feat: Migrating CLI to run on MemGPT Client

.github/workflows/test_ollama.yml

@@ -13,13 +13,13 @@ jobs:
     steps:
     - name: Checkout
       uses: actions/checkout@v4
-    
+
     - name: Start Ollama Server
       run: |
         curl -fsSL https://ollama.com/install.sh | sh
         ollama serve &
         sleep 10 # wait for server
-        ollama pull dolphin2.2-mistral:7b-q6_K 
+        ollama pull dolphin2.2-mistral:7b-q6_K
         ollama pull mxbai-embed-large
 
     - name: "Setup Python, Poetry and Dependencies"

README.md

@@ -23,13 +23,13 @@ You can also use MemGPT to deploy agents as a *service*. You can use a MemGPT se
 <img width="1000" alt="image" src="https://github.com/cpacker/MemGPT/assets/8505980/1096eb91-139a-4bc5-b908-fa585462da09">
 
 
-## Installation & Setup   
+## Installation & Setup
 Install MemGPT:
 ```sh
 pip install -U pymemgpt
 ```
 
-To use MemGPT with OpenAI, set the environment variable `OPENAI_API_KEY` to your OpenAI key then run: 
+To use MemGPT with OpenAI, set the environment variable `OPENAI_API_KEY` to your OpenAI key then run:
 ```
 memgpt quickstart --backend openai
 ```
@@ -54,20 +54,20 @@ MemGPT provides a developer portal that enables you to easily create, edit, moni
 
 <img width="1000" alt="image" src="https://github.com/cpacker/MemGPT/assets/5475622/071117c5-46a7-4953-bc9d-d74880e66258">
 
-## Quickstart (Server)  
+## Quickstart (Server)
 
-**Option 1 (Recommended)**: Run with docker compose  
+**Option 1 (Recommended)**: Run with docker compose
 1. [Install docker on your system](https://docs.docker.com/get-docker/)
 2. Clone the repo: `git clone https://github.com/cpacker/MemGPT.git`
 3. Copy-paste `.env.example` to `.env` and optionally modify
 4. Run `docker compose up`
-5. Go to `memgpt.localhost` in the browser to view the developer portal 
+5. Go to `memgpt.localhost` in the browser to view the developer portal
 
 **Option 2:** Run with the CLI:
 1. Run `memgpt server`
 2. Go to `localhost:8283` in the browser to view the developer portal
 
-Once the server is running, you can use the [Python client](https://memgpt.readme.io/docs/admin-client) or [REST API](https://memgpt.readme.io/reference/api) to connect to `memgpt.localhost` (if you're running with docker compose) or `localhost:8283` (if you're running with the CLI) to create users, agents, and more. The service requires authentication with a MemGPT admin password; it is the value of `MEMGPT_SERVER_PASS` in `.env`. 
+Once the server is running, you can use the [Python client](https://memgpt.readme.io/docs/admin-client) or [REST API](https://memgpt.readme.io/reference/api) to connect to `memgpt.localhost` (if you're running with docker compose) or `localhost:8283` (if you're running with the CLI) to create users, agents, and more. The service requires authentication with a MemGPT admin password; it is the value of `MEMGPT_SERVER_PASS` in `.env`.
 
 ## Supported Endpoints & Backends
 MemGPT is designed to be model and provider agnostic. The following LLM and embedding endpoints are supported:
@@ -96,12 +96,11 @@ When using MemGPT with open LLMs (such as those downloaded from HuggingFace), th
 * **Report Issues or Suggest Features**: Have an issue or a feature request? Please submit them through our [GitHub Issues page](https://github.com/cpacker/MemGPT/issues).
 * **Explore the Roadmap**: Curious about future developments? View and comment on our [project roadmap](https://github.com/cpacker/MemGPT/issues/1200).
 * **Benchmark the Performance**: Want to benchmark the performance of a model on MemGPT? Follow our [Benchmarking Guidance](#benchmarking-guidance).
-* **Join Community Events**: Stay updated with the [MemGPT event calendar](https://lu.ma/berkeley-llm-meetup) or follow our [Twitter account](https://twitter.com/MemGPT). 
+* **Join Community Events**: Stay updated with the [MemGPT event calendar](https://lu.ma/berkeley-llm-meetup) or follow our [Twitter account](https://twitter.com/MemGPT).
 
 
 ## Benchmarking Guidance
 To evaluate the performance of a model on MemGPT, simply configure the appropriate model settings using `memgpt configure`, and then initiate the benchmark via `memgpt benchmark`. The duration will vary depending on your hardware. This will run through a predefined set of prompts through multiple iterations to test the function calling capabilities of a model. You can help track what LLMs work well with MemGPT by contributing your benchmark results via [this form](https://forms.gle/XiBGKEEPFFLNSR348), which will be used to update the spreadsheet.
 
 ## Legal notices
 By using MemGPT and related MemGPT services (such as the MemGPT endpoint or hosted service), you agree to our [privacy policy](https://github.com/cpacker/MemGPT/tree/main/PRIVACY.md) and [terms of service](https://github.com/cpacker/MemGPT/tree/main/TERMS.md).
-

examples/resend_example/README.md

@@ -90,4 +90,3 @@ memgpt run --preset resend_preset --persona sam_pov --human cs_phd --stream
 Waiting in our inbox:
 
 <img width="500" alt="image" src="https://github.com/cpacker/MemGPT/assets/5475622/95f9b24a-98c3-493a-a787-72a2a956641a">
-

memgpt/cli/cli_config.py

@@ -1101,9 +1101,12 @@ class ListChoice(str, Enum):
 
 @app.command()
 def list(arg: Annotated[ListChoice, typer.Argument]):
+    from memgpt.client.client import create_client
+
     config = MemGPTConfig.load()
     ms = MetadataStore(config)
     user_id = uuid.UUID(config.anon_clientid)
+    client = create_client(base_url=os.getenv("MEMGPT_BASE_URL"), token=os.getenv("MEMGPT_SERVER_PASS"))
     table = ColorTable(theme=Themes.OCEAN)
     if arg == ListChoice.agents:
         """List all agents"""
@@ -1130,7 +1133,7 @@ def list(arg: Annotated[ListChoice, typer.Argument]):
     elif arg == ListChoice.humans:
         """List all humans"""
         table.field_names = ["Name", "Text"]
-        for human in ms.list_humans(user_id=user_id):
+        for human in client.list_humans(user_id=user_id):
             table.add_row([human.name, human.text.replace("\n", "")[:100]])
         print(table)
     elif arg == ListChoice.personas:
@@ -1194,9 +1197,12 @@ def add(
     filename: Annotated[Optional[str], typer.Option("-f", help="Specify filename")] = None,
 ):
     """Add a person/human"""
+    from memgpt.client.client import create_client
+
     config = MemGPTConfig.load()
     user_id = uuid.UUID(config.anon_clientid)
     ms = MetadataStore(config)
+    client = create_client(base_url=os.getenv("MEMGPT_BASE_URL"), token=os.getenv("MEMGPT_SERVER_PASS"))
     if filename:  # read from file
         assert text is None, "Cannot specify both text and filename"
         with open(filename, "r", encoding="utf-8") as f:
@@ -1214,16 +1220,16 @@ def add(
             ms.add_persona(persona)
 
     elif option == "human":
-        human = ms.get_human(name=name, user_id=user_id)
+        human = client.get_human(name=name, user_id=user_id)
         if human:
             # config if user wants to overwrite
             if not questionary.confirm(f"Human {name} already exists. Overwrite?").ask():
                 return
             human.text = text
-            ms.update_human(human)
+            client.update_human(human)
         else:
             human = HumanModel(name=name, text=text, user_id=user_id)
-            ms.add_human(HumanModel(name=name, text=text, user_id=user_id))
+            client.add_human(HumanModel(name=name, text=text, user_id=user_id))
     elif option == "preset":
         assert filename, "Must specify filename for preset"
         create_preset_from_file(filename, name, user_id, ms)
@@ -1234,9 +1240,11 @@ def add(
 @app.command()
 def delete(option: str, name: str):
     """Delete a source from the archival memory."""
+    from memgpt.client.client import create_client
 
     config = MemGPTConfig.load()
     user_id = uuid.UUID(config.anon_clientid)
+    client = create_client(base_url=os.getenv("MEMGPT_BASE_URL"), token=os.getenv("MEMGPT_API_KEY"))
     ms = MetadataStore(config)
     assert ms.get_user(user_id=user_id), f"User {user_id} does not exist"
 
@@ -1273,9 +1281,9 @@ def delete(option: str, name: str):
             ms.delete_agent(agent_id=agent.id)
 
         elif option == "human":
-            human = ms.get_human(name=name, user_id=user_id)
+            human = client.get_human(name=name, user_id=user_id)
             assert human is not None, f"Human {name} does not exist"
-            ms.delete_human(name=name, user_id=user_id)
+            client.delete_human(name=name, user_id=user_id)
         elif option == "persona":
             persona = ms.get_persona(name=name, user_id=user_id)
             assert persona is not None, f"Persona {name} does not exist"

memgpt/client/client.py

@@ -630,6 +630,8 @@ def __init__(
         self.interface = QueuingInterface(debug=debug)
         self.server = SyncServer(default_interface=self.interface)
 
+    # agents
+
     def list_agents(self):
         self.interface.clear()
         return self.server.list_agents(user_id=self.user_id)
@@ -665,6 +667,14 @@ def create_agent(
         )
         return agent_state
 
+    def delete_agent(self, agent_id: uuid.UUID):
+        self.server.delete_agent(user_id=self.user_id, agent_id=agent_id)
+
+    def get_agent_config(self, agent_id: str) -> AgentState:
+        self.interface.clear()
+        return self.server.get_agent_config(user_id=self.user_id, agent_id=agent_id)
+
+    # presets
     def create_preset(self, preset: Preset) -> Preset:
         if preset.user_id is None:
             preset.user_id = self.user_id
@@ -677,9 +687,7 @@ def delete_preset(self, preset_id: uuid.UUID):
     def list_presets(self) -> List[PresetModel]:
         return self.server.list_presets(user_id=self.user_id)
 
-    def get_agent_config(self, agent_id: str) -> AgentState:
-        self.interface.clear()
-        return self.server.get_agent_config(user_id=self.user_id, agent_id=agent_id)
+    # memory
 
     def get_agent_memory(self, agent_id: str) -> Dict:
         self.interface.clear()
@@ -689,6 +697,8 @@ def update_agent_core_memory(self, agent_id: str, new_memory_contents: Dict) ->
         self.interface.clear()
         return self.server.update_agent_core_memory(user_id=self.user_id, agent_id=agent_id, new_memory_contents=new_memory_contents)
 
+    # agent interactions
+
     def user_message(self, agent_id: str, message: str) -> Union[List[Dict], Tuple[List[Dict], int]]:
         self.interface.clear()
         self.server.user_message(user_id=self.user_id, agent_id=agent_id, message=message)
@@ -704,17 +714,7 @@ def run_command(self, agent_id: str, command: str) -> Union[str, None]:
     def save(self):
         self.server.save_agents()
 
-    def load_data(self, connector: DataConnector, source_name: str):
-        self.server.load_data(user_id=self.user_id, connector=connector, source_name=source_name)
-
-    def create_source(self, name: str):
-        self.server.create_source(user_id=self.user_id, name=name)
-
-    def attach_source_to_agent(self, source_id: uuid.UUID, agent_id: uuid.UUID):
-        self.server.attach_source_to_agent(user_id=self.user_id, source_id=source_id, agent_id=agent_id)
-
-    def delete_agent(self, agent_id: uuid.UUID):
-        self.server.delete_agent(user_id=self.user_id, agent_id=agent_id)
+    # archival memory
 
     def get_agent_archival_memory(
         self, agent_id: uuid.UUID, before: Optional[uuid.UUID] = None, after: Optional[uuid.UUID] = None, limit: Optional[int] = 1000
@@ -727,3 +727,35 @@ def get_agent_archival_memory(
             limit=limit,
         )
         return archival_json_records
+
+    # messages
+
+    # humans / personas
+
+    def list_humans(self, user_id: uuid.UUID):
+        return self.server.list_humans(user_id=user_id if user_id else self.user_id)
+
+    def get_human(self, name: str, user_id: uuid.UUID):
+        return self.server.get_human(name=name, user_id=user_id)
+
+    def add_human(self, human: HumanModel):
+        return self.server.add_human(human=human)
+
+    def update_human(self, human: HumanModel):
+        return self.server.update_human(human=human)
+
+    def delete_human(self, name: str, user_id: uuid.UUID):
+        return self.server.delete_human(name, user_id)
+
+    # tools
+
+    # data sources
+
+    def load_data(self, connector: DataConnector, source_name: str):
+        self.server.load_data(user_id=self.user_id, connector=connector, source_name=source_name)
+
+    def create_source(self, name: str):
+        self.server.create_source(user_id=self.user_id, name=name)
+
+    def attach_source_to_agent(self, source_id: uuid.UUID, agent_id: uuid.UUID):
+        self.server.attach_source_to_agent(user_id=self.user_id, source_id=source_id, agent_id=agent_id)

memgpt/server/server.py

@@ -40,6 +40,7 @@
 from memgpt.metadata import MetadataStore
 from memgpt.models.pydantic_models import (
     DocumentModel,
+    HumanModel,
     PassageModel,
     PresetModel,
     SourceModel,
@@ -897,6 +898,21 @@ def list_agents(
             "agents": agents_states_dicts,
         }
 
+    def list_humans(self, user_id: uuid.UUID):
+        return self.ms.list_humans(user_id=user_id)
+
+    def get_human(self, name: str, user_id: uuid.UUID):
+        return self.ms.get_human(name=name, user_id=user_id)
+
+    def add_human(self, human: HumanModel):
+        return self.ms.add_human(human=human)
+
+    def update_human(self, human: HumanModel):
+        return self.ms.update_human(human=human)
+
+    def delete_human(self, name: str, user_id: uuid.UUID):
+        return self.ms.delete_human(name, user_id)
+
     def get_agent(self, user_id: uuid.UUID, agent_id: uuid.UUID):
         """Get the agent state"""
         return self.ms.get_agent(agent_id=agent_id, user_id=user_id)

tests/test_new_cli.py

@@ -0,0 +1,102 @@
+import os
+import random
+import string
+import unittest.mock
+
+import pytest
+
+from memgpt.cli.cli_config import add, delete, list
+
+
+@pytest.mark.skip(reason="This is a helper function.")
+def generate_random_string(length):
+    characters = string.ascii_letters + string.digits
+    random_string = "".join(random.choices(characters, k=length))
+    return random_string
+
+
+@pytest.mark.skip(reason="Ensures LocalClient is used during testing.")
+def unset_env_variables():
+    server_url = os.environ.pop("MEMGPT_BASE_URL", None)
+    token = os.environ.pop("MEMGPT_SERVER_PASS", None)
+    return server_url, token
+
+
+@pytest.mark.skip(reason="Set env variables back to values before test.")
+def reset_env_variables(server_url, token):
+    if server_url is not None:
+        os.environ["MEMGPT_BASE_URL"] = server_url
+    if token is not None:
+        os.environ["MEMGPT_SERVER_PASS"] = token
+
+
+def test_crud_human(capsys):
+
+    server_url, token = unset_env_variables()
+
+    # Initialize values that won't interfere with existing ones
+    human_1 = generate_random_string(16)
+    text_1 = generate_random_string(32)
+    human_2 = generate_random_string(16)
+    text_2 = generate_random_string(32)
+    text_3 = generate_random_string(32)
+
+    # Add inital human
+    add("human", human_1, text_1)
+
+    # Expect inital human to be listed
+    list("humans")
+    captured = capsys.readouterr()
+    output = captured.out[captured.out.find(human_1) :]
+
+    assert human_1 in output
+    assert text_1 in output
+
+    # Add second human
+    add("human", human_2, text_2)
+
+    # Expect to see second human
+    list("humans")
+    captured = capsys.readouterr()
+    output = captured.out[captured.out.find(human_1) :]
+
+    assert human_1 in output
+    assert text_1 in output
+    assert human_2 in output
+    assert text_2 in output
+
+    with unittest.mock.patch("questionary.confirm") as mock_confirm:
+        mock_confirm.return_value.ask.return_value = True
+
+        # Update second human
+        add("human", human_2, text_3)
+
+        # Expect to see update text
+        list("humans")
+        captured = capsys.readouterr()
+        output = captured.out[captured.out.find(human_1) :]
+
+        assert human_1 in output
+        assert text_1 in output
+        assert human_2 in output
+        assert output.count(human_2) == 1
+        assert text_3 in output
+        assert text_2 not in output
+
+    # Delete second human
+    delete("human", human_2)
+
+    # Expect second human to be deleted
+    list("humans")
+    captured = capsys.readouterr()
+    output = captured.out[captured.out.find(human_1) :]
+
+    assert human_1 in output
+    assert text_1 in output
+    assert human_2 not in output
+    assert text_2 not in output
+
+    # Clean up
+    delete("human", human_1)
+
+    reset_env_variables(server_url, token)