# What does this PR do?
there is a lot of code in the agents API using the telemetry API and its
helpers without checking if that API is even enabled.
This is the only API besides inference actively using telemetry code, so
after this telemetry can be optional for the entire stack
resolves#3665
## Test Plan
existing agent tests.
Signed-off-by: Charlie Doern <cdoern@redhat.com>
# What does this PR do?
inference adapters can now configure `refresh_models: bool` to control
periodic model listing from their providers
BREAKING CHANGE: together inference adapter default changed. previously
always refreshed, now follows config.
addresses "models: refresh" on #3517
## Test Plan
ci w/ new tests
# What does this PR do?
when using a distro like starter where a bunch of providers are disabled
I should not see logs like:
```
in the provider data header, e.g. x-llamastack-provider-data: {"groq_api_key": "<API_KEY>"}, or in the provider config.
WARNING 2025-10-07 08:38:52,117 llama_stack.core.routing_tables.models:36 core::routing_tables: Model refresh failed for provider sambanova: API key is not set. Please provide a valid
API key in the provider data header, e.g. x-llamastack-provider-data: {"sambanova_api_key": "<API_KEY>"}, or in the provider config.
WARNING 2025-10-07 08:43:52,123 llama_stack.core.routing_tables.models:36 core::routing_tables: Model refresh failed for provider fireworks: Pass Fireworks API Key in the header
X-LlamaStack-Provider-Data as { "fireworks_api_key": <your api key>}
WARNING 2025-10-07 08:43:52,126 llama_stack.core.routing_tables.models:36 core::routing_tables: Model refresh failed for provider together: Pass Together API Key in the header
X-LlamaStack-Provider-Data as { "together_api_key": <your api key>}
WARNING 2025-10-07 08:43:52,129 llama_stack.core.routing_tables.models:36 core::routing_tables: Model refresh failed for provider openai: API key is not set. Please provide a valid API
key in the provider data header, e.g. x-llamastack-provider-data: {"openai_api_key": "<API_KEY>"}, or in the provider config.
WARNING 2025-10-07 08:43:52,132 llama_stack.core.routing_tables.models:36 core::routing_tables: Model refresh failed for provider anthropic: API key is not set. Please provide a valid
API key in the provider data header, e.g. x-llamastack-provider-data: {"anthropic_api_key": "<API_KEY>"}, or in the provider config.
WARNING 2025-10-07 08:43:52,136 llama_stack.core.routing_tables.models:36 core::routing_tables: Model refresh failed for provider gemini: API key is not set. Please provide a valid API
key in the provider data header, e.g. x-llamastack-provider-data: {"gemini_api_key": "<API_KEY>"}, or in the provider config.
WARNING 2025-10-07 08:43:52,139 llama_stack.core.routing_tables.models:36 core::routing_tables: Model refresh failed for provider groq: API key is not set. Please provide a valid API key
in the provider data header, e.g. x-llamastack-provider-data: {"groq_api_key": "<API_KEY>"}, or in the provider config.
WARNING 2025-10-07 08:43:52,142 llama_stack.core.routing_tables.models:36 core::routing_tables: Model refresh failed for provider sambanova: API key is not set. Please provide a valid
API key in the provider data header, e.g. x-llamastack-provider-data: {"sambanova_api_key": "<API_KEY>"}, or in the provider config.
^CINFO 2025-10-07 08:46:11,996 llama_stack.core.utils.exec:75 core:
```
as WARNING. Switch to Debug.
Signed-off-by: Charlie Doern <cdoern@redhat.com>
What does this PR do?
Updates pyproject.toml dependencies to fix vector processing
compatibility issues.
closes: #3495
Test Plan
Tested llama stack server with faiss vector database:
1. Built and ran server: llama stack build --distro starter --image-type
venv --image-name llamastack-faiss
3. Tested file upload: Successfully uploaded PDF via /v1/openai/v1/files
4. Tested vector operations:
- Created vector store with faiss backend
- Added PDF to vector store
- Performed semantic search queries
# What does this PR do?
Sorry to @mattf I thought I could close the other PR and reopen it.. But
I didn't have the option to reopen it now. I just didn't want it to keep
notifying maintainers if I would make other commits for testing.
Continuation of: https://github.com/llamastack/llama-stack/pull/3641
PR fixes Runpod Adapter
https://github.com/llamastack/llama-stack/issues/3517
## What I fixed from before:
Continuation of: https://github.com/llamastack/llama-stack/pull/3641
1. Made it all OpenAI
2. Fixed the class up since the OpenAIMixin had a couple changes with
the pydantic base model stuff.
3. Test to make sure that we could dynamically find models and use the
resulting identifier to make requests
```bash
curl -X GET \
-H "Content-Type: application/json" \
"http://localhost:8321/v1/models"
```
## 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.* -->
```
# RunPod Provider Quick Start
## Prerequisites
- Python 3.10+
- Git
- RunPod API token
## Setup for Development
```bash
# 1. Clone and enter the repository
cd (into the repo)
# 2. Create and activate virtual environment
python3 -m venv .venv
source .venv/bin/activate
# 3. Remove any existing llama-stack installation
pip uninstall llama-stack llama-stack-client -y
# 4. Install llama-stack in development mode
pip install -e .
# 5. Build using local development code
(Found this through the Discord)
LLAMA_STACK_DIR=. llama stack build
# When prompted during build:
# - Name: runpod-dev
# - Image type: venv
# - Inference provider: remote::runpod
# - Safety provider: "llama-guard"
# - Other providers: first defaults
```
## Configure the Stack
The RunPod adapter automatically discovers models from your endpoint via the `/v1/models` API.
No manual model configuration is required - just set your environment variables.
## Run the Server
### Important: Use the Build-Created Virtual Environment
```bash
# Exit the development venv if you're in it
deactivate
# Activate the build-created venv (NOT .venv)
cd (lama-stack folder github repo)
source llamastack-runpod-dev/bin/activate
```
### For Qwen3-32B-AWQ Public Endpoint (Recommended)
```bash
# Set environment variables
export RUNPOD_URL="https://api.runpod.ai/v2/qwen3-32b-awq/openai/v1"
export RUNPOD_API_TOKEN="your_runpod_api_key"
# Start server
llama stack run
~/.llama/distributions/llamastack-runpod-dev/llamastack-runpod-dev-run.yaml
```
## Quick Test
### 1. List Available Models (Dynamic Discovery)
First, check which models are available on your RunPod endpoint:
```bash
curl -X GET \
-H "Content-Type: application/json" \
"http://localhost:8321/v1/models"
```
**Example Response:**
```json
{
"data": [
{
"identifier": "qwen3-32b-awq",
"provider_resource_id": "Qwen/Qwen3-32B-AWQ",
"provider_id": "runpod",
"type": "model",
"metadata": {},
"model_type": "llm"
}
]
}
```
**Note:** Use the `identifier` value from the response above in your requests below.
### 2. Chat Completion (Non-streaming)
Replace `qwen3-32b-awq` with your model identifier from step 1:
```bash
curl -X POST http://localhost:8321/v1/chat/completions \
-H "Content-Type: application/json" \
-d '{
"model": "qwen3-32b-awq",
"messages": [{"role": "user", "content": "Hello, count to 3"}],
"stream": false
}'
```
### 3. Chat Completion (Streaming)
```bash
curl -X POST http://localhost:8321/v1/chat/completions \
-H "Content-Type: application/json" \
-d '{
"model": "qwen3-32b-awq",
"messages": [{"role": "user", "content": "Count to 5"}],
"stream": true
}'
```
**Clean streaming output:**
```bash
curl -N -X POST http://localhost:8321/v1/chat/completions \
-H "Content-Type: application/json" \
-d '{"model": "qwen3-32b-awq", "messages": [{"role": "user", "content":
"Count to 5"}], "stream": true}' \
2>/dev/null | while read -r line; do
echo "$line" | grep "^data: " | sed 's/^data: //' | jq -r
'.choices[0].delta.content // empty' 2>/dev/null
done
```
**Expected Output:**
```
1
2
3
4
5
```
# What does this PR do?
WARNING 2025-10-06 12:01:45,137 root:266 uncategorized: Unknown logging
category: tokenizer_utils. Falling back to default 'root' level: 20
## Test Plan
# What does this PR do?
* Cleans up API docstrings for better documentation rendering
<img width="2346" height="1126" alt="image"
src="https://github.com/user-attachments/assets/516b09a1-2d5b-4614-a3a9-13431fc21fc1"
/>
## Test Plan
* Manual testing
---------
Signed-off-by: Doug Edgar <dedgar@redhat.com>
Signed-off-by: Charlie Doern <cdoern@redhat.com>
Signed-off-by: Francisco Javier Arceo <farceo@redhat.com>
Signed-off-by: dependabot[bot] <support@github.com>
Co-authored-by: ehhuang <ehhuang@users.noreply.github.com>
Co-authored-by: Ashwin Bharambe <ashwin.bharambe@gmail.com>
Co-authored-by: Matthew Farrellee <matt@cs.wisc.edu>
Co-authored-by: Doug Edgar <dedgar@redhat.com>
Co-authored-by: Christian Zaccaria <73656840+ChristianZaccaria@users.noreply.github.com>
Co-authored-by: Anastas Stoyanovsky <contact@anastas.eu>
Co-authored-by: Charlie Doern <cdoern@redhat.com>
Co-authored-by: Francisco Arceo <arceofrancisco@gmail.com>
Co-authored-by: Claude <noreply@anthropic.com>
Co-authored-by: Young Han <110819238+seyeong-han@users.noreply.github.com>
Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com>
# What does this PR do?
- implement get_api_key instead of relying on
LiteLLMOpenAIMixin.get_api_key
- remove use of LiteLLMOpenAIMixin
- add default initialize/shutdown methods to OpenAIMixin
- remove __init__s to allow proper pydantic construction
- remove dead code from vllm adapter and associated / duplicate unit
tests
- update vllm adapter to use openaimixin for model registration
- remove ModelRegistryHelper from fireworks & together adapters
- remove Inference from nvidia adapter
- complete type hints on embedding_model_metadata
- allow extra fields on OpenAIMixin, for model_store, __provider_id__,
etc
- new recordings for ollama
- enhance the list models error handling
- update cerebras (remove cerebras-cloud-sdk) and anthropic (custom
model listing) inference adapters
- parametrized test_inference_client_caching
- remove cerebras, databricks, fireworks, together from blanket mypy
exclude
- removed unnecessary litellm deps
## Test Plan
ci
# What does this PR do?
when using the providers.d method of installation users could hand craft
their AdapterSpec's to use overlapping code meaning one repo could
contain an inline and remote impl. Currently installing a provider via
module does not allow for that as each repo is only allowed to have one
`get_provider_spec` method with one Spec returned
add an optional way for `get_provider_spec` to return a list of
`ProviderSpec` where each can be either an inline or remote impl.
Note: the `adapter_type` in `get_provider_spec` MUST match the
`provider_type` in the build/run yaml for this to work.
resolves#3226
## Test Plan
once this merges we need to re-enable the external provider test and
account for this functionality. Work needs to be done in the external
provider repos to support this functionality.
Signed-off-by: Charlie Doern <cdoern@redhat.com>
# What does this PR do?
https://github.com/llamastack/llama-stack/pull/3462 allows using uvicorn
to start llama stack server which supports spawning multiple workers.
This PR enables us to launch >1 workers from `llama stack run` (will add
the parameter in a follow-up PR, keeping this PR on simplifying) by
removing the old way of launching stack server and consolidates
launching via uvicorn.run only.
## Test Plan
ran `llama stack run starter`
CI
Bumps [pandas](https://github.com/pandas-dev/pandas) from 2.3.1 to
2.3.3.
<details>
<summary>Release notes</summary>
<p><em>Sourced from <a
href="https://github.com/pandas-dev/pandas/releases">pandas's
releases</a>.</em></p>
<blockquote>
<h2>Pandas 2.3.3</h2>
<p>We are pleased to announce the release of pandas 2.3.3.
This release includes some improvements and fixes to the future string
data type (preview feature for the upcoming pandas 3.0). We recommend
that all users upgrade to this version.</p>
<p>See the <a
href="https://pandas.pydata.org/pandas-docs/version/2.3/whatsnew/v2.3.3.html">full
whatsnew</a> for a list of all the changes.
Pandas 2.3.3 supports Python 3.9 and higher, and is the first release to
support Python 3.14.</p>
<p>The release will be available on the conda-forge channel:</p>
<pre><code>conda install pandas --channel conda-forge
</code></pre>
<p>Or via PyPI:</p>
<pre><code>python3 -m pip install --upgrade pandas
</code></pre>
<p>Please report any issues with the release on the <a
href="https://github.com/pandas-dev/pandas/issues">pandas issue
tracker</a>.</p>
<p>Thanks to all the contributors who made this release possible.</p>
<h2>Pandas 2.3.2</h2>
<p>We are pleased to announce the release of pandas 2.3.2.
This release includes some improvements and fixes to the future string
data type (preview feature for the upcoming pandas 3.0). We recommend
that all users upgrade to this version.</p>
<p>See the <a
href="https://pandas.pydata.org/pandas-docs/version/2.3/whatsnew/v2.3.2.html">full
whatsnew</a> for a list of all the changes.
Pandas 2.3.2 supports Python 3.9 and higher.</p>
<p>The release will be available on the conda-forge channel:</p>
<pre><code>conda install pandas --channel conda-forge
</code></pre>
<p>Or via PyPI:</p>
<pre><code>python3 -m pip install --upgrade pandas
</code></pre>
<p>Please report any issues with the release on the <a
href="https://github.com/pandas-dev/pandas/issues">pandas issue
tracker</a>.</p>
<p>Thanks to all the contributors who made this release possible.</p>
</blockquote>
</details>
<details>
<summary>Commits</summary>
<ul>
<li><a
href="9c8bc3e551"><code>9c8bc3e</code></a>
RLS: 2.3.3</li>
<li><a
href="6aa788a00b"><code>6aa788a</code></a>
[backport 2.3.x] DOC: prepare 2.3.3 whatsnew notes for release (<a
href="https://redirect.github.com/pandas-dev/pandas/issues/62499">#62499</a>)
(<a
href="https://redirect.github.com/pandas-dev/pandas/issues/62508">#62508</a>)</li>
<li><a
href="b64f0df403"><code>b64f0df</code></a>
[backport 2.3.x] BUG: avoid validation error for ufunc with
string[python] ar...</li>
<li><a
href="058eb2b0ed"><code>058eb2b</code></a>
[backport 2.3.x] BUG: String[pyarrow] comparison with mixed object (<a
href="https://redirect.github.com/pandas-dev/pandas/issues/62424">#62424</a>)
(...</li>
<li><a
href="2ca088daef"><code>2ca088d</code></a>
[backport 2.3.x] DEPR: remove the Period resampling deprecation (<a
href="https://redirect.github.com/pandas-dev/pandas/issues/62480">#62480</a>)
(<a
href="https://redirect.github.com/pandas-dev/pandas/issues/62">#62</a>...</li>
<li><a
href="92bf98f623"><code>92bf98f</code></a>
[backport 2.3.x] BUG: fix .str.isdigit to honor unicode superscript for
older...</li>
<li><a
href="e57c7d6a22"><code>e57c7d6</code></a>
Backport PR <a
href="https://redirect.github.com/pandas-dev/pandas/issues/62452">#62452</a>
on branch 2.3.x (TST: Adjust tests for numexpr 2.13) (<a
href="https://redirect.github.com/pandas-dev/pandas/issues/62454">#62454</a>)</li>
<li><a
href="e0fe9a03c9"><code>e0fe9a0</code></a>
Backport to 2.3.x: REGR: from_records not initializing subclasses
properly (#...</li>
<li><a
href="23a1085e64"><code>23a1085</code></a>
BUG: improve future warning for boolean operations with missaligned
indexes (...</li>
<li><a
href="61136969fb"><code>6113696</code></a>
Backport PR <a
href="https://redirect.github.com/pandas-dev/pandas/issues/62396">#62396</a>
on branch 2.3.x (PKG/DOC: indicate Python 3.14 support in ...</li>
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</details>
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Bumps [astral-sh/setup-uv](https://github.com/astral-sh/setup-uv) from
6.7.0 to 6.8.0.
<details>
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<li><a
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Bumps [requests](https://github.com/psf/requests) from 2.32.4 to 2.32.5.
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<blockquote>
<h2>v2.32.5</h2>
<h2>2.32.5 (2025-08-18)</h2>
<p><strong>Bugfixes</strong></p>
<ul>
<li>The SSLContext caching feature originally introduced in 2.32.0 has
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number
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<li>Added support for Python 3.14.</li>
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from psf/dependabot/github_actions/actions/checkout-...</li>
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Merge pull request <a
href="https://redirect.github.com/psf/requests/issues/6999">#6999</a>
from psf/dependabot/github_actions/step-security/har...</li>
<li><a
href="7618dbef01"><code>7618dbe</code></a>
Bump step-security/harden-runner from 2.12.0 to 2.13.0</li>
<li><a
href="2edca11103"><code>2edca11</code></a>
Add support for Python 3.14 and drop support for Python 3.8 (<a
href="https://redirect.github.com/psf/requests/issues/6993">#6993</a>)</li>
<li><a
href="fec96cd597"><code>fec96cd</code></a>
Update Makefile rules (<a
href="https://redirect.github.com/psf/requests/issues/6996">#6996</a>)</li>
<li><a
href="d58d8aa2f4"><code>d58d8aa</code></a>
docs: clarify timeout parameter uses seconds in Session.request (<a
href="https://redirect.github.com/psf/requests/issues/6994">#6994</a>)</li>
<li><a
href="91a3eabd3d"><code>91a3eab</code></a>
Bump github/codeql-action from 3.28.5 to 3.29.0</li>
<li>Additional commits viewable in <a
href="https://github.com/psf/requests/compare/v2.32.4...v2.32.5">compare
view</a></li>
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Uses test_id in request hashes and test-scoped subdirectories to prevent
cross-test contamination. Model list endpoints exclude test_id to enable
merging recordings from different servers.
Additionally, this PR adds a `record-if-missing` mode (which we will use
instead of `record` which records everything) which is very useful.
🤖 Co-authored with [Claude Code](https://claude.com/claude-code)
---------
Co-authored-by: Claude <noreply@anthropic.com>
# What does this PR do?
Added missing configuration files
## Test Plan
run ./scripts/telemetry/setup_telemetry.sh
```
OTEL_SERVICE_NAME=llama_stack OTEL_EXPORTER_OTLP_ENDPOINT=http://localhost:4318 TELEMETRY_SINKS=otel_trace,otel_metric uv run --with llama-stack llama stack build --distro=starter --image-type=venv --run
```
Navigate to grafana localhost:3000, query metrics and traces
IDs are now deterministic hashes based on request content, and
timestamps are normalized to constants, eliminating spurious changes
when re-recording tests.
## Changes
- Updated `inference_recorder.py` to normalize IDs and timestamps during
recording
- Added `scripts/normalize_recordings.py` utility to re-normalize
existing recordings
- Created documentation in `tests/integration/recordings/README.md`
- Normalized 350 existing recording files
