Temporal Cloud support

.env.example

@@ -4,3 +4,11 @@ RAPIDAPI_KEY=9df2cb5...
 RAPIDAPI_HOST=sky-scrapper.p.rapidapi.com
 
 STRIPE_API_KEY=sk_test_51J...
+
+# uncomment and unset these environment variables to connect to the local dev server
+TEMPORAL_ADDRESS=namespace.acct.tmprl.cloud:7233
+TEMPORAL_NAMESPACE=default
+TEMPORAL_TASK_QUEUE=agent-task-queue
+TEMPORAL_TLS_CERT='path/to/cert.pem'
+TEMPORAL_TLS_KEY='path/to/key.pem'
+# TEMPORAL_API_KEY=abcdef1234567890 # Uncomment not using mTLS

README.md

@@ -1,4 +1,4 @@
-# AI Agent execution using Temporal
+# Temporal AI Agent
 
 This demo shows a multi-turn conversation with an AI agent running inside a Temporal workflow. The goal is to collect information towards a goal. There's a simple DSL input for collecting information (currently set up to use mock functions to search for events, book flights around those events then create an invoice for those flights). The AI will respond with clarifications and ask for any missing information to that goal. It uses ChatGPT 4o but can be made to use a local LLM via [Ollama](https://ollama.com) (see the deprecated section below).
 
@@ -6,18 +6,51 @@ This demo shows a multi-turn conversation with an AI agent running inside a Temp
 
 [![Watch the demo](./agent-youtube-screenshot.jpeg)](https://www.youtube.com/watch?v=GEXllEH2XiQ)
 
-## Setup
+## Configuration
-* See `.env_example` for the required environment variables and copy to `.env` in the root directory.
+
-* Requires an OpenAI key for the gpt-4o model. Set this in the `OPENAI_API_KEY` environment variable in .env
+This application uses `.env` files for configuration. Copy the `.env.example` file to `.env` and update the values:
+
+```bash
+cp .env.example .env
+```
+
+The agent requires an OpenAI key for the gpt-4o model. Set this in the `OPENAI_API_KEY` environment variable in .env
+
+#### Using a local LLM instead of ChatGPT 4o
+* Install [Ollama](https://ollama.com) and the [Qwen2.5 14B](https://ollama.com/library/qwen2.5) model (`ollama run qwen2.5:14b`). (note this model is about 9GB to download).
+  * Local LLM is disabled as ChatGPT 4o was better for this use case. To use Ollama, examine `./activities/tool_activities.py` and rename the functions.
+
+## Agent Tools
 * Requires a Rapidapi key for sky-scrapper (how we find flights). Set this in the `RAPIDAPI_KEY` environment variable in .env
     * It's free to sign up and get a key at [RapidAPI](https://rapidapi.com/apiheya/api/sky-scrapper)
     * If you're lazy go to `tools/search_flights.py` and replace the `get_flights` function with the mock `search_flights_example` that exists in the same file.
 * 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.
-* Install and run Temporal. Follow the instructions in the [Temporal documentation](https://learn.temporal.io/getting_started/python/dev_environment/#set-up-a-local-temporal-service-for-development-with-temporal-cli) to install and run the Temporal server.
 
-### Python Environment
+## 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` 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.
 
@@ -27,44 +60,29 @@ Requires [Poetry](https://python-poetry.org/) to manage dependencies.
 
 3. `poetry install`
 
-### React UI
+Run the following commands in separate terminal windows:
-- `cd frontend`
-- `npm install` to install the dependencies.
 
-
+1. Start the Temporal worker:
-#### Deprecated:
-* Install [Ollama](https://ollama.com) and the [Qwen2.5 14B](https://ollama.com/library/qwen2.5) model (`ollama run qwen2.5:14b`). (note this model is about 9GB to download).
-  * Local LLM is disabled as ChatGPT 4o was better for this use case. To use Ollama, examine `./activities/tool_activities.py` and rename the functions.
-
-
-## Running the demo
-
-### Run a Temporal Dev Server
-
-On a Mac
 ```bash
-brew install temporal
+poetry run python scripts/run_worker.py
-temporal server start-dev
 ```
-See the [Temporal documentation](https://learn.temporal.io/getting_started/python/dev_environment/) for other platforms.
 
-### Run a Temporal Worker
+2. Start the API server:
+```bash
+poetry run uvicorn api.main:app --reload
+```
+Access the API at `/docs` to see the available endpoints.
 
-From the `/scripts` directory:
+### React UI
+Start the frontend:
+```bash
+cd frontend
+npm install
+npm run dev
+```
+Access the UI at `http://localhost:5173`
 
-- Run the worker: `poetry run python run_worker.py`
+## Customizing the Agent
-
-Then run the API and UI using the instructions below.
-
-### API
-- `poetry run uvicorn api.main:app --reload` to start the API server.
-- Access the API at `/docs` to see the available endpoints.
-
-### UI
-- `npm run dev` to start the dev server.
-- Access the UI at `http://localhost:5173`
-
-## 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`
@@ -74,4 +92,3 @@ Then run the API and UI using the instructions below.
 ## TODO
 - I should prove this out with other tool definitions outside of the event/flight search case (take advantage of my nice DSL).
 - Currently hardcoded to the Temporal dev server at localhost:7233. Need to support options incl Temporal Cloud.
-- UI: Make prettier

api/main.py

@@ -1,12 +1,21 @@
 from fastapi import FastAPI
+from typing import Optional
 from temporalio.client import Client
+
 from workflows.tool_workflow import ToolWorkflow
 from models.data_types import CombinedInput, ToolWorkflowParams
 from tools.goal_registry import goal_event_flight_invoice
 from temporalio.exceptions import TemporalError
 from fastapi.middleware.cors import CORSMiddleware
+from shared.config import get_temporal_client, TEMPORAL_TASK_QUEUE
 
 app = FastAPI()
+temporal_client: Optional[Client] = None
+
+@app.on_event("startup")
+async def startup_event():
+    global temporal_client
+    temporal_client = await get_temporal_client()
 
 app.add_middleware(
     CORSMiddleware,
@@ -25,10 +34,9 @@ def root():
 @app.get("/tool-data")
 async def get_tool_data():
     """Calls the workflow's 'get_tool_data' query."""
-    client = await Client.connect("localhost:7233")
     try:
         # Get workflow handle
-        handle = client.get_workflow_handle("agent-workflow")
+        handle = temporal_client.get_workflow_handle("agent-workflow")
 
         # Check if the workflow is completed
         workflow_status = await handle.describe()
@@ -48,9 +56,8 @@ async def get_tool_data():
 @app.get("/get-conversation-history")
 async def get_conversation_history():
     """Calls the workflow's 'get_conversation_history' query."""
-    client = await Client.connect("localhost:7233")
     try:
-        handle = client.get_workflow_handle("agent-workflow")
+        handle = temporal_client.get_workflow_handle("agent-workflow")
         conversation_history = await handle.query("get_conversation_history")
 
         return conversation_history
@@ -61,8 +68,6 @@ async def get_conversation_history():
 
 @app.post("/send-prompt")
 async def send_prompt(prompt: str):
-    client = await Client.connect("localhost:7233")
-
     # Create combined input
     combined_input = CombinedInput(
         tool_params=ToolWorkflowParams(None, None),
@@ -72,11 +77,11 @@ async def send_prompt(prompt: str):
     workflow_id = "agent-workflow"
 
     # Start (or signal) the workflow
-    await client.start_workflow(
+    await temporal_client.start_workflow(
         ToolWorkflow.run,
         combined_input,
         id=workflow_id,
-        task_queue="agent-task-queue",
+        task_queue=TEMPORAL_TASK_QUEUE,
         start_signal="user_prompt",
         start_signal_args=[prompt],
     )
@@ -87,9 +92,8 @@ async def send_prompt(prompt: str):
 @app.post("/confirm")
 async def send_confirm():
     """Sends a 'confirm' signal to the workflow."""
-    client = await Client.connect("localhost:7233")
     workflow_id = "agent-workflow"
-    handle = client.get_workflow_handle(workflow_id)
+    handle = temporal_client.get_workflow_handle(workflow_id)
     await handle.signal("confirm")
     return {"message": "Confirm signal sent."}
 
@@ -97,11 +101,10 @@ async def send_confirm():
 @app.post("/end-chat")
 async def end_chat():
     """Sends a 'end_chat' signal to the workflow."""
-    client = await Client.connect("localhost:7233")
     workflow_id = "agent-workflow"
 
     try:
-        handle = client.get_workflow_handle(workflow_id)
+        handle = temporal_client.get_workflow_handle(workflow_id)
         await handle.signal("end_chat")
         return {"message": "End chat signal sent."}
     except TemporalError as e:

scripts/end_chat.py

@@ -1,6 +1,6 @@
 import asyncio
 
-from temporalio.client import Client
+
 from workflows.tool_workflow import ToolWorkflow
 
 

scripts/get_history.py

@@ -1,12 +1,12 @@
 import asyncio
 
-from temporalio.client import Client
+from shared.config import get_temporal_client
-from workflows import ToolWorkflow
+from workflows.tool_workflow import ToolWorkflow
 
 
 async def main():
     # Create client connected to server at the given address
-    client = await Client.connect("localhost:7233")
+    client = await get_temporal_client()
     workflow_id = "agent-workflow"
 
     handle = client.get_workflow_handle(workflow_id)
@@ -15,16 +15,7 @@ async def main():
     history = await handle.query(ToolWorkflow.get_conversation_history)
 
     print("Conversation History")
-    print(
+    print(history)
-        *(f"{speaker.title()}: {message}\n" for speaker, message in history), sep="\n"
-    )
-
-    # Queries the workflow for the conversation summary
-    summary = await handle.query(ToolWorkflow.get_summary_from_history)
-
-    if summary is not None:
-        print("Conversation Summary:")
-        print(summary)
 
 
 if __name__ == "__main__":

scripts/get_tool_data.py

@@ -1,23 +0,0 @@
-import asyncio
-import json
-
-from temporalio.client import Client
-from workflows.tool_workflow import ToolWorkflow
-
-
-async def main():
-    # Create client connected to server at the given address
-    client = await Client.connect("localhost:7233")
-    workflow_id = "agent-workflow"
-
-    handle = client.get_workflow_handle(workflow_id)
-
-    # Queries the workflow for the conversation history
-    tool_data = await handle.query(ToolWorkflow.get_tool_data)
-
-    # pretty print
-    print(json.dumps(tool_data, indent=4))
-
-
-if __name__ == "__main__":
-    asyncio.run(main())

scripts/run_worker.py

@@ -1,27 +1,26 @@
 import asyncio
-import concurrent.futures
-import logging
 
-from temporalio.client import Client
+import concurrent.futures
+
 from temporalio.worker import Worker
 
 from activities.tool_activities import ToolActivities, dynamic_tool_activity
 from workflows.tool_workflow import ToolWorkflow
-from dotenv import load_dotenv
 
-load_dotenv()
+from shared.config import get_temporal_client, TEMPORAL_TASK_QUEUE
 
 
 async def main():
-    # Create client connected to server at the given address
+    # Create the client
-    client = await Client.connect("localhost:7233")
+    client = await get_temporal_client()
+
     activities = ToolActivities()
 
     # Run the worker
     with concurrent.futures.ThreadPoolExecutor(max_workers=100) as activity_executor:
         worker = Worker(
             client,
-            task_queue="agent-task-queue",
+            task_queue=TEMPORAL_TASK_QUEUE,
             workflows=[ToolWorkflow],
             activities=[
                 activities.prompt_llm,
@@ -29,12 +28,10 @@ async def main():
             ],
             activity_executor=activity_executor,
         )
+
+        print(f"Starting worker, connecting to task queue: {TEMPORAL_TASK_QUEUE}")
         await worker.run()
 
 
 if __name__ == "__main__":
-    print("Starting worker")
-
-    logging.basicConfig(level=logging.INFO)
-
     asyncio.run(main())

scripts/send_confirm.py

@@ -1,12 +1,14 @@
 import asyncio
 import sys
-from temporalio.client import Client
+
+from shared.config import get_temporal_client
+from workflows.tool_workflow import ToolWorkflow
 
 
 async def main():
 
-    # 1) Connect to Temporal and signal the workflow
+    # Connect to Temporal and signal the workflow
-    client = await Client.connect("localhost:7233")
+    client = await get_temporal_client()
 
     workflow_id = "agent-workflow"
 

shared/config.py

@@ -0,0 +1,56 @@
+import os
+from dotenv import load_dotenv
+from temporalio.client import Client
+from temporalio.service import TLSConfig
+
+load_dotenv()
+
+# Temporal connection settings
+TEMPORAL_ADDRESS = os.getenv("TEMPORAL_ADDRESS", "localhost:7233")
+TEMPORAL_NAMESPACE = os.getenv("TEMPORAL_NAMESPACE", "default")
+TEMPORAL_TASK_QUEUE = os.getenv("TEMPORAL_TASK_QUEUE", "agent-task-queue")
+
+# Authentication settings
+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.
+    Supports local server, mTLS, and API key authentication methods.
+    """
+    # Default to no TLS for local development
+    tls_config = False
+    print(f"Address: {TEMPORAL_ADDRESS}, Namespace {TEMPORAL_NAMESPACE}")
+    print("(If unset, then will try to connect to local server)")
+
+    # Configure mTLS if certificate and key are provided
+    if TEMPORAL_TLS_CERT and TEMPORAL_TLS_KEY:
+        print(f"TLS cert: {TEMPORAL_TLS_CERT}")
+        print(f"TLS key: {TEMPORAL_TLS_KEY}")
+        with open(TEMPORAL_TLS_CERT, "rb") as f:
+            client_cert = f.read()
+        with open(TEMPORAL_TLS_KEY, "rb") as f:
+            client_key = f.read()
+        tls_config = TLSConfig(
+            client_cert=client_cert,
+            client_private_key=client_key,
+        )
+
+    # Use API key authentication if provided
+    if TEMPORAL_API_KEY:
+        print(f"API key: {TEMPORAL_API_KEY}")
+        return await Client.connect(
+            TEMPORAL_ADDRESS,
+            namespace=TEMPORAL_NAMESPACE,
+            api_key=TEMPORAL_API_KEY,
+            tls=True,  # Always use TLS with API key
+        )
+    
+    # Use mTLS or local connection
+    return await Client.connect(
+        TEMPORAL_ADDRESS,
+        namespace=TEMPORAL_NAMESPACE,
+        tls=tls_config,
+    )