ecommerce installable

.env.example

@@ -5,8 +5,23 @@ FOOTBALL_DATA_API_KEY=....
 
 STRIPE_API_KEY=sk_test_51J...
 
-LLM_MODEL=openai/gpt-4o # default
-LLM_KEY=sk-proj-...
+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
+# LLM_PROVIDER=google
+# GOOGLE_API_KEY=your-google-api-key
+# or
+# LLM_PROVIDER=anthropic
+# ANTHROPIC_API_KEY=your-anthropic-api-key
+# or
+# LLM_PROVIDER=deepseek
+# DEEPSEEK_API_KEY=your-deepseek-api-key
 
 
 # uncomment and unset these environment variables to connect to the local dev server

Dockerfile

@@ -1,30 +0,0 @@
-FROM python:3.10-slim
-
-WORKDIR /app
-
-# Install system dependencies
-RUN apt-get update && \
-    apt-get install -y --no-install-recommends gcc build-essential && \
-    apt-get clean && \
-    rm -rf /var/lib/apt/lists/*
-
-# Copy requirements first for better caching
-RUN pip install --no-cache-dir poetry
-
-# Install Python dependencies without creating a virtualenv
-COPY pyproject.toml poetry.lock ./
-RUN poetry config virtualenvs.create false \
-    && poetry install --without dev --no-interaction --no-ansi --no-root
-
-# Copy application code
-COPY . .
-
-# Set Python to run in unbuffered mode (recommended for Docker)
-ENV PYTHONUNBUFFERED=1
-ENV PYTHONPATH=/app
-
-# Expose the port the app will run on
-EXPOSE 8000
-
-# Default to running only the API server; worker and train-api are separate Compose services
-CMD ["uvicorn", "api.main:app", "--host", "0.0.0.0", "--port", "8000"]

Makefile

@@ -1,62 +0,0 @@
-.PHONY: setup install run-worker run-api run-frontend run-train-api run-legacy-worker run-enterprise setup-venv check-python run-dev
-
-# Setup commands
-setup: check-python setup-venv install
-
-check-python:
-	@which python3 >/dev/null 2>&1 || (echo "Python 3 is required. Please install it first." && exit 1)
-
-setup-venv:
-	python3 -m venv venv
-	@echo "Virtual environment created. Don't forget to activate it with 'source venv/bin/activate'"
-
-install:
-	poetry install
-	cd frontend && npm install
-
-# Run commands
-run-worker:
-	poetry run python scripts/run_worker.py
-
-run-api:
-	poetry run uvicorn api.main:app --reload
-
-run-frontend:
-	cd frontend && npx vite
-
-run-train-api:
-	poetry run python thirdparty/train_api.py
-
-run-legacy-worker:
-	poetry run python scripts/run_legacy_worker.py
-
-run-enterprise:
-	cd enterprise && dotnet build && dotnet run
-
-# Development environment setup
-setup-temporal-mac:
-	brew install temporal
-	temporal server start-dev
-
-# Run all development services
-run-dev:
-	@echo "Starting all development services..."
-	@make run-worker & \
-	make run-api & \
-	make run-frontend & \
-	wait
-
-# Help command
-help:
-	@echo "Available commands:"
-	@echo "  make setup              - Create virtual environment and install dependencies"
-	@echo "  make setup-venv         - Create virtual environment only"
-	@echo "  make install            - Install all dependencies"
-	@echo "  make run-worker         - Start the Temporal worker"
-	@echo "  make run-api            - Start the API server"
-	@echo "  make run-frontend       - Start the frontend development server"
-	@echo "  make run-train-api      - Start the train API server"
-	@echo "  make run-legacy-worker  - Start the legacy worker"
-	@echo "  make run-enterprise     - Build and run the enterprise .NET worker"
-	@echo "  make setup-temporal-mac - Install and start Temporal server on Mac"
-	@echo "  make run-dev            - Start all development services (worker, API, frontend) in parallel" 

README.md

@@ -1,14 +1,11 @@
+### Branch 'ecommerce'
+This branch is for a demo, where I will live-install a new goal and tools.
+
 # Temporal AI Agent
 
 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 any LLM supported by [LiteLLM](https://docs.litellm.ai/docs/providers), including:
-- OpenAI models (GPT-4, GPT-3.5)
-- Anthropic Claude models
-- Google Gemini models
-- Deepseek models
-- Ollama models (local)
-- And many more!
+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).
 
 It's really helpful to [watch the demo (5 minute YouTube video)](https://www.youtube.com/watch?v=GEXllEH2XiQ) to understand how interaction works.
 
@@ -34,11 +31,7 @@ These are the key elements of an agentic framework:
 For a deeper dive into this, check out the [architecture guide](./architecture.md).
 
 ## Setup and Configuration
-See [the Setup guide](./setup.md) for detailed instructions. The basic configuration requires just two environment variables:
-```bash
-LLM_MODEL=openai/gpt-4o  # or any other model supported by LiteLLM
-LLM_KEY=your-api-key-here
-```
+See [the Setup guide](./setup.md).
 
 ## Customizing Interaction & Tools
 See [the guide to adding goals and tools](./adding-goals-and-tools.md).

activities/tool_activities.py

@@ -1,28 +1,142 @@
 import inspect
 from temporalio import activity
+from ollama import chat, ChatResponse
+from openai import OpenAI
 import json
-from typing import Optional, Sequence
+from typing import Sequence, Optional
 from temporalio.common import RawValue
 import os
 from datetime import datetime
+import google.generativeai as genai
+import anthropic
+import deepseek
 from dotenv import load_dotenv
 from models.data_types import EnvLookupOutput, ValidationInput, ValidationResult, ToolPromptInput, EnvLookupInput
-from litellm import completion
 
 load_dotenv(override=True)
+print(
+    "Using LLM provider: "
+    + os.environ.get("LLM_PROVIDER", "openai")
+    + " (set LLM_PROVIDER in .env to change)"
+)
+
+if os.environ.get("LLM_PROVIDER") == "ollama":
+    print(
+        "Using Ollama (local) model: "
+        + os.environ.get("OLLAMA_MODEL_NAME", "qwen2.5:14b")
+    )
+
 
 class ToolActivities:
     def __init__(self):
-        """Initialize LLM client using LiteLLM."""
-        self.llm_model = os.environ.get("LLM_MODEL", "openai/gpt-4")
-        self.llm_key = os.environ.get("LLM_KEY")
-        self.llm_base_url = os.environ.get("LLM_BASE_URL")
-        print(f"Initializing ToolActivities with LLM model: {self.llm_model}")
-        if self.llm_base_url:
-            print(f"Using custom base URL: {self.llm_base_url}")
+        """Initialize LLM clients based on environment configuration."""
+        self.llm_provider = os.environ.get("LLM_PROVIDER", "openai").lower()
+        print(f"Initializing ToolActivities with LLM provider: {self.llm_provider}")
+
+        # 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
+        self.ollama_model_name: Optional[str] = None
+        self.ollama_initialized: bool = False
+
+        # Only initialize the client specified by LLM_PROVIDER
+        if self.llm_provider == "openai":
+            if os.environ.get("OPENAI_API_KEY"):
+                self.openai_client = OpenAI(api_key=os.environ.get("OPENAI_API_KEY"))
+                print("Initialized OpenAI client")
+            else:
+                print("Warning: OPENAI_API_KEY not set but LLM_PROVIDER is 'openai'")
+        
+        elif 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(
+                    api_key=os.environ.get("ANTHROPIC_API_KEY")
+                )
+                print("Initialized Anthropic client")
+            else:
+                print(
+                    "Warning: ANTHROPIC_API_KEY not set but LLM_PROVIDER is 'anthropic'"
+                )
+
+        elif self.llm_provider == "google":
+            api_key = os.environ.get("GOOGLE_API_KEY")
+            if api_key:
+                genai.configure(api_key=api_key)
+                self.genai_configured = True
+                print("Configured Google Generative AI")
+            else:
+                print("Warning: GOOGLE_API_KEY not set but LLM_PROVIDER is 'google'")
+
+        elif self.llm_provider == "deepseek":
+            if os.environ.get("DEEPSEEK_API_KEY"):
+                self.deepseek_client = deepseek.DeepSeekAPI(
+                    api_key=os.environ.get("DEEPSEEK_API_KEY")
+                )
+                print("Initialized DeepSeek client")
+            else:
+                print(
+                    "Warning: DEEPSEEK_API_KEY not set but LLM_PROVIDER is 'deepseek'"
+                )
+
+        # For Ollama, we store the model name but actual initialization happens in warm_up_ollama
+        elif self.llm_provider == "ollama":
+            self.ollama_model_name = os.environ.get("OLLAMA_MODEL_NAME", "qwen2.5:14b")
+            print(
+                f"Using Ollama model: {self.ollama_model_name} (will be loaded on worker startup)"
+            )
+        else:
+            print(
+                f"Warning: Unknown LLM_PROVIDER '{self.llm_provider}', defaulting to OpenAI"
+            )
+
+    def warm_up_ollama(self):
+        """Pre-load the Ollama model to avoid cold start latency on first request"""
+        if self.llm_provider != "ollama" or self.ollama_initialized:
+            return False  # No need to warm up if not using Ollama or already warmed up
+
+        try:
+            print(
+                f"Pre-loading Ollama model '{self.ollama_model_name}' - this may take 30+ seconds..."
+            )
+            start_time = datetime.now()
+
+            # Make a simple request to load the model into memory
+            chat(
+                model=self.ollama_model_name,
+                messages=[
+                    {"role": "system", "content": "You are an AI assistant"},
+                    {
+                        "role": "user",
+                        "content": "Hello! This is a warm-up message to load the model.",
+                    },
+                ],
+            )
+
+            elapsed_time = (datetime.now() - start_time).total_seconds()
+            print(f"✅ Ollama model loaded successfully in {elapsed_time:.2f} seconds")
+            self.ollama_initialized = True
+            return True
+        except Exception as e:
+            print(f"❌ Error pre-loading Ollama model: {str(e)}")
+            print(
+                "The worker will continue, but the first actual request may experience a delay."
+            )
+            return False
 
     @activity.defn
-    async def agent_validatePrompt(self, validation_input: ValidationInput) -> ValidationResult:
+    async def agent_validatePrompt(
+        self, validation_input: ValidationInput
+    ) -> ValidationResult:
         """
         Validates the prompt in the context of the conversation history and agent goal.
         Returns a ValidationResult indicating if the prompt makes sense given the context.
@@ -73,7 +187,7 @@ class ToolActivities:
             prompt=validation_prompt, context_instructions=context_instructions
         )
 
-        result = await self.agent_toolPlanner(prompt_input)
+        result = self.agent_toolPlanner(prompt_input)
 
         return ValidationResult(
             validationResult=result.get("validationResult", False),
@@ -81,7 +195,41 @@ class ToolActivities:
         )
 
     @activity.defn
-    async def agent_toolPlanner(self, input: ToolPromptInput) -> dict:
+    def agent_toolPlanner(self, input: ToolPromptInput) -> dict:
+        if self.llm_provider == "ollama":
+            return self.prompt_llm_ollama(input)
+        elif self.llm_provider == "google":
+            return self.prompt_llm_google(input)
+        elif self.llm_provider == "anthropic":
+            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)
+
+    def parse_json_response(self, response_content: str) -> dict:
+        """
+        Parses the JSON response content and returns it as a dictionary.
+        """
+        try:
+            data = json.loads(response_content)
+            return data
+        except json.JSONDecodeError as e:
+            print(f"Invalid JSON: {e}")
+            raise
+
+    def prompt_llm_openai(self, input: ToolPromptInput) -> dict:
+        if not self.openai_client:
+            api_key = os.environ.get("OPENAI_API_KEY")
+            if not api_key:
+                raise ValueError(
+                    "OPENAI_API_KEY is not set in the environment variables but LLM_PROVIDER is 'openai'"
+                )
+            self.openai_client = OpenAI(api_key=api_key)
+            print("Initialized OpenAI client on demand")
+
         messages = [
             {
                 "role": "system",
@@ -95,53 +243,236 @@ class ToolActivities:
             },
         ]
 
-        try:
-            completion_kwargs = {
-                "model": self.llm_model,
-                "messages": messages,
-                "api_key": self.llm_key
-            }
-            
-            # Add base_url if configured
-            if self.llm_base_url:
-                completion_kwargs["base_url"] = self.llm_base_url
+        chat_completion = self.openai_client.chat.completions.create(
+            model="gpt-4o", messages=messages  # was gpt-4-0613
+        )
 
-            response = completion(**completion_kwargs)
-            
-            response_content = response.choices[0].message.content
-            activity.logger.info(f"LLM response: {response_content}")
+        response_content = chat_completion.choices[0].message.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:
+            print(
+                "Ollama model not pre-loaded. Loading now (this may take 30+ seconds)..."
+            )
+            try:
+                self.warm_up_ollama()
+            except Exception:
+                # We already logged the error in warm_up_ollama, continue with the actual request
+                pass
+
+        model_name = self.ollama_model_name or os.environ.get(
+            "OLLAMA_MODEL_NAME", "qwen2.5:14b"
+        )
+        messages = [
+            {
+                "role": "system",
+                "content": input.context_instructions
+                + ". The current date is "
+                + get_current_date_human_readable(),
+            },
+            {
+                "role": "user",
+                "content": input.prompt,
+            },
+        ]
+
+        try:
+            response: ChatResponse = chat(model=model_name, messages=messages)
+            print(f"Chat response: {response.message.content}")
 
             # Use the new sanitize function
-            response_content = self.sanitize_json_response(response_content)
-
+            response_content = self.sanitize_json_response(response.message.content)
             return self.parse_json_response(response_content)
+        except (json.JSONDecodeError, ValueError) as e:
+            # Re-raise JSON-related exceptions to let Temporal retry the activity
+            print(f"JSON parsing error with Ollama response: {str(e)}")
+            raise
         except Exception as e:
-            print(f"Error in LLM completion: {str(e)}")
+            # Log and raise other exceptions that may need retrying
+            print(f"Error in Ollama chat: {str(e)}")
             raise
 
-    def parse_json_response(self, response_content: str) -> dict:
-        """
-        Parses the JSON response content and returns it as a dictionary.
-        """
-        try:
-            data = json.loads(response_content)
-            return data
-        except json.JSONDecodeError as e:
-            print(f"Invalid JSON: {e}")
-            raise
+    def prompt_llm_google(self, input: ToolPromptInput) -> dict:
+        if not self.genai_configured:
+            api_key = os.environ.get("GOOGLE_API_KEY")
+            if not api_key:
+                raise ValueError(
+                    "GOOGLE_API_KEY is not set in the environment variables but LLM_PROVIDER is 'google'"
+                )
+            genai.configure(api_key=api_key)
+            self.genai_configured = True
+            print("Configured Google Generative AI on demand")
+
+        model = genai.GenerativeModel(
+            "models/gemini-1.5-flash",
+            system_instruction=input.context_instructions
+            + ". The current date is "
+            + datetime.now().strftime("%B %d, %Y"),
+        )
+        response = model.generate_content(input.prompt)
+        response_content = response.text
+        print(f"Google Gemini 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_anthropic(self, input: ToolPromptInput) -> dict:
+        if not self.anthropic_client:
+            api_key = os.environ.get("ANTHROPIC_API_KEY")
+            if not api_key:
+                raise ValueError(
+                    "ANTHROPIC_API_KEY is not set in the environment variables but LLM_PROVIDER is 'anthropic'"
+                )
+            self.anthropic_client = anthropic.Anthropic(api_key=api_key)
+            print("Initialized Anthropic client on demand")
+
+        response = self.anthropic_client.messages.create(
+            model="claude-3-5-sonnet-20241022",  
+            #model="claude-3-7-sonnet-20250219",  # doesn't do as well
+            max_tokens=1024,
+            system=input.context_instructions
+            + ". The current date is "
+            + get_current_date_human_readable(),
+            messages=[
+                {
+                    "role": "user",
+                    "content": input.prompt,
+                }
+            ],
+        )
+
+        response_content = response.content[0].text
+        print(f"Anthropic 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_deepseek(self, input: ToolPromptInput) -> dict:
+        if not self.deepseek_client:
+            api_key = os.environ.get("DEEPSEEK_API_KEY")
+            if not api_key:
+                raise ValueError(
+                    "DEEPSEEK_API_KEY is not set in the environment variables but LLM_PROVIDER is 'deepseek'"
+                )
+            self.deepseek_client = deepseek.DeepSeekAPI(api_key=api_key)
+            print("Initialized DeepSeek 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,
+            },
+        ]
+
+        response = self.deepseek_client.chat_completion(prompt=messages)
+        response_content = response
+        print(f"DeepSeek response: {response_content}")
+
+        # Use the new sanitize function
+        response_content = self.sanitize_json_response(response_content)
+
+        return self.parse_json_response(response_content)
 
     def sanitize_json_response(self, response_content: str) -> str:
         """
-        Sanitizes the response content to ensure it's valid JSON.
+        Extracts the JSON block from the response content as a string.
+        Supports:
+        - JSON surrounded by ```json and ```
+        - Raw JSON input
+        - JSON preceded or followed by extra text
+        Rejects invalid input that doesn't contain JSON.
         """
-        # Remove any markdown code block markers
-        response_content = response_content.replace("```json", "").replace("```", "")
-        
-        # Remove any leading/trailing whitespace
-        response_content = response_content.strip()
-        
-        return response_content
+        try:
+            start_marker = "```json"
+            end_marker = "```"
 
+            json_str = None
+
+            # Case 1: JSON surrounded by markers
+            if start_marker in response_content and end_marker in response_content:
+                json_start = response_content.index(start_marker) + len(start_marker)
+                json_end = response_content.index(end_marker, json_start)
+                json_str = response_content[json_start:json_end].strip()
+
+            # Case 2: Text with valid JSON
+            else:
+                # Try to locate the JSON block by scanning for the first `{` and last `}`
+                json_start = response_content.find("{")
+                json_end = response_content.rfind("}")
+
+                if json_start != -1 and json_end != -1 and json_start < json_end:
+                    json_str = response_content[json_start : json_end + 1].strip()
+
+            # Validate and ensure the extracted JSON is valid
+            if json_str:
+                json.loads(json_str)  # This will raise an error if the JSON is invalid
+                return json_str
+
+            # If no valid JSON found, raise an error
+            raise ValueError("Response does not contain valid JSON.")
+
+        except json.JSONDecodeError:
+            # Invalid JSON
+            print(f"Invalid JSON detected in response: {response_content}")
+            raise ValueError("Response does not contain valid JSON.")
+        except Exception as e:
+            # Other errors
+            print(f"Error processing response: {str(e)}")
+            print(f"Full response: {response_content}")
+            raise
+
+    # get env vars for workflow
     @activity.defn
     async def get_wf_env_vars(self, input: EnvLookupInput) -> EnvLookupOutput:
         """ gets env vars for workflow as an activity result so it's deterministic
@@ -167,6 +498,18 @@ class ToolActivities:
 
         return output
 
+
+def get_current_date_human_readable():
+    """
+    Returns the current date in a human-readable format.
+
+    Example: Wednesday, January 1, 2025
+    """
+    from datetime import datetime
+
+    return datetime.now().strftime("%A, %B %d, %Y")
+
+
 @activity.defn(dynamic=True)
 async def dynamic_tool_activity(args: Sequence[RawValue]) -> dict:
     from tools import get_handler

docker-compose.override.yml

@@ -1,20 +0,0 @@
-services:
-  api:
-    volumes:
-      - ./:/app:cached
-    command: uvicorn api.main:app --host 0.0.0.0 --port 8000 --reload
-
-  worker:
-    volumes:
-      - ./:/app:cached
-    command: python scripts/run_worker.py
-
-  train-api:
-    volumes:
-      - ./:/app:cached
-    command: python thirdparty/train_api.py
-
-  frontend:
-    volumes:
-      - ./frontend:/app:cached
-    command: sh -c "apk update && apk add --no-cache xdg-utils && npm install && npx vite --host 0.0.0.0 --port 5173"

docker-compose.yml

@@ -1,120 +0,0 @@
-services:
-  # Database service
-  postgresql:
-    image: postgres:14
-    container_name: temporal-postgresql
-    environment:
-      POSTGRES_USER: temporal
-      POSTGRES_PASSWORD: temporal
-      POSTGRES_DB: temporal
-    volumes:
-      - postgresql:/var/lib/postgresql/data
-    networks:
-      - temporal-network
-
-  # Temporal services
-  temporal:
-    image: temporalio/auto-setup:1.27.2
-    container_name: temporal
-    ports:
-      - "7233:7233"
-    environment:
-      - DB=postgres12
-      - DB_PORT=5432
-      - POSTGRES_USER=temporal
-      - POSTGRES_PWD=temporal
-      - POSTGRES_SEEDS=postgresql
-    depends_on:
-      - postgresql
-    networks:
-      - temporal-network
-
-  temporal-admin-tools:
-    image: temporalio/admin-tools:1.27
-    container_name: temporal-admin-tools
-    depends_on:
-      - temporal
-    environment:
-      - TEMPORAL_CLI_ADDRESS=temporal:7233
-    networks:
-      - temporal-network
-
-  temporal-ui:
-    image: temporalio/ui:2.37.2
-    container_name: temporal-ui
-    ports:
-      - "8080:8080"
-    environment:
-      - TEMPORAL_ADDRESS=temporal:7233
-      - TEMPORAL_CORS_ORIGINS=http://localhost:8080
-    depends_on:
-      - temporal
-    networks:
-      - temporal-network
-
-  api:
-    build:
-      context: .
-      dockerfile: Dockerfile
-    container_name: temporal-ai-agent-api
-    ports:
-      - "8000:8000"
-    depends_on:
-      - temporal
-    networks:
-      - temporal-network
-    env_file:
-      - .env
-    environment:
-      - TEMPORAL_ADDRESS=temporal:7233
-
-  worker:
-    build:
-      context: .
-      dockerfile: Dockerfile
-    container_name: temporal-ai-agent-worker
-    depends_on:
-      - temporal
-    env_file:
-      - .env
-    environment:
-      - TEMPORAL_ADDRESS=temporal:7233
-    command: python scripts/run_worker.py
-    networks:
-      - temporal-network
-
-  train-api:
-    build:
-      context: .
-      dockerfile: Dockerfile
-    container_name: temporal-ai-agent-train-api
-    depends_on:
-      - temporal
-    env_file:
-      - .env
-    environment:
-      - TEMPORAL_ADDRESS=temporal:7233
-    command: python thirdparty/train_api.py
-    networks:
-      - temporal-network
-
-  frontend:
-    image: node:18-alpine
-    container_name: temporal-ai-agent-frontend
-    working_dir: /app
-    volumes:
-      - ./frontend:/app
-    command: sh -c "apk update && apk add --no-cache xdg-utils && npm install && npx vite --host 0.0.0.0"
-    ports:
-      - "5173:5173"
-    depends_on:
-      - api
-    networks:
-      - temporal-network
-
-networks:
-  temporal-network:
-    driver: bridge
-
-volumes:
-  postgresql:

ecommerce_goal/goal_definition.py

@@ -0,0 +1,76 @@
+# ----- E-Commerce Goals ---
+# this tool checks account balances, and uses ./data/customer_account_data.json as dummy data
+goal_ecomm_order_status = AgentGoal(
+    id="goal_ecomm_order_status",
+    category_tag="ecommerce",
+    agent_name="Check Order Status",
+    agent_friendly_description="Check the status of your order.",
+    tools=[
+        tool_registry.ecomm_get_order,
+        tool_registry.ecomm_track_package,
+    ],
+    description="The user wants to learn the status of a specific order. If the status is 'shipped' or 'delivered', they might want to get the package tracking information. To assist with that goal, help the user gather args for these tools in order: "
+    "1. GetOrder: get information about an order"
+    "2. TrackPackage: provide tracking information for the package. This tool is optional and should only be offered if the status is 'shipped' OR 'delivered' - otherwise, skip this tool and do not mention it to the user.",
+    starter_prompt=starter_prompt_generic,
+    example_conversation_history="\n ".join(
+        [
+            "user: I'd like to know the status of my order",
+            "agent: Sure! I can help you out with that. May I have your order number?",
+            "user: 102 ",
+            "user_confirmed_tool_run: <user clicks confirm on GetOrderStatus tool>",
+            "tool_result: { 'id': '102', 'summary': 'Red Sunglasses', 'email': 'matt.murdock@nelsonmurdock.com', 'status': 'shipped', 'order_date': '2025-04-01', 'last_order_update': '2025-04-06', 'tracking_id': '039813852990618' }",
+            "agent: Your order 'Red Sunglasses,' placed April 1, 2025, was shipped on April 6, 2025. Would you like to see the tracking inforation?",
+            "user: Yes",
+            "user_confirmed_tool_run: <user clicks confirm on TrackPackage tool>",
+            "tool_result: { 'scheduled_delivery_date': 'April 30, 2025', 'carrier': 'USPS', 'status_summary': 'Your item has left our acceptance facility and is in transit to a sorting facility on April 10, 2025 at 7:06 am in IRON RIDGE, WI 53035.', 'tracking_link': 'https://tools.usps.com/go/TrackConfirmAction?qtc_tLabels1=12345','last_tracking_update': '2025-03-22T16:14:48'}",
+            "agent: Your package is scheduled to be delivered on April 30, 2025 via USPS. Here is the most recent status from them regarding your package, updated as of March 22: \n"
+            "Your item has left our acceptance facility and is in transit to a sorting facility on April 10, 2025 at 7:06 am in IRON RIDGE, WI 53035. \n"
+            "You can find the full tracking details here: tracking_link !",
+        ]
+    ),
+)
+
+goal_ecomm_list_orders = AgentGoal(
+    id="goal_ecomm_list_orders",
+    category_tag="ecommerce",
+    agent_name="List All Orders",
+    agent_friendly_description="List all orders for a user.",
+    tools=[
+        tool_registry.ecomm_list_orders,
+        tool_registry.ecomm_get_order,
+        tool_registry.ecomm_track_package,
+    ],
+    description="The user wants to see all of their orders. They may want more details about specific orders, and if the status of an order is 'shipped' or 'delivered', they might want to get the package tracking information. To assist with that goal, help the user gather args for this tool: "
+    "1. ListOrders: list orders for a user"
+    " and then offer the following tools, in a loop, until the user indicates they are done:"
+    "2. GetOrder: get information about an order. This tool is optional."
+    "3. TrackPackage: provide tracking information for the package. This tool is optional and should only be offered if the status is 'shipped' OR 'delivered' - otherwise, skip this tool and do not mention it to the user.",
+    starter_prompt=starter_prompt_generic,
+    example_conversation_history="\n ".join(
+        [
+            "user: I'd like to see all of my orders.",
+            "agent: Sure! I can help you out with that. May I have your email address?",
+            "user: email is bob.johnson@emailzzz.com ",
+            "user_confirmed_tool_run: <user clicks confirm on ListOrders tool>",
+            "tool_result: a list of orders including [{'id': '102', 'summary': 'Red Sunglasses', 'email': 'matt.murdock@nelsonmurdock.com', 'status': 'shipped', 'order_date': '2025-04-01', 'last_order_update': '2025-04-06', 'tracking_id': '039813852990618' }, { 'id': '103', 'summary': 'Blue Sunglasses', 'email': 'matt.murdock@nelsonmurdock.com', 'status': 'paid', 'order_date': '2025-04-03', 'last_order_update': '2025-04-07' }]",
+            "agent: Your orders are as follows: \n",
+            "1. Red Sunglasses, ordered 4/1/2025 \n",
+            "2. Blue Sunglasses, ordered 4/3/2025 \n",
+            "Would you like more information about any of your orders?"
+            "user: Yes, the Red Sunglasses",
+            "agent: Your order 'Red Sunglasses,' placed April 1, 2025, was shipped on April 6, 2025. Would you like to see the tracking inforation?",
+            "user: Yes",
+            "user_confirmed_tool_run: <user clicks confirm on TrackPackage tool>",
+            "tool_result: { 'scheduled_delivery_date': 'April 30, 2025', 'carrier': 'USPS', 'status_summary': 'Your item has left our acceptance facility and is in transit to a sorting facility on April 10, 2025 at 7:06 am in IRON RIDGE, WI 53035.', 'tracking_link': 'https://tools.usps.com/go/TrackConfirmAction?qtc_tLabels1=12345','last_tracking_update': '2025-03-22T16:14:48'}",
+            "agent: Your package is scheduled to be delivered on April 30, 2025 via USPS. Here is the most recent status from them regarding your package \n, updated as of March 22: \n"
+            "Your item has left our acceptance facility and is in transit to a sorting facility on April 10, 2025 at 7:06 am in IRON RIDGE, WI 53035. \n"
+            "You can find the full tracking details here: tracking_link ! \n"
+            "Would you like more information about any of your other orders?",
+            "user: No" "agent: Thanks, and have a great day!",
+        ]
+    ),
+)
+
+goal_list.append(goal_ecomm_list_orders)
+goal_list.append(goal_ecomm_order_status)

ecommerce_goal/tool_definitions.py

@@ -0,0 +1,49 @@
+# ----- ECommerce Use Case Tools -----
+ecomm_list_orders = ToolDefinition(
+    name="ListOrders",
+    description="Get all orders for a certain email address.",
+    arguments=[
+        ToolArgument(
+            name="email_address",
+            type="string",
+            description="Email address of user by which to find orders",
+        ),
+    ],
+)
+
+ecomm_get_order = ToolDefinition(
+    name="GetOrder",
+    description="Get infromation about an order by order ID.",
+    arguments=[
+        ToolArgument(
+            name="order_id",
+            type="string",
+            description="ID of order to determine status of",
+        ),
+    ],
+)
+
+ecomm_track_package = ToolDefinition(
+    name="TrackPackage",
+    description="Get tracking information for a package by shipping provider and tracking ID",
+    arguments=[
+        ToolArgument(
+            name="tracking_id",
+            type="string",
+            description="ID of package to track",
+        ),
+        ToolArgument(
+            name="userConfirmation",
+            type="string",
+            description="Indication of user's desire to get package tracking information",
+        ),
+    ],
+)
+
+# for __init__.py
+# if tool_name == "GetOrder":
+#     return get_order
+# if tool_name == "TrackPackage":
+#     return track_package
+# if tool_name == "ListOrders":
+#     return list_orders

pyproject.toml

@@ -31,11 +31,16 @@ temporalio = "^1.8.0"
 
 # Standard library modules (e.g. asyncio, collections) don't need to be added
 # since they're built-in for Python 3.8+.
-litellm = "^1.70.0"
+ollama = "^0.4.5"
 pyyaml = "^6.0.2"
 fastapi = "^0.115.6"
 uvicorn = "^0.34.0"
 python-dotenv = "^1.0.1"
+openai = "^1.59.2"
+stripe = "^11.4.1"
+google-generativeai = "^0.8.4"
+anthropic = "0.47.0"
+deepseek = "^1.0.0"
 requests = "^2.32.3"
 pandas = "^2.2.3"
 gtfs-kit = "^10.1.1"

scripts/run_ollama.py

@@ -0,0 +1,23 @@
+from ollama import chat, ChatResponse
+
+
+def main():
+    model_name = "mistral"
+
+    # The messages to pass to the model
+    messages = [
+        {
+            "role": "user",
+            "content": "Why is the sky blue?",
+        }
+    ]
+
+    # Call ollama's chat function
+    response: ChatResponse = chat(model=model_name, messages=messages)
+
+    # Print the full message content
+    print(response.message.content)
+
+
+if __name__ == "__main__":
+    main()

scripts/run_worker.py

@@ -17,18 +17,18 @@ async def main():
     load_dotenv(override=True)
 
     # Print LLM configuration info
-    llm_model = os.environ.get("LLM_MODEL", "openai/gpt-4")
-    print(f"Worker will use LLM model: {llm_model}")
+    llm_provider = os.environ.get("LLM_PROVIDER", "openai").lower()
+    print(f"Worker will use LLM provider: {llm_provider}")
 
     # Create the client
     client = await get_temporal_client()
 
-    # Initialize the activities class
+    # Initialize the activities class once with the specified LLM provider
     activities = ToolActivities()
-    print(f"ToolActivities initialized with LLM model: {llm_model}")
+    print(f"ToolActivities initialized with LLM provider: {llm_provider}")
 
     # If using Ollama, pre-load the model to avoid cold start latency
-    if llm_model.startswith("ollama"):
+    if llm_provider == "ollama":
         print("\n======== OLLAMA MODEL INITIALIZATION ========")
         print("Ollama models need to be loaded into memory on first use.")
         print("This may take 30+ seconds depending on your hardware and model size.")
@@ -51,6 +51,8 @@ async def main():
     print("Worker ready to process tasks!")
     logging.basicConfig(level=logging.WARN)
 
+
+
     # Run the worker
     with concurrent.futures.ThreadPoolExecutor(max_workers=100) as activity_executor:
         worker = Worker(

setup.md

@@ -14,37 +14,6 @@ If you want to show confirmations/enable the debugging UI that shows tool args,
 SHOW_CONFIRM=True
 ```
 
-### Quick Start with Makefile
-
-We've provided a Makefile to simplify the setup and running of the application. Here are the main commands:
-
-```bash
-# Initial setup
-make setup              # Creates virtual environment and installs dependencies
-make setup-venv         # Creates virtual environment only
-make install            # Installs all dependencies
-
-# Running the application
-make run-worker         # Starts the Temporal worker
-make run-api            # Starts the API server
-make run-frontend       # Starts the frontend development server
-
-# Additional services
-make run-train-api      # Starts the train API server
-make run-legacy-worker  # Starts the legacy worker
-make run-enterprise     # Builds and runs the enterprise .NET worker
-
-# Development environment setup
-make setup-temporal-mac # Installs and starts Temporal server on Mac
-
-# View all available commands
-make help
-```
-
-### Manual Setup (Alternative to Makefile)
-
-If you prefer to run commands manually, follow these steps:
-
 ### Agent Goal Configuration
 
 The agent can be configured to pursue different goals using the `AGENT_GOAL` environment variable in your `.env` file. If unset, default is `goal_choose_agent_type`. 
@@ -56,41 +25,54 @@ GOAL_CATEGORIES=hr,travel-flights,travel-trains,fin
 
 See the section Goal-Specific Tool Configuration below for tool configuration for specific goals.
 
-### LLM Configuration
+### LLM Provider Configuration
 
-Note: We recommend using OpenAI's GPT-4o or Claude 3.5 Sonnet for the best results. There can be significant differences in performance and capabilities between models, especially for complex tasks.
+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:
 
-The agent uses LiteLLM to interact with various LLM providers. Configure theqfollowing environment variables in your `.env` file:
+- `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)
 
-- `LLM_MODEL`: The model to use (e.g., "openai/gpt-4o", "anthropic/claude-3-sonnet", "google/gemini-pro", etc.)
-- `LLM_KEY`: Your API key for the selected provider
-- `LLM_BASE_URL`: (Optional) Custom base URL for the LLM provider. Useful for:
-  - Using Ollama with a custom endpoint
-  - Using a proxy or custom API gateway
-  - Testing with different API versions
+### Option 1: OpenAI
 
-LiteLLM will automatically detect the provider based on the model name. For example:
-- For OpenAI models: `openai/gpt-4o` or `openai/gpt-3.5-turbo`
-- For Anthropic models: `anthropic/claude-3-sonnet`
-- For Google models: `google/gemini-pro`
-- For Ollama models: `ollama/mistral` (requires `LLM_BASE_URL` set to your Ollama server)
+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`.
 
-Example configurations:
-```bash
-# For OpenAI
-LLM_MODEL=openai/gpt-4o
-LLM_KEY=your-api-key-here
+### Option 2: Google Gemini
 
-# For Anthropic
-LLM_MODEL=anthropic/claude-3-sonnet
-LLM_KEY=your-api-key-here
+To use Google Gemini:
 
-# For Ollama with custom URL
-LLM_MODEL=ollama/mistral
-LLM_BASE_URL=http://localhost:11434
-```
+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.
 
-For a complete list of supported models and providers, visit the [LiteLLM documentation](https://docs.litellm.ai/docs/providers).
+### 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
 
@@ -111,32 +93,10 @@ temporal server start-dev
 ```
 See the [Temporal documentation](https://learn.temporal.io/getting_started/python/dev_environment/) for other platforms.
 
-You can also run a local Temporal server using Docker Compose. See the `Development with Docker` section below.
 
 ## Running the Application
 
-### Docker
-- All services are defined in `docker-compose.yml` (includes a Temporal server).
-- **Dev overrides** (mounted code, live‑reload commands) live in `docker-compose.override.yml` and are **auto‑merged** on `docker compose up`.
-- To start **development** mode (with hot‑reload):
-  ```bash
-  docker compose up -d
-  # quick rebuild without infra:
-  docker compose up -d --no-deps --build api train-api worker frontend
-  ```
-- To run **production** mode (ignore dev overrides):
-  ```bash
-  docker compose -f docker-compose.yml up -d
-  ```
-
-Default urls:
-* Temporal UI: [http://localhost:8080](http://localhost:8080)
-* API: [http://localhost:8000](http://localhost:8000)
-* Frontend: [http://localhost:5173](http://localhost:5173)
-
-### Local Machine (no docker)
-
-**Python Backend**
+### Python Backend
 
 Requires [Poetry](https://python-poetry.org/) to manage dependencies.
 
@@ -159,7 +119,7 @@ poetry run uvicorn api.main:app --reload
 ```
 Access the API at `/docs` to see the available endpoints.
 
-**React UI**
+### React UI
 Start the frontend:
 ```bash
 cd frontend
@@ -167,7 +127,8 @@ npm install
 npx vite
 ```
 Access the UI at `http://localhost:5173`
-  
+
+
 
 ## Goal-Specific Tool Configuration
 Here is configuration guidance for specific goals. Travel and financial goals have configuration & setup as below.
@@ -184,7 +145,7 @@ Here is configuration guidance for specific goals. Travel and financial goals ha
 * 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/)
         * Set permissions for read-write on: `Credit Notes, Invoices, Customers and Customer Sessions`
-    * If you don't have a Stripe key, comment out the STRIPE_API_KEY in the .env file, and a dummy invoice will be created rather than a Stripe invoice. The function can be found in `tools/create_invoice.py`
+    * 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.
 
 ### Goal: Find a Premier League match, book train tickets to it and invoice the user for the cost (Replay 2025 Keynote)
 - `AGENT_GOAL=goal_match_train_invoice` - Focuses on Premier League match attendance with train booking and invoice generation
@@ -270,4 +231,4 @@ For more details, check out [adding goals and tools guide](./adding-goals-and-to
 [  ] `cd frontend`, `npm install`, `npx vite` <br />
 [ ] Access the UI at `http://localhost:5173` <br />
 
-And that's it! Happy AI Agent Exploring!
+And that's it! Happy AI Agent Exploring!

tools/__init__.py

@@ -18,9 +18,9 @@ from .fin.get_account_balances import get_account_balance
 from .fin.move_money import move_money
 from .fin.submit_loan_application import submit_loan_application
 
-from .ecommerce.get_order import get_order
-from .ecommerce.track_package import track_package
-from .ecommerce.list_orders import list_orders
+# from .ecommerce.get_order import get_order
+# from .ecommerce.track_package import track_package
+# from .ecommerce.list_orders import list_orders
 
 from .give_hint import give_hint
 from .guess_location import guess_location
@@ -61,12 +61,6 @@ def get_handler(tool_name: str):
         return move_money
     if tool_name == "FinCheckAccountSubmitLoanApproval":
         return submit_loan_application
-    if tool_name == "GetOrder":
-        return get_order
-    if tool_name == "TrackPackage":
-        return track_package
-    if tool_name == "ListOrders":
-        return list_orders
     if tool_name == "GiveHint":
         return give_hint
     if tool_name == "GuessLocation":

tools/create_invoice.py

@@ -27,7 +27,7 @@ def ensure_customer_exists(
 def create_invoice(args: dict) -> dict:
     """Create and finalize a Stripe invoice."""
     # If an API key exists in the env file, find or create customer
-    if stripe.api_key is not None and stripe.api_key != "":
+    if stripe.api_key is not None:
         customer_id = ensure_customer_exists(
             args.get("customer_id"), args.get("email", "default@example.com")
         )
@@ -69,3 +69,15 @@ def create_invoice(args: dict) -> dict:
             "invoiceURL": "https://pay.example.com/invoice/12345",
             "reference": "INV-12345",
         }
+
+def create_invoice_example(args: dict) -> dict:
+    """
+    This is an example implementation of the CreateInvoice tool
+    Doesn't call any external services, just returns a dummy response
+    """
+    print("[CreateInvoice] Creating invoice with:", args)
+    return {
+        "invoiceStatus": "generated",
+        "invoiceURL": "https://pay.example.com/invoice/12345",
+        "reference": "INV-12345",
+    }

tools/goal_registry.py

@@ -380,80 +380,6 @@ goal_fin_loan_application = AgentGoal(
     ),
 )
 
-# ----- E-Commerce Goals ---
-# this tool checks account balances, and uses ./data/customer_account_data.json as dummy data
-goal_ecomm_order_status = AgentGoal(
-    id="goal_ecomm_order_status",
-    category_tag="ecommerce",
-    agent_name="Check Order Status",
-    agent_friendly_description="Check the status of your order.",
-    tools=[
-        tool_registry.ecomm_get_order,
-        tool_registry.ecomm_track_package,
-    ],
-    description="The user wants to learn the status of a specific order. If the status is 'shipped' or 'delivered', they might want to get the package tracking information. To assist with that goal, help the user gather args for these tools in order: "
-    "1. GetOrder: get information about an order"
-    "2. TrackPackage: provide tracking information for the package. This tool is optional and should only be offered if the status is 'shipped' OR 'delivered' - otherwise, skip this tool and do not mention it to the user.",
-    starter_prompt=starter_prompt_generic,
-    example_conversation_history="\n ".join(
-        [
-            "user: I'd like to know the status of my order",
-            "agent: Sure! I can help you out with that. May I have your order number?",
-            "user: 102 ",
-            "user_confirmed_tool_run: <user clicks confirm on GetOrderStatus tool>",
-            "tool_result: { 'id': '102', 'summary': 'Red Sunglasses', 'email': 'matt.murdock@nelsonmurdock.com', 'status': 'shipped', 'order_date': '2025-04-01', 'last_order_update': '2025-04-06', 'tracking_id': '039813852990618' }",
-            "agent: Your order 'Red Sunglasses,' placed April 1, 2025, was shipped on April 6, 2025. Would you like to see the tracking inforation?",
-            "user: Yes",
-            "user_confirmed_tool_run: <user clicks confirm on TrackPackage tool>",
-            "tool_result: { 'scheduled_delivery_date': 'April 30, 2025', 'carrier': 'USPS', 'status_summary': 'Your item has left our acceptance facility and is in transit to a sorting facility on April 10, 2025 at 7:06 am in IRON RIDGE, WI 53035.', 'tracking_link': 'https://tools.usps.com/go/TrackConfirmAction?qtc_tLabels1=12345','last_tracking_update': '2025-03-22T16:14:48'}",
-            "agent: Your package is scheduled to be delivered on April 30, 2025 via USPS. Here is the most recent status from them regarding your package, updated as of March 22: \n"
-            "Your item has left our acceptance facility and is in transit to a sorting facility on April 10, 2025 at 7:06 am in IRON RIDGE, WI 53035. \n"
-            "You can find the full tracking details here: tracking_link !",
-        ]
-    ),
-)
-
-goal_ecomm_list_orders = AgentGoal(
-    id="goal_ecomm_list_orders",
-    category_tag="ecommerce",
-    agent_name="List All Orders",
-    agent_friendly_description="List all orders for a user.",
-    tools=[
-        tool_registry.ecomm_list_orders,
-        tool_registry.ecomm_get_order,
-        tool_registry.ecomm_track_package,
-    ],
-    description="The user wants to see all of their orders. They may want more details about specific orders, and if the status of an order is 'shipped' or 'delivered', they might want to get the package tracking information. To assist with that goal, help the user gather args for this tool: "
-    "1. ListOrders: list orders for a user"
-    " and then offer the following tools, in a loop, until the user indicates they are done:"
-    "2. GetOrder: get information about an order. This tool is optional."
-    "3. TrackPackage: provide tracking information for the package. This tool is optional and should only be offered if the status is 'shipped' OR 'delivered' - otherwise, skip this tool and do not mention it to the user.",
-    starter_prompt=starter_prompt_generic,
-    example_conversation_history="\n ".join(
-        [
-            "user: I'd like to see all of my orders.",
-            "agent: Sure! I can help you out with that. May I have your email address?",
-            "user: email is bob.johnson@emailzzz.com ",
-            "user_confirmed_tool_run: <user clicks confirm on ListOrders tool>",
-            "tool_result: a list of orders including [{'id': '102', 'summary': 'Red Sunglasses', 'email': 'matt.murdock@nelsonmurdock.com', 'status': 'shipped', 'order_date': '2025-04-01', 'last_order_update': '2025-04-06', 'tracking_id': '039813852990618' }, { 'id': '103', 'summary': 'Blue Sunglasses', 'email': 'matt.murdock@nelsonmurdock.com', 'status': 'paid', 'order_date': '2025-04-03', 'last_order_update': '2025-04-07' }]",
-            "agent: Your orders are as follows: \n",
-            "1. Red Sunglasses, ordered 4/1/2025 \n",
-            "2. Blue Sunglasses, ordered 4/3/2025 \n",
-            "Would you like more information about any of your orders?"
-            "user: Yes, the Red Sunglasses",
-            "agent: Your order 'Red Sunglasses,' placed April 1, 2025, was shipped on April 6, 2025. Would you like to see the tracking inforation?",
-            "user: Yes",
-            "user_confirmed_tool_run: <user clicks confirm on TrackPackage tool>",
-            "tool_result: { 'scheduled_delivery_date': 'April 30, 2025', 'carrier': 'USPS', 'status_summary': 'Your item has left our acceptance facility and is in transit to a sorting facility on April 10, 2025 at 7:06 am in IRON RIDGE, WI 53035.', 'tracking_link': 'https://tools.usps.com/go/TrackConfirmAction?qtc_tLabels1=12345','last_tracking_update': '2025-03-22T16:14:48'}",
-            "agent: Your package is scheduled to be delivered on April 30, 2025 via USPS. Here is the most recent status from them regarding your package \n, updated as of March 22: \n"
-            "Your item has left our acceptance facility and is in transit to a sorting facility on April 10, 2025 at 7:06 am in IRON RIDGE, WI 53035. \n"
-            "You can find the full tracking details here: tracking_link ! \n"
-            "Would you like more information about any of your other orders?",
-            "user: No" "agent: Thanks, and have a great day!",
-        ]
-    ),
-)
-
 # 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)
@@ -466,8 +392,6 @@ goal_list.append(goal_hr_check_paycheck_bank_integration_status)
 goal_list.append(goal_fin_check_account_balances)
 goal_list.append(goal_fin_move_money)
 goal_list.append(goal_fin_loan_application)
-goal_list.append(goal_ecomm_list_orders)
-goal_list.append(goal_ecomm_order_status)
 
 
 # for multi-goal, just set list agents as the last tool

tools/tool_registry.py

@@ -355,45 +355,3 @@ financial_submit_loan_approval = ToolDefinition(
         ),
     ],
 )
-
-# ----- ECommerce Use Case Tools -----
-ecomm_list_orders = ToolDefinition(
-    name="ListOrders",
-    description="Get all orders for a certain email address.",
-    arguments=[
-        ToolArgument(
-            name="email_address",
-            type="string",
-            description="Email address of user by which to find orders",
-        ),
-    ],
-)
-
-ecomm_get_order = ToolDefinition(
-    name="GetOrder",
-    description="Get infromation about an order by order ID.",
-    arguments=[
-        ToolArgument(
-            name="order_id",
-            type="string",
-            description="ID of order to determine status of",
-        ),
-    ],
-)
-
-ecomm_track_package = ToolDefinition(
-    name="TrackPackage",
-    description="Get tracking information for a package by shipping provider and tracking ID",
-    arguments=[
-        ToolArgument(
-            name="tracking_id",
-            type="string",
-            description="ID of package to track",
-        ),
-        ToolArgument(
-            name="userConfirmation",
-            type="string",
-            description="Indication of user's desire to get package tracking information",
-        ),
-    ],
-)