feat: add new providers (bedrock, deepseek) + bugfixes (#2445)

.pre-commit-config.yaml

@@ -3,7 +3,7 @@ repos:
     rev: v2.3.0
     hooks:
       - id: check-yaml
-        exclude: 'docs/.*|tests/data/.*|configs/.*'
+        exclude: 'docs/.*|tests/data/.*|configs/.*|helm/.*'
       - id: end-of-file-fixer
         exclude: 'docs/.*|tests/data/.*|letta/server/static_files/.*'
       - id: trailing-whitespace

Dockerfile

@@ -53,7 +53,8 @@ ENV LETTA_ENVIRONMENT=${LETTA_ENVIRONMENT} \
     PATH="/app/.venv/bin:$PATH" \
     POSTGRES_USER=letta \
     POSTGRES_PASSWORD=letta \
-    POSTGRES_DB=letta
+    POSTGRES_DB=letta \
+    COMPOSIO_DISABLE_VERSION_CHECK=true
 
 WORKDIR /app
 

alembic/versions/a113caac453e_add_identities_table.py

@@ -0,0 +1,66 @@
+"""add identities table
+
+Revision ID: a113caac453e
+Revises: 7980d239ea08
+Create Date: 2025-02-14 09:58:18.227122
+
+"""
+
+from typing import Sequence, Union
+
+import sqlalchemy as sa
+
+from alembic import op
+
+# revision identifiers, used by Alembic.
+revision: str = "a113caac453e"
+down_revision: Union[str, None] = "7980d239ea08"
+branch_labels: Union[str, Sequence[str], None] = None
+depends_on: Union[str, Sequence[str], None] = None
+
+
+def upgrade() -> None:
+    # Create identities table
+    op.create_table(
+        "identities",
+        sa.Column("id", sa.String(), nullable=False),
+        sa.Column("identifier_key", sa.String(), nullable=False),
+        sa.Column("name", sa.String(), nullable=False),
+        sa.Column("identity_type", sa.String(), nullable=False),
+        sa.Column("project_id", sa.String(), nullable=True),
+        # From OrganizationMixin
+        sa.Column("organization_id", sa.String(), nullable=False),
+        sa.Column("created_at", sa.DateTime(timezone=True), server_default=sa.text("CURRENT_TIMESTAMP"), nullable=True),
+        sa.Column("updated_at", sa.DateTime(timezone=True), server_default=sa.text("CURRENT_TIMESTAMP"), nullable=True),
+        sa.Column("is_deleted", sa.Boolean(), server_default=sa.text("FALSE"), nullable=False),
+        sa.Column("_created_by_id", sa.String(), nullable=True),
+        sa.Column("_last_updated_by_id", sa.String(), nullable=True),
+        # Foreign key to organizations
+        sa.ForeignKeyConstraint(
+            ["organization_id"],
+            ["organizations.id"],
+        ),
+        # Composite unique constraint
+        sa.UniqueConstraint(
+            "identifier_key",
+            "project_id",
+            "organization_id",
+            name="unique_identifier_pid_org_id",
+        ),
+        sa.PrimaryKeyConstraint("id"),
+    )
+
+    # Add identity_id column to agents table
+    op.add_column("agents", sa.Column("identity_id", sa.String(), nullable=True))
+
+    # Add foreign key constraint
+    op.create_foreign_key("fk_agents_identity_id", "agents", "identities", ["identity_id"], ["id"], ondelete="CASCADE")
+
+
+def downgrade() -> None:
+    # First remove the foreign key constraint and column from agents
+    op.drop_constraint("fk_agents_identity_id", "agents", type_="foreignkey")
+    op.drop_column("agents", "identity_id")
+
+    # Then drop the table
+    op.drop_table("identities")

alembic/versions/a3047a624130_add_identifier_key_to_agents.py

@@ -0,0 +1,27 @@
+"""add identifier key to agents
+
+Revision ID: a3047a624130
+Revises: a113caac453e
+Create Date: 2025-02-14 12:24:16.123456
+
+"""
+
+from typing import Sequence, Union
+
+import sqlalchemy as sa
+
+from alembic import op
+
+# revision identifiers, used by Alembic.
+revision: str = "a3047a624130"
+down_revision: Union[str, None] = "a113caac453e"
+branch_labels: Union[str, Sequence[str], None] = None
+depends_on: Union[str, Sequence[str], None] = None
+
+
+def upgrade() -> None:
+    op.add_column("agents", sa.Column("identifier_key", sa.String(), nullable=True))
+
+
+def downgrade() -> None:
+    op.drop_column("agents", "identifier_key")

letta/__init__.py

@@ -1,4 +1,4 @@
-__version__ = "0.6.27"
+__version__ = "0.6.28"
 
 # import clients
 from letta.client.client import LocalClient, RESTClient, create_client

letta/agent.py

@@ -60,6 +60,7 @@
 from letta.settings import summarizer_settings
 from letta.streaming_interface import StreamingRefreshCLIInterface
 from letta.system import get_heartbeat, get_token_limit_warning, package_function_response, package_summarize_message, package_user_message
+from letta.tracing import trace_method
 from letta.utils import (
     count_tokens,
     get_friendly_error_msg,
@@ -309,6 +310,7 @@ def _handle_function_error_response(
         # Return updated messages
         return messages
 
+    @trace_method("Get AI Reply")
     def _get_ai_reply(
         self,
         message_sequence: List[Message],
@@ -399,6 +401,7 @@ def _get_ai_reply(
         log_telemetry(self.logger, "_handle_ai_response finish catch-all exception")
         raise Exception("Retries exhausted and no valid response received.")
 
+    @trace_method("Handle AI Response")
     def _handle_ai_response(
         self,
         response_message: ChatCompletionMessage,  # TODO should we eventually move the Message creation outside of this function?
@@ -492,7 +495,10 @@ def _handle_ai_response(
             try:
                 raw_function_args = function_call.arguments
                 function_args = parse_json(raw_function_args)
-            except Exception:
+                if not isinstance(function_args, dict):
+                    raise ValueError(f"Function arguments are not a dictionary: {function_args} (raw={raw_function_args})")
+            except Exception as e:
+                print(e)
                 error_msg = f"Error parsing JSON for function '{function_name}' arguments: {function_call.arguments}"
                 function_response = "None"  # more like "never ran?"
                 messages = self._handle_function_error_response(
@@ -627,9 +633,15 @@ def _handle_ai_response(
         elif self.tool_rules_solver.is_terminal_tool(function_name):
             heartbeat_request = False
 
+        # if continue tool rule, then  must request a heartbeat
+        # TODO: dont even include heartbeats in the args
+        if self.tool_rules_solver.is_continue_tool(function_name):
+            heartbeat_request = True
+
         log_telemetry(self.logger, "_handle_ai_response finish")
         return messages, heartbeat_request, function_failed
 
+    @trace_method("Agent Step")
     def step(
         self,
         messages: Union[Message, List[Message]],

letta/client/client.py

@@ -2351,6 +2351,7 @@ def create_agent(
         tool_rules: Optional[List[BaseToolRule]] = None,
         include_base_tools: Optional[bool] = True,
         include_multi_agent_tools: bool = False,
+        include_base_tool_rules: bool = True,
         # metadata
         metadata: Optional[Dict] = {"human:": DEFAULT_HUMAN, "persona": DEFAULT_PERSONA},
         description: Optional[str] = None,
@@ -2402,6 +2403,7 @@ def create_agent(
             "tool_rules": tool_rules,
             "include_base_tools": include_base_tools,
             "include_multi_agent_tools": include_multi_agent_tools,
+            "include_base_tool_rules": include_base_tool_rules,
             "system": system,
             "agent_type": agent_type,
             "llm_config": llm_config if llm_config else self._default_llm_config,

letta/constants.py

@@ -86,6 +86,8 @@
 # The max amount of tokens supported by the underlying model (eg 8k for gpt-4 and Mistral 7B)
 LLM_MAX_TOKENS = {
     "DEFAULT": 8192,
+    "deepseek-chat": 64000,
+    "deepseek-reasoner": 64000,
     ## OpenAI models: https://platform.openai.com/docs/models/overview
     # "o1-preview
     "chatgpt-4o-latest": 128000,

letta/functions/schema_generator.py

@@ -394,12 +394,12 @@ def generate_schema(function, name: Optional[str] = None, description: Optional[
     # append the heartbeat
     # TODO: don't hard-code
     # TODO: if terminal, don't include this
-    if function.__name__ not in ["send_message"]:
-        schema["parameters"]["properties"]["request_heartbeat"] = {
-            "type": "boolean",
-            "description": "Request an immediate heartbeat after function execution. Set to `True` if you want to send a follow-up message or run a follow-up function.",
-        }
-        schema["parameters"]["required"].append("request_heartbeat")
+    # if function.__name__ not in ["send_message"]:
+    schema["parameters"]["properties"]["request_heartbeat"] = {
+        "type": "boolean",
+        "description": "Request an immediate heartbeat after function execution. Set to `True` if you want to send a follow-up message or run a follow-up function.",
+    }
+    schema["parameters"]["required"].append("request_heartbeat")
 
     return schema
 

letta/helpers/converters.py

@@ -0,0 +1,153 @@
+import base64
+from typing import Any, Dict, List, Optional, Union
+
+import numpy as np
+from openai.types.chat.chat_completion_message_tool_call import ChatCompletionMessageToolCall as OpenAIToolCall
+from openai.types.chat.chat_completion_message_tool_call import Function as OpenAIFunction
+from sqlalchemy import Dialect
+
+from letta.schemas.embedding_config import EmbeddingConfig
+from letta.schemas.enums import ToolRuleType
+from letta.schemas.llm_config import LLMConfig
+from letta.schemas.tool_rule import ChildToolRule, ConditionalToolRule, ContinueToolRule, InitToolRule, TerminalToolRule, ToolRule
+
+# --------------------------
+# LLMConfig Serialization
+# --------------------------
+
+
+def serialize_llm_config(config: Union[Optional[LLMConfig], Dict]) -> Optional[Dict]:
+    """Convert an LLMConfig object into a JSON-serializable dictionary."""
+    if config and isinstance(config, LLMConfig):
+        return config.model_dump()
+    return config
+
+
+def deserialize_llm_config(data: Optional[Dict]) -> Optional[LLMConfig]:
+    """Convert a dictionary back into an LLMConfig object."""
+    return LLMConfig(**data) if data else None
+
+
+# --------------------------
+# EmbeddingConfig Serialization
+# --------------------------
+
+
+def serialize_embedding_config(config: Union[Optional[EmbeddingConfig], Dict]) -> Optional[Dict]:
+    """Convert an EmbeddingConfig object into a JSON-serializable dictionary."""
+    if config and isinstance(config, EmbeddingConfig):
+        return config.model_dump()
+    return config
+
+
+def deserialize_embedding_config(data: Optional[Dict]) -> Optional[EmbeddingConfig]:
+    """Convert a dictionary back into an EmbeddingConfig object."""
+    return EmbeddingConfig(**data) if data else None
+
+
+# --------------------------
+# ToolRule Serialization
+# --------------------------
+
+
+def serialize_tool_rules(tool_rules: Optional[List[ToolRule]]) -> List[Dict[str, Any]]:
+    """Convert a list of ToolRules into a JSON-serializable format."""
+
+    if not tool_rules:
+        return []
+
+    data = [{**rule.model_dump(), "type": rule.type.value} for rule in tool_rules]  # Convert Enum to string for JSON compatibility
+
+    # Validate ToolRule structure
+    for rule_data in data:
+        if rule_data["type"] == ToolRuleType.constrain_child_tools.value and "children" not in rule_data:
+            raise ValueError(f"Invalid ToolRule serialization: 'children' field missing for rule {rule_data}")
+
+    return data
+
+
+def deserialize_tool_rules(data: Optional[List[Dict]]) -> List[Union[ChildToolRule, InitToolRule, TerminalToolRule, ConditionalToolRule]]:
+    """Convert a list of dictionaries back into ToolRule objects."""
+    if not data:
+        return []
+
+    return [deserialize_tool_rule(rule_data) for rule_data in data]
+
+
+def deserialize_tool_rule(data: Dict) -> Union[ChildToolRule, InitToolRule, TerminalToolRule, ConditionalToolRule, ContinueToolRule]:
+    """Deserialize a dictionary to the appropriate ToolRule subclass based on 'type'."""
+    rule_type = ToolRuleType(data.get("type"))
+
+    if rule_type == ToolRuleType.run_first:
+        return InitToolRule(**data)
+    elif rule_type == ToolRuleType.exit_loop:
+        return TerminalToolRule(**data)
+    elif rule_type == ToolRuleType.constrain_child_tools:
+        return ChildToolRule(**data)
+    elif rule_type == ToolRuleType.conditional:
+        return ConditionalToolRule(**data)
+    elif rule_type == ToolRuleType.continue_loop:
+        return ContinueToolRule(**data)
+    raise ValueError(f"Unknown ToolRule type: {rule_type}")
+
+
+# --------------------------
+# ToolCall Serialization
+# --------------------------
+
+
+def serialize_tool_calls(tool_calls: Optional[List[Union[OpenAIToolCall, dict]]]) -> List[Dict]:
+    """Convert a list of OpenAI ToolCall objects into JSON-serializable format."""
+    if not tool_calls:
+        return []
+
+    serialized_calls = []
+    for call in tool_calls:
+        if isinstance(call, OpenAIToolCall):
+            serialized_calls.append(call.model_dump())
+        elif isinstance(call, dict):
+            serialized_calls.append(call)  # Already a dictionary, leave it as-is
+        else:
+            raise TypeError(f"Unexpected tool call type: {type(call)}")
+
+    return serialized_calls
+
+
+def deserialize_tool_calls(data: Optional[List[Dict]]) -> List[OpenAIToolCall]:
+    """Convert a JSON list back into OpenAIToolCall objects."""
+    if not data:
+        return []
+
+    calls = []
+    for item in data:
+        func_data = item.pop("function", None)
+        tool_call_function = OpenAIFunction(**func_data) if func_data else None
+        calls.append(OpenAIToolCall(function=tool_call_function, **item))
+
+    return calls
+
+
+# --------------------------
+# Vector Serialization
+# --------------------------
+
+
+def serialize_vector(vector: Optional[Union[List[float], np.ndarray]]) -> Optional[bytes]:
+    """Convert a NumPy array or list into a base64-encoded byte string."""
+    if vector is None:
+        return None
+    if isinstance(vector, list):
+        vector = np.array(vector, dtype=np.float32)
+
+    return base64.b64encode(vector.tobytes())
+
+
+def deserialize_vector(data: Optional[bytes], dialect: Dialect) -> Optional[np.ndarray]:
+    """Convert a base64-encoded byte string back into a NumPy array."""
+    if not data:
+        return None
+
+    if dialect.name == "sqlite":
+        data = base64.b64decode(data)
+
+    return np.frombuffer(data, dtype=np.float32)