In NeMo Agent Toolkit, a workflow defines which functions and models are used to perform a given task or series of tasks. A workflow definition is specified in a YAML configuration file. The workflow section of the configuration file defines the workflow itself, and specifies a function, typically an agent, which will orchestrate which functions and models are called to complete the given task.
The workflow configuration file is a YAML file that specifies the tools and models to use in a workflow, along with general configuration settings. This section examines the configuration of the examples/getting_started/simple_web_query workflow to show how they are organized.
examples/getting_started/simple_web_query/configs/config.yml:
functions:
webpage_query:
_type: webpage_query
webpage_url: https://docs.smith.langchain.com
description: "Search for information about LangSmith. For any questions about LangSmith, you must use this tool!"
embedder_name: nemotron-3-embed-1b
chunk_size: 512
current_datetime:
_type: current_datetime
llms:
nim_llm:
_type: nim
model_name: nvidia/nemotron-3-super-120b-a12b
temperature: 0.0
embedders:
nemotron-3-embed-1b:
_type: nim
model_name: nvidia/nemotron-3-embed-1b
workflow:
_type: react_agent
tool_names: [webpage_query, current_datetime]
llm_name: nim_llm
verbose: true
parse_agent_response_max_retries: 3This workflow configuration is divided into four sections: functions, llms, embedders, and workflow. The functions section contains the tools used in the workflow, while llms and embedders define the models used in the workflow, and lastly the workflow section ties the other sections together and defines the workflow itself.
In this workflow, the webpage_query tool queries the LangSmith User Guide, and the current_datetime tool gets the current date and time. The description entry instructs the LLM when and how to use the tool. In this case, the workflow explicitly defines description for the webpage_query tool.
The webpage_query tool uses the nemotron-3-embed-1b embedder, which is defined in the embedders section.
The workflow itself is typically an agent, however any NeMo Agent Toolkit function can be used as a workflow. Refer to the Agents documentation for more details on the agents that are included in NeMo Agent Toolkit.
For details on workflow configuration, including sections not utilized in the above example, refer to the Workflow Configuration document.
Understanding these concepts will help you build workflows effectively.
A workflow defines which functions and models are used to perform a given task or series of tasks. The workflow section of the configuration file defines the workflow itself, and specifies a function, typically an agent, which will orchestrate which functions and models are called to complete the given task.
A workflow definition is specified in a YAML configuration file. The file specifies the tools and models to use in a workflow, along with general configuration settings, organized into the functions, llms, embedders, and workflow sections.
The functions section contains the tools used in the workflow. The description entry instructs the LLM when and how to use the tool.
The workflow itself is typically an agent, however any NeMo Agent Toolkit function can be used as a workflow. Refer to the Agents documentation for more details on the agents that are included in NeMo Agent Toolkit.
Control flow components are offered by NeMo Agent Toolkit to direct how a workflow runs, including the Router Agent and the Sequential Executor.
The following are agents offered by NeMo Agent Toolkit. Choose the approach that best fits your needs.
- Automatic Memory Wrapper Agent — Wraps any agent to provide automatic memory capture and retrieval without requiring the LLM to invoke memory tools explicitly.
- ReAct Agent — Performs ReAct (Reasoning and Acting) reasoning between tool calls.
- Reasoning Agent — Reasons ahead of time through planning rather than between steps (requires an LLM that supports reasoning).
- ReWOO Agent — Decouples reasoning from observations to improve tool usage and token efficiency for reasoning tasks.
- Responses API and Agent — Use tool with OpenAI's Responses API, including built-in tools, MCP remote tools, and NeMo Agent Toolkit tools.
- Tool Calling Agent — Directly invokes external tools based on structured function definitions (requires an LLM with tool-calling support).
The following are control flow components offered by NeMo Agent Toolkit. Use the following comparison to select the right component.
| Factor | Router Agent | Sequential Executor |
|---|---|---|
| What it does | Analyzes incoming requests and directs them to the most appropriate branch based on the request configuration. | Chains multiple functions together, where each function's output becomes the input for the next function. |
| How it runs | Pairs a single-pass architecture with intelligent request routing to analyze prompts and select one branch that best handles the request. | Creates a linear tool execution pipeline that executes functions in a predetermined sequence without requiring LLMs or agents for orchestration. |
| Best For | Scenarios where different types of requests need specialized handling. | Linear pipelines where each function feeds the next; supports better error handling and optional compatibility validation. |