Agent components in LLM Controls

Agent components define the behavior and capabilities of AI agents in your flow. Agents use LLMs as a reasoning engine to decide which of the connected tool components to use to solve a problem. Tools in agentic functions are essentially functions that the agent can call to perform tasks or access external resources. A function is wrapped as a Tool object with a common interface that the agent understands. Agents become aware of tools through tool registration, where the agent is provided a list of available tools typically at agent initialization. The Tool object’s description tells the agent what the tool can do. The agent then uses a connected LLM to reason through the problem to decide which tool is best for the job.

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Use an agent in a flow​

The simple agent starter project uses an agent component connected to a ,URL and Calculator tools to answer a user’s questions. The OpenAI LLM acts as a brain for the agent to decide which tool to use. Tools are connected to agent components at the Tools port.

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Agent component​

This component creates an agent that can use tools to answer questions and perform tasks based on given instructions. The component includes an LLM model integration, a system message prompt, and a Tools port to connect tools to extend its capabilities. For more information on this component,

Parameters

Name	Type	Description
agent_llm	Dropdown	The provider of the language model that the agent uses to generate responses. Options include OpenAI and other providers or Custom.
system_prompt	String	The system prompt provides initial instructions and context to guide the agent’s behavior.
tools	List	The list of tools available for the agent to use. This field is optional and can be empty.
input_value	String	The input task or question for the agent to process.
add_current_date_tool	Boolean	When true this adds a tool to the agent that returns the current date.
memory	Memory	An optional memory configuration for maintaining conversation history.
max_iterations	Integer	The maximum number of iterations the agent can perform.
handle_parsing_errors	Boolean	This determines whether to handle parsing errors during agent execution.
verbose	Boolean	This enables verbose output for detailed logging.

Outputs

Name	Type	Description
response	Message	The agent’s response to the given input task.

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JSON Agent​

This component creates a JSON agent from a JSON or YAML file and an LLM.

Parameters

Name	Type	Description
llm	LanguageModel	The language model to use for the agent.
path	File	The path to the JSON or YAML file.

Outputs

Name	Type	Description
agent	AgentExecutor	The JSON agent instance.

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Vector Store Agent​

This component creates a Vector Store Agent using LangChain.

Parameters

Inputs

Name	Type	Description
llm	LanguageModel	The language model to use for the agent.
vectorstore	VectorStoreInfo	The vector store information for the agent to use.

Outputs

Name	Type	Description
agent	AgentExecutor	The Vector Store Agent instance.

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Vector Store Router Agent​

This component creates a Vector Store Router Agent using LangChain.

Parameters

Inputs

Name	Type	Description
llm	LanguageModel	The language model to use for the agent.
vectorstores	List[VectorStoreInfo]	The list of vector store information for the agent to route between.

Outputs

Name	Type	Description
agent	AgentExecutor	The Vector Store Router Agent instance.