Agents

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 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.

Use an agent in a flow​

The simple agent starter project uses an agent component connected to 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.

Simple agent starter flow

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.

Inputs​

Name	Type	Description
agent_llm	Dropdown	The provider of the language model that the agent will use to generate responses. Options include OpenAI and other providers, or Custom.
system_prompt	String	System Prompt: Initial instructions and context provided to guide the agent's behavior.
tools	List	List of tools available for the agent to use.
input_value	String	The input task or question for the agent to process.
add_current_date_tool	Boolean	If true, adds a tool to the agent that returns the current date.
memory	Memory	Optional memory configuration for maintaining conversation history.
max_iterations	Integer	Maximum number of iterations the agent can perform.
handle_parsing_errors	Boolean	Whether to handle parsing errors during agent execution.
verbose	Boolean	Enables verbose output for detailed logging.

Outputs​

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

CSV Agent​

This component creates a CSV agent from a CSV file and LLM.

Inputs​

Name	Type	Description
llm	LanguageModel	Language model to use for the agent
path	File	Path to the CSV file
agent_type	String	Type of agent to create (zero-shot-react-description, openai-functions, or openai-tools)

Outputs​

Name	Type	Description
agent	AgentExecutor	CSV agent instance

CrewAI Agent​

This component represents an Agent of CrewAI, allowing for the creation of specialized AI agents with defined roles, goals, and capabilities within a crew.

Inputs​

Name	Display Name	Info
role	Role	The role of the agent
goal	Goal	The objective of the agent
backstory	Backstory	The backstory of the agent
tools	Tools	Tools at agent's disposal
llm	Language Model	Language model that will run the agent
memory	Memory	Whether the agent should have memory or not
verbose	Verbose	Enables verbose output
allow_delegation	Allow Delegation	Whether the agent is allowed to delegate tasks to other agents
allow_code_execution	Allow Code Execution	Whether the agent is allowed to execute code
kwargs	kwargs	Additional keyword arguments for the agent

Outputs​

Name	Display Name	Info
output	Agent	The constructed CrewAI Agent object

Hierarchical Crew​

This component represents a group of agents, managing how they should collaborate and the tasks they should perform in a hierarchical structure. This component allows for the creation of a crew with a manager overseeing the task execution.

Inputs​

Name	Display Name	Info
agents	Agents	List of Agent objects representing the crew members
tasks	Tasks	List of HierarchicalTask objects representing the tasks to be executed
manager_llm	Manager LLM	Language model for the manager agent (optional)
manager_agent	Manager Agent	Specific agent to act as the manager (optional)
verbose	Verbose	Enables verbose output for detailed logging
memory	Memory	Specifies the memory configuration for the crew
use_cache	Use Cache	Enables caching of results
max_rpm	Max RPM	Sets the maximum requests per minute
share_crew	Share Crew	Determines if the crew information is shared among agents
function_calling_llm	Function Calling LLM	Specifies the language model for function calling

Outputs​

Name	Display Name	Info
crew	Crew	The constructed Crew object with hierarchical task execution

JSON Agent​

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

Inputs​

Name	Type	Description
llm	LanguageModel	Language model to use for the agent
path	File	Path to the JSON or YAML file

Outputs​

Name	Type	Description
agent	AgentExecutor	JSON agent instance

OpenAI Tools Agent​

This component creates an OpenAI Tools Agent using LangChain.

Inputs​

Name	Type	Description
llm	LanguageModel	Language model to use for the agent (must be tool-enabled)
system_prompt	String	System prompt for the agent
user_prompt	String	User prompt template (must contain 'input' key)
chat_history	List[Data]	Optional chat history for the agent
tools	List[Tool]	List of tools available to the agent

Outputs​

Name	Type	Description
agent	AgentExecutor	OpenAI Tools Agent instance

OpenAPI Agent​

This component creates an OpenAPI Agent to interact with APIs defined by OpenAPI specifications.

Inputs​

Name	Type	Description
llm	LanguageModel	Language model to use for the agent
path	File	Path to the OpenAPI specification file (JSON or YAML)
allow_dangerous_requests	Boolean	Whether to allow potentially dangerous API requests

Outputs​

Name	Type	Description
agent	AgentExecutor	OpenAPI Agent instance

SQL Agent​

This component creates a SQL Agent to interact with SQL databases.

Inputs​

Name	Type	Description
llm	LanguageModel	Language model to use for the agent
database_uri	String	URI of the SQL database to connect to
extra_tools	List[Tool]	Additional tools to provide to the agent (optional)

Outputs​

Name	Type	Description
agent	AgentExecutor	SQL Agent instance

Sequential Crew​

This component represents a group of agents with tasks that are executed sequentially. This component allows for the creation of a crew that performs tasks in a specific order.

Inputs​

Name	Display Name	Info
tasks	Tasks	List of SequentialTask objects representing the tasks to be executed
verbose	Verbose	Enables verbose output for detailed logging
memory	Memory	Specifies the memory configuration for the crew
use_cache	Use Cache	Enables caching of results
max_rpm	Max RPM	Sets the maximum requests per minute
share_crew	Share Crew	Determines if the crew information is shared among agents
function_calling_llm	Function Calling LLM	Specifies the language model for function calling

Outputs​

Name	Display Name	Info
crew	Crew	The constructed Crew object with sequential task execution

Sequential task agent​

This component creates a CrewAI Task and its associated Agent, allowing for the definition of sequential tasks with specific agent roles and capabilities.

Inputs​

Name	Display Name	Info
role	Role	The role of the agent
goal	Goal	The objective of the agent
backstory	Backstory	The backstory of the agent
tools	Tools	Tools at agent's disposal
llm	Language Model	Language model that will run the agent
memory	Memory	Whether the agent should have memory or not
verbose	Verbose	Enables verbose output
allow_delegation	Allow Delegation	Whether the agent is allowed to delegate tasks to other agents
allow_code_execution	Allow Code Execution	Whether the agent is allowed to execute code
agent_kwargs	Agent kwargs	Additional kwargs for the agent
task_description	Task Description	Descriptive text detailing task's purpose and execution
expected_output	Expected Task Output	Clear definition of expected task outcome
async_execution	Async Execution	Boolean flag indicating asynchronous task execution
previous_task	Previous Task	The previous task in the sequence (for chaining)

Outputs​

Name	Display Name	Info
task_output	Sequential Task	List of SequentialTask objects representing the created tasks

Tool Calling Agent​

This component creates a Tool Calling Agent using LangChain.

Inputs​

Name	Type	Description
llm	LanguageModel	Language model to use for the agent
system_prompt	String	System prompt for the agent
user_prompt	String	User prompt template (must contain 'input' key)
chat_history	List[Data]	Optional chat history for the agent
tools	List[Tool]	List of tools available to the agent

Outputs​

Name	Type	Description
agent	AgentExecutor	Tool Calling Agent instance

Vector Store Agent​

This component creates a Vector Store Agent using LangChain.

Inputs​

Name	Type	Description
llm	LanguageModel	Language model to use for the agent
vectorstore	VectorStoreInfo	Vector store information for the agent to use

Outputs​

Name	Type	Description
agent	AgentExecutor	Vector Store Agent instance

Vector Store Router Agent​

This component creates a Vector Store Router Agent using LangChain.

Inputs​

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

Outputs​

Name	Type	Description
agent	AgentExecutor	Vector Store Router Agent instance

XML Agent​

This component creates an XML Agent using LangChain.

The agent uses XML formatting for tool instructions to the Language Model.

Inputs​

Name	Type	Description
llm	LanguageModel	Language model to use for the agent
user_prompt	String	Custom prompt template for the agent (includes XML formatting instructions)
tools	List[Tool]	List of tools available to the agent

Outputs​

Name	Type	Description
agent	AgentExecutor	XML Agent instance