- Switch from BRAVE_SEARCH_API_KEY to TAVILY_SEARCH_API_KEY
- Add provider_data to LlamaStackClient for API key passing
- Use builtin::websearch toolgroup instead of manual tool config
- Fix message types to use UserMessage instead of plain dict
- Add streaming support with proper type casting
- Remove async from EventLogger loop (bug fix)
Fixes websearch functionality in agents tutorial by properly configuring
Tavily search provider integration.
# What does this PR do?
Fixes the Agents101 tutorial notebook to work with the current Llama
Stack websearch implementation. The tutorial was using outdated Brave
Search configuration that no longer works with the current server setup.
**Key Changes:**
- **Switch API provider**: Change from `BRAVE_SEARCH_API_KEY` to
`TAVILY_SEARCH_API_KEY` to match server configuration
- **Fix client setup**: Add `provider_data` to `LlamaStackClient` to
properly pass API keys to server
- **Modernize tool usage**: Replace manual tool configuration with
`tools=["builtin::websearch"]`
- **Fix type safety**: Use `UserMessage` type instead of plain
dictionaries for messages
- **Fix streaming**: Add proper streaming support with `stream=True` and
type casting
- **Fix EventLogger**: Remove incorrect `async for` usage (should be
`for`)
**Why needed:** Users following the tutorial were getting 401
Unauthorized errors because the notebook wasn't properly configured for
the Tavily search provider that the server actually uses.
## Test Plan
**Prerequisites:**
1. Start Llama Stack server with Ollama template and
`TAVILY_SEARCH_API_KEY` environment variable
2. Set `TAVILY_SEARCH_API_KEY` in your `.env` file
**Testing Steps:**
1. **Clone and setup:**
```bash
git checkout fix-2558-update-agents101
cd docs/zero_to_hero_guide/
```
2. **Start server with API key:**
```bash
export TAVILY_SEARCH_API_KEY="your_tavily_api_key"
podman run -it --network=host -v ~/.llama:/root/.llama:Z \
--env INFERENCE_MODEL=$INFERENCE_MODEL \
--env OLLAMA_URL=http://localhost:11434 \
--env TAVILY_SEARCH_API_KEY=$TAVILY_SEARCH_API_KEY \
llamastack/distribution-ollama --port $LLAMA_STACK_PORT
```
3. **Run the notebook:**
- Open `07_Agents101.ipynb` in Jupyter
- Execute all cells in order
- Cell 5 should run without errors and show successful web search
results
**Expected Results:**
- ✅ No 401 Unauthorized errors
- ✅ Agent successfully calls `brave_search.call()` with web results
- ✅ Switzerland travel recommendations appear in output
- ✅ Follow-up questions work correctly
**Before this fix:** Users got `401 Unauthorized` errors and tutorial
failed
**After this fix:** Tutorial works end-to-end with proper web search
functionality
**Tested with:**
- Tavily API key (free tier)
- Ollama distribution template
- Llama-3.2-3B-Instruct model
# What does this PR do?
Adding nbformat version fixes this issue. Not sure exactly why this
needs to be done, but this version was rewritten to the bottom of a nb
file when I changed its name trying to get to the bottom of this. When I
opened it on GH the issue was no longer present
Closes#1837
## Test Plan
N/A
# What does this PR do?
Cleans up how we provide sampling params. Earlier, strategy was an enum
and all params (top_p, temperature, top_k) across all strategies were
grouped. We now have a strategy union object with each strategy (greedy,
top_p, top_k) having its corresponding params.
Earlier,
```
class SamplingParams:
strategy: enum ()
top_p, temperature, top_k and other params
```
However, the `strategy` field was not being used in any providers making
it confusing to know the exact sampling behavior purely based on the
params since you could pass temperature, top_p, top_k and how the
provider would interpret those would not be clear.
Hence we introduced -- a union where the strategy and relevant params
are all clubbed together to avoid this confusion.
Have updated all providers, tests, notebooks, readme and otehr places
where sampling params was being used to use the new format.
## Test Plan
`pytest llama_stack/providers/tests/inference/groq/test_groq_utils.py`
// inference on ollama, fireworks and together
`with-proxy pytest -v -s -k "ollama"
--inference-model="meta-llama/Llama-3.1-8B-Instruct"
llama_stack/providers/tests/inference/test_text_inference.py `
// agents on fireworks
`pytest -v -s -k 'fireworks and create_agent'
--inference-model="meta-llama/Llama-3.1-8B-Instruct"
llama_stack/providers/tests/agents/test_agents.py
--safety-shield="meta-llama/Llama-Guard-3-8B"`
## Before submitting
- [ ] This PR fixes a typo or improves the docs (you can dismiss the
other checks if that's the case).
- [X] Ran pre-commit to handle lint / formatting issues.
- [X] Read the [contributor
guideline](https://github.com/meta-llama/llama-stack/blob/main/CONTRIBUTING.md),
Pull Request section?
- [X] Updated relevant documentation.
- [X] Wrote necessary unit or integration tests.
---------
Co-authored-by: Hardik Shah <hjshah@fb.com>
# What does this PR do?
- updated the notebooks to reflect past changes up to llama-stack 0.0.53
- updated readme to provide accurate and up-to-date info
- improve the current zero to hero by integrating an example using
together api
## Before submitting
- [x] This PR fixes a typo or improves the docs (you can dismiss the
other checks if that's the case).
- [x] Ran pre-commit to handle lint / formatting issues.
- [x] Read the [contributor
guideline](https://github.com/meta-llama/llama-stack/blob/main/CONTRIBUTING.md),
Pull Request section?
- [ ] Updated relevant documentation.
- [ ] Wrote necessary unit or integration tests.
---------
Co-authored-by: Sanyam Bhutani <sanyambhutani@meta.com>
