Merge branch 'dev' into f/integrations-service

agents-api/agents_api/activities/execute_integration.py

@@ -0,0 +1,55 @@
+from typing import Any
+
+from beartype import beartype
+from temporalio import activity
+
+from ..autogen.openapi_model import IntegrationDef
+from ..common.protocol.tasks import StepContext
+from ..env import testing
+from ..models.tools import get_tool_args_from_metadata
+
+
+@beartype
+async def execute_integration(
+    context: StepContext,
+    tool_name: str,
+    integration: IntegrationDef,
+    arguments: dict[str, Any],
+) -> Any:
+    developer_id = context.execution_input.developer_id
+    agent_id = context.execution_input.agent.id
+    task_id = context.execution_input.task.id
+
+    merged_tool_args = get_tool_args_from_metadata(
+        developer_id=developer_id, agent_id=agent_id, task_id=task_id
+    )
+
+    arguments = merged_tool_args.get(tool_name, {}) | arguments
+
+    try:
+        if integration.provider == "dummy":
+            return arguments
+
+        else:
+            raise NotImplementedError(
+                f"Unknown integration provider: {integration.provider}"
+            )
+    except BaseException as e:
+        if activity.in_activity():
+            activity.logger.error(f"Error in execute_integration: {e}")
+
+        raise
+
+
+async def mock_execute_integration(
+    context: StepContext,
+    tool_name: str,
+    integration: IntegrationDef,
+    arguments: dict[str, Any],
+) -> Any:
+    return arguments
+
+
+execute_integration = activity.defn(name="execute_integration")(
+    execute_integration if not testing else mock_execute_integration
+)

agents-api/agents_api/activities/task_steps/prompt_step.py

@@ -1,5 +1,6 @@
 from beartype import beartype
 from temporalio import activity
+from temporalio.exceptions import ApplicationError
 
 from ...clients import (
     litellm,  # We dont directly import `acompletion` so we can mock it
@@ -63,18 +64,29 @@ async def prompt_step(context: StepContext) -> StepOutcome:
     else:
         passed_settings: dict = {}
 
+    # Wrap the prompt in a list if it is not already
+    if isinstance(prompt, str):
+        prompt = [{"role": "user", "content": prompt}]
+
     completion_data: dict = {
         "model": agent_model,
         "tools": formatted_agent_tools or None,
-        ("messages" if isinstance(prompt, list) else "prompt"): prompt,
+        "messages": prompt,
         **agent_default_settings,
         **passed_settings,
     }
+
     response = await litellm.acompletion(
         **completion_data,
     )
 
+    if context.current_step.unwrap:
+        if response.choices[0].finish_reason == "tool_calls":
+            raise ApplicationError("Tool calls cannot be unwrapped")
+
+        response = response.choices[0].message.content
+
     return StepOutcome(
-        output=response.model_dump(),
+        output=response.model_dump() if hasattr(response, "model_dump") else response,
         next=None,
     )

agents-api/agents_api/activities/task_steps/tool_call_step.py

@@ -3,9 +3,10 @@
 
 from beartype import beartype
 from temporalio import activity
+from temporalio.exceptions import ApplicationError
 
-from ...activities.task_steps import base_evaluate
-from ...autogen.openapi_model import ToolCallStep
+from ...activities.task_steps.base_evaluate import base_evaluate
+from ...autogen.openapi_model import Tool, ToolCallStep
 from ...common.protocol.tasks import (
     StepContext,
     StepOutcome,
@@ -26,24 +27,27 @@ def generate_call_id():
 async def tool_call_step(context: StepContext) -> StepOutcome:
     assert isinstance(context.current_step, ToolCallStep)
 
-    tool_type, tool_name = context.current_step.tool.split(".")
+    tools: list[Tool] = context.tools
+    tool_name = context.current_step.tool
+
+    tool = next((t for t in tools if t.name == tool_name), None)
+
+    if tool is None:
+        raise ApplicationError(f"Tool {tool_name} not found in the toolset")
+
     arguments = await base_evaluate(
         context.current_step.arguments, context.model_dump()
     )
 
-    tools = context.execution_input.tools
-
-    assert tool_name in [tool.name for tool in tools], f"Tool {tool_name} not found"
-
     call_id = generate_call_id()
 
     tool_call = {
-        tool_type: {
+        tool.type: {
             "arguments": arguments,
             "name": tool_name,
         },
         "id": call_id,
-        "type": tool_type,
+        "type": tool.type,
     }
 
     return StepOutcome(output=tool_call)

agents-api/agents_api/autogen/Common.py

@@ -9,6 +9,16 @@
 from pydantic import AwareDatetime, BaseModel, ConfigDict, Field, RootModel
 
 
+class JinjaTemplate(RootModel[str]):
+    model_config = ConfigDict(
+        populate_by_name=True,
+    )
+    root: str
+    """
+    A valid jinja template.
+    """
+
+
 class Limit(RootModel[int]):
     model_config = ConfigDict(
         populate_by_name=True,

agents-api/agents_api/autogen/Sessions.py

@@ -43,6 +43,16 @@ class CreateSessionRequest(BaseModel):
     """
     Action to start on context window overflow
     """
+    forward_tool_results: StrictBool | None = None
+    """
+    Whether to forward the tool results to the model when available.
+    "true" => always forward
+    "false" => never forward
+    null => forward if applicable (default)
+
+    If a tool call is made, the tool's output will be sent back to the model as the model's input.
+    If a tool call is not made, the model's output will be returned as is.
+    """
     metadata: dict[str, Any] | None = None
 
 
@@ -70,6 +80,16 @@ class PatchSessionRequest(BaseModel):
     """
     Action to start on context window overflow
     """
+    forward_tool_results: StrictBool | None = None
+    """
+    Whether to forward the tool results to the model when available.
+    "true" => always forward
+    "false" => never forward
+    null => forward if applicable (default)
+
+    If a tool call is made, the tool's output will be sent back to the model as the model's input.
+    If a tool call is not made, the model's output will be returned as is.
+    """
     metadata: dict[str, Any] | None = None
 
 
@@ -97,6 +117,16 @@ class Session(BaseModel):
     """
     Action to start on context window overflow
     """
+    forward_tool_results: StrictBool | None = None
+    """
+    Whether to forward the tool results to the model when available.
+    "true" => always forward
+    "false" => never forward
+    null => forward if applicable (default)
+
+    If a tool call is made, the tool's output will be sent back to the model as the model's input.
+    If a tool call is not made, the model's output will be returned as is.
+    """
     id: Annotated[UUID, Field(json_schema_extra={"readOnly": True})]
     metadata: dict[str, Any] | None = None
     created_at: Annotated[AwareDatetime, Field(json_schema_extra={"readOnly": True})]
@@ -160,6 +190,16 @@ class UpdateSessionRequest(BaseModel):
     """
     Action to start on context window overflow
     """
+    forward_tool_results: StrictBool | None = None
+    """
+    Whether to forward the tool results to the model when available.
+    "true" => always forward
+    "false" => never forward
+    null => forward if applicable (default)
+
+    If a tool call is made, the tool's output will be sent back to the model as the model's input.
+    If a tool call is not made, the model's output will be returned as is.
+    """
     metadata: dict[str, Any] | None = None
 
 
@@ -194,6 +234,16 @@ class CreateOrUpdateSessionRequest(CreateSessionRequest):
     """
     Action to start on context window overflow
     """
+    forward_tool_results: StrictBool | None = None
+    """
+    Whether to forward the tool results to the model when available.
+    "true" => always forward
+    "false" => never forward
+    null => forward if applicable (default)
+
+    If a tool call is made, the tool's output will be sent back to the model as the model's input.
+    If a tool call is not made, the model's output will be returned as is.
+    """
     metadata: dict[str, Any] | None = None