forked from phoenix-oss/llama-stack-mirror
ce33d02443
When registering a MCP endpoint, we cannot list tools (like we used to) since the MCP endpoint may be behind an auth wall. Registration can happen much sooner (via run.yaml). Instead, we do listing only when the _user_ actually calls listing. Furthermore, we cache the list in-memory in the server. Currently, the cache is not invalidated -- we may want to periodically re-list for MCP servers. Note that they must call `list_tools` before calling `invoke_tool` -- we use this critically. This will enable us to list MCP servers in run.yaml ## Test Plan Existing tests, updated tests accordingly. |
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| .. | ||
| _static | ||
| notebooks | ||
| openapi_generator | ||
| resources | ||
| source | ||
| zero_to_hero_guide | ||
| conftest.py | ||
| contbuild.sh | ||
| dog.jpg | ||
| getting_started.ipynb | ||
| getting_started_llama4.ipynb | ||
| getting_started_llama_api.ipynb | ||
| license_header.txt | ||
| make.bat | ||
| Makefile | ||
| readme.md | ||
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
From the llama-stack root directory, run the following command to render the docs locally:
uv run --with ".[docs]" sphinx-autobuild docs/source docs/build/html --write-all
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