PyPi package install README updates

README.md

@@ -81,13 +81,13 @@ pip install nvidia-nat
 NeMo Agent Toolkit has many optional dependencies which can be installed with the core package. Optional dependencies are grouped by framework and can be installed with the core package. For example, to install the LangChain/LangGraph plugin, run the following:
 
 ```bash
-pip install nvidia-nat[langchain]
+pip install "nvidia-nat[langchain]"
 ```
 
 Or for all optional dependencies:
 
 ```bash
-pip install nvidia-nat[all]
+pip install "nvidia-nat[all]"
 ```
 
 The full list of optional dependencies can be found [here](./docs/source/quick-start/installing.md#framework-integrations).

docs/source/extend/telemetry-exporters.md

@@ -266,14 +266,14 @@ Before creating a custom exporter, check if your observability service is alread
 | Service | Type | Installation | Configuration |
 |---------|------|-------------|---------------|
 | **File** | `file` | `pip install nvidia-nat` | local file or directory |
-| **Langfuse** | `langfuse` | `pip install nvidia-nat[opentelemetry]` | endpoint + API keys |
-| **LangSmith** | `langsmith` | `pip install nvidia-nat[opentelemetry]` | endpoint + API key |
-| **OpenTelemetry Collector** | `otelcollector` | `pip install nvidia-nat[opentelemetry]` | endpoint + headers |
-| **Patronus** | `patronus` | `pip install nvidia-nat[opentelemetry]` | endpoint + API key |
-| **Galileo** | `galileo` | `pip install nvidia-nat[opentelemetry]` | endpoint + API key |
-| **Phoenix** | `phoenix` | `pip install nvidia-nat[phoenix]` | endpoint |
-| **RagaAI/Catalyst** | `catalyst` | `pip install nvidia-nat[ragaai]` | API key + project |
-| **Weave** | `weave` | `pip install nvidia-nat[weave]` | project name |
+| **Langfuse** | `langfuse` | `pip install "nvidia-nat[opentelemetry]"` | endpoint + API keys |
+| **LangSmith** | `langsmith` | `pip install "nvidia-nat[opentelemetry]"` | endpoint + API key |
+| **OpenTelemetry Collector** | `otelcollector` | `pip install "nvidia-nat[opentelemetry]"` | endpoint + headers |
+| **Patronus** | `patronus` | `pip install "nvidia-nat[opentelemetry]"` | endpoint + API key |
+| **Galileo** | `galileo` | `pip install "nvidia-nat[opentelemetry]"` | endpoint + API key |
+| **Phoenix** | `phoenix` | `pip install "nvidia-nat[phoenix]"` | endpoint |
+| **RagaAI/Catalyst** | `catalyst` | `pip install "nvidia-nat[ragaai]"` | API key + project |
+| **Weave** | `weave` | `pip install "nvidia-nat[weave]"` | project name |
 
 ### Simple Configuration Example
 
@@ -412,7 +412,7 @@ class CustomSpanExporter(SpanExporter[Span, dict]):
 > **Note**: OpenTelemetry exporters require the `nvidia-nat-opentelemetry` subpackage. Install it with:
 
 > ```bash
-> pip install nvidia-nat[opentelemetry]
+> pip install "nvidia-nat[opentelemetry]"
 > ```
 
 For most OTLP-compatible services, use the pre-built `OTLPSpanAdapterExporter`:

docs/source/quick-start/installing.md

@@ -92,13 +92,13 @@ pip install nvidia-nat
 NeMo Agent toolkit has many optional dependencies which can be installed with the core package. Optional dependencies are grouped by framework and can be installed with the core package. For example, to install the LangChain/LangGraph plugin, run the following:
 
 ```bash
-pip install nvidia-nat[langchain]
+pip install "nvidia-nat[langchain]"
 ```
 
 Or for all optional dependencies:
 
 ```bash
-pip install nvidia-nat[all]
+pip install "nvidia-nat[all]"
 ```
 
 The full list of optional dependencies can be found [here](../quick-start/installing.md#framework-integrations).

docs/source/reference/api-server-endpoints.md

@@ -61,7 +61,7 @@ result back to the client. The transaction schema is defined by the workflow.
 ## Asynchronous Generate
 The asynchronous generate endpoint allows clients to submit a workflow to run in the background and return a response immediately with a unique identifier for the workflow. This can be used to query the status and results of the workflow at a later time. This is useful for long-running workflows, which would otherwise cause the client to time out.
 
-This endpoint is only available when the `async_endpoints` optional dependency extra is installed. For users installing from source, this can be done by running `uv pip install -e '.[async_endpoints]'` from the root directory of the NeMo Agent toolkit library. Similarly, for users installing from PyPI, this can be done by running `pip install 'nvidia-nat[async_endpoints]'`.
+This endpoint is only available when the `async_endpoints` optional dependency extra is installed. For users installing from source, this can be done by running `uv pip install -e '.[async_endpoints]'` from the root directory of the NeMo Agent toolkit library. Similarly, for users installing from PyPI, this can be done by running `pip install "nvidia-nat[async_endpoints]"`.
 
 Asynchronous jobs are managed using [Dask](https://docs.dask.org/en/stable/). By default, a local Dask cluster is created at start time, however you can also configure the server to connect to an existing Dask scheduler by setting the `scheduler_address` configuration parameter. The Dask scheduler is used to manage the execution of asynchronous jobs, and can be configured to run on a single machine or across a cluster of machines. Job history and metadata is stored in a SQL database using [SQLAlchemy](https://www.sqlalchemy.org/). By default, a temporary SQLite database is created at start time, however you can also configure the server to use a persistent database by setting the `db_url` configuration parameter. Refer to the [SQLAlchemy documentation](https://docs.sqlalchemy.org/en/20/core/engines.html#database-urls) for the format of the `db_url` parameter. Any database supported by [SQLAlchemy's Asynchronous I/O extension](https://docs.sqlalchemy.org/en/20/orm/extensions/asyncio.html) can be used. Refer to [SQLAlchemy's Dialects](https://docs.sqlalchemy.org/en/20/dialects/index.html) for a complete list (many but not all of these support Asynchronous I/O).
 

docs/source/reference/evaluate-api.md

@@ -20,7 +20,7 @@ limitations under the License.
 It is recommended that the [Evaluating NeMo Agent toolkit Workflows](./evaluate.md) guide be read before proceeding with this detailed documentation.
 :::
 
-The evaluation endpoint can be used to start evaluation jobs on a remote NeMo Agent toolkit server. This endpoint is only available when the `async_endpoints` optional dependency extra is installed. For users installing from source, this can be done by running `uv pip install -e '.[async_endpoints]'` from the root directory of the NeMo Agent toolkit library. Similarly, for users installing from PyPI, this can be done by running `pip install 'nvidia-nat[async_endpoints]'`.
+The evaluation endpoint can be used to start evaluation jobs on a remote NeMo Agent toolkit server. This endpoint is only available when the `async_endpoints` optional dependency extra is installed. For users installing from source, this can be done by running `uv pip install -e '.[async_endpoints]'` from the root directory of the NeMo Agent toolkit library. Similarly, for users installing from PyPI, this can be done by running `pip install "nvidia-nat[async_endpoints]"`.
 
 ## Evaluation Endpoint Overview
 ```{mermaid}

docs/source/workflows/evaluate.md

@@ -34,7 +34,7 @@ uv pip install -e '.[profiling]'
 
 If you are installing from a package, you can install the sub-package by running the following command:
 ```bash
-uv pip install nvidia-nat[profiling]
+uv pip install "nvidia-nat[profiling]"
 ```
 
 ## Evaluating a Workflow

docs/source/workflows/mcp/index.md

@@ -21,7 +21,7 @@ NeMo Agent toolkit [Model Context Protocol (MCP)](https://modelcontextprotocol.i
 * An [MCP client](./mcp-client.md) to connect to and use tools served by remote MCP servers.
 * An [MCP server](./mcp-server.md) to publish tools using MCP to be used by any MCP client.
 
-**Note:** MCP client functionality requires the `nvidia-nat-mcp` package. Install it with `uv pip install nvidia-nat[mcp]`.
+**Note:** MCP client functionality requires the `nvidia-nat-mcp` package. Install it with `uv pip install "nvidia-nat[mcp]"`.
 
 
 ```{toctree}

docs/source/workflows/mcp/mcp-client.md

@@ -28,7 +28,7 @@ This guide will cover how to use a NeMo Agent toolkit workflow as a MCP host wit
 MCP client functionality requires the `nvidia-nat-mcp` package. Install it with:
 
 ```bash
-uv pip install nvidia-nat[mcp]
+uv pip install "nvidia-nat[mcp]"
 ```
 ## Accessing Protected MCP Servers
 NeMo Agent toolkit can access protected MCP servers via the MCP client auth provider. For more information, see the [MCP Authentication](./mcp-auth.md) documentation.

docs/source/workflows/mcp/mcp-server.md

@@ -62,7 +62,7 @@ nat mcp serve --config_file examples/getting_started/simple_calculator/configs/c
 
 To list the tools published by the MCP server you can use the `nat mcp client tool list` command. This command acts as an MCP client and connects to the MCP server running on the specified URL (defaults to `http://localhost:9901/mcp` for streamable-http, with backwards compatibility for `http://localhost:9901/sse`).
 
-**Note:** The `nat mcp client` commands require the `nvidia-nat-mcp` package. If you encounter an error about missing MCP client functionality, install it with `uv pip install nvidia-nat[mcp]`.
+**Note:** The `nat mcp client` commands require the `nvidia-nat-mcp` package. If you encounter an error about missing MCP client functionality, install it with `uv pip install "nvidia-nat[mcp]"`.
 
 ```bash
 nat mcp client tool list

docs/source/workflows/profiler.md

@@ -41,7 +41,7 @@ uv pip install -e ".[profiling]"
 
 If you are installing from a package, you need to install the `nvidia-nat[profiling]` package by running the following command:
 ```bash
-uv pip install nvidia-nat[profiling]
+uv pip install "nvidia-nat[profiling]"
 ```
 
 ## Current Profiler Architecture

examples/MCP/simple_auth_mcp/README.md

@@ -26,7 +26,7 @@ It is recommended to read the [MCP Authentication](../../../docs/source/workflow
 1. **Agent toolkit**: Ensure you have the Agent toolkit installed. If you have not already done so, follow the instructions in the [Install Guide](../../../docs/source/quick-start/installing.md#install-from-source) to create the development environment and install NeMo Agent Toolkit.
 2. **MCP Server**: Access to an MCP server that requires authentication (e.g., corporate Jira system)
 
-**Note**: If you installed NeMo Agent toolkit from source, MCP client functionality is already included. If you installed from PyPI, you may need to install the MCP client package separately with `uv pip install nvidia-nat[mcp]`.
+**Note**: If you installed NeMo Agent toolkit from source, MCP client functionality is already included. If you installed from PyPI, you may need to install the MCP client package separately with `uv pip install "nvidia-nat[mcp]"`.
 
 ## Install this Workflow
 

examples/RAG/simple_rag/configs/milvus_memory_rag_tools_config.yml

@@ -13,7 +13,6 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
-
 memory:
   saas_memory:
     _type: mem0_memory
@@ -56,7 +55,7 @@ functions:
       The question should be about user preferences which will help you format your response.
       For example: "How does the user like responses formatted?"
 
-  # To use these tools you will need to install the nvidia-nat[langchain] package
+  # To use these tools you will need to install the "nvidia-nat[langchain]" package
   web_search_tool:
     _type: tavily_internet_search
     max_results: 5
@@ -85,11 +84,11 @@ embedders:
 workflow:
   _type: react_agent
   tool_names:
-   - cuda_retriever_tool
-   - mcp_retriever_tool
-   - add_memory
-   - get_memory
-   - web_search_tool
-   - code_generation_tool
+    - cuda_retriever_tool
+    - mcp_retriever_tool
+    - add_memory
+    - get_memory
+    - web_search_tool
+    - code_generation_tool
   verbose: true
   llm_name: nim_llm

examples/RAG/simple_rag/configs/milvus_rag_tools_config.yml

@@ -13,7 +13,6 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
-
 retrievers:
   cuda_retriever:
     _type: milvus_retriever
@@ -38,7 +37,7 @@ functions:
     retriever: mcp_retriever
     topic: Retrieve information about Model Context Protocol (MCP)
 
-  # To use these tools you will need to install the nvidia-nat[langchain] package
+  # To use these tools you will need to install the "nvidia-nat[langchain]" package
   web_search_tool:
     _type: tavily_internet_search
     max_results: 5
@@ -67,10 +66,10 @@ embedders:
 workflow:
   _type: react_agent
   tool_names:
-   - cuda_retriever_tool
-   - mcp_retriever_tool
-   - web_search_tool
-   - code_generation_tool
+    - cuda_retriever_tool
+    - mcp_retriever_tool
+    - web_search_tool
+    - code_generation_tool
   verbose: true
   llm_name: nim_llm
   additional_instructions: "If a tool call results in code or other artifacts being returned, you MUST include that in your thoughts and response."

pyproject.toml

@@ -66,7 +66,7 @@ maintainers = [{ name = "NVIDIA Corporation" }]
 
 
 [project.optional-dependencies]
-# Optional dependencies are things that users would want to install with NAT. i.e. `uv pip install nvidia-nat[langchain]`
+# Optional dependencies are things that users would want to install with NAT. i.e. `uv pip install "nvidia-nat[langchain]"`
 # Keep sorted!!!
 all = ["nvidia-nat-all"] # meta-package
 adk = ["nvidia-nat-adk"]

src/nat/agent/prompt_optimizer/register.py

@@ -51,7 +51,7 @@ async def prompt_optimizer_function(config: PromptOptimizerConfig, builder: Buil
         from .prompt import mutator_prompt
     except ImportError as exc:
         raise ImportError("langchain-core is not installed. Please install it to use MultiLLMPlanner.\n"
-                          "This error can be resolve by installing nvidia-nat[langchain]") from exc
+                          "This error can be resolve by installing \"nvidia-nat[langchain]\".") from exc
 
     llm = await builder.get_llm(config.optimizer_llm, wrapper_type=LLMFrameworkEnum.LANGCHAIN)
 
@@ -111,7 +111,7 @@ async def prompt_recombiner_function(config: PromptRecombinerConfig, builder: Bu
         from langchain_core.prompts import PromptTemplate
     except ImportError as exc:
         raise ImportError("langchain-core is not installed. Please install it to use MultiLLMPlanner.\n"
-                          "This error can be resolve by installing nvidia-nat[langchain].") from exc
+                          "This error can be resolve by installing \"nvidia-nat[langchain]\".") from exc
 
     llm = await builder.get_llm(config.optimizer_llm, wrapper_type=LLMFrameworkEnum.LANGCHAIN)
 

src/nat/profiler/decorators/framework_wrapper.py

@@ -123,7 +123,7 @@ async def wrapper(workflow_config, builder):
                 except ImportError as e:
                     logger.warning(
                         "ADK profiler not available. " +
-                        "Install NAT with ADK extras: pip install 'nvidia-nat[adk]'. Error: %s",
+                        "Install NAT with ADK extras: pip install \"nvidia-nat[adk]\". Error: %s",
                         e)
                 else:
                     handler = ADKProfilerHandler()

src/nat/profiler/forecasting/models/linear_model.py

@@ -36,7 +36,7 @@ def __init__(self):
         except ImportError:
             logger.error(
                 "scikit-learn is not installed. Please install scikit-learn to use the LinearModel "
-                "profiling model or install `nvidia-nat[profiler]` to install all necessary profiling packages.")
+                "profiling model or install \"nvidia-nat[profiler]\" to install all necessary profiling packages.")
 
             raise
 

src/nat/profiler/forecasting/models/random_forest_regressor.py

@@ -36,7 +36,7 @@ def __init__(self):
         except ImportError:
             logger.error(
                 "scikit-learn is not installed. Please install scikit-learn to use the RandomForest "
-                "profiling model or install `nvidia-nat[profiler]` to install all necessary profiling packages.")
+                "profiling model or install \"nvidia-nat[profiler]\" to install all necessary profiling packages.")
 
             raise
 

src/nat/profiler/inference_optimization/bottleneck_analysis/nested_stack_analysis.py

@@ -304,7 +304,7 @@ def save_gantt_chart(all_nodes: list[CallNode], output_path: str) -> None:
         import matplotlib.pyplot as plt
     except ImportError:
         logger.error("matplotlib is not installed. Please install matplotlib to use generate plots for the profiler "
-                     "or install `nvidia-nat[profiler]` to install all necessary profiling packages.")
+                     "or install \"nvidia-nat[profiler]\" to install all necessary profiling packages.")
 
         raise
 

src/nat/profiler/inference_optimization/experimental/prefix_span_analysis.py

@@ -212,7 +212,7 @@ def run_prefixspan(sequences_map: dict[int, list[PrefixCallNode]],
         from prefixspan import PrefixSpan
     except ImportError:
         logger.error("prefixspan is not installed. Please install prefixspan to run the prefix analysis in the "
-                     "profiler or install `nvidia-nat[profiler]` to install all necessary profiling packages.")
+                     "profiler or install \"nvidia-nat[profiler]\" to install all necessary profiling packages.")
 
         raise