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 write
  • contents - the contents to write to the file
• run - runs a command. Arguments:
  • command - the command to run
  • background - 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 command
  • id - 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.