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Version: DEV

Agent component

The component equipped with reasoning, tool usage, and multi-agent collaboration capabilities.

An Agent component fine-tunes the LLM and sets its prompt. From v0.20.0 onwards, an Agent component is able to work independently and with the following capabilities:

  • Autonomous reasoning with reflection and adjustment based on environmental feedback.
  • Use of tools or subagents to complete tasks.

Scenarios​

An Agent component is essential when you need the LLM to assist with summarizing, translating, or controlling various tasks.

Configurations​

Model​

Click the dropdown menu of Model to show the model configuration window.

  • Model: The chat model to use.
    • Ensure you set the chat model correctly on the Model providers page.
    • You can use different models for different components to increase flexibility or improve overall performance.
  • Freedom: A shortcut to Temperature, Top P, Presence penalty, and Frequency penalty settings, indicating the freedom level of the model. From Improvise, Precise, to Balance, each preset configuration corresponds to a unique combination of Temperature, Top P, Presence penalty, and Frequency penalty.
    This parameter has three options:
    • Improvise: Produces more creative responses.
    • Precise: (Default) Produces more conservative responses.
    • Balance: A middle ground between Improvise and Precise.
  • Temperature: The randomness level of the model's output.
    Defaults to 0.1.
    • Lower values lead to more deterministic and predictable outputs.
    • Higher values lead to more creative and varied outputs.
    • A temperature of zero results in the same output for the same prompt.
  • Top P: Nucleus sampling.
    • Reduces the likelihood of generating repetitive or unnatural text by setting a threshold P and restricting the sampling to tokens with a cumulative probability exceeding P.
    • Defaults to 0.3.
  • Presence penalty: Encourages the model to include a more diverse range of tokens in the response.
    • A higher presence penalty value results in the model being more likely to generate tokens not yet been included in the generated text.
    • Defaults to 0.4.
  • Frequency penalty: Discourages the model from repeating the same words or phrases too frequently in the generated text.
    • A higher frequency penalty value results in the model being more conservative in its use of repeated tokens.
    • Defaults to 0.7.
  • Max tokens:
NOTE
  • It is not necessary to stick with the same model for all components. If a specific model is not performing well for a particular task, consider using a different one.
  • If you are uncertain about the mechanism behind Temperature, Top P, Presence penalty, and Frequency penalty, simply choose one of the three options of Preset configurations.

System prompt​

Typically, you use the system prompt to describe the task for the LLM, specify how it should respond, and outline other miscellaneous requirements. We do not plan to elaborate on this topic, as it can be as extensive as prompt engineering. However, please be aware that the system prompt is often used in conjunction with keys (variables), which serve as various data inputs for the LLM.

IMPORTANT

An Agent component relies on keys (variables) to specify its data inputs. Its immediate upstream component is not necessarily its data input, and the arrows in the workflow indicate only the processing sequence. Keys in a Agent component are used in conjunction with the system prompt to specify data inputs for the LLM. Use a forward slash / or the (x) button to show the keys to use.

User prompt​

The user-defined prompt. Defaults to sys.query, the user query.

Tools​

You can use an Agent component as a collaborator that reasons and reflects with the aid of other tools; for instance, Retrieval can serve as one such tool for an Agent.

Agent​

You use an Agent component as a collaborator that reasons and reflects with the aid of subagents or other tools, forming a multi-agent system.

Message window size​

An integer specifying the number of previous dialogue rounds to input into the LLM. For example, if it is set to 12, the tokens from the last 12 dialogue rounds will be fed to the LLM. This feature consumes additional tokens.

IMPORTANT

This feature is used for multi-turn dialogue only.

Max retrieves​

Defines the maximum number of attempts the agent will make to retry a failed task or operation before stopping or reporting failure.

Delay after error​

The waiting period in seconds that the agent observes before retrying a failed task, helping to prevent immediate repeated attempts and allowing system conditions to improve. Defaults to 1 second.

Max rounds​

Defines the maximum number reflection rounds of the selected chat model. Defaults to 5 rounds.

Output​

The global variable name for the output of the Agent component, which can be referenced by other components in the workflow.