feat: Anthropic Claude API support

memgpt/cli/cli_config.py

@@ -21,6 +21,8 @@
 from memgpt.llm_api.openai import openai_get_model_list
 from memgpt.llm_api.azure_openai import azure_openai_get_model_list
 from memgpt.llm_api.google_ai import google_ai_get_model_list, google_ai_get_model_context_window
+from memgpt.llm_api.anthropic import anthropic_get_model_list, antropic_get_model_context_window
+from memgpt.llm_api.llm_api_tools import LLM_API_PROVIDER_OPTIONS
 from memgpt.local_llm.constants import DEFAULT_ENDPOINTS, DEFAULT_OLLAMA_MODEL, DEFAULT_WRAPPER_NAME
 from memgpt.local_llm.utils import get_available_wrappers
 from memgpt.server.utils import shorten_key_middle
@@ -64,14 +66,14 @@ def configure_llm_endpoint(config: MemGPTConfig, credentials: MemGPTCredentials)
     # get default
     default_model_endpoint_type = config.default_llm_config.model_endpoint_type
     if config.default_llm_config.model_endpoint_type is not None and config.default_llm_config.model_endpoint_type not in [
-        "openai",
-        "azure",
-        "google_ai",
+        provider for provider in LLM_API_PROVIDER_OPTIONS if provider != "local"
     ]:  # local model
         default_model_endpoint_type = "local"
 
     provider = questionary.select(
-        "Select LLM inference provider:", choices=["openai", "azure", "google_ai", "local"], default=default_model_endpoint_type
+        "Select LLM inference provider:",
+        choices=LLM_API_PROVIDER_OPTIONS,
+        default=default_model_endpoint_type,
     ).ask()
     if provider is None:
         raise KeyboardInterrupt
@@ -184,6 +186,46 @@ def configure_llm_endpoint(config: MemGPTConfig, credentials: MemGPTCredentials)
 
         model_endpoint_type = "google_ai"
 
+    elif provider == "anthropic":
+        # check for key
+        if credentials.anthropic_key is None:
+            # allow key to get pulled from env vars
+            anthropic_api_key = os.getenv("ANTHROPIC_API_KEY", None)
+            # if we still can't find it, ask for it as input
+            if anthropic_api_key is None:
+                while anthropic_api_key is None or len(anthropic_api_key) == 0:
+                    # Ask for API key as input
+                    anthropic_api_key = questionary.password(
+                        "Enter your Anthropic API key (starts with 'sk-', see https://console.anthropic.com/settings/keys):"
+                    ).ask()
+                    if anthropic_api_key is None:
+                        raise KeyboardInterrupt
+            credentials.anthropic_key = anthropic_api_key
+            credentials.save()
+        else:
+            # Give the user an opportunity to overwrite the key
+            anthropic_api_key = None
+            default_input = (
+                shorten_key_middle(credentials.anthropic_key) if credentials.anthropic_key.startswith("sk-") else credentials.anthropic_key
+            )
+            anthropic_api_key = questionary.password(
+                "Enter your Anthropic API key (starts with 'sk-', see https://console.anthropic.com/settings/keys):",
+                default=default_input,
+            ).ask()
+            if anthropic_api_key is None:
+                raise KeyboardInterrupt
+            # If the user modified it, use the new one
+            if anthropic_api_key != default_input:
+                credentials.anthropic_key = anthropic_api_key
+                credentials.save()
+
+        model_endpoint_type = "anthropic"
+        model_endpoint = "https://api.anthropic.com/v1"
+        model_endpoint = questionary.text("Override default endpoint:", default=model_endpoint).ask()
+        if model_endpoint is None:
+            raise KeyboardInterrupt
+        provider = "anthropic"
+
     else:  # local models
         # backend_options_old = ["webui", "webui-legacy", "llamacpp", "koboldcpp", "ollama", "lmstudio", "lmstudio-legacy", "vllm", "openai"]
         backend_options = builtins.list(DEFAULT_ENDPOINTS.keys())
@@ -291,6 +333,12 @@ def get_model_options(
             model_options = [mo for mo in model_options if str(mo).startswith("gemini") and "-pro" in str(mo)]
             # model_options = ["gemini-pro"]
 
+        elif model_endpoint_type == "anthropic":
+            if credentials.anthropic_key is None:
+                raise ValueError("Missing Anthropic API key")
+            fetched_model_options = anthropic_get_model_list(url=model_endpoint, api_key=credentials.anthropic_key)
+            model_options = [obj["name"] for obj in fetched_model_options]
+
         else:
             # Attempt to do OpenAI endpoint style model fetching
             # TODO support local auth with api-key header
@@ -382,6 +430,26 @@ def configure_model(config: MemGPTConfig, credentials: MemGPTCredentials, model_
         if model is None:
             raise KeyboardInterrupt
 
+    elif model_endpoint_type == "anthropic":
+        try:
+            fetched_model_options = get_model_options(
+                credentials=credentials, model_endpoint_type=model_endpoint_type, model_endpoint=model_endpoint
+            )
+        except Exception as e:
+            # NOTE: if this fails, it means the user's key is probably bad
+            typer.secho(
+                f"Failed to get model list from {model_endpoint} - make sure your API key and endpoints are correct!", fg=typer.colors.RED
+            )
+            raise e
+
+        model = questionary.select(
+            "Select default model:",
+            choices=fetched_model_options,
+            default=fetched_model_options[0],
+        ).ask()
+        if model is None:
+            raise KeyboardInterrupt
+
     else:  # local models
 
         # ask about local auth
@@ -522,8 +590,8 @@ def configure_model(config: MemGPTConfig, credentials: MemGPTCredentials, model_
                     fetched_context_window,
                     "custom",
                 ]
-            except:
-                print(f"Failed to get model details for model '{model}' on Google AI API")
+            except Exception as e:
+                print(f"Failed to get model details for model '{model}' on Google AI API ({str(e)})")
 
             context_window_input = questionary.select(
                 "Select your model's context window (see https://cloud.google.com/vertex-ai/generative-ai/docs/learn/model-versioning#gemini-model-versions):",
@@ -533,6 +601,27 @@ def configure_model(config: MemGPTConfig, credentials: MemGPTCredentials, model_
             if context_window_input is None:
                 raise KeyboardInterrupt
 
+        elif model_endpoint_type == "anthropic":
+            try:
+                fetched_context_window = str(
+                    antropic_get_model_context_window(url=model_endpoint, api_key=credentials.anthropic_key, model=model)
+                )
+                print(f"Got context window {fetched_context_window} for model {model}")
+                context_length_options = [
+                    fetched_context_window,
+                    "custom",
+                ]
+            except Exception as e:
+                print(f"Failed to get model details for model '{model}' ({str(e)})")
+
+            context_window_input = questionary.select(
+                "Select your model's context window (see https://docs.anthropic.com/claude/docs/models-overview):",
+                choices=context_length_options,
+                default=context_length_options[0],
+            ).ask()
+            if context_window_input is None:
+                raise KeyboardInterrupt
+
         else:
 
             # Ask the user to specify the context length

memgpt/credentials.py

@@ -32,6 +32,9 @@ class MemGPTCredentials:
     google_ai_key: Optional[str] = None
     google_ai_service_endpoint: Optional[str] = None
 
+    # anthropic config
+    anthropic_key: Optional[str] = None
+
     # azure config
     azure_auth_type: str = "api_key"
     azure_key: Optional[str] = None
@@ -77,6 +80,8 @@ def load(cls) -> "MemGPTCredentials":
                 # gemini
                 "google_ai_key": get_field(config, "google_ai", "key"),
                 "google_ai_service_endpoint": get_field(config, "google_ai", "service_endpoint"),
+                # anthropic
+                "anthropic_key": get_field(config, "anthropic", "key"),
                 # open llm
                 "openllm_auth_type": get_field(config, "openllm", "auth_type"),
                 "openllm_key": get_field(config, "openllm", "key"),
@@ -113,6 +118,9 @@ def save(self):
         set_field(config, "google_ai", "key", self.google_ai_key)
         set_field(config, "google_ai", "service_endpoint", self.google_ai_service_endpoint)
 
+        # anthropic
+        set_field(config, "anthropic", "key", self.anthropic_key)
+
         # openllm config
         set_field(config, "openllm", "auth_type", self.openllm_auth_type)
         set_field(config, "openllm", "key", self.openllm_key)

memgpt/data_types.py

@@ -294,6 +294,78 @@ def to_openai_dict(self, max_tool_id_length=TOOL_CALL_ID_MAX_LEN) -> dict:
 
         return openai_message
 
+    def to_anthropic_dict(self, inner_thoughts_xml_tag="thinking") -> dict:
+        # raise NotImplementedError
+
+        def add_xml_tag(string: str, xml_tag: Optional[str]):
+            # NOTE: Anthropic docs recommends using <thinking> tag when using CoT + tool use
+            return f"<{xml_tag}>{string}</{xml_tag}" if xml_tag else string
+
+        if self.role == "system":
+            raise ValueError(f"Anthropic 'system' role not supported")
+
+        elif self.role == "user":
+            assert all([v is not None for v in [self.text, self.role]]), vars(self)
+            anthropic_message = {
+                "content": self.text,
+                "role": self.role,
+            }
+            # Optional field, do not include if null
+            if self.name is not None:
+                anthropic_message["name"] = self.name
+
+        elif self.role == "assistant":
+            assert self.tool_calls is not None or self.text is not None
+            anthropic_message = {
+                "role": self.role,
+            }
+            content = []
+            if self.text is not None:
+                content.append(
+                    {
+                        "type": "text",
+                        "text": add_xml_tag(string=self.text, xml_tag=inner_thoughts_xml_tag),
+                    }
+                )
+            if self.tool_calls is not None:
+                for tool_call in self.tool_calls:
+                    content.append(
+                        {
+                            "type": "tool_use",
+                            "id": tool_call.id,
+                            "name": tool_call.function["name"],
+                            "input": json.loads(tool_call.function["arguments"]),
+                        }
+                    )
+
+            # If the only content was text, unpack it back into a singleton
+            # TODO
+            anthropic_message["content"] = content
+
+            # Optional fields, do not include if null
+            if self.name is not None:
+                anthropic_message["name"] = self.name
+
+        elif self.role == "tool":
+            # NOTE: Anthropic uses role "user" for "tool" responses
+            assert all([v is not None for v in [self.role, self.tool_call_id]]), vars(self)
+            anthropic_message = {
+                "role": "user",  # NOTE: diff
+                "content": [
+                    # TODO support error types etc
+                    {
+                        "type": "tool_result",
+                        "tool_use_id": self.tool_call_id,
+                        "content": self.text,
+                    }
+                ],
+            }
+
+        else:
+            raise ValueError(self.role)
+
+        return anthropic_message
+
     def to_google_ai_dict(self, put_inner_thoughts_in_kwargs: bool = True) -> dict:
         """Go from Message class to Google AI REST message object