Use Memories ​

As discussed in Concepts, Memory is one of the core components of an agent. Agent needs memory to have an essential context for making decision or perform actions. It also need memory to learn skills or accumulate experience. In this tutorial, we cover basic memory usage.

After this tutorial, you will be able to:

1. Understand what memories are like in MetaGPT
2. How to add or retrieve memories

What are memories like ​

Class Memory is the abstraction for an agent's memory in MetaGPT. When initialized, Role acquire its Memory as self._rc.memory, which will store every Message it later _observe in a list for future retrieval. The initialization and storage are handled by the framework. In short, memories of a Role are a list of Messages.

Retrieve memory ​

When recorded memories are needed, such as serving as context for a LLM call, you can use self.get_memories. The function definition is as follows:

def get_memories(self, k=0) -> list[Message]:
    """A wrapper to return the most recent k memories of this role, return all when k=0"""
    return self._rc.memory.get(k=k)

def get_memories(self, k=0) -> list[Message]:
    """A wrapper to return the most recent k memories of this role, return all when k=0"""
    return self._rc.memory.get(k=k)

For example, in MultiAgent101, we call this function to provide the tester with the full history. In this way, if the reviewer provides feedback, the tester can modify test cases with reference to their previous version. The snippet is as follows

async def _act(self) -> Message:
        logger.info(f"{self._setting}: ready to {self._rc.todo}")
        todo = self._rc.todo

        # context = self.get_memories(k=1)[0].content # use the most recent memory as context
        context = self.get_memories() # use all memories as context

        code_text = await todo.run(context, k=5) # specify arguments

        msg = Message(content=code_text, role=self.profile, cause_by=type(todo))

        return msg

async def _act(self) -> Message:
        logger.info(f"{self._setting}: ready to {self._rc.todo}")
        todo = self._rc.todo

        # context = self.get_memories(k=1)[0].content # use the most recent memory as context
        context = self.get_memories() # use all memories as context

        code_text = await todo.run(context, k=5) # specify arguments

        msg = Message(content=code_text, role=self.profile, cause_by=type(todo))

        return msg

Add memory ​

For adding memories, one can use self._rc.memory.add(msg) where msg must be an instance of Message. Check the snippet above for an example usage.

It is recommended to add Messages of action output to the Role's memory when defining the _act logic. Role normally needs to remember what it said or did previously in order to take a next step.

Next step ​

Memory is a huge topic in agents. To be precise, the memory this tutorial talks about corresponds to the concept of "short-term memory". The retrieval is also based on simple recency. However, there are multiple branches of memories as well as a wide range of memory generation and retrieval techniques. Please consult Memory for using memory to really boost your agent's performance.