OpenHands/docs/Agents.md

Agents and Capabilities

Monologue Agent:

Description:

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.

Actions:

Action, NullAction, CmdRunAction, FileWriteAction, FileReadAction, AgentRecallAction, BrowseURLAction, AgentThinkAction

Observations:

Observation, NullObservation, CmdOutputObservation, FileReadObservation, AgentRecallObservation, BrowserOutputObservation

Methods:

__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 recent 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.

Planner Agent:

Description:

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.

Actions:

NullAction, CmdRunAction, CmdKillAction, BrowseURLAction, FileReadAction, FileWriteAction, AgentRecallAction, AgentThinkAction, AgentFinishAction, AgentSummarizeAction, AddTaskAction, ModifyTaskAction,

Observations:

Observation, NullObservation, CmdOutputObservation, FileReadObservation, AgentRecallObservation, BrowserOutputObservation

Methods:

__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

CodeAct Agent:

Description:

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.

Actions:

Action, CmdRunAction, AgentEchoAction, AgentFinishAction,

Observations:

CmdOutputObservation, AgentMessageObservation,

Methods:

__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