llama-toolchain -> llama-stack (hyphens)

README.md

@@ -1,6 +1,6 @@
 # llama-stack
 
-[![PyPI - Downloads](https://img.shields.io/pypi/dm/llama-toolchain)](https://pypi.org/project/llama-toolchain/)
+[![PyPI - Downloads](https://img.shields.io/pypi/dm/llama-stack)](https://pypi.org/project/llama-stack/)
 [![Discord](https://img.shields.io/discord/1257833999603335178)](https://discord.gg/TZAAYNVtrU)
 
 This repository contains the specifications and implementations of the APIs which are part of the Llama Stack.
@@ -42,7 +42,7 @@ A Distribution is where APIs and Providers are assembled together to provide a c
 
 ## Installation
 
-You can install this repository as a [package](https://pypi.org/project/llama-toolchain/) with `pip install llama-toolchain`
+You can install this repository as a [package](https://pypi.org/project/llama-stack/) with `pip install llama-stack`
 
 If you want to install from source:
 

docs/cli_reference.md

@@ -1,6 +1,6 @@
 # Llama CLI Reference
 
-The `llama` CLI tool helps you setup and use the Llama toolchain & agentic systems. It should be available on your path after installing the `llama-toolchain` package.
+The `llama` CLI tool helps you setup and use the Llama toolchain & agentic systems. It should be available on your path after installing the `llama-stack` package.
 
 ### Subcommands
 1. `download`: `llama` cli tools supports downloading the model from Meta or HuggingFace.
@@ -480,7 +480,7 @@ This server is running a Llama model locally.
 Once the server is setup, we can test it with a client to see the example outputs.
 ```
 cd /path/to/llama-stack
-conda activate <env>  # any environment containing the llama-toolchain pip package will work
+conda activate <env>  # any environment containing the llama-stack pip package will work
 
 python -m llama_stack.apis.inference.client localhost 5000
 ```

docs/getting_started.md

@@ -1,6 +1,6 @@
 # Getting Started
 
-The `llama` CLI tool helps you setup and use the Llama toolchain & agentic systems. It should be available on your path after installing the `llama-toolchain` package.
+The `llama` CLI tool helps you setup and use the Llama toolchain & agentic systems. It should be available on your path after installing the `llama-stack` package.
 
 This guides allows you to quickly get started with building and running a Llama Stack server in < 5 minutes!
 
@@ -294,7 +294,7 @@ This server is running a Llama model locally.
 Once the server is setup, we can test it with a client to see the example outputs.
 ```
 cd /path/to/llama-stack
-conda activate <env>  # any environment containing the llama-toolchain pip package will work
+conda activate <env>  # any environment containing the llama-stack pip package will work
 
 python -m llama_stack.apis.inference.client localhost 5000
 ```

llama_stack/cli/download.py

@@ -106,7 +106,7 @@ def _hf_download(
             local_dir=output_dir,
             ignore_patterns=ignore_patterns,
             token=hf_token,
-            library_name="llama-toolchain",
+            library_name="llama-stack",
         )
     except GatedRepoError:
         parser.error(

llama_stack/core/build_conda_env.sh

@@ -11,7 +11,7 @@ LLAMA_TOOLCHAIN_DIR=${LLAMA_TOOLCHAIN_DIR:-}
 TEST_PYPI_VERSION=${TEST_PYPI_VERSION:-}
 
 if [ -n "$LLAMA_TOOLCHAIN_DIR" ]; then
-  echo "Using llama-toolchain-dir=$LLAMA_TOOLCHAIN_DIR"
+  echo "Using llama-stack-dir=$LLAMA_TOOLCHAIN_DIR"
 fi
 if [ -n "$LLAMA_MODELS_DIR" ]; then
   echo "Using llama-models-dir=$LLAMA_MODELS_DIR"
@@ -78,9 +78,9 @@ ensure_conda_env_python310() {
   if [ -n "$TEST_PYPI_VERSION" ]; then
     # these packages are damaged in test-pypi, so install them first
     pip install fastapi libcst
-    pip install --extra-index-url https://test.pypi.org/simple/ llama-models==$TEST_PYPI_VERSION llama-toolchain==$TEST_PYPI_VERSION $pip_dependencies
+    pip install --extra-index-url https://test.pypi.org/simple/ llama-models==$TEST_PYPI_VERSION llama-stack==$TEST_PYPI_VERSION $pip_dependencies
   else
-    # Re-installing llama-toolchain in the new conda environment
+    # Re-installing llama-stack in the new conda environment
     if [ -n "$LLAMA_TOOLCHAIN_DIR" ]; then
       if [ ! -d "$LLAMA_TOOLCHAIN_DIR" ]; then
         printf "${RED}Warning: LLAMA_TOOLCHAIN_DIR is set but directory does not exist: $LLAMA_TOOLCHAIN_DIR${NC}\n" >&2
@@ -90,7 +90,7 @@ ensure_conda_env_python310() {
       printf "Installing from LLAMA_TOOLCHAIN_DIR: $LLAMA_TOOLCHAIN_DIR\n"
       pip install --no-cache-dir -e "$LLAMA_TOOLCHAIN_DIR"
     else
-      pip install --no-cache-dir llama-toolchain
+      pip install --no-cache-dir llama-stack
     fi
 
     if [ -n "$LLAMA_MODELS_DIR" ]; then

llama_stack/core/build_container.sh

@@ -55,7 +55,7 @@ RUN apt-get update && apt-get install -y \
 
 EOF
 
-toolchain_mount="/app/llama-toolchain-source"
+toolchain_mount="/app/llama-stack-source"
 models_mount="/app/llama-models-source"
 
 if [ -n "$LLAMA_TOOLCHAIN_DIR" ]; then
@@ -65,7 +65,7 @@ if [ -n "$LLAMA_TOOLCHAIN_DIR" ]; then
   fi
   add_to_docker "RUN pip install $toolchain_mount"
 else
-  add_to_docker "RUN pip install llama-toolchain"
+  add_to_docker "RUN pip install llama-stack"
 fi
 
 if [ -n "$LLAMA_MODELS_DIR" ]; then

llama_stack/core/distribution.py

@@ -17,7 +17,7 @@ from llama_stack.apis.telemetry import Telemetry
 from .datatypes import Api, ApiEndpoint, ProviderSpec, remote_provider_spec
 
 # These are the dependencies needed by the distribution server.
-# `llama-toolchain` is automatically installed by the installation script.
+# `llama-stack` is automatically installed by the installation script.
 SERVER_DEPENDENCIES = [
     "fastapi",
     "uvicorn",

rfcs/RFC-0001-llama-stack.md

@@ -65,7 +65,7 @@ We define the Llama Stack as a layer cake shown below.
 
 
 
-The API is defined in the [YAML](RFC-0001-llama-stack-assets/llama-stack-spec.yaml) and [HTML](RFC-0001-llama-stack-assets/llama-stack-spec.html) files. These files were generated using the Pydantic definitions in (api/datatypes.py and api/endpoints.py) files that are in the llama-models, llama-toolchain, and llama-agentic-system repositories.
+The API is defined in the [YAML](RFC-0001-llama-stack-assets/llama-stack-spec.yaml) and [HTML](RFC-0001-llama-stack-assets/llama-stack-spec.html) files. These files were generated using the Pydantic definitions in (api/datatypes.py and api/endpoints.py) files that are in the llama-models, llama-stack, and llama-agentic-system repositories.
 
 
 
@@ -75,7 +75,7 @@ The API is defined in the [YAML](RFC-0001-llama-stack-assets/llama-stack-spec.ya
 
 To prove out the API, we implemented a handful of use cases to make things more concrete. The [llama-agentic-system](https://github.com/meta-llama/llama-agentic-system) repository contains [6 different examples](https://github.com/meta-llama/llama-agentic-system/tree/main/examples/scripts) ranging from very basic to a multi turn agent.
 
-There is also a sample inference endpoint implementation in the [llama-toolchain](https://github.com/meta-llama/llama-toolchain/blob/main/llama_stack/inference/server.py) repository.
+There is also a sample inference endpoint implementation in the [llama-stack](https://github.com/meta-llama/llama-stack/blob/main/llama_stack/inference/server.py) repository.
 
 
 ## Limitations

setup.py

@@ -28,7 +28,7 @@ setup(
     },
     long_description=open("README.md").read(),
     long_description_content_type="text/markdown",
-    url="https://github.com/meta-llama/llama-toolchain",
+    url="https://github.com/meta-llama/llama-stack",
     packages=find_packages(),
     classifiers=[],
     python_requires=">=3.10",