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# What does this PR do? Allow users to name an agent and use the name in telemetry instead of relying on randomly generated agent_ids. This improves the developer experience by making it easier to find specific agents in telemetry logs. Closes #1832 ## Test Plan - Added tests to verify the agent name is properly stored and retrieved - Ran `uv run -- pytest -v tests/integration/telemetry/test_telemetry.py::test_agent_name_filtering` from the root of the project and made sure the tests pass - Ran `uv run -- pytest -v tests/integration/telemetry/test_telemetry.py::test_agent_query_spans` to verify existing code without agent names still works correctly ## Use Example ``` agent = Agent( llama_stack_client, model=text_model_id, name="CustomerSupportAgent", # New parameter instructions="You are a helpful customer support assistant" ) session_id = agent.create_session(f"test-session-{uuid4()}") ``` ## Implementation Notes - Agent names are optional string parameters with no additional validation - Names are not required to be unique - multiple agents can have the same name - The agent_id remains the unique identifier for an agent --------- Co-authored-by: raghotham <raghotham@gmail.com> |
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| getting_started.ipynb | ||
| getting_started_llama4.ipynb | ||
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| requirements.txt | ||
Llama Stack Documentation
Here's a collection of comprehensive guides, examples, and resources for building AI applications with Llama Stack. For the complete documentation, visit our ReadTheDocs page.
Render locally
pip install -r requirements.txt
cd docs
python -m sphinx_autobuild source _build
You can open up the docs in your browser at http://localhost:8000
Content
Try out Llama Stack's capabilities through our detailed Jupyter notebooks:
- Building AI Applications Notebook - A comprehensive guide to building production-ready AI applications using Llama Stack
- Benchmark Evaluations Notebook - Detailed performance evaluations and benchmarking results
- Zero-to-Hero Guide - Step-by-step guide for getting started with Llama Stack