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* doc: Guide for using local LLM with Ollama * forgot to delete print statement * typos * Updated guide - new working method * Move to docs folder * Fixed front end overwrite local model name * Update llm.py * Delete docs/examples/images/example.png deleted example.png * Documentation of agent differences * rename examples to documentation * Docstrings for all agents * typo fix * typo fixes * Typo fixes * more typo fixes * typo fix * typo fixes * typos fixed * Typo fixes * top 10 list * typo fix * typo fix * typos to the moon * typos fixed * typo fix * typo fix * anotha one * The rest of the typos * Corrected agent descriptions * Agents markdown updated --------- Co-authored-by: Robert Brennan <accounts@rbren.io>
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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 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.
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-observaiton 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