Merge branch 'development' of https://github.com/joshmsmith/temporal-ai-agent into development

.env.example

@@ -37,3 +37,6 @@ OPENAI_API_KEY=sk-proj-...
 
 # Agent Goal Configuration
 # AGENT_GOAL=goal_event_flight_invoice  # (default) or goal_match_train_invoice
+
+# Set if the UI should force a user confirmation step or not
+SHOW_CONFIRM=True

api/main.py

@@ -1,3 +1,4 @@
+import os
 from fastapi import FastAPI
 from typing import Optional
 from temporalio.client import Client
@@ -11,7 +12,7 @@ from workflows.agent_goal_workflow import AgentGoalWorkflow
 from models.data_types import CombinedInput, AgentGoalWorkflowParams
 from tools.goal_registry import goal_list
 from fastapi.middleware.cors import CORSMiddleware
-from shared.config import get_temporal_client, TEMPORAL_TASK_QUEUE, AGENT_GOAL
+from shared.config import get_temporal_client, TEMPORAL_TASK_QUEUE
 
 app = FastAPI()
 temporal_client: Optional[Client] = None
@@ -22,13 +23,10 @@ load_dotenv()
 
 def get_initial_agent_goal():
     """Get the agent goal from environment variables."""
-    if AGENT_GOAL is not None:
-        for listed_goal in goal_list:
-            if listed_goal.id == AGENT_GOAL:
-                return listed_goal
-    else:
-        #if no goal is set in the config file, default to choosing an agent
-        return goal_list.get("goal_choose_agent_type")
+    env_goal = os.getenv("AGENT_GOAL", "goal_choose_agent_type") #if no goal is set in the env file, default to choosing an agent
+    for listed_goal in goal_list:
+        if listed_goal.id == env_goal:
+            return listed_goal
 
 
 @app.on_event("startup")
@@ -113,10 +111,14 @@ async def get_conversation_history():
                 status_code=404, detail="Workflow worker unavailable or not found."
             )
 
-        # For other Temporal errors, return a 500
-        raise HTTPException(
-            status_code=500, detail="Internal server error while querying workflow."
-        )
+        if "workflow not found" in error_message:
+            await start_workflow()
+            return []
+        else:
+            # For other Temporal errors, return a 500
+            raise HTTPException(
+                status_code=500, detail="Internal server error while querying workflow."
+            )
     
 @app.get("/agent-goal")
 async def get_agent_goal():

frontend/src/components/LLMResponse.jsx

@@ -27,7 +27,7 @@ const LLMResponse = memo(({ data, onConfirm, isLastMessage, onHeightChange }) =>
         : data?.response;
 
     const displayText = (response || '').trim();
-    const requiresConfirm = data.next === "confirm" && isLastMessage;
+    const requiresConfirm = data.force_confirm && data.next === "confirm" && isLastMessage;
     const defaultText = requiresConfirm 
         ? `Agent is ready to run "${data.tool}". Please confirm.` 
         : '';

shared/config.py

@@ -16,11 +16,6 @@ TEMPORAL_TLS_CERT = os.getenv("TEMPORAL_TLS_CERT", "")
 TEMPORAL_TLS_KEY = os.getenv("TEMPORAL_TLS_KEY", "")
 TEMPORAL_API_KEY = os.getenv("TEMPORAL_API_KEY", "")
 
-#Starting agent goal - 1st goal is always to help user pick a next goal
-AGENT_GOAL = "goal_choose_agent_type"
-#AGENT_GOAL = "goal_event_flight_invoice"
-
-
 async def get_temporal_client() -> Client:
     """
     Creates a Temporal client based on environment configuration.

todo.md

@@ -41,3 +41,5 @@
 
 [ ] non-retry the api key error - "Invalid API Key provided: sk_test_**J..." and "AuthenticationError" <br />
 [ ] make it so you can yeet yourself out of a goal and pick a new one <br />
+
+[ ] add visual feedback when workflow starting

workflows/agent_goal_workflow.py

@@ -1,5 +1,6 @@
 from collections import deque
 from datetime import timedelta
+import os
 from typing import Dict, Any, Union, List, Optional, Deque, TypedDict
 
 from temporalio.common import RetryPolicy
@@ -25,11 +26,19 @@ with workflow.unsafe.imports_passed_through():
 # Constants
 MAX_TURNS_BEFORE_CONTINUE = 250
 
+SHOW_CONFIRM = True
+show_confirm_env = os.getenv("SHOW_CONFIRM")
+if show_confirm_env is not None:
+    if show_confirm_env == "False":
+        SHOW_CONFIRM = False
+
+#ToolData as part of the workflow is what's accessible to the UI - see LLMResponse.jsx for example
 class ToolData(TypedDict, total=False):
     next: NextStep
     tool: str
     args: Dict[str, Any]
     response: str
+    force_confirm: bool = True
 
 @workflow.defn
 class AgentGoalWorkflow:
@@ -48,6 +57,7 @@ class AgentGoalWorkflow:
     # see ../api/main.py#temporal_client.start_workflow() for how the input parameters are set
     @workflow.run
     async def run(self, combined_input: CombinedInput) -> str:
+
         """Main workflow execution method."""
         # setup phase, starts with blank tool_params and agent_goal prompt as defined in tools/goal_registry.py
         params = combined_input.tool_params
@@ -77,12 +87,13 @@ class AgentGoalWorkflow:
 
             # handle chat-end signal
             if self.chat_ended:
+                workflow.logger.warning(f"workflow step: chat-end signal received, ending")
                 workflow.logger.info("Chat ended.")
-                
                 return f"{self.conversation_history}"
 
-            # execute tool 
+            # Execute the tool 
             if self.confirm and waiting_for_confirm and current_tool and self.tool_data:
+                workflow.logger.warning(f"workflow step: user has confirmed, executing the tool {current_tool}")
                 self.confirm = False
                 waiting_for_confirm = False
 
@@ -99,8 +110,6 @@ class AgentGoalWorkflow:
                     self.prompt_queue
                 )
 
-                workflow.logger.warning(f"tool_results keys: {self.tool_results[-1].keys()}")
-                workflow.logger.warning(f"tool_results values: {self.tool_results[-1].values()}")
                 #set new goal if we should
                 if len(self.tool_results) > 0:
                     if "ChangeGoal" in self.tool_results[-1].values() and "new_goal" in self.tool_results[-1].keys():
@@ -112,10 +121,11 @@ class AgentGoalWorkflow:
                         self.change_goal("goal_choose_agent_type")
                 continue
 
-            # push messages to UI if there are any
+            # if we've received messages to be processed on the prompt queue...
             if self.prompt_queue:
                 prompt = self.prompt_queue.popleft()
-                if not prompt.startswith("###"):
+                workflow.logger.warning(f"workflow step: processing message on the prompt queue, message is {prompt}")
+                if not prompt.startswith("###"): #if the message isn't from the LLM but is instead from the user
                     self.add_message("user", prompt)
 
                     # Validate the prompt before proceeding
@@ -134,27 +144,17 @@ class AgentGoalWorkflow:
                         ),
                     )
 
-                    #If validation fails, provide that feedback to the user - i.e., "your words make no sense, human"
+                    #If validation fails, provide that feedback to the user - i.e., "your words make no sense, puny human" end this iteration of processing
                     if not validation_result.validationResult:
-                        workflow.logger.warning(
-                            f"Prompt validation failed: {validation_result.validationFailedReason}"
-                        )
-                        self.add_message(
-                            "agent", validation_result.validationFailedReason
-                        )
+                        workflow.logger.warning(f"Prompt validation failed: {validation_result.validationFailedReason}")
+                        self.add_message("agent", validation_result.validationFailedReason)
                         continue
 
-                # Proceed with generating the context and prompt
-                context_instructions = generate_genai_prompt(
-                    self.goal, self.conversation_history, self.tool_data
-                )
+                # If valid, proceed with generating the context and prompt
+                context_instructions = generate_genai_prompt(self.goal, self.conversation_history, self.tool_data)
+                prompt_input = ToolPromptInput(prompt=prompt, context_instructions=context_instructions)
 
-                prompt_input = ToolPromptInput(
-                    prompt=prompt,
-                    context_instructions=context_instructions,
-                )
-
-                # connect to LLM and get it to create a prompt for the user about the tool
+                # connect to LLM and execute to get next steps
                 tool_data = await workflow.execute_activity(
                     ToolActivities.agent_toolPlanner,
                     prompt_input,
@@ -164,35 +164,39 @@ class AgentGoalWorkflow:
                         initial_interval=timedelta(seconds=5), backoff_coefficient=1
                     ),
                 )
+                tool_data["force_confirm"] = SHOW_CONFIRM
                 self.tool_data = tool_data
 
-                # move forward in the tool chain
+                # process the tool as dictated by the prompt response - what to do next, and with which tool
                 next_step = tool_data.get("next")
                 current_tool = tool_data.get("tool")
-                if "next" in self.tool_data.keys():
-                    workflow.logger.warning(f"ran the toolplanner, next step: {next_step}")
-                else: 
-                    workflow.logger.warning("ran the toolplanner, next step not set!")
 
+                workflow.logger.warning(f"next_step: {next_step}, current tool is {current_tool}")
+                #if the next step is to confirm...
                 if next_step == "confirm" and current_tool:
-                    workflow.logger.warning("next_step: confirm, ran the toolplanner, trying to confirm")
                     args = tool_data.get("args", {})
+                    #if we're missing arguments, go back to the top of the loop
                     if await helpers.handle_missing_args(current_tool, args, tool_data, self.prompt_queue):
                         continue
 
+                    #...otherwise, if we want to force the user to confirm, set that up
                     waiting_for_confirm = True
-                    self.confirm = False
-                    workflow.logger.info("Waiting for user confirm signal...")
+                    if SHOW_CONFIRM:
+                        self.confirm = False
+                        workflow.logger.info("Waiting for user confirm signal...")
+                    else:
+                        #theory - set self.confirm to true bc that's the signal, so we can get around the signal??
+                        self.confirm = True
 
-                # todo probably here we can set the next step to be change-goal 
+                # else if the next step is to pick a new goal...
                 elif next_step == "pick-new-goal":
                     workflow.logger.info("All steps completed. Resetting goal.")
-                    workflow.logger.warning("next_step = pick-new-goal, setting goal to goal_choose_agent_type")
                     self.change_goal("goal_choose_agent_type")
-                    
+                
+                # else if the next step is to be done - this should only happen if the user requests it via "end conversation"
                 elif next_step == "done":
-                    workflow.logger.warning("next_step = done")
                     self.add_message("agent", tool_data)
+                    # end the workflow
                     return str(self.conversation_history)
 
                 self.add_message("agent", tool_data)
@@ -208,8 +212,9 @@ class AgentGoalWorkflow:
     @workflow.signal
     async def user_prompt(self, prompt: str) -> None:
         """Signal handler for receiving user prompts."""
+        workflow.logger.warning(f"signal received: user_prompt, prompt is {prompt}")
         if self.chat_ended:
-            workflow.logger.warn(f"Message dropped due to chat closed: {prompt}")
+            workflow.logger.warning(f"Message dropped due to chat closed: {prompt}")
             return
         self.prompt_queue.append(prompt)
 
@@ -218,12 +223,14 @@ class AgentGoalWorkflow:
     async def confirm(self) -> None:
         """Signal handler for user confirmation of tool execution."""
         workflow.logger.info("Received user confirmation")
+        workflow.logger.warning(f"signal recieved: confirm")
         self.confirm = True
 
     #Signal that comes from api/main.py via a post to /end-chat
     @workflow.signal
     async def end_chat(self) -> None:
         """Signal handler for ending the chat session."""
+        workflow.logger.warning("signal received: end_chat")
         self.chat_ended = True
 
     @workflow.query