Memory

from camel.memories import (
    ChatHistoryBlock,
    LongtermAgentMemory,
    MemoryRecord,
    ScoreBasedContextCreator,
    VectorDBBlock,
)
from camel.messages import BaseMessage
from camel.types import ModelType, OpenAIBackendRole
from camel.utils import OpenAITokenCounter

# Initialize the memory
memory = LongtermAgentMemory(
    context_creator=ScoreBasedContextCreator(
        token_counter=OpenAITokenCounter(ModelType.GPT_4O_MINI),
        token_limit=1024,
    ),
    chat_history_block=ChatHistoryBlock(),
    vector_db_block=VectorDBBlock(),
)

# Create and write new records
records = [
    MemoryRecord(
        message=BaseMessage.make_user_message(
            role_name="User",
            meta_dict=None,
            content="What is CAMEL AI?",
        ),
        role_at_backend=OpenAIBackendRole.USER,
    ),
    MemoryRecord(
        message=BaseMessage.make_assistant_message(
            role_name="Agent",
            meta_dict=None,
            content="CAMEL-AI.org is the 1st LLM multi-agent framework and "
                    "an open-source community dedicated to finding the scaling law "
                    "of agents.",
        ),
        role_at_backend=OpenAIBackendRole.ASSISTANT,
    ),
]
memory.write_records(records)

# Get context for the agent
context, token_count = memory.get_context()

print(context)
print(f"Retrieved context (token count: {token_count}):")
for message in context:
    print(f"{message}")

>>> Retrieved context (token count: 49):
{'role': 'user', 'content': 'What is AI?'}
{'role': 'assistant', 'content': 'AI refers to systems that mimic human intelligence.'}

from camel.agents import ChatAgent

# Define system message for the agent
sys_msg = BaseMessage.make_assistant_message(
    role_name='Agent',
    content='You are a curious agent wondering about the universe.',
)

# Initialize agent
agent = ChatAgent(system_message=sys_msg)

# Set memory to the agent
agent.memory = memory

# Define a user message
usr_msg = BaseMessage.make_user_message(
    role_name='User',
    content="Tell me which is the 1st LLM multi-agent framework based on what we have discussed",
)

# Sending the message to the agent
response = agent.step(usr_msg)

# Check the response (just for illustrative purpose)
print(response.msgs[0].content)

>>> CAMEL AI is recognized as the first LLM (Large Language Model) multi-agent framework. It is an open-source community initiative focused on exploring the scaling laws of agents, enabling the development and interaction of multiple AI agents in a collaborative environment. This framework allows researchers and developers to experiment with various configurations and interactions among agents, facilitating advancements in AI capabilities and understanding.