Merge pull request #1 from joshmsmith/development

confirming

.env.example

@@ -7,6 +7,9 @@ STRIPE_API_KEY=sk_test_51J...
 LLM_PROVIDER=openai # default
 OPENAI_API_KEY=sk-proj-...
 # or
+#LLM_PROVIDER=grok
+#GROK_API_KEY=xai-your-grok-api-key
+# or
 # LLM_PROVIDER=ollama
 # OLLAMA_MODEL_NAME=qwen2.5:14b
 # or
@@ -34,3 +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

README.md

@@ -2,191 +2,29 @@
 
 This demo shows a multi-turn conversation with an AI agent running inside a Temporal workflow. The purpose of the agent is to collect information towards a goal, running tools along the way. There's a simple DSL input for collecting information (currently set up to use mock functions to search for public events, search for flights around those events, then create a test Stripe invoice for the trip).
 
-The AI will respond with clarifications and ask for any missing information to that goal. You can configure it to use [ChatGPT 4o](https://openai.com/index/hello-gpt-4o/), [Anthropic Claude](https://www.anthropic.com/claude), [Google Gemini](https://gemini.google.com), [Deepseek-V3](https://www.deepseek.com/) or a local LLM of your choice using [Ollama](https://ollama.com).
+The AI will respond with clarifications and ask for any missing information to that goal. You can configure it to use [ChatGPT 4o](https://openai.com/index/hello-gpt-4o/), [Anthropic Claude](https://www.anthropic.com/claude), [Google Gemini](https://gemini.google.com), [Deepseek-V3](https://www.deepseek.com/), [Grok](https://docs.x.ai/docs/overview) or a local LLM of your choice using [Ollama](https://ollama.com).
 
-[Watch the demo (5 minute YouTube video)](https://www.youtube.com/watch?v=GEXllEH2XiQ)
+It's really helpful to [watch the demo (5 minute YouTube video)](https://www.youtube.com/watch?v=GEXllEH2XiQ) to understand how interaction works.
 
-[![Watch the demo](./agent-youtube-screenshot.jpeg)](https://www.youtube.com/watch?v=GEXllEH2XiQ)
+[![Watch the demo](./assets/agent-youtube-screenshot.jpeg)](https://www.youtube.com/watch?v=GEXllEH2XiQ)
 
-## Configuration
+## Setup and Configuration
+See [the Setup guide](./setup.md).
 
-This application uses `.env` files for configuration. Copy the [.env.example](.env.example) file to `.env` and update the values:
+## Interaction
+TODO
 
-```bash
-cp .env.example .env
-```
+## Architecture
+See [the architecture guide](./architecture.md).
 
-### Agent Goal Configuration
-
-The agent can be configured to pursue different goals using the `AGENT_GOAL` environment variable in your `.env` file.
-
-#### Goal: Find an event in Australia / New Zealand, book flights to it and invoice the user for the cost
-- `AGENT_GOAL=goal_event_flight_invoice` (default) - Helps users find events, book flights, and arrange train travel with invoice generation
-    - This is the scenario in the video above
-
-#### Goal: Find a Premier League match, book train tickets to it and invoice the user for the cost
-- `AGENT_GOAL=goal_match_train_invoice` - Focuses on Premier League match attendance with train booking and invoice generation
-    - This is a new goal that is part of an upcoming conference talk
-
-If not specified, the agent defaults to `goal_event_flight_invoice`. Each goal comes with its own set of tools and conversation flows designed for specific use cases. You can examine `tools/goal_registry.py` to see the detailed configuration of each goal.
-
-See the next section for tool configuration for each goal.
-
-### Tool Configuration
-
-#### Agent Goal: goal_event_flight_invoice (default)
-* The agent uses a mock function to search for events. This has zero configuration.
-* By default the agent uses a mock function to search for flights.
-    * If you want to use the real flights API, go to `tools/search_flights.py` and replace the `search_flights` function with `search_flights_real_api` that exists in the same file.
-    * It's free to sign up at [RapidAPI](https://rapidapi.com/apiheya/api/sky-scrapper)
-    * This api might be slow to respond, so you may want to increase the start to close timeout, `TOOL_ACTIVITY_START_TO_CLOSE_TIMEOUT` in `workflows/workflow_helpers.py`
-* Requires a Stripe key for the `create_invoice` tool. Set this in the `STRIPE_API_KEY` environment variable in .env
-    * It's free to sign up and get a key at [Stripe](https://stripe.com/)
-    * If you're lazy go to `tools/create_invoice.py` and replace the `create_invoice` function with the mock `create_invoice_example` that exists in the same file.
-
-#### Agent Goal: goal_match_train_invoice
-
-* Finding a match requires a key from [Football Data](https://www.football-data.org). Sign up for a free account, then see the 'My Account' page to get your API token. Set `FOOTBALL_DATA_API_KEY` to this value.
-    * If you're lazy go to `tools/search_fixtures.py` and replace the `search_fixtures` function with the mock `search_fixtures_example` that exists in the same file.
-* We use a mock function to search for trains. Start the train API server to use the real API: `python thirdparty/train_api.py`
-* * The train activity is 'enterprise' so it's written in C# and requires a .NET runtime. See the [.NET backend](#net-(enterprise)-backend) section for details on running it.
-* Requires a Stripe key for the `create_invoice` tool. Set this in the `STRIPE_API_KEY` environment variable in .env
-    * It's free to sign up and get a key at [Stripe](https://stripe.com/)
-    * If you're lazy go to `tools/create_invoice.py` and replace the `create_invoice` function with the mock `create_invoice_example` that exists in the same file.
-
-### LLM Provider Configuration
-
-The agent can use OpenAI's GPT-4o, Google Gemini, Anthropic Claude, or a local LLM via Ollama. Set the `LLM_PROVIDER` environment variable in your `.env` file to choose the desired provider:
-
-- `LLM_PROVIDER=openai` for OpenAI's GPT-4o
-- `LLM_PROVIDER=google` for Google Gemini
-- `LLM_PROVIDER=anthropic` for Anthropic Claude
-- `LLM_PROVIDER=deepseek` for DeepSeek-V3
-- `LLM_PROVIDER=ollama` for running LLMs via [Ollama](https://ollama.ai) (not recommended for this use case)
-
-### Option 1: OpenAI
-
-If using OpenAI, ensure you have an OpenAI key for the GPT-4o model. Set this in the `OPENAI_API_KEY` environment variable in `.env`.
-
-### Option 2: Google Gemini
-
-To use Google Gemini:
-
-1. Obtain a Google API key and set it in the `GOOGLE_API_KEY` environment variable in `.env`.
-2. Set `LLM_PROVIDER=google` in your `.env` file.
-
-### Option 3: Anthropic Claude (recommended)
-
-I find that Claude Sonnet 3.5 performs better than the other hosted LLMs for this use case.
-
-To use Anthropic:
-
-1. Obtain an Anthropic API key and set it in the `ANTHROPIC_API_KEY` environment variable in `.env`.
-2. Set `LLM_PROVIDER=anthropic` in your `.env` file.
-
-### Option 4: Deepseek-V3
-
-To use Deepseek-V3:
-
-1. Obtain a Deepseek API key and set it in the `DEEPSEEK_API_KEY` environment variable in `.env`.
-2. Set `LLM_PROVIDER=deepseek` in your `.env` file.
-
-### Option 5: Local LLM via Ollama (not recommended)
-
-To use a local LLM with Ollama:
-
-1. Install [Ollama](https://ollama.com) and the [Qwen2.5 14B](https://ollama.com/library/qwen2.5) model.
-   - Run `ollama run <OLLAMA_MODEL_NAME>` to start the model. Note that this model is about 9GB to download.
-   - Example: `ollama run qwen2.5:14b`
-
-2. Set `LLM_PROVIDER=ollama` in your `.env` file and `OLLAMA_MODEL_NAME` to the name of the model you installed.
-
-Note: I found the other (hosted) LLMs to be MUCH more reliable for this use case. However, you can switch to Ollama if desired, and choose a suitably large model if your computer has the resources.
-
-## Configuring Temporal Connection
-
-By default, this application will connect to a local Temporal server (`localhost:7233`) in the default namespace, using the `agent-task-queue` task queue. You can override these settings in your `.env` file.
-
-### Use Temporal Cloud
-
-See [.env.example](.env.example) for details on connecting to Temporal Cloud using mTLS or API key authentication.
-
-[Sign up for Temporal Cloud](https://temporal.io/get-cloud)
-
-### Use a local Temporal Dev Server
-
-On a Mac
-```bash
-brew install temporal
-temporal server start-dev
-```
-See the [Temporal documentation](https://learn.temporal.io/getting_started/python/dev_environment/) for other platforms.
-
-
-## Running the Application
-
-### Python Backend
-
-Requires [Poetry](https://python-poetry.org/) to manage dependencies.
-
-1. `python -m venv venv`
-
-2. `source venv/bin/activate`
-
-3. `poetry install`
-
-Run the following commands in separate terminal windows:
-
-1. Start the Temporal worker:
-```bash
-poetry run python scripts/run_worker.py
-```
-
-2. Start the API server:
-```bash
-poetry run uvicorn api.main:app --reload
-```
-Access the API at `/docs` to see the available endpoints.
-
-### React UI
-Start the frontend:
-```bash
-cd frontend
-npm install
-npx vite
-```
-Access the UI at `http://localhost:5173`
-
-### Python Search Trains API
-> Agent Goal: goal_match_train_invoice only
-
-Required to search and book trains!
-```bash
-poetry run python thirdparty/train_api.py
-
-# example url
-# http://localhost:8080/api/search?from=london&to=liverpool&outbound_time=2025-04-18T09:00:00&inbound_time=2025-04-20T09:00:00
-```
-
-### .NET (enterprise) Backend ;)
-> Agent Goal: goal_match_train_invoice only
-
-We have activities written in C# to call the train APIs.
-```bash
-cd enterprise
-dotnet build # ensure you brew install dotnet@8 first!
-dotnet run
-```
-If you're running your train API above on a different host/port then change the API URL in `Program.cs`. Otherwise, be sure to run it using `python thirdparty/train_api.py`.
-
-## Customizing the Agent
-- `tool_registry.py` contains the mapping of tool names to tool definitions (so the AI understands how to use them)
-- `goal_registry.py` contains descriptions of goals and the tools used to achieve them
-- The tools themselves are defined in their own files in `/tools`
-- Note the mapping in `tools/__init__.py` to each tool
-
-## TODO
+## Productionalization & Adding Features
 - In a prod setting, I would need to ensure that payload data is stored separately (e.g. in S3 or a noSQL db - the claim-check pattern), or otherwise 'garbage collected'. Without these techniques, long conversations will fill up the workflow's conversation history, and start to breach Temporal event history payload limits.
 - Continue-as-new shouldn't be a big consideration for this use case (as it would take many conversational turns to trigger). Regardless, I should ensure that it's able to carry the agent state over to the new workflow execution.
 - Perhaps the UI should show when the LLM response is being retried (i.e. activity retry attempt because the LLM provided bad output)
 - Tests would be nice!
+See [the todo](./todo.md) for more details.
+
+See Customization for more details. <-- TODO
+
+## For Temporal SAs
+Check out the [slides](https://docs.google.com/presentation/d/1wUFY4v17vrtv8llreKEBDPLRtZte3FixxBUn0uWy5NU/edit#slide=id.g3333e5deaa9_0_0) here and the enablement guide here (TODO).

activities/tool_activities.py

@@ -34,6 +34,7 @@ class ToolActivities:
 
         # Initialize client variables (all set to None initially)
         self.openai_client: Optional[OpenAI] = None
+        self.grok_client: Optional[OpenAI] = None
         self.anthropic_client: Optional[anthropic.Anthropic] = None
         self.genai_configured: bool = False
         self.deepseek_client: Optional[deepseek.DeepSeekAPI] = None
@@ -48,6 +49,13 @@ class ToolActivities:
             else:
                 print("Warning: OPENAI_API_KEY not set but LLM_PROVIDER is 'openai'")
         
+        if self.llm_provider == "grok":
+            if os.environ.get("GROK_API_KEY"):
+                self.grok_client = OpenAI(api_key=os.environ.get("GROK_API_KEY"), base_url="https://api.x.ai/v1")
+                print("Initialized grok client")
+            else:
+                print("Warning: GROK_API_KEY not set but LLM_PROVIDER is 'grok'")
+
         elif self.llm_provider == "anthropic":
             if os.environ.get("ANTHROPIC_API_KEY"):
                 self.anthropic_client = anthropic.Anthropic(
@@ -195,6 +203,8 @@ class ToolActivities:
             return self.prompt_llm_anthropic(input)
         elif self.llm_provider == "deepseek":
             return self.prompt_llm_deepseek(input)
+        elif self.llm_provider == "grok":
+            return self.prompt_llm_grok(input)
         else:
             return self.prompt_llm_openai(input)
 
@@ -237,13 +247,47 @@ class ToolActivities:
         )
 
         response_content = chat_completion.choices[0].message.content
-        print(f"ChatGPT response: {response_content}")
+        activity.logger.info(f"ChatGPT response: {response_content}")
 
         # Use the new sanitize function
         response_content = self.sanitize_json_response(response_content)
 
         return self.parse_json_response(response_content)
 
+    def prompt_llm_grok(self, input: ToolPromptInput) -> dict:
+        if not self.grok_client:
+            api_key = os.environ.get("GROK_API_KEY")
+            if not api_key:
+                raise ValueError(
+                    "GROK_API_KEY is not set in the environment variables but LLM_PROVIDER is 'grok'"
+                )
+            self.grok_client = OpenAI(api_key=api_key, base_url="https://api.x.ai/v1")
+            print("Initialized grok client on demand")
+
+        messages = [
+            {
+                "role": "system",
+                "content": input.context_instructions
+                + ". The current date is "
+                + datetime.now().strftime("%B %d, %Y"),
+            },
+            {
+                "role": "user",
+                "content": input.prompt,
+            },
+        ]
+
+        chat_completion = self.grok_client.chat.completions.create(
+            model="grok-2-1212", messages=messages  
+        )
+
+        response_content = chat_completion.choices[0].message.content
+        activity.logger.info(f"Grok response: {response_content}")
+
+        # Use the new sanitize function
+        response_content = self.sanitize_json_response(response_content)
+
+        return self.parse_json_response(response_content)
     def prompt_llm_ollama(self, input: ToolPromptInput) -> dict:
         # If not yet initialized, try to do so now (this is a backup if warm_up_ollama wasn't called or failed)
         if not self.ollama_initialized:
@@ -449,6 +493,7 @@ def dynamic_tool_activity(args: Sequence[RawValue]) -> dict:
     # Delegate to the relevant function
     handler = get_handler(tool_name)
     result = handler(tool_args)
+    print(f"in dynamic tool activity, result: {result}")
 
     # Optionally log or augment the result
     activity.logger.info(f"Tool '{tool_name}' result: {result}")

api/main.py

@@ -1,3 +1,4 @@
+import os
 from fastapi import FastAPI
 from typing import Optional
 from temporalio.client import Client
@@ -6,11 +7,10 @@ from temporalio.api.enums.v1 import WorkflowExecutionStatus
 from fastapi import HTTPException
 from dotenv import load_dotenv
 import asyncio
-import os
 
 from workflows.agent_goal_workflow import AgentGoalWorkflow
 from models.data_types import CombinedInput, AgentGoalWorkflowParams
-from tools.goal_registry import goal_match_train_invoice, goal_event_flight_invoice
+from tools.goal_registry import goal_list
 from fastapi.middleware.cors import CORSMiddleware
 from shared.config import get_temporal_client, TEMPORAL_TASK_QUEUE
 
@@ -21,14 +21,12 @@ temporal_client: Optional[Client] = None
 load_dotenv()
 
 
-def get_agent_goal():
+def get_initial_agent_goal():
     """Get the agent goal from environment variables."""
-    goal_name = os.getenv("AGENT_GOAL", "goal_match_train_invoice")
-    goals = {
-        "goal_match_train_invoice": goal_match_train_invoice,
-        "goal_event_flight_invoice": goal_event_flight_invoice,
-    }
-    return goals.get(goal_name, goal_event_flight_invoice)
+    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")
@@ -113,18 +111,44 @@ async def get_conversation_history():
                 status_code=404, detail="Workflow worker unavailable or not found."
             )
 
+        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():
+    """Calls the workflow's 'get_agent_goal' query."""
+    try:
+        # Get workflow handle
+        handle = temporal_client.get_workflow_handle("agent-workflow")
+
+        # Check if the workflow is completed
+        workflow_status = await handle.describe()
+        if workflow_status.status == 2:
+            # Workflow is completed; return an empty response
+            return {}
+
+        # Query the workflow
+        agent_goal = await handle.query("get_agent_goal")
+        return agent_goal
+    except TemporalError as e:
+        # Workflow not found; return an empty response
+        print(e)
+        return {}
+
 
 @app.post("/send-prompt")
 async def send_prompt(prompt: str):
     # Create combined input with goal from environment
     combined_input = CombinedInput(
         tool_params=AgentGoalWorkflowParams(None, None),
-        agent_goal=get_agent_goal(),
+        agent_goal=get_initial_agent_goal(),
+        #change to get from workflow query
     )
 
     workflow_id = "agent-workflow"
@@ -168,13 +192,12 @@ async def end_chat():
 
 @app.post("/start-workflow")
 async def start_workflow():
-    # Get the configured goal
-    agent_goal = get_agent_goal()
+    initial_agent_goal = get_initial_agent_goal()
 
     # Create combined input
     combined_input = CombinedInput(
         tool_params=AgentGoalWorkflowParams(None, None),
-        agent_goal=agent_goal,
+        agent_goal=initial_agent_goal,
     )
 
     workflow_id = "agent-workflow"
@@ -186,9 +209,9 @@ async def start_workflow():
         id=workflow_id,
         task_queue=TEMPORAL_TASK_QUEUE,
         start_signal="user_prompt",
-        start_signal_args=["### " + agent_goal.starter_prompt],
+        start_signal_args=["### " + initial_agent_goal.starter_prompt],
     )
 
     return {
-        "message": f"Workflow started with goal's starter prompt: {agent_goal.starter_prompt}."
+        "message": f"Workflow started with goal's starter prompt: {initial_agent_goal.starter_prompt}."
     }

architecture.md

@@ -0,0 +1,12 @@
+# Elements
+![Architecture Elements](./assets/Architecture_elements.png "Architecture Elements")
+
+talk through the pieces
+
+# Architecture Model
+![Architecture](./assets/ai_agent_architecture_model.png "Architecture Model")
+
+explain elements
+
+# Adding features
+link to how to LLM interactions/how to change

frontend/package-lock.json

@@ -825,247 +825,228 @@
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       "cpu": [
         "arm"
       ],
-      "license": "MIT",
       "optional": true,
       "os": [
         "android"
       ]
     },
     "node_modules/@rollup/rollup-android-arm64": {
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+      "resolved": "https://registry.npmjs.org/@rollup/rollup-win32-ia32-msvc/-/rollup-win32-ia32-msvc-4.34.7.tgz",
+      "integrity": "sha512-aeawEKYswsFu1LhDM9RIgToobquzdtSc4jSVqHV8uApz4FVvhFl/mKh92wc8WpFc6aYCothV/03UjY6y7yLgbg==",
       "cpu": [
         "ia32"
       ],
-      "license": "MIT",
       "optional": true,
       "os": [
         "win32"
       ]
     },
     "node_modules/@rollup/rollup-win32-x64-msvc": {
-      "version": "4.29.1",
-      "resolved": "https://registry.npmjs.org/@rollup/rollup-win32-x64-msvc/-/rollup-win32-x64-msvc-4.29.1.tgz",
-      "integrity": "sha512-+10CMg9vt1MoHj6x1pxyjPSMjHTIlqs8/tBztXvPAx24SKs9jwVnKqHJumlH/IzhaPUaj3T6T6wfZr8okdXaIg==",
+      "version": "4.34.7",
+      "resolved": "https://registry.npmjs.org/@rollup/rollup-win32-x64-msvc/-/rollup-win32-x64-msvc-4.34.7.tgz",
+      "integrity": "sha512-4ZedScpxxIrVO7otcZ8kCX1mZArtH2Wfj3uFCxRJ9NO80gg1XV0U/b2f/MKaGwj2X3QopHfoWiDQ917FRpwY3w==",
       "cpu": [
         "x64"
       ],
-      "license": "MIT",
       "optional": true,
       "os": [
         "win32"
@@ -1115,8 +1096,7 @@
     "node_modules/@types/estree": {
       "version": "1.0.6",
       "resolved": "https://registry.npmjs.org/@types/estree/-/estree-1.0.6.tgz",
-      "integrity": "sha512-AYnb1nQyY49te+VRAVgmzfcgjYS91mY5P0TKUDCLEM+gNnA+3T6rWITXRLYCpahpqSQbN5cE+gHpnPyXjHWxcw==",
-      "license": "MIT"
+      "integrity": "sha512-AYnb1nQyY49te+VRAVgmzfcgjYS91mY5P0TKUDCLEM+gNnA+3T6rWITXRLYCpahpqSQbN5cE+gHpnPyXjHWxcw=="
     },
     "node_modules/@vitejs/plugin-react": {
       "version": "4.3.4",
@@ -2084,9 +2064,9 @@
       }
     },
     "node_modules/postcss": {
-      "version": "8.4.49",
-      "resolved": "https://registry.npmjs.org/postcss/-/postcss-8.4.49.tgz",
-      "integrity": "sha512-OCVPnIObs4N29kxTjzLfUryOkvZEq+pf8jTF0lg8E7uETuWHA+v7j3c/xJmiqpX450191LlmZfUKkXxkTry7nA==",
+      "version": "8.5.2",
+      "resolved": "https://registry.npmjs.org/postcss/-/postcss-8.5.2.tgz",
+      "integrity": "sha512-MjOadfU3Ys9KYoX0AdkBlFEF1Vx37uCCeN4ZHnmwm9FfpbsGWMZeBLMmmpY+6Ocqod7mkdZ0DT31OlbsFrLlkA==",
       "funding": [
         {
           "type": "opencollective",
@@ -2101,9 +2081,8 @@
           "url": "https://github.com/sponsors/ai"
         }
       ],
-      "license": "MIT",
       "dependencies": {
-        "nanoid": "^3.3.7",
+        "nanoid": "^3.3.8",
         "picocolors": "^1.1.1",
         "source-map-js": "^1.2.1"
       },
@@ -2339,10 +2318,9 @@
       }
     },
     "node_modules/rollup": {
-      "version": "4.29.1",
-      "resolved": "https://registry.npmjs.org/rollup/-/rollup-4.29.1.tgz",
-      "integrity": "sha512-RaJ45M/kmJUzSWDs1Nnd5DdV4eerC98idtUOVr6FfKcgxqvjwHmxc5upLF9qZU9EpsVzzhleFahrT3shLuJzIw==",
-      "license": "MIT",
+      "version": "4.34.7",
+      "resolved": "https://registry.npmjs.org/rollup/-/rollup-4.34.7.tgz",
+      "integrity": "sha512-8qhyN0oZ4x0H6wmBgfKxJtxM7qS98YJ0k0kNh5ECVtuchIJ7z9IVVvzpmtQyT10PXKMtBxYr1wQ5Apg8RS8kXQ==",
       "dependencies": {
         "@types/estree": "1.0.6"
       },
@@ -2354,25 +2332,25 @@
         "npm": ">=8.0.0"
       },
       "optionalDependencies": {
-        "@rollup/rollup-android-arm-eabi": "4.29.1",
-        "@rollup/rollup-android-arm64": "4.29.1",
-        "@rollup/rollup-darwin-arm64": "4.29.1",
-        "@rollup/rollup-darwin-x64": "4.29.1",
-        "@rollup/rollup-freebsd-arm64": "4.29.1",
-        "@rollup/rollup-freebsd-x64": "4.29.1",
-        "@rollup/rollup-linux-arm-gnueabihf": "4.29.1",
-        "@rollup/rollup-linux-arm-musleabihf": "4.29.1",
-        "@rollup/rollup-linux-arm64-gnu": "4.29.1",
-        "@rollup/rollup-linux-arm64-musl": "4.29.1",
-        "@rollup/rollup-linux-loongarch64-gnu": "4.29.1",
-        "@rollup/rollup-linux-powerpc64le-gnu": "4.29.1",
-        "@rollup/rollup-linux-riscv64-gnu": "4.29.1",
-        "@rollup/rollup-linux-s390x-gnu": "4.29.1",
-        "@rollup/rollup-linux-x64-gnu": "4.29.1",
-        "@rollup/rollup-linux-x64-musl": "4.29.1",
-        "@rollup/rollup-win32-arm64-msvc": "4.29.1",
-        "@rollup/rollup-win32-ia32-msvc": "4.29.1",
-        "@rollup/rollup-win32-x64-msvc": "4.29.1",
+        "@rollup/rollup-android-arm-eabi": "4.34.7",
+        "@rollup/rollup-android-arm64": "4.34.7",
+        "@rollup/rollup-darwin-arm64": "4.34.7",
+        "@rollup/rollup-darwin-x64": "4.34.7",
+        "@rollup/rollup-freebsd-arm64": "4.34.7",
+        "@rollup/rollup-freebsd-x64": "4.34.7",
+        "@rollup/rollup-linux-arm-gnueabihf": "4.34.7",
+        "@rollup/rollup-linux-arm-musleabihf": "4.34.7",
+        "@rollup/rollup-linux-arm64-gnu": "4.34.7",
+        "@rollup/rollup-linux-arm64-musl": "4.34.7",
+        "@rollup/rollup-linux-loongarch64-gnu": "4.34.7",
+        "@rollup/rollup-linux-powerpc64le-gnu": "4.34.7",
+        "@rollup/rollup-linux-riscv64-gnu": "4.34.7",
+        "@rollup/rollup-linux-s390x-gnu": "4.34.7",
+        "@rollup/rollup-linux-x64-gnu": "4.34.7",
+        "@rollup/rollup-linux-x64-musl": "4.34.7",
+        "@rollup/rollup-win32-arm64-msvc": "4.34.7",
+        "@rollup/rollup-win32-ia32-msvc": "4.34.7",
+        "@rollup/rollup-win32-x64-msvc": "4.34.7",
         "fsevents": "~2.3.2"
       }
     },
@@ -2719,14 +2697,13 @@
       "license": "MIT"
     },
     "node_modules/vite": {
-      "version": "6.0.7",
-      "resolved": "https://registry.npmjs.org/vite/-/vite-6.0.7.tgz",
-      "integrity": "sha512-RDt8r/7qx9940f8FcOIAH9PTViRrghKaK2K1jY3RaAURrEUbm9Du1mJ72G+jlhtG3WwodnfzY8ORQZbBavZEAQ==",
-      "license": "MIT",
+      "version": "6.1.0",
+      "resolved": "https://registry.npmjs.org/vite/-/vite-6.1.0.tgz",
+      "integrity": "sha512-RjjMipCKVoR4hVfPY6GQTgveinjNuyLw+qruksLDvA5ktI1150VmcMBKmQaEWJhg/j6Uaf6dNCNA0AfdzUb/hQ==",
       "dependencies": {
         "esbuild": "^0.24.2",
-        "postcss": "^8.4.49",
-        "rollup": "^4.23.0"
+        "postcss": "^8.5.1",
+        "rollup": "^4.30.1"
       },
       "bin": {
         "vite": "bin/vite.js"

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.` 
         : '';

models/data_types.py

@@ -17,7 +17,7 @@ class CombinedInput:
 
 Message = Dict[str, Union[str, Dict[str, Any]]]
 ConversationHistory = Dict[str, List[Message]]
-NextStep = Literal["confirm", "question", "done"]
+NextStep = Literal["confirm", "question", "pick-new-goal", "done"]
 
 
 @dataclass

models/tool_definitions.py

@@ -15,9 +15,11 @@ class ToolDefinition:
     description: str
     arguments: List[ToolArgument]
 
-
 @dataclass
 class AgentGoal:
+    id: str
+    agent_name: str
+    agent_friendly_description: str
     tools: List[ToolDefinition]
     description: str = "Description of the tools purpose and overall goal"
     starter_prompt: str = "Initial prompt to start the conversation"

prompts/agent_prompt_generators.py

@@ -68,7 +68,7 @@ def generate_genai_prompt(
         "Your JSON format must be:\n"
         "{\n"
         '  "response": "<plain text>",\n'
-        '  "next": "<question|confirm|done>",\n'
+        '  "next": "<question|confirm|pick-new-goal|done>",\n'
         '  "tool": "<tool_name or null>",\n'
         '  "args": {\n'
         '    "<arg1>": "<value1 or null>",\n'
@@ -81,9 +81,8 @@ def generate_genai_prompt(
         "1) If any required argument is missing, set next='question' and ask the user.\n"
         "2) If all required arguments are known, set next='confirm' and specify the tool.\n"
         "   The user will confirm before the tool is run.\n"
-        "3) If no more tools are needed (user_confirmed_tool_run has been run for all), set next='done' and tool=null.\n"
+        "3) If no more tools are needed (user_confirmed_tool_run has been run for all), set next='confirm' and tool='ListAgents'.\n"
         "4) response should be short and user-friendly.\n"
-        "5) Don't set next='done' until the final tool has returned user_confirmed_tool_run.\n"
     )
 
     # Validation Task (If raw_json is provided)
@@ -124,11 +123,10 @@ def generate_tool_completion_prompt(current_tool: str, dynamic_result: dict) ->
         f"### The '{current_tool}' tool completed successfully with {dynamic_result}. "
         "INSTRUCTIONS: Parse this tool result as plain text, and use the system prompt containing the list of tools in sequence and the conversation history (and previous tool_results) to figure out next steps, if any. "
         "You will need to use the tool_results to auto-fill arguments for subsequent tools and also to figure out if all tools have been run. "
-        '{"next": "<question|confirm|done>", "tool": "<tool_name or null>", "args": {"<arg1>": "<value1 or null>", "<arg2>": "<value2 or null>}, "response": "<plain text (can include \\n line breaks)>"}'
+        '{"next": "<question|confirm|pick-new-goal|done>", "tool": "<tool_name or null>", "args": {"<arg1>": "<value1 or null>", "<arg2>": "<value2 or null>}, "response": "<plain text (can include \\n line breaks)>"}'
         "ONLY return those json keys (next, tool, args, response), nothing else. "
-        'Next should only be "done" if all tools have been run (use the system prompt to figure that out).'
         'Next should be "question" if the tool is not the last one in the sequence. '
-        'Next should NOT be "confirm" at this point.'
+        'Next should only be "pick-new-goal" if all tools have been run (use the system prompt to figure that out).'
     )
 
 def generate_missing_args_prompt(current_tool: str, tool_data: dict, missing_args: list[str]) -> str:

setup.md

@@ -0,0 +1,176 @@
+## Configuration
+
+This application uses `.env` files for configuration. Copy the [.env.example](.env.example) file to `.env` and update the values:
+
+```bash
+cp .env.example .env
+```
+
+### Agent Goal Configuration
+
+The agent can be configured to pursue different goals using the `AGENT_GOAL` environment variable in your `.env` file.
+
+#### Goal: Find an event in Australia / New Zealand, book flights to it and invoice the user for the cost
+- `AGENT_GOAL=goal_event_flight_invoice` (default) - Helps users find events, book flights, and arrange train travel with invoice generation
+    - This is the scenario in the video above
+
+#### Goal: Find a Premier League match, book train tickets to it and invoice the user for the cost
+- `AGENT_GOAL=goal_match_train_invoice` - Focuses on Premier League match attendance with train booking and invoice generation
+    - This is a new goal that is part of an upcoming conference talk
+
+If not specified, the agent defaults to `goal_event_flight_invoice`. Each goal comes with its own set of tools and conversation flows designed for specific use cases. You can examine `tools/goal_registry.py` to see the detailed configuration of each goal.
+
+See the next section for tool configuration for each goal.
+
+### Tool Configuration
+
+#### Agent Goal: goal_event_flight_invoice (default)
+* The agent uses a mock function to search for events. This has zero configuration.
+* By default the agent uses a mock function to search for flights.
+    * If you want to use the real flights API, go to `tools/search_flights.py` and replace the `search_flights` function with `search_flights_real_api` that exists in the same file.
+    * It's free to sign up at [RapidAPI](https://rapidapi.com/apiheya/api/sky-scrapper)
+    * This api might be slow to respond, so you may want to increase the start to close timeout, `TOOL_ACTIVITY_START_TO_CLOSE_TIMEOUT` in `workflows/workflow_helpers.py`
+* Requires a Stripe key for the `create_invoice` tool. Set this in the `STRIPE_API_KEY` environment variable in .env
+    * It's free to sign up and get a key at [Stripe](https://stripe.com/)
+    * If you're lazy go to `tools/create_invoice.py` and replace the `create_invoice` function with the mock `create_invoice_example` that exists in the same file.
+
+#### Agent Goal: goal_match_train_invoice
+
+* Finding a match requires a key from [Football Data](https://www.football-data.org). Sign up for a free account, then see the 'My Account' page to get your API token. Set `FOOTBALL_DATA_API_KEY` to this value.
+    * If you're lazy go to `tools/search_fixtures.py` and replace the `search_fixtures` function with the mock `search_fixtures_example` that exists in the same file.
+* We use a mock function to search for trains. Start the train API server to use the real API: `python thirdparty/train_api.py`
+* * The train activity is 'enterprise' so it's written in C# and requires a .NET runtime. See the [.NET backend](#net-(enterprise)-backend) section for details on running it.
+* Requires a Stripe key for the `create_invoice` tool. Set this in the `STRIPE_API_KEY` environment variable in .env
+    * It's free to sign up and get a key at [Stripe](https://stripe.com/)
+    * If you're lazy go to `tools/create_invoice.py` and replace the `create_invoice` function with the mock `create_invoice_example` that exists in the same file.
+
+### LLM Provider Configuration
+
+The agent can use OpenAI's GPT-4o, Google Gemini, Anthropic Claude, or a local LLM via Ollama. Set the `LLM_PROVIDER` environment variable in your `.env` file to choose the desired provider:
+
+- `LLM_PROVIDER=openai` for OpenAI's GPT-4o
+- `LLM_PROVIDER=google` for Google Gemini
+- `LLM_PROVIDER=anthropic` for Anthropic Claude
+- `LLM_PROVIDER=deepseek` for DeepSeek-V3
+- `LLM_PROVIDER=ollama` for running LLMs via [Ollama](https://ollama.ai) (not recommended for this use case)
+
+### Option 1: OpenAI
+
+If using OpenAI, ensure you have an OpenAI key for the GPT-4o model. Set this in the `OPENAI_API_KEY` environment variable in `.env`.
+
+### Option 2: Google Gemini
+
+To use Google Gemini:
+
+1. Obtain a Google API key and set it in the `GOOGLE_API_KEY` environment variable in `.env`.
+2. Set `LLM_PROVIDER=google` in your `.env` file.
+
+### Option 3: Anthropic Claude (recommended)
+
+I find that Claude Sonnet 3.5 performs better than the other hosted LLMs for this use case.
+
+To use Anthropic:
+
+1. Obtain an Anthropic API key and set it in the `ANTHROPIC_API_KEY` environment variable in `.env`.
+2. Set `LLM_PROVIDER=anthropic` in your `.env` file.
+
+### Option 4: Deepseek-V3
+
+To use Deepseek-V3:
+
+1. Obtain a Deepseek API key and set it in the `DEEPSEEK_API_KEY` environment variable in `.env`.
+2. Set `LLM_PROVIDER=deepseek` in your `.env` file.
+
+### Option 5: Local LLM via Ollama (not recommended)
+
+To use a local LLM with Ollama:
+
+1. Install [Ollama](https://ollama.com) and the [Qwen2.5 14B](https://ollama.com/library/qwen2.5) model.
+   - Run `ollama run <OLLAMA_MODEL_NAME>` to start the model. Note that this model is about 9GB to download.
+   - Example: `ollama run qwen2.5:14b`
+
+2. Set `LLM_PROVIDER=ollama` in your `.env` file and `OLLAMA_MODEL_NAME` to the name of the model you installed.
+
+Note: I found the other (hosted) LLMs to be MUCH more reliable for this use case. However, you can switch to Ollama if desired, and choose a suitably large model if your computer has the resources.
+
+## Configuring Temporal Connection
+
+By default, this application will connect to a local Temporal server (`localhost:7233`) in the default namespace, using the `agent-task-queue` task queue. You can override these settings in your `.env` file.
+
+### Use Temporal Cloud
+
+See [.env.example](.env.example) for details on connecting to Temporal Cloud using mTLS or API key authentication.
+
+[Sign up for Temporal Cloud](https://temporal.io/get-cloud)
+
+### Use a local Temporal Dev Server
+
+On a Mac
+```bash
+brew install temporal
+temporal server start-dev
+```
+See the [Temporal documentation](https://learn.temporal.io/getting_started/python/dev_environment/) for other platforms.
+
+
+## Running the Application
+
+### Python Backend
+
+Requires [Poetry](https://python-poetry.org/) to manage dependencies.
+
+1. `python -m venv venv`
+
+2. `source venv/bin/activate`
+
+3. `poetry install`
+
+Run the following commands in separate terminal windows:
+
+1. Start the Temporal worker:
+```bash
+poetry run python scripts/run_worker.py
+```
+
+2. Start the API server:
+```bash
+poetry run uvicorn api.main:app --reload
+```
+Access the API at `/docs` to see the available endpoints.
+
+### React UI
+Start the frontend:
+```bash
+cd frontend
+npm install
+npx vite
+```
+Access the UI at `http://localhost:5173`
+
+### Python Search Trains API
+> Agent Goal: goal_match_train_invoice only
+
+Required to search and book trains!
+```bash
+poetry run python thirdparty/train_api.py
+
+# example url
+# http://localhost:8080/api/search?from=london&to=liverpool&outbound_time=2025-04-18T09:00:00&inbound_time=2025-04-20T09:00:00
+```
+
+### .NET (enterprise) Backend ;)
+> Agent Goal: goal_match_train_invoice only
+
+We have activities written in C# to call the train APIs.
+```bash
+cd enterprise
+dotnet build # ensure you brew install dotnet@8 first!
+dotnet run
+```
+If you're running your train API above on a different host/port then change the API URL in `Program.cs`. Otherwise, be sure to run it using `python thirdparty/train_api.py`.
+
+## Customizing the Agent
+- `tool_registry.py` contains the mapping of tool names to tool definitions (so the AI understands how to use them)
+- `goal_registry.py` contains descriptions of goals and the tools used to achieve them
+- The tools themselves are defined in their own files in `/tools`
+- Note the mapping in `tools/__init__.py` to each tool

shared/config.py

@@ -16,7 +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", "")
 
-
 async def get_temporal_client() -> Client:
     """
     Creates a Temporal client based on environment configuration.

tests/agent_goal_workflow_test.py

@@ -0,0 +1,55 @@
+import asyncio
+
+from temporalio.client import Client, WorkflowExecutionStatus
+from temporalio.worker import Worker
+from temporalio.testing import TestWorkflowEnvironment
+from api.main import get_initial_agent_goal
+from models.data_types import AgentGoalWorkflowParams, CombinedInput
+from workflows import AgentGoalWorkflow
+from activities.tool_activities import ToolActivities, dynamic_tool_activity
+
+
+async def asyncSetUp(self):
+    # Set up the test environment
+    self.env = await TestWorkflowEnvironment.create_local()
+
+async def asyncTearDown(self):
+    # Clean up after tests
+    await self.env.shutdown()
+
+async def test_workflow_success(client: Client):
+    # Register the workflow and activity
+    # self.env.register_workflow(AgentGoalWorkflow)
+    # self.env.register_activity(ToolActivities.agent_validatePrompt)
+    # self.env.register_activity(ToolActivities.agent_toolPlanner)
+    # self.env.register_activity(dynamic_tool_activity)
+
+    task_queue_name = "agent-ai-workflow"
+    workflow_id = "agent-workflow"
+    
+    initial_agent_goal = get_initial_agent_goal()
+
+    # Create combined input
+    combined_input = CombinedInput(
+        tool_params=AgentGoalWorkflowParams(None, None),
+        agent_goal=initial_agent_goal,
+    )
+
+    workflow_id = "agent-workflow"
+    async with Worker(client, task_queue=task_queue_name, workflows=[AgentGoalWorkflow], activities=[ToolActivities.agent_validatePrompt, ToolActivities.agent_toolPlanner, dynamic_tool_activity]):
+        handle = await client.start_workflow(
+            AgentGoalWorkflow.run, id=workflow_id, task_queue=task_queue_name
+        )
+        # todo fix signals
+        await handle.signal(AgentGoalWorkflow.submit_greeting, "user1")
+        await handle.signal(AgentGoalWorkflow.submit_greeting, "user2")
+        assert WorkflowExecutionStatus.RUNNING == (await handle.describe()).status
+
+        await handle.signal(AgentGoalWorkflow.exit)
+        assert ["Hello, user1", "Hello, user2"] == await handle.result()
+        assert WorkflowExecutionStatus.COMPLETED == (await handle.describe()).status
+
+   
+
+
+    

todo.md

@@ -0,0 +1,45 @@
+# todo list
+[ ] clean up workflow/make functions
+
+[ ] make the debugging confirms optional <br />
+ <br />
+[ ] document *why* temporal for ai agents - scalability, durability, visibility in the readme <br />
+[ ] fix readme: move setup to its own page, demo to its own page, add the why /|\ section <br />
+[ ] add architecture to readme <br />
+- elements of app <br />
+- dive into llm interaction <br />
+- workflow breakdown - interactive loop <br />
+- why temporal <br />
+
+[ ] setup readme, why readme, architecture readme, what this is in main readme with temporal value props and pictures <br />
+[ ] how to add more scenarios, tools <br />
+ <br />
+ <br />
+[ ] create tests<br />
+
+[ ] create people management scenario <br />
+- check pay status <br />
+- book work travel <br />
+- check PTO levels <br />
+- check insurance coverages <br />
+- book PTO around a date (https://developers.google.com/calendar/api/guides/overview)?  <br />
+- scenario should use multiple tools <br />
+- expense management <br />
+- check in on the health of the team <br />
+
+[ ] demo the reasons why: <br />
+- Orchestrate interactions across distributed data stores and tools <br />
+- Hold state, potentially over long periods of time <br />
+- Ability to ‘self-heal’ and retry until the (probabilistic) LLM returns valid data <br />
+- Support for human intervention such as approvals <br />
+- Parallel processing for efficiency of data retrieval and tool use <br />
+- Insight into the agent’s performance <br />
+    - ask the ai agent how it did at the end of the conversation, was it efficient? successful? insert a search attribute to document that before return
+
+[ ] customize prompts in [workflow to manage scenario](./workflows/tool_workflow.py)<br />
+[ ] add in new tools? <br />
+
+[ ] 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

tools/__init__.py

@@ -4,6 +4,9 @@ from .search_trains import search_trains
 from .search_trains import book_trains
 from .create_invoice import create_invoice
 from .find_events import find_events
+from .list_agents import list_agents
+from .change_goal import change_goal
+from .transfer_control import transfer_control
 
 
 def get_handler(tool_name: str):
@@ -19,5 +22,11 @@ def get_handler(tool_name: str):
         return create_invoice
     if tool_name == "FindEvents":
         return find_events
+    if tool_name == "ListAgents":
+        return list_agents
+    if tool_name == "ChangeGoal":
+        return change_goal
+    if tool_name == "TransferControl":
+        return transfer_control
 
     raise ValueError(f"Unknown tool: {tool_name}")

tools/change_goal.py

@@ -0,0 +1,9 @@
+def change_goal(args: dict) -> dict:
+
+    new_goal = args.get("goalID")
+    if new_goal is None:
+        new_goal = "goal_choose_agent_type"
+
+    return {
+        "new_goal": new_goal,
+    }

tools/create_invoice.py

@@ -4,7 +4,7 @@ from dotenv import load_dotenv
 
 load_dotenv(override=True)  # Load environment variables from a .env file
 
-stripe.api_key = os.getenv("STRIPE_API_KEY", "YOUR_DEFAULT_KEY")
+stripe.api_key = os.getenv("STRIPE_API_KEY")
 
 
 def ensure_customer_exists(
@@ -26,7 +26,8 @@ def ensure_customer_exists(
 
 def create_invoice(args: dict) -> dict:
     """Create and finalize a Stripe invoice."""
-    # Find or create customer
+    # If an API key exists in the env file, find or create customer
+    if stripe.api_key is not None:
         customer_id = ensure_customer_exists(
             args.get("customer_id"), args.get("email", "default@example.com")
         )
@@ -60,7 +61,14 @@ def create_invoice(args: dict) -> dict:
             "invoiceURL": finalized_invoice.hosted_invoice_url,
             "reference": finalized_invoice.number,
         }
-
+    # if no API key is in the env file, return dummy info
+    else:
+        print("[CreateInvoice] Creating invoice with:", args)
+        return {
+            "invoiceStatus": "generated",
+            "invoiceURL": "https://pay.example.com/invoice/12345",
+            "reference": "INV-12345",
+        }
 
 def create_invoice_example(args: dict) -> dict:
     """

tools/goal_registry.py

@@ -1,3 +1,4 @@
+from typing import List
 from models.tool_definitions import AgentGoal
 from tools.tool_registry import (
     search_fixtures_tool,
@@ -6,14 +7,50 @@ from tools.tool_registry import (
     book_trains_tool,
     create_invoice_tool,
     find_events_tool,
+    change_goal_tool,
+    list_agents_tool
+)
+
+starter_prompt_generic = "Welcome me, give me a description of what you can do, then ask me for the details you need to do your job"
+
+goal_choose_agent_type = AgentGoal(
+    id = "goal_choose_agent_type",
+    agent_name="Choose Agent",
+    agent_friendly_description="Choose the type of agent to assist you today.",
+    tools=[
+        list_agents_tool, 
+        change_goal_tool,
+    ],
+    description="The user wants to choose which type of agent they will interact with. "
+        "Help the user gather args for these tools, in order: "
+        "1. ListAgents: List agents available to interact with "
+        "2. ChangeGoal: Change goal of agent "
+        "After these tools are complete, change your goal to the new goal as chosen by the user. ",
+    starter_prompt=starter_prompt_generic,
+    example_conversation_history="\n ".join(
+        [
+            "user: I'd like to choose an agent",
+            "agent: Sure! Would you like me to list the available agents?",
+            "user_confirmed_tool_run: <user clicks confirm on ListAgents tool>",
+            "tool_result: { 'agent_name': 'Event Flight Finder', 'goal_id': 'goal_event_flight_invoice', 'agent_description': 'Helps users find interesting events and arrange travel to them' }",
+            "agent: The available agents are: 1. Event Flight Finder. Which agent would you like to speak to?",
+            "user: 1",
+            "user_confirmed_tool_run: <user clicks confirm on ChangeGoal tool>",
+            "tool_result: { 'new_goal': 'goal_event_flight_invoice' }",
+        ]
+    ),
 )
 
 goal_match_train_invoice = AgentGoal(
+    id = "goal_match_train_invoice",
+    agent_name="UK Premier League Match Trip Booking",
+    agent_friendly_description="Book a trip to a city in the UK around the dates of a premier league match.",
     tools=[
         search_fixtures_tool,
         search_trains_tool,
         book_trains_tool,
         create_invoice_tool,
+        list_agents_tool, #last tool must be list_agents to fasciliate changing back to picking an agent again at the end
     ],
     description="The user wants to book a trip to a city in the UK around the dates of a premier league match. "
     "Help the user find a premier league match to attend, search and book trains for that match and offers to invoice them for the cost of train tickets. "
@@ -23,7 +60,7 @@ goal_match_train_invoice = AgentGoal(
     "2. SearchTrains: Search for trains to the city of the match and list them for the customer to choose from "
     "3. BookTrains: Book the train tickets, used to invoice the user for the cost of the train tickets "
     "4. CreateInvoice: Invoices the user for the cost of train tickets, with total and details inferred from the conversation history ",
-    starter_prompt="Welcome me, give me a description of what you can do, then ask me for the details you need to begin your job as an agent ",
+    starter_prompt=starter_prompt_generic,
     example_conversation_history="\n ".join(
         [
             "user: I'd like to travel to a premier league match",
@@ -51,18 +88,21 @@ goal_match_train_invoice = AgentGoal(
     ),
 )
 
-# unused
 goal_event_flight_invoice = AgentGoal(
+    id = "goal_event_flight_invoice",
+    agent_name="Australia and New Zealand Event Flight Booking",
+    agent_friendly_description="Book a trip to a city in Australia or New Zealand around the dates of events in that city.",    
     tools=[
         find_events_tool,
         search_flights_tool,
         create_invoice_tool,
+        list_agents_tool, #last tool must be list_agents to fasciliate changing back to picking an agent again at the end
     ],
     description="Help the user gather args for these tools in order: "
     "1. FindEvents: Find an event to travel to "
     "2. SearchFlights: search for a flight around the event dates "
     "3. CreateInvoice: Create a simple invoice for the cost of that flight ",
-    starter_prompt="Welcome me, give me a description of what you can do, then ask me for the details you need to do your job",
+    starter_prompt=starter_prompt_generic,
     example_conversation_history="\n ".join(
         [
             "user: I'd like to travel to an event",
@@ -85,3 +125,9 @@ goal_event_flight_invoice = AgentGoal(
         ]
     ),
 )
+
+#Add the goals to a list for more generic processing, like listing available agents
+goal_list: List[AgentGoal] = []
+goal_list.append(goal_choose_agent_type)
+goal_list.append(goal_event_flight_invoice)
+goal_list.append(goal_match_train_invoice)

tools/list_agents.py

@@ -0,0 +1,17 @@
+import tools.goal_registry as goals
+
+def list_agents(args: dict) -> dict:
+
+    agents = []
+    if goals.goal_list is not None:
+        for goal in goals.goal_list:
+            agents.append(
+                {
+                    "agent_name": goal.agent_name,
+                    "goal_id": goal.id,
+                    "agent_description": goal.agent_friendly_description,
+                }
+            )
+    return {
+        "agents": agents,
+    }

tools/tool_registry.py

@@ -1,5 +1,23 @@
 from models.tool_definitions import ToolDefinition, ToolArgument
 
+list_agents_tool = ToolDefinition(
+    name="ListAgents",
+    description="List available agents to interact with, pulled from goal_registry. ",
+    arguments=[],
+)
+
+change_goal_tool = ToolDefinition(
+    name="ChangeGoal",
+    description="Change the goal of the active agent. ",
+    arguments=[
+        ToolArgument(
+            name="goalID",
+            type="string",
+            description="Which goal to change to",
+        ),
+    ],
+)
+
 search_flights_tool = ToolDefinition(
     name="SearchFlights",
     description="Search for return flights from an origin to a destination within a date range (dateDepart, dateReturn).",

tools/transfer_control.py

@@ -0,0 +1,7 @@
+import shared.config
+
+def transfer_control(args: dict) -> dict:
+
+    return {
+        "new_goal": shared.config.AGENT_GOAL,
+    }

workflows/agent_goal_workflow.py

@@ -1,11 +1,13 @@
 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
 from temporalio import workflow
 
 from models.data_types import ConversationHistory, NextStep, ValidationInput
+from models.tool_definitions import AgentGoal
 from workflows.workflow_helpers import LLM_ACTIVITY_START_TO_CLOSE_TIMEOUT, \
     LLM_ACTIVITY_SCHEDULE_TO_CLOSE_TIMEOUT
 from workflows import workflow_helpers as helpers
@@ -19,15 +21,24 @@ with workflow.unsafe.imports_passed_through():
         CombinedInput,
         ToolPromptInput,
     )
+    from tools.goal_registry import goal_list
 
 # 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:
@@ -41,13 +52,18 @@ class AgentGoalWorkflow:
         self.tool_data: Optional[ToolData] = None
         self.confirm: bool = False
         self.tool_results: List[Dict[str, Any]] = []
+        self.goal: AgentGoal = {"tools": []}
 
+    # 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."""
-        params = combined_input.tool_params
-        agent_goal = combined_input.agent_goal
 
+        """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
+        self.goal = combined_input.agent_goal
+
+        # add message from sample conversation provided in tools/goal_registry.py, if it exists
         if params and params.conversation_summary:
             self.add_message("conversation_summary", params.conversation_summary)
             self.conversation_summary = params.conversation_summary
@@ -58,16 +74,26 @@ class AgentGoalWorkflow:
         waiting_for_confirm = False 
         current_tool = None
 
+        # This is the main interactive loop. Main responsibilities:
+        #   - Selecting and changing goals as directed by the user
+        #   - reacting to user input (from signals) 
+        #   - calling activities to determine next steps and prompts
+        #   - executing the selected tools 
         while True:
+            # wait indefinitely for input from signals - user_prompt, end_chat, or confirm as defined below
             await workflow.wait_condition(
                 lambda: bool(self.prompt_queue) or self.chat_ended or self.confirm
             )
 
+            # 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 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
 
@@ -75,6 +101,7 @@ class AgentGoalWorkflow:
                 confirmed_tool_data["next"] = "user_confirmed_tool_run"
                 self.add_message("user_confirmed_tool_run", confirmed_tool_data)
 
+                # execute the tool by key as defined in tools/__init__.py
                 await helpers.handle_tool_execution(
                     current_tool,
                     self.tool_data,
@@ -82,18 +109,30 @@ class AgentGoalWorkflow:
                     self.add_message,
                     self.prompt_queue
                 )
+
+                #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():
+                        new_goal = self.tool_results[-1].get("new_goal")
+                        workflow.logger.warning(f"Booya new goal!: {new_goal}")
+                        self.change_goal(new_goal)
+                    elif "ListAgents" in self.tool_results[-1].values() and self.goal.id != "goal_choose_agent_type":
+                        workflow.logger.warning("setting goal to goal_choose_agent_type")
+                        self.change_goal("goal_choose_agent_type")
                 continue
 
+            # 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
                     validation_input = ValidationInput(
                         prompt=prompt,
                         conversation_history=self.conversation_history,
-                        agent_goal=agent_goal,
+                        agent_goal=self.goal,
                     )
                     validation_result = await workflow.execute_activity(
                         ToolActivities.agent_validatePrompt,
@@ -105,25 +144,17 @@ class AgentGoalWorkflow:
                         ),
                     )
 
+                    #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(
-                    agent_goal, self.conversation_history, self.tool_data
-                )
-
-                prompt_input = ToolPromptInput(
-                    prompt=prompt,
-                    context_instructions=context_instructions,
-                )
+                # 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)
 
+                # connect to LLM and execute to get next steps
                 tool_data = await workflow.execute_activity(
                     ToolActivities.agent_toolPlanner,
                     prompt_input,
@@ -133,51 +164,73 @@ class AgentGoalWorkflow:
                         initial_interval=timedelta(seconds=5), backoff_coefficient=1
                     ),
                 )
+                tool_data["force_confirm"] = SHOW_CONFIRM
                 self.tool_data = tool_data
 
+                # 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")
 
+                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:
                     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
+                    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
 
+                # 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.")
+                    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.info("All steps completed. Exiting workflow.")
                     self.add_message("agent", tool_data)
+                    # end the workflow
                     return str(self.conversation_history)
 
                 self.add_message("agent", tool_data)
                 await helpers.continue_as_new_if_needed(
                     self.conversation_history,
                     self.prompt_queue,
-                    agent_goal,
+                    self.goal,
                     MAX_TURNS_BEFORE_CONTINUE,
                     self.add_message
                 )
 
+    #Signal that comes from api/main.py via a post to /send-prompt
     @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)
 
+    #Signal that comes from api/main.py via a post to /confirm
     @workflow.signal
     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
@@ -185,6 +238,11 @@ class AgentGoalWorkflow:
         """Query handler to retrieve the full conversation history."""
         return self.conversation_history
     
+    @workflow.query
+    def get_agent_goal(self) -> AgentGoal:
+        """Query handler to retrieve the current goal of the agent."""
+        return self.goal
+
     @workflow.query
     def get_summary_from_history(self) -> Optional[str]:
         """Query handler to retrieve the conversation summary if available. 
@@ -212,3 +270,18 @@ class AgentGoalWorkflow:
         self.conversation_history["messages"].append(
             {"actor": actor, "response": response}
         )
+
+    def change_goal(self, goal: str) -> None:
+        '''goalsLocal = {
+            "goal_match_train_invoice": goal_match_train_invoice,
+            "goal_event_flight_invoice": goal_event_flight_invoice,
+            "goal_choose_agent_type": goal_choose_agent_type,
+        }'''
+
+        if goal is not None:
+            for listed_goal in goal_list:
+                if listed_goal.id == goal:
+                    self.goal = listed_goal
+        #    self.goal = goals.get(goal)
+                    workflow.logger.warning("Changed goal to " + goal)
+        #todo reset goal or tools if this doesn't work or whatever