Merge branch 'main' into fix-anthropic-kevin

.github/workflows/docker-image.yml

@@ -6,11 +6,9 @@ on:
   workflow_dispatch:
 
 jobs:
-
   build:
-
     runs-on: ubuntu-latest
-
+    
     steps:
     - name: Login to Docker Hub
       uses: docker/login-action@v3
@@ -19,19 +17,25 @@ jobs:
         password: ${{ secrets.DOCKERHUB_TOKEN }}
 
     - uses: actions/checkout@v3
-    - name: Build and push the Docker image (memgpt)
-      run: |
-        # Extract the version number from pyproject.toml using awk
-        CURRENT_VERSION=$(awk -F '"' '/version =/ { print $2 }' pyproject.toml | head -n 1)
-        docker build . --file Dockerfile --tag memgpt/letta:$CURRENT_VERSION --tag memgpt/letta:latest
-        docker push memgpt/letta:$CURRENT_VERSION
-        docker push memgpt/letta:latest
+
+    - name: Set up QEMU
+      uses: docker/setup-qemu-action@v3
 
-    - uses: actions/checkout@v3
-    - name: Build and push the Docker image (letta)
-      run: |
-        # Extract the version number from pyproject.toml using awk
-        CURRENT_VERSION=$(awk -F '"' '/version =/ { print $2 }' pyproject.toml | head -n 1)
-        docker build . --file Dockerfile --tag letta/letta:$CURRENT_VERSION --tag letta/letta:latest
-        docker push letta/letta:$CURRENT_VERSION
-        docker push letta/letta:latest
+    - name: Set up Docker Buildx
+      uses: docker/setup-buildx-action@v3
+
+    - name: Extract version number
+      id: extract_version
+      run: echo "CURRENT_VERSION=$(awk -F '\"' '/version =/ { print $2 }' pyproject.toml | head -n 1)" >> $GITHUB_ENV
+
+    - name: Build and push
+      uses: docker/build-push-action@v6
+      with:
+        platforms: linux/amd64,linux/arm64
+        push: true
+        tags: |
+          letta/letta:${{ env.CURRENT_VERSION }}
+          letta/letta:latest
+          memgpt/letta:${{ env.CURRENT_VERSION }}
+          memgpt/letta:latest
+          

letta/agent.py

@@ -18,6 +18,7 @@
     MESSAGE_SUMMARY_WARNING_FRAC,
     O1_BASE_TOOLS,
     REQ_HEARTBEAT_MESSAGE,
+    STRUCTURED_OUTPUT_MODELS
 )
 from letta.errors import LLMError
 from letta.helpers import ToolRulesSolver
@@ -276,6 +277,7 @@ def __init__(
 
         # gpt-4, gpt-3.5-turbo, ...
         self.model = self.agent_state.llm_config.model
+        self.check_tool_rules()
 
         # state managers
         self.block_manager = BlockManager()
@@ -381,6 +383,14 @@ def __init__(
         # Create the agent in the DB
         self.update_state()
 
+    def check_tool_rules(self):
+        if self.model not in STRUCTURED_OUTPUT_MODELS:
+            if len(self.tool_rules_solver.init_tool_rules) > 1:
+                raise ValueError("Multiple initial tools are not supported for non-structured models. Please use only one initial tool rule.")
+            self.supports_structured_output = False
+        else:
+            self.supports_structured_output = True
+
     def update_memory_if_change(self, new_memory: Memory) -> bool:
         """
         Update internal memory object and system prompt if there have been modifications.
@@ -588,6 +598,7 @@ def _get_ai_reply(
         empty_response_retry_limit: int = 3,
         backoff_factor: float = 0.5,  # delay multiplier for exponential backoff
         max_delay: float = 10.0,  # max delay between retries
+        step_count: Optional[int] = None,
     ) -> ChatCompletionResponse:
         """Get response from LLM API with robust retry mechanism."""
 
@@ -596,6 +607,16 @@ def _get_ai_reply(
             self.functions if not allowed_tool_names else [func for func in self.functions if func["name"] in allowed_tool_names]
         )
 
+        # For the first message, force the initial tool if one is specified
+        force_tool_call = None
+        if (
+            step_count is not None
+            and step_count == 0
+            and not self.supports_structured_output
+            and len(self.tool_rules_solver.init_tool_rules) > 0
+        ):
+            force_tool_call = self.tool_rules_solver.init_tool_rules[0].tool_name
+
         for attempt in range(1, empty_response_retry_limit + 1):
             try:
                 response = create(
@@ -606,6 +627,7 @@ def _get_ai_reply(
                     functions_python=self.functions_python,
                     function_call=function_call,
                     first_message=first_message,
+                    force_tool_call=force_tool_call,
                     stream=stream,
                     stream_interface=self.interface,
                 )
@@ -897,6 +919,7 @@ def step(
         step_count = 0
         while True:
             kwargs["first_message"] = False
+            kwargs["step_count"] = step_count
             step_response = self.inner_step(
                 messages=next_input_message,
                 **kwargs,
@@ -972,6 +995,7 @@ def inner_step(
         first_message_retry_limit: int = FIRST_MESSAGE_ATTEMPTS,
         skip_verify: bool = False,
         stream: bool = False,  # TODO move to config?
+        step_count: Optional[int] = None,
     ) -> AgentStepResponse:
         """Runs a single step in the agent loop (generates at most one LLM call)"""
 
@@ -1014,7 +1038,9 @@ def inner_step(
             else:
                 response = self._get_ai_reply(
                     message_sequence=input_message_sequence,
+                    first_message=first_message,
                     stream=stream,
+                    step_count=step_count,
                 )
 
             # Step 3: check if LLM wanted to call a function

letta/client/client.py

@@ -2156,6 +2156,7 @@ def create_agent(
             "block_ids": [b.id for b in memory.get_blocks()] + block_ids,
             "tool_ids": tool_ids,
             "tool_rules": tool_rules,
+            "include_base_tools": include_base_tools,
             "system": system,
             "agent_type": agent_type,
             "llm_config": llm_config if llm_config else self._default_llm_config,

letta/constants.py

@@ -48,6 +48,9 @@
 DEFAULT_MESSAGE_TOOL = "send_message"
 DEFAULT_MESSAGE_TOOL_KWARG = "message"
 
+# Structured output models
+STRUCTURED_OUTPUT_MODELS = {"gpt-4o", "gpt-4o-mini"}
+
 # LOGGER_LOG_LEVEL is use to convert Text to Logging level value for logging mostly for Cli input to setting level
 LOGGER_LOG_LEVELS = {"CRITICAL": CRITICAL, "ERROR": ERROR, "WARN": WARN, "WARNING": WARNING, "INFO": INFO, "DEBUG": DEBUG, "NOTSET": NOTSET}
 

letta/embeddings.py

@@ -234,16 +234,10 @@ def embedding_model(config: EmbeddingConfig, user_id: Optional[uuid.UUID] = None
         )
     elif endpoint_type == "ollama":
 
-        from llama_index.embeddings.ollama import OllamaEmbedding
-
-        ollama_additional_kwargs = {}
-        callback_manager = None
-
-        model = OllamaEmbedding(
-            model_name=config.embedding_model,
+        model = OllamaEmbeddings(
+            model=config.embedding_model,
             base_url=config.embedding_endpoint,
-            ollama_additional_kwargs=ollama_additional_kwargs or {},
-            callback_manager=callback_manager or None,
+            ollama_additional_kwargs={},
         )
         return model
 

letta/llm_api/anthropic.py

@@ -99,16 +99,20 @@ def convert_tools_to_anthropic_format(tools: List[Tool]) -> List[dict]:
         - 1 level less of nesting
         - "parameters" -> "input_schema"
     """
-    tools_dict_list = []
+    formatted_tools = []
     for tool in tools:
-        tools_dict_list.append(
-            {
-                "name": tool.function.name,
-                "description": tool.function.description,
-                "input_schema": tool.function.parameters,
+        formatted_tool = {
+            "name"         : tool.function.name,
+            "description"  : tool.function.description,
+            "input_schema"   : tool.function.parameters or {
+                "type": "object",
+                "properties": {},
+                "required": []
             }
-        )
-    return tools_dict_list
+        }
+        formatted_tools.append(formatted_tool)
+
+    return formatted_tools
 
 
 def merge_tool_results_into_user_messages(messages: List[dict]):

letta/llm_api/llm_api_tools.py

@@ -113,6 +113,7 @@ def create(
     function_call: str = "auto",
     # hint
     first_message: bool = False,
+    force_tool_call: Optional[str] = None,  # Force a specific tool to be called
     # use tool naming?
     # if false, will use deprecated 'functions' style
     use_tool_naming: bool = True,
@@ -252,14 +253,24 @@ def create(
         if not use_tool_naming:
             raise NotImplementedError("Only tool calling supported on Anthropic API requests")
 
+        tool_call = None
+        if force_tool_call is not None:
+            tool_call = {
+                "type": "function",
+                "function": {
+                    "name": force_tool_call
+                }
+            }
+            assert functions is not None
+
         return anthropic_chat_completions_request(
             url=llm_config.model_endpoint,
             api_key=model_settings.anthropic_api_key,
             data=ChatCompletionRequest(
                 model=llm_config.model,
                 messages=[cast_message_to_subtype(m.to_openai_dict()) for m in messages],
                 tools=[{"type": "function", "function": f} for f in functions] if functions else None,
-                # tool_choice=function_call,
+                tool_choice=tool_call,
                 # user=str(user_id),
                 # NOTE: max_tokens is required for Anthropic API
                 max_tokens=1024,  # TODO make dynamic

letta/orm/errors.py

@@ -12,3 +12,11 @@ class UniqueConstraintViolationError(ValueError):
 
 class ForeignKeyConstraintViolationError(ValueError):
     """Custom exception for foreign key constraint violations."""
+
+
+class DatabaseTimeoutError(Exception):
+    """Custom exception for database timeout issues."""
+
+    def __init__(self, message="Database operation timed out", original_exception=None):
+        super().__init__(message)
+        self.original_exception = original_exception

letta/orm/sqlalchemy_base.py

@@ -1,14 +1,16 @@
 from datetime import datetime
 from enum import Enum
+from functools import wraps
 from typing import TYPE_CHECKING, List, Literal, Optional
 
 from sqlalchemy import String, desc, func, or_, select
-from sqlalchemy.exc import DBAPIError, IntegrityError
+from sqlalchemy.exc import DBAPIError, IntegrityError, TimeoutError
 from sqlalchemy.orm import Mapped, Session, mapped_column
 
 from letta.log import get_logger
 from letta.orm.base import Base, CommonSqlalchemyMetaMixins
 from letta.orm.errors import (
+    DatabaseTimeoutError,
     ForeignKeyConstraintViolationError,
     NoResultFound,
     UniqueConstraintViolationError,
@@ -23,6 +25,20 @@
 logger = get_logger(__name__)
 
 
+def handle_db_timeout(func):
+    """Decorator to handle SQLAlchemy TimeoutError and wrap it in a custom exception."""
+
+    @wraps(func)
+    def wrapper(*args, **kwargs):
+        try:
+            return func(*args, **kwargs)
+        except TimeoutError as e:
+            logger.error(f"Timeout while executing {func.__name__} with args {args} and kwargs {kwargs}: {e}")
+            raise DatabaseTimeoutError(message=f"Timeout occurred in {func.__name__}.", original_exception=e)
+
+    return wrapper
+
+
 class AccessType(str, Enum):
     ORGANIZATION = "organization"
     USER = "user"
@@ -36,22 +52,7 @@ class SqlalchemyBase(CommonSqlalchemyMetaMixins, Base):
     id: Mapped[str] = mapped_column(String, primary_key=True)
 
     @classmethod
-    def get(cls, *, db_session: Session, id: str) -> Optional["SqlalchemyBase"]:
-        """Get a record by ID.
-
-        Args:
-            db_session: SQLAlchemy session
-            id: Record ID to retrieve
-
-        Returns:
-            Optional[SqlalchemyBase]: The record if found, None otherwise
-        """
-        try:
-            return db_session.query(cls).filter(cls.id == id).first()
-        except DBAPIError:
-            return None
-
-    @classmethod
+    @handle_db_timeout
     def list(
         cls,
         *,
@@ -180,6 +181,7 @@ def list(
             return list(session.execute(query).scalars())
 
     @classmethod
+    @handle_db_timeout
     def read(
         cls,
         db_session: "Session",
@@ -231,6 +233,7 @@ def read(
         conditions_str = ", ".join(query_conditions) if query_conditions else "no specific conditions"
         raise NoResultFound(f"{cls.__name__} not found with {conditions_str}")
 
+    @handle_db_timeout
     def create(self, db_session: "Session", actor: Optional["User"] = None) -> "SqlalchemyBase":
         logger.debug(f"Creating {self.__class__.__name__} with ID: {self.id} with actor={actor}")
 
@@ -245,6 +248,7 @@ def create(self, db_session: "Session", actor: Optional["User"] = None) -> "Sqla
         except (DBAPIError, IntegrityError) as e:
             self._handle_dbapi_error(e)
 
+    @handle_db_timeout
     def delete(self, db_session: "Session", actor: Optional["User"] = None) -> "SqlalchemyBase":
         logger.debug(f"Soft deleting {self.__class__.__name__} with ID: {self.id} with actor={actor}")
 
@@ -254,6 +258,7 @@ def delete(self, db_session: "Session", actor: Optional["User"] = None) -> "Sqla
         self.is_deleted = True
         return self.update(db_session)
 
+    @handle_db_timeout
     def hard_delete(self, db_session: "Session", actor: Optional["User"] = None) -> None:
         """Permanently removes the record from the database."""
         logger.debug(f"Hard deleting {self.__class__.__name__} with ID: {self.id} with actor={actor}")
@@ -269,6 +274,7 @@ def hard_delete(self, db_session: "Session", actor: Optional["User"] = None) ->
             else:
                 logger.debug(f"{self.__class__.__name__} with ID {self.id} successfully hard deleted")
 
+    @handle_db_timeout
     def update(self, db_session: "Session", actor: Optional["User"] = None) -> "SqlalchemyBase":
         logger.debug(f"Updating {self.__class__.__name__} with ID: {self.id} with actor={actor}")
         if actor:
@@ -281,6 +287,7 @@ def update(self, db_session: "Session", actor: Optional["User"] = None) -> "Sqla
             return self
 
     @classmethod
+    @handle_db_timeout
     def size(
         cls,
         *,