# Sequential Agent The **Sequential Agent** in THub is a component within the AgentFlow V1 architecture designed to execute a series of tasks in a predetermined order. This setup is particularly useful for workflows that require a structured sequence of operations, such as data collection, processing, and response generation. #### 1) **Agent Node** The **Agent Node** serves as the central decision-making component within a workflow. It leverages a Large Language Model (LLM) to process inputs, access tools, and retrieve information from knowledge bases to generate context-aware responses. **Key Features:** * **Tool Integration**: Configure which tools the agent can utilize. * **Knowledge Access**: Connect to document stores or vector databases for information retrieval. * **Memory Management**: Enable or disable memory to consider past interactions. * **Flow State Updates**: Modify the workflow's runtime state (`$flow.state`) during execution. **Inputs**: Data from prior nodes or initial triggers. **Outputs**: Agent-generated responses, either as plain text or structured JSON.
#### **2)Condition Node** The **Condition Node** introduces deterministic branching logic, allowing the workflow to diverge based on specified conditions. **Key Features:** * **Conditional Evaluation**: Assess input values using logical operators (e.g., equals, contains). * **Branching Paths**: Direct the workflow along different routes based on evaluation results. **Inputs**: Data to be evaluated. **Outputs**: Branches corresponding to condition outcomes (e.g., true or false).
#### **3)Condition Agent** The Condition agent is an agent that uses a large language model (LLM) to decide which actions to take based on specific conditions or criteria. This means the agent's behavior isn't solely determined by a predefined workflow, but it uses its reasoning capabilities to select the most appropriate course of action based on the current state and input.
#### **4)Custom JS Function Node** The **Custom JS Function Node** allows the execution of bespoke JavaScript code within the workflow, enabling complex data transformations or integrations. **Key Features:** * **Custom Logic**: Implement specific operations not covered by standard nodes. * **Access to Flow Context**: Utilize variables like `$flow.state`, `$flow.input`, and custom-defined variables. * **Flow State Updates**: Modify the workflow's runtime state based on function outcomes. **Inputs**: Defined input variables and access to flow context. **Outputs**: String value returned by the executed JavaScript function.
#### **5)END Node** The **END Node** signifies the termination point of a workflow or a specific branch within it. **Key Features:** * **Final Output**: Deliver a concluding message to the user, which can be static or dynamically generated. * **Workflow Termination**: No further nodes are processed beyond this point. **Inputs**: Message content from previous nodes or flow state. **Outputs**: None; this node concludes the execution path.
#### 6) **Execute Flow Node** The **Execute Flow Node** enables the invocation of another complete THub Chatflow or AgentFlow from within the current workflow, promoting modular design. **Key Features:** * **Sub-Workflow Execution**: Trigger separate, pre-existing workflows. * **Input Passing**: Provide initial inputs to the invoked workflow. * **Flow State Updates**: Incorporate outputs from the executed sub-flow into the current workflow's state. **Inputs**: Execution signal and input data for the sub-flow. **Outputs**: Results returned by the executed sub-flow.
#### 7)**LLM Node** The **LLM Node** provides direct access to a configured Large Language Model for executing AI tasks, such as data extraction or content generation. **Key Features:** * **Structured Output**: Configure the LLM to return responses in a specific JSON schema. * **Flow State Updates**: Store LLM outputs in the workflow's runtime state for downstream use. **Inputs**: Data from the workflow's initial trigger or preceding nodes. **Outputs**: LLM-generated responses, either as plain text or structured data.
#### **8)Loop Node** The **Loop Node** introduces controlled cycles within the workflow, enabling iterative processes where a sequence of nodes is executed multiple times based on defined conditions. **Key Features:** * **Iteration Control**: Define the maximum number of loop iterations to prevent infinite cycles. * **Loop Back Target**: Specify the node to which the workflow should return after each iteration. **Inputs**: Execution signal to activate the loop. **Outputs**: Redirects execution flow back to the specified node.
#### **9)Start Node** The **Start Node** is the designated entry point for initiating any AgentFlow V2 workflow execution. **Key Features:** * **Input Configuration**: Determine how the workflow is triggered, either by chat input or a submitted form. * **Flow State Initialization**: Define the initial key-value pairs for the workflow's runtime state. * **Memory Settings**: Configure whether to consider past messages from the conversation thread. **Inputs**: Initial data that triggers the workflow. **Outputs**: Passes along the initial input data and the initialized Flow State to the first operational node.
#### 10) State Node The **State Node** provides a mechanism for storing and managing intermediate data within the workflow, allowing information to be shared across different nodes. **Key Features:** • **Data Storage**: Stores intermediate values during workflow execution.\ • **State Persistence:** Maintains data across multiple steps in the flow.\ • **Flexible Usage:** Can store different types of data as required.\ • **Workflow Integration:** Enables smooth data sharing between nodes. **Inputs:** Necessary data or values to be stored, usually coming from previous node outputs. **Outputs:** Stored state data that can be accessed and used by subsequent nodes in the workflow.
#### 11) **Tool Node** The **Tool Node** provides a mechanism for directly and deterministically executing a specific, pre-defined Flowise Tool within the workflow sequence. **Key Features:** * **Deterministic Execution**: Executes the selected tool without involving LLM reasoning for tool selection. * **Input Argument Mapping**: Define how data from your workflow is supplied to the selected tool. * **Flow State Updates**: Store the tool's output in the workflow's runtime state for use in subsequent nodes. **Inputs**: Necessary data for the tool's arguments, sourced from previous node outputs or flow state. **Outputs**: Raw output generated by the executed tool.
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