The Monologue Agent utilizes long and short term memory to complete tasks. Long term memory is stored as a LongTermMemory object and the model uses it to search for examples from the past. Short term memory is stored as a Monologue object and the model can condense it as necessary.
Action,
NullAction,
CmdRunAction,
FileWriteAction,
FileReadAction,
AgentRecallAction,
BrowseURLAction,
AgentThinkAction
Observation,
NullObservation,
CmdOutputObservation,
FileReadObservation,
AgentRecallObservation,
BrowserOutputObservation
__init__: Initializes the agent with a long term memory, and an internal monologue
_add_event: Appends events to the monologue of the agent and condenses with summary automatically if the monologue is too long
_initialize: Utilizes the INITIAL_THOUGHTS list to give the agent a context for its capabilities and how to navigate the /workspace
step: Modifies the current state by adding the most rescent actions and observations, then prompts the model to think about its next action to take.
search_memory: Uses VectorIndexRetriever to find related memories within the long term memory.
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.
NullAction,
CmdRunAction,
CmdKillAction,
BrowseURLAction,
FileReadAction,
FileWriteAction,
AgentRecallAction,
AgentThinkAction,
AgentFinishAction,
AgentSummarizeAction,
AddTaskAction,
ModifyTaskAction,
Observation,
NullObservation,
CmdOutputObservation,
FileReadObservation,
AgentRecallObservation,
BrowserOutputObservation
__init__: Initializes an agent with llm
step: Checks to see if current step is completed, returns AgentFinishAction if True. Otherwise, creates a plan prompt and sends to model for inference, adding the result as the next action.
search_memory: Not yet implemented
The Code Act Agent is a minimalist agent. The agent works by passing the model a list of action-observation pairs and prompting the model to take the next step.
Action,
CmdRunAction,
AgentEchoAction,
AgentFinishAction,
CmdOutputObservation,
AgentMessageObservation,
__init__: Initializes an agent with llm and a list of messages List[Mapping[str, str]]
step: First, gets messages from state and then compiles them into a list for context. Next, pass the context list with the prompt to get the next command to execute. Finally, Execute command if valid, else return AgentEchoAction(INVALID_INPUT_MESSAGE)
search_memory: Not yet implemented