letta-server/docs/python_client.md

title, excerpt, category

title	excerpt	category
Python client	Developing using the MemGPT Python client	6580dab16cade8003f996d17

The fastest way to integrate MemGPT with your own Python projects is through the MemGPT client class:

from memgpt import MemGPT

# Create a MemGPT client object (sets up the persistent state)
client = MemGPT(
    quickstart="openai",
    config={
      "openai_api_key": "YOUR_API_KEY"
    }
)

# You can set many more parameters, this is just a basic example
agent_id = client.create_agent(
    agent_config={
      "persona": "sam_pov",
      "user": "cs_phd",
    }
)

# Now that we have an agent_name identifier, we can send it a message!
# The response will have data from the MemGPT agent
my_message = "Hi MemGPT! How's it going?"
response = client.user_message(agent_id=agent_id, message=my_message)

More in-depth example of using the MemGPT Python client

from memgpt.config import AgentConfig
from memgpt import MemGPT
from memgpt import constants
from memgpt.cli.cli import QuickstartChoice


client = MemGPT(
    # When auto_save is 'True' then the agent(s) will be saved after every
    # user message.  This may have performance implications, so you
    # can otherwise choose when to save explicitly using client.save().
    auto_save=True,

    # Quickstart will automatically configure MemGPT (without having to run `memgpt configure`
    # If you choose 'openai' then you must set the api key (env or in config)
    quickstart=QuickstartChoice.memgpt_hosted,

    # Allows you to override default config generated by quickstart or `memgpt configure`
    config={}
)

# Create an AgentConfig with default persona and human txt
# In this case, assume we wrote a custom persona file "my_persona.txt", located at ~/.memgpt/personas/my_persona.txt
# Same for a custom user file "my_user.txt", located at ~/.memgpt/humans/my_user.txt
agent_config = AgentConfig(
    name="CustomAgent",
    persona="my_persona",
    human="my_user",
    preset="memgpt_chat",
    model="gpt-4",
)

# Create the agent according to AgentConfig we set up. If an agent with
# the same name already exists it will simply return, unless you set
# throw_if_exists to 'True'
agent_id = client.create_agent(agent_config=agent_config)

# Create a helper that sends a message and prints the assistant response only
def send_message(message: str):
    """
    sends a message and prints the assistant output only.
    :param message: the message to send
    """
    response = client.user_message(agent_id=agent_id, message=message)
    for r in response:
        # Can also handle other types "function_call", "function_return", "function_message"
        if "assistant_message" in r:
            print("ASSISTANT:", r["assistant_message"])
        elif "thoughts" in r:
            print("THOUGHTS:", r["internal_monologue"])

# Send a message and see the response
send_message("Please introduce yourself and tell me about your abilities!")