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agenthub.planner_agent.agent

PlannerAgent Objects​

class PlannerAgent(Agent)

VERSION​

The planner agent utilizes a special prompting strategy to create long term plans for solving problems. The agent is given its previous action-observation pairs, current task, and hint based on last action taken at every step.

__init__​

def __init__(llm: LLM)

Initialize the Planner Agent with an LLM

Arguments:

  • llm (LLM): The llm to be used by this agent

step​

def step(state: State) -> Action

Checks to see if current step is completed, returns AgentFinishAction if True. Otherwise, creates a plan prompt and sends to model for inference, returning the result as the next action.

Arguments:

  • state (State): The current state given the previous actions and observations

Returns:

  • AgentFinishAction: If the last state was 'completed', 'verified', or 'abandoned'
  • Action: The next action to take based on llm response