter", "pythainlp", "langdetect", "emoji", "nltk"],
             module="llama_stack.providers.inline.eval.meta_reference",
             config_class="llama_stack.providers.inline.eval.meta_reference.MetaReferenceEvalConfig",
             api_dependencies=[

llama_stack/providers/registry/tool_runtime.py

@@ -20,16 +20,16 @@ def available_providers() -> list[ProviderSpec]:
             api=Api.tool_runtime,
             provider_type="inline::rag-runtime",
             pip_packages=[
-                "blobfile",
-                "chardet",
-                "pypdf",
-                "tqdm",
-                "numpy",
-                "scikit-learn",
-                "scipy",
-                "nltk",
-                "sentencepiece",
-                "transformers",
+                # "blobfile",
+                # "chardet",
+                # "pypdf",
+                # "tqdm",
+                # "numpy",
+                # "scikit-learn",
+                # "scipy",
+                # "nltk",
+                # "sentencepiece",
+                # "transformers",
             ],
             module="llama_stack.providers.inline.tool_runtime.rag",
             config_class="llama_stack.providers.inline.tool_runtime.rag.config.RagToolRuntimeConfig",

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

@@ -4,8 +4,9 @@
 # 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 pydantic import BaseModel, Field
+from pydantic import BaseModel, Field, field_validator
 
 from llama_stack.schema_utils import json_schema_type
 
@@ -24,11 +25,27 @@ class VLLMInferenceAdapterConfig(BaseModel):
         default="fake",
         description="The API token",
     )
-    tls_verify: bool = Field(
+    tls_verify: bool | str = Field(
         default=True,
-        description="Whether to verify TLS certificates",
+        description="Whether to verify TLS certificates. Can be a boolean or a path to a CA certificate file.",
     )
 
+    @field_validator("tls_verify")
+    @classmethod
+    def validate_tls_verify(cls, v):
+        if isinstance(v, str):
+            # Check if it's a boolean string
+            if v.lower() in ("true", "false"):
+                return v.lower() == "true"
+            # Otherwise, treat it as a cert path
+            cert_path = Path(v).expanduser().resolve()
+            if not cert_path.exists():
+                raise ValueError(f"TLS certificate file does not exist: {v}")
+            if not cert_path.is_file():
+                raise ValueError(f"TLS certificate path is not a file: {v}")
+            return v
+        return v
+
     @classmethod
     def sample_run_config(
         cls,

llama_stack/providers/remote/inference/vllm/vllm.py

@@ -313,7 +313,7 @@ class VLLMInferenceAdapter(Inference, ModelsProtocolPrivate):
         return AsyncOpenAI(
             base_url=self.config.url,
             api_key=self.config.api_token,
-            http_client=None if self.config.tls_verify else httpx.AsyncClient(verify=False),
+            http_client=httpx.AsyncClient(verify=self.config.tls_verify),
         )
 
     async def completion(

llama_stack/providers/remote/tool_runtime/bing_search/bing_search.py

@@ -12,19 +12,19 @@ import httpx
 from llama_stack.apis.common.content_types import URL
 from llama_stack.apis.tools import (
     ListToolDefsResponse,
-    Tool,
     ToolDef,
+    ToolGroup,
     ToolInvocationResult,
     ToolParameter,
     ToolRuntime,
 )
 from llama_stack.distribution.request_headers import NeedsRequestProviderData
-from llama_stack.providers.datatypes import ToolsProtocolPrivate
+from llama_stack.providers.datatypes import ToolGroupsProtocolPrivate
 
 from .config import BingSearchToolConfig
 
 
-class BingSearchToolRuntimeImpl(ToolsProtocolPrivate, ToolRuntime, NeedsRequestProviderData):
+class BingSearchToolRuntimeImpl(ToolGroupsProtocolPrivate, ToolRuntime, NeedsRequestProviderData):
     def __init__(self, config: BingSearchToolConfig):
         self.config = config
         self.url = "https://api.bing.microsoft.com/v7.0/search"
@@ -32,10 +32,10 @@ class BingSearchToolRuntimeImpl(ToolsProtocolPrivate, ToolRuntime, NeedsRequestP
     async def initialize(self):
         pass
 
-    async def register_tool(self, tool: Tool) -> None:
+    async def register_toolgroup(self, toolgroup: ToolGroup) -> None:
         pass
 
-    async def unregister_tool(self, tool_id: str) -> None:
+    async def unregister_toolgroup(self, toolgroup_id: str) -> None:
         return
 
     def _get_api_key(self) -> str:

llama_stack/providers/remote/tool_runtime/brave_search/brave_search.py

@@ -11,30 +11,30 @@ import httpx
 from llama_stack.apis.common.content_types import URL
 from llama_stack.apis.tools import (
     ListToolDefsResponse,
-    Tool,
     ToolDef,
+    ToolGroup,
     ToolInvocationResult,
     ToolParameter,
     ToolRuntime,
 )
 from llama_stack.distribution.request_headers import NeedsRequestProviderData
 from llama_stack.models.llama.datatypes import BuiltinTool
-from llama_stack.providers.datatypes import ToolsProtocolPrivate
+from llama_stack.providers.datatypes import ToolGroupsProtocolPrivate
 
 from .config import BraveSearchToolConfig
 
 
-class BraveSearchToolRuntimeImpl(ToolsProtocolPrivate, ToolRuntime, NeedsRequestProviderData):
+class BraveSearchToolRuntimeImpl(ToolGroupsProtocolPrivate, ToolRuntime, NeedsRequestProviderData):
     def __init__(self, config: BraveSearchToolConfig):
         self.config = config
 
     async def initialize(self):
         pass
 
-    async def register_tool(self, tool: Tool) -> None:
+    async def register_toolgroup(self, toolgroup: ToolGroup) -> None:
         pass
 
-    async def unregister_tool(self, tool_id: str) -> None:
+    async def unregister_toolgroup(self, toolgroup_id: str) -> None:
         return
 
     def _get_api_key(self) -> str:

llama_stack/providers/remote/tool_runtime/model_context_protocol/config.py

@@ -10,8 +10,8 @@ from pydantic import BaseModel
 
 
 class MCPProviderDataValidator(BaseModel):
-    # mcp_endpoint => list of headers to send
-    mcp_headers: dict[str, list[str]] | None = None
+    # mcp_endpoint => dict of headers to send
+    mcp_headers: dict[str, dict[str, str]] | None = None
 
 
 class MCPProviderConfig(BaseModel):

llama_stack/providers/remote/tool_runtime/model_context_protocol/model_context_protocol.py

@@ -11,26 +11,33 @@ from llama_stack.apis.common.content_types import URL
 from llama_stack.apis.datatypes import Api
 from llama_stack.apis.tools import (
     ListToolDefsResponse,
+    ToolGroup,
     ToolInvocationResult,
     ToolRuntime,
 )
 from llama_stack.distribution.request_headers import NeedsRequestProviderData
 from llama_stack.log import get_logger
-from llama_stack.providers.datatypes import ToolsProtocolPrivate
-from llama_stack.providers.utils.tools.mcp import convert_header_list_to_dict, invoke_mcp_tool, list_mcp_tools
+from llama_stack.providers.datatypes import ToolGroupsProtocolPrivate
+from llama_stack.providers.utils.tools.mcp import invoke_mcp_tool, list_mcp_tools
 
 from .config import MCPProviderConfig
 
 logger = get_logger(__name__, category="tools")
 
 
-class ModelContextProtocolToolRuntimeImpl(ToolsProtocolPrivate, ToolRuntime, NeedsRequestProviderData):
+class ModelContextProtocolToolRuntimeImpl(ToolGroupsProtocolPrivate, ToolRuntime, NeedsRequestProviderData):
     def __init__(self, config: MCPProviderConfig, _deps: dict[Api, Any]):
         self.config = config
 
     async def initialize(self):
         pass
 
+    async def register_toolgroup(self, toolgroup: ToolGroup) -> None:
+        pass
+
+    async def unregister_toolgroup(self, toolgroup_id: str) -> None:
+        return
+
     async def list_runtime_tools(
         self, tool_group_id: str | None = None, mcp_endpoint: URL | None = None
     ) -> ListToolDefsResponse:
@@ -62,5 +69,5 @@ class ModelContextProtocolToolRuntimeImpl(ToolsProtocolPrivate, ToolRuntime, Nee
             for uri, values in provider_data.mcp_headers.items():
                 if canonicalize_uri(uri) != canonicalize_uri(mcp_endpoint_uri):
                     continue
-                headers.update(convert_header_list_to_dict(values))
+                headers.update(values)
         return headers

llama_stack/providers/remote/tool_runtime/tavily_search/tavily_search.py

@@ -12,29 +12,29 @@ import httpx
 from llama_stack.apis.common.content_types import URL
 from llama_stack.apis.tools import (
     ListToolDefsResponse,
-    Tool,
     ToolDef,
+    ToolGroup,
     ToolInvocationResult,
     ToolParameter,
     ToolRuntime,
 )
 from llama_stack.distribution.request_headers import NeedsRequestProviderData
-from llama_stack.providers.datatypes import ToolsProtocolPrivate
+from llama_stack.providers.datatypes import ToolGroupsProtocolPrivate
 
 from .config import TavilySearchToolConfig
 
 
-class TavilySearchToolRuntimeImpl(ToolsProtocolPrivate, ToolRuntime, NeedsRequestProviderData):
+class TavilySearchToolRuntimeImpl(ToolGroupsProtocolPrivate, ToolRuntime, NeedsRequestProviderData):
     def __init__(self, config: TavilySearchToolConfig):
         self.config = config
 
     async def initialize(self):
         pass
 
-    async def register_tool(self, tool: Tool) -> None:
+    async def register_toolgroup(self, toolgroup: ToolGroup) -> None:
         pass
 
-    async def unregister_tool(self, tool_id: str) -> None:
+    async def unregister_toolgroup(self, toolgroup_id: str) -> None:
         return
 
     def _get_api_key(self) -> str:

llama_stack/providers/remote/tool_runtime/wolfram_alpha/wolfram_alpha.py

@@ -12,19 +12,19 @@ import httpx
 from llama_stack.apis.common.content_types import URL
 from llama_stack.apis.tools import (
     ListToolDefsResponse,
-    Tool,
     ToolDef,
+    ToolGroup,
     ToolInvocationResult,
     ToolParameter,
     ToolRuntime,
 )
 from llama_stack.distribution.request_headers import NeedsRequestProviderData
-from llama_stack.providers.datatypes import ToolsProtocolPrivate
+from llama_stack.providers.datatypes import ToolGroupsProtocolPrivate
 
 from .config import WolframAlphaToolConfig
 
 
-class WolframAlphaToolRuntimeImpl(ToolsProtocolPrivate, ToolRuntime, NeedsRequestProviderData):
+class WolframAlphaToolRuntimeImpl(ToolGroupsProtocolPrivate, ToolRuntime, NeedsRequestProviderData):
     def __init__(self, config: WolframAlphaToolConfig):
         self.config = config
         self.url = "https://api.wolframalpha.com/v2/query"
@@ -32,10 +32,10 @@ class WolframAlphaToolRuntimeImpl(ToolsProtocolPrivate, ToolRuntime, NeedsReques
     async def initialize(self):
         pass
 
-    async def register_tool(self, tool: Tool) -> None:
+    async def register_toolgroup(self, toolgroup: ToolGroup) -> None:
         pass
 
-    async def unregister_tool(self, tool_id: str) -> None:
+    async def unregister_toolgroup(self, toolgroup_id: str) -> None:
         return
 
     def _get_api_key(self) -> str:

llama_stack/providers/utils/inference/openai_compat.py

@@ -1402,9 +1402,8 @@ class OpenAIChatCompletionToLlamaStackMixin:
         outstanding_responses: list[Awaitable[AsyncIterator[ChatCompletionResponseStreamChunk]]],
     ):
         id = f"chatcmpl-{uuid.uuid4()}"
-        for outstanding_response in outstanding_responses:
+        for i, outstanding_response in enumerate(outstanding_responses):
             response = await outstanding_response
-            i = 0
             async for chunk in response:
                 event = chunk.event
                 finish_reason = _convert_stop_reason_to_openai_finish_reason(event.stop_reason)
@@ -1459,7 +1458,6 @@ class OpenAIChatCompletionToLlamaStackMixin:
                             model=model,
                             object="chat.completion.chunk",
                         )
-                i = i + 1
 
     async def _process_non_stream_response(
         self, model: str, outstanding_responses: list[Awaitable[ChatCompletionResponse]]

llama_stack/providers/utils/tools/mcp.py

@@ -51,16 +51,6 @@ async def sse_client_wrapper(endpoint: str, headers: dict[str, str]):
         raise
 
 
-def convert_header_list_to_dict(header_list: list[str]) -> dict[str, str]:
-    headers = {}
-    for header in header_list:
-        parts = header.split(":")
-        if len(parts) == 2:
-            k, v = parts
-            headers[k.strip()] = v.strip()
-    return headers
-
-
 async def list_mcp_tools(endpoint: str, headers: dict[str, str]) -> ListToolDefsResponse:
     tools = []
     async with sse_client_wrapper(endpoint, headers) as session:

llama_stack/templates/dependencies.json

@@ -1,855 +0,0 @@
-{
-  "bedrock": [
-    "aiosqlite",
-    "autoevals",
-    "blobfile",
-    "boto3",
-    "chardet",
-    "chromadb-client",
-    "datasets",
-    "emoji",
-    "faiss-cpu",
-    "fastapi",
-    "fire",
-    "httpx",
-    "langdetect",
-    "matplotlib",
-    "mcp",
-    "nltk",
-    "numpy",
-    "openai",
-    "opentelemetry-exporter-otlp-proto-http",
-    "opentelemetry-sdk",
-    "pandas",
-    "pillow",
-    "psycopg2-binary",
-    "pymongo",
-    "pypdf",
-    "pythainlp",
-    "redis",
-    "requests",
-    "scikit-learn",
-    "scipy",
-    "sentencepiece",
-    "sqlalchemy[asyncio]",
-    "tqdm",
-    "transformers",
-    "tree_sitter",
-    "uvicorn"
-  ],
-  "cerebras": [
-    "aiosqlite",
-    "autoevals",
-    "blobfile",
-    "cerebras_cloud_sdk",
-    "chardet",
-    "chromadb-client",
-    "datasets",
-    "emoji",
-    "faiss-cpu",
-    "fastapi",
-    "fire",
-    "httpx",
-    "langdetect",
-    "matplotlib",
-    "nltk",
-    "numpy",
-    "openai",
-    "opentelemetry-exporter-otlp-proto-http",
-    "opentelemetry-sdk",
-    "pandas",
-    "pillow",
-    "psycopg2-binary",
-    "pymongo",
-    "pypdf",
-    "pythainlp",
-    "redis",
-    "requests",
-    "scikit-learn",
-    "scipy",
-    "sentencepiece",
-    "sqlalchemy[asyncio]",
-    "tqdm",
-    "transformers",
-    "tree_sitter",
-    "uvicorn",
-    "sentence-transformers --no-deps",
-    "torch torchvision --index-url https://download.pytorch.org/whl/cpu"
-  ],
-  "ci-tests": [
-    "aiosqlite",
-    "autoevals",
-    "blobfile",
-    "chardet",
-    "chromadb-client",
-    "datasets",
-    "emoji",
-    "fastapi",
-    "fire",
-    "fireworks-ai",
-    "httpx",
-    "langdetect",
-    "matplotlib",
-    "mcp",
-    "nltk",
-    "numpy",
-    "openai",
-    "opentelemetry-exporter-otlp-proto-http",
-    "opentelemetry-sdk",
-    "pandas",
-    "pillow",
-    "psycopg2-binary",
-    "pymongo",
-    "pypdf",
-    "pythainlp",
-    "redis",
-    "requests",
-    "scikit-learn",
-    "scipy",
-    "sentencepiece",
-    "sqlalchemy[asyncio]",
-    "sqlite-vec",
-    "tqdm",
-    "transformers",
-    "tree_sitter",
-    "uvicorn",
-    "sentence-transformers --no-deps",
-    "torch torchvision --index-url https://download.pytorch.org/whl/cpu"
-  ],
-  "dell": [
-    "aiohttp",
-    "aiosqlite",
-    "autoevals",
-    "blobfile",
-    "chardet",
-    "chromadb-client",
-    "datasets",
-    "emoji",
-    "faiss-cpu",
-    "fastapi",
-    "fire",
-    "httpx",
-    "huggingface_hub",
-    "langdetect",
-    "matplotlib",
-    "nltk",
-    "numpy",
-    "openai",
-    "opentelemetry-exporter-otlp-proto-http",
-    "opentelemetry-sdk",
-    "pandas",
-    "pillow",
-    "psycopg2-binary",
-    "pymongo",
-    "pypdf",
-    "pythainlp",
-    "redis",
-    "requests",
-    "scikit-learn",
-    "scipy",
-    "sentencepiece",
-    "sqlalchemy[asyncio]",
-    "tqdm",
-    "transformers",
-    "tree_sitter",
-    "uvicorn",
-    "sentence-transformers --no-deps",
-    "torch torchvision --index-url https://download.pytorch.org/whl/cpu"
-  ],
-  "fireworks": [
-    "aiosqlite",
-    "autoevals",
-    "blobfile",
-    "chardet",
-    "chromadb-client",
-    "datasets",
-    "emoji",
-    "faiss-cpu",
-    "fastapi",
-    "fire",
-    "fireworks-ai",
-    "httpx",
-    "langdetect",
-    "matplotlib",
-    "mcp",
-    "nltk",
-    "numpy",
-    "openai",
-    "opentelemetry-exporter-otlp-proto-http",
-    "opentelemetry-sdk",
-    "pandas",
-    "pillow",
-    "psycopg2-binary",
-    "pymongo",
-    "pypdf",
-    "pythainlp",
-    "redis",
-    "requests",
-    "scikit-learn",
-    "scipy",
-    "sentencepiece",
-    "sqlalchemy[asyncio]",
-    "tqdm",
-    "transformers",
-    "tree_sitter",
-    "uvicorn",
-    "sentence-transformers --no-deps",
-    "torch torchvision --index-url https://download.pytorch.org/whl/cpu"
-  ],
-  "groq": [
-    "aiosqlite",
-    "autoevals",
-    "blobfile",
-    "chardet",
-    "datasets",
-    "emoji",
-    "faiss-cpu",
-    "fastapi",
-    "fire",
-    "httpx",
-    "langdetect",
-    "litellm",
-    "matplotlib",
-    "nltk",
-    "numpy",
-    "openai",
-    "opentelemetry-exporter-otlp-proto-http",
-    "opentelemetry-sdk",
-    "pandas",
-    "pillow",
-    "psycopg2-binary",
-    "pymongo",
-    "pypdf",
-    "pythainlp",
-    "redis",
-    "requests",
-    "scikit-learn",
-    "scipy",
-    "sentencepiece",
-    "sqlalchemy[asyncio]",
-    "tqdm",
-    "transformers",
-    "tree_sitter",
-    "uvicorn"
-  ],
-  "hf-endpoint": [
-    "aiohttp",
-    "aiosqlite",
-    "autoevals",
-    "blobfile",
-    "chardet",
-    "chromadb-client",
-    "datasets",
-    "emoji",
-    "faiss-cpu",
-    "fastapi",
-    "fire",
-    "httpx",
-    "huggingface_hub",
-    "langdetect",
-    "matplotlib",
-    "mcp",
-    "nltk",
-    "numpy",
-    "openai",
-    "opentelemetry-exporter-otlp-proto-http",
-    "opentelemetry-sdk",
-    "pandas",
-    "pillow",
-    "psycopg2-binary",
-    "pymongo",
-    "pypdf",
-    "pythainlp",
-    "redis",
-    "requests",
-    "scikit-learn",
-    "scipy",
-    "sentencepiece",
-    "sqlalchemy[asyncio]",
-    "tqdm",
-    "transformers",
-    "tree_sitter",
-    "uvicorn"
-  ],
-  "hf-serverless": [
-    "aiohttp",
-    "aiosqlite",
-    "autoevals",
-    "blobfile",
-    "chardet",
-    "chromadb-client",
-    "datasets",
-    "emoji",
-    "faiss-cpu",
-    "fastapi",
-    "fire",
-    "httpx",
-    "huggingface_hub",
-    "langdetect",
-    "matplotlib",
-    "mcp",
-    "nltk",
-    "numpy",
-    "openai",
-    "opentelemetry-exporter-otlp-proto-http",
-    "opentelemetry-sdk",
-    "pandas",
-    "pillow",
-    "psycopg2-binary",
-    "pymongo",
-    "pypdf",
-    "pythainlp",
-    "redis",
-    "requests",
-    "scikit-learn",
-    "scipy",
-    "sentencepiece",
-    "sqlalchemy[asyncio]",
-    "tqdm",
-    "transformers",
-    "tree_sitter",
-    "uvicorn",
-    "sentence-transformers --no-deps",
-    "torch torchvision --index-url https://download.pytorch.org/whl/cpu"
-  ],
-  "llama_api": [
-    "aiosqlite",
-    "autoevals",
-    "blobfile",
-    "chardet",
-    "chromadb-client",
-    "datasets",
-    "emoji",
-    "fastapi",
-    "fire",
-    "httpx",
-    "langdetect",
-    "litellm",
-    "matplotlib",
-    "mcp",
-    "nltk",
-    "numpy",
-    "openai",
-    "opentelemetry-exporter-otlp-proto-http",
-    "opentelemetry-sdk",
-    "pandas",
-    "pillow",
-    "psycopg2-binary",
-    "pymongo",
-    "pypdf",
-    "pythainlp",
-    "redis",
-    "requests",
-    "scikit-learn",
-    "scipy",
-    "sentencepiece",
-    "sqlalchemy[asyncio]",
-    "sqlite-vec",
-    "tqdm",
-    "transformers",
-    "tree_sitter",
-    "uvicorn",
-    "sentence-transformers --no-deps",
-    "torch torchvision --index-url https://download.pytorch.org/whl/cpu"
-  ],
-  "meta-reference-gpu": [
-    "accelerate",
-    "aiosqlite",
-    "autoevals",
-    "blobfile",
-    "chardet",
-    "chromadb-client",
-    "datasets",
-    "emoji",
-    "fairscale",
-    "faiss-cpu",
-    "fastapi",
-    "fbgemm-gpu-genai==1.1.2",
-    "fire",
-    "httpx",
-    "langdetect",
-    "lm-format-enforcer",
-    "matplotlib",
-    "mcp",
-    "nltk",
-    "numpy",
-    "openai",
-    "opentelemetry-exporter-otlp-proto-http",
-    "opentelemetry-sdk",
-    "pandas",
-    "pillow",
-    "psycopg2-binary",
-    "pymongo",
-    "pypdf",
-    "pythainlp",
-    "redis",
-    "requests",
-    "scikit-learn",
-    "scipy",
-    "sentence-transformers",
-    "sentencepiece",
-    "sqlalchemy[asyncio]",
-    "torch",
-    "torchao==0.8.0",
-    "torchvision",
-    "tqdm",
-    "transformers",
-    "tree_sitter",
-    "uvicorn",
-    "zmq"
-  ],
-  "nvidia": [
-    "aiohttp",
-    "aiosqlite",
-    "blobfile",
-    "chardet",
-    "datasets",
-    "faiss-cpu",
-    "fastapi",
-    "fire",
-    "httpx",
-    "matplotlib",
-    "nltk",
-    "numpy",
-    "openai",
-    "opentelemetry-exporter-otlp-proto-http",
-    "opentelemetry-sdk",
-    "pandas",
-    "pillow",
-    "psycopg2-binary",
-    "pymongo",
-    "pypdf",
-    "redis",
-    "requests",
-    "scikit-learn",
-    "scipy",
-    "sentencepiece",
-    "sqlalchemy[asyncio]",
-    "tqdm",
-    "transformers",
-    "uvicorn"
-  ],
-  "ollama": [
-    "aiohttp",
-    "aiosqlite",
-    "blobfile",
-    "chardet",
-    "chromadb-client",
-    "emoji",
-    "faiss-cpu",
-    "fastapi",
-    "fire",
-    "httpx",
-    "langdetect",
-    "matplotlib",
-    "mcp",
-    "nltk",
-    "ollama",
-    "openai",
-    "opentelemetry-exporter-otlp-proto-http",
-    "opentelemetry-sdk",
-    "peft",
-    "pillow",
-    "psycopg2-binary",
-    "pymongo",
-    "pypdf",
-    "pythainlp",
-    "requests",
-    "sqlalchemy[asyncio]",
-    "tqdm",
-    "tree_sitter",
-    "trl",
-    "uvicorn"
-  ],
-  "open-benchmark": [
-    "aiosqlite",
-    "autoevals",
-    "blobfile",
-    "chardet",
-    "chromadb-client",
-    "datasets",
-    "emoji",
-    "fastapi",
-    "fire",
-    "httpx",
-    "langdetect",
-    "litellm",
-    "matplotlib",
-    "mcp",
-    "nltk",
-    "numpy",
-    "openai",
-    "opentelemetry-exporter-otlp-proto-http",
-    "opentelemetry-sdk",
-    "pandas",
-    "pillow",
-    "psycopg2-binary",
-    "pymongo",
-    "pypdf",
-    "pythainlp",
-    "redis",
-    "requests",
-    "scikit-learn",
-    "scipy",
-    "sentencepiece",
-    "sqlalchemy[asyncio]",
-    "sqlite-vec",
-    "together",
-    "tqdm",
-    "transformers",
-    "tree_sitter",
-    "uvicorn"
-  ],
-  "passthrough": [
-    "aiosqlite",
-    "autoevals",
-    "blobfile",
-    "chardet",
-    "chromadb-client",
-    "datasets",
-    "emoji",
-    "faiss-cpu",
-    "fastapi",
-    "fire",
-    "httpx",
-    "langdetect",
-    "matplotlib",
-    "mcp",
-    "nltk",
-    "numpy",
-    "openai",
-    "opentelemetry-exporter-otlp-proto-http",
-    "opentelemetry-sdk",
-    "pandas",
-    "pillow",
-    "psycopg2-binary",
-    "pymongo",
-    "pypdf",
-    "pythainlp",
-    "redis",
-    "requests",
-    "scikit-learn",
-    "scipy",
-    "sentencepiece",
-    "sqlalchemy[asyncio]",
-    "tqdm",
-    "transformers",
-    "tree_sitter",
-    "uvicorn",
-    "sentence-transformers --no-deps",
-    "torch torchvision --index-url https://download.pytorch.org/whl/cpu"
-  ],
-  "remote-vllm": [
-    "aiosqlite",
-    "autoevals",
-    "blobfile",
-    "chardet",
-    "chromadb-client",
-    "datasets",
-    "emoji",
-    "faiss-cpu",
-    "fastapi",
-    "fire",
-    "httpx",
-    "langdetect",
-    "matplotlib",
-    "mcp",
-    "nltk",
-    "numpy",
-    "openai",
-    "opentelemetry-exporter-otlp-proto-http",
-    "opentelemetry-sdk",
-    "pandas",
-    "pillow",
-    "psycopg2-binary",
-    "pymongo",
-    "pypdf",
-    "pythainlp",
-    "redis",
-    "requests",
-    "scikit-learn",
-    "scipy",
-    "sentencepiece",
-    "sqlalchemy[asyncio]",
-    "tqdm",
-    "transformers",
-    "tree_sitter",
-    "uvicorn",
-    "sentence-transformers --no-deps",
-    "torch torchvision --index-url https://download.pytorch.org/whl/cpu"
-  ],
-  "sambanova": [
-    "aiosqlite",
-    "blobfile",
-    "chardet",
-    "chromadb-client",
-    "faiss-cpu",
-    "fastapi",
-    "fire",
-    "httpx",
-    "litellm",
-    "matplotlib",
-    "mcp",
-    "nltk",
-    "numpy",
-    "opentelemetry-exporter-otlp-proto-http",
-    "opentelemetry-sdk",
-    "pandas",
-    "pillow",
-    "psycopg2-binary",
-    "pymongo",
-    "pypdf",
-    "redis",
-    "requests",
-    "scikit-learn",
-    "scipy",
-    "sentencepiece",
-    "sqlalchemy[asyncio]",
-    "tqdm",
-    "transformers",
-    "uvicorn",
-    "sentence-transformers --no-deps",
-    "torch torchvision --index-url https://download.pytorch.org/whl/cpu"
-  ],
-  "starter": [
-    "aiosqlite",
-    "autoevals",
-    "blobfile",
-    "chardet",
-    "chromadb-client",
-    "datasets",
-    "emoji",
-    "fastapi",
-    "fire",
-    "fireworks-ai",
-    "httpx",
-    "langdetect",
-    "litellm",
-    "matplotlib",
-    "mcp",
-    "nltk",
-    "numpy",
-    "openai",
-    "opentelemetry-exporter-otlp-proto-http",
-    "opentelemetry-sdk",
-    "pandas",
-    "pillow",
-    "psycopg2-binary",
-    "pymongo",
-    "pypdf",
-    "pythainlp",
-    "redis",
-    "requests",
-    "scikit-learn",
-    "scipy",
-    "sentencepiece",
-    "sqlalchemy[asyncio]",
-    "sqlite-vec",
-    "tqdm",
-    "transformers",
-    "tree_sitter",
-    "uvicorn",
-    "sentence-transformers --no-deps",
-    "torch torchvision --index-url https://download.pytorch.org/whl/cpu"
-  ],
-  "tgi": [
-    "aiohttp",
-    "aiosqlite",
-    "autoevals",
-    "blobfile",
-    "chardet",
-    "chromadb-client",
-    "datasets",
-    "emoji",
-    "faiss-cpu",
-    "fastapi",
-    "fire",
-    "httpx",
-    "huggingface_hub",
-    "langdetect",
-    "matplotlib",
-    "mcp",
-    "nltk",
-    "numpy",
-    "openai",
-    "opentelemetry-exporter-otlp-proto-http",
-    "opentelemetry-sdk",
-    "pandas",
-    "pillow",
-    "psycopg2-binary",
-    "pymongo",
-    "pypdf",
-    "pythainlp",
-    "redis",
-    "requests",
-    "scikit-learn",
-    "scipy",
-    "sentencepiece",
-    "sqlalchemy[asyncio]",
-    "tqdm",
-    "transformers",
-    "tree_sitter",
-    "uvicorn",
-    "sentence-transformers --no-deps",
-    "torch torchvision --index-url https://download.pytorch.org/whl/cpu"
-  ],
-  "together": [
-    "aiosqlite",
-    "autoevals",
-    "blobfile",
-    "chardet",
-    "chromadb-client",
-    "datasets",
-    "emoji",
-    "faiss-cpu",
-    "fastapi",
-    "fire",
-    "httpx",
-    "langdetect",
-    "matplotlib",
-    "mcp",
-    "nltk",
-    "numpy",
-    "openai",
-    "opentelemetry-exporter-otlp-proto-http",
-    "opentelemetry-sdk",
-    "pandas",
-    "pillow",
-    "psycopg2-binary",
-    "pymongo",
-    "pypdf",
-    "pythainlp",
-    "redis",
-    "requests",
-    "scikit-learn",
-    "scipy",
-    "sentencepiece",
-    "sqlalchemy[asyncio]",
-    "together",
-    "tqdm",
-    "transformers",
-    "tree_sitter",
-    "uvicorn",
-    "sentence-transformers --no-deps",
-    "torch torchvision --index-url https://download.pytorch.org/whl/cpu"
-  ],
-  "verification": [
-    "aiosqlite",
-    "autoevals",
-    "blobfile",
-    "chardet",
-    "chromadb-client",
-    "datasets",
-    "emoji",
-    "fastapi",
-    "fire",
-    "httpx",
-    "langdetect",
-    "litellm",
-    "matplotlib",
-    "mcp",
-    "nltk",
-    "numpy",
-    "openai",
-    "opentelemetry-exporter-otlp-proto-http",
-    "opentelemetry-sdk",
-    "pandas",
-    "pillow",
-    "psycopg2-binary",
-    "pymongo",
-    "pypdf",
-    "pythainlp",
-    "redis",
-    "requests",
-    "scikit-learn",
-    "scipy",
-    "sentencepiece",
-    "sqlalchemy[asyncio]",
-    "sqlite-vec",
-    "tqdm",
-    "transformers",
-    "tree_sitter",
-    "uvicorn",
-    "sentence-transformers --no-deps",
-    "torch torchvision --index-url https://download.pytorch.org/whl/cpu"
-  ],
-  "vllm-gpu": [
-    "aiosqlite",
-    "autoevals",
-    "blobfile",
-    "chardet",
-    "chromadb-client",
-    "datasets",
-    "emoji",
-    "faiss-cpu",
-    "fastapi",
-    "fire",
-    "httpx",
-    "langdetect",
-    "matplotlib",
-    "mcp",
-    "nltk",
-    "numpy",
-    "openai",
-    "opentelemetry-exporter-otlp-proto-http",
-    "opentelemetry-sdk",
-    "pandas",
-    "pillow",
-    "psycopg2-binary",
-    "pymongo",
-    "pypdf",
-    "pythainlp",
-    "redis",
-    "requests",
-    "scikit-learn",
-    "scipy",
-    "sentencepiece",
-    "sqlalchemy[asyncio]",
-    "tqdm",
-    "transformers",
-    "tree_sitter",
-    "uvicorn",
-    "vllm",
-    "sentence-transformers --no-deps",
-    "torch torchvision --index-url https://download.pytorch.org/whl/cpu"
-  ],
-  "watsonx": [
-    "aiosqlite",
-    "autoevals",
-    "blobfile",
-    "chardet",
-    "datasets",
-    "emoji",
-    "faiss-cpu",
-    "fastapi",
-    "fire",
-    "httpx",
-    "ibm_watson_machine_learning",
-    "langdetect",
-    "matplotlib",
-    "mcp",
-    "nltk",
-    "numpy",
-    "openai",
-    "opentelemetry-exporter-otlp-proto-http",
-    "opentelemetry-sdk",
-    "pandas",
-    "pillow",
-    "psycopg2-binary",
-    "pymongo",
-    "pypdf",
-    "pythainlp",
-    "redis",
-    "requests",
-    "scikit-learn",
-    "scipy",
-    "sentencepiece",
-    "sqlalchemy[asyncio]",
-    "tqdm",
-    "transformers",
-    "tree_sitter",
-    "uvicorn",
-    "sentence-transformers --no-deps",
-    "torch torchvision --index-url https://download.pytorch.org/whl/cpu"
-  ]
-}

llama_stack/templates/ollama/build.yaml

@@ -25,23 +25,7 @@ distribution_spec:
     - inline::rag-runtime
     - remote::model-context-protocol
     - remote::wolfram-alpha
-  metadata_store:
-    type: sqlite
-    db_path: ${env.SQLITE_STORE_DIR:~/.llama/distributions/ollama}/registry.db
-  inference_store:
-    type: sqlite
-    db_path: ${env.SQLITE_STORE_DIR:~/.llama/distributions/ollama}/inference_store.db
-  models:
-  - metadata: {}
-    model_id: ${env.INFERENCE_MODEL}
-    provider_id: remote::ollama
-    model_type: llm
-  - metadata:
-      embedding_dimension: 384
-    model_id: all-MiniLM-L6-v2
-    provider_id: remote::ollama
-    provider_model_id: all-minilm:latest
-    model_type: embedding
-image_type: container
+image_type: conda
 additional_pip_packages:
 - sqlalchemy[asyncio]
+- blobfile

llama_stack/templates/ollama/ollama.py

@@ -13,8 +13,8 @@ from llama_stack.distribution.datatypes import (
     ShieldInput,
     ToolGroupInput,
 )
-from llama_stack.providers.inline.post_training.huggingface import HuggingFacePostTrainingConfig
-from llama_stack.providers.inline.vector_io.faiss.config import FaissVectorIOConfig
+#from llama_stack.providers.inline.post_training.huggingface import HuggingFacePostTrainingConfig
+#from llama_stack.providers.inline.vector_io.faiss.config import FaissVectorIOConfig
 from llama_stack.providers.remote.inference.ollama import OllamaImplConfig
 from llama_stack.templates.template import DistributionTemplate, RunConfigSettings
 
@@ -32,7 +32,6 @@ def get_distribution_template() -> DistributionTemplate:
         "tool_runtime": [
             "remote::brave-search",
             "remote::tavily-search",
-            "inline::rag-runtime",
             "remote::model-context-protocol",
             "remote::wolfram-alpha",
         ],
@@ -43,11 +42,11 @@ def get_distribution_template() -> DistributionTemplate:
         provider_type="remote::ollama",
         config=OllamaImplConfig.sample_run_config(),
     )
-    vector_io_provider_faiss = Provider(
-        provider_id="faiss",
-        provider_type="inline::faiss",
-        config=FaissVectorIOConfig.sample_run_config(f"~/.llama/distributions/{name}"),
-    )
+    #vector_io_provider_faiss = Provider(
+    #    provider_id="faiss",
+    #    provider_type="inline::faiss",
+    #    config=FaissVectorIOConfig.sample_run_config(f"~/.llama/distributions/{name}"),
+    #)
     inference_model = ModelInput(
         model_id="${env.INFERENCE_MODEL}",
         provider_id="ollama",
@@ -70,10 +69,6 @@ def get_distribution_template() -> DistributionTemplate:
             toolgroup_id="builtin::websearch",
             provider_id="tavily-search",
         ),
-        ToolGroupInput(
-            toolgroup_id="builtin::rag",
-            provider_id="rag-runtime",
-        ),
         ToolGroupInput(
             toolgroup_id="builtin::wolfram_alpha",
             provider_id="wolfram-alpha",

llama_stack/templates/ollama/run.yaml

@@ -24,6 +24,10 @@ providers:
         type: sqlite
         namespace: null
         db_path: ${env.SQLITE_STORE_DIR:~/.llama/distributions/ollama}/faiss_store.db
+  - provider_id: chromadb
+    provider_type: remote::chromadb
+    config:
+      url: ${env.CHROMADB_URL:http://host.docker.internal:8000}
   safety:
   - provider_id: llama-guard
     provider_type: inline::llama-guard

llama_stack/ui/app/logs/chat-completions/[id]/page.tsx

@@ -2,9 +2,9 @@
 
 import { useEffect, useState } from "react";
 import { useParams } from "next/navigation";
-import LlamaStackClient from "llama-stack-client";
 import { ChatCompletion } from "@/lib/types";
 import { ChatCompletionDetailView } from "@/components/chat-completions/chat-completion-detail";
+import { client } from "@/lib/client";
 
 export default function ChatCompletionDetailPage() {
   const params = useParams();
@@ -22,10 +22,6 @@ export default function ChatCompletionDetailPage() {
       return;
     }
 
-    const client = new LlamaStackClient({
-      baseURL: process.env.NEXT_PUBLIC_LLAMA_STACK_BASE_URL,
-    });
-
     const fetchCompletionDetail = async () => {
       setIsLoading(true);
       setError(null);

llama_stack/ui/app/logs/chat-completions/layout.tsx

@@ -1,45 +1,19 @@
 "use client";
 
 import React from "react";
-import { usePathname, useParams } from "next/navigation";
-import {
-  PageBreadcrumb,
-  BreadcrumbSegment,
-} from "@/components/layout/page-breadcrumb";
-import { truncateText } from "@/lib/truncate-text";
+import LogsLayout from "@/components/layout/logs-layout";
 
 export default function ChatCompletionsLayout({
   children,
 }: {
   children: React.ReactNode;
 }) {
-  const pathname = usePathname();
-  const params = useParams();
-
-  let segments: BreadcrumbSegment[] = [];
-
-  // Default for /logs/chat-completions
-  if (pathname === "/logs/chat-completions") {
-    segments = [{ label: "Chat Completions" }];
-  }
-
-  // For /logs/chat-completions/[id]
-  const idParam = params?.id;
-  if (idParam && typeof idParam === "string") {
-    segments = [
-      { label: "Chat Completions", href: "/logs/chat-completions" },
-      { label: `Details (${truncateText(idParam, 20)})` },
-    ];
-  }
-
   return (
-    <div className="container mx-auto p-4">
-      <>
-        {segments.length > 0 && (
-          <PageBreadcrumb segments={segments} className="mb-4" />
-        )}
+    <LogsLayout
+      sectionLabel="Chat Completions"
+      basePath="/logs/chat-completions"
+    >
       {children}
-      </>
-    </div>
+    </LogsLayout>
   );
 }

llama_stack/ui/app/logs/chat-completions/page.tsx

@@ -1,9 +1,9 @@
 "use client";
 
 import { useEffect, useState } from "react";
-import LlamaStackClient from "llama-stack-client";
 import { ChatCompletion } from "@/lib/types";
-import { ChatCompletionsTable } from "@/components/chat-completions/chat-completion-table";
+import { ChatCompletionsTable } from "@/components/chat-completions/chat-completions-table";
+import { client } from "@/lib/client";
 
 export default function ChatCompletionsPage() {
   const [completions, setCompletions] = useState<ChatCompletion[]>([]);
@@ -11,9 +11,6 @@ export default function ChatCompletionsPage() {
   const [error, setError] = useState<Error | null>(null);
 
   useEffect(() => {
-    const client = new LlamaStackClient({
-      baseURL: process.env.NEXT_PUBLIC_LLAMA_STACK_BASE_URL,
-    });
     const fetchCompletions = async () => {
       setIsLoading(true);
       setError(null);
@@ -21,7 +18,7 @@ export default function ChatCompletionsPage() {
         const response = await client.chat.completions.list();
         const data = Array.isArray(response)
           ? response
-          : (response as any).data;
+          : (response as { data: ChatCompletion[] }).data;
 
         if (Array.isArray(data)) {
           setCompletions(data);
@@ -46,7 +43,7 @@ export default function ChatCompletionsPage() {
 
   return (
     <ChatCompletionsTable
-      completions={completions}
+      data={completions}
       isLoading={isLoading}
       error={error}
     />

llama_stack/ui/app/logs/responses/[id]/page.tsx

@@ -0,0 +1,125 @@
+"use client";
+
+import { useEffect, useState } from "react";
+import { useParams } from "next/navigation";
+import type { ResponseObject } from "llama-stack-client/resources/responses/responses";
+import { OpenAIResponse, InputItemListResponse } from "@/lib/types";
+import { ResponseDetailView } from "@/components/responses/responses-detail";
+import { client } from "@/lib/client";
+
+export default function ResponseDetailPage() {
+  const params = useParams();
+  const id = params.id as string;
+
+  const [responseDetail, setResponseDetail] = useState<OpenAIResponse | null>(
+    null,
+  );
+  const [inputItems, setInputItems] = useState<InputItemListResponse | null>(
+    null,
+  );
+  const [isLoading, setIsLoading] = useState<boolean>(true);
+  const [isLoadingInputItems, setIsLoadingInputItems] = useState<boolean>(true);
+  const [error, setError] = useState<Error | null>(null);
+  const [inputItemsError, setInputItemsError] = useState<Error | null>(null);
+
+  // Helper function to convert ResponseObject to OpenAIResponse
+  const convertResponseObject = (
+    responseData: ResponseObject,
+  ): OpenAIResponse => {
+    return {
+      id: responseData.id,
+      created_at: responseData.created_at,
+      model: responseData.model,
+      object: responseData.object,
+      status: responseData.status,
+      output: responseData.output as OpenAIResponse["output"],
+      input: [], // ResponseObject doesn't include input; component uses inputItems prop instead
+      error: responseData.error,
+      parallel_tool_calls: responseData.parallel_tool_calls,
+      previous_response_id: responseData.previous_response_id,
+      temperature: responseData.temperature,
+      top_p: responseData.top_p,
+      truncation: responseData.truncation,
+      user: responseData.user,
+    };
+  };
+
+  useEffect(() => {
+    if (!id) {
+      setError(new Error("Response ID is missing."));
+      setIsLoading(false);
+      return;
+    }
+
+    const fetchResponseDetail = async () => {
+      setIsLoading(true);
+      setIsLoadingInputItems(true);
+      setError(null);
+      setInputItemsError(null);
+      setResponseDetail(null);
+      setInputItems(null);
+
+      try {
+        const [responseResult, inputItemsResult] = await Promise.allSettled([
+          client.responses.retrieve(id),
+          client.responses.inputItems.list(id, { order: "asc" }),
+        ]);
+
+        // Handle response detail result
+        if (responseResult.status === "fulfilled") {
+          const convertedResponse = convertResponseObject(responseResult.value);
+          setResponseDetail(convertedResponse);
+        } else {
+          console.error(
+            `Error fetching response detail for ID ${id}:`,
+            responseResult.reason,
+          );
+          setError(
+            responseResult.reason instanceof Error
+              ? responseResult.reason
+              : new Error("Failed to fetch response detail"),
+          );
+        }
+
+        // Handle input items result
+        if (inputItemsResult.status === "fulfilled") {
+          const inputItemsData =
+            inputItemsResult.value as unknown as InputItemListResponse;
+          setInputItems(inputItemsData);
+        } else {
+          console.error(
+            `Error fetching input items for response ID ${id}:`,
+            inputItemsResult.reason,
+          );
+          setInputItemsError(
+            inputItemsResult.reason instanceof Error
+              ? inputItemsResult.reason
+              : new Error("Failed to fetch input items"),
+          );
+        }
+      } catch (err) {
+        console.error(`Unexpected error fetching data for ID ${id}:`, err);
+        setError(
+          err instanceof Error ? err : new Error("Unexpected error occurred"),
+        );
+      } finally {
+        setIsLoading(false);
+        setIsLoadingInputItems(false);
+      }
+    };
+
+    fetchResponseDetail();
+  }, [id]);
+
+  return (
+    <ResponseDetailView
+      response={responseDetail}
+      inputItems={inputItems}
+      isLoading={isLoading}
+      isLoadingInputItems={isLoadingInputItems}
+      error={error}
+      inputItemsError={inputItemsError}
+      id={id}
+    />
+  );
+}

llama_stack/ui/app/logs/responses/layout.tsx

@@ -0,0 +1,16 @@
+"use client";
+
+import React from "react";
+import LogsLayout from "@/components/layout/logs-layout";
+
+export default function ResponsesLayout({
+  children,
+}: {
+  children: React.ReactNode;
+}) {
+  return (
+    <LogsLayout sectionLabel="Responses" basePath="/logs/responses">
+      {children}
+    </LogsLayout>
+  );
+}

llama_stack/ui/app/logs/responses/page.tsx

@@ -1,7 +1,66 @@
-export default function Responses() {
+"use client";
+
+import { useEffect, useState } from "react";
+import type { ResponseListResponse } from "llama-stack-client/resources/responses/responses";
+import { OpenAIResponse } from "@/lib/types";
+import { ResponsesTable } from "@/components/responses/responses-table";
+import { client } from "@/lib/client";
+
+export default function ResponsesPage() {
+  const [responses, setResponses] = useState<OpenAIResponse[]>([]);
+  const [isLoading, setIsLoading] = useState<boolean>(true);
+  const [error, setError] = useState<Error | null>(null);
+
+  // Helper function to convert ResponseListResponse.Data to OpenAIResponse
+  const convertResponseListData = (
+    responseData: ResponseListResponse.Data,
+  ): OpenAIResponse => {
+    return {
+      id: responseData.id,
+      created_at: responseData.created_at,
+      model: responseData.model,
+      object: responseData.object,
+      status: responseData.status,
+      output: responseData.output as OpenAIResponse["output"],
+      input: responseData.input as OpenAIResponse["input"],
+      error: responseData.error,
+      parallel_tool_calls: responseData.parallel_tool_calls,
+      previous_response_id: responseData.previous_response_id,
+      temperature: responseData.temperature,
+      top_p: responseData.top_p,
+      truncation: responseData.truncation,
+      user: responseData.user,
+    };
+  };
+
+  useEffect(() => {
+    const fetchResponses = async () => {
+      setIsLoading(true);
+      setError(null);
+      try {
+        const response = await client.responses.list();
+        const responseListData = response as ResponseListResponse;
+
+        const convertedResponses: OpenAIResponse[] = responseListData.data.map(
+          convertResponseListData,
+        );
+
+        setResponses(convertedResponses);
+      } catch (err) {
+        console.error("Error fetching responses:", err);
+        setError(
+          err instanceof Error ? err : new Error("Failed to fetch responses"),
+        );
+        setResponses([]);
+      } finally {
+        setIsLoading(false);
+      }
+    };
+
+    fetchResponses();
+  }, []);
+
   return (
-    <div>
-      <h1>Under Construction</h1>
-    </div>
+    <ResponsesTable data={responses} isLoading={isLoading} error={error} />
   );
 }

llama_stack/ui/components/chat-completions/chat-completion-detail.test.tsx

@@ -75,7 +75,7 @@ describe("ChatCompletionDetailView", () => {
       />,
     );
     expect(
-      screen.getByText("No details found for completion ID: notfound-id."),
+      screen.getByText("No details found for ID: notfound-id."),
     ).toBeInTheDocument();
   });
 

llama_stack/ui/components/chat-completions/chat-completion-detail.tsx

@@ -3,45 +3,14 @@
 import { ChatMessage, ChatCompletion } from "@/lib/types";
 import { ChatMessageItem } from "@/components/chat-completions/chat-messasge-item";
 import { Card, CardContent, CardHeader, CardTitle } from "@/components/ui/card";
-import { Skeleton } from "@/components/ui/skeleton";
-
-function ChatCompletionDetailLoadingView() {
-  return (
-    <>
-      <Skeleton className="h-8 w-3/4 mb-6" /> {/* Title Skeleton */}
-      <div className="flex flex-col md:flex-row gap-6">
-        <div className="flex-grow md:w-2/3 space-y-6">
-          {[...Array(2)].map((_, i) => (
-            <Card key={`main-skeleton-card-${i}`}>
-              <CardHeader>
-                <CardTitle>
-                  <Skeleton className="h-6 w-1/2" />
-                </CardTitle>
-              </CardHeader>
-              <CardContent className="space-y-2">
-                <Skeleton className="h-4 w-full" />
-                <Skeleton className="h-4 w-full" />
-                <Skeleton className="h-4 w-3/4" />
-              </CardContent>
-            </Card>
-          ))}
-        </div>
-        <div className="md:w-1/3">
-          <div className="p-4 border rounded-lg shadow-sm bg-white space-y-3">
-            <Skeleton className="h-6 w-1/3 mb-3" />{" "}
-            {/* Properties Title Skeleton */}
-            {[...Array(5)].map((_, i) => (
-              <div key={`prop-skeleton-${i}`} className="space-y-1">
-                <Skeleton className="h-4 w-1/4" />
-                <Skeleton className="h-4 w-1/2" />
-              </div>
-            ))}
-          </div>
-        </div>
-      </div>
-    </>
-  );
-}
+import {
+  DetailLoadingView,
+  DetailErrorView,
+  DetailNotFoundView,
+  DetailLayout,
+  PropertiesCard,
+  PropertyItem,
+} from "@/components/layout/detail-layout";
 
 interface ChatCompletionDetailViewProps {
   completion: ChatCompletion | null;
@@ -56,39 +25,23 @@ export function ChatCompletionDetailView({
   error,
   id,
 }: ChatCompletionDetailViewProps) {
+  const title = "Chat Completion Details";
+
   if (error) {
-    return (
-      <>
-        {/* We still want a title for consistency on error pages */}
-        <h1 className="text-2xl font-bold mb-6">Chat Completion Details</h1>
-        <p>
-          Error loading details for ID {id}: {error.message}
-        </p>
-      </>
-    );
+    return <DetailErrorView title={title} id={id} error={error} />;
   }
 
   if (isLoading) {
-    return <ChatCompletionDetailLoadingView />;
+    return <DetailLoadingView title={title} />;
   }
 
   if (!completion) {
-    // This state means: not loading, no error, but no completion data
-    return (
-      <>
-        {/* We still want a title for consistency on not-found pages */}
-        <h1 className="text-2xl font-bold mb-6">Chat Completion Details</h1>
-        <p>No details found for completion ID: {id}.</p>
-      </>
-    );
+    return <DetailNotFoundView title={title} id={id} />;
   }
 
-  // If no error, not loading, and completion exists, render the details:
-  return (
+  // Main content cards
+  const mainContent = (
     <>
-      <h1 className="text-2xl font-bold mb-6">Chat Completion Details</h1>
-      <div className="flex flex-col md:flex-row gap-6">
-        <div className="flex-grow md:w-2/3 space-y-6">
       <Card>
         <CardHeader>
           <CardTitle>Input</CardTitle>
@@ -98,13 +51,15 @@ export function ChatCompletionDetailView({
             <ChatMessageItem key={`input-msg-${index}`} message={msg} />
           ))}
           {completion.choices?.[0]?.message?.tool_calls &&
+          Array.isArray(completion.choices[0].message.tool_calls) &&
           !completion.input_messages?.some(
             (im) =>
               im.role === "assistant" &&
               im.tool_calls &&
+              Array.isArray(im.tool_calls) &&
               im.tool_calls.length > 0,
-                ) &&
-                completion.choices[0].message.tool_calls.map(
+          )
+            ? completion.choices[0].message.tool_calls.map(
                 (toolCall: any, index: number) => {
                   const assistantToolCallMessage: ChatMessage = {
                     role: "assistant",
@@ -118,7 +73,8 @@ export function ChatCompletionDetailView({
                     />
                   );
                 },
-                )}
+              )
+            : null}
         </CardContent>
       </Card>
 
@@ -138,61 +94,52 @@ export function ChatCompletionDetailView({
           )}
         </CardContent>
       </Card>
-        </div>
+    </>
+  );
 
-        <div className="md:w-1/3">
-          <Card>
-            <CardHeader>
-              <CardTitle>Properties</CardTitle>
-            </CardHeader>
-            <CardContent>
-              <ul className="space-y-2 text-sm text-gray-600">
-                <li>
-                  <strong>Created:</strong>{" "}
-                  <span className="text-gray-900 font-medium">
-                    {new Date(completion.created * 1000).toLocaleString()}
-                  </span>
-                </li>
-                <li>
-                  <strong>ID:</strong>{" "}
-                  <span className="text-gray-900 font-medium">
-                    {completion.id}
-                  </span>
-                </li>
-                <li>
-                  <strong>Model:</strong>{" "}
-                  <span className="text-gray-900 font-medium">
-                    {completion.model}
-                  </span>
-                </li>
-                <li className="pt-1 mt-1 border-t border-gray-200">
-                  <strong>Finish Reason:</strong>{" "}
-                  <span className="text-gray-900 font-medium">
-                    {completion.choices?.[0]?.finish_reason || "N/A"}
-                  </span>
-                </li>
-                {completion.choices?.[0]?.message?.tool_calls &&
-                  completion.choices[0].message.tool_calls.length > 0 && (
-                    <li className="pt-1 mt-1 border-t border-gray-200">
-                      <strong>Functions/Tools Called:</strong>
+  // Properties sidebar
+  const sidebar = (
+    <PropertiesCard>
+      <PropertyItem
+        label="Created"
+        value={new Date(completion.created * 1000).toLocaleString()}
+      />
+      <PropertyItem label="ID" value={completion.id} />
+      <PropertyItem label="Model" value={completion.model} />
+      <PropertyItem
+        label="Finish Reason"
+        value={completion.choices?.[0]?.finish_reason || "N/A"}
+        hasBorder
+      />
+      {(() => {
+        const toolCalls = completion.choices?.[0]?.message?.tool_calls;
+        if (toolCalls && Array.isArray(toolCalls) && toolCalls.length > 0) {
+          return (
+            <PropertyItem
+              label="Functions/Tools Called"
+              value={
+                <div>
                   <ul className="list-disc list-inside pl-4 mt-1">
-                        {completion.choices[0].message.tool_calls.map(
-                          (toolCall: any, index: number) => (
+                    {toolCalls.map((toolCall: any, index: number) => (
                       <li key={index}>
                         <span className="text-gray-900 font-medium">
                           {toolCall.function?.name || "N/A"}
                         </span>
                       </li>
-                          ),
-                        )}
+                    ))}
                   </ul>
-                    </li>
-                  )}
-              </ul>
-            </CardContent>
-          </Card>
                 </div>
-      </div>
-    </>
+              }
+              hasBorder
+            />
+          );
+        }
+        return null;
+      })()}
+    </PropertiesCard>
+  );
+
+  return (
+    <DetailLayout title={title} mainContent={mainContent} sidebar={sidebar} />
   );
 }

llama_stack/ui/components/chat-completions/chat-completion-table.test.tsx

@@ -1,8 +1,8 @@
 import React from "react";
 import { render, screen, fireEvent } from "@testing-library/react";
 import "@testing-library/jest-dom";
-import { ChatCompletionsTable } from "./chat-completion-table";
-import { ChatCompletion } from "@/lib/types"; // Assuming this path is correct
+import { ChatCompletionsTable } from "./chat-completions-table";
+import { ChatCompletion } from "@/lib/types";
 
 // Mock next/navigation
 const mockPush = jest.fn();
@@ -13,21 +13,25 @@ jest.mock("next/navigation", () => ({
 }));
 
 // Mock helper functions
-// These are hoisted, so their mocks are available throughout the file
 jest.mock("@/lib/truncate-text");
-jest.mock("@/lib/format-tool-call");
+jest.mock("@/lib/format-message-content");
 
 // Import the mocked functions to set up default or specific implementations
 import { truncateText as originalTruncateText } from "@/lib/truncate-text";
-import { formatToolCallToString as originalFormatToolCallToString } from "@/lib/format-tool-call";
+import {
+  extractTextFromContentPart as originalExtractTextFromContentPart,
+  extractDisplayableText as originalExtractDisplayableText,
+} from "@/lib/format-message-content";
 
 // Cast to jest.Mock for typings
 const truncateText = originalTruncateText as jest.Mock;
-const formatToolCallToString = originalFormatToolCallToString as jest.Mock;
+const extractTextFromContentPart =
+  originalExtractTextFromContentPart as jest.Mock;
+const extractDisplayableText = originalExtractDisplayableText as jest.Mock;
 
 describe("ChatCompletionsTable", () => {
   const defaultProps = {
-    completions: [] as ChatCompletion[],
+    data: [] as ChatCompletion[],
     isLoading: false,
     error: null,
   };
@@ -36,28 +40,26 @@ describe("ChatCompletionsTable", () => {
     // Reset all mocks before each test
     mockPush.mockClear();
     truncateText.mockClear();
-    formatToolCallToString.mockClear();
+    extractTextFromContentPart.mockClear();
+    extractDisplayableText.mockClear();
 
-    // Default pass-through implementation for tests not focusing on truncation/formatting
+    // Default pass-through implementations
     truncateText.mockImplementation((text: string | undefined) => text);
-    formatToolCallToString.mockImplementation((toolCall: any) =>
-      toolCall && typeof toolCall === "object" && toolCall.name
-        ? `[DefaultToolCall:${toolCall.name}]`
-        : "[InvalidToolCall]",
+    extractTextFromContentPart.mockImplementation((content: unknown) =>
+      typeof content === "string" ? content : "extracted text",
+    );
+    extractDisplayableText.mockImplementation(
+      (message: unknown) =>
+        (message as { content?: string })?.content || "extracted output",
     );
   });
 
   test("renders without crashing with default props", () => {
     render(<ChatCompletionsTable {...defaultProps} />);
-    // Check for a unique element that should be present in the non-empty, non-loading, non-error state
-    // For now, as per Task 1, we will test the empty state message
     expect(screen.getByText("No chat completions found.")).toBeInTheDocument();
   });
 
   test("click on a row navigates to the correct URL", () => {
-    const { rerender } = render(<ChatCompletionsTable {...defaultProps} />);
-
-    // Simulate a scenario where a completion exists and is clicked
     const mockCompletion: ChatCompletion = {
       id: "comp_123",
       object: "chat.completion",
@@ -73,9 +75,12 @@ describe("ChatCompletionsTable", () => {
       input_messages: [{ role: "user", content: "Test input" }],
     };
 
-    rerender(
-      <ChatCompletionsTable {...defaultProps} completions={[mockCompletion]} />,
-    );
+    // Set up mocks to return expected values
+    extractTextFromContentPart.mockReturnValue("Test input");
+    extractDisplayableText.mockReturnValue("Test output");
+
+    render(<ChatCompletionsTable {...defaultProps} data={[mockCompletion]} />);
+
     const row = screen.getByText("Test input").closest("tr");
     if (row) {
       fireEvent.click(row);
@@ -91,14 +96,13 @@ describe("ChatCompletionsTable", () => {
         <ChatCompletionsTable {...defaultProps} isLoading={true} />,
       );
 
-      // The Skeleton component uses data-slot="skeleton"
-      const skeletonSelector = '[data-slot="skeleton"]';
-
       // Check for skeleton in the table caption
       const tableCaption = container.querySelector("caption");
       expect(tableCaption).toBeInTheDocument();
       if (tableCaption) {
-        const captionSkeleton = tableCaption.querySelector(skeletonSelector);
+        const captionSkeleton = tableCaption.querySelector(
+          '[data-slot="skeleton"]',
+        );
         expect(captionSkeleton).toBeInTheDocument();
       }
 
@@ -107,16 +111,10 @@ describe("ChatCompletionsTable", () => {
       expect(tableBody).toBeInTheDocument();
       if (tableBody) {
         const bodySkeletons = tableBody.querySelectorAll(
-          `td ${skeletonSelector}`,
+          '[data-slot="skeleton"]',
         );
-        expect(bodySkeletons.length).toBeGreaterThan(0); // Ensure at least one skeleton cell exists
+        expect(bodySkeletons.length).toBeGreaterThan(0);
       }
-
-      // General check: ensure multiple skeleton elements are present in the table overall
-      const allSkeletonsInTable = container.querySelectorAll(
-        `table ${skeletonSelector}`,
-      );
-      expect(allSkeletonsInTable.length).toBeGreaterThan(3); // e.g., caption + at least one row of 3 cells, or just a few
     });
   });
 
@@ -140,14 +138,14 @@ describe("ChatCompletionsTable", () => {
           {...defaultProps}
           error={{ name: "Error", message: "" }}
         />,
-      ); // Error with empty message
+      );
       expect(
         screen.getByText("Error fetching data: An unknown error occurred"),
       ).toBeInTheDocument();
     });
 
     test("renders default error message when error prop is an object without message", () => {
-      render(<ChatCompletionsTable {...defaultProps} error={{} as Error} />); // Empty error object
+      render(<ChatCompletionsTable {...defaultProps} error={{} as Error} />);
       expect(
         screen.getByText("Error fetching data: An unknown error occurred"),
       ).toBeInTheDocument();
@@ -155,14 +153,8 @@ describe("ChatCompletionsTable", () => {
   });
 
   describe("Empty State", () => {
-    test('renders "No chat completions found." and no table when completions array is empty', () => {
-      render(
-        <ChatCompletionsTable
-          completions={[]}
-          isLoading={false}
-          error={null}
-        />,
-      );
+    test('renders "No chat completions found." and no table when data array is empty', () => {
+      render(<ChatCompletionsTable data={[]} isLoading={false} error={null} />);
       expect(
         screen.getByText("No chat completions found."),
       ).toBeInTheDocument();
@@ -179,7 +171,7 @@ describe("ChatCompletionsTable", () => {
         {
           id: "comp_1",
           object: "chat.completion",
-          created: 1710000000, // Fixed timestamp for test
+          created: 1710000000,
           model: "llama-test-model",
           choices: [
             {
@@ -206,9 +198,22 @@ describe("ChatCompletionsTable", () => {
         },
       ];
 
+      // Set up mocks to return expected values
+      extractTextFromContentPart.mockImplementation((content: unknown) => {
+        if (content === "Test input") return "Test input";
+        if (content === "Another input") return "Another input";
+        return "extracted text";
+      });
+      extractDisplayableText.mockImplementation((message: unknown) => {
+        const msg = message as { content?: string };
+        if (msg?.content === "Test output") return "Test output";
+        if (msg?.content === "Another output") return "Another output";
+        return "extracted output";
+      });
+
       render(
         <ChatCompletionsTable
-          completions={mockCompletions}
+          data={mockCompletions}
           isLoading={false}
           error={null}
         />,
@@ -242,7 +247,7 @@ describe("ChatCompletionsTable", () => {
     });
   });
 
-  describe("Text Truncation and Tool Call Formatting", () => {
+  describe("Text Truncation and Content Extraction", () => {
     test("truncates long input and output text", () => {
       // Specific mock implementation for this test
       truncateText.mockImplementation(
@@ -259,6 +264,10 @@ describe("ChatCompletionsTable", () => {
         "This is a very long input message that should be truncated.";
       const longOutput =
         "This is a very long output message that should also be truncated.";
+
+      extractTextFromContentPart.mockReturnValue(longInput);
+      extractDisplayableText.mockReturnValue(longOutput);
+
       const mockCompletions = [
         {
           id: "comp_trunc",
@@ -278,7 +287,7 @@ describe("ChatCompletionsTable", () => {
 
       render(
         <ChatCompletionsTable
-          completions={mockCompletions}
+          data={mockCompletions}
           isLoading={false}
           error={null}
         />,
@@ -289,52 +298,50 @@ describe("ChatCompletionsTable", () => {
         longInput.slice(0, 10) + "...",
       );
       expect(truncatedTexts.length).toBe(2); // one for input, one for output
-      // Optionally, verify each one is in the document if getAllByText doesn't throw on not found
       truncatedTexts.forEach((textElement) =>
         expect(textElement).toBeInTheDocument(),
       );
     });
 
-    test("formats tool call output using formatToolCallToString", () => {
-      // Specific mock implementation for this test
-      formatToolCallToString.mockImplementation(
-        (toolCall: any) => `[TOOL:${toolCall.name}]`,
-      );
-      // Ensure no truncation interferes for this specific test for clarity of tool call format
-      truncateText.mockImplementation((text: string | undefined) => text);
-
-      const toolCall = { name: "search", args: { query: "llama" } };
-      const mockCompletions = [
-        {
-          id: "comp_tool",
+    test("uses content extraction functions correctly", () => {
+      const mockCompletion = {
+        id: "comp_extract",
         object: "chat.completion",
         created: 1710003000,
-          model: "llama-tool-model",
+        model: "llama-extract-model",
         choices: [
           {
             index: 0,
-              message: {
-                role: "assistant",
-                content: "Tool output", // Content that will be prepended
-                tool_calls: [toolCall],
-              },
+            message: { role: "assistant", content: "Extracted output" },
             finish_reason: "stop",
           },
         ],
-          input_messages: [{ role: "user", content: "Tool input" }],
-        },
-      ];
+        input_messages: [{ role: "user", content: "Extracted input" }],
+      };
+
+      extractTextFromContentPart.mockReturnValue("Extracted input");
+      extractDisplayableText.mockReturnValue("Extracted output");
 
       render(
         <ChatCompletionsTable
-          completions={mockCompletions}
+          data={[mockCompletion]}
           isLoading={false}
           error={null}
         />,
       );
 
-      // The component concatenates message.content and the formatted tool call
-      expect(screen.getByText("Tool output [TOOL:search]")).toBeInTheDocument();
+      // Verify the extraction functions were called
+      expect(extractTextFromContentPart).toHaveBeenCalledWith(
+        "Extracted input",
+      );
+      expect(extractDisplayableText).toHaveBeenCalledWith({
+        role: "assistant",
+        content: "Extracted output",
+      });
+
+      // Verify the extracted content is displayed
+      expect(screen.getByText("Extracted input")).toBeInTheDocument();
+      expect(screen.getByText("Extracted output")).toBeInTheDocument();
     });
   });
 });

llama_stack/ui/components/chat-completions/chat-completions-table.tsx

@@ -0,0 +1,43 @@
+"use client";
+
+import { ChatCompletion } from "@/lib/types";
+import { LogsTable, LogTableRow } from "@/components/logs/logs-table";
+import {
+  extractTextFromContentPart,
+  extractDisplayableText,
+} from "@/lib/format-message-content";
+
+interface ChatCompletionsTableProps {
+  data: ChatCompletion[];
+  isLoading: boolean;
+  error: Error | null;
+}
+
+function formatChatCompletionToRow(completion: ChatCompletion): LogTableRow {
+  return {
+    id: completion.id,
+    input: extractTextFromContentPart(completion.input_messages?.[0]?.content),
+    output: extractDisplayableText(completion.choices?.[0]?.message),
+    model: completion.model,
+    createdTime: new Date(completion.created * 1000).toLocaleString(),
+    detailPath: `/logs/chat-completions/${completion.id}`,
+  };
+}
+
+export function ChatCompletionsTable({
+  data,
+  isLoading,
+  error,
+}: ChatCompletionsTableProps) {
+  const formattedData = data.map(formatChatCompletionToRow);
+
+  return (
+    <LogsTable
+      data={formattedData}
+      isLoading={isLoading}
+      error={error}
+      caption="A list of your recent chat completions."
+      emptyMessage="No chat completions found."
+    />
+  );
+}

llama_stack/ui/components/chat-completions/chat-messasge-item.tsx

@@ -4,45 +4,10 @@ import { ChatMessage } from "@/lib/types";
 import React from "react";
 import { formatToolCallToString } from "@/lib/format-tool-call";
 import { extractTextFromContentPart } from "@/lib/format-message-content";
-
-// Sub-component or helper for the common label + content structure
-const MessageBlock: React.FC<{
-  label: string;
-  labelDetail?: string;
-  content: React.ReactNode;
-}> = ({ label, labelDetail, content }) => {
-  return (
-    <div>
-      <p className="py-1 font-semibold text-gray-800 mb-1">
-        {label}
-        {labelDetail && (
-          <span className="text-xs text-gray-500 font-normal ml-1">
-            {labelDetail}
-          </span>
-        )}
-      </p>
-      <div className="py-1">{content}</div>
-    </div>
-  );
-};
-
-interface ToolCallBlockProps {
-  children: React.ReactNode;
-  className?: string;
-}
-
-const ToolCallBlock = ({ children, className }: ToolCallBlockProps) => {
-  // Common styling for both function call arguments and tool output blocks
-  // Let's use slate-50 background as it's good for code-like content.
-  const baseClassName =
-    "p-3 bg-slate-50 border border-slate-200 rounded-md text-sm";
-
-  return (
-    <div className={`${baseClassName} ${className || ""}`}>
-      <pre className="whitespace-pre-wrap text-xs">{children}</pre>
-    </div>
-  );
-};
+import {
+  MessageBlock,
+  ToolCallBlock,
+} from "@/components/ui/message-components";
 
 interface ChatMessageItemProps {
   message: ChatMessage;
@@ -65,7 +30,11 @@ export function ChatMessageItem({ message }: ChatMessageItemProps) {
       );
 
     case "assistant":
-      if (message.tool_calls && message.tool_calls.length > 0) {
+      if (
+        message.tool_calls &&
+        Array.isArray(message.tool_calls) &&
+        message.tool_calls.length > 0
+      ) {
         return (
           <>
             {message.tool_calls.map((toolCall: any, index: number) => {

llama_stack/ui/components/layout/detail-layout.tsx

@@ -0,0 +1,141 @@
+import React from "react";
+import { Card, CardContent, CardHeader, CardTitle } from "@/components/ui/card";
+import { Skeleton } from "@/components/ui/skeleton";
+
+export function DetailLoadingView({ title }: { title: string }) {
+  return (
+    <>
+      <Skeleton className="h-8 w-3/4 mb-6" /> {/* Title Skeleton */}
+      <div className="flex flex-col md:flex-row gap-6">
+        <div className="flex-grow md:w-2/3 space-y-6">
+          {[...Array(2)].map((_, i) => (
+            <Card key={`main-skeleton-card-${i}`}>
+              <CardHeader>
+                <CardTitle>
+                  <Skeleton className="h-6 w-1/2" />
+                </CardTitle>
+              </CardHeader>
+              <CardContent className="space-y-2">
+                <Skeleton className="h-4 w-full" />
+                <Skeleton className="h-4 w-full" />
+                <Skeleton className="h-4 w-3/4" />
+              </CardContent>
+            </Card>
+          ))}
+        </div>
+        <div className="md:w-1/3">
+          <div className="p-4 border rounded-lg shadow-sm bg-white space-y-3">
+            <Skeleton className="h-6 w-1/3 mb-3" />{" "}
+            {/* Properties Title Skeleton */}
+            {[...Array(5)].map((_, i) => (
+              <div key={`prop-skeleton-${i}`} className="space-y-1">
+                <Skeleton className="h-4 w-1/4" />
+                <Skeleton className="h-4 w-1/2" />
+              </div>
+            ))}
+          </div>
+        </div>
+      </div>
+    </>
+  );
+}
+
+export function DetailErrorView({
+  title,
+  id,
+  error,
+}: {
+  title: string;
+  id: string;
+  error: Error;
+}) {
+  return (
+    <>
+      <h1 className="text-2xl font-bold mb-6">{title}</h1>
+      <p>
+        Error loading details for ID {id}: {error.message}
+      </p>
+    </>
+  );
+}
+
+export function DetailNotFoundView({
+  title,
+  id,
+}: {
+  title: string;
+  id: string;
+}) {
+  return (
+    <>
+      <h1 className="text-2xl font-bold mb-6">{title}</h1>
+      <p>No details found for ID: {id}.</p>
+    </>
+  );
+}
+
+export interface PropertyItemProps {
+  label: string;
+  value: React.ReactNode;
+  className?: string;
+  hasBorder?: boolean;
+}
+
+export function PropertyItem({
+  label,
+  value,
+  className = "",
+  hasBorder = false,
+}: PropertyItemProps) {
+  return (
+    <li
+      className={`${hasBorder ? "pt-1 mt-1 border-t border-gray-200" : ""} ${className}`}
+    >
+      <strong>{label}:</strong>{" "}
+      {typeof value === "string" || typeof value === "number" ? (
+        <span className="text-gray-900 font-medium">{value}</span>
+      ) : (
+        value
+      )}
+    </li>
+  );
+}
+
+export interface PropertiesCardProps {
+  children: React.ReactNode;
+}
+
+export function PropertiesCard({ children }: PropertiesCardProps) {
+  return (
+    <Card>
+      <CardHeader>
+        <CardTitle>Properties</CardTitle>
+      </CardHeader>
+      <CardContent>
+        <ul className="space-y-2 text-sm text-gray-600">{children}</ul>
+      </CardContent>
+    </Card>
+  );
+}
+
+export interface DetailLayoutProps {
+  title: string;
+  mainContent: React.ReactNode;
+  sidebar: React.ReactNode;
+}
+
+export function DetailLayout({
+  title,
+  mainContent,
+  sidebar,
+}: DetailLayoutProps) {
+  return (
+    <>
+      <h1 className="text-2xl font-bold mb-6">{title}</h1>
+      <div className="flex flex-col md:flex-row gap-6">
+        <div className="flex-grow md:w-2/3 space-y-6">{mainContent}</div>
+        <div className="md:w-1/3">{sidebar}</div>
+      </div>
+    </>
+  );
+}

llama_stack/ui/components/layout/logs-layout.tsx

@@ -0,0 +1,49 @@
+"use client";
+
+import React from "react";
+import { usePathname, useParams } from "next/navigation";
+import {
+  PageBreadcrumb,
+  BreadcrumbSegment,
+} from "@/components/layout/page-breadcrumb";
+import { truncateText } from "@/lib/truncate-text";
+
+interface LogsLayoutProps {
+  children: React.ReactNode;
+  sectionLabel: string;
+  basePath: string;
+}
+
+export default function LogsLayout({
+  children,
+  sectionLabel,
+  basePath,
+}: LogsLayoutProps) {
+  const pathname = usePathname();
+  const params = useParams();
+
+  let segments: BreadcrumbSegment[] = [];
+
+  if (pathname === basePath) {
+    segments = [{ label: sectionLabel }];
+  }
+
+  const idParam = params?.id;
+  if (idParam && typeof idParam === "string") {
+    segments = [
+      { label: sectionLabel, href: basePath },
+      { label: `Details (${truncateText(idParam, 20)})` },
+    ];
+  }
+
+  return (
+    <div className="container mx-auto p-4">
+      <>
+        {segments.length > 0 && (
+          <PageBreadcrumb segments={segments} className="mb-4" />
+        )}
+        {children}
+      </>
+    </div>
+  );
+}

llama_stack/ui/components/logs/logs-table.test.tsx

@@ -0,0 +1,350 @@
+import React from "react";
+import { render, screen, fireEvent } from "@testing-library/react";
+import "@testing-library/jest-dom";
+import { LogsTable, LogTableRow } from "./logs-table";
+
+// Mock next/navigation
+const mockPush = jest.fn();
+jest.mock("next/navigation", () => ({
+  useRouter: () => ({
+    push: mockPush,
+  }),
+}));
+
+// Mock helper functions
+jest.mock("@/lib/truncate-text");
+
+// Import the mocked functions
+import { truncateText as originalTruncateText } from "@/lib/truncate-text";
+
+// Cast to jest.Mock for typings
+const truncateText = originalTruncateText as jest.Mock;
+
+describe("LogsTable", () => {
+  const defaultProps = {
+    data: [] as LogTableRow[],
+    isLoading: false,
+    error: null,
+    caption: "Test table caption",
+    emptyMessage: "No data found",
+  };
+
+  beforeEach(() => {
+    // Reset all mocks before each test
+    mockPush.mockClear();
+    truncateText.mockClear();
+
+    // Default pass-through implementation
+    truncateText.mockImplementation((text: string | undefined) => text);
+  });
+
+  test("renders without crashing with default props", () => {
+    render(<LogsTable {...defaultProps} />);
+    expect(screen.getByText("No data found")).toBeInTheDocument();
+  });
+
+  test("click on a row navigates to the correct URL", () => {
+    const mockData: LogTableRow[] = [
+      {
+        id: "row_123",
+        input: "Test input",
+        output: "Test output",
+        model: "test-model",
+        createdTime: "2024-01-01 12:00:00",
+        detailPath: "/test/path/row_123",
+      },
+    ];
+
+    render(<LogsTable {...defaultProps} data={mockData} />);
+
+    const row = screen.getByText("Test input").closest("tr");
+    if (row) {
+      fireEvent.click(row);
+      expect(mockPush).toHaveBeenCalledWith("/test/path/row_123");
+    } else {
+      throw new Error('Row with "Test input" not found for router mock test.');
+    }
+  });
+
+  describe("Loading State", () => {
+    test("renders skeleton UI when isLoading is true", () => {
+      const { container } = render(
+        <LogsTable {...defaultProps} isLoading={true} />,
+      );
+
+      // Check for skeleton in the table caption
+      const tableCaption = container.querySelector("caption");
+      expect(tableCaption).toBeInTheDocument();
+      if (tableCaption) {
+        const captionSkeleton = tableCaption.querySelector(
+          '[data-slot="skeleton"]',
+        );
+        expect(captionSkeleton).toBeInTheDocument();
+      }
+
+      // Check for skeletons in the table body cells
+      const tableBody = container.querySelector("tbody");
+      expect(tableBody).toBeInTheDocument();
+      if (tableBody) {
+        const bodySkeletons = tableBody.querySelectorAll(
+          '[data-slot="skeleton"]',
+        );
+        expect(bodySkeletons.length).toBeGreaterThan(0);
+      }
+
+      // Check that table headers are still rendered
+      expect(screen.getByText("Input")).toBeInTheDocument();
+      expect(screen.getByText("Output")).toBeInTheDocument();
+      expect(screen.getByText("Model")).toBeInTheDocument();
+      expect(screen.getByText("Created")).toBeInTheDocument();
+    });
+
+    test("renders correct number of skeleton rows", () => {
+      const { container } = render(
+        <LogsTable {...defaultProps} isLoading={true} />,
+      );
+
+      const skeletonRows = container.querySelectorAll("tbody tr");
+      expect(skeletonRows.length).toBe(3); // Should render 3 skeleton rows
+    });
+  });
+
+  describe("Error State", () => {
+    test("renders error message when error prop is provided", () => {
+      const errorMessage = "Network Error";
+      render(
+        <LogsTable
+          {...defaultProps}
+          error={{ name: "Error", message: errorMessage }}
+        />,
+      );
+      expect(
+        screen.getByText(`Error fetching data: ${errorMessage}`),
+      ).toBeInTheDocument();
+    });
+
+    test("renders default error message when error.message is not available", () => {
+      render(
+        <LogsTable {...defaultProps} error={{ name: "Error", message: "" }} />,
+      );
+      expect(
+        screen.getByText("Error fetching data: An unknown error occurred"),
+      ).toBeInTheDocument();
+    });
+
+    test("renders default error message when error prop is an object without message", () => {
+      render(<LogsTable {...defaultProps} error={{} as Error} />);
+      expect(
+        screen.getByText("Error fetching data: An unknown error occurred"),
+      ).toBeInTheDocument();
+    });
+
+    test("does not render table when in error state", () => {
+      render(
+        <LogsTable
+          {...defaultProps}
+          error={{ name: "Error", message: "Test error" }}
+        />,
+      );
+      const table = screen.queryByRole("table");
+      expect(table).not.toBeInTheDocument();
+    });
+  });
+
+  describe("Empty State", () => {
+    test("renders custom empty message when data array is empty", () => {
+      render(
+        <LogsTable
+          {...defaultProps}
+          data={[]}
+          emptyMessage="Custom empty message"
+        />,
+      );
+      expect(screen.getByText("Custom empty message")).toBeInTheDocument();
+
+      // Ensure that the table structure is NOT rendered in the empty state
+      const table = screen.queryByRole("table");
+      expect(table).not.toBeInTheDocument();
+    });
+  });
+
+  describe("Data Rendering", () => {
+    test("renders table caption, headers, and data correctly", () => {
+      const mockData: LogTableRow[] = [
+        {
+          id: "row_1",
+          input: "First input",
+          output: "First output",
+          model: "model-1",
+          createdTime: "2024-01-01 12:00:00",
+          detailPath: "/path/1",
+        },
+        {
+          id: "row_2",
+          input: "Second input",
+          output: "Second output",
+          model: "model-2",
+          createdTime: "2024-01-02 13:00:00",
+          detailPath: "/path/2",
+        },
+      ];
+
+      render(
+        <LogsTable
+          {...defaultProps}
+          data={mockData}
+          caption="Custom table caption"
+        />,
+      );
+
+      // Table caption
+      expect(screen.getByText("Custom table caption")).toBeInTheDocument();
+
+      // Table headers
+      expect(screen.getByText("Input")).toBeInTheDocument();
+      expect(screen.getByText("Output")).toBeInTheDocument();
+      expect(screen.getByText("Model")).toBeInTheDocument();
+      expect(screen.getByText("Created")).toBeInTheDocument();
+
+      // Data rows
+      expect(screen.getByText("First input")).toBeInTheDocument();
+      expect(screen.getByText("First output")).toBeInTheDocument();
+      expect(screen.getByText("model-1")).toBeInTheDocument();
+      expect(screen.getByText("2024-01-01 12:00:00")).toBeInTheDocument();
+
+      expect(screen.getByText("Second input")).toBeInTheDocument();
+      expect(screen.getByText("Second output")).toBeInTheDocument();
+      expect(screen.getByText("model-2")).toBeInTheDocument();
+      expect(screen.getByText("2024-01-02 13:00:00")).toBeInTheDocument();
+    });
+
+    test("applies correct CSS classes to table rows", () => {
+      const mockData: LogTableRow[] = [
+        {
+          id: "row_1",
+          input: "Test input",
+          output: "Test output",
+          model: "test-model",
+          createdTime: "2024-01-01 12:00:00",
+          detailPath: "/test/path",
+        },
+      ];
+
+      render(<LogsTable {...defaultProps} data={mockData} />);
+
+      const row = screen.getByText("Test input").closest("tr");
+      expect(row).toHaveClass("cursor-pointer");
+      expect(row).toHaveClass("hover:bg-muted/50");
+    });
+
+    test("applies correct alignment to Created column", () => {
+      const mockData: LogTableRow[] = [
+        {
+          id: "row_1",
+          input: "Test input",
+          output: "Test output",
+          model: "test-model",
+          createdTime: "2024-01-01 12:00:00",
+          detailPath: "/test/path",
+        },
+      ];
+
+      render(<LogsTable {...defaultProps} data={mockData} />);
+
+      const createdCell = screen.getByText("2024-01-01 12:00:00").closest("td");
+      expect(createdCell).toHaveClass("text-right");
+    });
+  });
+
+  describe("Text Truncation", () => {
+    test("truncates input and output text using truncateText function", () => {
+      // Mock truncateText to return truncated versions
+      truncateText.mockImplementation((text: string | undefined) => {
+        if (typeof text === "string" && text.length > 10) {
+          return text.slice(0, 10) + "...";
+        }
+        return text;
+      });
+
+      const longInput =
+        "This is a very long input text that should be truncated";
+      const longOutput =
+        "This is a very long output text that should be truncated";
+
+      const mockData: LogTableRow[] = [
+        {
+          id: "row_1",
+          input: longInput,
+          output: longOutput,
+          model: "test-model",
+          createdTime: "2024-01-01 12:00:00",
+          detailPath: "/test/path",
+        },
+      ];
+
+      render(<LogsTable {...defaultProps} data={mockData} />);
+
+      // Verify truncateText was called
+      expect(truncateText).toHaveBeenCalledWith(longInput);
+      expect(truncateText).toHaveBeenCalledWith(longOutput);
+
+      // Verify truncated text is displayed
+      const truncatedTexts = screen.getAllByText("This is a ...");
+      expect(truncatedTexts).toHaveLength(2); // one for input, one for output
+      truncatedTexts.forEach((textElement) =>
+        expect(textElement).toBeInTheDocument(),
+      );
+    });
+
+    test("does not truncate model names", () => {
+      const mockData: LogTableRow[] = [
+        {
+          id: "row_1",
+          input: "Test input",
+          output: "Test output",
+          model: "very-long-model-name-that-should-not-be-truncated",
+          createdTime: "2024-01-01 12:00:00",
+          detailPath: "/test/path",
+        },
+      ];
+
+      render(<LogsTable {...defaultProps} data={mockData} />);
+
+      // Model name should not be passed to truncateText
+      expect(truncateText).not.toHaveBeenCalledWith(
+        "very-long-model-name-that-should-not-be-truncated",
+      );
+
+      // Full model name should be displayed
+      expect(
+        screen.getByText("very-long-model-name-that-should-not-be-truncated"),
+      ).toBeInTheDocument();
+    });
+  });
+
+  describe("Accessibility", () => {
+    test("table has proper role and structure", () => {
+      const mockData: LogTableRow[] = [
+        {
+          id: "row_1",
+          input: "Test input",
+          output: "Test output",
+          model: "test-model",
+          createdTime: "2024-01-01 12:00:00",
+          detailPath: "/test/path",
+        },
+      ];
+
+      render(<LogsTable {...defaultProps} data={mockData} />);
+
+      const table = screen.getByRole("table");
+      expect(table).toBeInTheDocument();
+
+      const columnHeaders = screen.getAllByRole("columnheader");
+      expect(columnHeaders).toHaveLength(4);
+
+      const rows = screen.getAllByRole("row");
+      expect(rows).toHaveLength(2); // 1 header row + 1 data row
+    });
+  });
+});

llama_stack/ui/components/logs/logs-table.tsx

@@ -1,12 +1,7 @@
 "use client";
 
 import { useRouter } from "next/navigation";
-import { ChatCompletion } from "@/lib/types";
 import { truncateText } from "@/lib/truncate-text";
-import {
-  extractTextFromContentPart,
-  extractDisplayableText,
-} from "@/lib/format-message-content";
 import {
   Table,
   TableBody,
@@ -18,17 +13,31 @@ import {
 } from "@/components/ui/table";
 import { Skeleton } from "@/components/ui/skeleton";
 
-interface ChatCompletionsTableProps {
-  completions: ChatCompletion[];
-  isLoading: boolean;
-  error: Error | null;
+// Generic table row data interface
+export interface LogTableRow {
+  id: string;
+  input: string;
+  output: string;
+  model: string;
+  createdTime: string;
+  detailPath: string;
 }
 
-export function ChatCompletionsTable({
-  completions,
+interface LogsTableProps {
+  data: LogTableRow[];
+  isLoading: boolean;
+  error: Error | null;
+  caption: string;
+  emptyMessage: string;
+}
+
+export function LogsTable({
+  data,
   isLoading,
   error,
-}: ChatCompletionsTableProps) {
+  caption,
+  emptyMessage,
+}: LogsTableProps) {
   const router = useRouter();
 
   const tableHeader = (
@@ -77,41 +86,25 @@ export function ChatCompletionsTable({
     );
   }
 
-  if (completions.length === 0) {
-    return <p>No chat completions found.</p>;
+  if (data.length === 0) {
+    return <p>{emptyMessage}</p>;
   }
 
   return (
     <Table>
-      <TableCaption>A list of your recent chat completions.</TableCaption>
+      <TableCaption>{caption}</TableCaption>
       {tableHeader}
       <TableBody>
-        {completions.map((completion) => (
+        {data.map((row) => (
           <TableRow
-            key={completion.id}
-            onClick={() =>
-              router.push(`/logs/chat-completions/${completion.id}`)
-            }
+            key={row.id}
+            onClick={() => router.push(row.detailPath)}
             className="cursor-pointer hover:bg-muted/50"
           >
-            <TableCell>
-              {truncateText(
-                extractTextFromContentPart(
-                  completion.input_messages?.[0]?.content,
-                ),
-              )}
-            </TableCell>
-            <TableCell>
-              {(() => {
-                const message = completion.choices?.[0]?.message;
-                const outputText = extractDisplayableText(message);
-                return truncateText(outputText);
-              })()}
-            </TableCell>
-            <TableCell>{completion.model}</TableCell>
-            <TableCell className="text-right">
-              {new Date(completion.created * 1000).toLocaleString()}
-            </TableCell>
+            <TableCell>{truncateText(row.input)}</TableCell>
+            <TableCell>{truncateText(row.output)}</TableCell>
+            <TableCell>{row.model}</TableCell>
+            <TableCell className="text-right">{row.createdTime}</TableCell>
           </TableRow>
         ))}
       </TableBody>

llama_stack/ui/components/responses/grouping/grouped-items-display.tsx

@@ -0,0 +1,56 @@
+import { useFunctionCallGrouping } from "../hooks/function-call-grouping";
+import { ItemRenderer } from "../items/item-renderer";
+import { GroupedFunctionCallItemComponent } from "../items/grouped-function-call-item";
+import {
+  isFunctionCallItem,
+  isFunctionCallOutputItem,
+  AnyResponseItem,
+} from "../utils/item-types";
+
+interface GroupedItemsDisplayProps {
+  items: AnyResponseItem[];
+  keyPrefix: string;
+  defaultRole?: string;
+}
+
+export function GroupedItemsDisplay({
+  items,
+  keyPrefix,
+  defaultRole = "unknown",
+}: GroupedItemsDisplayProps) {
+  const groupedItems = useFunctionCallGrouping(items);
+
+  return (
+    <>
+      {groupedItems.map((groupedItem) => {
+        // If this is a function call with an output, render the grouped component
+        if (
+          groupedItem.outputItem &&
+          isFunctionCallItem(groupedItem.item) &&
+          isFunctionCallOutputItem(groupedItem.outputItem)
+        ) {
+          return (
+            <GroupedFunctionCallItemComponent
+              key={`${keyPrefix}-${groupedItem.index}`}
+              functionCall={groupedItem.item}
+              output={groupedItem.outputItem}
+              index={groupedItem.index}
+              keyPrefix={keyPrefix}
+            />
+          );
+        }
+
+        // Otherwise, render the individual item
+        return (
+          <ItemRenderer
+            key={`${keyPrefix}-${groupedItem.index}`}
+            item={groupedItem.item}
+            index={groupedItem.index}
+            keyPrefix={keyPrefix}
+            defaultRole={defaultRole}
+          />
+        );
+      })}
+    </>
+  );
+}

llama_stack/ui/components/responses/hooks/function-call-grouping.ts

@@ -0,0 +1,92 @@
+import { useMemo } from "react";
+import {
+  isFunctionCallOutputItem,
+  AnyResponseItem,
+  FunctionCallOutputItem,
+} from "../utils/item-types";
+
+export interface GroupedItem {
+  item: AnyResponseItem;
+  index: number;
+  outputItem?: AnyResponseItem;
+  outputIndex?: number;
+}
+
+/**
+ * Hook to group function calls with their corresponding outputs
+ * @param items Array of items to group
+ * @returns Array of grouped items with their outputs
+ */
+export function useFunctionCallGrouping(
+  items: AnyResponseItem[],
+): GroupedItem[] {
+  return useMemo(() => {
+    const groupedItems: GroupedItem[] = [];
+    const processedIndices = new Set<number>();
+
+    // Build a map of call_id to indices for function_call_output items
+    const callIdToIndices = new Map<string, number[]>();
+
+    for (let i = 0; i < items.length; i++) {
+      const item = items[i];
+      if (isFunctionCallOutputItem(item)) {
+        if (!callIdToIndices.has(item.call_id)) {
+          callIdToIndices.set(item.call_id, []);
+        }
+        callIdToIndices.get(item.call_id)!.push(i);
+      }
+    }
+
+    // Process items and group function calls with their outputs
+    for (let i = 0; i < items.length; i++) {
+      if (processedIndices.has(i)) {
+        continue;
+      }
+
+      const currentItem = items[i];
+
+      if (
+        currentItem.type === "function_call" &&
+        "name" in currentItem &&
+        "call_id" in currentItem
+      ) {
+        const functionCallId = currentItem.call_id as string;
+        let outputIndex = -1;
+        let outputItem: FunctionCallOutputItem | null = null;
+
+        const relatedIndices = callIdToIndices.get(functionCallId) || [];
+        for (const idx of relatedIndices) {
+          const potentialOutput = items[idx];
+          outputIndex = idx;
+          outputItem = potentialOutput as FunctionCallOutputItem;
+          break;
+        }
+
+        if (outputItem && outputIndex !== -1) {
+          // Group function call with its function_call_output
+          groupedItems.push({
+            item: currentItem,
+            index: i,
+            outputItem,
+            outputIndex,
+          });
+
+          // Mark both items as processed
+          processedIndices.add(i);
+          processedIndices.add(outputIndex);
+
+          // Matching function call and output found, skip to next item
+          continue;
+        }
+      }
+      // render normally
+      groupedItems.push({
+        item: currentItem,
+        index: i,
+      });
+      processedIndices.add(i);
+    }
+
+    return groupedItems;
+  }, [items]);
+}

llama_stack/ui/components/responses/items/function-call-item.tsx

@@ -0,0 +1,29 @@
+import {
+  MessageBlock,
+  ToolCallBlock,
+} from "@/components/ui/message-components";
+import { FunctionCallItem } from "../utils/item-types";
+
+interface FunctionCallItemProps {
+  item: FunctionCallItem;
+  index: number;
+  keyPrefix: string;
+}
+
+export function FunctionCallItemComponent({
+  item,
+  index,
+  keyPrefix,
+}: FunctionCallItemProps) {
+  const name = item.name || "unknown";
+  const args = item.arguments || "{}";
+  const formattedFunctionCall = `${name}(${args})`;
+
+  return (
+    <MessageBlock
+      key={`${keyPrefix}-${index}`}
+      label="Function Call"
+      content={<ToolCallBlock>{formattedFunctionCall}</ToolCallBlock>}
+    />
+  );
+}

llama_stack/ui/components/responses/items/generic-item.tsx

@@ -0,0 +1,37 @@
+import {
+  MessageBlock,
+  ToolCallBlock,
+} from "@/components/ui/message-components";
+import { BaseItem } from "../utils/item-types";
+
+interface GenericItemProps {
+  item: BaseItem;
+  index: number;
+  keyPrefix: string;
+}
+
+export function GenericItemComponent({
+  item,
+  index,
+  keyPrefix,
+}: GenericItemProps) {
+  // Handle other types like function calls, tool outputs, etc.
+  const itemData = item as Record<string, unknown>;
+
+  const content = itemData.content
+    ? typeof itemData.content === "string"
+      ? itemData.content
+      : JSON.stringify(itemData.content, null, 2)
+    : JSON.stringify(itemData, null, 2);
+
+  const label = keyPrefix === "input" ? "Input" : "Output";
+
+  return (
+    <MessageBlock
+      key={`${keyPrefix}-${index}`}
+      label={label}
+      labelDetail={`(${itemData.type})`}
+      content={<ToolCallBlock>{content}</ToolCallBlock>}
+    />
+  );
+}

llama_stack/ui/components/responses/items/grouped-function-call-item.tsx

@@ -0,0 +1,54 @@
+import {
+  MessageBlock,
+  ToolCallBlock,
+} from "@/components/ui/message-components";
+import { FunctionCallItem, FunctionCallOutputItem } from "../utils/item-types";
+
+interface GroupedFunctionCallItemProps {
+  functionCall: FunctionCallItem;
+  output: FunctionCallOutputItem;
+  index: number;
+  keyPrefix: string;
+}
+
+export function GroupedFunctionCallItemComponent({
+  functionCall,
+  output,
+  index,
+  keyPrefix,
+}: GroupedFunctionCallItemProps) {
+  const name = functionCall.name || "unknown";
+  const args = functionCall.arguments || "{}";
+
+  // Extract the output content from function_call_output
+  let outputContent = "";
+  if (output.output) {
+    outputContent =
+      typeof output.output === "string"
+        ? output.output
+        : JSON.stringify(output.output);
+  } else {
+    outputContent = JSON.stringify(output, null, 2);
+  }
+
+  const functionCallContent = (
+    <div>
+      <div className="mb-2">
+        <span className="text-sm text-gray-600">Arguments</span>
+        <ToolCallBlock>{`${name}(${args})`}</ToolCallBlock>
+      </div>
+      <div>
+        <span className="text-sm text-gray-600">Output</span>
+        <ToolCallBlock>{outputContent}</ToolCallBlock>
+      </div>
+    </div>
+  );
+
+  return (
+    <MessageBlock
+      key={`${keyPrefix}-${index}`}
+      label="Function Call"
+      content={functionCallContent}
+    />
+  );
+}

llama_stack/ui/components/responses/items/index.ts

@@ -0,0 +1,6 @@
+export { MessageItemComponent } from "./message-item";
+export { FunctionCallItemComponent } from "./function-call-item";
+export { WebSearchItemComponent } from "./web-search-item";
+export { GenericItemComponent } from "./generic-item";
+export { GroupedFunctionCallItemComponent } from "./grouped-function-call-item";
+export { ItemRenderer } from "./item-renderer";

llama_stack/ui/components/responses/items/item-renderer.tsx

@@ -0,0 +1,60 @@
+import {
+  isMessageItem,
+  isFunctionCallItem,
+  isWebSearchCallItem,
+  AnyResponseItem,
+} from "../utils/item-types";
+import { MessageItemComponent } from "./message-item";
+import { FunctionCallItemComponent } from "./function-call-item";
+import { WebSearchItemComponent } from "./web-search-item";
+import { GenericItemComponent } from "./generic-item";
+
+interface ItemRendererProps {
+  item: AnyResponseItem;
+  index: number;
+  keyPrefix: string;
+  defaultRole?: string;
+}
+
+export function ItemRenderer({
+  item,
+  index,
+  keyPrefix,
+  defaultRole = "unknown",
+}: ItemRendererProps) {
+  if (isMessageItem(item)) {
+    return (
+      <MessageItemComponent
+        item={item}
+        index={index}
+        keyPrefix={keyPrefix}
+        defaultRole={defaultRole}
+      />
+    );
+  }
+
+  if (isFunctionCallItem(item)) {
+    return (
+      <FunctionCallItemComponent
+        item={item}
+        index={index}
+        keyPrefix={keyPrefix}
+      />
+    );
+  }
+
+  if (isWebSearchCallItem(item)) {
+    return (
+      <WebSearchItemComponent item={item} index={index} keyPrefix={keyPrefix} />
+    );
+  }
+
+  // Fallback to generic item for unknown types
+  return (
+    <GenericItemComponent
+      item={item as any}
+      index={index}
+      keyPrefix={keyPrefix}
+    />
+  );
+}

llama_stack/ui/components/responses/items/message-item.tsx

@@ -0,0 +1,41 @@
+import { MessageBlock } from "@/components/ui/message-components";
+import { MessageItem } from "../utils/item-types";
+
+interface MessageItemProps {
+  item: MessageItem;
+  index: number;
+  keyPrefix: string;
+  defaultRole?: string;
+}
+
+export function MessageItemComponent({
+  item,
+  index,
+  keyPrefix,
+  defaultRole = "unknown",
+}: MessageItemProps) {
+  let content = "";
+
+  if (typeof item.content === "string") {
+    content = item.content;
+  } else if (Array.isArray(item.content)) {
+    content = item.content
+      .map((c) => {
+        return c.type === "input_text" || c.type === "output_text"
+          ? c.text
+          : JSON.stringify(c);
+      })
+      .join(" ");
+  }
+
+  const role = item.role || defaultRole;
+  const label = role.charAt(0).toUpperCase() + role.slice(1);
+
+  return (
+    <MessageBlock
+      key={`${keyPrefix}-${index}`}
+      label={label}
+      content={content}
+    />
+  );
+}

llama_stack/ui/components/responses/items/web-search-item.tsx

@@ -0,0 +1,28 @@
+import {
+  MessageBlock,
+  ToolCallBlock,
+} from "@/components/ui/message-components";
+import { WebSearchCallItem } from "../utils/item-types";
+
+interface WebSearchItemProps {
+  item: WebSearchCallItem;
+  index: number;
+  keyPrefix: string;
+}
+
+export function WebSearchItemComponent({
+  item,
+  index,
+  keyPrefix,
+}: WebSearchItemProps) {
+  const formattedWebSearch = `web_search_call(status: ${item.status})`;
+
+  return (
+    <MessageBlock
+      key={`${keyPrefix}-${index}`}
+      label="Function Call"
+      labelDetail="(Web Search)"
+      content={<ToolCallBlock>{formattedWebSearch}</ToolCallBlock>}
+    />
+  );
+}

llama_stack/ui/components/responses/responses-detail.test.tsx

@@ -0,0 +1,777 @@
+import React from "react";
+import { render, screen } from "@testing-library/react";
+import "@testing-library/jest-dom";
+import { ResponseDetailView } from "./responses-detail";
+import { OpenAIResponse, InputItemListResponse } from "@/lib/types";
+
+describe("ResponseDetailView", () => {
+  const defaultProps = {
+    response: null,
+    inputItems: null,
+    isLoading: false,
+    isLoadingInputItems: false,
+    error: null,
+    inputItemsError: null,
+    id: "test_id",
+  };
+
+  describe("Loading State", () => {
+    test("renders loading skeleton when isLoading is true", () => {
+      const { container } = render(
+        <ResponseDetailView {...defaultProps} isLoading={true} />,
+      );
+
+      // Check for skeleton elements
+      const skeletons = container.querySelectorAll('[data-slot="skeleton"]');
+      expect(skeletons.length).toBeGreaterThan(0);
+
+      // The title is replaced by a skeleton when loading, so we shouldn't expect the text
+    });
+  });
+
+  describe("Error State", () => {
+    test("renders error message when error prop is provided", () => {
+      const errorMessage = "Network Error";
+      render(
+        <ResponseDetailView
+          {...defaultProps}
+          error={{ name: "Error", message: errorMessage }}
+        />,
+      );
+
+      expect(screen.getByText("Responses Details")).toBeInTheDocument();
+      // The error message is split across elements, so we check for parts
+      expect(
+        screen.getByText(/Error loading details for ID/),
+      ).toBeInTheDocument();
+      expect(screen.getByText(/test_id/)).toBeInTheDocument();
+      expect(screen.getByText(/Network Error/)).toBeInTheDocument();
+    });
+
+    test("renders default error message when error.message is not available", () => {
+      render(
+        <ResponseDetailView
+          {...defaultProps}
+          error={{ name: "Error", message: "" }}
+        />,
+      );
+
+      expect(
+        screen.getByText(/Error loading details for ID/),
+      ).toBeInTheDocument();
+      expect(screen.getByText(/test_id/)).toBeInTheDocument();
+    });
+  });
+
+  describe("Not Found State", () => {
+    test("renders not found message when response is null and not loading/error", () => {
+      render(<ResponseDetailView {...defaultProps} response={null} />);
+
+      expect(screen.getByText("Responses Details")).toBeInTheDocument();
+      // The message is split across elements
+      expect(screen.getByText(/No details found for ID:/)).toBeInTheDocument();
+      expect(screen.getByText(/test_id/)).toBeInTheDocument();
+    });
+  });
+
+  describe("Response Data Rendering", () => {
+    const mockResponse: OpenAIResponse = {
+      id: "resp_123",
+      object: "response",
+      created_at: 1710000000,
+      model: "llama-test-model",
+      status: "completed",
+      output: [
+        {
+          type: "message",
+          role: "assistant",
+          content: "Test response output",
+        },
+      ],
+      input: [
+        {
+          type: "message",
+          role: "user",
+          content: "Test input message",
+        },
+      ],
+      temperature: 0.7,
+      top_p: 0.9,
+      parallel_tool_calls: true,
+      previous_response_id: "prev_resp_456",
+    };
+
+    test("renders response data with input and output sections", () => {
+      render(<ResponseDetailView {...defaultProps} response={mockResponse} />);
+
+      // Check main sections
+      expect(screen.getByText("Responses Details")).toBeInTheDocument();
+      expect(screen.getByText("Input")).toBeInTheDocument();
+      expect(screen.getByText("Output")).toBeInTheDocument();
+
+      // Check input content
+      expect(screen.getByText("Test input message")).toBeInTheDocument();
+      expect(screen.getByText("User")).toBeInTheDocument();
+
+      // Check output content
+      expect(screen.getByText("Test response output")).toBeInTheDocument();
+      expect(screen.getByText("Assistant")).toBeInTheDocument();
+    });
+
+    test("renders properties sidebar with all response metadata", () => {
+      render(<ResponseDetailView {...defaultProps} response={mockResponse} />);
+
+      // Check properties - use regex to handle text split across elements
+      expect(screen.getByText(/Created/)).toBeInTheDocument();
+      expect(
+        screen.getByText(new Date(1710000000 * 1000).toLocaleString()),
+      ).toBeInTheDocument();
+
+      // Check for the specific ID label (not Previous Response ID)
+      expect(
+        screen.getByText((content, element) => {
+          return element?.tagName === "STRONG" && content === "ID:";
+        }),
+      ).toBeInTheDocument();
+      expect(screen.getByText("resp_123")).toBeInTheDocument();
+
+      expect(screen.getByText(/Model/)).toBeInTheDocument();
+      expect(screen.getByText("llama-test-model")).toBeInTheDocument();
+
+      expect(screen.getByText(/Status/)).toBeInTheDocument();
+      expect(screen.getByText("completed")).toBeInTheDocument();
+
+      expect(screen.getByText(/Temperature/)).toBeInTheDocument();
+      expect(screen.getByText("0.7")).toBeInTheDocument();
+
+      expect(screen.getByText(/Top P/)).toBeInTheDocument();
+      expect(screen.getByText("0.9")).toBeInTheDocument();
+
+      expect(screen.getByText(/Parallel Tool Calls/)).toBeInTheDocument();
+      expect(screen.getByText("Yes")).toBeInTheDocument();
+
+      expect(screen.getByText(/Previous Response ID/)).toBeInTheDocument();
+      expect(screen.getByText("prev_resp_456")).toBeInTheDocument();
+    });
+
+    test("handles optional properties correctly", () => {
+      const minimalResponse: OpenAIResponse = {
+        id: "resp_minimal",
+        object: "response",
+        created_at: 1710000000,
+        model: "test-model",
+        status: "completed",
+        output: [],
+        input: [],
+      };
+
+      render(
+        <ResponseDetailView {...defaultProps} response={minimalResponse} />,
+      );
+
+      // Should show required properties
+      expect(screen.getByText("resp_minimal")).toBeInTheDocument();
+      expect(screen.getByText("test-model")).toBeInTheDocument();
+      expect(screen.getByText("completed")).toBeInTheDocument();
+
+      // Should not show optional properties
+      expect(screen.queryByText("Temperature")).not.toBeInTheDocument();
+      expect(screen.queryByText("Top P")).not.toBeInTheDocument();
+      expect(screen.queryByText("Parallel Tool Calls")).not.toBeInTheDocument();
+      expect(
+        screen.queryByText("Previous Response ID"),
+      ).not.toBeInTheDocument();
+    });
+
+    test("renders error information when response has error", () => {
+      const errorResponse: OpenAIResponse = {
+        ...mockResponse,
+        error: {
+          code: "invalid_request",
+          message: "The request was invalid",
+        },
+      };
+
+      render(<ResponseDetailView {...defaultProps} response={errorResponse} />);
+
+      // The error is shown in the properties sidebar, not as a separate "Error" label
+      expect(
+        screen.getByText("invalid_request: The request was invalid"),
+      ).toBeInTheDocument();
+    });
+  });
+
+  describe("Input Items Handling", () => {
+    const mockResponse: OpenAIResponse = {
+      id: "resp_123",
+      object: "response",
+      created_at: 1710000000,
+      model: "test-model",
+      status: "completed",
+      output: [{ type: "message", role: "assistant", content: "output" }],
+      input: [{ type: "message", role: "user", content: "fallback input" }],
+    };
+
+    test("shows loading state for input items", () => {
+      render(
+        <ResponseDetailView
+          {...defaultProps}
+          response={mockResponse}
+          isLoadingInputItems={true}
+        />,
+      );
+
+      // Check for skeleton loading in input items section
+      const { container } = render(
+        <ResponseDetailView
+          {...defaultProps}
+          response={mockResponse}
+          isLoadingInputItems={true}
+        />,
+      );
+
+      const skeletons = container.querySelectorAll('[data-slot="skeleton"]');
+      expect(skeletons.length).toBeGreaterThan(0);
+    });
+
+    test("shows error message for input items with fallback", () => {
+      render(
+        <ResponseDetailView
+          {...defaultProps}
+          response={mockResponse}
+          inputItemsError={{
+            name: "Error",
+            message: "Failed to load input items",
+          }}
+        />,
+      );
+
+      expect(
+        screen.getByText(
+          "Error loading input items: Failed to load input items",
+        ),
+      ).toBeInTheDocument();
+      expect(
+        screen.getByText("Falling back to response input data."),
+      ).toBeInTheDocument();
+
+      // Should still show fallback input data
+      expect(screen.getByText("fallback input")).toBeInTheDocument();
+    });
+
+    test("uses input items data when available", () => {
+      const mockInputItems: InputItemListResponse = {
+        object: "list",
+        data: [
+          {
+            type: "message",
+            role: "user",
+            content: "input from items API",
+          },
+        ],
+      };
+
+      render(
+        <ResponseDetailView
+          {...defaultProps}
+          response={mockResponse}
+          inputItems={mockInputItems}
+        />,
+      );
+
+      // Should show input items data, not response.input
+      expect(screen.getByText("input from items API")).toBeInTheDocument();
+      expect(screen.queryByText("fallback input")).not.toBeInTheDocument();
+    });
+
+    test("falls back to response.input when input items is empty", () => {
+      const emptyInputItems: InputItemListResponse = {
+        object: "list",
+        data: [],
+      };
+
+      render(
+        <ResponseDetailView
+          {...defaultProps}
+          response={mockResponse}
+          inputItems={emptyInputItems}
+        />,
+      );
+
+      // Should show fallback input data
+      expect(screen.getByText("fallback input")).toBeInTheDocument();
+    });
+
+    test("shows no input message when no data available", () => {
+      const responseWithoutInput: OpenAIResponse = {
+        ...mockResponse,
+        input: [],
+      };
+
+      render(
+        <ResponseDetailView
+          {...defaultProps}
+          response={responseWithoutInput}
+          inputItems={null}
+        />,
+      );
+
+      expect(screen.getByText("No input data available.")).toBeInTheDocument();
+    });
+  });
+
+  describe("Input Display Components", () => {
+    test("renders string content input correctly", () => {
+      const mockResponse: OpenAIResponse = {
+        id: "resp_123",
+        object: "response",
+        created_at: 1710000000,
+        model: "test-model",
+        status: "completed",
+        output: [],
+        input: [
+          {
+            type: "message",
+            role: "user",
+            content: "Simple string input",
+          },
+        ],
+      };
+
+      render(<ResponseDetailView {...defaultProps} response={mockResponse} />);
+
+      expect(screen.getByText("Simple string input")).toBeInTheDocument();
+      expect(screen.getByText("User")).toBeInTheDocument();
+    });
+
+    test("renders array content input correctly", () => {
+      const mockResponse: OpenAIResponse = {
+        id: "resp_123",
+        object: "response",
+        created_at: 1710000000,
+        model: "test-model",
+        status: "completed",
+        output: [],
+        input: [
+          {
+            type: "message",
+            role: "user",
+            content: [
+              { type: "input_text", text: "First part" },
+              { type: "output_text", text: "Second part" },
+            ],
+          },
+        ],
+      };
+
+      render(<ResponseDetailView {...defaultProps} response={mockResponse} />);
+
+      expect(screen.getByText("First part Second part")).toBeInTheDocument();
+      expect(screen.getByText("User")).toBeInTheDocument();
+    });
+
+    test("renders non-message input types correctly", () => {
+      const mockResponse: OpenAIResponse = {
+        id: "resp_123",
+        object: "response",
+        created_at: 1710000000,
+        model: "test-model",
+        status: "completed",
+        output: [],
+        input: [
+          {
+            type: "function_call",
+            content: "function call content",
+          },
+        ],
+      };
+
+      render(<ResponseDetailView {...defaultProps} response={mockResponse} />);
+
+      expect(screen.getByText("function call content")).toBeInTheDocument();
+      // Use getAllByText to find the specific "Input" with the type detail
+      const inputElements = screen.getAllByText("Input");
+      expect(inputElements.length).toBeGreaterThan(0);
+      expect(screen.getByText("(function_call)")).toBeInTheDocument();
+    });
+
+    test("handles input with object content", () => {
+      const mockResponse: OpenAIResponse = {
+        id: "resp_123",
+        object: "response",
+        created_at: 1710000000,
+        model: "test-model",
+        status: "completed",
+        output: [],
+        input: [
+          {
+            type: "custom_type",
+            content: JSON.stringify({ key: "value", nested: { data: "test" } }),
+          },
+        ],
+      };
+
+      render(<ResponseDetailView {...defaultProps} response={mockResponse} />);
+
+      // Should show JSON stringified content (without quotes around keys in the rendered output)
+      expect(screen.getByText(/key.*value/)).toBeInTheDocument();
+      // Use getAllByText to find the specific "Input" with the type detail
+      const inputElements = screen.getAllByText("Input");
+      expect(inputElements.length).toBeGreaterThan(0);
+      expect(screen.getByText("(custom_type)")).toBeInTheDocument();
+    });
+
+    test("renders function call input correctly", () => {
+      const mockResponse: OpenAIResponse = {
+        id: "resp_123",
+        object: "response",
+        created_at: 1710000000,
+        model: "test-model",
+        status: "completed",
+        output: [],
+        input: [
+          {
+            type: "function_call",
+            id: "call_456",
+            status: "completed",
+            name: "input_function",
+            arguments: '{"param": "value"}',
+          },
+        ],
+      };
+
+      render(<ResponseDetailView {...defaultProps} response={mockResponse} />);
+
+      expect(
+        screen.getByText('input_function({"param": "value"})'),
+      ).toBeInTheDocument();
+      expect(screen.getByText("Function Call")).toBeInTheDocument();
+    });
+
+    test("renders web search call input correctly", () => {
+      const mockResponse: OpenAIResponse = {
+        id: "resp_123",
+        object: "response",
+        created_at: 1710000000,
+        model: "test-model",
+        status: "completed",
+        output: [],
+        input: [
+          {
+            type: "web_search_call",
+            id: "search_789",
+            status: "completed",
+          },
+        ],
+      };
+
+      render(<ResponseDetailView {...defaultProps} response={mockResponse} />);
+
+      expect(
+        screen.getByText("web_search_call(status: completed)"),
+      ).toBeInTheDocument();
+      expect(screen.getByText("Function Call")).toBeInTheDocument();
+      expect(screen.getByText("(Web Search)")).toBeInTheDocument();
+    });
+  });
+
+  describe("Output Display Components", () => {
+    test("renders message output with string content", () => {
+      const mockResponse: OpenAIResponse = {
+        id: "resp_123",
+        object: "response",
+        created_at: 1710000000,
+        model: "test-model",
+        status: "completed",
+        output: [
+          {
+            type: "message",
+            role: "assistant",
+            content: "Simple string output",
+          },
+        ],
+        input: [],
+      };
+
+      render(<ResponseDetailView {...defaultProps} response={mockResponse} />);
+
+      expect(screen.getByText("Simple string output")).toBeInTheDocument();
+      expect(screen.getByText("Assistant")).toBeInTheDocument();
+    });
+
+    test("renders message output with array content", () => {
+      const mockResponse: OpenAIResponse = {
+        id: "resp_123",
+        object: "response",
+        created_at: 1710000000,
+        model: "test-model",
+        status: "completed",
+        output: [
+          {
+            type: "message",
+            role: "assistant",
+            content: [
+              { type: "output_text", text: "First output" },
+              { type: "input_text", text: "Second output" },
+            ],
+          },
+        ],
+        input: [],
+      };
+
+      render(<ResponseDetailView {...defaultProps} response={mockResponse} />);
+
+      expect(
+        screen.getByText("First output Second output"),
+      ).toBeInTheDocument();
+      expect(screen.getByText("Assistant")).toBeInTheDocument();
+    });
+
+    test("renders function call output correctly", () => {
+      const mockResponse: OpenAIResponse = {
+        id: "resp_123",
+        object: "response",
+        created_at: 1710000000,
+        model: "test-model",
+        status: "completed",
+        output: [
+          {
+            type: "function_call",
+            id: "call_123",
+            status: "completed",
+            name: "search_function",
+            arguments: '{"query": "test"}',
+          },
+        ],
+        input: [],
+      };
+
+      render(<ResponseDetailView {...defaultProps} response={mockResponse} />);
+
+      expect(
+        screen.getByText('search_function({"query": "test"})'),
+      ).toBeInTheDocument();
+      expect(screen.getByText("Function Call")).toBeInTheDocument();
+    });
+
+    test("renders function call output without arguments", () => {
+      const mockResponse: OpenAIResponse = {
+        id: "resp_123",
+        object: "response",
+        created_at: 1710000000,
+        model: "test-model",
+        status: "completed",
+        output: [
+          {
+            type: "function_call",
+            id: "call_123",
+            status: "completed",
+            name: "simple_function",
+          },
+        ],
+        input: [],
+      };
+
+      render(<ResponseDetailView {...defaultProps} response={mockResponse} />);
+
+      expect(screen.getByText("simple_function({})")).toBeInTheDocument();
+      expect(screen.getByText(/Function Call/)).toBeInTheDocument();
+    });
+
+    test("renders web search call output correctly", () => {
+      const mockResponse: OpenAIResponse = {
+        id: "resp_123",
+        object: "response",
+        created_at: 1710000000,
+        model: "test-model",
+        status: "completed",
+        output: [
+          {
+            type: "web_search_call",
+            id: "search_123",
+            status: "completed",
+          },
+        ],
+        input: [],
+      };
+
+      render(<ResponseDetailView {...defaultProps} response={mockResponse} />);
+
+      expect(
+        screen.getByText("web_search_call(status: completed)"),
+      ).toBeInTheDocument();
+      expect(screen.getByText(/Function Call/)).toBeInTheDocument();
+      expect(screen.getByText("(Web Search)")).toBeInTheDocument();
+    });
+
+    test("renders unknown output types with JSON fallback", () => {
+      const mockResponse: OpenAIResponse = {
+        id: "resp_123",
+        object: "response",
+        created_at: 1710000000,
+        model: "test-model",
+        status: "completed",
+        output: [
+          {
+            type: "unknown_type",
+            custom_field: "custom_value",
+            data: { nested: "object" },
+          } as any,
+        ],
+        input: [],
+      };
+
+      render(<ResponseDetailView {...defaultProps} response={mockResponse} />);
+
+      // Should show JSON stringified content
+      expect(
+        screen.getByText(/custom_field.*custom_value/),
+      ).toBeInTheDocument();
+      expect(screen.getByText("(unknown_type)")).toBeInTheDocument();
+    });
+
+    test("shows no output message when output array is empty", () => {
+      const mockResponse: OpenAIResponse = {
+        id: "resp_123",
+        object: "response",
+        created_at: 1710000000,
+        model: "test-model",
+        status: "completed",
+        output: [],
+        input: [],
+      };
+
+      render(<ResponseDetailView {...defaultProps} response={mockResponse} />);
+
+      expect(screen.getByText("No output data available.")).toBeInTheDocument();
+    });
+
+    test("groups function call with its output correctly", () => {
+      const mockResponse: OpenAIResponse = {
+        id: "resp_123",
+        object: "response",
+        created_at: 1710000000,
+        model: "test-model",
+        status: "completed",
+        output: [
+          {
+            type: "function_call",
+            id: "call_123",
+            status: "completed",
+            name: "get_weather",
+            arguments: '{"city": "Tokyo"}',
+          },
+          {
+            type: "message",
+            role: "assistant",
+            call_id: "call_123",
+            content: "sunny and warm",
+          } as any, // Using any to bypass the type restriction for this test
+        ],
+        input: [],
+      };
+
+      render(<ResponseDetailView {...defaultProps} response={mockResponse} />);
+
+      // Should show the function call and message as separate items (not grouped)
+      expect(screen.getByText("Function Call")).toBeInTheDocument();
+      expect(
+        screen.getByText('get_weather({"city": "Tokyo"})'),
+      ).toBeInTheDocument();
+      expect(screen.getByText("Assistant")).toBeInTheDocument();
+      expect(screen.getByText("sunny and warm")).toBeInTheDocument();
+
+      // Should NOT have the grouped "Arguments" and "Output" labels
+      expect(screen.queryByText("Arguments")).not.toBeInTheDocument();
+    });
+
+    test("groups function call with function_call_output correctly", () => {
+      const mockResponse: OpenAIResponse = {
+        id: "resp_123",
+        object: "response",
+        created_at: 1710000000,
+        model: "test-model",
+        status: "completed",
+        output: [
+          {
+            type: "function_call",
+            call_id: "call_123",
+            status: "completed",
+            name: "get_weather",
+            arguments: '{"city": "Tokyo"}',
+          },
+          {
+            type: "function_call_output",
+            id: "fc_68364957013081...",
+            status: "completed",
+            call_id: "call_123",
+            output: "sunny and warm",
+          } as any, // Using any to bypass the type restriction for this test
+        ],
+        input: [],
+      };
+
+      render(<ResponseDetailView {...defaultProps} response={mockResponse} />);
+
+      // Should show the function call grouped with its clean output
+      expect(screen.getByText("Function Call")).toBeInTheDocument();
+      expect(screen.getByText("Arguments")).toBeInTheDocument();
+      expect(
+        screen.getByText('get_weather({"city": "Tokyo"})'),
+      ).toBeInTheDocument();
+      // Use getAllByText since there are multiple "Output" elements (card title and output label)
+      const outputElements = screen.getAllByText("Output");
+      expect(outputElements.length).toBeGreaterThan(0);
+      expect(screen.getByText("sunny and warm")).toBeInTheDocument();
+    });
+  });
+
+  describe("Edge Cases and Error Handling", () => {
+    test("handles missing role in message input", () => {
+      const mockResponse: OpenAIResponse = {
+        id: "resp_123",
+        object: "response",
+        created_at: 1710000000,
+        model: "test-model",
+        status: "completed",
+        output: [],
+        input: [
+          {
+            type: "message",
+            content: "Message without role",
+          },
+        ],
+      };
+
+      render(<ResponseDetailView {...defaultProps} response={mockResponse} />);
+
+      expect(screen.getByText("Message without role")).toBeInTheDocument();
+      expect(screen.getByText("Unknown")).toBeInTheDocument(); // Default role
+    });
+
+    test("handles missing name in function call output", () => {
+      const mockResponse: OpenAIResponse = {
+        id: "resp_123",
+        object: "response",
+        created_at: 1710000000,
+        model: "test-model",
+        status: "completed",
+        output: [
+          {
+            type: "function_call",
+            id: "call_123",
+            status: "completed",
+          },
+        ],
+        input: [],
+      };
+
+      render(<ResponseDetailView {...defaultProps} response={mockResponse} />);
+
+      // When name is missing, it falls back to JSON.stringify of the entire output
+      const functionCallElements = screen.getAllByText(/function_call/);
+      expect(functionCallElements.length).toBeGreaterThan(0);
+      expect(screen.getByText(/call_123/)).toBeInTheDocument();
+    });
+  });
+});

llama_stack/ui/components/responses/responses-detail.tsx

@@ -0,0 +1,171 @@
+"use client";
+
+import { OpenAIResponse, InputItemListResponse } from "@/lib/types";
+import { Card, CardContent, CardHeader, CardTitle } from "@/components/ui/card";
+import { Skeleton } from "@/components/ui/skeleton";
+import {
+  DetailLoadingView,
+  DetailErrorView,
+  DetailNotFoundView,
+  DetailLayout,
+  PropertiesCard,
+  PropertyItem,
+} from "@/components/layout/detail-layout";
+import { GroupedItemsDisplay } from "./grouping/grouped-items-display";
+
+interface ResponseDetailViewProps {
+  response: OpenAIResponse | null;
+  inputItems: InputItemListResponse | null;
+  isLoading: boolean;
+  isLoadingInputItems: boolean;
+  error: Error | null;
+  inputItemsError: Error | null;
+  id: string;
+}
+
+export function ResponseDetailView({
+  response,
+  inputItems,
+  isLoading,
+  isLoadingInputItems,
+  error,
+  inputItemsError,
+  id,
+}: ResponseDetailViewProps) {
+  const title = "Responses Details";
+
+  if (error) {
+    return <DetailErrorView title={title} id={id} error={error} />;
+  }
+
+  if (isLoading) {
+    return <DetailLoadingView title={title} />;
+  }
+
+  if (!response) {
+    return <DetailNotFoundView title={title} id={id} />;
+  }
+
+  // Main content cards
+  const mainContent = (
+    <>
+      <Card>
+        <CardHeader>
+          <CardTitle>Input</CardTitle>
+        </CardHeader>
+        <CardContent>
+          {/* Show loading state for input items */}
+          {isLoadingInputItems ? (
+            <div className="space-y-2">
+              <Skeleton className="h-4 w-full" />
+              <Skeleton className="h-4 w-3/4" />
+              <Skeleton className="h-4 w-1/2" />
+            </div>
+          ) : inputItemsError ? (
+            <div className="text-red-500 text-sm">
+              Error loading input items: {inputItemsError.message}
+              <br />
+              <span className="text-gray-500 text-xs">
+                Falling back to response input data.
+              </span>
+            </div>
+          ) : null}
+
+          {/* Display input items if available, otherwise fall back to response.input */}
+          {(() => {
+            const dataToDisplay =
+              inputItems?.data && inputItems.data.length > 0
+                ? inputItems.data
+                : response.input;
+
+            if (dataToDisplay && dataToDisplay.length > 0) {
+              return (
+                <GroupedItemsDisplay
+                  items={dataToDisplay}
+                  keyPrefix="input"
+                  defaultRole="unknown"
+                />
+              );
+            } else {
+              return (
+                <p className="text-gray-500 italic text-sm">
+                  No input data available.
+                </p>
+              );
+            }
+          })()}
+        </CardContent>
+      </Card>
+
+      <Card>
+        <CardHeader>
+          <CardTitle>Output</CardTitle>
+        </CardHeader>
+        <CardContent>
+          {response.output?.length > 0 ? (
+            <GroupedItemsDisplay
+              items={response.output}
+              keyPrefix="output"
+              defaultRole="assistant"
+            />
+          ) : (
+            <p className="text-gray-500 italic text-sm">
+              No output data available.
+            </p>
+          )}
+        </CardContent>
+      </Card>
+    </>
+  );
+
+  // Properties sidebar
+  const sidebar = (
+    <PropertiesCard>
+      <PropertyItem
+        label="Created"
+        value={new Date(response.created_at * 1000).toLocaleString()}
+      />
+      <PropertyItem label="ID" value={response.id} />
+      <PropertyItem label="Model" value={response.model} />
+      <PropertyItem label="Status" value={response.status} hasBorder />
+      {response.temperature && (
+        <PropertyItem
+          label="Temperature"
+          value={response.temperature}
+          hasBorder
+        />
+      )}
+      {response.top_p && <PropertyItem label="Top P" value={response.top_p} />}
+      {response.parallel_tool_calls && (
+        <PropertyItem
+          label="Parallel Tool Calls"
+          value={response.parallel_tool_calls ? "Yes" : "No"}
+        />
+      )}
+      {response.previous_response_id && (
+        <PropertyItem
+          label="Previous Response ID"
+          value={
+            <span className="text-xs">{response.previous_response_id}</span>
+          }
+          hasBorder
+        />
+      )}
+      {response.error && (
+        <PropertyItem
+          label="Error"
+          value={
+            <span className="text-red-900 font-medium">
+              {response.error.code}: {response.error.message}
+            </span>
+          }
+          className="pt-1 mt-1 border-t border-red-200"
+        />
+      )}
+    </PropertiesCard>
+  );
+
+  return (
+    <DetailLayout title={title} mainContent={mainContent} sidebar={sidebar} />
+  );
+}

llama_stack/ui/components/responses/responses-table.test.tsx

@@ -0,0 +1,537 @@
+import React from "react";
+import { render, screen, fireEvent } from "@testing-library/react";
+import "@testing-library/jest-dom";
+import { ResponsesTable } from "./responses-table";
+import { OpenAIResponse } from "@/lib/types";
+
+// Mock next/navigation
+const mockPush = jest.fn();
+jest.mock("next/navigation", () => ({
+  useRouter: () => ({
+    push: mockPush,
+  }),
+}));
+
+// Mock helper functions
+jest.mock("@/lib/truncate-text");
+
+// Import the mocked functions
+import { truncateText as originalTruncateText } from "@/lib/truncate-text";
+
+// Cast to jest.Mock for typings
+const truncateText = originalTruncateText as jest.Mock;
+
+describe("ResponsesTable", () => {
+  const defaultProps = {
+    data: [] as OpenAIResponse[],
+    isLoading: false,
+    error: null,
+  };
+
+  beforeEach(() => {
+    // Reset all mocks before each test
+    mockPush.mockClear();
+    truncateText.mockClear();
+
+    // Default pass-through implementation
+    truncateText.mockImplementation((text: string | undefined) => text);
+  });
+
+  test("renders without crashing with default props", () => {
+    render(<ResponsesTable {...defaultProps} />);
+    expect(screen.getByText("No responses found.")).toBeInTheDocument();
+  });
+
+  test("click on a row navigates to the correct URL", () => {
+    const mockResponse: OpenAIResponse = {
+      id: "resp_123",
+      object: "response",
+      created_at: Math.floor(Date.now() / 1000),
+      model: "llama-test-model",
+      status: "completed",
+      output: [
+        {
+          type: "message",
+          role: "assistant",
+          content: "Test output",
+        },
+      ],
+      input: [
+        {
+          type: "message",
+          role: "user",
+          content: "Test input",
+        },
+      ],
+    };
+
+    render(<ResponsesTable {...defaultProps} data={[mockResponse]} />);
+
+    const row = screen.getByText("Test input").closest("tr");
+    if (row) {
+      fireEvent.click(row);
+      expect(mockPush).toHaveBeenCalledWith("/logs/responses/resp_123");
+    } else {
+      throw new Error('Row with "Test input" not found for router mock test.');
+    }
+  });
+
+  describe("Loading State", () => {
+    test("renders skeleton UI when isLoading is true", () => {
+      const { container } = render(
+        <ResponsesTable {...defaultProps} isLoading={true} />,
+      );
+
+      // Check for skeleton in the table caption
+      const tableCaption = container.querySelector("caption");
+      expect(tableCaption).toBeInTheDocument();
+      if (tableCaption) {
+        const captionSkeleton = tableCaption.querySelector(
+          '[data-slot="skeleton"]',
+        );
+        expect(captionSkeleton).toBeInTheDocument();
+      }
+
+      // Check for skeletons in the table body cells
+      const tableBody = container.querySelector("tbody");
+      expect(tableBody).toBeInTheDocument();
+      if (tableBody) {
+        const bodySkeletons = tableBody.querySelectorAll(
+          '[data-slot="skeleton"]',
+        );
+        expect(bodySkeletons.length).toBeGreaterThan(0);
+      }
+    });
+  });
+
+  describe("Error State", () => {
+    test("renders error message when error prop is provided", () => {
+      const errorMessage = "Network Error";
+      render(
+        <ResponsesTable
+          {...defaultProps}
+          error={{ name: "Error", message: errorMessage }}
+        />,
+      );
+      expect(
+        screen.getByText(`Error fetching data: ${errorMessage}`),
+      ).toBeInTheDocument();
+    });
+
+    test("renders default error message when error.message is not available", () => {
+      render(
+        <ResponsesTable
+          {...defaultProps}
+          error={{ name: "Error", message: "" }}
+        />,
+      );
+      expect(
+        screen.getByText("Error fetching data: An unknown error occurred"),
+      ).toBeInTheDocument();
+    });
+
+    test("renders default error message when error prop is an object without message", () => {
+      render(<ResponsesTable {...defaultProps} error={{} as Error} />);
+      expect(
+        screen.getByText("Error fetching data: An unknown error occurred"),
+      ).toBeInTheDocument();
+    });
+  });
+
+  describe("Empty State", () => {
+    test('renders "No responses found." and no table when data array is empty', () => {
+      render(<ResponsesTable data={[]} isLoading={false} error={null} />);
+      expect(screen.getByText("No responses found.")).toBeInTheDocument();
+
+      // Ensure that the table structure is NOT rendered in the empty state
+      const table = screen.queryByRole("table");
+      expect(table).not.toBeInTheDocument();
+    });
+  });
+
+  describe("Data Rendering", () => {
+    test("renders table caption, headers, and response data correctly", () => {
+      const mockResponses = [
+        {
+          id: "resp_1",
+          object: "response" as const,
+          created_at: 1710000000,
+          model: "llama-test-model",
+          status: "completed",
+          output: [
+            {
+              type: "message" as const,
+              role: "assistant" as const,
+              content: "Test output",
+            },
+          ],
+          input: [
+            {
+              type: "message",
+              role: "user",
+              content: "Test input",
+            },
+          ],
+        },
+        {
+          id: "resp_2",
+          object: "response" as const,
+          created_at: 1710001000,
+          model: "llama-another-model",
+          status: "completed",
+          output: [
+            {
+              type: "message" as const,
+              role: "assistant" as const,
+              content: "Another output",
+            },
+          ],
+          input: [
+            {
+              type: "message",
+              role: "user",
+              content: "Another input",
+            },
+          ],
+        },
+      ];
+
+      render(
+        <ResponsesTable data={mockResponses} isLoading={false} error={null} />,
+      );
+
+      // Table caption
+      expect(
+        screen.getByText("A list of your recent responses."),
+      ).toBeInTheDocument();
+
+      // Table headers
+      expect(screen.getByText("Input")).toBeInTheDocument();
+      expect(screen.getByText("Output")).toBeInTheDocument();
+      expect(screen.getByText("Model")).toBeInTheDocument();
+      expect(screen.getByText("Created")).toBeInTheDocument();
+
+      // Data rows
+      expect(screen.getByText("Test input")).toBeInTheDocument();
+      expect(screen.getByText("Test output")).toBeInTheDocument();
+      expect(screen.getByText("llama-test-model")).toBeInTheDocument();
+      expect(
+        screen.getByText(new Date(1710000000 * 1000).toLocaleString()),
+      ).toBeInTheDocument();
+
+      expect(screen.getByText("Another input")).toBeInTheDocument();
+      expect(screen.getByText("Another output")).toBeInTheDocument();
+      expect(screen.getByText("llama-another-model")).toBeInTheDocument();
+      expect(
+        screen.getByText(new Date(1710001000 * 1000).toLocaleString()),
+      ).toBeInTheDocument();
+    });
+  });
+
+  describe("Input Text Extraction", () => {
+    test("extracts text from string content", () => {
+      const mockResponse: OpenAIResponse = {
+        id: "resp_string",
+        object: "response",
+        created_at: 1710000000,
+        model: "test-model",
+        status: "completed",
+        output: [{ type: "message", role: "assistant", content: "output" }],
+        input: [
+          {
+            type: "message",
+            role: "user",
+            content: "Simple string input",
+          },
+        ],
+      };
+
+      render(
+        <ResponsesTable data={[mockResponse]} isLoading={false} error={null} />,
+      );
+      expect(screen.getByText("Simple string input")).toBeInTheDocument();
+    });
+
+    test("extracts text from array content with input_text type", () => {
+      const mockResponse: OpenAIResponse = {
+        id: "resp_array",
+        object: "response",
+        created_at: 1710000000,
+        model: "test-model",
+        status: "completed",
+        output: [{ type: "message", role: "assistant", content: "output" }],
+        input: [
+          {
+            type: "message",
+            role: "user",
+            content: [
+              { type: "input_text", text: "Array input text" },
+              { type: "input_text", text: "Should not be used" },
+            ],
+          },
+        ],
+      };
+
+      render(
+        <ResponsesTable data={[mockResponse]} isLoading={false} error={null} />,
+      );
+      expect(screen.getByText("Array input text")).toBeInTheDocument();
+    });
+
+    test("returns empty string when no message input found", () => {
+      const mockResponse: OpenAIResponse = {
+        id: "resp_no_input",
+        object: "response",
+        created_at: 1710000000,
+        model: "test-model",
+        status: "completed",
+        output: [{ type: "message", role: "assistant", content: "output" }],
+        input: [
+          {
+            type: "other_type",
+            content: "Not a message",
+          },
+        ],
+      };
+
+      const { container } = render(
+        <ResponsesTable data={[mockResponse]} isLoading={false} error={null} />,
+      );
+
+      // Find the input cell (first cell in the data row) and verify it's empty
+      const inputCell = container.querySelector("tbody tr td:first-child");
+      expect(inputCell).toBeInTheDocument();
+      expect(inputCell).toHaveTextContent("");
+    });
+  });
+
+  describe("Output Text Extraction", () => {
+    test("extracts text from string message content", () => {
+      const mockResponse: OpenAIResponse = {
+        id: "resp_string_output",
+        object: "response",
+        created_at: 1710000000,
+        model: "test-model",
+        status: "completed",
+        output: [
+          {
+            type: "message",
+            role: "assistant",
+            content: "Simple string output",
+          },
+        ],
+        input: [{ type: "message", content: "input" }],
+      };
+
+      render(
+        <ResponsesTable data={[mockResponse]} isLoading={false} error={null} />,
+      );
+      expect(screen.getByText("Simple string output")).toBeInTheDocument();
+    });
+
+    test("extracts text from array message content with output_text type", () => {
+      const mockResponse: OpenAIResponse = {
+        id: "resp_array_output",
+        object: "response",
+        created_at: 1710000000,
+        model: "test-model",
+        status: "completed",
+        output: [
+          {
+            type: "message",
+            role: "assistant",
+            content: [
+              { type: "output_text", text: "Array output text" },
+              { type: "output_text", text: "Should not be used" },
+            ],
+          },
+        ],
+        input: [{ type: "message", content: "input" }],
+      };
+
+      render(
+        <ResponsesTable data={[mockResponse]} isLoading={false} error={null} />,
+      );
+      expect(screen.getByText("Array output text")).toBeInTheDocument();
+    });
+
+    test("formats function call output", () => {
+      const mockResponse: OpenAIResponse = {
+        id: "resp_function_call",
+        object: "response",
+        created_at: 1710000000,
+        model: "test-model",
+        status: "completed",
+        output: [
+          {
+            type: "function_call",
+            id: "call_123",
+            status: "completed",
+            name: "search_function",
+            arguments: '{"query": "test"}',
+          },
+        ],
+        input: [{ type: "message", content: "input" }],
+      };
+
+      render(
+        <ResponsesTable data={[mockResponse]} isLoading={false} error={null} />,
+      );
+      expect(
+        screen.getByText('search_function({"query": "test"})'),
+      ).toBeInTheDocument();
+    });
+
+    test("formats function call output without arguments", () => {
+      const mockResponse: OpenAIResponse = {
+        id: "resp_function_no_args",
+        object: "response",
+        created_at: 1710000000,
+        model: "test-model",
+        status: "completed",
+        output: [
+          {
+            type: "function_call",
+            id: "call_123",
+            status: "completed",
+            name: "simple_function",
+          },
+        ],
+        input: [{ type: "message", content: "input" }],
+      };
+
+      render(
+        <ResponsesTable data={[mockResponse]} isLoading={false} error={null} />,
+      );
+      expect(screen.getByText("simple_function({})")).toBeInTheDocument();
+    });
+
+    test("formats web search call output", () => {
+      const mockResponse: OpenAIResponse = {
+        id: "resp_web_search",
+        object: "response",
+        created_at: 1710000000,
+        model: "test-model",
+        status: "completed",
+        output: [
+          {
+            type: "web_search_call",
+            id: "search_123",
+            status: "completed",
+          },
+        ],
+        input: [{ type: "message", content: "input" }],
+      };
+
+      render(
+        <ResponsesTable data={[mockResponse]} isLoading={false} error={null} />,
+      );
+      expect(
+        screen.getByText("web_search_call(status: completed)"),
+      ).toBeInTheDocument();
+    });
+
+    test("falls back to JSON.stringify for unknown tool call types", () => {
+      const mockResponse: OpenAIResponse = {
+        id: "resp_unknown_tool",
+        object: "response",
+        created_at: 1710000000,
+        model: "test-model",
+        status: "completed",
+        output: [
+          {
+            type: "unknown_call",
+            id: "unknown_123",
+            status: "completed",
+            custom_field: "custom_value",
+          } as any,
+        ],
+        input: [{ type: "message", content: "input" }],
+      };
+
+      render(
+        <ResponsesTable data={[mockResponse]} isLoading={false} error={null} />,
+      );
+      // Should contain the JSON stringified version
+      expect(screen.getByText(/unknown_call/)).toBeInTheDocument();
+    });
+
+    test("falls back to JSON.stringify for entire output when no message or tool call found", () => {
+      const mockResponse: OpenAIResponse = {
+        id: "resp_fallback",
+        object: "response",
+        created_at: 1710000000,
+        model: "test-model",
+        status: "completed",
+        output: [
+          {
+            type: "unknown_type",
+            data: "some data",
+          } as any,
+        ],
+        input: [{ type: "message", content: "input" }],
+      };
+
+      render(
+        <ResponsesTable data={[mockResponse]} isLoading={false} error={null} />,
+      );
+      // Should contain the JSON stringified version of the output array
+      expect(screen.getByText(/unknown_type/)).toBeInTheDocument();
+    });
+  });
+
+  describe("Text Truncation", () => {
+    test("truncates long input and output text", () => {
+      // Specific mock implementation for this test
+      truncateText.mockImplementation(
+        (text: string | undefined, maxLength?: number) => {
+          const defaultTestMaxLength = 10;
+          const effectiveMaxLength = maxLength ?? defaultTestMaxLength;
+          return typeof text === "string" && text.length > effectiveMaxLength
+            ? text.slice(0, effectiveMaxLength) + "..."
+            : text;
+        },
+      );
+
+      const longInput =
+        "This is a very long input message that should be truncated.";
+      const longOutput =
+        "This is a very long output message that should also be truncated.";
+
+      const mockResponse: OpenAIResponse = {
+        id: "resp_trunc",
+        object: "response",
+        created_at: 1710002000,
+        model: "llama-trunc-model",
+        status: "completed",
+        output: [
+          {
+            type: "message",
+            role: "assistant",
+            content: longOutput,
+          },
+        ],
+        input: [
+          {
+            type: "message",
+            role: "user",
+            content: longInput,
+          },
+        ],
+      };
+
+      render(
+        <ResponsesTable data={[mockResponse]} isLoading={false} error={null} />,
+      );
+
+      // The truncated text should be present for both input and output
+      const truncatedTexts = screen.getAllByText(
+        longInput.slice(0, 10) + "...",
+      );
+      expect(truncatedTexts.length).toBe(2); // one for input, one for output
+      truncatedTexts.forEach((textElement) =>
+        expect(textElement).toBeInTheDocument(),
+      );
+    });
+  });
+});

llama_stack/ui/components/responses/responses-table.tsx

@@ -0,0 +1,117 @@
+"use client";
+
+import {
+  OpenAIResponse,
+  ResponseInput,
+  ResponseInputMessageContent,
+} from "@/lib/types";
+import { LogsTable, LogTableRow } from "@/components/logs/logs-table";
+import {
+  isMessageInput,
+  isMessageItem,
+  isFunctionCallItem,
+  isWebSearchCallItem,
+  MessageItem,
+  FunctionCallItem,
+  WebSearchCallItem,
+} from "./utils/item-types";
+
+interface ResponsesTableProps {
+  data: OpenAIResponse[];
+  isLoading: boolean;
+  error: Error | null;
+}
+
+function getInputText(response: OpenAIResponse): string {
+  const firstInput = response.input.find(isMessageInput);
+  if (firstInput) {
+    return extractContentFromItem(firstInput);
+  }
+  return "";
+}
+
+function getOutputText(response: OpenAIResponse): string {
+  const firstMessage = response.output.find((item) =>
+    isMessageItem(item as any),
+  );
+  if (firstMessage) {
+    const content = extractContentFromItem(firstMessage as MessageItem);
+    if (content) {
+      return content;
+    }
+  }
+
+  const functionCall = response.output.find((item) =>
+    isFunctionCallItem(item as any),
+  );
+  if (functionCall) {
+    return formatFunctionCall(functionCall as FunctionCallItem);
+  }
+
+  const webSearchCall = response.output.find((item) =>
+    isWebSearchCallItem(item as any),
+  );
+  if (webSearchCall) {
+    return formatWebSearchCall(webSearchCall as WebSearchCallItem);
+  }
+
+  return JSON.stringify(response.output);
+}
+
+function extractContentFromItem(item: {
+  content?: string | ResponseInputMessageContent[];
+}): string {
+  if (!item.content) {
+    return "";
+  }
+
+  if (typeof item.content === "string") {
+    return item.content;
+  } else if (Array.isArray(item.content)) {
+    const textContent = item.content.find(
+      (c: ResponseInputMessageContent) =>
+        c.type === "input_text" || c.type === "output_text",
+    );
+    return textContent?.text || "";
+  }
+  return "";
+}
+
+function formatFunctionCall(functionCall: FunctionCallItem): string {
+  const args = functionCall.arguments || "{}";
+  const name = functionCall.name || "unknown";
+  return `${name}(${args})`;
+}
+
+function formatWebSearchCall(webSearchCall: WebSearchCallItem): string {
+  return `web_search_call(status: ${webSearchCall.status})`;
+}
+
+function formatResponseToRow(response: OpenAIResponse): LogTableRow {
+  return {
+    id: response.id,
+    input: getInputText(response),
+    output: getOutputText(response),
+    model: response.model,
+    createdTime: new Date(response.created_at * 1000).toLocaleString(),
+    detailPath: `/logs/responses/${response.id}`,
+  };
+}
+
+export function ResponsesTable({
+  data,
+  isLoading,
+  error,
+}: ResponsesTableProps) {
+  const formattedData = data.map(formatResponseToRow);
+
+  return (
+    <LogsTable
+      data={formattedData}
+      isLoading={isLoading}
+      error={error}
+      caption="A list of your recent responses."
+      emptyMessage="No responses found."
+    />
+  );
+}

llama_stack/ui/components/responses/utils/item-types.ts

@@ -0,0 +1,61 @@
+/**
+ * Type guards for different item types in responses
+ */
+
+import type {
+  ResponseInput,
+  ResponseOutput,
+  ResponseMessage,
+  ResponseToolCall,
+} from "@/lib/types";
+
+export interface BaseItem {
+  type: string;
+  [key: string]: unknown;
+}
+
+export type MessageItem = ResponseMessage;
+export type FunctionCallItem = ResponseToolCall & { type: "function_call" };
+export type WebSearchCallItem = ResponseToolCall & { type: "web_search_call" };
+export type FunctionCallOutputItem = BaseItem & {
+  type: "function_call_output";
+  call_id: string;
+  output?: string | object;
+};
+
+export type AnyResponseItem =
+  | ResponseInput
+  | ResponseOutput
+  | FunctionCallOutputItem;
+
+export function isMessageInput(
+  item: ResponseInput,
+): item is ResponseInput & { type: "message" } {
+  return item.type === "message";
+}
+
+export function isMessageItem(item: AnyResponseItem): item is MessageItem {
+  return item.type === "message" && "content" in item;
+}
+
+export function isFunctionCallItem(
+  item: AnyResponseItem,
+): item is FunctionCallItem {
+  return item.type === "function_call" && "name" in item;
+}
+
+export function isWebSearchCallItem(
+  item: AnyResponseItem,
+): item is WebSearchCallItem {
+  return item.type === "web_search_call";
+}
+
+export function isFunctionCallOutputItem(
+  item: AnyResponseItem,
+): item is FunctionCallOutputItem {
+  return (
+    item.type === "function_call_output" &&
+    "call_id" in item &&
+    typeof (item as any).call_id === "string"
+  );
+}

llama_stack/ui/components/ui/message-components.tsx

@@ -0,0 +1,49 @@
+import React from "react";
+
+export interface MessageBlockProps {
+  label: string;
+  labelDetail?: string;
+  content: React.ReactNode;
+  className?: string;
+  contentClassName?: string;
+}
+
+export const MessageBlock: React.FC<MessageBlockProps> = ({
+  label,
+  labelDetail,
+  content,
+  className = "",
+  contentClassName = "",
+}) => {
+  return (
+    <div className={`mb-4 ${className}`}>
+      <p className="py-1 font-semibold text-gray-800 mb-1">
+        {label}
+        {labelDetail && (
+          <span className="text-xs text-gray-500 font-normal ml-1">
+            {labelDetail}
+          </span>
+        )}
+      </p>
+      <div className={`py-1 whitespace-pre-wrap ${contentClassName}`}>
+        {content}
+      </div>
+    </div>
+  );
+};
+
+export interface ToolCallBlockProps {
+  children: React.ReactNode;
+  className?: string;
+}
+
+export const ToolCallBlock = ({ children, className }: ToolCallBlockProps) => {
+  const baseClassName =
+    "p-3 bg-slate-50 border border-slate-200 rounded-md text-sm";
+
+  return (
+    <div className={`${baseClassName} ${className || ""}`}>
+      <pre className="whitespace-pre-wrap text-xs">{children}</pre>
+    </div>
+  );
+};

llama_stack/ui/lib/client.ts

@@ -0,0 +1,12 @@
+import LlamaStackClient from "llama-stack-client";
+import OpenAI from "openai";
+
+export const client =
+  process.env.NEXT_PUBLIC_USE_OPENAI_CLIENT === "true" // useful for testing
+    ? new OpenAI({
+        apiKey: process.env.NEXT_PUBLIC_OPENAI_API_KEY,
+        dangerouslyAllowBrowser: true,
+      })
+    : new LlamaStackClient({
+        baseURL: process.env.NEXT_PUBLIC_LLAMA_STACK_BASE_URL,
+      });

llama_stack/ui/lib/format-message-content.ts

@@ -43,10 +43,14 @@ export function extractDisplayableText(
     return "";
   }
 
-  let textPart = extractTextFromContentPart(message.content);
+  const textPart = extractTextFromContentPart(message.content);
   let toolCallPart = "";
 
-  if (message.tool_calls && message.tool_calls.length > 0) {
+  if (
+    message.tool_calls &&
+    Array.isArray(message.tool_calls) &&
+    message.tool_calls.length > 0
+  ) {
     // For summary, usually the first tool call is sufficient
     toolCallPart = formatToolCallToString(message.tool_calls[0]);
   }

llama_stack/ui/lib/types.ts

@@ -18,20 +18,20 @@ export interface ImageUrlContentBlock {
 export type ChatMessageContentPart =
   | TextContentBlock
   | ImageUrlContentBlock
-  | { type: string; [key: string]: any }; // Fallback for other potential types
+  | { type: string; [key: string]: unknown }; // Fallback for other potential types
 
 export interface ChatMessage {
   role: string;
   content: string | ChatMessageContentPart[]; // Updated content type
   name?: string | null;
-  tool_calls?: any | null; // This could also be refined to a more specific ToolCall[] type
+  tool_calls?: unknown | null; // This could also be refined to a more specific ToolCall[] type
 }
 
 export interface Choice {
   message: ChatMessage;
   finish_reason: string;
   index: number;
-  logprobs?: any | null;
+  logprobs?: unknown | null;
 }
 
 export interface ChatCompletion {
@@ -42,3 +42,62 @@ export interface ChatCompletion {
   model: string;
   input_messages: ChatMessage[];
 }
+
+// Response types for OpenAI Responses API
+export interface ResponseInputMessageContent {
+  text?: string;
+  type: "input_text" | "input_image" | "output_text";
+  image_url?: string;
+  detail?: "low" | "high" | "auto";
+}
+
+export interface ResponseMessage {
+  content: string | ResponseInputMessageContent[];
+  role: "system" | "developer" | "user" | "assistant";
+  type: "message";
+  id?: string;
+  status?: string;
+}
+
+export interface ResponseToolCall {
+  id: string;
+  status: string;
+  type: "web_search_call" | "function_call";
+  arguments?: string;
+  call_id?: string;
+  name?: string;
+}
+
+export type ResponseOutput = ResponseMessage | ResponseToolCall;
+
+export interface ResponseInput {
+  type: string;
+  content?: string | ResponseInputMessageContent[];
+  role?: string;
+  [key: string]: unknown; // Flexible for various input types
+}
+
+export interface OpenAIResponse {
+  id: string;
+  created_at: number;
+  model: string;
+  object: "response";
+  status: string;
+  output: ResponseOutput[];
+  input: ResponseInput[];
+  error?: {
+    code: string;
+    message: string;
+  };
+  parallel_tool_calls?: boolean;
+  previous_response_id?: string;
+  temperature?: number;
+  top_p?: number;
+  truncation?: string;
+  user?: string;
+}
+
+export interface InputItemListResponse {
+  data: ResponseInput[];
+  object: "list";
+}

llama_stack/ui/package-lock.json

@@ -19,6 +19,7 @@
         "lucide-react": "^0.510.0",
         "next": "15.3.2",
         "next-themes": "^0.4.6",
+        "openai": "^4.103.0",
         "react": "^19.0.0",
         "react-dom": "^19.0.0",
         "tailwind-merge": "^3.3.0"
@@ -9092,7 +9093,7 @@
     },
     "node_modules/llama-stack-client": {
       "version": "0.0.1-alpha.0",
-      "resolved": "git+ssh://git@github.com/stainless-sdks/llama-stack-node.git#efa814980d44b3b2c92944377a086915137b2134",
+      "resolved": "git+ssh://git@github.com/stainless-sdks/llama-stack-node.git#5d34d229fb53b6dad02da0f19f4b310b529c6b15",
       "license": "Apache-2.0",
       "dependencies": {
         "@types/node": "^18.11.18",
@@ -9804,6 +9805,51 @@
         "url": "https://github.com/sponsors/sindresorhus"
       }
     },
+    "node_modules/openai": {
+      "version": "4.103.0",
+      "resolved": "https://registry.npmjs.org/openai/-/openai-4.103.0.tgz",
+      "integrity": "sha512-eWcz9kdurkGOFDtd5ySS5y251H2uBgq9+1a2lTBnjMMzlexJ40Am5t6Mu76SSE87VvitPa0dkIAp75F+dZVC0g==",
+      "license": "Apache-2.0",
+      "dependencies": {
+        "@types/node": "^18.11.18",
+        "@types/node-fetch": "^2.6.4",
+        "abort-controller": "^3.0.0",
+        "agentkeepalive": "^4.2.1",
+        "form-data-encoder": "1.7.2",
+        "formdata-node": "^4.3.2",
+        "node-fetch": "^2.6.7"
+      },
+      "bin": {
+        "openai": "bin/cli"
+      },
+      "peerDependencies": {
+        "ws": "^8.18.0",
+        "zod": "^3.23.8"
+      },
+      "peerDependenciesMeta": {
+        "ws": {
+          "optional": true
+        },
+        "zod": {
+          "optional": true
+        }
+      }
+    },
+    "node_modules/openai/node_modules/@types/node": {
+      "version": "18.19.103",
+      "resolved": "https://registry.npmjs.org/@types/node/-/node-18.19.103.tgz",
+      "integrity": "sha512-hHTHp+sEz6SxFsp+SA+Tqrua3AbmlAw+Y//aEwdHrdZkYVRWdvWD3y5uPZ0flYOkgskaFWqZ/YGFm3FaFQ0pRw==",
+      "license": "MIT",
+      "dependencies": {
+        "undici-types": "~5.26.4"
+      }
+    },
+    "node_modules/openai/node_modules/undici-types": {
+      "version": "5.26.5",
+      "resolved": "https://registry.npmjs.org/undici-types/-/undici-types-5.26.5.tgz",
+      "integrity": "sha512-JlCMO+ehdEIKqlFxk6IfVoAUVmgz7cU7zD/h9XZ0qzeosSHmUJVOzSQvvYSYWXkFXC+IfLKSIffhv0sVZup6pA==",
+      "license": "MIT"
+    },
     "node_modules/optionator": {
       "version": "0.9.4",
       "resolved": "https://registry.npmjs.org/optionator/-/optionator-0.9.4.tgz",
@@ -12223,7 +12269,7 @@
       "version": "8.18.2",
       "resolved": "https://registry.npmjs.org/ws/-/ws-8.18.2.tgz",
       "integrity": "sha512-DMricUmwGZUVr++AEAe2uiVM7UoO9MAVZMDu05UQOaUII0lp+zOzLLU4Xqh/JvTqklB1T4uELaaPBKyjE1r4fQ==",
-      "dev": true,
+      "devOptional": true,
       "license": "MIT",
       "engines": {
         "node": ">=10.0.0"
@@ -12334,7 +12380,7 @@
       "version": "3.24.4",
       "resolved": "https://registry.npmjs.org/zod/-/zod-3.24.4.tgz",
       "integrity": "sha512-OdqJE9UDRPwWsrHjLN2F8bPxvwJBK22EHLWtanu0LSYr5YqzsaaW3RMgmjwr8Rypg5k+meEJdSPXJZXE/yqOMg==",
-      "dev": true,
+      "devOptional": true,
       "license": "MIT",
       "funding": {
         "url": "https://github.com/sponsors/colinhacks"

llama_stack/ui/package.json

@@ -19,7 +19,7 @@
     "@radix-ui/react-tooltip": "^1.2.6",
     "class-variance-authority": "^0.7.1",
     "clsx": "^2.1.1",
-    "llama-stack-client": "github:stainless-sdks/llama-stack-node#ehhuang/dev",
+    "llama-stack-client": "0.2.8",
     "lucide-react": "^0.510.0",
     "next": "15.3.2",
     "next-themes": "^0.4.6",

pyproject.toml

@@ -4,7 +4,7 @@ build-backend = "setuptools.build_meta"
 
 [project]
 name = "llama_stack"
-version = "0.2.7"
+version = "0.2.8"
 authors = [{ name = "Meta Llama", email = "llama-oss@meta.com" }]
 description = "Llama Stack"
 readme = "README.md"
@@ -21,13 +21,13 @@ classifiers = [
     "Topic :: Scientific/Engineering :: Information Analysis",
 ]
 dependencies = [
-    "blobfile",
+    "aiohttp",
     "fire",
     "httpx",
     "huggingface-hub",
     "jinja2>=3.1.6",
     "jsonschema",
-    "llama-stack-client>=0.2.7",
+    "llama-stack-client>=0.2.8",
     "openai>=1.66",
     "prompt-toolkit",
     "python-dotenv",
@@ -36,6 +36,7 @@ dependencies = [
     "requests",
     "rich",
     "setuptools",
+    "starlette",
     "termcolor",
     "tiktoken",
     "pillow",
@@ -43,6 +44,14 @@ dependencies = [
 ]
 
 [project.optional-dependencies]
+ui = [
+    "streamlit",
+    "pandas",
+    "llama-stack-client>=0.2.8",
+    "streamlit-option-menu",
+]
+
+[dependency-groups]
 dev = [
     "pytest",
     "pytest-timeout",
@@ -73,10 +82,11 @@ unit = [
     "opentelemetry-exporter-otlp-proto-http",
     "sqlalchemy",
     "sqlalchemy[asyncio]>=2.0.41",
+    "blobfile",
 ]
 # These are the core dependencies required for running integration tests. They are shared across all
 # providers. If a provider requires additional dependencies, please add them to your environment
-# separately. If you are using "uv" to execute your tests, you can use the "--with" flag to specify extra
+# separately. If you are using "uv" to execute your tests, you can use the "--group" flag to specify extra
 # dependencies.
 test = [
     "openai",
@@ -112,12 +122,6 @@ docs = [
     "sphinxcontrib.openapi",
 ]
 codegen = ["rich", "pydantic", "jinja2>=3.1.6"]
-ui = [
-    "streamlit",
-    "pandas",
-    "llama-stack-client>=0.2.7",
-    "streamlit-option-menu",
-]
 
 [project.urls]
 Homepage = "https://github.com/meta-llama/llama-stack"
@@ -138,7 +142,6 @@ explicit = true
 [tool.uv.sources]
 torch = [{ index = "pytorch-cpu" }]
 torchvision = [{ index = "pytorch-cpu" }]
-llama-stack = { workspace = true }
 
 [tool.ruff]
 line-length = 120
@@ -332,10 +335,5 @@ init_forbid_extra = true
 init_typed = true
 warn_required_dynamic_aliases = true
 
-[dependency-groups]
-dev = [
-    "llama-stack",
-]
-
 [tool.ruff.lint.pep8-naming]
 classmethod-decorators = ["classmethod", "pydantic.field_validator"]

requirements.txt

@@ -1,63 +1,203 @@
 # This file was autogenerated by uv via the following command:
-#    uv export --frozen --no-hashes --no-emit-project --output-file=requirements.txt
+#    uv export --frozen --no-hashes --no-emit-project --no-default-groups --output-file=requirements.txt
+aiohappyeyeballs==2.5.0
+    # via aiohttp
+aiohttp==3.11.13
+    # via llama-stack
+aiosignal==1.3.2
+    # via aiohttp
 annotated-types==0.7.0
+    # via pydantic
 anyio==4.8.0
+    # via
+    #   httpx
+    #   llama-stack-client
+    #   openai
+    #   starlette
+async-timeout==5.0.1 ; python_full_version < '3.11'
+    # via aiohttp
 attrs==25.1.0
-blobfile==3.0.0
+    # via
+    #   aiohttp
+    #   jsonschema
+    #   referencing
 certifi==2025.1.31
+    # via
+    #   httpcore
+    #   httpx
+    #   requests
 charset-normalizer==3.4.1
+    # via requests
 click==8.1.8
+    # via llama-stack-client
 colorama==0.4.6 ; sys_platform == 'win32'
+    # via
+    #   click
+    #   tqdm
 distro==1.9.0
+    # via
+    #   llama-stack-client
+    #   openai
 ecdsa==0.19.1
+    # via python-jose
 exceptiongroup==1.2.2 ; python_full_version < '3.11'
+    # via anyio
 filelock==3.17.0
+    # via huggingface-hub
 fire==0.7.0
+    # via llama-stack
+frozenlist==1.5.0
+    # via
+    #   aiohttp
+    #   aiosignal
 fsspec==2024.12.0
+    # via huggingface-hub
 h11==0.16.0
+    # via
+    #   httpcore
+    #   llama-stack
 httpcore==1.0.9
+    # via httpx
 httpx==0.28.1
+    # via
+    #   llama-stack
+    #   llama-stack-client
+    #   openai
 huggingface-hub==0.29.0
+    # via llama-stack
 idna==3.10
+    # via
+    #   anyio
+    #   httpx
+    #   requests
+    #   yarl
 jinja2==3.1.6
+    # via llama-stack
 jiter==0.8.2
+    # via openai
 jsonschema==4.23.0
+    # via llama-stack
 jsonschema-specifications==2024.10.1
-llama-stack-client==0.2.7
-lxml==5.3.1
+    # via jsonschema
+llama-stack-client==0.2.8
+    # via llama-stack
 markdown-it-py==3.0.0
+    # via rich
 markupsafe==3.0.2
+    # via jinja2
 mdurl==0.1.2
+    # via markdown-it-py
+multidict==6.1.0
+    # via
+    #   aiohttp
+    #   yarl
 numpy==2.2.3
+    # via pandas
 openai==1.71.0
+    # via llama-stack
 packaging==24.2
+    # via huggingface-hub
 pandas==2.2.3
+    # via llama-stack-client
 pillow==11.1.0
+    # via llama-stack
 prompt-toolkit==3.0.50
+    # via
+    #   llama-stack
+    #   llama-stack-client
+propcache==0.3.0
+    # via
+    #   aiohttp
+    #   yarl
 pyaml==25.1.0
+    # via llama-stack-client
 pyasn1==0.4.8
-pycryptodomex==3.21.0
+    # via
+    #   python-jose
+    #   rsa
 pydantic==2.10.6
+    # via
+    #   llama-stack
+    #   llama-stack-client
+    #   openai
 pydantic-core==2.27.2
+    # via pydantic
 pygments==2.19.1
+    # via rich
 python-dateutil==2.9.0.post0
+    # via pandas
 python-dotenv==1.0.1
+    # via llama-stack
 python-jose==3.4.0
+    # via llama-stack
 pytz==2025.1
+    # via pandas
 pyyaml==6.0.2
+    # via
+    #   huggingface-hub
+    #   pyaml
 referencing==0.36.2
+    # via
+    #   jsonschema
+    #   jsonschema-specifications
 regex==2024.11.6
+    # via tiktoken
 requests==2.32.3
+    # via
+    #   huggingface-hub
+    #   llama-stack
+    #   tiktoken
 rich==13.9.4
+    # via
+    #   llama-stack
+    #   llama-stack-client
 rpds-py==0.22.3
+    # via
+    #   jsonschema
+    #   referencing
 rsa==4.9
+    # via python-jose
 setuptools==80.8.0
+    # via llama-stack
 six==1.17.0
+    # via
+    #   ecdsa
+    #   python-dateutil
 sniffio==1.3.1
+    # via
+    #   anyio
+    #   llama-stack-client
+    #   openai
+starlette==0.45.3
+    # via llama-stack
 termcolor==2.5.0
+    # via
+    #   fire
+    #   llama-stack
+    #   llama-stack-client
 tiktoken==0.9.0
+    # via llama-stack
 tqdm==4.67.1
+    # via
+    #   huggingface-hub
+    #   llama-stack-client
+    #   openai
 typing-extensions==4.12.2
+    # via
+    #   anyio
+    #   huggingface-hub
+    #   llama-stack-client
+    #   multidict
+    #   openai
+    #   pydantic
+    #   pydantic-core
+    #   referencing
+    #   rich
 tzdata==2025.1
+    # via pandas
 urllib3==2.3.0
+    # via requests
 wcwidth==0.2.13
+    # via prompt-toolkit
+yarl==1.18.3
+    # via aiohttp

scripts/distro_codegen.py

@@ -7,7 +7,6 @@
 
 import concurrent.futures
 import importlib
-import json
 import subprocess
 import sys
 from collections.abc import Iterable
@@ -108,21 +107,6 @@ def collect_template_dependencies(template_dir: Path) -> tuple[str | None, list[
     return None, []
 
 
-def generate_dependencies_file(change_tracker: ChangedPathTracker):
-    templates_dir = REPO_ROOT / "llama_stack" / "templates"
-    distribution_deps = {}
-
-    for template_dir in find_template_dirs(templates_dir):
-        name, deps = collect_template_dependencies(template_dir)
-        if name:
-            distribution_deps[name] = deps
-
-    deps_file = REPO_ROOT / "llama_stack" / "templates" / "dependencies.json"
-    change_tracker.add_paths(deps_file)
-    with open(deps_file, "w") as f:
-        f.write(json.dumps(distribution_deps, indent=2) + "\n")
-
-
 def main():
     templates_dir = REPO_ROOT / "llama_stack" / "templates"
     change_tracker = ChangedPathTracker()
@@ -143,8 +127,6 @@ def main():
             list(executor.map(process_func, template_dirs))
             progress.update(task, advance=len(template_dirs))
 
-    generate_dependencies_file(change_tracker)
-
     if check_for_changes(change_tracker):
         print(
             "Distribution template changes detected. Please commit the changes.",

scripts/unit-tests.sh

@@ -10,10 +10,10 @@ 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 $PYTHON_VERSION
+uv python find "$PYTHON_VERSION"
 FOUND_PYTHON=$?
 if [ $FOUND_PYTHON -ne 0 ]; then
-     uv python install $PYTHON_VERSION
+     uv python install "$PYTHON_VERSION"
 fi
 
-uv run --python $PYTHON_VERSION --with-editable . --with-editable ".[dev]" --with-editable ".[unit]" pytest --asyncio-mode=auto -s -v tests/unit/ $@
+uv run --python "$PYTHON_VERSION" --with-editable . --group unit pytest --asyncio-mode=auto -s -v tests/unit/ $@

tests/external-provider/llama-stack-provider-ollama/pyproject.toml

@@ -6,7 +6,6 @@ dependencies = [
     "aiohttp",
     "aiosqlite",
     "autoevals",
-    "blobfile",
     "chardet",
     "chromadb-client",
     "datasets",

tests/integration/agents/test_openai_responses.py

@@ -41,7 +41,6 @@ def openai_client(client_with_models):
         ],
     ],
 )
-@pytest.mark.skip(reason="Very flaky, sometimes there is a message not a function call, standard tool calling issues")
 def test_responses_store(openai_client, client_with_models, text_model_id, stream, tools):
     if isinstance(client_with_models, LlamaStackAsLibraryClient):
         pytest.skip("OpenAI responses are not supported when testing with library client yet.")
@@ -68,13 +67,15 @@ def test_responses_store(openai_client, client_with_models, text_model_id, strea
         for chunk in response:
             if response_id is None:
                 response_id = chunk.response.id
-            if not tools:
             if chunk.type == "response.completed":
                 response_id = chunk.response.id
+                output_type = chunk.response.output[0].type
+                if output_type == "message":
                     content = chunk.response.output[0].content[0].text
     else:
         response_id = response.id
-        if not tools:
+        output_type = response.output[0].type
+        if output_type == "message":
             content = response.output[0].content[0].text
 
     # list responses - use the underlying HTTP client for endpoints not in SDK
@@ -87,9 +88,8 @@ def test_responses_store(openai_client, client_with_models, text_model_id, strea
     retrieved_response = client.responses.retrieve(response_id)
     assert retrieved_response.id == response_id
     assert retrieved_response.model == text_model_id
-    if tools:
-        assert retrieved_response.output[0].type == "function_call"
-    else:
+    assert retrieved_response.output[0].type == output_type, retrieved_response
+    if output_type == "message":
         assert retrieved_response.output[0].content[0].text == content
 
 

tests/integration/inference/test_openai_completion.py

@@ -224,6 +224,43 @@ def test_openai_chat_completion_streaming(compat_client, client_with_models, tex
     assert expected.lower() in "".join(streamed_content)
 
 
+@pytest.mark.parametrize(
+    "test_case",
+    [
+        "inference:chat_completion:streaming_01",
+        "inference:chat_completion:streaming_02",
+    ],
+)
+def test_openai_chat_completion_streaming_with_n(compat_client, client_with_models, text_model_id, test_case):
+    skip_if_model_doesnt_support_openai_chat_completion(client_with_models, text_model_id)
+
+    provider = provider_from_model(client_with_models, text_model_id)
+    if provider.provider_type == "remote::ollama":
+        pytest.skip(f"Model {text_model_id} hosted by {provider.provider_type} doesn't support n > 1.")
+
+    tc = TestCase(test_case)
+    question = tc["question"]
+    expected = tc["expected"]
+
+    response = compat_client.chat.completions.create(
+        model=text_model_id,
+        messages=[{"role": "user", "content": question}],
+        stream=True,
+        timeout=120,  # Increase timeout to 2 minutes for large conversation history,
+        n=2,
+    )
+    streamed_content = {}
+    for chunk in response:
+        for choice in chunk.choices:
+            if choice.delta.content:
+                streamed_content[choice.index] = (
+                    streamed_content.get(choice.index, "") + choice.delta.content.lower().strip()
+                )
+    assert len(streamed_content) == 2
+    for i, content in streamed_content.items():
+        assert expected.lower() in content, f"Choice {i}: Expected {expected.lower()} in {content}"
+
+
 @pytest.mark.parametrize(
     "stream",
     [
@@ -253,6 +290,7 @@ def test_inference_store(openai_client, client_with_models, text_model_id, strea
         for chunk in response:
             if response_id is None:
                 response_id = chunk.id
+            if chunk.choices[0].delta.content:
                 content += chunk.choices[0].delta.content
     else:
         response_id = response.id
@@ -263,8 +301,8 @@ def test_inference_store(openai_client, client_with_models, text_model_id, strea
 
     retrieved_response = client.chat.completions.retrieve(response_id)
     assert retrieved_response.id == response_id
-    assert retrieved_response.input_messages[0]["content"] == message
-    assert retrieved_response.choices[0].message.content == content
+    assert retrieved_response.input_messages[0]["content"] == message, retrieved_response
+    assert retrieved_response.choices[0].message.content == content, retrieved_response
 
 
 @pytest.mark.parametrize(
@@ -274,7 +312,6 @@ def test_inference_store(openai_client, client_with_models, text_model_id, strea
         False,
     ],
 )
-@pytest.mark.skip(reason="Very flaky, tool calling really wacky on CI")
 def test_inference_store_tool_calls(openai_client, client_with_models, text_model_id, stream):
     skip_if_model_doesnt_support_openai_chat_completion(client_with_models, text_model_id)
     client = openai_client
@@ -312,7 +349,9 @@ def test_inference_store_tool_calls(openai_client, client_with_models, text_mode
         for chunk in response:
             if response_id is None:
                 response_id = chunk.id
-            content += chunk.choices[0].delta.content
+            if delta := chunk.choices[0].delta:
+                if delta.content:
+                    content += delta.content
     else:
         response_id = response.id
         content = response.choices[0].message.content
@@ -323,5 +362,11 @@ def test_inference_store_tool_calls(openai_client, client_with_models, text_mode
     retrieved_response = client.chat.completions.retrieve(response_id)
     assert retrieved_response.id == response_id
     assert retrieved_response.input_messages[0]["content"] == message
-    assert retrieved_response.choices[0].message.tool_calls[0].function.name == "get_weather"
-    assert retrieved_response.choices[0].message.tool_calls[0].function.arguments == '{"city":"Tokyo"}'
+    tool_calls = retrieved_response.choices[0].message.tool_calls
+    # sometimes model doesn't ouptut tool calls, but we still want to test that the tool was called
+    if tool_calls:
+        assert len(tool_calls) == 1
+        assert tool_calls[0].function.name == "get_weather"
+        assert "tokyo" in tool_calls[0].function.arguments.lower()
+    else:
+        assert retrieved_response.choices[0].message.content == content

tests/integration/tool_runtime/test_builtin_tools.py

@@ -25,10 +25,12 @@ def test_web_search_tool(llama_stack_client, sample_search_query):
     if "TAVILY_SEARCH_API_KEY" not in os.environ:
         pytest.skip("TAVILY_SEARCH_API_KEY not set, skipping test")
 
+    tools = llama_stack_client.tool_runtime.list_tools()
+    assert any(tool.identifier == "web_search" for tool in tools)
+
     response = llama_stack_client.tool_runtime.invoke_tool(
         tool_name="web_search", kwargs={"query": sample_search_query}
     )
-
     # Verify the response
     assert response.content is not None
     assert len(response.content) > 0
@@ -49,11 +51,12 @@ def test_wolfram_alpha_tool(llama_stack_client, sample_wolfram_alpha_query):
     if "WOLFRAM_ALPHA_API_KEY" not in os.environ:
         pytest.skip("WOLFRAM_ALPHA_API_KEY not set, skipping test")
 
+    tools = llama_stack_client.tool_runtime.list_tools()
+    assert any(tool.identifier == "wolfram_alpha" for tool in tools)
     response = llama_stack_client.tool_runtime.invoke_tool(
         tool_name="wolfram_alpha", kwargs={"query": sample_wolfram_alpha_query}
     )
 
-    print(response.content)
     assert response.content is not None
     assert len(response.content) > 0
     assert isinstance(response.content, str)

tests/integration/tool_runtime/test_mcp.py

@@ -31,8 +31,7 @@ def test_mcp_invocation(llama_stack_client, mcp_server):
     test_toolgroup_id = MCP_TOOLGROUP_ID
     uri = mcp_server["server_url"]
 
-    # registering itself should fail since it requires listing tools
-    with pytest.raises(Exception, match="Unauthorized"):
+    # registering should not raise an error anymore even if you don't specify the auth token
     llama_stack_client.toolgroups.register(
         toolgroup_id=test_toolgroup_id,
         provider_id="model-context-protocol",
@@ -41,27 +40,18 @@ def test_mcp_invocation(llama_stack_client, mcp_server):
 
     provider_data = {
         "mcp_headers": {
-            uri: [
-                f"Authorization: Bearer {AUTH_TOKEN}",
-            ],
+            uri: {
+                "Authorization": f"Bearer {AUTH_TOKEN}",
+            },
         },
     }
     auth_headers = {
         "X-LlamaStack-Provider-Data": json.dumps(provider_data),
     }
 
-    try:
-        llama_stack_client.toolgroups.unregister(toolgroup_id=test_toolgroup_id, extra_headers=auth_headers)
-    except Exception as e:
-        # An error is OK since the toolgroup may not exist
-        print(f"Error unregistering toolgroup: {e}")
+    with pytest.raises(Exception, match="Unauthorized"):
+        llama_stack_client.tools.list()
 
-    llama_stack_client.toolgroups.register(
-        toolgroup_id=test_toolgroup_id,
-        provider_id="model-context-protocol",
-        mcp_endpoint=dict(uri=uri),
-        extra_headers=auth_headers,
-    )
     response = llama_stack_client.tools.list(
         toolgroup_id=test_toolgroup_id,
         extra_headers=auth_headers,

tests/integration/tool_runtime/test_registration.py

@@ -51,7 +51,5 @@ def test_register_and_unregister_toolgroup(llama_stack_client):
         with pytest.raises(Exception, match=f"Tool group '{test_toolgroup_id}' not found"):
             llama_stack_client.toolgroups.get(toolgroup_id=test_toolgroup_id)
 
-        # Verify tools are also unregistered
-        unregister_tools_list_response = llama_stack_client.tools.list(toolgroup_id=test_toolgroup_id)
-        assert isinstance(unregister_tools_list_response, list)
-        assert not unregister_tools_list_response
+        with pytest.raises(Exception, match=f"Tool group '{test_toolgroup_id}' not found"):
+            llama_stack_client.tools.list(toolgroup_id=test_toolgroup_id)

tests/unit/distribution/routers/test_routing_tables.py

@@ -15,7 +15,7 @@ from llama_stack.apis.datasets.datasets import Dataset, DatasetPurpose, URIDataS
 from llama_stack.apis.datatypes import Api
 from llama_stack.apis.models.models import Model, ModelType
 from llama_stack.apis.shields.shields import Shield
-from llama_stack.apis.tools import ListToolDefsResponse, ToolDef, ToolParameter
+from llama_stack.apis.tools import ListToolDefsResponse, ToolDef, ToolGroup, ToolParameter
 from llama_stack.apis.vector_dbs.vector_dbs import VectorDB
 from llama_stack.distribution.routing_tables.benchmarks import BenchmarksRoutingTable
 from llama_stack.distribution.routing_tables.datasets import DatasetsRoutingTable
@@ -101,11 +101,11 @@ class ToolGroupsImpl(Impl):
     def __init__(self):
         super().__init__(Api.tool_runtime)
 
-    async def register_tool(self, tool):
-        return tool
+    async def register_toolgroup(self, toolgroup: ToolGroup):
+        return toolgroup
 
-    async def unregister_tool(self, tool_name: str):
-        return tool_name
+    async def unregister_toolgroup(self, toolgroup_id: str):
+        return toolgroup_id
 
     async def list_runtime_tools(self, toolgroup_id, mcp_endpoint):
         return ListToolDefsResponse(

tests/unit/providers/agents/meta_reference/test_openai_responses.py

@@ -232,9 +232,17 @@ async def test_create_openai_response_with_tool_call_type_none(openai_responses_
 
     # Check that we got the content from our mocked tool execution result
     chunks = [chunk async for chunk in result]
-    assert len(chunks) > 0
-    assert chunks[0].response.output[0].type == "function_call"
-    assert chunks[0].response.output[0].name == "get_weather"
+    assert len(chunks) == 2  # Should have response.created and response.completed
+
+    # Check response.created event (should have empty output)
+    assert chunks[0].type == "response.created"
+    assert len(chunks[0].response.output) == 0
+
+    # Check response.completed event (should have the tool call)
+    assert chunks[1].type == "response.completed"
+    assert len(chunks[1].response.output) == 1
+    assert chunks[1].response.output[0].type == "function_call"
+    assert chunks[1].response.output[0].name == "get_weather"
 
 
 @pytest.mark.asyncio
@@ -620,3 +628,69 @@ async def test_responses_store_list_input_items_logic():
     result = await responses_store.list_response_input_items("resp_123", limit=0, order=Order.asc)
     assert result.object == "list"
     assert len(result.data) == 0  # Should return no items
+
+
+@pytest.mark.asyncio
+async def test_store_response_uses_rehydrated_input_with_previous_response(
+    openai_responses_impl, mock_responses_store, mock_inference_api
+):
+    """Test that _store_response uses the full re-hydrated input (including previous responses)
+    rather than just the original input when previous_response_id is provided."""
+
+    # Setup - Create a previous response that should be included in the stored input
+    previous_response = OpenAIResponseObjectWithInput(
+        id="resp-previous-123",
+        object="response",
+        created_at=1234567890,
+        model="meta-llama/Llama-3.1-8B-Instruct",
+        status="completed",
+        input=[
+            OpenAIResponseMessage(
+                id="msg-prev-user", role="user", content=[OpenAIResponseInputMessageContentText(text="What is 2+2?")]
+            )
+        ],
+        output=[
+            OpenAIResponseMessage(
+                id="msg-prev-assistant",
+                role="assistant",
+                content=[OpenAIResponseOutputMessageContentOutputText(text="2+2 equals 4.")],
+            )
+        ],
+    )
+
+    mock_responses_store.get_response_object.return_value = previous_response
+
+    current_input = "Now what is 3+3?"
+    model = "meta-llama/Llama-3.1-8B-Instruct"
+    mock_chat_completion = load_chat_completion_fixture("simple_chat_completion.yaml")
+    mock_inference_api.openai_chat_completion.return_value = mock_chat_completion
+
+    # Execute - Create response with previous_response_id
+    result = await openai_responses_impl.create_openai_response(
+        input=current_input,
+        model=model,
+        previous_response_id="resp-previous-123",
+        store=True,
+    )
+
+    store_call_args = mock_responses_store.store_response_object.call_args
+    stored_input = store_call_args.kwargs["input"]
+
+    # Verify that the stored input contains the full re-hydrated conversation:
+    # 1. Previous user message
+    # 2. Previous assistant response
+    # 3. Current user message
+    assert len(stored_input) == 3
+
+    assert stored_input[0].role == "user"
+    assert stored_input[0].content[0].text == "What is 2+2?"
+
+    assert stored_input[1].role == "assistant"
+    assert stored_input[1].content[0].text == "2+2 equals 4."
+
+    assert stored_input[2].role == "user"
+    assert stored_input[2].content == "Now what is 3+3?"
+
+    # Verify the response itself is correct
+    assert result.model == model
+    assert result.status == "completed"

tests/verifications/README.md

@@ -27,7 +27,7 @@ export TOGETHER_API_KEY=<your_together_api_key>
 ```
 then run
 ```bash
-uv run --with-editable ".[dev]" python tests/verifications/generate_report.py --run-tests
+uv run python tests/verifications/generate_report.py --run-tests
 ```
 
 ## Running Tests

tests/verifications/openai_api/conftest.py

@@ -10,17 +10,17 @@ from tests.verifications.openai_api.fixtures.fixtures import _load_all_verificat
 def pytest_generate_tests(metafunc):
     """Dynamically parametrize tests based on the selected provider and config."""
     if "model" in metafunc.fixturenames:
+        model = metafunc.config.getoption("model")
+        if model:
+            metafunc.parametrize("model", [model])
+            return
+
         provider = metafunc.config.getoption("provider")
         if not provider:
             print("Warning: --provider not specified. Skipping model parametrization.")
             metafunc.parametrize("model", [])
             return
 
-        model = metafunc.config.getoption("model")
-        if model:
-            metafunc.parametrize("model", [model])
-            return
-
         try:
             config_data = _load_all_verification_configs()
         except (OSError, FileNotFoundError) as e:

tests/verifications/openai_api/fixtures/test_cases/responses.yaml

@@ -77,11 +77,12 @@ test_response_image:
           image_url: "https://upload.wikimedia.org/wikipedia/commons/f/f7/Llamas%2C_Vernagt-Stausee%2C_Italy.jpg"
       output: "llama"
 
+# the models are really poor at tool calling after seeing images :/
 test_response_multi_turn_image:
   test_name: test_response_multi_turn_image
   test_params:
     case:
-    - case_id: "llama_image_search"
+    - case_id: "llama_image_understanding"
       turns:
       - input:
         - role: user
@@ -91,7 +92,5 @@ test_response_multi_turn_image:
           - type: input_image
             image_url: "https://upload.wikimedia.org/wikipedia/commons/f/f7/Llamas%2C_Vernagt-Stausee%2C_Italy.jpg"
         output: "llama"
-      - input: "Search the web using the search tool for the animal from the previous response. Your search query should be a single phrase that includes the animal's name and the words 'maverick', 'scout' and 'llm'"
-        tools:
-        - type: web_search
-        output: "model"
+      - input: "What country do you find this animal primarily in? What continent?"
+        output: "peru"