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* docs(docs): start implementing docs website * update video url * add autogenerated codebase docs for backend * precommit * update links * fix config and video * gh actions * rename * workdirs * path * path * fix doc1 * redo markdown * docs * change main folder name * simplify readme * add back architecture * Fix lint errors * lint * update poetry lock --------- Co-authored-by: Jim Su <jimsu@protonmail.com>
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1.0 KiB
sidebar_label, title
| sidebar_label | title |
|---|---|
| agent | agenthub.delegator_agent.agent |
DelegatorAgent Objects
class DelegatorAgent(Agent)
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 Delegator 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