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Available Components

Agent Node

Executes tools and manages complex interactions within the conversational AI system

Condition Agent Node

Uses an agent (typically an LLM) to evaluate the current state and decide on the next action

Condition Node

Evaluates conditions to determine the next step in the workflow

End Node

Signifies the termination point of a conversation or process flow

LLM Node

Runs a Chat Model and returns its output, serving as an intermediary step for reasoning and decision-making

Loop Node

Creates loops within the workflow, allowing for repetitive or conditional execution of certain steps

Start Node

Initializes the conversation flow and sets up essential components

State Node

Provides a centralized state object that can be updated and passed between nodes in the graph

Tool Node

Executes specific tools and returns their output within the sequential workflow

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Use Cases

Sequential Agents are beneficial for a wide range of complex, multi-step processes:

  1. Advanced Chatbots: Create chatbots that can handle multi-turn conversations, maintain context, and make decisions based on user input and previous interactions.

  2. Decision Trees: Implement sophisticated decision-making processes that can adapt based on user responses or external data.

  3. Workflow Automation: Design complex workflows that involve multiple steps, conditional branching, and integration with various tools and APIs.

  4. Information Gathering: Create agents that can collect and synthesize information from multiple sources, making decisions on what to query next based on previous results.

  5. Problem-Solving Agents: Develop agents that can break down complex problems into smaller steps, using different tools and reasoning processes to arrive at a solution.

  6. Interactive Tutorials or Guides: Build adaptive learning experiences that adjust based on user progress and responses.

  7. Customer Support Systems: Create intelligent support systems that can handle complex queries, route issues to appropriate departments, and provide step-by-step assistance.

  8. Data Analysis Pipelines: Construct workflows that can process, analyze, and make decisions based on large datasets, using various analytical tools and models.

By combining these components, developers can create highly flexible and powerful agent-based systems capable of handling a wide variety of tasks and scenarios.

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