82f934d4cd
* rearrange workspace_dir and max_step as arguments to controller * remove unused output * abstract each action into dataclass * move actions * fix action import * move cmd manager and change method to private * move controller * rename action folder * add state * a draft of Controller & new agent abstraction * add agent actions * remove controller file * add observation to perform a refractor on langchains agent * revert to make this compatible via translation * fix typo and translate error * add error to observation * index thought as dict * refractor controller * fix circular dependency caused by type hint * add runnable attribute to agent * add mixin to denote executable * change baseclass * make file read/write action compatible w/ docker directory * remove event * fix some merge issue * fix sandbox w/ permission issue * cleanup history abstraction since langchains agent is not really using it * tweak to make langchains agent working * make all actions return observation * fix missing import * add echo action for agent * add error code to cmd output obs * make cmd manager returns cmd output obs * fix codeact agent to make it work * fix all ruff issue * fix mypy * add import agenthub back * add message for Action attribute (migrate from previous event) * fix typo * fix instruction setting * fix instruction setting * attempt to fix session * ruff fix * add .to_dict method for base and observation * add message for recall * try to simplify the state_updated_info with tuple of action and obs * update_info to Tuple[Action, Observation] * make codeact agent and langchains compatible with Tuple[Action, Observation] * fix ruff * fix ruff * change to base path to fix minimal langchains agent * add NullAction to potentially handle for chat scenario * Update opendevin/controller/command_manager.py Co-authored-by: Robert Brennan <accounts@rbren.io> * fix event args * set the default workspace to "workspace" * make directory relative (so it does not show up to agent in File*Action) * fix typo * await to yield for sending observation * fix message format --------- Co-authored-by: Robert Brennan <accounts@rbren.io>
Agent Framework Research
In this folder, there may exist multiple implementations of Agent that will be used by the
For example, agenthub/langchain_agent, agenthub/metagpt_agent, agenthub/codeact_agent, etc.
Contributors from different backgrounds and interests can choose to contribute to any (or all!) of these directions.
Constructing an Agent
Your agent must implement the following methods:
step
def step(self, cmd_mgr: CommandManager) -> Event:
step moves the agent forward one step towards its goal. This probably means
sending a prompt to the LLM, then parsing the response into an action Event.
Each Event has an action and a dict of args. Supported Events include:
read- reads the contents of a file. Arguments:path- the path of the file to read
write- writes the contents to a file. Arguments:path- the path of the file to writecontents- the contents to write to the file
run- runs a command. Arguments:command- the command to runbackground- if true, run the command in the background, so that other commands can be run concurrently. Useful for e.g. starting a server. You won't be able to see the logs. You don't need to end the command with&, just set this to true.
kill- kills a background commandid- the ID of the background command to kill
browse- opens a web page. Arguments:url- the URL to open
recall- recalls a past memory. Arguments:query- the query to search for
think- make a plan, set a goal, or record your thoughts. Arguments:thought- the thought to record
finish- if you're absolutely certain that you've completed your task and have tested your work, use the finish action to stop working.
For Events like read and run, a follow-up event will be added via add_event with the output.
add_event
def add_event(self, event: Event) -> None:
add_event adds an event to the agent's history. This could be a user message,
an action taken by the agent, log output, file contents, or anything else.
You'll probably want to keep a history of events, and use them in your prompts so that the agent knows what it did recently. You may also want to keep events in a vector database so the agent can refer back to them.
The output of step will automatically be passed to this method.
search_memory
def search_memory(self, query: str) -> List[str]:
search_memory should return a list of events that match the query. This will be used
for the recall action.