chore: Various bug fixes (#1350)

Co-authored-by: Sarah Wooders <sarahwooders@gmail.com>
Co-authored-by: cthomas <caren@letta.com>
Co-authored-by: tarunkumark <tkksctwo@gmail.com>
Co-authored-by: Kevin Lin <klin5061@gmail.com>
Co-authored-by: Charles Packer <packercharles@gmail.com>
Co-authored-by: Miao <one.lemorage@gmail.com>
Co-authored-by: Krishnakumar R (KK) <65895020+kk-src@users.noreply.github.com>
Co-authored-by: Shubham Naik <shub@memgpt.ai>
Co-authored-by: Shubham Naik <shub@letta.com>
Co-authored-by: Will Sargent <will.sargent@gmail.com>
Co-authored-by: Shubham Naik <shubham.naik10@gmail.com>
Co-authored-by: mlong93 <35275280+mlong93@users.noreply.github.com>
Co-authored-by: Mindy Long <mindy@letta.com>
Co-authored-by: Stephan Fitzpatrick <stephan@knowsuchagency.com>
Co-authored-by: dboyliao <qmalliao@gmail.com>
Co-authored-by: Jyotirmaya Mahanta <jyotirmaya.mahanta@gmail.com>
Co-authored-by: Nicholas <102550462+ndisalvio3@users.noreply.github.com>
Co-authored-by: Tristan Morris <tristanbmorris@gmail.com>
Co-authored-by: Daniel Shin <88547237+kyuds@users.noreply.github.com>
Co-authored-by: Jindřich Šíma <67415662+JindrichSima@users.noreply.github.com>
Co-authored-by: Azin Asgarian <31479845+azinasg@users.noreply.github.com>
Co-authored-by: Connor Shorten <connorshorten300@gmail.com>
Co-authored-by: Lucas Mohallem Ferraz <ferraz.m.lucas@gmail.com>
Co-authored-by: kyuds <kyuds@everspin.co.kr>

CONTRIBUTING.md

@@ -60,8 +60,8 @@ alembic upgrade head
 Now when you want to use `letta`, make sure you first activate the `poetry` environment using poetry shell:
 
 ```shell
-$ poetry shell
+$ eval $(poetry env activate)
-(pyletta-py3.12) $ letta run
+(letta-py3.12) $ letta run
 ```
 
 Alternatively, you can use `poetry run` (which will activate the `poetry` environment for the `letta run` command only):

Dockerfile

@@ -67,6 +67,7 @@ ENV LETTA_ENVIRONMENT=${LETTA_ENVIRONMENT} \
     COMPOSIO_DISABLE_VERSION_CHECK=true \
     LETTA_OTEL_EXPORTER_OTLP_ENDPOINT="http://localhost:4317"
 
+
 WORKDIR /app
 
 # Copy virtual environment and app from builder

letta/__init__.py

@@ -1,4 +1,4 @@
-__version__ = "0.6.41"
+__version__ = "0.6.43"
 
 # import clients
 from letta.client.client import LocalClient, RESTClient, create_client

letta/client/client.py

@@ -2937,7 +2937,6 @@ class LocalClient(AbstractClient):
 
         Args:
             func (callable): The function to create a tool for.
-            name: (str): Name of the tool (must be unique per-user.)
             tags (Optional[List[str]], optional): Tags for the tool. Defaults to None.
             description (str, optional): The description.
             return_char_limit (int): The character limit for the tool's return value. Defaults to FUNCTION_RETURN_CHAR_LIMIT.
@@ -2950,6 +2949,7 @@ class LocalClient(AbstractClient):
         # parse source code/schema
         source_code = parse_source_code(func)
         source_type = "python"
+        name = func.__name__  # Initialize name using function's __name__
         if not tags:
             tags = []
 

letta/embeddings.py

@@ -246,19 +246,6 @@ def embedding_model(config: EmbeddingConfig, user_id: Optional[uuid.UUID] = None
                 model_settings.azure_api_version is not None,
             ]
         )
-        # from llama_index.embeddings.azure_openai import AzureOpenAIEmbedding
-
-        ## https://learn.microsoft.com/en-us/azure/ai-services/openai/reference#embeddings
-        # model = "text-embedding-ada-002"
-        # deployment = credentials.azure_embedding_deployment if credentials.azure_embedding_deployment is not None else model
-        # return AzureOpenAIEmbedding(
-        #    model=model,
-        #    deployment_name=deployment,
-        #    api_key=credentials.azure_key,
-        #    azure_endpoint=credentials.azure_endpoint,
-        #    api_version=credentials.azure_version,
-        # )
-
         return AzureOpenAIEmbedding(
             api_endpoint=model_settings.azure_base_url,
             api_key=model_settings.azure_api_key,

letta/llm_api/anthropic.py

@@ -844,6 +844,7 @@ def anthropic_chat_completions_process_stream(
             total_tokens=prompt_tokens,
         ),
     )
+
     log_event(name="llm_request_sent", attributes=chat_completion_request.model_dump())
 
     if stream_interface:

letta/llm_api/aws_bedrock.py

@@ -72,7 +72,7 @@ def bedrock_get_model_details(region_name: str, model_id: str) -> Dict[str, Any]
         response = bedrock.get_foundation_model(modelIdentifier=model_id)
         return response["modelDetails"]
     except ClientError as e:
-        print(f"Error getting model details: {str(e)}")
+        logger.exception(f"Error getting model details: {str(e)}")
         raise e
 
 

letta/llm_api/azure_openai.py

@@ -1,14 +1,13 @@
 from collections import defaultdict
 
 import requests
+from openai import AzureOpenAI
 
-from letta.llm_api.helpers import make_post_request
+from letta.llm_api.openai import prepare_openai_payload
 from letta.schemas.llm_config import LLMConfig
 from letta.schemas.openai.chat_completion_response import ChatCompletionResponse
 from letta.schemas.openai.chat_completions import ChatCompletionRequest
-from letta.schemas.openai.embedding_response import EmbeddingResponse
 from letta.settings import ModelSettings
-from letta.tracing import log_event
 
 
 def get_azure_chat_completions_endpoint(base_url: str, model: str, api_version: str):
@@ -33,20 +32,20 @@ def get_azure_deployment_list_endpoint(base_url: str):
 def azure_openai_get_deployed_model_list(base_url: str, api_key: str, api_version: str) -> list:
     """https://learn.microsoft.com/en-us/rest/api/azureopenai/models/list?view=rest-azureopenai-2023-05-15&tabs=HTTP"""
 
+    client = AzureOpenAI(api_key=api_key, api_version=api_version, azure_endpoint=base_url)
+
+    try:
+        models_list = client.models.list()
+    except requests.RequestException as e:
+        raise RuntimeError(f"Failed to retrieve model list: {e}")
+
+    all_available_models = [model.to_dict() for model in models_list.data]
+
     # https://xxx.openai.azure.com/openai/models?api-version=xxx
     headers = {"Content-Type": "application/json"}
     if api_key is not None:
         headers["api-key"] = f"{api_key}"
 
-    # 1. Get all available models
-    url = get_azure_model_list_endpoint(base_url, api_version)
-    try:
-        response = requests.get(url, headers=headers)
-        response.raise_for_status()
-    except requests.RequestException as e:
-        raise RuntimeError(f"Failed to retrieve model list: {e}")
-    all_available_models = response.json().get("data", [])
-
     # 2. Get all the deployed models
     url = get_azure_deployment_list_endpoint(base_url)
     try:
@@ -102,42 +101,18 @@ def azure_openai_get_embeddings_model_list(base_url: str, api_key: str, api_vers
 
 
 def azure_openai_chat_completions_request(
-    model_settings: ModelSettings, llm_config: LLMConfig, api_key: str, chat_completion_request: ChatCompletionRequest
+    model_settings: ModelSettings, llm_config: LLMConfig, chat_completion_request: ChatCompletionRequest
 ) -> ChatCompletionResponse:
     """https://learn.microsoft.com/en-us/azure/ai-services/openai/reference#chat-completions"""
 
-    assert api_key is not None, "Missing required field when calling Azure OpenAI"
+    assert model_settings.azure_api_key is not None, "Missing required api key field when calling Azure OpenAI"
+    assert model_settings.azure_api_version is not None, "Missing required api version field when calling Azure OpenAI"
+    assert model_settings.azure_base_url is not None, "Missing required base url field when calling Azure OpenAI"
 
-    headers = {"Content-Type": "application/json", "api-key": f"{api_key}"}
+    data = prepare_openai_payload(chat_completion_request)
-    data = chat_completion_request.model_dump(exclude_none=True)
+    client = AzureOpenAI(
+        api_key=model_settings.azure_api_key, api_version=model_settings.azure_api_version, azure_endpoint=model_settings.azure_base_url
+    )
+    chat_completion = client.chat.completions.create(**data)
 
-    # If functions == None, strip from the payload
+    return ChatCompletionResponse(**chat_completion.model_dump())
-    if "functions" in data and data["functions"] is None:
-        data.pop("functions")
-        data.pop("function_call", None)  # extra safe,  should exist always (default="auto")
-
-    if "tools" in data and data["tools"] is None:
-        data.pop("tools")
-        data.pop("tool_choice", None)  # extra safe,  should exist always (default="auto")
-
-    url = get_azure_chat_completions_endpoint(model_settings.azure_base_url, llm_config.model, model_settings.azure_api_version)
-    log_event(name="llm_request_sent", attributes=data)
-    response_json = make_post_request(url, headers, data)
-    # NOTE: azure openai does not include "content" in the response when it is None, so we need to add it
-    if "content" not in response_json["choices"][0].get("message"):
-        response_json["choices"][0]["message"]["content"] = None
-    log_event(name="llm_response_received", attributes=response_json)
-    response = ChatCompletionResponse(**response_json)  # convert to 'dot-dict' style which is the openai python client default
-    return response
-
-
-def azure_openai_embeddings_request(
-    resource_name: str, deployment_id: str, api_version: str, api_key: str, data: dict
-) -> EmbeddingResponse:
-    """https://learn.microsoft.com/en-us/azure/ai-services/openai/reference#embeddings"""
-
-    url = f"https://{resource_name}.openai.azure.com/openai/deployments/{deployment_id}/embeddings?api-version={api_version}"
-    headers = {"Content-Type": "application/json", "api-key": f"{api_key}"}
-
-    response_json = make_post_request(url, headers, data)
-    return EmbeddingResponse(**response_json)

letta/llm_api/llm_api_tools.py

@@ -306,7 +306,6 @@ def create(
         response = azure_openai_chat_completions_request(
             model_settings=model_settings,
             llm_config=llm_config,
-            api_key=model_settings.azure_api_key,
             chat_completion_request=chat_completion_request,
         )
 

letta/orm/sqlalchemy_base.py

@@ -286,7 +286,45 @@ class SqlalchemyBase(CommonSqlalchemyMetaMixins, Base):
         Raises:
             NoResultFound: if the object is not found
         """
-        logger.debug(f"Reading {cls.__name__} with ID: {identifier} with actor={actor}")
+        # this is ok because read_multiple will check if the
+        identifiers = [] if identifier is None else [identifier]
+        found = cls.read_multiple(db_session, identifiers, actor, access, access_type, **kwargs)
+        if len(found) == 0:
+            # for backwards compatibility.
+            conditions = []
+            if identifier:
+                conditions.append(f"id={identifier}")
+            if actor:
+                conditions.append(f"access level in {access} for {actor}")
+            if hasattr(cls, "is_deleted"):
+                conditions.append("is_deleted=False")
+            raise NoResultFound(f"{cls.__name__} not found with {', '.join(conditions if conditions else ['no conditions'])}")
+        return found[0]
+
+    @classmethod
+    @handle_db_timeout
+    def read_multiple(
+        cls,
+        db_session: "Session",
+        identifiers: List[str] = [],
+        actor: Optional["User"] = None,
+        access: Optional[List[Literal["read", "write", "admin"]]] = ["read"],
+        access_type: AccessType = AccessType.ORGANIZATION,
+        **kwargs,
+    ) -> List["SqlalchemyBase"]:
+        """The primary accessor for ORM record(s)
+        Args:
+            db_session: the database session to use when retrieving the record
+            identifiers: a list of identifiers of the records to read, can be the id string or the UUID object for backwards compatibility
+            actor: if specified, results will be scoped only to records the user is able to access
+            access: if actor is specified, records will be filtered to the minimum permission level for the actor
+            kwargs: additional arguments to pass to the read, used for more complex objects
+        Returns:
+            The matching object
+        Raises:
+            NoResultFound: if the object is not found
+        """
+        logger.debug(f"Reading {cls.__name__} with ID(s): {identifiers} with actor={actor}")
 
         # Start the query
         query = select(cls)
@@ -294,9 +332,9 @@ class SqlalchemyBase(CommonSqlalchemyMetaMixins, Base):
         query_conditions = []
 
         # If an identifier is provided, add it to the query conditions
-        if identifier is not None:
+        if len(identifiers) > 0:
-            query = query.where(cls.id == identifier)
+            query = query.where(cls.id.in_(identifiers))
-            query_conditions.append(f"id='{identifier}'")
+            query_conditions.append(f"id='{identifiers}'")
 
         if kwargs:
             query = query.filter_by(**kwargs)
@@ -309,12 +347,29 @@ class SqlalchemyBase(CommonSqlalchemyMetaMixins, Base):
         if hasattr(cls, "is_deleted"):
             query = query.where(cls.is_deleted == False)
             query_conditions.append("is_deleted=False")
-        if found := db_session.execute(query).scalar():
+
-            return found
+        results = db_session.execute(query).scalars().all()
+        if results:  # if empty list a.k.a. no results
+            if len(identifiers) > 0:
+                # find which identifiers were not found
+                # only when identifier length is greater than 0 (so it was used in the actual query)
+                identifier_set = set(identifiers)
+                results_set = set(map(lambda obj: obj.id, results))
+
+                # we log a warning message if any of the queried IDs were not found.
+                # TODO: should we error out instead?
+                if identifier_set != results_set:
+                    # Construct a detailed error message based on query conditions
+                    conditions_str = ", ".join(query_conditions) if query_conditions else "no specific conditions"
+                    logger.warning(
+                        f"{cls.__name__} not found with {conditions_str}. Queried ids: {identifier_set}, Found ids: {results_set}"
+                    )
+            return results
 
         # Construct a detailed error message based on query conditions
         conditions_str = ", ".join(query_conditions) if query_conditions else "no specific conditions"
-        raise NoResultFound(f"{cls.__name__} not found with {conditions_str}")
+        logger.warning(f"{cls.__name__} not found with {conditions_str}")
+        return []
 
     @handle_db_timeout
     def create(self, db_session: "Session", actor: Optional["User"] = None) -> "SqlalchemyBase":

letta/schemas/message.py

@@ -748,6 +748,12 @@ class Message(BaseMessage):
         else:
             raise ValueError(self.role)
 
+        # Validate that parts is never empty before returning
+        if "parts" not in google_ai_message or not google_ai_message["parts"]:
+            # If parts is empty, add a default text part
+            google_ai_message["parts"] = [{"text": "empty message"}]
+            warnings.warn(f"Empty 'parts' detected in message with role '{self.role}'. Added default empty text part.")
+
         return google_ai_message
 
     def to_cohere_dict(

letta/schemas/providers.py

@@ -207,9 +207,259 @@ class OpenAIProvider(Provider):
     def get_model_context_window_size(self, model_name: str):
         if model_name in LLM_MAX_TOKENS:
             return LLM_MAX_TOKENS[model_name]
+        else:
+            return LLM_MAX_TOKENS["DEFAULT"]
+
+
+class xAIProvider(OpenAIProvider):
+    """https://docs.x.ai/docs/api-reference"""
+
+    name: str = "xai"
+    api_key: str = Field(..., description="API key for the xAI/Grok API.")
+    base_url: str = Field("https://api.x.ai/v1", description="Base URL for the xAI/Grok API.")
+
+    def get_model_context_window_size(self, model_name: str) -> Optional[int]:
+        # xAI doesn't return context window in the model listing,
+        # so these are hardcoded from their website
+        if model_name == "grok-2-1212":
+            return 131072
         else:
             return None
 
+    def list_llm_models(self) -> List[LLMConfig]:
+        from letta.llm_api.openai import openai_get_model_list
+
+        response = openai_get_model_list(self.base_url, api_key=self.api_key)
+
+        if "data" in response:
+            data = response["data"]
+        else:
+            data = response
+
+        configs = []
+        for model in data:
+            assert "id" in model, f"xAI/Grok model missing 'id' field: {model}"
+            model_name = model["id"]
+
+            # In case xAI starts supporting it in the future:
+            if "context_length" in model:
+                context_window_size = model["context_length"]
+            else:
+                context_window_size = self.get_model_context_window_size(model_name)
+
+            if not context_window_size:
+                warnings.warn(f"Couldn't find context window size for model {model_name}")
+                continue
+
+            configs.append(
+                LLMConfig(
+                    model=model_name,
+                    model_endpoint_type="xai",
+                    model_endpoint=self.base_url,
+                    context_window=context_window_size,
+                    handle=self.get_handle(model_name),
+                )
+            )
+
+        return configs
+
+    def list_embedding_models(self) -> List[EmbeddingConfig]:
+        # No embeddings supported
+        return []
+
+
+class DeepSeekProvider(OpenAIProvider):
+    """
+    DeepSeek ChatCompletions API is similar to OpenAI's reasoning API,
+    but with slight differences:
+    * For example, DeepSeek's API requires perfect interleaving of user/assistant
+    * It also does not support native function calling
+    """
+
+    name: str = "deepseek"
+    base_url: str = Field("https://api.deepseek.com/v1", description="Base URL for the DeepSeek API.")
+    api_key: str = Field(..., description="API key for the DeepSeek API.")
+
+    def get_model_context_window_size(self, model_name: str) -> Optional[int]:
+        # DeepSeek doesn't return context window in the model listing,
+        # so these are hardcoded from their website
+        if model_name == "deepseek-reasoner":
+            return 64000
+        elif model_name == "deepseek-chat":
+            return 64000
+        else:
+            return None
+
+    def list_llm_models(self) -> List[LLMConfig]:
+        from letta.llm_api.openai import openai_get_model_list
+
+        response = openai_get_model_list(self.base_url, api_key=self.api_key)
+
+        if "data" in response:
+            data = response["data"]
+        else:
+            data = response
+
+        configs = []
+        for model in data:
+            assert "id" in model, f"DeepSeek model missing 'id' field: {model}"
+            model_name = model["id"]
+
+            # In case DeepSeek starts supporting it in the future:
+            if "context_length" in model:
+                # Context length is returned in OpenRouter as "context_length"
+                context_window_size = model["context_length"]
+            else:
+                context_window_size = self.get_model_context_window_size(model_name)
+
+            if not context_window_size:
+                warnings.warn(f"Couldn't find context window size for model {model_name}")
+                continue
+
+            # Not used for deepseek-reasoner, but otherwise is true
+            put_inner_thoughts_in_kwargs = False if model_name == "deepseek-reasoner" else True
+
+            configs.append(
+                LLMConfig(
+                    model=model_name,
+                    model_endpoint_type="deepseek",
+                    model_endpoint=self.base_url,
+                    context_window=context_window_size,
+                    handle=self.get_handle(model_name),
+                    put_inner_thoughts_in_kwargs=put_inner_thoughts_in_kwargs,
+                )
+            )
+
+        return configs
+
+    def list_embedding_models(self) -> List[EmbeddingConfig]:
+        # No embeddings supported
+        return []
+
+
+class LMStudioOpenAIProvider(OpenAIProvider):
+    name: str = "lmstudio-openai"
+    base_url: str = Field(..., description="Base URL for the LMStudio OpenAI API.")
+    api_key: Optional[str] = Field(None, description="API key for the LMStudio API.")
+
+    def list_llm_models(self) -> List[LLMConfig]:
+        from letta.llm_api.openai import openai_get_model_list
+
+        # For LMStudio, we want to hit 'GET /api/v0/models' instead of 'GET /v1/models'
+        MODEL_ENDPOINT_URL = f"{self.base_url.strip('/v1')}/api/v0"
+        response = openai_get_model_list(MODEL_ENDPOINT_URL)
+
+        """
+        Example response:
+
+        {
+          "object": "list",
+          "data": [
+            {
+              "id": "qwen2-vl-7b-instruct",
+              "object": "model",
+              "type": "vlm",
+              "publisher": "mlx-community",
+              "arch": "qwen2_vl",
+              "compatibility_type": "mlx",
+              "quantization": "4bit",
+              "state": "not-loaded",
+              "max_context_length": 32768
+            },
+            ...
+        """
+        if "data" not in response:
+            warnings.warn(f"LMStudio OpenAI model query response missing 'data' field: {response}")
+            return []
+
+        configs = []
+        for model in response["data"]:
+            assert "id" in model, f"Model missing 'id' field: {model}"
+            model_name = model["id"]
+
+            if "type" not in model:
+                warnings.warn(f"LMStudio OpenAI model missing 'type' field: {model}")
+                continue
+            elif model["type"] not in ["vlm", "llm"]:
+                continue
+
+            if "max_context_length" in model:
+                context_window_size = model["max_context_length"]
+            else:
+                warnings.warn(f"LMStudio OpenAI model missing 'max_context_length' field: {model}")
+                continue
+
+            configs.append(
+                LLMConfig(
+                    model=model_name,
+                    model_endpoint_type="openai",
+                    model_endpoint=self.base_url,
+                    context_window=context_window_size,
+                    handle=self.get_handle(model_name),
+                )
+            )
+
+        return configs
+
+    def list_embedding_models(self) -> List[EmbeddingConfig]:
+        from letta.llm_api.openai import openai_get_model_list
+
+        # For LMStudio, we want to hit 'GET /api/v0/models' instead of 'GET /v1/models'
+        MODEL_ENDPOINT_URL = f"{self.base_url.strip('/v1')}/api/v0"
+        response = openai_get_model_list(MODEL_ENDPOINT_URL)
+
+        """
+        Example response:
+        {
+          "object": "list",
+          "data": [
+            {
+              "id": "text-embedding-nomic-embed-text-v1.5",
+              "object": "model",
+              "type": "embeddings",
+              "publisher": "nomic-ai",
+              "arch": "nomic-bert",
+              "compatibility_type": "gguf",
+              "quantization": "Q4_0",
+              "state": "not-loaded",
+              "max_context_length": 2048
+            }
+            ...
+        """
+        if "data" not in response:
+            warnings.warn(f"LMStudio OpenAI model query response missing 'data' field: {response}")
+            return []
+
+        configs = []
+        for model in response["data"]:
+            assert "id" in model, f"Model missing 'id' field: {model}"
+            model_name = model["id"]
+
+            if "type" not in model:
+                warnings.warn(f"LMStudio OpenAI model missing 'type' field: {model}")
+                continue
+            elif model["type"] not in ["embeddings"]:
+                continue
+
+            if "max_context_length" in model:
+                context_window_size = model["max_context_length"]
+            else:
+                warnings.warn(f"LMStudio OpenAI model missing 'max_context_length' field: {model}")
+                continue
+
+            configs.append(
+                EmbeddingConfig(
+                    embedding_model=model_name,
+                    embedding_endpoint_type="openai",
+                    embedding_endpoint=self.base_url,
+                    embedding_dim=context_window_size,
+                    embedding_chunk_size=300,  # NOTE: max is 2048
+                    handle=self.get_handle(model_name),
+                ),
+            )
+
+        return configs
+
 
 class xAIProvider(OpenAIProvider):
     """https://docs.x.ai/docs/api-reference"""

letta/server/rest_api/app.py

@@ -43,16 +43,6 @@ interface: StreamingServerInterface = StreamingServerInterface
 server = SyncServer(default_interface_factory=lambda: interface())
 logger = get_logger(__name__)
 
-# TODO: remove
-password = None
-## TODO(ethan): eventuall remove
-# if password := settings.server_pass:
-#    # if the pass was specified in the environment, use it
-#    print(f"Using existing admin server password from environment.")
-# else:
-#    # Autogenerate a password for this session and dump it to stdout
-#    password = secrets.token_urlsafe(16)
-#    #typer.secho(f"Generated admin server password for this session: {password}", fg=typer.colors.GREEN)
 
 import logging
 import platform
@@ -287,7 +277,7 @@ def create_application() -> "FastAPI":
     app.include_router(openai_chat_completions_router, prefix=OPENAI_API_PREFIX)
 
     # /api/auth endpoints
-    app.include_router(setup_auth_router(server, interface, password), prefix=API_PREFIX)
+    app.include_router(setup_auth_router(server, interface, random_password), prefix=API_PREFIX)
 
     # / static files
     mount_static_files(app)

letta/server/rest_api/optimistic_json_parser.py

@@ -32,7 +32,7 @@ class OptimisticJSONParser:
         self.on_extra_token = self.default_on_extra_token
 
     def default_on_extra_token(self, text, data, reminding):
-        pass
+        print(f"Parsed JSON with extra tokens: {data}, remaining: {reminding}")
 
     def parse(self, input_str):
         """
@@ -130,8 +130,8 @@ class OptimisticJSONParser:
         if end == -1:
             # Incomplete string
             if not self.strict:
-                return input_str[1:], ""
+                return input_str[1:], ""  # Lenient mode returns partial string
-            return json.loads(f'"{input_str[1:]}"'), ""
+            raise decode_error  # Raise error for incomplete string in strict mode
 
         str_val = input_str[: end + 1]
         input_str = input_str[end + 1 :]
@@ -152,8 +152,8 @@ class OptimisticJSONParser:
         num_str = input_str[:idx]
         remainder = input_str[idx:]
 
-        # If it's only a sign or just '.', return as-is with empty remainder
+        # If not strict, and it's only a sign or just '.', return as-is with empty remainder
-        if not num_str or num_str in {"-", "."}:
+        if not self.strict and (not num_str or num_str in {"-", "."}):
             return num_str, ""
 
         try:

letta/services/block_manager.py

@@ -106,12 +106,14 @@ class BlockManager:
 
     @enforce_types
     def get_all_blocks_by_ids(self, block_ids: List[str], actor: Optional[PydanticUser] = None) -> List[PydanticBlock]:
-        # TODO: We can do this much more efficiently by listing, instead of executing individual queries per block_id
+        """Retrieve blocks by their names."""
-        blocks = []
+        with self.session_maker() as session:
-        for block_id in block_ids:
+            blocks = list(
-            block = self.get_block_by_id(block_id, actor=actor)
+                map(lambda obj: obj.to_pydantic(), BlockModel.read_multiple(db_session=session, identifiers=block_ids, actor=actor))
-            blocks.append(block)
+            )
-        return blocks
+            # backwards compatibility. previous implementation added None for every block not found.
+            blocks.extend([None for _ in range(len(block_ids) - len(blocks))])
+            return blocks
 
     @enforce_types
     def add_default_blocks(self, actor: PydanticUser):

letta/services/message_manager.py

@@ -129,6 +129,44 @@ class MessageManager:
         # raise error if message type got modified
         raise ValueError(f"Message type got modified: {letta_message_update.message_type}")
 
+    @enforce_types
+    def update_message_by_letta_message(
+        self, message_id: str, letta_message_update: LettaMessageUpdateUnion, actor: PydanticUser
+    ) -> PydanticMessage:
+        """
+        Updated the underlying messages table giving an update specified to the user-facing LettaMessage
+        """
+        message = self.get_message_by_id(message_id=message_id, actor=actor)
+        if letta_message_update.message_type == "assistant_message":
+            # modify the tool call for send_message
+            # TODO: fix this if we add parallel tool calls
+            # TODO: note this only works if the AssistantMessage is generated by the standard send_message
+            assert (
+                message.tool_calls[0].function.name == "send_message"
+            ), f"Expected the first tool call to be send_message, but got {message.tool_calls[0].function.name}"
+            original_args = json.loads(message.tool_calls[0].function.arguments)
+            original_args["message"] = letta_message_update.content  # override the assistant message
+            update_tool_call = message.tool_calls[0].__deepcopy__()
+            update_tool_call.function.arguments = json.dumps(original_args)
+
+            update_message = MessageUpdate(tool_calls=[update_tool_call])
+        elif letta_message_update.message_type == "reasoning_message":
+            update_message = MessageUpdate(content=letta_message_update.reasoning)
+        elif letta_message_update.message_type == "user_message" or letta_message_update.message_type == "system_message":
+            update_message = MessageUpdate(content=letta_message_update.content)
+        else:
+            raise ValueError(f"Unsupported message type for modification: {letta_message_update.message_type}")
+
+        message = self.update_message_by_id(message_id=message_id, message_update=update_message, actor=actor)
+
+        # convert back to LettaMessage
+        for letta_msg in message.to_letta_message(use_assistant_message=True):
+            if letta_msg.message_type == letta_message_update.message_type:
+                return letta_msg
+
+        # raise error if message type got modified
+        raise ValueError(f"Message type got modified: {letta_message_update.message_type}")
+
     @enforce_types
     def update_message_by_id(self, message_id: str, message_update: MessageUpdate, actor: PydanticUser) -> PydanticMessage:
         """

poetry.lock

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@@ -534,24 +524,8 @@ files = [
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-    {file = "Brotli-1.1.0-cp38-cp38-musllinux_1_2_ppc64le.whl", hash = "sha256:2feb1d960f760a575dbc5ab3b1c00504b24caaf6986e2dc2b01c09c87866a943"},
-    {file = "Brotli-1.1.0-cp38-cp38-musllinux_1_2_x86_64.whl", hash = "sha256:4410f84b33374409552ac9b6903507cdb31cd30d2501fc5ca13d18f73548444a"},
     {file = "Brotli-1.1.0-cp38-cp38-win32.whl", hash = "sha256:db85ecf4e609a48f4b29055f1e144231b90edc90af7481aa731ba2d059226b1b"},
     {file = "Brotli-1.1.0-cp38-cp38-win_amd64.whl", hash = "sha256:3d7954194c36e304e1523f55d7042c59dc53ec20dd4e9ea9d151f1b62b4415c0"},
     {file = "Brotli-1.1.0-cp39-cp39-macosx_10_9_universal2.whl", hash = "sha256:5fb2ce4b8045c78ebbc7b8f3c15062e435d47e7393cc57c25115cfd49883747a"},
@@ -608,10 +570,6 @@ files = [
     {file = "Brotli-1.1.0-cp39-cp39-musllinux_1_1_i686.whl", hash = "sha256:949f3b7c29912693cee0afcf09acd6ebc04c57af949d9bf77d6101ebb61e388c"},
     {file = "Brotli-1.1.0-cp39-cp39-musllinux_1_1_ppc64le.whl", hash = "sha256:89f4988c7203739d48c6f806f1e87a1d96e0806d44f0fba61dba81392c9e474d"},
     {file = "Brotli-1.1.0-cp39-cp39-musllinux_1_1_x86_64.whl", hash = "sha256:de6551e370ef19f8de1807d0a9aa2cdfdce2e85ce88b122fe9f6b2b076837e59"},
-    {file = "Brotli-1.1.0-cp39-cp39-musllinux_1_2_aarch64.whl", hash = "sha256:0737ddb3068957cf1b054899b0883830bb1fec522ec76b1098f9b6e0f02d9419"},
-    {file = "Brotli-1.1.0-cp39-cp39-musllinux_1_2_i686.whl", hash = "sha256:4f3607b129417e111e30637af1b56f24f7a49e64763253bbc275c75fa887d4b2"},
-    {file = "Brotli-1.1.0-cp39-cp39-musllinux_1_2_ppc64le.whl", hash = "sha256:6c6e0c425f22c1c719c42670d561ad682f7bfeeef918edea971a79ac5252437f"},
-    {file = "Brotli-1.1.0-cp39-cp39-musllinux_1_2_x86_64.whl", hash = "sha256:494994f807ba0b92092a163a0a283961369a65f6cbe01e8891132b7a320e61eb"},
     {file = "Brotli-1.1.0-cp39-cp39-win32.whl", hash = "sha256:f0d8a7a6b5983c2496e364b969f0e526647a06b075d034f3297dc66f3b360c64"},
     {file = "Brotli-1.1.0-cp39-cp39-win_amd64.whl", hash = "sha256:cdad5b9014d83ca68c25d2e9444e28e967ef16e80f6b436918c700c117a85467"},
     {file = "Brotli-1.1.0.tar.gz", hash = "sha256:81de08ac11bcb85841e440c13611c00b67d3bf82698314928d0b676362546724"},
@@ -1035,9 +993,9 @@ isort = ">=4.3.21,<6.0"
 jinja2 = ">=2.10.1,<4.0"
 packaging = "*"
 pydantic = [
+    {version = ">=1.10.0,<2.0.0 || >2.0.0,<2.0.1 || >2.0.1,<2.4.0 || >2.4.0,<3.0", extras = ["email"], markers = "python_version >= \"3.12\" and python_version < \"4.0\""},
     {version = ">=1.10.0,<2.4.0 || >2.4.0,<3.0", extras = ["email"], markers = "python_version >= \"3.11\" and python_version < \"3.12\""},
     {version = ">=1.9.0,<2.4.0 || >2.4.0,<3.0", extras = ["email"], markers = "python_version >= \"3.10\" and python_version < \"3.11\""},
-    {version = ">=1.10.0,<2.0.0 || >2.0.0,<2.0.1 || >2.0.1,<2.4.0 || >2.4.0,<3.0", extras = ["email"], markers = "python_version >= \"3.12\" and python_version < \"4.0\""},
 ]
 pyyaml = ">=6.0.1"
 toml = {version = ">=0.10.0,<1.0.0", markers = "python_version < \"3.11\""}
@@ -3035,8 +2993,8 @@ psutil = ">=5.9.1"
 pywin32 = {version = "*", markers = "sys_platform == \"win32\""}
 pyzmq = ">=25.0.0"
 requests = [
-    {version = ">=2.26.0", markers = "python_version <= \"3.11\""},
     {version = ">=2.32.2", markers = "python_version > \"3.11\""},
+    {version = ">=2.26.0", markers = "python_version <= \"3.11\""},
 ]
 setuptools = ">=70.0.0"
 tomli = {version = ">=1.1.0", markers = "python_version < \"3.11\""}
@@ -3899,9 +3857,9 @@ files = [
 
 [package.dependencies]
 numpy = [
+    {version = ">=1.26.0", markers = "python_version >= \"3.12\""},
     {version = ">=1.23.2", markers = "python_version == \"3.11\""},
     {version = ">=1.22.4", markers = "python_version < \"3.11\""},
-    {version = ">=1.26.0", markers = "python_version >= \"3.12\""},
 ]
 python-dateutil = ">=2.8.2"
 pytz = ">=2020.1"
@@ -4387,7 +4345,6 @@ files = [
     {file = "psycopg2-2.9.10-cp311-cp311-win_amd64.whl", hash = "sha256:0435034157049f6846e95103bd8f5a668788dd913a7c30162ca9503fdf542cb4"},
     {file = "psycopg2-2.9.10-cp312-cp312-win32.whl", hash = "sha256:65a63d7ab0e067e2cdb3cf266de39663203d38d6a8ed97f5ca0cb315c73fe067"},
     {file = "psycopg2-2.9.10-cp312-cp312-win_amd64.whl", hash = "sha256:4a579d6243da40a7b3182e0430493dbd55950c493d8c68f4eec0b302f6bbf20e"},
-    {file = "psycopg2-2.9.10-cp313-cp313-win_amd64.whl", hash = "sha256:91fd603a2155da8d0cfcdbf8ab24a2d54bca72795b90d2a3ed2b6da8d979dee2"},
     {file = "psycopg2-2.9.10-cp39-cp39-win32.whl", hash = "sha256:9d5b3b94b79a844a986d029eee38998232451119ad653aea42bb9220a8c5066b"},
     {file = "psycopg2-2.9.10-cp39-cp39-win_amd64.whl", hash = "sha256:88138c8dedcbfa96408023ea2b0c369eda40fe5d75002c0964c78f46f11fa442"},
     {file = "psycopg2-2.9.10.tar.gz", hash = "sha256:12ec0b40b0273f95296233e8750441339298e6a572f7039da5b260e3c8b60e11"},
@@ -4447,7 +4404,6 @@ files = [
     {file = "psycopg2_binary-2.9.10-cp313-cp313-musllinux_1_2_i686.whl", hash = "sha256:bb89f0a835bcfc1d42ccd5f41f04870c1b936d8507c6df12b7737febc40f0909"},
     {file = "psycopg2_binary-2.9.10-cp313-cp313-musllinux_1_2_ppc64le.whl", hash = "sha256:f0c2d907a1e102526dd2986df638343388b94c33860ff3bbe1384130828714b1"},
     {file = "psycopg2_binary-2.9.10-cp313-cp313-musllinux_1_2_x86_64.whl", hash = "sha256:f8157bed2f51db683f31306aa497311b560f2265998122abe1dce6428bd86567"},
-    {file = "psycopg2_binary-2.9.10-cp313-cp313-win_amd64.whl", hash = "sha256:27422aa5f11fbcd9b18da48373eb67081243662f9b46e6fd07c3eb46e4535142"},
     {file = "psycopg2_binary-2.9.10-cp38-cp38-macosx_12_0_x86_64.whl", hash = "sha256:eb09aa7f9cecb45027683bb55aebaaf45a0df8bf6de68801a6afdc7947bb09d4"},
     {file = "psycopg2_binary-2.9.10-cp38-cp38-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:b73d6d7f0ccdad7bc43e6d34273f70d587ef62f824d7261c4ae9b8b1b6af90e8"},
     {file = "psycopg2_binary-2.9.10-cp38-cp38-manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:ce5ab4bf46a211a8e924d307c1b1fcda82368586a19d0a24f8ae166f5c784864"},
@@ -5192,8 +5148,8 @@ grpcio = ">=1.41.0"
 grpcio-tools = ">=1.41.0"
 httpx = {version = ">=0.20.0", extras = ["http2"]}
 numpy = [
-    {version = ">=1.21", markers = "python_version >= \"3.10\" and python_version < \"3.12\""},
     {version = ">=1.26", markers = "python_version == \"3.12\""},
+    {version = ">=1.21", markers = "python_version >= \"3.10\" and python_version < \"3.12\""},
 ]
 portalocker = ">=2.7.0,<3.0.0"
 pydantic = ">=1.10.8"

pyproject.toml

@@ -1,6 +1,6 @@
 [tool.poetry]
 name = "letta"
-version = "0.6.42"
+version = "0.6.43"
 packages = [
     {include = "letta"},
 ]

tests/test_optimistic_json_parser.py

@@ -96,7 +96,7 @@ def test_parse_number_cases(strict_parser):
 def test_parse_boolean_true(strict_parser):
     assert strict_parser.parse("true") is True, "Should parse 'true'."
     # Check leftover
-    assert strict_parser.last_parse_reminding == "", "No extra tokens expected."
+    assert strict_parser.last_parse_reminding == None, "No extra tokens expected."
 
 
 def test_parse_boolean_false(strict_parser):
@@ -246,3 +246,35 @@ def test_multiple_parse_calls(strict_parser):
     result_2 = strict_parser.parse(input_2)
     assert result_2 == [2, 3]
     assert strict_parser.last_parse_reminding.strip() == "trailing2"
+
+
+def test_parse_incomplete_string_streaming_strict(strict_parser):
+    """
+    Test how a strict parser handles an incomplete string received in chunks.
+    """
+    # Simulate streaming chunks
+    chunk1 = '{"message": "This is an incomplete'
+    chunk2 = " string with a newline\\n"
+    chunk3 = 'and more text"}'
+
+    with pytest.raises(json.JSONDecodeError, match="Unterminated string"):
+        strict_parser.parse(chunk1)
+
+    incomplete_json = chunk1 + chunk2
+    with pytest.raises(json.JSONDecodeError, match="Unterminated string"):
+        strict_parser.parse(incomplete_json)
+
+    complete_json = incomplete_json + chunk3
+    result = strict_parser.parse(complete_json)
+    expected = {"message": "This is an incomplete string with a newline\nand more text"}
+    assert result == expected, "Should parse complete JSON correctly"
+
+
+def test_unescaped_control_characters_strict(strict_parser):
+    """
+    Test parsing JSON containing unescaped control characters in strict mode.
+    """
+    input_str = '{"message": "This has a newline\nand tab\t"}'
+
+    with pytest.raises(json.JSONDecodeError, match="Invalid control character"):
+        strict_parser.parse(input_str)