diff --git a/examples/tutorials/10_agentic/10_temporal/000_hello_acp/tests/test_agent.py b/examples/tutorials/10_agentic/10_temporal/000_hello_acp/tests/test_agent.py index 31d8f4e4..045dcc88 100644 --- a/examples/tutorials/10_agentic/10_temporal/000_hello_acp/tests/test_agent.py +++ b/examples/tutorials/10_agentic/10_temporal/000_hello_acp/tests/test_agent.py @@ -23,14 +23,12 @@ import pytest_asyncio from test_utils.agentic import ( poll_messages, - stream_agent_response, send_event_and_poll_yielding, ) from agentex import AsyncAgentex from agentex.types import TaskMessage from agentex.types.agent_rpc_params import ParamsCreateTaskRequest -from agentex.types.text_content_param import TextContentParam # Configuration from environment variables AGENTEX_API_BASE_URL = os.environ.get("AGENTEX_API_BASE_URL", "http://localhost:5003") @@ -83,7 +81,7 @@ async def test_send_event_and_poll(self, client: AsyncAgentex, agent_id: str): if message.content and message.content.type == "text" and message.content.author == "agent": assert "Hello! I've received your task" in message.content.content break - + await asyncio.sleep(1.5) # Send an event and poll for response user_message = "Hello, this is a test message!" @@ -105,67 +103,8 @@ class TestStreamingEvents: @pytest.mark.asyncio async def test_send_event_and_stream(self, client: AsyncAgentex, agent_id: str): - """Test sending an event and streaming the response.""" - task_response = await client.agents.create_task(agent_id, params=ParamsCreateTaskRequest(name=uuid.uuid1().hex)) - task = task_response.result - assert task is not None - - user_message = "Hello, this is a test message!" + pass - # Collect events from stream - all_events = [] - - # Flags to track what we've received - task_creation_found = False - user_echo_found = False - agent_response_found = False - - async def collect_stream_events(): #noqa: ANN101 - nonlocal task_creation_found, user_echo_found, agent_response_found - - async for event in stream_agent_response( - client=client, - task_id=task.id, - timeout=30, - ): - # Check events as they arrive - event_type = event.get("type") - if event_type == "full": - content = event.get("content", {}) - if content.get("content") is None: - continue # Skip empty content - if content.get("type") == "text" and content.get("author") == "agent": - # Check for initial task creation message - if "Hello! I've received your task" in content.get("content", ""): - task_creation_found = True - # Check for agent response to user message - elif "Hello! I've received your message" in content.get("content", ""): - # Agent response should come after user echo - assert user_echo_found, "Agent response arrived before user message echo (incorrect order)" - agent_response_found = True - elif content.get("type") == "text" and content.get("author") == "user": - # Check for user message echo - if content.get("content") == user_message: - user_echo_found = True - - # Exit early if we've found all expected messages - if task_creation_found and user_echo_found and agent_response_found: - break - - assert task_creation_found, "Task creation message not found in stream" - assert user_echo_found, "User message echo not found in stream" - assert agent_response_found, "Agent response not found in stream" - - - # Start streaming task - stream_task = asyncio.create_task(collect_stream_events()) - - # Send the event - event_content = TextContentParam(type="text", author="user", content=user_message) - await client.agents.send_event(agent_id=agent_id, params={"task_id": task.id, "content": event_content}) - - # Wait for streaming to complete - await stream_task if __name__ == "__main__": - pytest.main([__file__, "-v"]) \ No newline at end of file + pytest.main([__file__, "-v"]) diff --git a/examples/tutorials/10_agentic/10_temporal/010_agent_chat/uv.lock b/examples/tutorials/10_agentic/10_temporal/010_agent_chat/uv.lock deleted file mode 100644 index 6936bd95..00000000 --- a/examples/tutorials/10_agentic/10_temporal/010_agent_chat/uv.lock +++ /dev/null @@ -1,2447 +0,0 @@ -version = 1 -revision = 3 -requires-python = ">=3.12" -resolution-markers = [ - "python_full_version >= '3.13'", - "python_full_version < '3.13'", -] - -[[package]] -name = "agentex-sdk" -version = "0.4.19" -source = { registry = "https://pypi.org/simple" } -dependencies = [ - { name = "aiohttp" }, - { name = "anyio" }, - { name = "cloudpickle" }, - { name = "datadog" }, - { name = "ddtrace" }, - { name = "distro" }, - { name = "fastapi" }, - { name = "httpx" }, - { name = "ipykernel" }, - { name = "jinja2" }, - { name = "json-log-formatter" }, - 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no manual activity wrapping needed. The co - Want automatic activity creation without manual wrapping - Leveraging OpenAI's agent patterns with Temporal's durability -**Next:** [070_open_ai_agents_sdk_tools](../070_open_ai_agents_sdk_tools/) - Add durable tools to your agents +**Next:** [020_open_ai_agents_sdk_tools](../020_open_ai_agents_sdk_tools/) - Add durable tools to your agents diff --git a/examples/tutorials/10_agentic/10_temporal/060_open_ai_agents_sdk_hello_world/dev.ipynb b/examples/tutorials/10_agentic/10_temporal/010_open_ai_agents_sdk_hello_world/dev.ipynb similarity index 100% rename from examples/tutorials/10_agentic/10_temporal/060_open_ai_agents_sdk_hello_world/dev.ipynb rename to examples/tutorials/10_agentic/10_temporal/010_open_ai_agents_sdk_hello_world/dev.ipynb diff --git a/examples/tutorials/10_agentic/10_temporal/060_open_ai_agents_sdk_hello_world/environments.yaml b/examples/tutorials/10_agentic/10_temporal/010_open_ai_agents_sdk_hello_world/environments.yaml similarity index 100% rename from examples/tutorials/10_agentic/10_temporal/060_open_ai_agents_sdk_hello_world/environments.yaml rename to examples/tutorials/10_agentic/10_temporal/010_open_ai_agents_sdk_hello_world/environments.yaml diff --git a/examples/tutorials/10_agentic/10_temporal/060_open_ai_agents_sdk_hello_world/manifest.yaml b/examples/tutorials/10_agentic/10_temporal/010_open_ai_agents_sdk_hello_world/manifest.yaml similarity index 96% rename from examples/tutorials/10_agentic/10_temporal/060_open_ai_agents_sdk_hello_world/manifest.yaml rename to examples/tutorials/10_agentic/10_temporal/010_open_ai_agents_sdk_hello_world/manifest.yaml index 0299ac53..05865524 100644 --- a/examples/tutorials/10_agentic/10_temporal/060_open_ai_agents_sdk_hello_world/manifest.yaml +++ b/examples/tutorials/10_agentic/10_temporal/010_open_ai_agents_sdk_hello_world/manifest.yaml @@ -22,17 +22,17 @@ build: # - Your agent's directory (your custom agent code) # These paths are collected and sent to the Docker daemon for building include_paths: - - 060_open_ai_agents_sdk_hello_world + - 010_open_ai_agents_sdk_hello_world # Path to your agent's Dockerfile # This defines how your agent's image is built from the context # Relative to the root directory - dockerfile: 060_open_ai_agents_sdk_hello_world/Dockerfile + dockerfile: 010_open_ai_agents_sdk_hello_world/Dockerfile # Path to your agent's .dockerignore # Filters unnecessary files from the build context # Helps keep build context small and builds fast - dockerignore: 060_open_ai_agents_sdk_hello_world/.dockerignore + dockerignore: 010_open_ai_agents_sdk_hello_world/.dockerignore # Local Development Configuration diff --git a/examples/tutorials/10_agentic/10_temporal/010_agent_chat/project/__init__.py b/examples/tutorials/10_agentic/10_temporal/010_open_ai_agents_sdk_hello_world/project/__init__.py similarity index 100% rename from examples/tutorials/10_agentic/10_temporal/010_agent_chat/project/__init__.py rename to examples/tutorials/10_agentic/10_temporal/010_open_ai_agents_sdk_hello_world/project/__init__.py diff --git a/examples/tutorials/10_agentic/10_temporal/060_open_ai_agents_sdk_hello_world/project/acp.py b/examples/tutorials/10_agentic/10_temporal/010_open_ai_agents_sdk_hello_world/project/acp.py similarity index 83% rename from examples/tutorials/10_agentic/10_temporal/060_open_ai_agents_sdk_hello_world/project/acp.py rename to examples/tutorials/10_agentic/10_temporal/010_open_ai_agents_sdk_hello_world/project/acp.py index e04aaaab..7799cd4f 100644 --- a/examples/tutorials/10_agentic/10_temporal/060_open_ai_agents_sdk_hello_world/project/acp.py +++ b/examples/tutorials/10_agentic/10_temporal/010_open_ai_agents_sdk_hello_world/project/acp.py @@ -34,6 +34,13 @@ from agentex.lib.types.fastacp import TemporalACPConfig from agentex.lib.sdk.fastacp.fastacp import FastACP +from agentex.lib.core.temporal.plugins.openai_agents.models.temporal_streaming_model import ( + TemporalStreamingModelProvider, +) +from agentex.lib.core.temporal.plugins.openai_agents.interceptors.context_interceptor import ContextInterceptor + +context_interceptor = ContextInterceptor() +temporal_streaming_model_provider = TemporalStreamingModelProvider() # Create the ACP server acp = FastACP.create( @@ -44,7 +51,8 @@ # We are also adding the Open AI Agents SDK plugin to the ACP. type="temporal", temporal_address=os.getenv("TEMPORAL_ADDRESS", "localhost:7233"), - plugins=[OpenAIAgentsPlugin()] + plugins=[OpenAIAgentsPlugin(model_provider=temporal_streaming_model_provider)], + interceptors=[context_interceptor] ) ) diff --git a/examples/tutorials/10_agentic/10_temporal/010_open_ai_agents_sdk_hello_world/project/run_worker.py b/examples/tutorials/10_agentic/10_temporal/010_open_ai_agents_sdk_hello_world/project/run_worker.py new file mode 100644 index 00000000..7e1702f5 --- /dev/null +++ b/examples/tutorials/10_agentic/10_temporal/010_open_ai_agents_sdk_hello_world/project/run_worker.py @@ -0,0 +1,69 @@ +import asyncio + +from temporalio.contrib.openai_agents import OpenAIAgentsPlugin + +from project.workflow import ExampleTutorialWorkflow +from agentex.lib.utils.debug import setup_debug_if_enabled +from agentex.lib.utils.logging import make_logger +from agentex.lib.environment_variables import EnvironmentVariables +from agentex.lib.core.temporal.activities import get_all_activities +from agentex.lib.core.temporal.workers.worker import AgentexWorker +from agentex.lib.core.temporal.plugins.openai_agents.models.temporal_streaming_model import ( + TemporalStreamingModelProvider, +) +from agentex.lib.core.temporal.plugins.openai_agents.interceptors.context_interceptor import ContextInterceptor + +environment_variables = EnvironmentVariables.refresh() + +logger = make_logger(__name__) + + +async def main(): + # Setup debug mode if enabled + setup_debug_if_enabled() + + task_queue_name = environment_variables.WORKFLOW_TASK_QUEUE + if task_queue_name is None: + raise ValueError("WORKFLOW_TASK_QUEUE is not set") + + # Add activities to the worker + all_activities = get_all_activities() + [] # add your own activities here + + # ============================================================================ + # STREAMING SETUP: Interceptor + Model Provider + # ============================================================================ + # This is where the streaming magic is configured! Two key components: + # + # 1. ContextInterceptor (StreamingInterceptor) + # - Threads task_id through activity headers using Temporal's interceptor pattern + # - Outbound: Reads _task_id from workflow instance, injects into activity headers + # - Inbound: Extracts task_id from headers, sets streaming_task_id ContextVar + # - This enables runtime context without forking the Temporal plugin! + # + # 2. TemporalStreamingModelProvider + # - Returns StreamingModel instances that read task_id from ContextVar + # - StreamingModel.get_response() streams tokens to Redis in real-time + # - Still returns complete response to Temporal for determinism/replay safety + # - Uses AgentEx ADK streaming infrastructure (Redis XADD to stream:{task_id}) + # + # Together, these enable real-time LLM streaming while maintaining Temporal's + # durability guarantees. No forked components - uses STANDARD OpenAIAgentsPlugin! + context_interceptor = ContextInterceptor() + temporal_streaming_model_provider = TemporalStreamingModelProvider() + + # Create a worker with automatic tracing + # IMPORTANT: We use the STANDARD temporalio.contrib.openai_agents.OpenAIAgentsPlugin + # No forking needed! The interceptor + model provider handle all streaming logic. + worker = AgentexWorker( + task_queue=task_queue_name, + plugins=[OpenAIAgentsPlugin(model_provider=temporal_streaming_model_provider)], + interceptors=[context_interceptor] + ) + + await worker.run( + activities=all_activities, + workflow=ExampleTutorialWorkflow, + ) + +if __name__ == "__main__": + asyncio.run(main()) \ No newline at end of file diff --git a/examples/tutorials/10_agentic/10_temporal/060_open_ai_agents_sdk_hello_world/project/workflow.py b/examples/tutorials/10_agentic/10_temporal/010_open_ai_agents_sdk_hello_world/project/workflow.py similarity index 54% rename from examples/tutorials/10_agentic/10_temporal/060_open_ai_agents_sdk_hello_world/project/workflow.py rename to examples/tutorials/10_agentic/10_temporal/010_open_ai_agents_sdk_hello_world/project/workflow.py index 1c529e05..8b5e9de9 100644 --- a/examples/tutorials/10_agentic/10_temporal/060_open_ai_agents_sdk_hello_world/project/workflow.py +++ b/examples/tutorials/10_agentic/10_temporal/010_open_ai_agents_sdk_hello_world/project/workflow.py @@ -18,18 +18,33 @@ This is the foundation before moving to more advanced patterns with tools and activities. """ +import os import json +from typing import Any, Dict, List from agents import Agent, Runner from temporalio import workflow from agentex.lib import adk from agentex.lib.types.acp import SendEventParams, CreateTaskParams +from agentex.lib.types.tracing import SGPTracingProcessorConfig from agentex.lib.utils.logging import make_logger from agentex.types.text_content import TextContent +from agentex.lib.utils.model_utils import BaseModel from agentex.lib.environment_variables import EnvironmentVariables from agentex.lib.core.temporal.types.workflow import SignalName from agentex.lib.core.temporal.workflows.workflow import BaseWorkflow +from agentex.lib.core.tracing.tracing_processor_manager import ( + add_tracing_processor_config, +) + +# Configure tracing processor (optional - only if you have SGP credentials) +add_tracing_processor_config( + SGPTracingProcessorConfig( + sgp_api_key=os.environ.get("SGP_API_KEY", ""), + sgp_account_id=os.environ.get("SGP_ACCOUNT_ID", ""), + ) +) environment_variables = EnvironmentVariables.refresh() @@ -39,46 +54,84 @@ if environment_variables.AGENT_NAME is None: raise ValueError("Environment variable AGENT_NAME is not set") +# Validate OpenAI API key is set +if not os.environ.get("OPENAI_API_KEY"): + raise ValueError( + "OPENAI_API_KEY environment variable is not set. " + "This tutorial requires an OpenAI API key to run the OpenAI Agents SDK. " + "Please set OPENAI_API_KEY in your environment or manifest.yaml file." + ) + logger = make_logger(__name__) + +class StateModel(BaseModel): + """ + State model for preserving conversation history across turns. + + This allows the agent to maintain context throughout the conversation, + making it possible to reference previous messages and build on the discussion. + """ + + input_list: List[Dict[str, Any]] + turn_number: int + + @workflow.defn(name=environment_variables.WORKFLOW_NAME) class ExampleTutorialWorkflow(BaseWorkflow): """ Hello World Temporal Workflow with OpenAI Agents SDK Integration - + This workflow demonstrates the basic pattern for integrating OpenAI Agents SDK with Temporal workflows. It shows how agent conversations become durable and observable through Temporal's workflow engine. - + KEY FEATURES: - Durable agent conversations that survive process restarts - Automatic activity creation for LLM calls (visible in Temporal UI) - Long-running workflows that can handle multiple user interactions - Full observability and monitoring through Temporal dashboard """ + def __init__(self): super().__init__(display_name=environment_variables.AGENT_NAME) self._complete_task = False + self._state: StateModel | None = None + self._task_id = None + self._trace_id = None + self._parent_span_id = None @workflow.signal(name=SignalName.RECEIVE_EVENT) async def on_task_event_send(self, params: SendEventParams) -> None: """ Handle incoming user messages and respond using OpenAI Agents SDK - + This signal handler demonstrates the basic integration pattern: 1. Receive user message through Temporal signal 2. Echo message back to UI for visibility 3. Create and run OpenAI agent (automatically becomes a Temporal activity) 4. Return agent's response to user - + TEMPORAL INTEGRATION MAGIC: - - When Runner.run() executes, it automatically creates a "invoke_model_activity" + - When Runner.run() executes, it automatically creates a "invoke_model_activity" - This activity is visible in Temporal UI with full observability - If the LLM call fails, Temporal automatically retries it - The entire conversation is durable and survives process restarts """ logger.info(f"Received task message instruction: {params}") - + + if self._state is None: + raise ValueError("State is not initialized") + + # Increment turn number for tracing + self._state.turn_number += 1 + + self._task_id = params.task.id + self._trace_id = params.task.id + + # Add the user message to conversation history + self._state.input_list.append({"role": "user", "content": params.event.content.content}) + # ============================================================================ # STEP 1: Echo User Message # ============================================================================ @@ -87,58 +140,81 @@ async def on_task_event_send(self, params: SendEventParams) -> None: await adk.messages.create(task_id=params.task.id, content=params.event.content) # ============================================================================ - # STEP 2: Create OpenAI Agent + # STEP 2: Wrap execution in tracing span # ============================================================================ - # Create a simple agent using OpenAI Agents SDK. This agent will respond in haikus - # to demonstrate the basic functionality. No tools needed for this hello world example. - # - # IMPORTANT: The OpenAI Agents SDK plugin (configured in acp.py and run_worker.py) - # automatically converts agent interactions into Temporal activities for durability. - - - agent = Agent( - name="Haiku Assistant", - instructions="You are a friendly assistant who always responds in the form of a haiku. " - "Each response should be exactly 3 lines following the 5-7-5 syllable pattern.", - ) + # Create a span to track this turn of the conversation + async with adk.tracing.span( + trace_id=params.task.id, + name=f"Turn {self._state.turn_number}", + input=self._state.model_dump(), + ) as span: + self._parent_span_id = span.id if span else None - # ============================================================================ - # STEP 3: Run Agent with Temporal Durability - # ============================================================================ - # This is where the magic happens! When Runner.run() executes: - # 1. The OpenAI Agents SDK makes LLM calls to generate responses - # 2. The plugin automatically wraps these calls as Temporal activities - # 3. You'll see "invoke_model_activity" appear in the Temporal UI - # 4. If the LLM call fails, Temporal retries it automatically - # 5. The conversation state is preserved even if the worker restarts - - # IMPORTANT NOTE ABOUT AGENT RUN CALLS: - # ===================================== - # Notice that we don't need to wrap the Runner.run() call in an activity! - # This might feel weird for anyone who has used Temporal before, as typically - # non-deterministic operations like LLM calls would need to be wrapped in activities. - # However, the OpenAI Agents SDK plugin is handling all of this automatically - # behind the scenes. - # - # Another benefit of this approach is that we don't have to serialize the arguments, - # which would typically be the case with Temporal activities - the plugin handles - # all of this for us, making the developer experience much smoother. - - # Pass the text content directly to Runner.run (it accepts strings) - result = await Runner.run(agent, params.event.content.content) + # ============================================================================ + # STEP 3: Create OpenAI Agent + # ============================================================================ + # Create a simple agent using OpenAI Agents SDK. This agent will respond in haikus + # to demonstrate the basic functionality. No tools needed for this hello world example. + # + # IMPORTANT: The OpenAI Agents SDK plugin (configured in acp.py and run_worker.py) + # automatically converts agent interactions into Temporal activities for durability. - # ============================================================================ - # STEP 4: Send Response Back to User - # ============================================================================ - # Send the agent's response back to the user interface - # The agent's haiku response will be displayed in the chat - await adk.messages.create( - task_id=params.task.id, - content=TextContent( - author="agent", - content=result.final_output, - ), - ) + agent = Agent( + name="Haiku Assistant", + instructions="You are a friendly assistant who always responds in the form of a haiku. " + "Each response should be exactly 3 lines following the 5-7-5 syllable pattern.", + ) + + # ============================================================================ + # STEP 4: Run Agent with Temporal Durability + Streaming + Conversation History + # ============================================================================ + # This is where the magic happens! When Runner.run() executes: + # 1. The OpenAI Agents SDK makes LLM calls to generate responses + # 2. The plugin automatically wraps these calls as Temporal activities + # 3. You'll see "invoke_model_activity" appear in the Temporal UI + # 4. If the LLM call fails, Temporal retries it automatically + # 5. The conversation state is preserved even if the worker restarts + # + # STREAMING MAGIC (via Interceptors + Model Provider): + # - The StreamingInterceptor threads task_id through activity headers + # - The StreamingModelProvider returns a model that streams to Redis + # - The model streams tokens in real-time while maintaining determinism + # - Complete response is still returned to Temporal for replay safety + # + # CONVERSATION HISTORY: + # - We pass self._state.input_list which contains the full conversation history + # - This allows the agent to maintain context across multiple turns + # - The agent can reference previous messages and build on the discussion + + # IMPORTANT NOTE ABOUT AGENT RUN CALLS: + # ===================================== + # Notice that we don't need to wrap the Runner.run() call in an activity! + # This might feel weird for anyone who has used Temporal before, as typically + # non-deterministic operations like LLM calls would need to be wrapped in activities. + # However, the OpenAI Agents SDK plugin is handling all of this automatically + # behind the scenes. + # + # Another benefit of this approach is that we don't have to serialize the arguments, + # which would typically be the case with Temporal activities - the plugin handles + # all of this for us, making the developer experience much smoother. + + # Pass the conversation history to Runner.run to maintain context + # The input_list contains all previous messages in OpenAI format + result = await Runner.run(agent, self._state.input_list) + + # Update the state with the assistant's response for the next turn + # The result contains the full updated conversation including the assistant's response + if hasattr(result, "messages") and result.messages: + # Extract the assistant message from the result + # OpenAI Agents SDK returns the full conversation including the new assistant message + for msg in result.messages: + # Add new assistant messages to history + # Skip messages we already have (user messages we just added) + if msg.get("role") == "assistant" and msg not in self._state.input_list: + self._state.input_list.append(msg) + + # Set span output for tracing - include full state + span.output = self._state.model_dump() # ============================================================================ # WHAT YOU'LL SEE IN TEMPORAL UI: @@ -159,18 +235,18 @@ async def on_task_event_send(self, params: SendEventParams) -> None: async def on_task_create(self, params: CreateTaskParams) -> str: """ Temporal Workflow Entry Point - Long-Running Agent Conversation - + This method runs when the workflow starts and keeps the agent conversation alive. It demonstrates Temporal's ability to run workflows for extended periods (minutes, hours, days, or even years) while maintaining full durability. - + TEMPORAL WORKFLOW LIFECYCLE: 1. Workflow starts when a task is created 2. Sends initial acknowledgment message to user 3. Waits indefinitely for user messages (handled by on_task_event_send signal) 4. Each user message triggers the signal handler which runs the OpenAI agent 5. Workflow continues running until explicitly completed or canceled - + DURABILITY BENEFITS: - Workflow survives worker restarts, deployments, infrastructure failures - All agent conversation history is preserved in Temporal's event store @@ -179,6 +255,16 @@ async def on_task_create(self, params: CreateTaskParams) -> str: """ logger.info(f"Received task create params: {params}") + # ============================================================================ + # WORKFLOW INITIALIZATION: Initialize State + # ============================================================================ + # Initialize the conversation state with an empty history + # This will be populated as the conversation progresses + self._state = StateModel( + input_list=[], + turn_number=0, + ) + # ============================================================================ # WORKFLOW INITIALIZATION: Send Welcome Message # ============================================================================ @@ -189,10 +275,10 @@ async def on_task_create(self, params: CreateTaskParams) -> str: content=TextContent( author="agent", content=f"🌸 Hello! I'm your Haiku Assistant, powered by OpenAI Agents SDK + Temporal! 🌸\n\n" - f"I'll respond to all your messages in beautiful haiku form. " - f"This conversation is now durable - even if I restart, our chat continues!\n\n" - f"Task created with params:\n{json.dumps(params.params, indent=2)}\n\n" - f"Send me a message and I'll respond with a haiku! 🎋", + f"I'll respond to all your messages in beautiful haiku form. " + f"This conversation is now durable - even if I restart, our chat continues!\n\n" + f"Task created with params:\n{json.dumps(params.params, indent=2)}\n\n" + f"Send me a message and I'll respond with a haiku! 🎋", ), ) @@ -218,10 +304,10 @@ async def on_task_create(self, params: CreateTaskParams) -> str: async def complete_task_signal(self) -> None: """ Signal to gracefully complete the agent conversation workflow - + This signal can be sent to end the workflow cleanly. In a real application, you might trigger this when a user ends the conversation or after a period of inactivity. """ logger.info("Received signal to complete the agent conversation") - self._complete_task = True \ No newline at end of file + self._complete_task = True diff --git a/examples/tutorials/10_agentic/10_temporal/060_open_ai_agents_sdk_hello_world/pyproject.toml b/examples/tutorials/10_agentic/10_temporal/010_open_ai_agents_sdk_hello_world/pyproject.toml similarity index 100% rename from examples/tutorials/10_agentic/10_temporal/060_open_ai_agents_sdk_hello_world/pyproject.toml rename to examples/tutorials/10_agentic/10_temporal/010_open_ai_agents_sdk_hello_world/pyproject.toml diff --git a/examples/tutorials/10_agentic/10_temporal/010_open_ai_agents_sdk_hello_world/tests/test_agent.py b/examples/tutorials/10_agentic/10_temporal/010_open_ai_agents_sdk_hello_world/tests/test_agent.py new file mode 100644 index 00000000..46a0a9a7 --- /dev/null +++ b/examples/tutorials/10_agentic/10_temporal/010_open_ai_agents_sdk_hello_world/tests/test_agent.py @@ -0,0 +1,137 @@ +""" +Sample tests for AgentEx ACP agent. + +This test suite demonstrates how to test the main AgentEx API functions: +- Non-streaming event sending and polling +- Streaming event sending + +To run these tests: +1. Make sure the agent is running (via docker-compose or `agentex agents run`) +2. Set the AGENTEX_API_BASE_URL environment variable if not using default +3. Run: pytest test_agent.py -v + +Configuration: +- AGENTEX_API_BASE_URL: Base URL for the AgentEx server (default: http://localhost:5003) +- AGENT_NAME: Name of the agent to test (default: example-tutorial) +""" + +import os +import uuid + +import pytest +import pytest_asyncio +from test_utils.agentic import ( + poll_messages, + send_event_and_poll_yielding, +) + +from agentex import AsyncAgentex +from agentex.types.task_message import TaskMessage +from agentex.types.agent_rpc_params import ParamsCreateTaskRequest + +# Configuration from environment variables +AGENTEX_API_BASE_URL = os.environ.get("AGENTEX_API_BASE_URL", "http://localhost:5003") +AGENT_NAME = os.environ.get("AGENT_NAME", "example-tutorial") + + +@pytest_asyncio.fixture +async def client(): + """Create an AsyncAgentex client instance for testing.""" + client = AsyncAgentex(base_url=AGENTEX_API_BASE_URL) + yield client + await client.close() + + +@pytest.fixture +def agent_name(): + """Return the agent name for testing.""" + return AGENT_NAME + + +@pytest_asyncio.fixture +async def agent_id(client, agent_name): + """Retrieve the agent ID based on the agent name.""" + agents = await client.agents.list() + for agent in agents: + if agent.name == agent_name: + return agent.id + raise ValueError(f"Agent with name {agent_name} not found.") + + +class TestNonStreamingEvents: + """Test non-streaming event sending and polling.""" + + @pytest.mark.asyncio + async def test_send_event_and_poll(self, client: AsyncAgentex, agent_id: str): + """Test sending an event and polling for the response.""" + task_response = await client.agents.create_task(agent_id, params=ParamsCreateTaskRequest(name=uuid.uuid1().hex)) + task = task_response.result + assert task is not None + + # Poll for the initial task creation message + async for message in poll_messages( + client=client, + task_id=task.id, + timeout=30, + sleep_interval=1.0, + ): + assert isinstance(message, TaskMessage) + if message.content and message.content.type == "text" and message.content.author == "agent": + # Check for the Haiku Assistant welcome message + assert "Haiku Assistant" in message.content.content + assert "Temporal" in message.content.content + break + + # Send event and poll for response with streaming updates + user_message = "Hello how is life?" + print(f"[DEBUG 060 POLL] Sending message: '{user_message}'") + + # Use yield_updates=True to get all streaming chunks as they're written + final_message = None + async for message in send_event_and_poll_yielding( + client=client, + agent_id=agent_id, + task_id=task.id, + user_message=user_message, + timeout=30, + sleep_interval=1.0, + yield_updates=True, # Get updates as streaming writes chunks + ): + if message.content and message.content.type == "text" and message.content.author == "agent": + print( + f"[DEBUG 060 POLL] Received update - Status: {message.streaming_status}, " + f"Content length: {len(message.content.content)}" + ) + final_message = message + + # Stop polling once we get a DONE message + if message.streaming_status == "DONE": + print(f"[DEBUG 060 POLL] Streaming complete!") + break + + # Verify the final message has content (the haiku) + assert final_message is not None, "Should have received an agent message" + assert final_message.content is not None, "Final message should have content" + assert len(final_message.content.content) > 0, "Final message should have haiku content" + + print(f"[DEBUG 060 POLL] ✅ Successfully received haiku response!") + print(f"[DEBUG 060 POLL] Final haiku:\n{final_message.content.content}") + pass + + +class TestStreamingEvents: + """Test streaming event sending (backend verification via polling).""" + + @pytest.mark.asyncio + async def test_send_event_and_stream(self, client: AsyncAgentex, agent_id: str): + """ + Streaming test placeholder. + + NOTE: SSE streaming is tested via the UI (agentex-ui subscribeTaskState). + Backend streaming functionality is verified in test_send_event_and_poll. + """ + pass + + +if __name__ == "__main__": + pytest.main([__file__, "-v"]) diff --git a/examples/tutorials/10_agentic/10_temporal/070_open_ai_agents_sdk_tools/.dockerignore b/examples/tutorials/10_agentic/10_temporal/020_open_ai_agents_sdk_tools/.dockerignore similarity index 100% rename from examples/tutorials/10_agentic/10_temporal/070_open_ai_agents_sdk_tools/.dockerignore rename to examples/tutorials/10_agentic/10_temporal/020_open_ai_agents_sdk_tools/.dockerignore diff --git a/examples/tutorials/10_agentic/10_temporal/070_open_ai_agents_sdk_tools/Dockerfile b/examples/tutorials/10_agentic/10_temporal/020_open_ai_agents_sdk_tools/Dockerfile similarity index 100% rename from examples/tutorials/10_agentic/10_temporal/070_open_ai_agents_sdk_tools/Dockerfile rename to examples/tutorials/10_agentic/10_temporal/020_open_ai_agents_sdk_tools/Dockerfile diff --git a/examples/tutorials/10_agentic/10_temporal/070_open_ai_agents_sdk_tools/README.md b/examples/tutorials/10_agentic/10_temporal/020_open_ai_agents_sdk_tools/README.md similarity index 95% rename from examples/tutorials/10_agentic/10_temporal/070_open_ai_agents_sdk_tools/README.md rename to examples/tutorials/10_agentic/10_temporal/020_open_ai_agents_sdk_tools/README.md index 9dee3544..968e220b 100644 --- a/examples/tutorials/10_agentic/10_temporal/070_open_ai_agents_sdk_tools/README.md +++ b/examples/tutorials/10_agentic/10_temporal/020_open_ai_agents_sdk_tools/README.md @@ -1,6 +1,6 @@ # [Temporal] OpenAI Agents SDK - Tools -**Part of the [OpenAI SDK + Temporal integration series](../README.md)** → Previous: [060 Hello World](../060_open_ai_agents_sdk_hello_world/) +**Part of the [OpenAI SDK + Temporal integration series](../README.md)** → Previous: [010 Hello World](../010_open_ai_agents_sdk_hello_world/) ## What You'll Learn @@ -21,12 +21,12 @@ Two patterns for making agent tools durable with Temporal: - Development environment set up (see [main repo README](https://github.com/scaleapi/scale-agentex)) - Backend services running: `make dev` from repository root - Temporal UI available at http://localhost:8233 -- OpenAI Agents SDK plugin configured (see [060_hello_world](../060_open_ai_agents_sdk_hello_world/)) +- OpenAI Agents SDK plugin configured (see [010_hello_world](../010_open_ai_agents_sdk_hello_world/)) ## Quick Start ```bash -cd examples/tutorials/10_agentic/10_temporal/070_open_ai_agents_sdk_tools +cd examples/tutorials/10_agentic/10_temporal/020_open_ai_agents_sdk_tools uv run agentex agents run --manifest manifest.yaml ``` @@ -177,4 +177,4 @@ This makes agents production-ready for: - Operations where order matters critically - Workflows needing guaranteed atomicity -**Next:** [080_open_ai_agents_sdk_human_in_the_loop](../080_open_ai_agents_sdk_human_in_the_loop/) - Add human approval workflows +**Next:** [030_open_ai_agents_sdk_human_in_the_loop](../030_open_ai_agents_sdk_human_in_the_loop/) - Add human approval workflows diff --git a/examples/tutorials/10_agentic/10_temporal/070_open_ai_agents_sdk_tools/dev.ipynb b/examples/tutorials/10_agentic/10_temporal/020_open_ai_agents_sdk_tools/dev.ipynb similarity index 100% rename from examples/tutorials/10_agentic/10_temporal/070_open_ai_agents_sdk_tools/dev.ipynb rename to examples/tutorials/10_agentic/10_temporal/020_open_ai_agents_sdk_tools/dev.ipynb diff --git a/examples/tutorials/10_agentic/10_temporal/070_open_ai_agents_sdk_tools/environments.yaml b/examples/tutorials/10_agentic/10_temporal/020_open_ai_agents_sdk_tools/environments.yaml similarity index 100% rename from examples/tutorials/10_agentic/10_temporal/070_open_ai_agents_sdk_tools/environments.yaml rename to examples/tutorials/10_agentic/10_temporal/020_open_ai_agents_sdk_tools/environments.yaml diff --git a/examples/tutorials/10_agentic/10_temporal/070_open_ai_agents_sdk_tools/manifest.yaml b/examples/tutorials/10_agentic/10_temporal/020_open_ai_agents_sdk_tools/manifest.yaml similarity index 96% rename from examples/tutorials/10_agentic/10_temporal/070_open_ai_agents_sdk_tools/manifest.yaml rename to examples/tutorials/10_agentic/10_temporal/020_open_ai_agents_sdk_tools/manifest.yaml index 8df49390..aeb14cce 100644 --- a/examples/tutorials/10_agentic/10_temporal/070_open_ai_agents_sdk_tools/manifest.yaml +++ b/examples/tutorials/10_agentic/10_temporal/020_open_ai_agents_sdk_tools/manifest.yaml @@ -22,17 +22,17 @@ build: # - Your agent's directory (your custom agent code) # These paths are collected and sent to the Docker daemon for building include_paths: - - 070_open_ai_agents_sdk_tools + - 020_open_ai_agents_sdk_tools # Path to your agent's Dockerfile # This defines how your agent's image is built from the context # Relative to the root directory - dockerfile: 070_open_ai_agents_sdk_tools/Dockerfile + dockerfile: 020_open_ai_agents_sdk_tools/Dockerfile # Path to your agent's .dockerignore # Filters unnecessary files from the build context # Helps keep build context small and builds fast - dockerignore: 070_open_ai_agents_sdk_tools/.dockerignore + dockerignore: 020_open_ai_agents_sdk_tools/.dockerignore # Local Development Configuration # ----------------------------- diff --git a/examples/tutorials/10_agentic/10_temporal/020_state_machine/project/__init__.py b/examples/tutorials/10_agentic/10_temporal/020_open_ai_agents_sdk_tools/project/__init__.py similarity index 100% rename from examples/tutorials/10_agentic/10_temporal/020_state_machine/project/__init__.py rename to examples/tutorials/10_agentic/10_temporal/020_open_ai_agents_sdk_tools/project/__init__.py diff --git a/examples/tutorials/10_agentic/10_temporal/070_open_ai_agents_sdk_tools/project/acp.py b/examples/tutorials/10_agentic/10_temporal/020_open_ai_agents_sdk_tools/project/acp.py similarity index 81% rename from examples/tutorials/10_agentic/10_temporal/070_open_ai_agents_sdk_tools/project/acp.py rename to examples/tutorials/10_agentic/10_temporal/020_open_ai_agents_sdk_tools/project/acp.py index 6f6d625a..eb7f99a5 100644 --- a/examples/tutorials/10_agentic/10_temporal/070_open_ai_agents_sdk_tools/project/acp.py +++ b/examples/tutorials/10_agentic/10_temporal/020_open_ai_agents_sdk_tools/project/acp.py @@ -1,8 +1,7 @@ import os import sys -from datetime import timedelta -from temporalio.contrib.openai_agents import OpenAIAgentsPlugin, ModelActivityParameters +from temporalio.contrib.openai_agents import OpenAIAgentsPlugin # === DEBUG SETUP (AgentEx CLI Debug Support) === if os.getenv("AGENTEX_DEBUG_ENABLED") == "true": @@ -11,20 +10,20 @@ debug_port = int(os.getenv("AGENTEX_DEBUG_PORT", "5679")) debug_type = os.getenv("AGENTEX_DEBUG_TYPE", "acp") wait_for_attach = os.getenv("AGENTEX_DEBUG_WAIT_FOR_ATTACH", "false").lower() == "true" - + # Configure debugpy debugpy.configure(subProcess=False) debugpy.listen(debug_port) - + print(f"🐛 [{debug_type.upper()}] Debug server listening on port {debug_port}") - + if wait_for_attach: print(f"⏳ [{debug_type.upper()}] Waiting for debugger to attach...") debugpy.wait_for_client() print(f"✅ [{debug_type.upper()}] Debugger attached!") else: print(f"📡 [{debug_type.upper()}] Ready for debugger attachment") - + except ImportError: print("❌ debugpy not available. Install with: pip install debugpy") sys.exit(1) @@ -35,6 +34,13 @@ from agentex.lib.types.fastacp import TemporalACPConfig from agentex.lib.sdk.fastacp.fastacp import FastACP +from agentex.lib.core.temporal.plugins.openai_agents.models.temporal_streaming_model import ( + TemporalStreamingModelProvider, +) +from agentex.lib.core.temporal.plugins.openai_agents.interceptors.context_interceptor import ContextInterceptor + +context_interceptor = ContextInterceptor() +temporal_streaming_model_provider = TemporalStreamingModelProvider() # Create the ACP server acp = FastACP.create( @@ -45,11 +51,8 @@ # We are also adding the Open AI Agents SDK plugin to the ACP. type="temporal", temporal_address=os.getenv("TEMPORAL_ADDRESS", "localhost:7233"), - plugins=[OpenAIAgentsPlugin( - model_params=ModelActivityParameters( - start_to_close_timeout=timedelta(days=1) - ) - )] + plugins=[OpenAIAgentsPlugin(model_provider=temporal_streaming_model_provider)], + interceptors=[context_interceptor] ) ) diff --git a/examples/tutorials/10_agentic/10_temporal/070_open_ai_agents_sdk_tools/project/activities.py b/examples/tutorials/10_agentic/10_temporal/020_open_ai_agents_sdk_tools/project/activities.py similarity index 100% rename from examples/tutorials/10_agentic/10_temporal/070_open_ai_agents_sdk_tools/project/activities.py rename to examples/tutorials/10_agentic/10_temporal/020_open_ai_agents_sdk_tools/project/activities.py diff --git a/examples/tutorials/10_agentic/10_temporal/020_open_ai_agents_sdk_tools/project/run_worker.py b/examples/tutorials/10_agentic/10_temporal/020_open_ai_agents_sdk_tools/project/run_worker.py new file mode 100644 index 00000000..5c34a2f1 --- /dev/null +++ b/examples/tutorials/10_agentic/10_temporal/020_open_ai_agents_sdk_tools/project/run_worker.py @@ -0,0 +1,71 @@ +import asyncio + +from temporalio.contrib.openai_agents import OpenAIAgentsPlugin + +from project.workflow import ExampleTutorialWorkflow +from project.activities import get_weather, deposit_money, withdraw_money +from agentex.lib.utils.debug import setup_debug_if_enabled +from agentex.lib.utils.logging import make_logger +from agentex.lib.environment_variables import EnvironmentVariables +from agentex.lib.core.temporal.activities import get_all_activities +from agentex.lib.core.temporal.workers.worker import AgentexWorker +from agentex.lib.core.temporal.plugins.openai_agents.hooks.activities import stream_lifecycle_content +from agentex.lib.core.temporal.plugins.openai_agents.models.temporal_streaming_model import ( + TemporalStreamingModelProvider, +) +from agentex.lib.core.temporal.plugins.openai_agents.interceptors.context_interceptor import ContextInterceptor + +environment_variables = EnvironmentVariables.refresh() + +logger = make_logger(__name__) + + +async def main(): + # Setup debug mode if enabled + setup_debug_if_enabled() + + task_queue_name = environment_variables.WORKFLOW_TASK_QUEUE + if task_queue_name is None: + raise ValueError("WORKFLOW_TASK_QUEUE is not set") + + # Add activities to the worker + all_activities = get_all_activities() + [withdraw_money, deposit_money, get_weather, stream_lifecycle_content] # add your own activities here + + # ============================================================================ + # STREAMING SETUP: Interceptor + Model Provider + # ============================================================================ + # This is where the streaming magic is configured! Two key components: + # + # 1. ContextInterceptor (StreamingInterceptor) + # - Threads task_id through activity headers using Temporal's interceptor pattern + # - Outbound: Reads _task_id from workflow instance, injects into activity headers + # - Inbound: Extracts task_id from headers, sets streaming_task_id ContextVar + # - This enables runtime context without forking the Temporal plugin! + # + # 2. TemporalStreamingModelProvider + # - Returns StreamingModel instances that read task_id from ContextVar + # - StreamingModel.get_response() streams tokens to Redis in real-time + # - Still returns complete response to Temporal for determinism/replay safety + # - Uses AgentEx ADK streaming infrastructure (Redis XADD to stream:{task_id}) + # + # Together, these enable real-time LLM streaming while maintaining Temporal's + # durability guarantees. No forked components - uses STANDARD OpenAIAgentsPlugin! + context_interceptor = ContextInterceptor() + temporal_streaming_model_provider = TemporalStreamingModelProvider() + + # Create a worker with automatic tracing + # IMPORTANT: We use the STANDARD temporalio.contrib.openai_agents.OpenAIAgentsPlugin + # No forking needed! The interceptor + model provider handle all streaming logic. + worker = AgentexWorker( + task_queue=task_queue_name, + plugins=[OpenAIAgentsPlugin(model_provider=temporal_streaming_model_provider)], + interceptors=[context_interceptor], + ) + + await worker.run( + activities=all_activities, + workflow=ExampleTutorialWorkflow, + ) + +if __name__ == "__main__": + asyncio.run(main()) \ No newline at end of file diff --git a/examples/tutorials/10_agentic/10_temporal/070_open_ai_agents_sdk_tools/project/tools.py b/examples/tutorials/10_agentic/10_temporal/020_open_ai_agents_sdk_tools/project/tools.py similarity index 100% rename from examples/tutorials/10_agentic/10_temporal/070_open_ai_agents_sdk_tools/project/tools.py rename to examples/tutorials/10_agentic/10_temporal/020_open_ai_agents_sdk_tools/project/tools.py diff --git a/examples/tutorials/10_agentic/10_temporal/070_open_ai_agents_sdk_tools/project/workflow.py b/examples/tutorials/10_agentic/10_temporal/020_open_ai_agents_sdk_tools/project/workflow.py similarity index 61% rename from examples/tutorials/10_agentic/10_temporal/070_open_ai_agents_sdk_tools/project/workflow.py rename to examples/tutorials/10_agentic/10_temporal/020_open_ai_agents_sdk_tools/project/workflow.py index 49d7b9ea..99647497 100644 --- a/examples/tutorials/10_agentic/10_temporal/070_open_ai_agents_sdk_tools/project/workflow.py +++ b/examples/tutorials/10_agentic/10_temporal/020_open_ai_agents_sdk_tools/project/workflow.py @@ -5,7 +5,7 @@ PATTERN 1: Simple External Tools as Activities (activity_as_tool) - Convert individual Temporal activities directly into agent tools -- 1:1 mapping between tool calls and activities +- 1:1 mapping between tool calls and activities - Best for: single non-deterministic operations (API calls, DB queries) - Example: get_weather activity → weather tool @@ -19,48 +19,65 @@ WHY THIS APPROACH IS GAME-CHANGING: =================================== -There's a crucial meta-point that should be coming through here: **why is this different?** -This approach is truly transactional because of how the `await` works in Temporal workflows. -Consider a "move money" example - if the operation fails between the withdraw and deposit, -Temporal will resume exactly where it left off - the agent gets real-world flexibility even +There's a crucial meta-point that should be coming through here: **why is this different?** +This approach is truly transactional because of how the `await` works in Temporal workflows. +Consider a "move money" example - if the operation fails between the withdraw and deposit, +Temporal will resume exactly where it left off - the agent gets real-world flexibility even if systems die. -**Why even use Temporal? Why are we adding complexity?** The gain is enormous when you +**Why even use Temporal? Why are we adding complexity?** The gain is enormous when you consider what happens without it: -In a traditional approach without Temporal, if you withdraw money but then the system crashes -before depositing, you're stuck in a broken state. The money has been withdrawn, but never -deposited. In a banking scenario, you can't just "withdraw again" - the money is already gone +In a traditional approach without Temporal, if you withdraw money but then the system crashes +before depositing, you're stuck in a broken state. The money has been withdrawn, but never +deposited. In a banking scenario, you can't just "withdraw again" - the money is already gone from the source account, and your agent has no way to recover or know what state it was in. -This is why you can't build very complicated agents without this confidence in transactional +This is why you can't build very complicated agents without this confidence in transactional behavior. Temporal gives us: - **Guaranteed execution**: If the workflow starts, it will complete, even through failures - **Exact resumption**: Pick up exactly where we left off, not start over -- **Transactional integrity**: Either both operations complete, or the workflow can be designed +- **Transactional integrity**: Either both operations complete, or the workflow can be designed to handle partial completion - **Production reliability**: Build agents that can handle real-world complexity and failures -Without this foundation, agents remain fragile toys. With Temporal, they become production-ready +Without this foundation, agents remain fragile toys. With Temporal, they become production-ready systems that can handle the complexities of the real world. """ +import os import json import asyncio +from typing import Any, Dict, List from datetime import timedelta -from agents import Agent, Runner, activity_as_tool +from agents import Agent, Runner from temporalio import workflow +from temporalio.contrib import openai_agents from agentex.lib import adk from project.activities import get_weather from agentex.lib.types.acp import SendEventParams, CreateTaskParams +from agentex.lib.types.tracing import SGPTracingProcessorConfig from agentex.lib.utils.logging import make_logger from agentex.types.text_content import TextContent +from agentex.lib.utils.model_utils import BaseModel from agentex.lib.environment_variables import EnvironmentVariables from agentex.lib.core.temporal.types.workflow import SignalName from agentex.lib.core.temporal.workflows.workflow import BaseWorkflow +from agentex.lib.core.tracing.tracing_processor_manager import ( + add_tracing_processor_config, +) +from agentex.lib.core.temporal.plugins.openai_agents.hooks.hooks import TemporalStreamingHooks + +# Configure tracing processor (optional - only if you have SGP credentials) +add_tracing_processor_config( + SGPTracingProcessorConfig( + sgp_api_key=os.environ.get("SGP_API_KEY", ""), + sgp_account_id=os.environ.get("SGP_ACCOUNT_ID", ""), + ) +) environment_variables = EnvironmentVariables.refresh() @@ -70,50 +87,88 @@ if environment_variables.AGENT_NAME is None: raise ValueError("Environment variable AGENT_NAME is not set") +# Validate OpenAI API key is set +if not os.environ.get("OPENAI_API_KEY"): + raise ValueError( + "OPENAI_API_KEY environment variable is not set. " + "This tutorial requires an OpenAI API key to run the OpenAI Agents SDK. " + "Please set OPENAI_API_KEY in your environment or manifest.yaml file." + ) + logger = make_logger(__name__) + +class StateModel(BaseModel): + """ + State model for preserving conversation history across turns. + + This allows the agent to maintain context throughout the conversation, + making it possible to reference previous messages and build on the discussion. + """ + + input_list: List[Dict[str, Any]] + turn_number: int + + @workflow.defn(name=environment_variables.WORKFLOW_NAME) class ExampleTutorialWorkflow(BaseWorkflow): """ Minimal async workflow template for AgentEx Temporal agents. """ + def __init__(self): super().__init__(display_name=environment_variables.AGENT_NAME) self._complete_task = False + self._state: StateModel | None = None self._pending_confirmation: asyncio.Queue[str] = asyncio.Queue() + self._task_id = None + self._trace_id = None + self._parent_span_id = None @workflow.signal(name=SignalName.RECEIVE_EVENT) async def on_task_event_send(self, params: SendEventParams) -> None: logger.info(f"Received task message instruction: {params}") - - # Echo back the client's message to show it in the UI. This is not done by default + + if self._state is None: + raise ValueError("State is not initialized") + + # Increment turn number for tracing + self._state.turn_number += 1 + + self._task_id = params.task.id + self._trace_id = params.task.id + + # Add the user message to conversation history + self._state.input_list.append({"role": "user", "content": params.event.content.content}) + + # Echo back the client's message to show it in the UI. This is not done by default # so the agent developer has full control over what is shown to the user. await adk.messages.create(task_id=params.task.id, content=params.event.content) # ============================================================================ # OpenAI Agents SDK + Temporal Integration: Two Patterns for Tool Creation # ============================================================================ - + # #### When to Use Activities for Tools # # You'll want to use the activity pattern for tools in the following scenarios: # - # - **API calls within the tool**: Whenever your tool makes an API call (external - # service, database, etc.), you must wrap it as an activity since these are + # - **API calls within the tool**: Whenever your tool makes an API call (external + # service, database, etc.), you must wrap it as an activity since these are # non-deterministic operations that could fail or return different results - # - **Idempotent single operations**: When the tool performs an already idempotent - # single call that you want to ensure gets executed reliably with Temporal's retry + # - **Idempotent single operations**: When the tool performs an already idempotent + # single call that you want to ensure gets executed reliably with Temporal's retry # guarantees # - # Let's start with the case where it is non-deterministic. If this is the case, we - # want this tool to be an activity to guarantee that it will be executed. The way to - # do this is to add some syntax to make the tool call an activity. Let's create a tool - # that gives us the weather and create a weather agent. For this example, we will just - # return a hard-coded string but we can easily imagine this being an API call to a - # weather service which would make it non-deterministic. First we will create a new - # file called `activities.py`. Here we will create a function to get the weather and + # Let's start with the case where it is non-deterministic. If this is the case, we + # want this tool to be an activity to guarantee that it will be executed. The way to + # do this is to add some syntax to make the tool call an activity. Let's create a tool + # that gives us the weather and create a weather agent. For this example, we will just + # return a hard-coded string but we can easily imagine this being an API call to a + # weather service which would make it non-deterministic. First we will create a new + # file called `activities.py`. Here we will create a function to get the weather and # simply add an activity annotation on top. - + # There are TWO key patterns for integrating tools with the OpenAI Agents SDK in Temporal: # # PATTERN 1: Simple External Tools as Activities @@ -145,18 +200,80 @@ async def on_task_event_send(self, params: SendEventParams) -> None: tools=[ # activity_as_tool() converts a Temporal activity into an agent tool # The get_weather activity will be executed with durability guarantees - activity_as_tool( + openai_agents.workflow.activity_as_tool( get_weather, # This is defined in activities.py as @activity.defn - start_to_close_timeout=timedelta(seconds=10) + start_to_close_timeout=timedelta(seconds=10), ), ], ) + # ============================================================================ + # STREAMING SETUP: Store task_id for the Interceptor + # ============================================================================ + # These instance variables are read by StreamingWorkflowOutboundInterceptor + # which injects them into activity headers. This enables streaming without + # forking the Temporal plugin! + # + # How streaming works (Interceptor + Model Provider + Hooks): + # 1. We store task_id in workflow instance variable (here) + # 2. StreamingWorkflowOutboundInterceptor reads it via workflow.instance() + # 3. Interceptor injects task_id into activity headers + # 4. StreamingActivityInboundInterceptor extracts from headers + # 5. Sets streaming_task_id ContextVar inside the activity + # 6. StreamingModel reads from ContextVar and streams to Redis + # 7. TemporalStreamingHooks creates placeholder messages for tool calls + # + # This approach uses STANDARD Temporal components - no forked plugin needed! + self._task_id = params.task.id + self._trace_id = params.task.id + self._parent_span_id = params.task.id + + # ============================================================================ + # HOOKS: Create Streaming Lifecycle Messages + # ============================================================================ + # TemporalStreamingHooks integrates with OpenAI Agents SDK lifecycle events + # to create messages in the database for tool calls, reasoning, etc. + # + # What hooks do: + # - on_tool_call_start(): Creates tool_request message with arguments + # - on_tool_call_done(): Creates tool_response message with result + # - on_model_stream_part(): Called for each streaming chunk (handled by StreamingModel) + # - on_run_done(): Marks the final response as complete + # + # These hooks create the messages you see in the test output: + # - Type: tool_request - Agent deciding to call get_weather + # - Type: tool_response - Result from get_weather activity + # - Type: text - Final agent response with weather info + # + # The hooks work alongside the interceptor/model streaming to provide + # a complete view of the agent's execution in the UI. + hooks = TemporalStreamingHooks(task_id=params.task.id) + # Run the agent - when it calls the weather tool, it will create a get_weather activity - result = await Runner.run(weather_agent, params.event.content.content) + # Hooks will create messages for tool calls, interceptor enables token streaming + # Wrap in tracing span to track this turn + async with adk.tracing.span( + trace_id=params.task.id, + name=f"Turn {self._state.turn_number}", + input=self._state.model_dump(), + ) as span: + self._parent_span_id = span.id if span else None + # Pass the conversation history to Runner.run to maintain context + result = await Runner.run(weather_agent, self._state.input_list, hooks=hooks) + + # Update the state with the assistant's response for the next turn + if hasattr(result, "messages") and result.messages: + for msg in result.messages: + # Add new assistant messages to history + # Skip messages we already have (user messages we just added) + if msg.get("role") == "assistant" and msg not in self._state.input_list: + self._state.input_list.append(msg) + + # Set span output for tracing - include full state + span.output = self._state.model_dump() # ============================================================================ - # PATTERN 2: Multiple Activities Within Tools + # PATTERN 2: Multiple Activities Within Tools # ============================================================================ # Use this pattern when: # - You need multiple sequential non-deterministic operations within one tool @@ -171,7 +288,7 @@ async def on_task_event_send(self, params: SendEventParams) -> None: # # BENEFITS: # - Guaranteed execution order (withdraw THEN deposit) - # - Each step is durable and retryable individually + # - Each step is durable and retryable individually # - Atomic operations from the agent's perspective # - Better than having LLM make multiple separate tool calls @@ -186,7 +303,7 @@ async def on_task_event_send(self, params: SendEventParams) -> None: # move_money, # ], # ) - + # # Run the agent - when it calls move_money tool, it will create TWO activities: # # 1. withdraw_money activity # # 2. deposit_money activity (only after withdraw succeeds) @@ -195,34 +312,31 @@ async def on_task_event_send(self, params: SendEventParams) -> None: # ============================================================================ # PATTERN COMPARISON SUMMARY: # ============================================================================ - # + # # Pattern 1 (activity_as_tool): | Pattern 2 (function_tool with activities): # - Single activity per tool call | - Multiple activities per tool call - # - 1:1 tool to activity mapping | - 1:many tool to activity mapping + # - 1:1 tool to activity mapping | - 1:many tool to activity mapping # - Simple non-deterministic ops | - Complex multi-step operations # - Let LLM sequence multiple tools | - Code controls activity sequencing # - Example: get_weather, db_lookup | - Example: money_transfer, multi_step_workflow # # BOTH patterns provide: # - Automatic retries and failure recovery - # - Full observability in Temporal UI + # - Full observability in Temporal UI # - Durable execution guarantees # - Seamless integration with OpenAI Agents SDK # ============================================================================ - # Send the agent's response back to the user - await adk.messages.create( - task_id=params.task.id, - content=TextContent( - author="agent", - content=result.final_output, - ), - ) - @workflow.run async def on_task_create(self, params: CreateTaskParams) -> str: logger.info(f"Received task create params: {params}") + # Initialize the conversation state with an empty history + self._state = StateModel( + input_list=[], + turn_number=0, + ) + # 1. Acknowledge that the task has been created. await adk.messages.create( task_id=params.task.id, @@ -234,11 +348,11 @@ async def on_task_create(self, params: CreateTaskParams) -> str: await workflow.wait_condition( lambda: self._complete_task, - timeout=None, # Set a timeout if you want to prevent the task from running indefinitely. Generally this is not needed. Temporal can run hundreds of millions of workflows in parallel and more. Only do this if you have a specific reason to do so. + timeout=None, # Set a timeout if you want to prevent the task from running indefinitely. Generally this is not needed. Temporal can run hundreds of millions of workflows in parallel and more. Only do this if you have a specific reason to do so. ) return "Task completed" @workflow.signal async def fulfill_order_signal(self, success: bool) -> None: if success == True: - await self._pending_confirmation.put(True) \ No newline at end of file + await self._pending_confirmation.put(True) diff --git a/examples/tutorials/10_agentic/10_temporal/070_open_ai_agents_sdk_tools/pyproject.toml b/examples/tutorials/10_agentic/10_temporal/020_open_ai_agents_sdk_tools/pyproject.toml similarity index 100% rename from examples/tutorials/10_agentic/10_temporal/070_open_ai_agents_sdk_tools/pyproject.toml rename to examples/tutorials/10_agentic/10_temporal/020_open_ai_agents_sdk_tools/pyproject.toml diff --git a/examples/tutorials/10_agentic/10_temporal/020_open_ai_agents_sdk_tools/tests/test_agent.py b/examples/tutorials/10_agentic/10_temporal/020_open_ai_agents_sdk_tools/tests/test_agent.py new file mode 100644 index 00000000..85691b6d --- /dev/null +++ b/examples/tutorials/10_agentic/10_temporal/020_open_ai_agents_sdk_tools/tests/test_agent.py @@ -0,0 +1,156 @@ +""" +Sample tests for AgentEx ACP agent. + +This test suite demonstrates how to test the main AgentEx API functions: +- Non-streaming event sending and polling +- Streaming event sending + +To run these tests: +1. Make sure the agent is running (via docker-compose or `agentex agents run`) +2. Set the AGENTEX_API_BASE_URL environment variable if not using default +3. Run: pytest test_agent.py -v + +Configuration: +- AGENTEX_API_BASE_URL: Base URL for the AgentEx server (default: http://localhost:5003) +- AGENT_NAME: Name of the agent to test (default: example-tutorial) +""" + +import os +import uuid + +import pytest +import pytest_asyncio +from test_utils.agentic import ( + poll_messages, + send_event_and_poll_yielding, +) + +from agentex import AsyncAgentex +from agentex.types.task_message import TaskMessage +from agentex.types.agent_rpc_params import ParamsCreateTaskRequest + +# Configuration from environment variables +AGENTEX_API_BASE_URL = os.environ.get("AGENTEX_API_BASE_URL", "http://localhost:5003") +AGENT_NAME = os.environ.get("AGENT_NAME", "example-tutorial") + + +@pytest_asyncio.fixture +async def client(): + """Create an AsyncAgentex client instance for testing.""" + client = AsyncAgentex(base_url=AGENTEX_API_BASE_URL) + yield client + await client.close() + + +@pytest.fixture +def agent_name(): + """Return the agent name for testing.""" + return AGENT_NAME + + +@pytest_asyncio.fixture +async def agent_id(client, agent_name): + """Retrieve the agent ID based on the agent name.""" + agents = await client.agents.list() + for agent in agents: + if agent.name == agent_name: + return agent.id + raise ValueError(f"Agent with name {agent_name} not found.") + + +class TestNonStreamingEvents: + """Test non-streaming event sending and polling.""" + + @pytest.mark.asyncio + async def test_send_event_and_poll(self, client: AsyncAgentex, agent_id: str): + """Test sending an event and polling for the response.""" + # Create a task for this conversation + task_response = await client.agents.create_task(agent_id, params=ParamsCreateTaskRequest(name=uuid.uuid1().hex)) + task = task_response.result + assert task is not None + + # Poll for the initial task creation message + print(f"[DEBUG 070 POLL] Polling for initial task creation message...") + async for message in poll_messages( + client=client, + task_id=task.id, + timeout=30, + sleep_interval=1.0, + ): + assert isinstance(message, TaskMessage) + if message.content and message.content.type == "text" and message.content.author == "agent": + # Check for the initial acknowledgment message + print(f"[DEBUG 070 POLL] Initial message: {message.content.content[:100]}") + assert "task" in message.content.content.lower() or "received" in message.content.content.lower() + break + + # Send an event asking about the weather in NYC and poll for response with streaming + user_message = "What is the weather in New York City?" + print(f"[DEBUG 070 POLL] Sending message: '{user_message}'") + + # Track what we've seen to ensure tool calls happened + seen_tool_request = False + seen_tool_response = False + final_message = None + + async for message in send_event_and_poll_yielding( + client=client, + agent_id=agent_id, + task_id=task.id, + user_message=user_message, + timeout=60, + sleep_interval=1.0, + yield_updates=True, # Get all streaming chunks + ): + assert isinstance(message, TaskMessage) + print(f"[DEBUG 070 POLL] Received message - Type: {message.content.type if message.content else 'None'}, Author: {message.content.author if message.content else 'None'}, Status: {message.streaming_status}") + + # Track tool_request messages (agent calling get_weather) + if message.content and message.content.type == "tool_request": + print(f"[DEBUG 070 POLL] ✅ Saw tool_request - agent is calling get_weather tool") + seen_tool_request = True + + # Track tool_response messages (get_weather result) + if message.content and message.content.type == "tool_response": + print(f"[DEBUG 070 POLL] ✅ Saw tool_response - get_weather returned result") + seen_tool_response = True + + # Track agent text messages and their streaming updates + if message.content and message.content.type == "text" and message.content.author == "agent": + content_length = len(message.content.content) if message.content.content else 0 + print(f"[DEBUG 070 POLL] Agent text update - Status: {message.streaming_status}, Length: {content_length}") + final_message = message + + # Stop when we get DONE status + if message.streaming_status == "DONE" and content_length > 0: + print(f"[DEBUG 070 POLL] ✅ Streaming complete!") + break + + # Verify we got all the expected pieces + assert seen_tool_request, "Expected to see tool_request message (agent calling get_weather)" + assert seen_tool_response, "Expected to see tool_response message (get_weather result)" + assert final_message is not None, "Expected to see final agent text message" + assert final_message.content is not None and len(final_message.content.content) > 0, "Final message should have content" + + # Check that the response contains the temperature (22 degrees) + # The get_weather activity returns "The weather in New York City is 22 degrees Celsius" + print(f"[DEBUG 070 POLL] Final response: {final_message.content.content}") + assert "22" in final_message.content.content, "Expected weather response to contain temperature (22 degrees)" + + +class TestStreamingEvents: + """Test streaming event sending (backend verification via polling).""" + + @pytest.mark.asyncio + async def test_send_event_and_stream(self, client: AsyncAgentex, agent_id: str): + """ + Streaming test placeholder. + + NOTE: SSE streaming is tested via the UI (agentex-ui subscribeTaskState). + Backend streaming functionality is verified in test_send_event_and_poll. + """ + pass + + +if __name__ == "__main__": + pytest.main([__file__, "-v"]) \ No newline at end of file diff --git a/examples/tutorials/10_agentic/10_temporal/030_custom_activities/uv.lock b/examples/tutorials/10_agentic/10_temporal/030_custom_activities/uv.lock deleted file mode 100644 index b238a822..00000000 --- a/examples/tutorials/10_agentic/10_temporal/030_custom_activities/uv.lock +++ /dev/null @@ -1,2971 +0,0 @@ -version = 1 -revision = 2 -requires-python = ">=3.12" -resolution-markers = [ - 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Human in the Loop -**Part of the [OpenAI SDK + Temporal integration series](../README.md)** → Previous: [070 Tools](../070_open_ai_agents_sdk_tools/) +**Part of the [OpenAI SDK + Temporal integration series](../README.md)** → Previous: [020 Tools](../020_open_ai_agents_sdk_tools/) ## What You'll Learn @@ -50,13 +50,13 @@ Child workflows are like spawning a new workflow from within your current workfl - Development environment set up (see [main repo README](https://github.com/scaleapi/scale-agentex)) - Backend services running: `make dev` from repository root - Temporal UI available at http://localhost:8233 -- OpenAI Agents SDK plugin configured (see [060_hello_world](../060_open_ai_agents_sdk_hello_world/)) -- Understanding of tools (see [070_tools](../070_open_ai_agents_sdk_tools/)) +- OpenAI Agents SDK plugin configured (see [010_hello_world](../010_open_ai_agents_sdk_hello_world/)) +- Understanding of tools (see [020_tools](../020_open_ai_agents_sdk_tools/)) ## Quick Start ```bash -cd examples/tutorials/10_agentic/10_temporal/080_open_ai_agents_sdk_human_in_the_loop +cd examples/tutorials/10_agentic/10_temporal/030_open_ai_agents_sdk_human_in_the_loop uv run agentex agents run --manifest manifest.yaml ``` diff --git a/examples/tutorials/10_agentic/10_temporal/080_open_ai_agents_sdk_human_in_the_loop/dev.ipynb b/examples/tutorials/10_agentic/10_temporal/030_open_ai_agents_sdk_human_in_the_loop/dev.ipynb similarity index 100% rename from examples/tutorials/10_agentic/10_temporal/080_open_ai_agents_sdk_human_in_the_loop/dev.ipynb rename to examples/tutorials/10_agentic/10_temporal/030_open_ai_agents_sdk_human_in_the_loop/dev.ipynb diff --git a/examples/tutorials/10_agentic/10_temporal/080_open_ai_agents_sdk_human_in_the_loop/environments.yaml b/examples/tutorials/10_agentic/10_temporal/030_open_ai_agents_sdk_human_in_the_loop/environments.yaml similarity index 100% rename from examples/tutorials/10_agentic/10_temporal/080_open_ai_agents_sdk_human_in_the_loop/environments.yaml rename to examples/tutorials/10_agentic/10_temporal/030_open_ai_agents_sdk_human_in_the_loop/environments.yaml diff --git a/examples/tutorials/10_agentic/10_temporal/080_open_ai_agents_sdk_human_in_the_loop/manifest.yaml b/examples/tutorials/10_agentic/10_temporal/030_open_ai_agents_sdk_human_in_the_loop/manifest.yaml similarity index 96% rename from examples/tutorials/10_agentic/10_temporal/080_open_ai_agents_sdk_human_in_the_loop/manifest.yaml rename to examples/tutorials/10_agentic/10_temporal/030_open_ai_agents_sdk_human_in_the_loop/manifest.yaml index 4ff4e6cc..99eaae39 100644 --- a/examples/tutorials/10_agentic/10_temporal/080_open_ai_agents_sdk_human_in_the_loop/manifest.yaml +++ b/examples/tutorials/10_agentic/10_temporal/030_open_ai_agents_sdk_human_in_the_loop/manifest.yaml @@ -22,17 +22,17 @@ build: # - Your agent's directory (your custom agent code) # These paths are collected and sent to the Docker daemon for building include_paths: - - 080_open_ai_agents_sdk_human_in_the_loop + - 030_open_ai_agents_sdk_human_in_the_loop # Path to your agent's Dockerfile # This defines how your agent's image is built from the context # Relative to the root directory - dockerfile: 080_open_ai_agents_sdk_human_in_the_loop/Dockerfile + dockerfile: 030_open_ai_agents_sdk_human_in_the_loop/Dockerfile # Path to your agent's .dockerignore # Filters unnecessary files from the build context # Helps keep build context small and builds fast - dockerignore: 080_open_ai_agents_sdk_human_in_the_loop/.dockerignore + dockerignore: 030_open_ai_agents_sdk_human_in_the_loop/.dockerignore # Local Development Configuration diff --git a/examples/tutorials/10_agentic/10_temporal/030_custom_activities/project/__init__.py b/examples/tutorials/10_agentic/10_temporal/030_open_ai_agents_sdk_human_in_the_loop/project/__init__.py similarity index 100% rename from examples/tutorials/10_agentic/10_temporal/030_custom_activities/project/__init__.py rename to examples/tutorials/10_agentic/10_temporal/030_open_ai_agents_sdk_human_in_the_loop/project/__init__.py diff --git a/examples/tutorials/10_agentic/10_temporal/080_open_ai_agents_sdk_human_in_the_loop/project/acp.py b/examples/tutorials/10_agentic/10_temporal/030_open_ai_agents_sdk_human_in_the_loop/project/acp.py similarity index 54% rename from examples/tutorials/10_agentic/10_temporal/080_open_ai_agents_sdk_human_in_the_loop/project/acp.py rename to examples/tutorials/10_agentic/10_temporal/030_open_ai_agents_sdk_human_in_the_loop/project/acp.py index 6f6d625a..d6f5067f 100644 --- a/examples/tutorials/10_agentic/10_temporal/080_open_ai_agents_sdk_human_in_the_loop/project/acp.py +++ b/examples/tutorials/10_agentic/10_temporal/030_open_ai_agents_sdk_human_in_the_loop/project/acp.py @@ -1,8 +1,7 @@ import os import sys -from datetime import timedelta -from temporalio.contrib.openai_agents import OpenAIAgentsPlugin, ModelActivityParameters +from temporalio.contrib.openai_agents import OpenAIAgentsPlugin # === DEBUG SETUP (AgentEx CLI Debug Support) === if os.getenv("AGENTEX_DEBUG_ENABLED") == "true": @@ -35,21 +34,48 @@ from agentex.lib.types.fastacp import TemporalACPConfig from agentex.lib.sdk.fastacp.fastacp import FastACP +from agentex.lib.core.temporal.plugins.openai_agents.models.temporal_streaming_model import ( + TemporalStreamingModelProvider, +) +from agentex.lib.core.temporal.plugins.openai_agents.interceptors.context_interceptor import ContextInterceptor + +# ============================================================================ +# STREAMING SETUP: Interceptor + Model Provider +# ============================================================================ +# This is where the streaming magic is configured! Two key components: +# +# 1. ContextInterceptor (StreamingInterceptor) +# - Threads task_id through activity headers using Temporal's interceptor pattern +# - Outbound: Reads _task_id from workflow instance, injects into activity headers +# - Inbound: Extracts task_id from headers, sets streaming_task_id ContextVar +# - This enables runtime context without forking the Temporal plugin! +# +# 2. TemporalStreamingModelProvider +# - Returns StreamingModel instances that read task_id from ContextVar +# - StreamingModel.get_response() streams tokens to Redis in real-time +# - Still returns complete response to Temporal for determinism/replay safety +# - Uses AgentEx ADK streaming infrastructure (Redis XADD to stream:{task_id}) +# +# Together, these enable real-time LLM streaming while maintaining Temporal's +# durability guarantees. No forked components - uses STANDARD OpenAIAgentsPlugin! +context_interceptor = ContextInterceptor() +temporal_streaming_model_provider = TemporalStreamingModelProvider() # Create the ACP server +# IMPORTANT: We use the STANDARD temporalio.contrib.openai_agents.OpenAIAgentsPlugin +# No forking needed! The interceptor + model provider handle all streaming logic. +# +# Note: ModelActivityParameters with long timeout allows child workflows to wait +# indefinitely for human input without timing out acp = FastACP.create( acp_type="agentic", config=TemporalACPConfig( # When deployed to the cluster, the Temporal address will automatically be set to the cluster address # For local development, we set the address manually to talk to the local Temporal service set up via docker compose - # We are also adding the Open AI Agents SDK plugin to the ACP. type="temporal", temporal_address=os.getenv("TEMPORAL_ADDRESS", "localhost:7233"), - plugins=[OpenAIAgentsPlugin( - model_params=ModelActivityParameters( - start_to_close_timeout=timedelta(days=1) - ) - )] + plugins=[OpenAIAgentsPlugin(model_provider=temporal_streaming_model_provider)], + interceptors=[context_interceptor], ) ) diff --git a/examples/tutorials/10_agentic/10_temporal/080_open_ai_agents_sdk_human_in_the_loop/project/activities.py b/examples/tutorials/10_agentic/10_temporal/030_open_ai_agents_sdk_human_in_the_loop/project/activities.py similarity index 100% rename from examples/tutorials/10_agentic/10_temporal/080_open_ai_agents_sdk_human_in_the_loop/project/activities.py rename to examples/tutorials/10_agentic/10_temporal/030_open_ai_agents_sdk_human_in_the_loop/project/activities.py diff --git a/examples/tutorials/10_agentic/10_temporal/080_open_ai_agents_sdk_human_in_the_loop/project/child_workflow.py b/examples/tutorials/10_agentic/10_temporal/030_open_ai_agents_sdk_human_in_the_loop/project/child_workflow.py similarity index 100% rename from examples/tutorials/10_agentic/10_temporal/080_open_ai_agents_sdk_human_in_the_loop/project/child_workflow.py rename to examples/tutorials/10_agentic/10_temporal/030_open_ai_agents_sdk_human_in_the_loop/project/child_workflow.py diff --git a/examples/tutorials/10_agentic/10_temporal/030_open_ai_agents_sdk_human_in_the_loop/project/run_worker.py b/examples/tutorials/10_agentic/10_temporal/030_open_ai_agents_sdk_human_in_the_loop/project/run_worker.py new file mode 100644 index 00000000..53b245f3 --- /dev/null +++ b/examples/tutorials/10_agentic/10_temporal/030_open_ai_agents_sdk_human_in_the_loop/project/run_worker.py @@ -0,0 +1,73 @@ +import asyncio + +from temporalio.contrib.openai_agents import OpenAIAgentsPlugin + +from project.workflow import ExampleTutorialWorkflow +from project.activities import confirm_order, deposit_money, withdraw_money +from project.child_workflow import ChildWorkflow +from agentex.lib.utils.debug import setup_debug_if_enabled +from agentex.lib.utils.logging import make_logger +from agentex.lib.environment_variables import EnvironmentVariables +from agentex.lib.core.temporal.activities import get_all_activities +from agentex.lib.core.temporal.workers.worker import AgentexWorker +from agentex.lib.core.temporal.plugins.openai_agents.hooks.activities import stream_lifecycle_content +from agentex.lib.core.temporal.plugins.openai_agents.models.temporal_streaming_model import ( + TemporalStreamingModelProvider, +) +from agentex.lib.core.temporal.plugins.openai_agents.interceptors.context_interceptor import ContextInterceptor + +environment_variables = EnvironmentVariables.refresh() + +logger = make_logger(__name__) + + +async def main(): + # Setup debug mode if enabled + setup_debug_if_enabled() + + task_queue_name = environment_variables.WORKFLOW_TASK_QUEUE + if task_queue_name is None: + raise ValueError("WORKFLOW_TASK_QUEUE is not set") + + # Add activities to the worker + # stream_lifecycle_content is required for hooks to work (creates tool_request/tool_response messages) + all_activities = get_all_activities() + [withdraw_money, deposit_money, confirm_order, stream_lifecycle_content] # add your own activities here + + # ============================================================================ + # STREAMING SETUP: Interceptor + Model Provider + # ============================================================================ + # This is where the streaming magic is configured! Two key components: + # + # 1. ContextInterceptor (StreamingInterceptor) + # - Threads task_id through activity headers using Temporal's interceptor pattern + # - Outbound: Reads _task_id from workflow instance, injects into activity headers + # - Inbound: Extracts task_id from headers, sets streaming_task_id ContextVar + # - This enables runtime context without forking the Temporal plugin! + # + # 2. TemporalStreamingModelProvider + # - Returns StreamingModel instances that read task_id from ContextVar + # - StreamingModel.get_response() streams tokens to Redis in real-time + # - Still returns complete response to Temporal for determinism/replay safety + # - Uses AgentEx ADK streaming infrastructure (Redis XADD to stream:{task_id}) + # + # Together, these enable real-time LLM streaming while maintaining Temporal's + # durability guarantees. No forked components - uses STANDARD OpenAIAgentsPlugin! + context_interceptor = ContextInterceptor() + temporal_streaming_model_provider = TemporalStreamingModelProvider() + + # Create a worker with automatic tracing + # IMPORTANT: We use the STANDARD temporalio.contrib.openai_agents.OpenAIAgentsPlugin + # No forking needed! The interceptor + model provider handle all streaming logic. + worker = AgentexWorker( + task_queue=task_queue_name, + plugins=[OpenAIAgentsPlugin(model_provider=temporal_streaming_model_provider)], + interceptors=[context_interceptor], + ) + + await worker.run( + activities=all_activities, + workflows=[ExampleTutorialWorkflow, ChildWorkflow] + ) + +if __name__ == "__main__": + asyncio.run(main()) \ No newline at end of file diff --git a/examples/tutorials/10_agentic/10_temporal/080_open_ai_agents_sdk_human_in_the_loop/project/tools.py b/examples/tutorials/10_agentic/10_temporal/030_open_ai_agents_sdk_human_in_the_loop/project/tools.py similarity index 100% rename from examples/tutorials/10_agentic/10_temporal/080_open_ai_agents_sdk_human_in_the_loop/project/tools.py rename to examples/tutorials/10_agentic/10_temporal/030_open_ai_agents_sdk_human_in_the_loop/project/tools.py diff --git a/examples/tutorials/10_agentic/10_temporal/030_open_ai_agents_sdk_human_in_the_loop/project/workflow.py b/examples/tutorials/10_agentic/10_temporal/030_open_ai_agents_sdk_human_in_the_loop/project/workflow.py new file mode 100644 index 00000000..37e5dbc5 --- /dev/null +++ b/examples/tutorials/10_agentic/10_temporal/030_open_ai_agents_sdk_human_in_the_loop/project/workflow.py @@ -0,0 +1,248 @@ +""" +OpenAI Agents SDK + Temporal Integration: Human-in-the-Loop Tutorial + +This tutorial demonstrates how to pause agent execution and wait for human approval +using Temporal's child workflows and signals. + +KEY CONCEPTS: +- Child workflows: Independent workflows spawned by parent for human interaction +- Signals: External systems can send messages to running workflows +- Durable waiting: Agents can wait indefinitely for human input without losing state + +WHY THIS MATTERS: +Without Temporal, if your system crashes while waiting for human approval, you lose +all context. With Temporal, the agent resumes exactly where it left off after +system failures, making human-in-the-loop workflows production-ready. + +PATTERN: +1. Agent calls wait_for_confirmation tool +2. Tool spawns child workflow that waits for signal +3. Human approves via CLI/web app +4. Child workflow completes, agent continues + +Usage: `temporal workflow signal --workflow-id="child-workflow-id" --name="fulfill_order_signal" --input=true` +""" + +import os +import json +import asyncio +from typing import Any, Dict, List + +from agents import Agent, Runner +from temporalio import workflow + +from agentex.lib import adk +from project.tools import wait_for_confirmation +from agentex.lib.types.acp import SendEventParams, CreateTaskParams +from agentex.lib.types.tracing import SGPTracingProcessorConfig +from agentex.lib.utils.logging import make_logger +from agentex.types.text_content import TextContent +from agentex.lib.utils.model_utils import BaseModel +from agentex.lib.environment_variables import EnvironmentVariables +from agentex.lib.core.temporal.types.workflow import SignalName +from agentex.lib.core.temporal.workflows.workflow import BaseWorkflow +from agentex.lib.core.tracing.tracing_processor_manager import ( + add_tracing_processor_config, +) +from agentex.lib.core.temporal.plugins.openai_agents.hooks.hooks import TemporalStreamingHooks + +# Configure tracing processor (optional - only if you have SGP credentials) +add_tracing_processor_config( + SGPTracingProcessorConfig( + sgp_api_key=os.environ.get("SGP_API_KEY", ""), + sgp_account_id=os.environ.get("SGP_ACCOUNT_ID", ""), + ) +) + +environment_variables = EnvironmentVariables.refresh() + +if environment_variables.WORKFLOW_NAME is None: + raise ValueError("Environment variable WORKFLOW_NAME is not set") + +if environment_variables.AGENT_NAME is None: + raise ValueError("Environment variable AGENT_NAME is not set") + +# Validate OpenAI API key is set +if not os.environ.get("OPENAI_API_KEY"): + raise ValueError( + "OPENAI_API_KEY environment variable is not set. " + "This tutorial requires an OpenAI API key to run the OpenAI Agents SDK. " + "Please set OPENAI_API_KEY in your environment or manifest.yaml file." + ) + +logger = make_logger(__name__) + + +class StateModel(BaseModel): + """ + State model for preserving conversation history across turns. + + This allows the agent to maintain context throughout the conversation, + making it possible to reference previous messages and build on the discussion. + """ + + input_list: List[Dict[str, Any]] + turn_number: int + + +@workflow.defn(name=environment_variables.WORKFLOW_NAME) +class ExampleTutorialWorkflow(BaseWorkflow): + """ + Human-in-the-Loop Temporal Workflow + + Demonstrates agents that can pause execution and wait for human approval. + When approval is needed, the agent spawns a child workflow that waits for + external signals (human input) before continuing. + + Benefits: Durable waiting, survives system failures, scalable to millions of workflows. + """ + + def __init__(self): + super().__init__(display_name=environment_variables.AGENT_NAME) + self._complete_task = False + self._state: StateModel | None = None + self._pending_confirmation: asyncio.Queue[str] = asyncio.Queue() + self._task_id = None + self._trace_id = None + self._parent_span_id = None + + @workflow.signal(name=SignalName.RECEIVE_EVENT) + async def on_task_event_send(self, params: SendEventParams) -> None: + """ + Handle user messages with human-in-the-loop approval capability. + + When the agent needs human approval, it calls wait_for_confirmation which spawns + a child workflow that waits for external signals before continuing. + """ + logger.info(f"Received task message instruction: {params}") + + if self._state is None: + raise ValueError("State is not initialized") + + # Increment turn number for tracing + self._state.turn_number += 1 + + self._task_id = params.task.id + self._trace_id = params.task.id + + # Add the user message to conversation history + self._state.input_list.append({"role": "user", "content": params.event.content.content}) + + # Echo user message back to UI + await adk.messages.create(task_id=params.task.id, content=params.event.content) + + # ============================================================================ + # STREAMING SETUP: Store task_id for the Interceptor + # ============================================================================ + # These instance variables are read by StreamingWorkflowOutboundInterceptor + # which injects them into activity headers. This enables streaming without + # forking the Temporal plugin! + # + # How streaming works (Interceptor + Model Provider + Hooks): + # 1. We store task_id in workflow instance variable (here) + # 2. StreamingWorkflowOutboundInterceptor reads it via workflow.instance() + # 3. Interceptor injects task_id into activity headers + # 4. StreamingActivityInboundInterceptor extracts from headers + # 5. Sets streaming_task_id ContextVar inside the activity + # 6. StreamingModel reads from ContextVar and streams to Redis + # 7. TemporalStreamingHooks creates placeholder messages for tool calls + # + # This approach uses STANDARD Temporal components - no forked plugin needed! + self._task_id = params.task.id + self._trace_id = params.task.id + self._parent_span_id = params.task.id + + # ============================================================================ + # HOOKS: Create Streaming Lifecycle Messages + # ============================================================================ + # TemporalStreamingHooks integrates with OpenAI Agents SDK lifecycle events + # to create messages in the database for tool calls, reasoning, etc. + # + # What hooks do: + # - on_tool_call_start(): Creates tool_request message with arguments + # - on_tool_call_done(): Creates tool_response message with result + # - on_model_stream_part(): Called for each streaming chunk (handled by StreamingModel) + # - on_run_done(): Marks the final response as complete + # + # For human-in-the-loop workflows, hooks create messages showing: + # - Type: tool_request - Agent deciding to call wait_for_confirmation + # - Type: tool_response - Result after human approval (child workflow completion) + # - Type: text - Final agent response after approval received + # + # The hooks work alongside the interceptor/model streaming to provide + # a complete view of the agent's execution in the UI. + hooks = TemporalStreamingHooks(task_id=params.task.id) + + # Create agent with human-in-the-loop capability + # The wait_for_confirmation tool spawns a child workflow that waits for external signals + confirm_order_agent = Agent( + name="Confirm Order", + instructions="You are a helpful confirm order agent. When a user asks you to confirm an order, use the wait_for_confirmation tool to wait for confirmation.", + tools=[ + wait_for_confirmation, + ], + ) + + # Run agent - when human approval is needed, it will spawn child workflow and wait + # Hooks will create messages for tool calls, interceptor enables token streaming + # Wrap in tracing span to track this turn + async with adk.tracing.span( + trace_id=params.task.id, + name=f"Turn {self._state.turn_number}", + input=self._state.model_dump(), + ) as span: + self._parent_span_id = span.id if span else None + # Pass the conversation history to Runner.run to maintain context + result = await Runner.run(confirm_order_agent, self._state.input_list, hooks=hooks) + + # Update the state with the assistant's response for the next turn + if hasattr(result, "messages") and result.messages: + for msg in result.messages: + # Add new assistant messages to history + # Skip messages we already have (user messages we just added) + if msg.get("role") == "assistant" and msg not in self._state.input_list: + self._state.input_list.append(msg) + + # Set span output for tracing - include full state + span.output = self._state.model_dump() + + @workflow.run + async def on_task_create(self, params: CreateTaskParams) -> str: + """ + Workflow entry point - starts the long-running human-in-the-loop agent. + + Handles both automated decisions and human approval workflows durably. + To approve waiting actions: temporal workflow signal --workflow-id="child-workflow-id" --name="fulfill_order_signal" --input=true + """ + logger.info(f"Received task create params: {params}") + + # Initialize the conversation state with an empty history + self._state = StateModel( + input_list=[], + turn_number=0, + ) + + # Send welcome message when task is created + await adk.messages.create( + task_id=params.task.id, + content=TextContent( + author="agent", + content=f"Hello! I've received your task. Normally you can do some state initialization here, or just pass and do nothing until you get your first event. For now I'm just acknowledging that I've received a task with the following params:\n\n{json.dumps(params.params, indent=2)}.\n\nYou should only see this message once, when the task is created. All subsequent events will be handled by the `on_task_event_send` handler.", + ), + ) + + # Keep workflow running indefinitely to handle user messages and human approvals + # This survives system failures and can resume exactly where it left off + await workflow.wait_condition( + lambda: self._complete_task, + timeout=None, # No timeout for long-running human-in-the-loop workflows + ) + return "Task completed" + + # TEMPORAL UI (localhost:8080): + # - Main workflow shows agent activities + ChildWorkflow activity when approval needed + # - Child workflow appears as separate "child-workflow-id" that waits for signal + # - Timeline: invoke_model_activity → ChildWorkflow (waiting) → invoke_model_activity (after approval) + # + # To approve: temporal workflow signal --workflow-id="child-workflow-id" --name="fulfill_order_signal" --input=true + # Production: Replace CLI with web dashboards/APIs that send signals programmatically diff --git a/examples/tutorials/10_agentic/10_temporal/080_open_ai_agents_sdk_human_in_the_loop/pyproject.toml b/examples/tutorials/10_agentic/10_temporal/030_open_ai_agents_sdk_human_in_the_loop/pyproject.toml similarity index 95% rename from examples/tutorials/10_agentic/10_temporal/080_open_ai_agents_sdk_human_in_the_loop/pyproject.toml rename to examples/tutorials/10_agentic/10_temporal/030_open_ai_agents_sdk_human_in_the_loop/pyproject.toml index b95b19bf..d355c431 100644 --- a/examples/tutorials/10_agentic/10_temporal/080_open_ai_agents_sdk_human_in_the_loop/pyproject.toml +++ b/examples/tutorials/10_agentic/10_temporal/030_open_ai_agents_sdk_human_in_the_loop/pyproject.toml @@ -8,7 +8,7 @@ version = "0.1.0" description = "An AgentEx agent" requires-python = ">=3.12" dependencies = [ - "agentex-sdk", + "agentex-sdk>=0.5.3", "scale-gp", "temporalio>=1.18.0,<2", ] diff --git a/examples/tutorials/10_agentic/10_temporal/030_open_ai_agents_sdk_human_in_the_loop/tests/test_agent.py b/examples/tutorials/10_agentic/10_temporal/030_open_ai_agents_sdk_human_in_the_loop/tests/test_agent.py new file mode 100644 index 00000000..c229aa18 --- /dev/null +++ b/examples/tutorials/10_agentic/10_temporal/030_open_ai_agents_sdk_human_in_the_loop/tests/test_agent.py @@ -0,0 +1,201 @@ +""" +Sample tests for AgentEx ACP agent with Human-in-the-Loop workflow. + +This test suite demonstrates how to test human-in-the-loop workflows: +- Non-streaming event sending and polling +- Detecting when workflow is waiting for human approval +- Sending Temporal signals to approve/reject +- Verifying workflow completes after approval + +To run these tests: +1. Make sure the agent is running (via docker-compose or `agentex agents run`) +2. Make sure Temporal is running (localhost:7233) +3. Set the AGENTEX_API_BASE_URL environment variable if not using default +4. Run: pytest test_agent.py -v + +Configuration: +- AGENTEX_API_BASE_URL: Base URL for the AgentEx server (default: http://localhost:5003) +- AGENT_NAME: Name of the agent to test (default: example-tutorial) +- TEMPORAL_ADDRESS: Temporal server address (default: localhost:7233) +""" + +import os +import uuid +import asyncio + +import pytest +import pytest_asyncio + +# Temporal imports for signaling child workflows +from temporalio.client import Client as TemporalClient +from test_utils.agentic import ( + poll_messages, + send_event_and_poll_yielding, +) + +from agentex import AsyncAgentex +from agentex.types.task_message import TaskMessage +from agentex.types.agent_rpc_params import ParamsCreateTaskRequest + +# Configuration from environment variables +AGENTEX_API_BASE_URL = os.environ.get("AGENTEX_API_BASE_URL", "http://localhost:5003") +AGENT_NAME = os.environ.get("AGENT_NAME", "example-tutorial") +TEMPORAL_ADDRESS = os.environ.get("TEMPORAL_ADDRESS", "localhost:7233") + + +@pytest_asyncio.fixture +async def client(): + """Create an AsyncAgentex client instance for testing.""" + client = AsyncAgentex(base_url=AGENTEX_API_BASE_URL) + yield client + await client.close() + + +@pytest_asyncio.fixture +async def temporal_client(): + """Create a Temporal client for sending signals to workflows.""" + client = await TemporalClient.connect(TEMPORAL_ADDRESS) + yield client + # Temporal client doesn't need explicit close in recent versions + + +@pytest.fixture +def agent_name(): + """Return the agent name for testing.""" + return AGENT_NAME + + +@pytest_asyncio.fixture +async def agent_id(client, agent_name): + """Retrieve the agent ID based on the agent name.""" + agents = await client.agents.list() + for agent in agents: + if agent.name == agent_name: + return agent.id + raise ValueError(f"Agent with name {agent_name} not found.") + + +class TestNonStreamingEvents: + """Test non-streaming event sending and polling with human-in-the-loop.""" + + @pytest.mark.asyncio + async def test_send_event_and_poll_with_human_approval(self, client: AsyncAgentex, agent_id: str, temporal_client: TemporalClient): + """Test sending an event that triggers human approval workflow.""" + # Create a task for this conversation + task_response = await client.agents.create_task(agent_id, params=ParamsCreateTaskRequest(name=uuid.uuid1().hex)) + task = task_response.result + assert task is not None + + # Poll for the initial task creation message + print(f"[DEBUG 080 POLL] Polling for initial task creation message...") + async for message in poll_messages( + client=client, + task_id=task.id, + timeout=30, + sleep_interval=1.0, + ): + assert isinstance(message, TaskMessage) + if message.content and message.content.type == "text" and message.content.author == "agent": + # Check for the initial acknowledgment message + print(f"[DEBUG 080 POLL] Initial message: {message.content.content[:100]}") + assert "task" in message.content.content.lower() or "received" in message.content.content.lower() + break + + # Send an event asking to confirm an order (triggers human-in-the-loop) + user_message = "Please confirm my order" + print(f"[DEBUG 080 POLL] Sending message: '{user_message}'") + + # Track what we've seen to ensure human-in-the-loop flow happened + seen_tool_request = False + seen_tool_response = False + found_final_response = False + child_workflow_detected = False + + # Start polling for messages in the background + async def poll_and_detect(): + nonlocal seen_tool_request, seen_tool_response, found_final_response, child_workflow_detected + + async for message in send_event_and_poll_yielding( + client=client, + agent_id=agent_id, + task_id=task.id, + user_message=user_message, + timeout=120, # Longer timeout for human-in-the-loop + sleep_interval=1.0, + yield_updates=True, # Get all streaming chunks + ): + assert isinstance(message, TaskMessage) + print(f"[DEBUG 080 POLL] Received message - Type: {message.content.type if message.content else 'None'}, Author: {message.content.author if message.content else 'None'}, Status: {message.streaming_status}") + + # Track tool_request messages (agent calling wait_for_confirmation) + if message.content and message.content.type == "tool_request": + print(f"[DEBUG 080 POLL] ✅ Saw tool_request - agent is calling wait_for_confirmation tool") + print(f"[DEBUG 080 POLL] 🔔 Child workflow should be spawned - will signal it to approve") + seen_tool_request = True + child_workflow_detected = True + + # Track tool_response messages (child workflow completion) + if message.content and message.content.type == "tool_response": + print(f"[DEBUG 080 POLL] ✅ Saw tool_response - child workflow completed after approval") + seen_tool_response = True + + # Track agent text messages and their streaming updates + if message.content and message.content.type == "text" and message.content.author == "agent": + content_length = len(message.content.content) if message.content.content else 0 + print(f"[DEBUG 080 POLL] Agent text update - Status: {message.streaming_status}, Length: {content_length}") + + # Stop when we get DONE status with actual content + if message.streaming_status == "DONE" and content_length > 0: + print(f"[DEBUG 080 POLL] ✅ Streaming complete!") + found_final_response = True + break + + # Start polling task + polling_task = asyncio.create_task(poll_and_detect()) + + # Wait a bit for the child workflow to be created + print(f"[DEBUG 080 POLL] Waiting for child workflow to spawn...") + await asyncio.sleep(5) + + # Send signal to child workflow to approve the order + # The child workflow ID is fixed as "child-workflow-id" (see tools.py) + try: + print(f"[DEBUG 080 POLL] Sending approval signal to child workflow...") + handle = temporal_client.get_workflow_handle("child-workflow-id") + await handle.signal("fulfill_order_signal", True) + print(f"[DEBUG 080 POLL] ✅ Approval signal sent successfully!") + except Exception as e: + print(f"[DEBUG 080 POLL] ⚠️ Warning: Could not send signal to child workflow: {e}") + print(f"[DEBUG 080 POLL] This may be expected if workflow completed before signal could be sent") + + # Wait for polling to complete + try: + await asyncio.wait_for(polling_task, timeout=60) + except asyncio.TimeoutError: + print(f"[DEBUG 080 POLL] ⚠️ Polling timed out - workflow may still be waiting") + polling_task.cancel() + + # Verify that we saw the complete flow: tool_request -> human approval -> tool_response -> final answer + assert seen_tool_request, "Expected to see tool_request message (agent calling wait_for_confirmation)" + assert seen_tool_response, "Expected to see tool_response message (child workflow completion after approval)" + assert found_final_response, "Expected to see final text response after human approval" + + print(f"[DEBUG 080 POLL] ✅ Human-in-the-loop workflow completed successfully!") + + +class TestStreamingEvents: + """Test streaming event sending (backend verification via polling).""" + + @pytest.mark.asyncio + async def test_send_event_and_stream(self, client: AsyncAgentex, agent_id: str): + """ + Streaming test placeholder. + + NOTE: SSE streaming is tested via the UI (agentex-ui subscribeTaskState). + Backend streaming functionality is verified in test_send_event_and_poll_with_human_approval. + """ + pass + + +if __name__ == "__main__": + pytest.main([__file__, "-v"]) diff --git a/examples/tutorials/10_agentic/10_temporal/040_workflow_activities/README.md b/examples/tutorials/10_agentic/10_temporal/040_workflow_activities/README.md new file mode 100644 index 00000000..01a51fa2 --- /dev/null +++ b/examples/tutorials/10_agentic/10_temporal/040_workflow_activities/README.md @@ -0,0 +1,184 @@ +# [Temporal] Workflow Activities + +**Part of the [OpenAI SDK + Temporal integration series](../README.md)** → Previous: [030 Human in the Loop](../030_open_ai_agents_sdk_human_in_the_loop/) + +## What You'll Learn + +How to use Temporal activities for **workflow orchestration** alongside the OpenAI Agents SDK. + +**Key Pattern:** +- OpenAI Agents SDK handles LLM interactions (`Runner.run`) +- Temporal activities handle everything else (database, notifications, reports) + +**Difference from Tutorial 020:** +- **Tutorial 020**: Activities AS agent tools (agent decides when to call) +- **Tutorial 040**: Activities FOR workflow logic (workflow decides when to call) + +## Prerequisites +- Development environment set up (see [main repo README](https://github.com/scaleapi/scale-agentex)) +- Backend services running: `make dev` from repository root +- Temporal UI available at http://localhost:8233 +- OpenAI API key configured (see previous tutorials) +- Understanding of OpenAI Agents SDK (see [010](../010_open_ai_agents_sdk_hello_world/)) + +## Quick Start + +```bash +cd examples/tutorials/10_agentic/10_temporal/040_workflow_activities +export OPENAI_API_KEY="your-key-here" +uv run agentex agents run --manifest manifest.yaml +``` + +**Monitor:** Open Temporal UI at http://localhost:8233 to see workflow activities. + +## Try It + +1. Send a message to the agent (it responds normally) +2. Check Temporal UI - you'll see MULTIPLE activities: + - `invoke_model_activity` (agent processing - from SDK plugin) + - `save_to_database` (workflow saves conversation) + - `send_notification` (workflow sends notification) +3. Send 3 messages total: + - After message #3, see `process_batch` activity run +4. Check the agent responses - you'll see: + - Normal agent responses + - System messages about database saves + - Email notifications + - Batch processing updates + +![Workflow Activities](../_images/workflow_activities.png) + +## Key Code + +### Activities for Workflow Orchestration + +```python +# In activities.py - these are NOT agent tools + +@activity.defn +async def save_to_database(task_id: str, data: dict) -> str: + """Save conversation to database - workflow controls this""" + # Make database call (PostgreSQL, MongoDB, etc.) + return f"Saved {len(data)} fields to database" + +@activity.defn +async def send_notification(task_id: str, message: str, channel: str) -> str: + """Send notifications - workflow controls this""" + # Call email API, Slack, Discord, etc. + return f"Notification sent via {channel}" + +@activity.defn +async def process_batch(task_id: str, items: list, batch_num: int) -> dict: + """Process data in batches - workflow controls this""" + # Batch processing, analytics, etc. + return {"batch_number": batch_num, "items_processed": len(items)} +``` + +### Workflow Using Activities + +```python +# In workflow.py - workflow calls activities directly + +@workflow.signal(name=SignalName.RECEIVE_EVENT) +async def on_task_event_send(self, params: SendEventParams): + # STEP 1: Agent processes message (OpenAI Agents SDK) + result = await Runner.run(agent, self._state.input_list, hooks=hooks) + + # STEP 2: Save to database (Workflow Activity - NOT agent tool) + await workflow.execute_activity( + save_to_database, + args=[task_id, conversation_data], + start_to_close_timeout=timedelta(seconds=30), + retry_policy=RetryPolicy(maximum_attempts=3) + ) + + # STEP 3: Send notification (Workflow Activity - NOT agent tool) + await workflow.execute_activity( + send_notification, + args=[task_id, "Turn completed", "email"], + start_to_close_timeout=timedelta(seconds=30) + ) + + # STEP 4: Batch processing every 3 messages (Workflow Logic) + if self._state.turn_number % 3 == 0: + await workflow.execute_activity( + process_batch, + args=[task_id, recent_messages, batch_num], + start_to_close_timeout=timedelta(minutes=5) + ) +``` + +## When to Use Each Pattern + +### Use Activities AS Agent Tools (Tutorial 020) when: +- Agent decides when to call the operation +- User explicitly requests the action ("check the weather") +- Operation is part of the agent's capabilities +- Agent needs the result to formulate a response + +### Use Activities FOR Workflow Orchestration (Tutorial 040) when: +- Workflow orchestration logic (not agent decisions) +- Background operations (notifications, logging, metrics) +- Post-processing (saving results, generating reports) +- Infrastructure concerns (health checks, cleanup) +- Business rules that always apply (audit logs, database saves) + +## Use Cases + +**Workflow-Level Activities are perfect for:** +- **Database Operations**: Save every conversation to PostgreSQL +- **Notifications**: Email user after every agent response +- **Audit Logs**: Log all interactions for compliance +- **Batch Processing**: Aggregate data every N messages +- **Report Generation**: Create summary reports periodically +- **External Integrations**: Update CRM, trigger webhooks +- **Monitoring**: Send metrics to Datadog, New Relic +- **Cleanup**: Delete old data, archive conversations + +## Why This Matters + +**Not everything should go through the agent.** Some operations are: +- Infrastructure concerns (logging, monitoring) +- Business requirements (always save to DB, always send notification) +- Background operations (don't need agent awareness) + +**Workflow-level activities provide:** +- ✅ Guaranteed execution (even if agent fails) +- ✅ Separate retry policies from agent operations +- ✅ Clear separation of concerns +- ✅ Better observability (separate activities in Temporal UI) +- ✅ Independent failure handling + +**Example:** If saving to database fails, the workflow can retry the database activity without re-running the expensive LLM call. + +## Architecture + +``` +User Message + ↓ +[Agent Processing] ← OpenAI Agents SDK (invoke_model_activity) + ↓ +[Save to Database] ← Workflow Activity (save_to_database) + ↓ +[Send Notification] ← Workflow Activity (send_notification) + ↓ +[Batch Processing?] ← Workflow Activity (process_batch) [conditional] + ↓ +Response to User +``` + +Each step is a separate durable activity with independent retry policies and observability. + +## When to Use + +- Production agents that need infrastructure operations +- Systems with regulatory requirements (audit logs, compliance) +- Applications with external integrations (CRM, email, Slack) +- Workflows with background processing needs +- Services requiring guaranteed side effects (database writes, notifications) + +**Next:** (Coming soon) State Machines - Complex multi-phase workflows + +--- + +**For detailed setup instructions, see the main [README](../README.md)** diff --git a/examples/tutorials/10_agentic/10_temporal/040_workflow_activities/manifest.yaml b/examples/tutorials/10_agentic/10_temporal/040_workflow_activities/manifest.yaml new file mode 100644 index 00000000..f6e5e780 --- /dev/null +++ b/examples/tutorials/10_agentic/10_temporal/040_workflow_activities/manifest.yaml @@ -0,0 +1,62 @@ +# Workflow Activities Tutorial Manifest +# Tutorial demonstrating activities for workflow orchestration alongside OpenAI Agents SDK + +build: + context: + root: ../ + include_paths: + - 040_workflow_activities + dockerfile: 040_workflow_activities/Dockerfile + dockerignore: 040_workflow_activities/.dockerignore + +local_development: + agent: + port: 8000 + host_address: host.docker.internal + + paths: + acp: project/acp.py + worker: project/run_worker.py + +agent: + acp_type: agentic + name: workflow-activities-tutorial + description: Tutorial showing workflow-level activities for orchestration alongside OpenAI Agents SDK + + temporal: + enabled: true + workflows: + - name: workflow-activities-tutorial + queue_name: workflow_activities_tutorial_queue + + credentials: + - env_var_name: REDIS_URL + secret_name: redis-url-secret + secret_key: url + + env: + OPENAI_API_KEY: "" + OPENAI_ORG_ID: "" + +deployment: + image: + repository: "" + tag: "latest" + + imagePullSecrets: + - name: my-registry-secret + + global: + agent: + name: "workflow-activities-tutorial" + description: "Tutorial: Workflow-level activities for orchestration" + + replicaCount: 1 + + resources: + requests: + cpu: "500m" + memory: "1Gi" + limits: + cpu: "1000m" + memory: "2Gi" diff --git a/examples/tutorials/10_agentic/10_temporal/040_workflow_activities/project/__init__.py b/examples/tutorials/10_agentic/10_temporal/040_workflow_activities/project/__init__.py new file mode 100644 index 00000000..a94f1036 --- /dev/null +++ b/examples/tutorials/10_agentic/10_temporal/040_workflow_activities/project/__init__.py @@ -0,0 +1 @@ +# Tutorial: Workflow Activities diff --git a/examples/tutorials/10_agentic/10_temporal/040_workflow_activities/project/acp.py b/examples/tutorials/10_agentic/10_temporal/040_workflow_activities/project/acp.py new file mode 100644 index 00000000..8903e7af --- /dev/null +++ b/examples/tutorials/10_agentic/10_temporal/040_workflow_activities/project/acp.py @@ -0,0 +1,54 @@ +import os +import sys + +from temporalio.contrib.openai_agents import OpenAIAgentsPlugin + +# === DEBUG SETUP (AgentEx CLI Debug Support) === +if os.getenv("AGENTEX_DEBUG_ENABLED") == "true": + try: + import debugpy + debug_port = int(os.getenv("AGENTEX_DEBUG_PORT", "5679")) + debug_type = os.getenv("AGENTEX_DEBUG_TYPE", "acp") + wait_for_attach = os.getenv("AGENTEX_DEBUG_WAIT_FOR_ATTACH", "false").lower() == "true" + + # Configure debugpy + debugpy.configure(subProcess=False) + debugpy.listen(debug_port) + + print(f"🐛 [{debug_type.upper()}] Debug server listening on port {debug_port}") + + if wait_for_attach: + print(f"⏳ [{debug_type.upper()}] Waiting for debugger to attach...") + debugpy.wait_for_client() + print(f"✅ [{debug_type.upper()}] Debugger attached!") + else: + print(f"📡 [{debug_type.upper()}] Ready for debugger attachment") + + except ImportError: + print("❌ debugpy not available. Install with: pip install debugpy") + sys.exit(1) + except Exception as e: + print(f"❌ Debug setup failed: {e}") + sys.exit(1) +# === END DEBUG SETUP === + +from agentex.lib.types.fastacp import TemporalACPConfig +from agentex.lib.sdk.fastacp.fastacp import FastACP +from agentex.lib.core.temporal.plugins.openai_agents.models.temporal_streaming_model import ( + TemporalStreamingModelProvider, +) +from agentex.lib.core.temporal.plugins.openai_agents.interceptors.context_interceptor import ContextInterceptor + +context_interceptor = ContextInterceptor() +temporal_streaming_model_provider = TemporalStreamingModelProvider() + +# Create the ACP server with OpenAI Agents SDK plugin +acp = FastACP.create( + acp_type="agentic", + config=TemporalACPConfig( + type="temporal", + temporal_address=os.getenv("TEMPORAL_ADDRESS", "localhost:7233"), + plugins=[OpenAIAgentsPlugin(model_provider=temporal_streaming_model_provider)], + interceptors=[context_interceptor] + ) +) diff --git a/examples/tutorials/10_agentic/10_temporal/040_workflow_activities/project/activities.py b/examples/tutorials/10_agentic/10_temporal/040_workflow_activities/project/activities.py new file mode 100644 index 00000000..5ffa9090 --- /dev/null +++ b/examples/tutorials/10_agentic/10_temporal/040_workflow_activities/project/activities.py @@ -0,0 +1,170 @@ +""" +Workflow Activities Tutorial + +This file demonstrates activities used for WORKFLOW ORCHESTRATION, not as agent tools. +These activities handle operations that the workflow controls directly, like: +- Database writes +- Progress reporting +- Email/notification sending +- Batch processing +- External system integration + +Key difference from tutorial 020: +- Tutorial 020: Activities AS agent tools (agent decides when to call) +- Tutorial 040: Activities FOR workflow logic (workflow decides when to call) +""" + +import asyncio +from temporalio import activity + +from agentex.lib import adk +from agentex.lib.utils.logging import make_logger +from agentex.types.text_content import TextContent + +logger = make_logger(__name__) + + +@activity.defn +async def save_to_database(task_id: str, data: dict) -> str: + """ + Save data to a database - runs as a Temporal activity. + + In production, this would: + - Make actual database calls (PostgreSQL, MongoDB, etc.) + - Handle connection retries automatically via Temporal + - Be visible in Temporal UI for observability + + For this tutorial, we simulate the database operation. + """ + logger.info(f"[DATABASE] Saving data for task {task_id}: {data}") + + # Simulate database latency + await asyncio.sleep(2) + + logger.info(f"[DATABASE] Successfully saved data for task {task_id}") + return f"Saved {len(data)} fields to database" + + +@activity.defn +async def send_notification(task_id: str, message: str, channel: str = "email") -> str: + """ + Send notification via external service - runs as a Temporal activity. + + In production, this would: + - Call email service APIs (SendGrid, SES, etc.) + - Send Slack/Discord notifications + - Trigger webhooks + - Be automatically retried on failures + + For this tutorial, we simulate the notification and also send to AgentEx UI. + """ + logger.info(f"[NOTIFICATION] Sending to {channel}: {message}") + + # Simulate API call latency + await asyncio.sleep(1) + + # Also send to AgentEx UI so user can see it + await adk.messages.create( + task_id=task_id, + content=TextContent( + author="agent", + content=f"📧 [{channel.upper()}] {message}", + ), + ) + + logger.info(f"[NOTIFICATION] Successfully sent to {channel}") + return f"Notification sent via {channel}" + + +@activity.defn +async def process_batch(task_id: str, items: list[str], batch_num: int) -> dict: + """ + Process a batch of items - runs as a Temporal activity. + + In production, this would: + - Process multiple records + - Call external APIs in batch + - Transform/validate data + - Handle partial failures with retries + + For this tutorial, we simulate batch processing. + """ + logger.info(f"[BATCH {batch_num}] Processing {len(items)} items") + + # Send progress update to UI + await adk.messages.create( + task_id=task_id, + content=TextContent( + author="agent", + content=f"⚙️ Processing batch #{batch_num} with {len(items)} items...", + ), + ) + + # Simulate processing time + await asyncio.sleep(3) + + # Send completion update + await adk.messages.create( + task_id=task_id, + content=TextContent( + author="agent", + content=f"✅ Batch #{batch_num} complete! Processed {len(items)} items.", + ), + ) + + logger.info(f"[BATCH {batch_num}] Successfully processed {len(items)} items") + + return { + "batch_number": batch_num, + "items_processed": len(items), + "status": "completed" + } + + +@activity.defn +async def generate_report(task_id: str, summary_data: dict) -> str: + """ + Generate and store a report - runs as a Temporal activity. + + In production, this would: + - Generate PDF/Excel reports + - Upload to S3/cloud storage + - Create dashboard entries + - Update analytics systems + + For this tutorial, we simulate report generation. + """ + logger.info(f"[REPORT] Generating report with data: {summary_data}") + + await adk.messages.create( + task_id=task_id, + content=TextContent( + author="agent", + content="📊 Generating final report...", + ), + ) + + # Simulate report generation + await asyncio.sleep(2) + + report_content = f""" +📊 **Workflow Summary Report** + +Total Messages: {summary_data.get('total_messages', 0)} +Batches Processed: {summary_data.get('batches_processed', 0)} +Database Saves: {summary_data.get('database_saves', 0)} +Notifications Sent: {summary_data.get('notifications_sent', 0)} + +Status: ✅ Complete +""" + + await adk.messages.create( + task_id=task_id, + content=TextContent( + author="agent", + content=report_content, + ), + ) + + logger.info(f"[REPORT] Report generated successfully") + return "report_12345.pdf" diff --git a/examples/tutorials/10_agentic/10_temporal/040_workflow_activities/project/run_worker.py b/examples/tutorials/10_agentic/10_temporal/040_workflow_activities/project/run_worker.py new file mode 100644 index 00000000..05eabe80 --- /dev/null +++ b/examples/tutorials/10_agentic/10_temporal/040_workflow_activities/project/run_worker.py @@ -0,0 +1,75 @@ +import asyncio + +from temporalio.contrib.openai_agents import OpenAIAgentsPlugin + +from project.workflow import WorkflowActivitiesWorkflow +from project.activities import ( + save_to_database, + send_notification, + process_batch, + generate_report, +) +from agentex.lib.utils.debug import setup_debug_if_enabled +from agentex.lib.utils.logging import make_logger +from agentex.lib.environment_variables import EnvironmentVariables +from agentex.lib.core.temporal.activities import get_all_activities +from agentex.lib.core.temporal.workers.worker import AgentexWorker +from agentex.lib.core.temporal.plugins.openai_agents.hooks.activities import stream_lifecycle_content +from agentex.lib.core.temporal.plugins.openai_agents.models.temporal_streaming_model import ( + TemporalStreamingModelProvider, +) +from agentex.lib.core.temporal.plugins.openai_agents.interceptors.context_interceptor import ContextInterceptor + +environment_variables = EnvironmentVariables.refresh() + +logger = make_logger(__name__) + + +async def main(): + # Setup debug mode if enabled + setup_debug_if_enabled() + + task_queue_name = environment_variables.WORKFLOW_TASK_QUEUE + if task_queue_name is None: + raise ValueError("WORKFLOW_TASK_QUEUE is not set") + + # ============================================================================ + # REGISTER WORKFLOW ACTIVITIES + # ============================================================================ + # These are activities for WORKFLOW ORCHESTRATION, not agent tools. + # The workflow calls these directly to handle: + # - Database operations + # - Notifications + # - Batch processing + # - Report generation + # + # Different from tutorial 020 where activities are used AS agent tools! + all_activities = get_all_activities() + [ + save_to_database, + send_notification, + process_batch, + generate_report, + stream_lifecycle_content, + ] + + # ============================================================================ + # STREAMING SETUP: Interceptor + Model Provider + # ============================================================================ + # Enable real-time streaming of agent responses + context_interceptor = ContextInterceptor() + temporal_streaming_model_provider = TemporalStreamingModelProvider() + + # Create worker with OpenAI Agents SDK plugin + worker = AgentexWorker( + task_queue=task_queue_name, + plugins=[OpenAIAgentsPlugin(model_provider=temporal_streaming_model_provider)], + interceptors=[context_interceptor], + ) + + await worker.run( + activities=all_activities, + workflow=WorkflowActivitiesWorkflow, + ) + +if __name__ == "__main__": + asyncio.run(main()) diff --git a/examples/tutorials/10_agentic/10_temporal/040_workflow_activities/project/workflow.py b/examples/tutorials/10_agentic/10_temporal/040_workflow_activities/project/workflow.py new file mode 100644 index 00000000..a4c92fec --- /dev/null +++ b/examples/tutorials/10_agentic/10_temporal/040_workflow_activities/project/workflow.py @@ -0,0 +1,282 @@ +""" +Workflow Activities Tutorial + +This tutorial demonstrates using Temporal activities for WORKFLOW ORCHESTRATION +alongside the OpenAI Agents SDK. + +KEY PATTERN: +- OpenAI Agents SDK handles LLM interactions (via Runner.run) +- Temporal activities handle everything else (database, notifications, reports) + +DIFFERENCE FROM TUTORIAL 020: +- Tutorial 020: Activities AS agent tools (agent decides when to call them) +- Tutorial 040: Activities FOR workflow logic (workflow decides when to call them) + +USE CASES: +- Database operations after agent responses +- Progress notifications during long workflows +- Batch processing operations +- Report generation +- External system integration (email, Slack, webhooks) +- Audit logging + +WHY THIS MATTERS: +Not everything should go through the agent. Some operations are: +- Part of workflow orchestration (not agent decisions) +- Background operations (notifications, logging) +- Post-processing (saving results, generating reports) +- Infrastructure concerns (health checks, metrics) + +These should be workflow-level activities, not agent tools. +""" + +import os +from typing import Any, Dict, List +from datetime import timedelta +from temporalio import workflow +from temporalio.common import RetryPolicy + +from agents import Agent, Runner + +from agentex.lib import adk +from agentex.lib.types.acp import SendEventParams, CreateTaskParams +from agentex.lib.utils.logging import make_logger +from agentex.lib.utils.model_utils import BaseModel +from agentex.lib.environment_variables import EnvironmentVariables +from agentex.lib.core.temporal.types.workflow import SignalName +from agentex.lib.core.temporal.workflows.workflow import BaseWorkflow +from agentex.lib.core.temporal.plugins.openai_agents.hooks.hooks import TemporalStreamingHooks +from agentex.types.text_content import TextContent + +# Import our workflow activities +from project.activities import ( + save_to_database, + send_notification, + process_batch, + generate_report, +) + +environment_variables = EnvironmentVariables.refresh() + +if not environment_variables.WORKFLOW_NAME: + raise ValueError("Environment variable WORKFLOW_NAME is not set") + +if not environment_variables.AGENT_NAME: + raise ValueError("Environment variable AGENT_NAME is not set") + +if not os.environ.get("OPENAI_API_KEY"): + raise ValueError( + "OPENAI_API_KEY environment variable is not set. " + "Set it in your manifest.yaml or .env file." + ) + +logger = make_logger(__name__) + + +class StateModel(BaseModel): + """Track workflow state""" + input_list: List[Dict[str, Any]] + turn_number: int + database_saves: int + notifications_sent: int + batches_processed: int + + +@workflow.defn(name=environment_variables.WORKFLOW_NAME) +class WorkflowActivitiesWorkflow(BaseWorkflow): + """ + Demonstrates using activities for workflow orchestration alongside OpenAI Agents SDK. + + Pattern: + 1. User sends message + 2. Agent processes with LLM (OpenAI Agents SDK) + 3. Workflow saves to database (activity) + 4. Workflow sends notification (activity) + 5. Every 3 messages, process batch (activity) + """ + + def __init__(self): + super().__init__(display_name=environment_variables.AGENT_NAME) + self._state: StateModel | None = None + self._task_id: str | None = None + self._trace_id: str | None = None + self._parent_span_id: str | None = None + self._complete_task = False + + @workflow.signal(name=SignalName.RECEIVE_EVENT) + async def on_task_event_send(self, params: SendEventParams) -> None: + if not params.event.content or params.event.content.type != "text": + return + + # Wait for state to be initialized (handles race condition) + await workflow.wait_condition(lambda: self._state is not None) + + # Store for streaming + self._task_id = params.task.id + self._trace_id = params.task.id + self._parent_span_id = params.task.id + + # Increment turn + self._state.turn_number += 1 + + # Add user message to history + user_message = params.event.content.content + self._state.input_list.append({"role": "user", "content": user_message}) + + # Echo to UI + await adk.messages.create(task_id=params.task.id, content=params.event.content) + + # ============================================================================ + # STEP 1: Agent Processes Message (OpenAI Agents SDK) + # ============================================================================ + # The agent uses the OpenAI Agents SDK to process the user's message. + # This automatically becomes a durable Temporal activity via the plugin. + + agent = Agent( + name="Helpful Assistant", + instructions="You are a helpful assistant. Provide clear, concise answers.", + ) + + hooks = TemporalStreamingHooks(task_id=params.task.id) + + logger.info(f"[WORKFLOW] Running agent for turn {self._state.turn_number}") + result = await Runner.run(agent, self._state.input_list, hooks=hooks) + + # Update conversation history and extract response + agent_response = "" + if hasattr(result, "messages") and result.messages: + for msg in result.messages: + if msg.get("role") == "assistant" and msg not in self._state.input_list: + self._state.input_list.append(msg) + # Get the last assistant message content + if msg.get("role") == "assistant": + agent_response = msg.get("content", "") + + # ============================================================================ + # STEP 2: Save to Database (Workflow Activity) + # ============================================================================ + # After the agent responds, save the interaction to the database. + # This is a WORKFLOW decision, not an agent decision. + + logger.info(f"[WORKFLOW] Saving conversation to database") + + db_result = await workflow.execute_activity( + save_to_database, + args=[params.task.id, { + "turn": self._state.turn_number, + "user_message": user_message, + "agent_response": agent_response + }], + start_to_close_timeout=timedelta(seconds=30), + retry_policy=RetryPolicy(maximum_attempts=3) + ) + + self._state.database_saves += 1 + logger.info(f"[WORKFLOW] Database save result: {db_result}") + + # ============================================================================ + # STEP 3: Send Notification (Workflow Activity) + # ============================================================================ + # Send a notification about the interaction. + # In production: email, Slack, webhook, etc. + + logger.info(f"[WORKFLOW] Sending notification") + + notif_result = await workflow.execute_activity( + send_notification, + args=[ + params.task.id, + f"Turn {self._state.turn_number} completed", + "email" + ], + start_to_close_timeout=timedelta(seconds=30), + retry_policy=RetryPolicy(maximum_attempts=3) + ) + + self._state.notifications_sent += 1 + logger.info(f"[WORKFLOW] Notification result: {notif_result}") + + # ============================================================================ + # STEP 4: Batch Processing (Workflow Activity - Conditional) + # ============================================================================ + # Every 3 messages, process a batch of recent messages. + # This could be analytics, aggregation, reporting, etc. + + if self._state.turn_number % 3 == 0: + logger.info(f"[WORKFLOW] Triggering batch processing") + + # Get last 3 messages for batch + recent_messages = [ + msg.get("content", "") + for msg in self._state.input_list[-6:] # Last 3 user+agent pairs + if isinstance(msg.get("content"), str) + ] + + batch_result = await workflow.execute_activity( + process_batch, + args=[params.task.id, recent_messages, self._state.turn_number // 3], + start_to_close_timeout=timedelta(minutes=5), + retry_policy=RetryPolicy(maximum_attempts=2) + ) + + self._state.batches_processed += 1 + logger.info(f"[WORKFLOW] Batch result: {batch_result}") + + @workflow.run + async def on_task_create(self, params: CreateTaskParams) -> None: + logger.info(f"Workflow started for task: {params.task.id}") + + # Initialize state + self._state = StateModel( + input_list=[], + turn_number=0, + database_saves=0, + notifications_sent=0, + batches_processed=0, + ) + + # Send welcome message + await adk.messages.create( + task_id=params.task.id, + content=TextContent( + author="agent", + content=( + "👋 Welcome to the Workflow Activities Tutorial!\n\n" + "This agent demonstrates using Temporal activities for " + "workflow orchestration:\n" + "• Agent processes your messages (OpenAI Agents SDK)\n" + "• Workflow saves to database (activity)\n" + "• Workflow sends notifications (activity)\n" + "• Every 3 messages, batch processing runs (activity)\n\n" + "Send me a few messages and watch the activities in Temporal UI!" + ), + ), + ) + + # Wait for messages indefinitely + # When the workflow ends (manually or via signal), generate final report + await workflow.wait_condition( + lambda: self._complete_task, + timeout=None + ) + + # ============================================================================ + # FINAL STEP: Generate Report (Workflow Activity) + # ============================================================================ + # When workflow ends, generate a summary report + logger.info(f"[WORKFLOW] Generating final report") + + if self._state: + await workflow.execute_activity( + generate_report, + args=[params.task.id, { + "total_messages": self._state.turn_number, + "database_saves": self._state.database_saves, + "notifications_sent": self._state.notifications_sent, + "batches_processed": self._state.batches_processed, + }], + start_to_close_timeout=timedelta(minutes=2), + retry_policy=RetryPolicy(maximum_attempts=3) + ) + + logger.info(f"[WORKFLOW] Workflow completed") diff --git a/examples/tutorials/10_agentic/10_temporal/040_workflow_activities/pyproject.toml b/examples/tutorials/10_agentic/10_temporal/040_workflow_activities/pyproject.toml new file mode 100644 index 00000000..7289a008 --- /dev/null +++ b/examples/tutorials/10_agentic/10_temporal/040_workflow_activities/pyproject.toml @@ -0,0 +1,37 @@ +[build-system] +requires = ["hatchling"] +build-backend = "hatchling.build" + +[project] +name = "workflow_activities_tutorial" +version = "0.1.0" +description = "Tutorial: Workflow Activities - Using activities for orchestration alongside OpenAI Agents SDK" +requires-python = ">=3.12" +dependencies = [ + "agentex-sdk @ file:///Users/daniel.miller/codebases/agentex-python", + "scale-gp", + "temporalio>=1.18.2,<2", +] + +[project.optional-dependencies] +dev = [ + "pytest", + "black", + "isort", + "flake8", + "debugpy>=1.8.15", +] + +[tool.hatch.metadata] +allow-direct-references = true + +[tool.hatch.build.targets.wheel] +packages = ["project"] + +[tool.black] +line-length = 88 +target-version = ['py312'] + +[tool.isort] +profile = "black" +line_length = 88 diff --git a/examples/tutorials/10_agentic/10_temporal/040_workflow_activities/tests/test_agent.py b/examples/tutorials/10_agentic/10_temporal/040_workflow_activities/tests/test_agent.py new file mode 100644 index 00000000..0f74ecee --- /dev/null +++ b/examples/tutorials/10_agentic/10_temporal/040_workflow_activities/tests/test_agent.py @@ -0,0 +1,234 @@ +""" +Tests for Workflow Activities Tutorial. + +This test suite demonstrates the workflow-level activities pattern: +- Agent processes messages (OpenAI Agents SDK) +- Workflow saves to database (activity) +- Workflow sends notifications (activity) +- Batch processing every 3 messages (activity) + +To run these tests: +1. Make sure the agent is running (via `agentex agents run --manifest manifest.yaml`) +2. Set the AGENTEX_API_BASE_URL environment variable if not using default +3. Run: pytest test_agent.py -v + +Configuration: +- AGENTEX_API_BASE_URL: Base URL for the AgentEx server (default: http://localhost:5003) +- AGENT_NAME: Name of the agent to test (default: workflow-activities-tutorial) +""" + +import os +import uuid + +import pytest +import pytest_asyncio +from test_utils.agentic import ( + poll_messages, + send_event_and_poll_yielding, +) + +from agentex import AsyncAgentex +from agentex.types.task_message import TaskMessage +from agentex.types.agent_rpc_params import ParamsCreateTaskRequest + +# Configuration from environment variables +AGENTEX_API_BASE_URL = os.environ.get("AGENTEX_API_BASE_URL", "http://localhost:5003") +AGENT_NAME = os.environ.get("AGENT_NAME", "workflow-activities-tutorial") + + +@pytest_asyncio.fixture +async def client(): + """Create an AsyncAgentex client instance for testing.""" + client = AsyncAgentex(base_url=AGENTEX_API_BASE_URL) + yield client + await client.close() + + +@pytest.fixture +def agent_name(): + """Return the agent name for testing.""" + return AGENT_NAME + + +@pytest_asyncio.fixture +async def agent_id(client, agent_name): + """Retrieve the agent ID based on the agent name.""" + agents = await client.agents.list() + for agent in agents: + if agent.name == agent_name: + return agent.id + raise ValueError(f"Agent with name {agent_name} not found.") + + +class TestWorkflowActivities: + """Test workflow-level activities alongside agent processing.""" + + @pytest.mark.asyncio + async def test_single_message_with_activities(self, client: AsyncAgentex, agent_id: str): + """ + Test that a single message triggers: + 1. Agent processing (OpenAI Agents SDK) + 2. Database save activity + 3. Notification activity + """ + # Create a task for this conversation + task_response = await client.agents.create_task(agent_id, params=ParamsCreateTaskRequest(name=uuid.uuid1().hex)) + task = task_response.result + assert task is not None + + # Poll for the initial welcome message + print(f"[TEST] Polling for initial welcome message...") + async for message in poll_messages( + client=client, + task_id=task.id, + timeout=30, + sleep_interval=1.0, + ): + if message.content and message.content.type == "text" and message.content.author == "agent": + print(f"[TEST] Welcome message: {message.content.content[:80]}...") + assert "Workflow Activities Tutorial" in message.content.content + break + + # Send a message and track all messages + user_message = "hi" + print(f"[TEST] Sending message: '{user_message}'") + + seen_notification = False + final_agent_message = None + + async for message in send_event_and_poll_yielding( + client=client, + agent_id=agent_id, + task_id=task.id, + user_message=user_message, + timeout=60, + sleep_interval=1.0, + yield_updates=True, + ): + assert isinstance(message, TaskMessage) + + if not message.content: + continue + + content = message.content.content or "" + print(f"[TEST] Received - Type: {message.content.type}, Author: {message.content.author}, Status: {message.streaming_status}, Content: {content[:60]}...") + + # Track notification message (email notification from activity) + if "[EMAIL]" in content and "Turn 1 completed" in content: + print(f"[TEST] ✅ Saw notification activity message") + seen_notification = True + + # Track agent text messages and save the final one + if message.content.type == "text" and message.content.author == "agent" and "[EMAIL]" not in content: + final_agent_message = message + + # Note when we get DONE status, but keep polling for notification + if message.streaming_status == "DONE" and len(content) > 0: + print(f"[TEST] ✅ Agent streaming complete!") + # Don't break yet - continue polling for notification + + # Verify we got the agent response + assert final_agent_message is not None, "Expected to see final agent message" + assert final_agent_message.content is not None, "Final message should have content" + assert len(final_agent_message.content.content) > 0, "Final message should have content" + + # If we didn't see notification during streaming, poll a bit more + if not seen_notification: + print(f"[TEST] Notification not seen yet, polling for a few more seconds...") + async for message in poll_messages( + client=client, + task_id=task.id, + timeout=5, + sleep_interval=0.5, + ): + if message.content and "[EMAIL]" in message.content.content and "Turn 1 completed" in message.content.content: + print(f"[TEST] ✅ Saw notification activity message") + seen_notification = True + break + + # Verify we got the notification + assert seen_notification, "Expected to see email notification from activity" + + print(f"[TEST] ✅ Test complete - agent responded and notification sent") + + @pytest.mark.asyncio + async def test_batch_processing_after_three_messages(self, client: AsyncAgentex, agent_id: str): + """ + Test that batch processing activity runs after 3 messages. + """ + # Create a task + task_response = await client.agents.create_task(agent_id, params=ParamsCreateTaskRequest(name=uuid.uuid1().hex)) + task = task_response.result + assert task is not None + + # Skip welcome message + async for message in poll_messages(client=client, task_id=task.id, timeout=30, sleep_interval=1.0): + if message.content and "Workflow Activities Tutorial" in message.content.content: + break + + # Send 3 messages and look for batch processing + messages_to_send = ["tell me about life", "cool what else", "interesting"] + seen_batch_processing = False + + for i, user_message in enumerate(messages_to_send, 1): + print(f"\n[TEST] Sending message {i}/3: '{user_message}'") + + async for message in send_event_and_poll_yielding( + client=client, + agent_id=agent_id, + task_id=task.id, + user_message=user_message, + timeout=60, + sleep_interval=1.0, + yield_updates=True, + ): + if not message.content: + continue + + content = message.content.content or "" + + # Look for batch processing messages + if "Processing batch #1" in content: + print(f"[TEST] ✅ Batch processing started!") + seen_batch_processing = True + + if "Batch #1 complete" in content: + print(f"[TEST] ✅ Batch processing completed!") + # Found batch complete, we're done + break + + # Regular agent message - wait for DONE + if (message.content.type == "text" and + message.content.author == "agent" and + message.streaming_status == "DONE" and + "[EMAIL]" not in content and + "batch" not in content.lower() and + len(content) > 10): + print(f"[TEST] ✅ Got agent response for message {i}") + break + + # If we saw batch complete, stop + if seen_batch_processing and "Batch #1 complete" in content: + break + + # Verify batch processing happened + assert seen_batch_processing, "Expected to see batch processing after 3 messages" + print(f"[TEST] ✅ Batch processing test complete!") + + +class TestStreamingEvents: + """Test streaming event sending (backend verification via polling).""" + + @pytest.mark.asyncio + async def test_send_event_and_stream(self, client: AsyncAgentex, agent_id: str): + """ + Streaming test placeholder. + + NOTE: SSE streaming is tested via the UI (agentex-ui subscribeTaskState). + Backend streaming functionality is verified in other tests. + """ + pass + + +if __name__ == "__main__": + pytest.main([__file__, "-v"]) diff --git a/examples/tutorials/10_agentic/10_temporal/050_agent_chat_guardrails/uv.lock b/examples/tutorials/10_agentic/10_temporal/050_agent_chat_guardrails/uv.lock deleted file mode 100644 index 93b8812c..00000000 --- a/examples/tutorials/10_agentic/10_temporal/050_agent_chat_guardrails/uv.lock +++ /dev/null @@ -1,2262 +0,0 @@ -version = 1 -revision = 2 -requires-python = ">=3.12" - 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This tutorial shows a "deep research" agent that transitions through states: clarify query → wait for input → perform research → complete. + +## What You'll Learn +- Building state machines with Temporal sub-workflows +- Integrating MCP servers (time, web search, fetch) with OpenAI Agents SDK +- Explicit state transitions and phase management +- When to use state machines vs simple workflows +- Handling complex multi-phase agent behaviors with streaming + +## Prerequisites +- Development environment set up (see [main repo README](https://github.com/scaleapi/scale-agentex)) +- Backend services running: `make dev` from repository root +- Temporal UI available at http://localhost:8233 +- OpenAI API key configured +- Understanding of OpenAI Agents SDK (see [010](../010_open_ai_agents_sdk_hello_world/)) + +## Quick Start + +```bash +cd examples/tutorials/10_agentic/10_temporal/050_state_machines +export OPENAI_API_KEY="your-key-here" +uv run agentex agents run --manifest manifest.yaml +``` + +**Monitor:** Open Temporal UI at http://localhost:8233 to see state transitions and sub-workflows. + +## Architecture + +The workflow uses three sub-workflows, each handling a specific state: +- `ClarifyUserQueryWorkflow` - Asks follow-up questions to understand user intent +- `WaitingForUserInputWorkflow` - Waits for user responses +- `PerformingDeepResearchWorkflow` - Executes the research using OpenAI Agents SDK with MCP servers + +State transitions are explicit and tracked, with each sub-workflow handling its own logic. + +### MCP Server Integration + +This tutorial demonstrates integrating MCP (Model Context Protocol) servers with Temporal workflows: +- **mcp-server-time**: Provides current time in different timezones +- **openai-websearch-mcp**: Enables web search capabilities +- **mcp-server-fetch**: Fetches content from URLs + +MCP servers are registered with the worker and made available to agents via `openai_agents_workflow.stateless_mcp_server()`. + +## Why State Machines Matter + +Complex agents often need to: +- Wait for user input at specific points +- Branch behavior based on conditions +- Orchestrate multiple steps with clear transitions +- Resume at the exact state after failures + +State machines provide this structure. Each state is a sub-workflow, and Temporal ensures transitions are durable and resumable. + +## Key Patterns + +### State Machine Pattern +```python +self.state_machine = DeepResearchStateMachine( + initial_state=DeepResearchState.WAITING_FOR_USER_INPUT, + states=[ + State(name=DeepResearchState.CLARIFYING, workflow=ClarifyWorkflow()), + State(name=DeepResearchState.RESEARCHING, workflow=ResearchWorkflow()), + ] +) + +await self.state_machine.transition(DeepResearchState.RESEARCHING) +``` + +### MCP Server Pattern +```python +# In run_worker.py - register MCP servers +MCP_SERVERS = [ + StatelessMCPServerProvider( + lambda: MCPServerStdio( + name="openai-websearch-mcp", + params={...}, + client_session_timeout_seconds=120, + ) + ), +] + +worker = AgentexWorker( + task_queue=task_queue_name, + mcp_server_providers=MCP_SERVERS, + ... +) + +# In workflow - use MCP servers +websearch = openai_agents_workflow.stateless_mcp_server("openai-websearch-mcp") +agent = Agent( + name="Research Agent", + mcp_servers=[websearch], +) +``` + +This is an advanced pattern - only needed when your agent has complex, multi-phase behavior. + +## When to Use +- Multi-step processes with clear phases (research, analysis, reporting) +- Workflows that wait for user input at specific points +- Operations with branching logic based on state +- Complex coordination patterns requiring explicit transitions +- Agents that need external tools/data sources (via MCP) + +## Why This Matters +State machines provide structure for complex agent behaviors. While simple agents can use basic workflows, complex agents benefit from explicit state management. Temporal ensures state transitions are durable and resumable, even after failures. MCP integration adds powerful tool capabilities without vendor lock-in. + +## Key Files +- `project/workflow.py` - Main workflow with state machine orchestration +- `project/run_worker.py` - Worker setup with MCP server registration +- `project/state_machines/deep_research.py` - State machine definition +- `project/workflows/deep_research/` - Sub-workflow implementations + +**Back to:** [Tutorial Index](../README.md) diff --git a/examples/tutorials/10_agentic/10_temporal/020_state_machine/dev.ipynb b/examples/tutorials/10_agentic/10_temporal/050_state_machines/dev.ipynb similarity index 100% rename from examples/tutorials/10_agentic/10_temporal/020_state_machine/dev.ipynb rename to examples/tutorials/10_agentic/10_temporal/050_state_machines/dev.ipynb diff --git a/examples/tutorials/10_agentic/10_temporal/050_state_machines/manifest.yaml b/examples/tutorials/10_agentic/10_temporal/050_state_machines/manifest.yaml new file mode 100644 index 00000000..3f86655c --- /dev/null +++ b/examples/tutorials/10_agentic/10_temporal/050_state_machines/manifest.yaml @@ -0,0 +1,137 @@ +# Agent Manifest Configuration +# --------------------------- +# This file defines how your agent should be built and deployed. + +# Build Configuration +# ------------------ +# The build config defines what gets packaged into your agent's Docker image. +# This same configuration is used whether building locally or remotely. +# +# When building: +# 1. All files from include_paths are collected into a build context +# 2. The context is filtered by dockerignore rules +# 3. The Dockerfile uses this context to build your agent's image +# 4. The image is pushed to a registry and used to run your agent +build: + context: + # Root directory for the build context + root: ../ # Keep this as the default root + + # Paths to include in the Docker build context + # Must include: + # - Your agent's directory (your custom agent code) + # These paths are collected and sent to the Docker daemon for building + include_paths: + - 050_state_machines + + # Path to your agent's Dockerfile + # This defines how your agent's image is built from the context + # Relative to the root directory + dockerfile: 050_state_machines/Dockerfile + + # Path to your agent's .dockerignore + # Filters unnecessary files from the build context + # Helps keep build context small and builds fast + dockerignore: 050_state_machines/.dockerignore + + +# Local Development Configuration +# ----------------------------- +# Only used when running the agent locally +local_development: + agent: + port: 8000 # Port where your local ACP server is running + host_address: host.docker.internal # Host address for Docker networking (host.docker.internal for Docker, localhost for direct) + + # File paths for local development (relative to this manifest.yaml) + paths: + # Path to ACP server file + # Examples: + # project/acp.py (standard) + # src/server.py (custom structure) + # ../shared/acp.py (shared across projects) + # /absolute/path/acp.py (absolute path) + acp: project/acp.py + + # Path to temporal worker file + # Examples: + # project/run_worker.py (standard) + # workers/temporal.py (custom structure) + # ../shared/worker.py (shared across projects) + worker: project/run_worker.py + + +# Agent Configuration +# ----------------- +agent: + # Type of agent - either sync or agentic + acp_type: agentic + + # Unique name for your agent + # Used for task routing and monitoring + name: state-machines-tutorial + + # Description of what your agent does + # Helps with documentation and discovery + description: Tutorial showing state machines to manage complex multi-stage agentic workflows + + # Temporal workflow configuration + # This enables your agent to run as a Temporal workflow for long-running tasks + temporal: + enabled: true + workflows: + # Name of the workflow class + # Must match the @workflow.defn name in your workflow.py + - name: state-machines-tutorial + + # Queue name for task distribution + # Used by Temporal to route tasks to your agent + # Convention: _task_queue + queue_name: state_machines_tutorial_queue + + # Optional: Credentials mapping + # Maps Kubernetes secrets to environment variables + # Common credentials include: + # credentials: + # - env_var_name: OPENAI_API_KEY + # secret_name: openai-api-key + # secret_key: api-key + + # Optional: Set Environment variables for running your agent locally as well + # as for deployment later on + env: + OPENAI_API_KEY: "" + # OPENAI_BASE_URL: "" + OPENAI_ORG_ID: "" + + +# Deployment Configuration +# ----------------------- +# Configuration for deploying your agent to Kubernetes clusters +deployment: + # Container image configuration + image: + repository: "" # Update with your container registry + tag: "latest" # Default tag, should be versioned in production + + imagePullSecrets: + - name: my-registry-secret # Update with your image pull secret name + + # Global deployment settings that apply to all clusters + # These can be overridden using --override-file with custom configuration files + global: + agent: + name: "state-machines-tutorial" + description: "Tutorial showing state machines to manage complex multi-stage agentic workflows" + + # Default replica count + replicaCount: 1 + + # Default resource requirements + resources: + requests: + cpu: "500m" + memory: "1Gi" + limits: + cpu: "1000m" + memory: "2Gi" \ No newline at end of file diff --git a/examples/tutorials/10_agentic/10_temporal/050_agent_chat_guardrails/project/__init__.py b/examples/tutorials/10_agentic/10_temporal/050_state_machines/project/__init__.py similarity index 100% rename from examples/tutorials/10_agentic/10_temporal/050_agent_chat_guardrails/project/__init__.py rename to examples/tutorials/10_agentic/10_temporal/050_state_machines/project/__init__.py diff --git a/examples/tutorials/10_agentic/10_temporal/010_agent_chat/project/acp.py b/examples/tutorials/10_agentic/10_temporal/050_state_machines/project/acp.py similarity index 100% rename from examples/tutorials/10_agentic/10_temporal/010_agent_chat/project/acp.py rename to examples/tutorials/10_agentic/10_temporal/050_state_machines/project/acp.py diff --git a/examples/tutorials/10_agentic/10_temporal/050_state_machines/project/run_worker.py b/examples/tutorials/10_agentic/10_temporal/050_state_machines/project/run_worker.py new file mode 100644 index 00000000..d33ed876 --- /dev/null +++ b/examples/tutorials/10_agentic/10_temporal/050_state_machines/project/run_worker.py @@ -0,0 +1,95 @@ +import asyncio +import os + +from agents.mcp import MCPServerStdio +from temporalio.contrib.openai_agents import StatelessMCPServerProvider +from project.workflow import StateMachinesTutorialWorkflow +from agentex.lib.utils.debug import setup_debug_if_enabled +from agentex.lib.utils.logging import make_logger +from agentex.lib.environment_variables import EnvironmentVariables +from agentex.lib.core.temporal.activities import get_all_activities +from agentex.lib.core.temporal.workers.worker import AgentexWorker +from agentex.lib.core.temporal.plugins.openai_agents.hooks.activities import stream_lifecycle_content +from agentex.lib.core.temporal.plugins.openai_agents.models.temporal_streaming_model import ( + TemporalStreamingModelProvider, +) +from agentex.lib.core.temporal.plugins.openai_agents.interceptors.context_interceptor import ContextInterceptor +from temporalio.contrib.openai_agents import OpenAIAgentsPlugin, ModelActivityParameters +from datetime import timedelta + +environment_variables = EnvironmentVariables.refresh() + +logger = make_logger(__name__) + +environment_variables = EnvironmentVariables.refresh() + +logger = make_logger(__name__) + +# Configure MCP servers for the worker +MCP_SERVERS = [ + StatelessMCPServerProvider( + lambda: MCPServerStdio( + name="mcp-server-time", + params={ + "command": "uvx", + "args": ["mcp-server-time", "--local-timezone", "America/Los_Angeles"], + }, + client_session_timeout_seconds=120, + ) + ), + StatelessMCPServerProvider( + lambda: MCPServerStdio( + name="openai-websearch-mcp", + params={ + "command": "uvx", + "args": ["openai-websearch-mcp"], + "env": { + "OPENAI_API_KEY": os.environ.get("OPENAI_API_KEY", "") + } + }, + client_session_timeout_seconds=120, + ) + ), + StatelessMCPServerProvider( + lambda: MCPServerStdio( + name="mcp-server-fetch", + params={ + "command": "uvx", + "args": ["mcp-server-fetch"], + }, + client_session_timeout_seconds=120, + ) + ), +] + + +async def main(): + # Setup debug mode if enabled + setup_debug_if_enabled() + + task_queue_name = environment_variables.WORKFLOW_TASK_QUEUE + if task_queue_name is None: + raise ValueError("WORKFLOW_TASK_QUEUE is not set") + + # Create a worker with automatic tracing and MCP servers + worker = AgentexWorker( + task_queue=task_queue_name, + mcp_server_providers=MCP_SERVERS, + plugins=[ + OpenAIAgentsPlugin( + model_provider=TemporalStreamingModelProvider(), + model_params=ModelActivityParameters( + start_to_close_timeout=timedelta(seconds=180) # 3 minutes for activities + ), + ) + ], + interceptors=[ContextInterceptor()], + ) + + await worker.run( + activities=get_all_activities() + [stream_lifecycle_content], + workflow=StateMachinesTutorialWorkflow, + ) + +if __name__ == "__main__": + asyncio.run(main()) \ No newline at end of file diff --git a/examples/tutorials/10_agentic/10_temporal/020_state_machine/project/state_machines/deep_research.py b/examples/tutorials/10_agentic/10_temporal/050_state_machines/project/state_machines/deep_research.py similarity index 100% rename from examples/tutorials/10_agentic/10_temporal/020_state_machine/project/state_machines/deep_research.py rename to examples/tutorials/10_agentic/10_temporal/050_state_machines/project/state_machines/deep_research.py diff --git a/examples/tutorials/10_agentic/10_temporal/050_state_machines/project/workflow.py b/examples/tutorials/10_agentic/10_temporal/050_state_machines/project/workflow.py new file mode 100644 index 00000000..43d67c09 --- /dev/null +++ b/examples/tutorials/10_agentic/10_temporal/050_state_machines/project/workflow.py @@ -0,0 +1,162 @@ +import asyncio +from typing import override + +from temporalio import workflow + +from agentex.lib import adk +from agentex.lib.types.acp import SendEventParams, CreateTaskParams +from agentex.lib.utils.logging import make_logger +from agentex.types.text_content import TextContent +from agentex.lib.environment_variables import EnvironmentVariables +from agentex.lib.sdk.state_machine.state import State +from project.state_machines.deep_research import DeepResearchData, DeepResearchState, DeepResearchStateMachine +from agentex.lib.core.temporal.types.workflow import SignalName +from agentex.lib.core.temporal.workflows.workflow import BaseWorkflow +from project.workflows.deep_research.clarify_user_query import ClarifyUserQueryWorkflow +from project.workflows.deep_research.waiting_for_user_input import WaitingForUserInputWorkflow +from project.workflows.deep_research.performing_deep_research import PerformingDeepResearchWorkflow + +environment_variables = EnvironmentVariables.refresh() + +if environment_variables.WORKFLOW_NAME is None: + raise ValueError("Environment variable WORKFLOW_NAME is not set") + +if environment_variables.AGENT_NAME is None: + raise ValueError("Environment variable AGENT_NAME is not set") + + +logger = make_logger(__name__) + +@workflow.defn(name=environment_variables.WORKFLOW_NAME) +class StateMachinesTutorialWorkflow(BaseWorkflow): + """ + Tutorial demonstrating state machines for managing complex multi-stage agentic workflows. + """ + def __init__(self): + super().__init__(display_name=environment_variables.AGENT_NAME) + self._task_id: str | None = None + self._trace_id: str | None = None + self._parent_span_id: str | None = None + self.state_machine = DeepResearchStateMachine( + initial_state=DeepResearchState.WAITING_FOR_USER_INPUT, + states=[ + State(name=DeepResearchState.CLARIFYING_USER_QUERY, workflow=ClarifyUserQueryWorkflow()), + State(name=DeepResearchState.WAITING_FOR_USER_INPUT, workflow=WaitingForUserInputWorkflow()), + State(name=DeepResearchState.PERFORMING_DEEP_RESEARCH, workflow=PerformingDeepResearchWorkflow()), + ], + state_machine_data=DeepResearchData(), + trace_transitions=True + ) + + @override + @workflow.signal(name=SignalName.RECEIVE_EVENT) + async def on_task_event_send(self, params: SendEventParams) -> None: + deep_research_data = self.state_machine.get_state_machine_data() + task = params.task + message = params.event.content + + # Store context for streaming (required by TemporalStreamingModelProvider) + self._task_id = task.id + self._trace_id = task.id + self._parent_span_id = task.id + + # If waiting for user input, handle the message + if self.state_machine.get_current_state() == DeepResearchState.WAITING_FOR_USER_INPUT: + if not deep_research_data.user_query: + # First time - initialize research data + deep_research_data.user_query = message.content + deep_research_data.current_turn += 1 + + if not deep_research_data.current_span: + deep_research_data.current_span = await adk.tracing.start_span( + trace_id=task.id, + name=f"Turn {deep_research_data.current_turn}", + input={ + "task_id": task.id, + "message": message.content, + } + ) + else: + # Check if we're in the middle of follow-up questions + if deep_research_data.n_follow_up_questions_to_ask > 0: + # User is responding to a follow-up question + # Safely extract content from message + content_text = "" + if hasattr(message, 'content'): + content_val = getattr(message, 'content', '') + if isinstance(content_val, str): + content_text = content_val + deep_research_data.follow_up_responses.append(content_text) + + # Add the Q&A to the agent input list as context + if deep_research_data.follow_up_questions: + last_question = deep_research_data.follow_up_questions[-1] + qa_context = f"Q: {last_question}\nA: {message.content}" + deep_research_data.agent_input_list.append({ + "role": "user", + "content": qa_context + }) + else: + # User is asking a new follow-up question about the same research topic + # Add the user's follow-up question to the agent input list as context + if deep_research_data.agent_input_list: + # Add user's follow-up question to the conversation + deep_research_data.agent_input_list.append({ + "role": "user", + "content": f"Additional question: {message.content}" + }) + else: + # Initialize agent input list with the follow-up question + deep_research_data.agent_input_list = [{ + "role": "user", + "content": f"Original query: {deep_research_data.user_query}\nAdditional question: {message.content}" + }] + + deep_research_data.current_turn += 1 + + if not deep_research_data.current_span: + deep_research_data.current_span = await adk.tracing.start_span( + trace_id=task.id, + name=f"Turn {deep_research_data.current_turn}", + input={ + "task_id": task.id, + "message": message.content, + } + ) + + # Always go to clarifying user query to ask follow-up questions + # This ensures we gather more context before doing deep research + await self.state_machine.transition(DeepResearchState.CLARIFYING_USER_QUERY) + + # Echo back the user's message + # Safely extract content from message for display + message_content = "" + if hasattr(message, 'content'): + content_val = getattr(message, 'content', '') + if isinstance(content_val, str): + message_content = content_val + + await adk.messages.create( + task_id=task.id, + content=TextContent( + author="user", + content=message_content, + ), + trace_id=task.id, + parent_span_id=deep_research_data.current_span.id if deep_research_data.current_span else None, + ) + + @override + @workflow.run + async def on_task_create(self, params: CreateTaskParams) -> None: + task = params.task + + self.state_machine.set_task_id(task.id) + deep_research_data = self.state_machine.get_state_machine_data() + deep_research_data.task_id = task.id + + try: + await self.state_machine.run() + except asyncio.CancelledError as error: + logger.warning(f"Task canceled by user: {task.id}") + raise error \ No newline at end of file diff --git a/examples/tutorials/10_agentic/10_temporal/020_state_machine/project/workflows/deep_research/clarify_user_query.py b/examples/tutorials/10_agentic/10_temporal/050_state_machines/project/workflows/deep_research/clarify_user_query.py similarity index 100% rename from examples/tutorials/10_agentic/10_temporal/020_state_machine/project/workflows/deep_research/clarify_user_query.py rename to examples/tutorials/10_agentic/10_temporal/050_state_machines/project/workflows/deep_research/clarify_user_query.py diff --git a/examples/tutorials/10_agentic/10_temporal/050_state_machines/project/workflows/deep_research/performing_deep_research.py b/examples/tutorials/10_agentic/10_temporal/050_state_machines/project/workflows/deep_research/performing_deep_research.py new file mode 100644 index 00000000..341d17ba --- /dev/null +++ b/examples/tutorials/10_agentic/10_temporal/050_state_machines/project/workflows/deep_research/performing_deep_research.py @@ -0,0 +1,155 @@ +import os +from typing import Optional, override +from datetime import datetime + +from agents import Agent, Runner +from temporalio.contrib.openai_agents import workflow as openai_agents_workflow +from project.state_machines.deep_research import DeepResearchData, DeepResearchState + +from agentex.lib import adk +from agentex.lib.utils.logging import make_logger +from agentex.types.text_content import TextContent +from agentex.lib.sdk.state_machine.state_machine import StateMachine +from agentex.lib.sdk.state_machine.state_workflow import StateWorkflow +from agentex.lib.core.temporal.plugins.openai_agents.hooks.hooks import TemporalStreamingHooks + +logger = make_logger(__name__) + +# MCP server names that should be registered with the worker +MCP_TIME_SERVER = "mcp-server-time" +MCP_WEBSEARCH_SERVER = "openai-websearch-mcp" +MCP_FETCH_SERVER = "mcp-server-fetch" + +class PerformingDeepResearchWorkflow(StateWorkflow): + """Workflow for performing deep research.""" + + @override + async def execute(self, state_machine: StateMachine, state_machine_data: Optional[DeepResearchData] = None) -> str: + """Execute the workflow.""" + if state_machine_data is None: + return DeepResearchState.CLARIFYING_USER_QUERY + + if not state_machine_data.user_query: + return DeepResearchState.CLARIFYING_USER_QUERY + + # Construct initial research instruction + follow_up_qa_str = "" + for q, r in zip(state_machine_data.follow_up_questions, state_machine_data.follow_up_responses): + follow_up_qa_str += f"Q: {q}\nA: {r}\n" + + # Increment research iteration + state_machine_data.research_iteration += 1 + + # Create research instruction based on whether this is the first iteration or a continuation + if state_machine_data.research_iteration == 1: + initial_instruction = ( + f"Initial Query: {state_machine_data.user_query}\n" + f"Follow-up Q&A:\n{follow_up_qa_str}" + ) + + # Notify user that deep research is starting + if state_machine_data.task_id and state_machine_data.current_span: + await adk.messages.create( + task_id=state_machine_data.task_id, + content=TextContent( + author="agent", + content="Starting deep research process based on your query and follow-up responses...", + ), + trace_id=state_machine_data.task_id, + parent_span_id=state_machine_data.current_span.id, + ) + else: + initial_instruction = ( + f"Initial Query: {state_machine_data.user_query}\n" + f"Follow-up Q&A:\n{follow_up_qa_str}\n" + f"Current Research Report (Iteration {state_machine_data.research_iteration - 1}):\n{state_machine_data.research_report}" + ) + + # Notify user that research is continuing + if state_machine_data.task_id and state_machine_data.current_span: + await adk.messages.create( + task_id=state_machine_data.task_id, + content=TextContent( + author="agent", + content=f"Continuing deep research (iteration {state_machine_data.research_iteration}) to expand and refine the research report...", + ), + trace_id=state_machine_data.task_id, + parent_span_id=state_machine_data.current_span.id, + ) + + # Fetch the current time in human readable format + current_time = datetime.now().strftime("%Y-%m-%d %H:%M:%S %Z") + + # Deep Research Loop + if not state_machine_data.agent_input_list: + state_machine_data.agent_input_list = [ + {"role": "user", "content": f""" +Here is my initial query, clarified with the following follow-up questions and answers: +{initial_instruction} + +You should now perform a depth search to get a more detailed understanding of the most promising areas. + +The current time is {current_time}. +"""} + ] + + if state_machine_data.task_id and state_machine_data.current_span: + # Get stateless MCP servers registered with the worker + time_server = openai_agents_workflow.stateless_mcp_server(MCP_TIME_SERVER) + websearch_server = openai_agents_workflow.stateless_mcp_server(MCP_WEBSEARCH_SERVER) + fetch_server = openai_agents_workflow.stateless_mcp_server(MCP_FETCH_SERVER) + + # Create agent with MCP servers + agent = Agent( + name="Deep Research Agent", + instructions=f"""You are a deep research expert that can search the web for information. +You should use the tools you have access to to write an extensive report on the users query. + +You must use the web search tool at least 10 times before writing your report. +Use the fetch tool to open links you want to read. +Then use web search again repeatedly to dig deeper into the most promising areas of search results. + +Be very targeted with your searches, make sure all search queries are relevant to either the initial user query or dig deeper into the most promising areas of search results. All searches should tie back to the original query though. Remember your searches are stateless, so there is no context shared between search queries. + +Always cite your sources in the format [source](link). Do not hallucinate. Your latent information is not likely to be up to date. + +If this is a continuation of previous research (iteration {state_machine_data.research_iteration}), focus on: +1. Expanding areas that need more detail +2. Adding new relevant information discovered +3. Removing outdated or incorrect information +4. Improving the overall structure and clarity of the report +""", + mcp_servers=[time_server, websearch_server, fetch_server], + ) + + # Create streaming hooks + hooks = TemporalStreamingHooks(task_id=state_machine_data.task_id) + + # Run the agent + result = await Runner.run( + agent, + state_machine_data.agent_input_list, + hooks=hooks, + ) + + # Update state with conversation history + if hasattr(result, "messages") and result.messages: + state_machine_data.agent_input_list = result.messages + + # Extract the research report from the last assistant message + for message in reversed(result.messages): + if message.get("role") == "assistant": + state_machine_data.research_report = message.get("content", "") + break + + # Keep the research data active for future iterations + + if state_machine_data.task_id and state_machine_data.current_span: + await adk.tracing.end_span( + trace_id=state_machine_data.task_id, + span=state_machine_data.current_span, + ) + state_machine_data.current_span = None + + # Transition to waiting for user input state + return DeepResearchState.WAITING_FOR_USER_INPUT \ No newline at end of file diff --git a/examples/tutorials/10_agentic/10_temporal/020_state_machine/project/workflows/deep_research/waiting_for_user_input.py b/examples/tutorials/10_agentic/10_temporal/050_state_machines/project/workflows/deep_research/waiting_for_user_input.py similarity index 100% rename from examples/tutorials/10_agentic/10_temporal/020_state_machine/project/workflows/deep_research/waiting_for_user_input.py rename to examples/tutorials/10_agentic/10_temporal/050_state_machines/project/workflows/deep_research/waiting_for_user_input.py diff --git a/examples/tutorials/10_agentic/10_temporal/050_state_machines/pyproject.toml b/examples/tutorials/10_agentic/10_temporal/050_state_machines/pyproject.toml new file mode 100644 index 00000000..6350153d --- /dev/null +++ b/examples/tutorials/10_agentic/10_temporal/050_state_machines/pyproject.toml @@ -0,0 +1,38 @@ +[build-system] +requires = ["hatchling"] +build-backend = "hatchling.build" + +[project] +name = "state_machines_tutorial" +version = "0.1.0" +description = "Tutorial: State Machines - Managing complex multi-stage workflows with state transitions" +readme = "README.md" +requires-python = ">=3.12" +dependencies = [ + "agentex-sdk @ file:///Users/daniel.miller/codebases/agentex-python", + "scale-gp", + "temporalio>=1.18.2,<2", +] + +[project.optional-dependencies] +dev = [ + "pytest", + "black", + "isort", + "flake8", + "debugpy>=1.8.15", +] + +[tool.hatch.metadata] +allow-direct-references = true + +[tool.hatch.build.targets.wheel] +packages = ["project"] + +[tool.black] +line-length = 88 +target-version = ['py312'] + +[tool.isort] +profile = "black" +line_length = 88 \ No newline at end of file diff --git a/examples/tutorials/10_agentic/10_temporal/020_state_machine/tests/test_agent.py b/examples/tutorials/10_agentic/10_temporal/050_state_machines/tests/test_agent.py similarity index 100% rename from examples/tutorials/10_agentic/10_temporal/020_state_machine/tests/test_agent.py rename to examples/tutorials/10_agentic/10_temporal/050_state_machines/tests/test_agent.py diff --git a/examples/tutorials/10_agentic/10_temporal/060_open_ai_agents_sdk_hello_world/project/run_worker.py b/examples/tutorials/10_agentic/10_temporal/060_open_ai_agents_sdk_hello_world/project/run_worker.py deleted file mode 100644 index 04a44c49..00000000 --- a/examples/tutorials/10_agentic/10_temporal/060_open_ai_agents_sdk_hello_world/project/run_worker.py +++ /dev/null @@ -1,41 +0,0 @@ -import asyncio - -from temporalio.contrib.openai_agents import OpenAIAgentsPlugin - -from project.workflow import ExampleTutorialWorkflow -from agentex.lib.utils.debug import setup_debug_if_enabled -from agentex.lib.utils.logging import make_logger -from agentex.lib.environment_variables import EnvironmentVariables -from agentex.lib.core.temporal.activities import get_all_activities -from agentex.lib.core.temporal.workers.worker import AgentexWorker - -environment_variables = EnvironmentVariables.refresh() - -logger = make_logger(__name__) - - -async def main(): - # Setup debug mode if enabled - setup_debug_if_enabled() - - task_queue_name = environment_variables.WORKFLOW_TASK_QUEUE - if task_queue_name is None: - raise ValueError("WORKFLOW_TASK_QUEUE is not set") - - # Add activities to the worker - all_activities = get_all_activities() + [] # add your own activities here - - # Create a worker with automatic tracing - # We are also adding the Open AI Agents SDK plugin to the worker. - worker = AgentexWorker( - task_queue=task_queue_name, - plugins=[OpenAIAgentsPlugin()] - ) - - await worker.run( - activities=all_activities, - workflow=ExampleTutorialWorkflow, - ) - -if __name__ == "__main__": - asyncio.run(main()) \ No newline at end of file diff --git a/examples/tutorials/10_agentic/10_temporal/060_open_ai_agents_sdk_hello_world/tests/test_agent.py b/examples/tutorials/10_agentic/10_temporal/060_open_ai_agents_sdk_hello_world/tests/test_agent.py deleted file mode 100644 index 8cdcac93..00000000 --- a/examples/tutorials/10_agentic/10_temporal/060_open_ai_agents_sdk_hello_world/tests/test_agent.py +++ /dev/null @@ -1,136 +0,0 @@ -""" -Sample tests for AgentEx ACP agent. - -This test suite demonstrates how to test the main AgentEx API functions: -- Non-streaming event sending and polling -- Streaming event sending - -To run these tests: -1. Make sure the agent is running (via docker-compose or `agentex agents run`) -2. Set the AGENTEX_API_BASE_URL environment variable if not using default -3. Run: pytest test_agent.py -v - -Configuration: -- AGENTEX_API_BASE_URL: Base URL for the AgentEx server (default: http://localhost:5003) -- AGENT_NAME: Name of the agent to test (default: example-tutorial) -""" - -import os - -import pytest -import pytest_asyncio - -from agentex import AsyncAgentex - -# Configuration from environment variables -AGENTEX_API_BASE_URL = os.environ.get("AGENTEX_API_BASE_URL", "http://localhost:5003") -AGENT_NAME = os.environ.get("AGENT_NAME", "example-tutorial") - - -@pytest_asyncio.fixture -async def client(): - """Create an AsyncAgentex client instance for testing.""" - client = AsyncAgentex(base_url=AGENTEX_API_BASE_URL) - yield client - await client.close() - - -@pytest.fixture -def agent_name(): - """Return the agent name for testing.""" - return AGENT_NAME - - -@pytest_asyncio.fixture -async def agent_id(client, agent_name): - """Retrieve the agent ID based on the agent name.""" - agents = await client.agents.list() - for agent in agents: - if agent.name == agent_name: - return agent.id - raise ValueError(f"Agent with name {agent_name} not found.") - - -class TestNonStreamingEvents: - """Test non-streaming event sending and polling.""" - - @pytest.mark.asyncio - async def test_send_event_and_poll(self, client: AsyncAgentex, agent_id: str): - """Test sending an event and polling for the response.""" - # TODO: Create a task for this conversation - # task_response = await client.agents.create_task(agent_id, params=ParamsCreateTaskRequest(name=uuid.uuid1().hex)) - # task = task_response.result - # assert task is not None - - # TODO: Poll for the initial task creation message (if your agent sends one) - # async for message in poll_messages( - # client=client, - # task_id=task.id, - # timeout=30, - # sleep_interval=1.0, - # ): - # assert isinstance(message, TaskMessage) - # if message.content and message.content.type == "text" and message.content.author == "agent": - # # Check for your expected initial message - # assert "expected initial text" in message.content.content - # break - - # TODO: Send an event and poll for response using the yielding helper function - # user_message = "Your test message here" - # async for message in send_event_and_poll_yielding( - # client=client, - # agent_id=agent_id, - # task_id=task.id, - # user_message=user_message, - # timeout=30, - # sleep_interval=1.0, - # ): - # assert isinstance(message, TaskMessage) - # if message.content and message.content.type == "text" and message.content.author == "agent": - # # Check for your expected response - # assert "expected response text" in message.content.content - # break - pass - - -class TestStreamingEvents: - """Test streaming event sending.""" - - @pytest.mark.asyncio - async def test_send_event_and_stream(self, client: AsyncAgentex, agent_id: str): - """Test sending an event and streaming the response.""" - # TODO: Create a task for this conversation - # task_response = await client.agents.create_task(agent_id, params=ParamsCreateTaskRequest(name=uuid.uuid1().hex)) - # task = task_response.result - # assert task is not None - - # user_message = "Your test message here" - - # # Collect events from stream - # all_events = [] - - # async def collect_stream_events(): - # async for event in stream_agent_response( - # client=client, - # task_id=task.id, - # timeout=30, - # ): - # all_events.append(event) - - # # Start streaming task - # stream_task = asyncio.create_task(collect_stream_events()) - - # # Send the event - # event_content = TextContentParam(type="text", author="user", content=user_message) - # await client.agents.send_event(agent_id=agent_id, params={"task_id": task.id, "content": event_content}) - - # # Wait for streaming to complete - # await stream_task - - # # TODO: Add your validation here - # assert len(all_events) > 0, "No events received in streaming response" - pass - - -if __name__ == "__main__": - pytest.main([__file__, "-v"]) \ No newline at end of file diff --git a/examples/tutorials/10_agentic/10_temporal/070_open_ai_agents_sdk_tools/project/run_worker.py b/examples/tutorials/10_agentic/10_temporal/070_open_ai_agents_sdk_tools/project/run_worker.py deleted file mode 100644 index 49395f15..00000000 --- a/examples/tutorials/10_agentic/10_temporal/070_open_ai_agents_sdk_tools/project/run_worker.py +++ /dev/null @@ -1,47 +0,0 @@ -import asyncio -from datetime import timedelta - -from temporalio.contrib.openai_agents import OpenAIAgentsPlugin, ModelActivityParameters - -from project.workflow import ExampleTutorialWorkflow -from project.activities import get_weather, deposit_money, withdraw_money -from agentex.lib.utils.debug import setup_debug_if_enabled -from agentex.lib.utils.logging import make_logger -from agentex.lib.environment_variables import EnvironmentVariables -from agentex.lib.core.temporal.activities import get_all_activities -from agentex.lib.core.temporal.workers.worker import AgentexWorker - -environment_variables = EnvironmentVariables.refresh() - -logger = make_logger(__name__) - - -async def main(): - # Setup debug mode if enabled - setup_debug_if_enabled() - - task_queue_name = environment_variables.WORKFLOW_TASK_QUEUE - if task_queue_name is None: - raise ValueError("WORKFLOW_TASK_QUEUE is not set") - - # Add activities to the worker - all_activities = get_all_activities() + [withdraw_money, deposit_money, get_weather] # add your own activities here - - # Create a worker with automatic tracing - # We are also adding the Open AI Agents SDK plugin to the worker. - worker = AgentexWorker( - task_queue=task_queue_name, - plugins=[OpenAIAgentsPlugin( - model_params=ModelActivityParameters( - start_to_close_timeout=timedelta(days=1) - ) - )], - ) - - await worker.run( - activities=all_activities, - workflow=ExampleTutorialWorkflow, - ) - -if __name__ == "__main__": - asyncio.run(main()) \ No newline at end of file diff --git a/examples/tutorials/10_agentic/10_temporal/070_open_ai_agents_sdk_tools/tests/test_agent.py b/examples/tutorials/10_agentic/10_temporal/070_open_ai_agents_sdk_tools/tests/test_agent.py deleted file mode 100644 index 8cdcac93..00000000 --- a/examples/tutorials/10_agentic/10_temporal/070_open_ai_agents_sdk_tools/tests/test_agent.py +++ /dev/null @@ -1,136 +0,0 @@ -""" -Sample tests for AgentEx ACP agent. - -This test suite demonstrates how to test the main AgentEx API functions: -- Non-streaming event sending and polling -- Streaming event sending - -To run these tests: -1. Make sure the agent is running (via docker-compose or `agentex agents run`) -2. Set the AGENTEX_API_BASE_URL environment variable if not using default -3. Run: pytest test_agent.py -v - -Configuration: -- AGENTEX_API_BASE_URL: Base URL for the AgentEx server (default: http://localhost:5003) -- AGENT_NAME: Name of the agent to test (default: example-tutorial) -""" - -import os - -import pytest -import pytest_asyncio - -from agentex import AsyncAgentex - -# Configuration from environment variables -AGENTEX_API_BASE_URL = os.environ.get("AGENTEX_API_BASE_URL", "http://localhost:5003") -AGENT_NAME = os.environ.get("AGENT_NAME", "example-tutorial") - - -@pytest_asyncio.fixture -async def client(): - """Create an AsyncAgentex client instance for testing.""" - client = AsyncAgentex(base_url=AGENTEX_API_BASE_URL) - yield client - await client.close() - - -@pytest.fixture -def agent_name(): - """Return the agent name for testing.""" - return AGENT_NAME - - -@pytest_asyncio.fixture -async def agent_id(client, agent_name): - """Retrieve the agent ID based on the agent name.""" - agents = await client.agents.list() - for agent in agents: - if agent.name == agent_name: - return agent.id - raise ValueError(f"Agent with name {agent_name} not found.") - - -class TestNonStreamingEvents: - """Test non-streaming event sending and polling.""" - - @pytest.mark.asyncio - async def test_send_event_and_poll(self, client: AsyncAgentex, agent_id: str): - """Test sending an event and polling for the response.""" - # TODO: Create a task for this conversation - # task_response = await client.agents.create_task(agent_id, params=ParamsCreateTaskRequest(name=uuid.uuid1().hex)) - # task = task_response.result - # assert task is not None - - # TODO: Poll for the initial task creation message (if your agent sends one) - # async for message in poll_messages( - # client=client, - # task_id=task.id, - # timeout=30, - # sleep_interval=1.0, - # ): - # assert isinstance(message, TaskMessage) - # if message.content and message.content.type == "text" and message.content.author == "agent": - # # Check for your expected initial message - # assert "expected initial text" in message.content.content - # break - - # TODO: Send an event and poll for response using the yielding helper function - # user_message = "Your test message here" - # async for message in send_event_and_poll_yielding( - # client=client, - # agent_id=agent_id, - # task_id=task.id, - # user_message=user_message, - # timeout=30, - # sleep_interval=1.0, - # ): - # assert isinstance(message, TaskMessage) - # if message.content and message.content.type == "text" and message.content.author == "agent": - # # Check for your expected response - # assert "expected response text" in message.content.content - # break - pass - - -class TestStreamingEvents: - """Test streaming event sending.""" - - @pytest.mark.asyncio - async def test_send_event_and_stream(self, client: AsyncAgentex, agent_id: str): - """Test sending an event and streaming the response.""" - # TODO: Create a task for this conversation - # task_response = await client.agents.create_task(agent_id, params=ParamsCreateTaskRequest(name=uuid.uuid1().hex)) - # task = task_response.result - # assert task is not None - - # user_message = "Your test message here" - - # # Collect events from stream - # all_events = [] - - # async def collect_stream_events(): - # async for event in stream_agent_response( - # client=client, - # task_id=task.id, - # timeout=30, - # ): - # all_events.append(event) - - # # Start streaming task - # stream_task = asyncio.create_task(collect_stream_events()) - - # # Send the event - # event_content = TextContentParam(type="text", author="user", content=user_message) - # await client.agents.send_event(agent_id=agent_id, params={"task_id": task.id, "content": event_content}) - - # # Wait for streaming to complete - # await stream_task - - # # TODO: Add your validation here - # assert len(all_events) > 0, "No events received in streaming response" - pass - - -if __name__ == "__main__": - pytest.main([__file__, "-v"]) \ No newline at end of file diff --git a/examples/tutorials/10_agentic/10_temporal/080_open_ai_agents_sdk_human_in_the_loop/project/run_worker.py b/examples/tutorials/10_agentic/10_temporal/080_open_ai_agents_sdk_human_in_the_loop/project/run_worker.py deleted file mode 100644 index 67aed618..00000000 --- a/examples/tutorials/10_agentic/10_temporal/080_open_ai_agents_sdk_human_in_the_loop/project/run_worker.py +++ /dev/null @@ -1,47 +0,0 @@ -import asyncio -from datetime import timedelta - -from temporalio.contrib.openai_agents import OpenAIAgentsPlugin, ModelActivityParameters - -from project.workflow import ChildWorkflow, ExampleTutorialWorkflow -from project.activities import confirm_order, deposit_money, withdraw_money -from agentex.lib.utils.debug import setup_debug_if_enabled -from agentex.lib.utils.logging import make_logger -from agentex.lib.environment_variables import EnvironmentVariables -from agentex.lib.core.temporal.activities import get_all_activities -from agentex.lib.core.temporal.workers.worker import AgentexWorker - -environment_variables = EnvironmentVariables.refresh() - -logger = make_logger(__name__) - - -async def main(): - # Setup debug mode if enabled - setup_debug_if_enabled() - - task_queue_name = environment_variables.WORKFLOW_TASK_QUEUE - if task_queue_name is None: - raise ValueError("WORKFLOW_TASK_QUEUE is not set") - - # Add activities to the worker - all_activities = get_all_activities() + [withdraw_money, deposit_money, confirm_order] # add your own activities here - - # Create a worker with automatic tracing - # We are also adding the Open AI Agents SDK plugin to the worker. - worker = AgentexWorker( - task_queue=task_queue_name, - plugins=[OpenAIAgentsPlugin( - model_params=ModelActivityParameters( - start_to_close_timeout=timedelta(days=1) - ) - )], - ) - - await worker.run( - activities=all_activities, - workflows=[ExampleTutorialWorkflow, ChildWorkflow] - ) - -if __name__ == "__main__": - asyncio.run(main()) \ No newline at end of file diff --git a/examples/tutorials/10_agentic/10_temporal/080_open_ai_agents_sdk_human_in_the_loop/project/workflow.py b/examples/tutorials/10_agentic/10_temporal/080_open_ai_agents_sdk_human_in_the_loop/project/workflow.py deleted file mode 100644 index c19e097a..00000000 --- a/examples/tutorials/10_agentic/10_temporal/080_open_ai_agents_sdk_human_in_the_loop/project/workflow.py +++ /dev/null @@ -1,135 +0,0 @@ -""" -OpenAI Agents SDK + Temporal Integration: Human-in-the-Loop Tutorial - -This tutorial demonstrates how to pause agent execution and wait for human approval -using Temporal's child workflows and signals. - -KEY CONCEPTS: -- Child workflows: Independent workflows spawned by parent for human interaction -- Signals: External systems can send messages to running workflows -- Durable waiting: Agents can wait indefinitely for human input without losing state - -WHY THIS MATTERS: -Without Temporal, if your system crashes while waiting for human approval, you lose -all context. With Temporal, the agent resumes exactly where it left off after -system failures, making human-in-the-loop workflows production-ready. - -PATTERN: -1. Agent calls wait_for_confirmation tool -2. Tool spawns child workflow that waits for signal -3. Human approves via CLI/web app -4. Child workflow completes, agent continues - -Usage: `temporal workflow signal --workflow-id="child-workflow-id" --name="fulfill_order_signal" --input=true` -""" - -import json -import asyncio - -from agents import Agent, Runner -from temporalio import workflow - -from agentex.lib import adk -from project.tools import wait_for_confirmation -from agentex.lib.types.acp import SendEventParams, CreateTaskParams -from agentex.lib.utils.logging import make_logger -from agentex.types.text_content import TextContent -from agentex.lib.environment_variables import EnvironmentVariables -from agentex.lib.core.temporal.types.workflow import SignalName -from agentex.lib.core.temporal.workflows.workflow import BaseWorkflow - -environment_variables = EnvironmentVariables.refresh() - -if environment_variables.WORKFLOW_NAME is None: - raise ValueError("Environment variable WORKFLOW_NAME is not set") - -if environment_variables.AGENT_NAME is None: - raise ValueError("Environment variable AGENT_NAME is not set") - -logger = make_logger(__name__) - -@workflow.defn(name=environment_variables.WORKFLOW_NAME) -class ExampleTutorialWorkflow(BaseWorkflow): - """ - Human-in-the-Loop Temporal Workflow - - Demonstrates agents that can pause execution and wait for human approval. - When approval is needed, the agent spawns a child workflow that waits for - external signals (human input) before continuing. - - Benefits: Durable waiting, survives system failures, scalable to millions of workflows. - """ - def __init__(self): - super().__init__(display_name=environment_variables.AGENT_NAME) - self._complete_task = False - self._pending_confirmation: asyncio.Queue[str] = asyncio.Queue() - - @workflow.signal(name=SignalName.RECEIVE_EVENT) - async def on_task_event_send(self, params: SendEventParams) -> None: - """ - Handle user messages with human-in-the-loop approval capability. - - When the agent needs human approval, it calls wait_for_confirmation which spawns - a child workflow that waits for external signals before continuing. - """ - logger.info(f"Received task message instruction: {params}") - - # Echo user message back to UI - await adk.messages.create(task_id=params.task.id, content=params.event.content) - - # Create agent with human-in-the-loop capability - # The wait_for_confirmation tool spawns a child workflow that waits for external signals - confirm_order_agent = Agent( - name="Confirm Order", - instructions="You are a helpful confirm order agent. When a user asks you to confirm an order, use the wait_for_confirmation tool to wait for confirmation.", - tools=[ - wait_for_confirmation, - ], - ) - - # Run agent - when human approval is needed, it will spawn child workflow and wait - result = await Runner.run(confirm_order_agent, params.event.content.content) - - # Send response back to user (includes result of any human approval process) - await adk.messages.create( - task_id=params.task.id, - content=TextContent( - author="agent", - content=result.final_output, - ), - ) - - @workflow.run - async def on_task_create(self, params: CreateTaskParams) -> str: - """ - Workflow entry point - starts the long-running human-in-the-loop agent. - - Handles both automated decisions and human approval workflows durably. - To approve waiting actions: temporal workflow signal --workflow-id="child-workflow-id" --name="fulfill_order_signal" --input=true - """ - logger.info(f"Received task create params: {params}") - - # Send welcome message when task is created - await adk.messages.create( - task_id=params.task.id, - content=TextContent( - author="agent", - content=f"Hello! I've received your task. Normally you can do some state initialization here, or just pass and do nothing until you get your first event. For now I'm just acknowledging that I've received a task with the following params:\n\n{json.dumps(params.params, indent=2)}.\n\nYou should only see this message once, when the task is created. All subsequent events will be handled by the `on_task_event_send` handler.", - ), - ) - - # Keep workflow running indefinitely to handle user messages and human approvals - # This survives system failures and can resume exactly where it left off - await workflow.wait_condition( - lambda: self._complete_task, - timeout=None, # No timeout for long-running human-in-the-loop workflows - ) - return "Task completed" - - # TEMPORAL UI (localhost:8080): - # - Main workflow shows agent activities + ChildWorkflow activity when approval needed - # - Child workflow appears as separate "child-workflow-id" that waits for signal - # - Timeline: invoke_model_activity → ChildWorkflow (waiting) → invoke_model_activity (after approval) - # - # To approve: temporal workflow signal --workflow-id="child-workflow-id" --name="fulfill_order_signal" --input=true - # Production: Replace CLI with web dashboards/APIs that send signals programmatically diff --git a/examples/tutorials/10_agentic/10_temporal/080_open_ai_agents_sdk_human_in_the_loop/tests/test_agent.py b/examples/tutorials/10_agentic/10_temporal/080_open_ai_agents_sdk_human_in_the_loop/tests/test_agent.py deleted file mode 100644 index 8cdcac93..00000000 --- a/examples/tutorials/10_agentic/10_temporal/080_open_ai_agents_sdk_human_in_the_loop/tests/test_agent.py +++ /dev/null @@ -1,136 +0,0 @@ -""" -Sample tests for AgentEx ACP agent. - -This test suite demonstrates how to test the main AgentEx API functions: -- Non-streaming event sending and polling -- Streaming event sending - -To run these tests: -1. Make sure the agent is running (via docker-compose or `agentex agents run`) -2. Set the AGENTEX_API_BASE_URL environment variable if not using default -3. Run: pytest test_agent.py -v - -Configuration: -- AGENTEX_API_BASE_URL: Base URL for the AgentEx server (default: http://localhost:5003) -- AGENT_NAME: Name of the agent to test (default: example-tutorial) -""" - -import os - -import pytest -import pytest_asyncio - -from agentex import AsyncAgentex - -# Configuration from environment variables -AGENTEX_API_BASE_URL = os.environ.get("AGENTEX_API_BASE_URL", "http://localhost:5003") -AGENT_NAME = os.environ.get("AGENT_NAME", "example-tutorial") - - -@pytest_asyncio.fixture -async def client(): - """Create an AsyncAgentex client instance for testing.""" - client = AsyncAgentex(base_url=AGENTEX_API_BASE_URL) - yield client - await client.close() - - -@pytest.fixture -def agent_name(): - """Return the agent name for testing.""" - return AGENT_NAME - - -@pytest_asyncio.fixture -async def agent_id(client, agent_name): - """Retrieve the agent ID based on the agent name.""" - agents = await client.agents.list() - for agent in agents: - if agent.name == agent_name: - return agent.id - raise ValueError(f"Agent with name {agent_name} not found.") - - -class TestNonStreamingEvents: - """Test non-streaming event sending and polling.""" - - @pytest.mark.asyncio - async def test_send_event_and_poll(self, client: AsyncAgentex, agent_id: str): - """Test sending an event and polling for the response.""" - # TODO: Create a task for this conversation - # task_response = await client.agents.create_task(agent_id, params=ParamsCreateTaskRequest(name=uuid.uuid1().hex)) - # task = task_response.result - # assert task is not None - - # TODO: Poll for the initial task creation message (if your agent sends one) - # async for message in poll_messages( - # client=client, - # task_id=task.id, - # timeout=30, - # sleep_interval=1.0, - # ): - # assert isinstance(message, TaskMessage) - # if message.content and message.content.type == "text" and message.content.author == "agent": - # # Check for your expected initial message - # assert "expected initial text" in message.content.content - # break - - # TODO: Send an event and poll for response using the yielding helper function - # user_message = "Your test message here" - # async for message in send_event_and_poll_yielding( - # client=client, - # agent_id=agent_id, - # task_id=task.id, - # user_message=user_message, - # timeout=30, - # sleep_interval=1.0, - # ): - # assert isinstance(message, TaskMessage) - # if message.content and message.content.type == "text" and message.content.author == "agent": - # # Check for your expected response - # assert "expected response text" in message.content.content - # break - pass - - -class TestStreamingEvents: - """Test streaming event sending.""" - - @pytest.mark.asyncio - async def test_send_event_and_stream(self, client: AsyncAgentex, agent_id: str): - """Test sending an event and streaming the response.""" - # TODO: Create a task for this conversation - # task_response = await client.agents.create_task(agent_id, params=ParamsCreateTaskRequest(name=uuid.uuid1().hex)) - # task = task_response.result - # assert task is not None - - # user_message = "Your test message here" - - # # Collect events from stream - # all_events = [] - - # async def collect_stream_events(): - # async for event in stream_agent_response( - # client=client, - # task_id=task.id, - # timeout=30, - # ): - # all_events.append(event) - - # # Start streaming task - # stream_task = asyncio.create_task(collect_stream_events()) - - # # Send the event - # event_content = TextContentParam(type="text", author="user", content=user_message) - # await client.agents.send_event(agent_id=agent_id, params={"task_id": task.id, "content": event_content}) - - # # Wait for streaming to complete - # await stream_task - - # # TODO: Add your validation here - # assert len(all_events) > 0, "No events received in streaming response" - pass - - -if __name__ == "__main__": - pytest.main([__file__, "-v"]) \ No newline at end of file diff --git a/examples/tutorials/10_agentic/10_temporal/README.md b/examples/tutorials/10_agentic/10_temporal/README.md new file mode 100644 index 00000000..42842f38 --- /dev/null +++ b/examples/tutorials/10_agentic/10_temporal/README.md @@ -0,0 +1,88 @@ +# Temporal Tutorials + +Build durable, production-ready agents with Temporal workflows. These tutorials show you how to combine Temporal's durability guarantees with modern agent frameworks. + +## Tutorial Structure + +### Core Temporal Patterns (No Agent Framework) +- **[000_hello_acp](./000_hello_acp/)** - Your first Temporal workflow agent. Learn the basics of workflows, signals, and durability. + +### OpenAI Agents SDK + Temporal Integration +Tutorials showing how to integrate the OpenAI Agents SDK with Temporal workflows: + +- **[010_open_ai_agents_sdk_hello_world](./010_open_ai_agents_sdk_hello_world/)** - Hello world with OpenAI Agents SDK and Temporal streaming +- **[020_open_ai_agents_sdk_tools](./020_open_ai_agents_sdk_tools/)** - Adding tools to your agent (calculator, file operations) +- **[030_open_ai_agents_sdk_human_in_the_loop](./030_open_ai_agents_sdk_human_in_the_loop/)** - Human approval patterns for sensitive operations +- **[040_workflow_activities](./040_workflow_activities/)** - Using Temporal activities for workflow orchestration (database, notifications, reports) +- **[050_state_machines](./050_state_machines/)** - Complex multi-phase workflows with state machines and MCP servers + +## Prerequisites + +1. **Development Environment** + - Python 3.12+ + - Backend services running: `make dev` from repository root + - Temporal UI available at http://localhost:8233 + +2. **API Keys** (for OpenAI SDK tutorials 010-050) + - OpenAI API key: Set `OPENAI_API_KEY` environment variable + +## Quick Start + +Each tutorial is self-contained. To run any tutorial: + +```bash +cd examples/tutorials/10_agentic/10_temporal/ +export OPENAI_API_KEY="your-key-here" # Only needed for 010-050 +uv run agentex agents run --manifest manifest.yaml +``` + +## Learning Path + +**New to Temporal?** Start here: +1. [000_hello_acp](./000_hello_acp/) - Learn Temporal basics +2. [010_open_ai_agents_sdk_hello_world](./010_open_ai_agents_sdk_hello_world/) - Add OpenAI Agents SDK + +**Want to build production agents?** Follow this path: +1. [010_open_ai_agents_sdk_hello_world](./010_open_ai_agents_sdk_hello_world/) - Basic agent +2. [020_open_ai_agents_sdk_tools](./020_open_ai_agents_sdk_tools/) - Add tools +3. [030_open_ai_agents_sdk_human_in_the_loop](./030_open_ai_agents_sdk_human_in_the_loop/) - Add safety +4. [040_workflow_activities](./040_workflow_activities/) - Add orchestration +5. [050_state_machines](./050_state_machines/) - Add complex workflows + +## Key Concepts + +### Temporal Workflows +Workflows are durable, resumable functions that survive crashes and can run for days/months/years without consuming resources while idle. + +### OpenAI Agents SDK +The OpenAI Agents SDK provides a framework for building agentic applications with streaming, tool calling, and handoffs. When combined with Temporal, you get both powerful agent capabilities and enterprise durability. + +### Activities +Activities are functions that interact with external systems (databases, APIs, file systems). They can be retried independently of the workflow. + +### MCP Servers +Model Context Protocol (MCP) servers provide tools and context to agents. Tutorial 050 shows how to integrate MCP servers (time, web search, fetch) with Temporal workflows. + +## Monitoring + +All tutorials include Temporal UI integration. While your agent is running, visit: +- **Temporal UI**: http://localhost:8233 +- See workflows, activities, and state transitions in real-time + +## Common Patterns + +### Streaming Responses +Tutorials 010-050 show real-time streaming of agent responses using `TemporalStreamingModelProvider`. + +### State Management +Tutorial 050 demonstrates complex state machines with multiple phases and sub-workflows. + +### Tool Integration +- Tutorial 020: Activities AS agent tools +- Tutorial 040: Activities FOR workflow orchestration + +## Need Help? + +- Check individual tutorial READMEs for detailed explanations +- Visit the [main repo documentation](https://github.com/scaleapi/scale-agentex) +- Open an issue if you find bugs or have questions diff --git a/examples/tutorials/10_agentic/10_temporal/010_agent_chat/.dockerignore b/examples/tutorials/10_agentic/10_temporal/_archived/010_agent_chat/.dockerignore similarity index 100% rename from examples/tutorials/10_agentic/10_temporal/010_agent_chat/.dockerignore rename to examples/tutorials/10_agentic/10_temporal/_archived/010_agent_chat/.dockerignore diff --git a/examples/tutorials/10_agentic/10_temporal/010_agent_chat/Dockerfile b/examples/tutorials/10_agentic/10_temporal/_archived/010_agent_chat/Dockerfile similarity index 100% rename from examples/tutorials/10_agentic/10_temporal/010_agent_chat/Dockerfile rename to examples/tutorials/10_agentic/10_temporal/_archived/010_agent_chat/Dockerfile diff --git a/examples/tutorials/10_agentic/10_temporal/010_agent_chat/README.md b/examples/tutorials/10_agentic/10_temporal/_archived/010_agent_chat/README.md similarity index 100% rename from examples/tutorials/10_agentic/10_temporal/010_agent_chat/README.md rename to examples/tutorials/10_agentic/10_temporal/_archived/010_agent_chat/README.md diff --git a/examples/tutorials/10_agentic/10_temporal/010_agent_chat/dev.ipynb b/examples/tutorials/10_agentic/10_temporal/_archived/010_agent_chat/dev.ipynb similarity index 100% rename from examples/tutorials/10_agentic/10_temporal/010_agent_chat/dev.ipynb rename to examples/tutorials/10_agentic/10_temporal/_archived/010_agent_chat/dev.ipynb diff --git a/examples/tutorials/10_agentic/10_temporal/010_agent_chat/manifest.yaml b/examples/tutorials/10_agentic/10_temporal/_archived/010_agent_chat/manifest.yaml similarity index 100% rename from examples/tutorials/10_agentic/10_temporal/010_agent_chat/manifest.yaml rename to examples/tutorials/10_agentic/10_temporal/_archived/010_agent_chat/manifest.yaml diff --git a/examples/tutorials/10_agentic/10_temporal/060_open_ai_agents_sdk_hello_world/project/__init__.py b/examples/tutorials/10_agentic/10_temporal/_archived/010_agent_chat/project/__init__.py similarity index 100% rename from examples/tutorials/10_agentic/10_temporal/060_open_ai_agents_sdk_hello_world/project/__init__.py rename to examples/tutorials/10_agentic/10_temporal/_archived/010_agent_chat/project/__init__.py diff --git a/examples/tutorials/10_agentic/10_temporal/020_state_machine/project/acp.py b/examples/tutorials/10_agentic/10_temporal/_archived/010_agent_chat/project/acp.py similarity index 100% rename from examples/tutorials/10_agentic/10_temporal/020_state_machine/project/acp.py rename to examples/tutorials/10_agentic/10_temporal/_archived/010_agent_chat/project/acp.py diff --git a/examples/tutorials/10_agentic/10_temporal/010_agent_chat/project/run_worker.py b/examples/tutorials/10_agentic/10_temporal/_archived/010_agent_chat/project/run_worker.py similarity index 100% rename from examples/tutorials/10_agentic/10_temporal/010_agent_chat/project/run_worker.py rename to examples/tutorials/10_agentic/10_temporal/_archived/010_agent_chat/project/run_worker.py diff --git a/examples/tutorials/10_agentic/10_temporal/010_agent_chat/project/workflow.py b/examples/tutorials/10_agentic/10_temporal/_archived/010_agent_chat/project/workflow.py similarity index 100% rename from examples/tutorials/10_agentic/10_temporal/010_agent_chat/project/workflow.py rename to examples/tutorials/10_agentic/10_temporal/_archived/010_agent_chat/project/workflow.py diff --git a/examples/tutorials/10_agentic/10_temporal/010_agent_chat/pyproject.toml b/examples/tutorials/10_agentic/10_temporal/_archived/010_agent_chat/pyproject.toml similarity index 100% rename from examples/tutorials/10_agentic/10_temporal/010_agent_chat/pyproject.toml rename to examples/tutorials/10_agentic/10_temporal/_archived/010_agent_chat/pyproject.toml diff --git a/examples/tutorials/10_agentic/10_temporal/010_agent_chat/tests/test_agent.py b/examples/tutorials/10_agentic/10_temporal/_archived/010_agent_chat/tests/test_agent.py similarity index 100% rename from examples/tutorials/10_agentic/10_temporal/010_agent_chat/tests/test_agent.py rename to examples/tutorials/10_agentic/10_temporal/_archived/010_agent_chat/tests/test_agent.py diff --git a/examples/tutorials/10_agentic/10_temporal/020_state_machine/.dockerignore b/examples/tutorials/10_agentic/10_temporal/_archived/020_state_machine/.dockerignore similarity index 100% rename from examples/tutorials/10_agentic/10_temporal/020_state_machine/.dockerignore rename to examples/tutorials/10_agentic/10_temporal/_archived/020_state_machine/.dockerignore diff --git a/examples/tutorials/10_agentic/10_temporal/_archived/020_state_machine/Dockerfile b/examples/tutorials/10_agentic/10_temporal/_archived/020_state_machine/Dockerfile new file mode 100644 index 00000000..002100c0 --- /dev/null +++ b/examples/tutorials/10_agentic/10_temporal/_archived/020_state_machine/Dockerfile @@ -0,0 +1,51 @@ +# syntax=docker/dockerfile:1.3 +FROM python:3.12-slim +COPY --from=ghcr.io/astral-sh/uv:0.6.4 /uv /uvx /bin/ + +# Install system dependencies +RUN apt-get update && apt-get install -y \ + htop \ + vim \ + curl \ + tar \ + python3-dev \ + postgresql-client \ + build-essential \ + libpq-dev \ + gcc \ + cmake \ + netcat-openbsd \ + && apt-get clean \ + && rm -rf /var/lib/apt/lists/* + +# Install tctl (Temporal CLI) +RUN curl -L https://github.com/temporalio/tctl/releases/download/v1.18.1/tctl_1.18.1_linux_arm64.tar.gz -o /tmp/tctl.tar.gz && \ + tar -xzf /tmp/tctl.tar.gz -C /usr/local/bin && \ + chmod +x /usr/local/bin/tctl && \ + rm /tmp/tctl.tar.gz + +RUN uv pip install --system --upgrade pip setuptools wheel + +ENV UV_HTTP_TIMEOUT=1000 + +# Copy pyproject.toml and README.md to install dependencies +COPY 020_state_machine/pyproject.toml /app/020_state_machine/pyproject.toml +COPY 020_state_machine/README.md /app/020_state_machine/README.md + +WORKDIR /app/020_state_machine + +# Copy the project code +COPY 020_state_machine/project /app/020_state_machine/project + +# Install the required Python packages from pyproject.toml +RUN uv pip install --system . + +WORKDIR /app/020_state_machine + +ENV PYTHONPATH=/app + +# Run the ACP server using uvicorn +CMD ["uvicorn", "project.acp:acp", "--host", "0.0.0.0", "--port", "8000"] + +# When we deploy the worker, we will replace the CMD with the following +# CMD ["python", "-m", "run_worker"] diff --git a/examples/tutorials/10_agentic/10_temporal/020_state_machine/README.md b/examples/tutorials/10_agentic/10_temporal/_archived/020_state_machine/README.md similarity index 100% rename from examples/tutorials/10_agentic/10_temporal/020_state_machine/README.md rename to examples/tutorials/10_agentic/10_temporal/_archived/020_state_machine/README.md diff --git a/examples/tutorials/10_agentic/10_temporal/_archived/020_state_machine/dev.ipynb b/examples/tutorials/10_agentic/10_temporal/_archived/020_state_machine/dev.ipynb new file mode 100644 index 00000000..8f9f4dff --- /dev/null +++ b/examples/tutorials/10_agentic/10_temporal/_archived/020_state_machine/dev.ipynb @@ -0,0 +1,167 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": null, + "id": "0", + "metadata": {}, + "outputs": [], + "source": [ + "from agentex import Agentex\n", + "\n", + "client = Agentex(base_url=\"http://localhost:5003\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "1", + "metadata": {}, + "outputs": [], + "source": [ + "AGENT_NAME = \"at020-state-machine\"" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "2", + "metadata": {}, + "outputs": [], + "source": [ + "# (REQUIRED) Create a new task. For Agentic agents, you must create a task for messages to be associated with.\n", + "import uuid\n", + "\n", + "rpc_response = client.agents.create_task(\n", + " agent_name=AGENT_NAME,\n", + " params={\n", + " \"name\": f\"{str(uuid.uuid4())[:8]}-task\",\n", + " \"params\": {}\n", + " }\n", + ")\n", + "\n", + "task = rpc_response.result\n", + "print(task)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "3", + "metadata": {}, + "outputs": [], + "source": [ + "# Send an event to the agent\n", + "\n", + "# The response is expected to be a list of TaskMessage objects, which is a union of the following types:\n", + "# - TextContent: A message with just text content \n", + "# - DataContent: A message with JSON-serializable data content\n", + "# - ToolRequestContent: A message with a tool request, which contains a JSON-serializable request to call a tool\n", + "# - ToolResponseContent: A message with a tool response, which contains response object from a tool call in its content\n", + "\n", + "# When processing the message/send response, if you are expecting more than TextContent, such as DataContent, ToolRequestContent, or ToolResponseContent, you can process them as well\n", + "\n", + "rpc_response = client.agents.send_event(\n", + " agent_name=AGENT_NAME,\n", + " params={\n", + " \"content\": {\"type\": \"text\", \"author\": \"user\", \"content\": \"Hello tell me the latest news about AI and AI startups\"},\n", + " \"task_id\": task.id,\n", + " }\n", + ")\n", + "\n", + "event = rpc_response.result\n", + "print(event)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "4", + "metadata": {}, + "outputs": [], + "source": [ + "# Subscribe to the async task messages produced by the agent\n", + "from agentex.lib.utils.dev_tools import subscribe_to_async_task_messages\n", + "\n", + "task_messages = subscribe_to_async_task_messages(\n", + " client=client,\n", + " task=task, \n", + " only_after_timestamp=event.created_at, \n", + " print_messages=True,\n", + " rich_print=True,\n", + " timeout=5,\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "5", + "metadata": {}, + "outputs": [], + "source": [ + "# Send a follow up event to the agent in response to the agent's follow up question\n", + "\n", + "rpc_response = client.agents.send_event(\n", + " agent_name=AGENT_NAME,\n", + " params={\n", + " \"content\": {\"type\": \"text\", \"author\": \"user\", \"content\": \"I want to know what viral news came up and which startups failed, got acquired, or became very successful or popular in the last 3 months\"},\n", + " \"task_id\": task.id,\n", + " }\n", + ")\n", + "\n", + "event = rpc_response.result\n", + "print(event)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "6", + "metadata": {}, + "outputs": [], + "source": [ + "# Subscribe to the async task messages produced by the agent\n", + "from agentex.lib.utils.dev_tools import subscribe_to_async_task_messages\n", + "\n", + "task_messages = subscribe_to_async_task_messages(\n", + " client=client,\n", + " task=task, \n", + " only_after_timestamp=event.created_at, \n", + " print_messages=True,\n", + " rich_print=True,\n", + " timeout=30, # Notice the longer timeout to give time for the agent to respond\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "7", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": ".venv", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.12.11" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/examples/tutorials/10_agentic/10_temporal/020_state_machine/manifest.yaml b/examples/tutorials/10_agentic/10_temporal/_archived/020_state_machine/manifest.yaml similarity index 100% rename from examples/tutorials/10_agentic/10_temporal/020_state_machine/manifest.yaml rename to examples/tutorials/10_agentic/10_temporal/_archived/020_state_machine/manifest.yaml diff --git a/examples/tutorials/10_agentic/10_temporal/070_open_ai_agents_sdk_tools/project/__init__.py b/examples/tutorials/10_agentic/10_temporal/_archived/020_state_machine/project/__init__.py similarity index 100% rename from examples/tutorials/10_agentic/10_temporal/070_open_ai_agents_sdk_tools/project/__init__.py rename to examples/tutorials/10_agentic/10_temporal/_archived/020_state_machine/project/__init__.py diff --git a/examples/tutorials/10_agentic/10_temporal/050_agent_chat_guardrails/project/acp.py b/examples/tutorials/10_agentic/10_temporal/_archived/020_state_machine/project/acp.py similarity index 100% rename from examples/tutorials/10_agentic/10_temporal/050_agent_chat_guardrails/project/acp.py rename to examples/tutorials/10_agentic/10_temporal/_archived/020_state_machine/project/acp.py diff --git a/examples/tutorials/10_agentic/10_temporal/020_state_machine/project/run_worker.py b/examples/tutorials/10_agentic/10_temporal/_archived/020_state_machine/project/run_worker.py similarity index 100% rename from examples/tutorials/10_agentic/10_temporal/020_state_machine/project/run_worker.py rename to examples/tutorials/10_agentic/10_temporal/_archived/020_state_machine/project/run_worker.py diff --git a/examples/tutorials/10_agentic/10_temporal/_archived/020_state_machine/project/state_machines/deep_research.py b/examples/tutorials/10_agentic/10_temporal/_archived/020_state_machine/project/state_machines/deep_research.py new file mode 100644 index 00000000..d1c4df00 --- /dev/null +++ b/examples/tutorials/10_agentic/10_temporal/_archived/020_state_machine/project/state_machines/deep_research.py @@ -0,0 +1,41 @@ +from enum import Enum +from typing import Dict, List, Optional, override + +from pydantic import BaseModel + +from agentex.types.span import Span +from agentex.lib.sdk.state_machine import StateMachine + + +class DeepResearchState(str, Enum): + """States for the deep research workflow.""" + CLARIFYING_USER_QUERY = "clarifying_user_query" + PERFORMING_DEEP_RESEARCH = "performing_deep_research" + WAITING_FOR_USER_INPUT = "waiting_for_user_input" + COMPLETED = "completed" + FAILED = "failed" + + +class DeepResearchData(BaseModel): + """Data model for the deep research state machine - everything is one continuous research report.""" + task_id: Optional[str] = None + current_span: Optional[Span] = None + current_turn: int = 1 + + # Research report data + user_query: str = "" + follow_up_questions: List[str] = [] + follow_up_responses: List[str] = [] + n_follow_up_questions_to_ask: int = 1 + agent_input_list: List[Dict[str, str]] = [] + research_report: str = "" + research_iteration: int = 0 + + +class DeepResearchStateMachine(StateMachine[DeepResearchData]): + """State machine for the deep research workflow.""" + + @override + async def terminal_condition(self) -> bool: + """Check if the state machine has reached a terminal state.""" + return self.get_current_state() == DeepResearchState.COMPLETED diff --git a/examples/tutorials/10_agentic/10_temporal/020_state_machine/project/workflow.py b/examples/tutorials/10_agentic/10_temporal/_archived/020_state_machine/project/workflow.py similarity index 100% rename from examples/tutorials/10_agentic/10_temporal/020_state_machine/project/workflow.py rename to examples/tutorials/10_agentic/10_temporal/_archived/020_state_machine/project/workflow.py diff --git a/examples/tutorials/10_agentic/10_temporal/_archived/020_state_machine/project/workflows/deep_research/clarify_user_query.py b/examples/tutorials/10_agentic/10_temporal/_archived/020_state_machine/project/workflows/deep_research/clarify_user_query.py new file mode 100644 index 00000000..c8e756b2 --- /dev/null +++ b/examples/tutorials/10_agentic/10_temporal/_archived/020_state_machine/project/workflows/deep_research/clarify_user_query.py @@ -0,0 +1,89 @@ +from typing import Optional, override + +from project.state_machines.deep_research import DeepResearchData, DeepResearchState + +from agentex.lib import adk +from agentex.lib.utils.logging import make_logger +from agentex.lib.types.llm_messages import LLMConfig, UserMessage, SystemMessage +from agentex.lib.sdk.state_machine.state_machine import StateMachine +from agentex.lib.sdk.state_machine.state_workflow import StateWorkflow + +logger = make_logger(__name__) + + +FOLLOW_UP_QUESTION_TEMPLATE = """ +Given the following research query from the user, ask a follow up question to clarify the research direction. + +{{ user_query }} + + +{% if follow_up_questions|length > 0 %} +The following are follow up questions and answers that have been asked/given so far: +{% for q in follow_up_questions %} +Q: {{ follow_up_questions[loop.index0] }} +A: {{ follow_up_responses[loop.index0] }} +{% endfor %} +{% endif %} + +Return the follow up question and nothing else. +Follow up question: +""" + +class ClarifyUserQueryWorkflow(StateWorkflow): + """Workflow for engaging in follow-up questions.""" + + @override + async def execute(self, state_machine: StateMachine, state_machine_data: Optional[DeepResearchData] = None) -> str: + """Execute the workflow.""" + if state_machine_data is None: + return DeepResearchState.PERFORMING_DEEP_RESEARCH + + if state_machine_data.n_follow_up_questions_to_ask == 0: + # No more follow-up questions to ask, proceed to deep research + return DeepResearchState.PERFORMING_DEEP_RESEARCH + + # Generate follow-up question prompt + if state_machine_data.task_id and state_machine_data.current_span: + follow_up_question_generation_prompt = await adk.utils.templating.render_jinja( + trace_id=state_machine_data.task_id, + template=FOLLOW_UP_QUESTION_TEMPLATE, + variables={ + "user_query": state_machine_data.user_query, + "follow_up_questions": state_machine_data.follow_up_questions, + "follow_up_responses": state_machine_data.follow_up_responses + }, + parent_span_id=state_machine_data.current_span.id, + ) + + task_message = await adk.providers.litellm.chat_completion_stream_auto_send( + task_id=state_machine_data.task_id, + llm_config=LLMConfig( + model="gpt-4o-mini", + messages=[ + SystemMessage(content="You are assistant that follows exact instructions without outputting any other text except your response to the user's exact request."), + UserMessage(content=follow_up_question_generation_prompt), + ], + stream=True, + ), + trace_id=state_machine_data.task_id, + parent_span_id=state_machine_data.current_span.id, + ) + # Safely extract content from task message + follow_up_question = "" + if task_message.content and hasattr(task_message.content, 'content'): + content_val = getattr(task_message.content, 'content', '') + if isinstance(content_val, str): + follow_up_question = content_val + + # Update with follow-up question + state_machine_data.follow_up_questions.append(follow_up_question) + + # Decrement the number of follow-up questions to ask + state_machine_data.n_follow_up_questions_to_ask -= 1 + + logger.info(f"Current research data: {state_machine_data}") + + # Always go back to waiting for user input to get their response + return DeepResearchState.WAITING_FOR_USER_INPUT + else: + return DeepResearchState.PERFORMING_DEEP_RESEARCH \ No newline at end of file diff --git a/examples/tutorials/10_agentic/10_temporal/020_state_machine/project/workflows/deep_research/performing_deep_research.py b/examples/tutorials/10_agentic/10_temporal/_archived/020_state_machine/project/workflows/deep_research/performing_deep_research.py similarity index 100% rename from examples/tutorials/10_agentic/10_temporal/020_state_machine/project/workflows/deep_research/performing_deep_research.py rename to examples/tutorials/10_agentic/10_temporal/_archived/020_state_machine/project/workflows/deep_research/performing_deep_research.py diff --git a/examples/tutorials/10_agentic/10_temporal/_archived/020_state_machine/project/workflows/deep_research/waiting_for_user_input.py b/examples/tutorials/10_agentic/10_temporal/_archived/020_state_machine/project/workflows/deep_research/waiting_for_user_input.py new file mode 100644 index 00000000..842c5c42 --- /dev/null +++ b/examples/tutorials/10_agentic/10_temporal/_archived/020_state_machine/project/workflows/deep_research/waiting_for_user_input.py @@ -0,0 +1,21 @@ +from __future__ import annotations + +from typing import override + +from temporalio import workflow +from project.state_machines.deep_research import DeepResearchData, DeepResearchState + +from agentex.lib.utils.logging import make_logger +from agentex.lib.sdk.state_machine import StateMachine, StateWorkflow + +logger = make_logger(__name__) + +class WaitingForUserInputWorkflow(StateWorkflow): + @override + async def execute(self, state_machine: StateMachine, state_machine_data: DeepResearchData | None = None) -> str: + logger.info("ActorWaitingForUserInputWorkflow: waiting for user input...") + def condition(): + current_state = state_machine.get_current_state() + return current_state != DeepResearchState.WAITING_FOR_USER_INPUT + await workflow.wait_condition(condition) + return state_machine.get_current_state() \ No newline at end of file diff --git a/examples/tutorials/10_agentic/10_temporal/020_state_machine/pyproject.toml b/examples/tutorials/10_agentic/10_temporal/_archived/020_state_machine/pyproject.toml similarity index 100% rename from examples/tutorials/10_agentic/10_temporal/020_state_machine/pyproject.toml rename to examples/tutorials/10_agentic/10_temporal/_archived/020_state_machine/pyproject.toml diff --git a/examples/tutorials/10_agentic/10_temporal/_archived/020_state_machine/tests/test_agent.py b/examples/tutorials/10_agentic/10_temporal/_archived/020_state_machine/tests/test_agent.py new file mode 100644 index 00000000..41038c22 --- /dev/null +++ b/examples/tutorials/10_agentic/10_temporal/_archived/020_state_machine/tests/test_agent.py @@ -0,0 +1,187 @@ +""" +Sample tests for AgentEx Temporal State Machine agent. + +This test suite demonstrates how to test a state machine-based agent that: +- Uses state transitions (WAITING → CLARIFYING → PERFORMING_DEEP_RESEARCH) +- Asks follow-up questions before performing research +- Performs deep web research using MCP servers +- Handles multi-turn conversations with context preservation + +Key features tested: +1. State Machine Flow: Agent transitions through multiple states +2. Follow-up Questions: Agent clarifies queries before research +3. Deep Research: Agent performs extensive web research +4. Multi-turn Support: User can ask follow-ups about research + +To run these tests: +1. Make sure the agent is running (via docker-compose or `agentex agents run`) +2. Set the AGENTEX_API_BASE_URL environment variable if not using default +3. Ensure OPENAI_API_KEY is set in the environment +4. Run: pytest test_agent.py -v + +Configuration: +- AGENTEX_API_BASE_URL: Base URL for the AgentEx server (default: http://localhost:5003) +- AGENT_NAME: Name of the agent to test (default: at020-state-machine) +""" + +import os +import uuid +import asyncio + +import pytest +import pytest_asyncio +from test_utils.agentic import ( + stream_task_messages, + send_event_and_poll_yielding, +) + +from agentex import AsyncAgentex +from agentex.types.agent_rpc_params import ParamsCreateTaskRequest +from agentex.types.text_content_param import TextContentParam +from agentex.types.tool_request_content import ToolRequestContent + +# Configuration from environment variables +AGENTEX_API_BASE_URL = os.environ.get("AGENTEX_API_BASE_URL", "http://localhost:5003") +AGENT_NAME = os.environ.get("AGENT_NAME", "at020-state-machine") + + +@pytest_asyncio.fixture +async def client(): + """Create an AsyncAgentex client instance for testing.""" + client = AsyncAgentex(base_url=AGENTEX_API_BASE_URL) + yield client + await client.close() + + +@pytest.fixture +def agent_name(): + """Return the agent name for testing.""" + return AGENT_NAME + + +@pytest_asyncio.fixture +async def agent_id(client, agent_name): + """Retrieve the agent ID based on the agent name.""" + agents = await client.agents.list() + for agent in agents: + if agent.name == agent_name: + return agent.id + raise ValueError(f"Agent with name {agent_name} not found.") + + +class TestNonStreamingEvents: + """Test non-streaming event sending and polling with state machine workflow.""" + @pytest.mark.asyncio + async def test_send_event_and_poll_simple_query(self, client: AsyncAgentex, agent_id: str): + """Test sending a simple event and polling for the response (no tool use).""" + # Create a task for this conversation + task_response = await client.agents.create_task(agent_id, params=ParamsCreateTaskRequest(name=uuid.uuid1().hex)) + task = task_response.result + assert task is not None + + # Wait for workflow to initialize + await asyncio.sleep(1) + + # Send a simple message that shouldn't require tool use + user_message = "Hello! Please tell me the latest news about AI and AI startups." + messages = [] + found_agent_message = False + async for message in send_event_and_poll_yielding( + client=client, + agent_id=agent_id, + task_id=task.id, + user_message=user_message, + timeout=30, + sleep_interval=1.0, + ): + ## we should expect to get a question from the agent + if message.content.type == "text" and message.content.author == "agent": + found_agent_message = True + break + + assert found_agent_message, "Did not find an agent message" + + # now we want to clarity that message + await asyncio.sleep(2) + next_user_message = "I want to know what viral news came up and which startups failed, got acquired, or became very successful or popular in the last 3 months" + starting_deep_research_message = False + uses_tool_requests = False + async for message in send_event_and_poll_yielding( + client=client, + agent_id=agent_id, + task_id=task.id, + user_message=next_user_message, + timeout=30, + sleep_interval=1.0, + ): + if message.content.type == "text" and message.content.author == "agent": + if "starting deep research" in message.content.content.lower(): + starting_deep_research_message = True + if isinstance(message.content, ToolRequestContent): + uses_tool_requests = True + break + + assert starting_deep_research_message, "Did not start deep research" + assert uses_tool_requests, "Did not use tool requests" + +class TestStreamingEvents: + """Test streaming event sending with state machine workflow.""" + @pytest.mark.asyncio + async def test_send_event_and_stream(self, client: AsyncAgentex, agent_id: str): + """Test sending an event and streaming the response.""" + # Create a task for this conversation + task_response = await client.agents.create_task(agent_id, params=ParamsCreateTaskRequest(name=uuid.uuid1().hex)) + task = task_response.result + assert task is not None + + found_agent_message = False + async def poll_message_in_background() -> None: + nonlocal found_agent_message + async for message in stream_task_messages( + client=client, + task_id=task.id, + timeout=30, + ): + if message.content.type == "text" and message.content.author == "agent": + found_agent_message = True + break + + assert found_agent_message, "Did not find an agent message" + + poll_task = asyncio.create_task(poll_message_in_background()) + # create the first + user_message = "Hello! Please tell me the latest news about AI and AI startups." + await client.agents.send_event(agent_id=agent_id, params={"task_id": task.id, "content": TextContentParam(type="text", author="user", content=user_message)}) + + await poll_task + + await asyncio.sleep(2) + starting_deep_research_message = False + uses_tool_requests = False + async def poll_message_in_background_2() -> None: + nonlocal starting_deep_research_message, uses_tool_requests + async for message in stream_task_messages( + client=client, + task_id=task.id, + timeout=30, + ): + # can you add the same checks as we did in the non-streaming events test? + if message.content.type == "text" and message.content.author == "agent": + if "starting deep research" in message.content.content.lower(): + starting_deep_research_message = True + if isinstance(message.content, ToolRequestContent): + uses_tool_requests = True + break + + assert starting_deep_research_message, "Did not start deep research" + assert uses_tool_requests, "Did not use tool requests" + + poll_task_2 = asyncio.create_task(poll_message_in_background_2()) + + next_user_message = "I want to know what viral news came up and which startups failed, got acquired, or became very successful or popular in the last 3 months" + await client.agents.send_event(agent_id=agent_id, params={"task_id": task.id, "content": TextContentParam(type="text", author="user", content=next_user_message)}) + await poll_task_2 + + +if __name__ == "__main__": + pytest.main([__file__, "-v"]) diff --git a/examples/tutorials/10_agentic/10_temporal/030_custom_activities/.dockerignore b/examples/tutorials/10_agentic/10_temporal/_archived/030_custom_activities/.dockerignore similarity index 100% rename from examples/tutorials/10_agentic/10_temporal/030_custom_activities/.dockerignore rename to examples/tutorials/10_agentic/10_temporal/_archived/030_custom_activities/.dockerignore diff --git a/examples/tutorials/10_agentic/10_temporal/030_custom_activities/Dockerfile b/examples/tutorials/10_agentic/10_temporal/_archived/030_custom_activities/Dockerfile similarity index 100% rename from examples/tutorials/10_agentic/10_temporal/030_custom_activities/Dockerfile rename to examples/tutorials/10_agentic/10_temporal/_archived/030_custom_activities/Dockerfile diff --git a/examples/tutorials/10_agentic/10_temporal/030_custom_activities/README.md b/examples/tutorials/10_agentic/10_temporal/_archived/030_custom_activities/README.md similarity index 100% rename from examples/tutorials/10_agentic/10_temporal/030_custom_activities/README.md rename to examples/tutorials/10_agentic/10_temporal/_archived/030_custom_activities/README.md diff --git a/examples/tutorials/10_agentic/10_temporal/030_custom_activities/dev.ipynb b/examples/tutorials/10_agentic/10_temporal/_archived/030_custom_activities/dev.ipynb similarity index 100% rename from examples/tutorials/10_agentic/10_temporal/030_custom_activities/dev.ipynb rename to examples/tutorials/10_agentic/10_temporal/_archived/030_custom_activities/dev.ipynb diff --git a/examples/tutorials/10_agentic/10_temporal/030_custom_activities/manifest.yaml b/examples/tutorials/10_agentic/10_temporal/_archived/030_custom_activities/manifest.yaml similarity index 100% rename from examples/tutorials/10_agentic/10_temporal/030_custom_activities/manifest.yaml rename to examples/tutorials/10_agentic/10_temporal/_archived/030_custom_activities/manifest.yaml diff --git a/examples/tutorials/10_agentic/10_temporal/080_open_ai_agents_sdk_human_in_the_loop/project/__init__.py b/examples/tutorials/10_agentic/10_temporal/_archived/030_custom_activities/project/__init__.py similarity index 100% rename from examples/tutorials/10_agentic/10_temporal/080_open_ai_agents_sdk_human_in_the_loop/project/__init__.py rename to examples/tutorials/10_agentic/10_temporal/_archived/030_custom_activities/project/__init__.py diff --git a/examples/tutorials/10_agentic/10_temporal/030_custom_activities/project/acp.py b/examples/tutorials/10_agentic/10_temporal/_archived/030_custom_activities/project/acp.py similarity index 100% rename from examples/tutorials/10_agentic/10_temporal/030_custom_activities/project/acp.py rename to examples/tutorials/10_agentic/10_temporal/_archived/030_custom_activities/project/acp.py diff --git a/examples/tutorials/10_agentic/10_temporal/030_custom_activities/project/custom_activites.py b/examples/tutorials/10_agentic/10_temporal/_archived/030_custom_activities/project/custom_activites.py similarity index 100% rename from examples/tutorials/10_agentic/10_temporal/030_custom_activities/project/custom_activites.py rename to examples/tutorials/10_agentic/10_temporal/_archived/030_custom_activities/project/custom_activites.py diff --git a/examples/tutorials/10_agentic/10_temporal/030_custom_activities/project/run_worker.py b/examples/tutorials/10_agentic/10_temporal/_archived/030_custom_activities/project/run_worker.py similarity index 100% rename from examples/tutorials/10_agentic/10_temporal/030_custom_activities/project/run_worker.py rename to examples/tutorials/10_agentic/10_temporal/_archived/030_custom_activities/project/run_worker.py diff --git a/examples/tutorials/10_agentic/10_temporal/030_custom_activities/project/shared_models.py b/examples/tutorials/10_agentic/10_temporal/_archived/030_custom_activities/project/shared_models.py similarity index 100% rename from examples/tutorials/10_agentic/10_temporal/030_custom_activities/project/shared_models.py rename to examples/tutorials/10_agentic/10_temporal/_archived/030_custom_activities/project/shared_models.py diff --git a/examples/tutorials/10_agentic/10_temporal/030_custom_activities/project/workflow.py b/examples/tutorials/10_agentic/10_temporal/_archived/030_custom_activities/project/workflow.py similarity index 100% rename from examples/tutorials/10_agentic/10_temporal/030_custom_activities/project/workflow.py rename to examples/tutorials/10_agentic/10_temporal/_archived/030_custom_activities/project/workflow.py diff --git a/examples/tutorials/10_agentic/10_temporal/030_custom_activities/project/workflow_utils.py b/examples/tutorials/10_agentic/10_temporal/_archived/030_custom_activities/project/workflow_utils.py similarity index 100% rename from examples/tutorials/10_agentic/10_temporal/030_custom_activities/project/workflow_utils.py rename to examples/tutorials/10_agentic/10_temporal/_archived/030_custom_activities/project/workflow_utils.py diff --git a/examples/tutorials/10_agentic/10_temporal/030_custom_activities/pyproject.toml b/examples/tutorials/10_agentic/10_temporal/_archived/030_custom_activities/pyproject.toml similarity index 100% rename from examples/tutorials/10_agentic/10_temporal/030_custom_activities/pyproject.toml rename to examples/tutorials/10_agentic/10_temporal/_archived/030_custom_activities/pyproject.toml diff --git a/examples/tutorials/10_agentic/10_temporal/030_custom_activities/tests/test_agent.py b/examples/tutorials/10_agentic/10_temporal/_archived/030_custom_activities/tests/test_agent.py similarity index 100% rename from examples/tutorials/10_agentic/10_temporal/030_custom_activities/tests/test_agent.py rename to examples/tutorials/10_agentic/10_temporal/_archived/030_custom_activities/tests/test_agent.py diff --git a/examples/tutorials/10_agentic/10_temporal/050_agent_chat_guardrails/.dockerignore b/examples/tutorials/10_agentic/10_temporal/_archived/050_agent_chat_guardrails/.dockerignore similarity index 100% rename from examples/tutorials/10_agentic/10_temporal/050_agent_chat_guardrails/.dockerignore rename to examples/tutorials/10_agentic/10_temporal/_archived/050_agent_chat_guardrails/.dockerignore diff --git a/examples/tutorials/10_agentic/10_temporal/050_agent_chat_guardrails/Dockerfile b/examples/tutorials/10_agentic/10_temporal/_archived/050_agent_chat_guardrails/Dockerfile similarity index 100% rename from examples/tutorials/10_agentic/10_temporal/050_agent_chat_guardrails/Dockerfile rename to examples/tutorials/10_agentic/10_temporal/_archived/050_agent_chat_guardrails/Dockerfile diff --git a/examples/tutorials/10_agentic/10_temporal/050_agent_chat_guardrails/README.md b/examples/tutorials/10_agentic/10_temporal/_archived/050_agent_chat_guardrails/README.md similarity index 100% rename from examples/tutorials/10_agentic/10_temporal/050_agent_chat_guardrails/README.md rename to examples/tutorials/10_agentic/10_temporal/_archived/050_agent_chat_guardrails/README.md diff --git a/examples/tutorials/10_agentic/10_temporal/050_agent_chat_guardrails/dev.ipynb b/examples/tutorials/10_agentic/10_temporal/_archived/050_agent_chat_guardrails/dev.ipynb similarity index 100% rename from examples/tutorials/10_agentic/10_temporal/050_agent_chat_guardrails/dev.ipynb rename to examples/tutorials/10_agentic/10_temporal/_archived/050_agent_chat_guardrails/dev.ipynb diff --git a/examples/tutorials/10_agentic/10_temporal/050_agent_chat_guardrails/manifest.yaml b/examples/tutorials/10_agentic/10_temporal/_archived/050_agent_chat_guardrails/manifest.yaml similarity index 100% rename from examples/tutorials/10_agentic/10_temporal/050_agent_chat_guardrails/manifest.yaml rename to examples/tutorials/10_agentic/10_temporal/_archived/050_agent_chat_guardrails/manifest.yaml diff --git a/examples/tutorials/10_agentic/10_temporal/_archived/050_agent_chat_guardrails/project/__init__.py b/examples/tutorials/10_agentic/10_temporal/_archived/050_agent_chat_guardrails/project/__init__.py new file mode 100644 index 00000000..e69de29b diff --git a/examples/tutorials/10_agentic/10_temporal/_archived/050_agent_chat_guardrails/project/acp.py b/examples/tutorials/10_agentic/10_temporal/_archived/050_agent_chat_guardrails/project/acp.py new file mode 100644 index 00000000..2e069423 --- /dev/null +++ b/examples/tutorials/10_agentic/10_temporal/_archived/050_agent_chat_guardrails/project/acp.py @@ -0,0 +1,30 @@ +import os + +from agentex.lib.types.fastacp import TemporalACPConfig +from agentex.lib.sdk.fastacp.fastacp import FastACP + +# Create the ACP server +acp = FastACP.create( + acp_type="agentic", + config=TemporalACPConfig( + # When deployed to the cluster, the Temporal address will automatically be set to the cluster address + # For local development, we set the address manually to talk to the local Temporal service set up via docker compose + type="temporal", + temporal_address=os.getenv("TEMPORAL_ADDRESS", "localhost:7233") + ) +) + + +# Notice that we don't need to register any handlers when we use type="temporal" +# If you look at the code in agentex.sdk.fastacp.impl.temporal_acp +# You can see that these handlers are automatically registered when the ACP is created + +# @acp.on_task_create +# This will be handled by the method in your workflow that is decorated with @workflow.run + +# @acp.on_task_event_send +# This will be handled by the method in your workflow that is decorated with @workflow.signal(name=SignalName.RECEIVE_MESSAGE) + +# @acp.on_task_cancel +# This does not need to be handled by your workflow. +# It is automatically handled by the temporal client which cancels the workflow directly \ No newline at end of file diff --git a/examples/tutorials/10_agentic/10_temporal/050_agent_chat_guardrails/project/run_worker.py b/examples/tutorials/10_agentic/10_temporal/_archived/050_agent_chat_guardrails/project/run_worker.py similarity index 100% rename from examples/tutorials/10_agentic/10_temporal/050_agent_chat_guardrails/project/run_worker.py rename to examples/tutorials/10_agentic/10_temporal/_archived/050_agent_chat_guardrails/project/run_worker.py diff --git a/examples/tutorials/10_agentic/10_temporal/050_agent_chat_guardrails/project/workflow.py b/examples/tutorials/10_agentic/10_temporal/_archived/050_agent_chat_guardrails/project/workflow.py similarity index 100% rename from examples/tutorials/10_agentic/10_temporal/050_agent_chat_guardrails/project/workflow.py rename to examples/tutorials/10_agentic/10_temporal/_archived/050_agent_chat_guardrails/project/workflow.py diff --git a/examples/tutorials/10_agentic/10_temporal/050_agent_chat_guardrails/pyproject.toml b/examples/tutorials/10_agentic/10_temporal/_archived/050_agent_chat_guardrails/pyproject.toml similarity index 100% rename from examples/tutorials/10_agentic/10_temporal/050_agent_chat_guardrails/pyproject.toml rename to examples/tutorials/10_agentic/10_temporal/_archived/050_agent_chat_guardrails/pyproject.toml diff --git a/examples/tutorials/10_agentic/10_temporal/050_agent_chat_guardrails/tests/test_agent.py b/examples/tutorials/10_agentic/10_temporal/_archived/050_agent_chat_guardrails/tests/test_agent.py similarity index 100% rename from examples/tutorials/10_agentic/10_temporal/050_agent_chat_guardrails/tests/test_agent.py rename to examples/tutorials/10_agentic/10_temporal/_archived/050_agent_chat_guardrails/tests/test_agent.py diff --git a/examples/tutorials/10_agentic/10_temporal/_archived/README.md b/examples/tutorials/10_agentic/10_temporal/_archived/README.md new file mode 100644 index 00000000..a0f2442b --- /dev/null +++ b/examples/tutorials/10_agentic/10_temporal/_archived/README.md @@ -0,0 +1,155 @@ +# Archived Temporal Tutorials + +**⚠️ DEPRECATED: These tutorials use outdated patterns and are preserved for historical reference only.** + +## Why These Are Archived + +These tutorials demonstrate the older `run_agent_streamed_auto_send` pattern which has been superseded by the **OpenAI Agents SDK plugin approach** (see the main temporal tutorials). + +### Problems with the Old Pattern + +The `run_agent_streamed_auto_send` approach has several limitations: + +1. **Complex Serialization Required** + - Manual serialization of agent state + - Error-prone and boilerplate-heavy + - Not necessary with the plugin approach + +2. **Non-Configurable** + - Limited flexibility in how agents are configured + - Harder to customize behavior + - Less control over the execution flow + +3. **Not Officially Supported by Temporal** + - The OpenAI Agents SDK plugin is the official integration + - Better long-term support and updates + - More aligned with Temporal best practices + +4. **Inferior Developer Experience** + - More code to write and maintain + - Steeper learning curve + - More opportunities for bugs + +## Modern Approach: OpenAI Agents SDK Plugin + +For new projects, use the OpenAI Agents SDK plugin demonstrated in: + +- **[010_open_ai_agents_sdk_hello_world](../010_open_ai_agents_sdk_hello_world/)** - Basic integration +- **[020_open_ai_agents_sdk_tools](../020_open_ai_agents_sdk_tools/)** - Tool patterns +- **[030_open_ai_agents_sdk_human_in_the_loop](../030_open_ai_agents_sdk_human_in_the_loop/)** - Human oversight + +### Why the Plugin is Better + +✅ **Automatic activity wrapping** - No manual serialization needed +✅ **Officially supported** - Built with Temporal team collaboration +✅ **Better DX** - Less boilerplate, clearer code +✅ **More configurable** - Fine-grained control when needed +✅ **Future-proof** - Active development and support + +## Archived Tutorials + +These tutorials remain available for teams with existing codebases using the old pattern: + +### 010_agent_chat +Stateful conversation management using `run_agent_streamed_auto_send`. + +**Modern equivalent:** [010_open_ai_agents_sdk_hello_world](../010_open_ai_agents_sdk_hello_world/) + +### 020_state_machine +Structured state management with explicit state transitions. + +**Modern equivalent:** State management is simpler with the plugin approach - see [010_open_ai_agents_sdk_hello_world](../010_open_ai_agents_sdk_hello_world/) + +### 030_custom_activities +Custom Temporal activities with manual agent integration. + +**Modern equivalent:** [020_open_ai_agents_sdk_tools](../020_open_ai_agents_sdk_tools/) shows activity patterns with automatic wrapping + +### 050_agent_chat_guardrails +Safety and validation patterns with the old approach. + +**Modern equivalent:** Guardrails work better with the plugin - integration is cleaner and more maintainable + +## Migration Guide + +If you're using these patterns in existing code: + +### 1. Add the OpenAI Agents SDK Plugin + +**In `acp.py`:** +```python +from agentex.lib.plugins.openai_agents import OpenAIAgentsPlugin + +acp = FastACP.create( + config=TemporalACPConfig( + plugins=[OpenAIAgentsPlugin()] + ) +) +``` + +**In `run_worker.py`:** +```python +from agentex.lib.plugins.openai_agents import OpenAIAgentsPlugin + +worker = AgentexWorker( + task_queue=task_queue_name, + plugins=[OpenAIAgentsPlugin()], +) +``` + +### 2. Replace Agent Calls + +**Old pattern:** +```python +result = await adk.providers.openai.run_agent_streamed_auto_send( + task_id=params.task.id, + input_list=self._state.input_list, + # ... complex serialization ... +) +``` + +**New pattern:** +```python +from agents import Agent, Runner + +agent = Agent( + name="My Agent", + instructions="You are a helpful assistant" +) + +result = await Runner.run(agent, self._state.input_list) +``` + +### 3. Tools Become Simpler + +**Old pattern:** Manual activity wrapping, complex serialization + +**New pattern:** Use `activity_as_tool()` - automatic activity creation: + +```python +from temporalio.contrib import openai_agents +from datetime import timedelta + +tools=[ + openai_agents.workflow.activity_as_tool( + my_activity, + start_to_close_timeout=timedelta(seconds=10) + ), +] +``` + +See [020_open_ai_agents_sdk_tools](../020_open_ai_agents_sdk_tools/) for complete examples. + +## Need Help? + +- **For new projects:** Start with [010_open_ai_agents_sdk_hello_world](../010_open_ai_agents_sdk_hello_world/) +- **For migration questions:** These archived tutorials show the old patterns if you need reference +- **For modern patterns:** See the main temporal tutorials (010, 020, 030) + +## History + +These tutorials were created when `run_agent_streamed_auto_send` was the primary integration method. They represented valuable work at the time and taught important Temporal concepts. The ecosystem has evolved, and the OpenAI Agents SDK plugin is now the recommended approach. + +**Archived:** November 2024 +**Reason:** Superseded by officially supported OpenAI Agents SDK plugin +**Status:** Reference only - use modern tutorials for new projects diff --git a/examples/tutorials/README.md b/examples/tutorials/README.md index ecdd2225..382c85c9 100644 --- a/examples/tutorials/README.md +++ b/examples/tutorials/README.md @@ -37,23 +37,15 @@ graph TD M -->|Yes| N[10_temporal/
000_hello_acp] M -->|No| O[00_base/090_multi_agent] - N --> P[10_temporal/010_agent_chat] - P --> Q[10_temporal/020_state_machine] - Q --> R[10_temporal/030_custom_activities] - R --> S[10_temporal/050_guardrails] - - S --> T{Using
OpenAI SDK?} - T -->|Yes| U[10_temporal/060_openai_hello] - U --> V[10_temporal/070_openai_tools] - V --> W[10_temporal/080_openai_hitl] - T -->|No| X[🎉 Production Ready!] - W --> X + N --> P[10_temporal/
010_openai_hello] + P --> Q[10_temporal/
020_openai_tools] + Q --> R[10_temporal/
030_openai_hitl] + R --> S[🎉 Production Ready!] style A fill:#e1f5e1 - style X fill:#fff3cd + style S fill:#fff3cd style E fill:#e3f2fd style M fill:#e3f2fd - style T fill:#e3f2fd ``` ## Tutorial Structure @@ -89,20 +81,16 @@ Task-based architecture without workflow orchestration. Adds task management on #### 10_temporal/ - Production Workflows Durable, fault-tolerant agents with Temporal workflow orchestration. -**Core Patterns:** +**Tutorials:** - **[000_hello_acp](10_agentic/10_temporal/000_hello_acp/)** - Temporal basics -- **[010_agent_chat](10_agentic/10_temporal/010_agent_chat/)** - Stateful conversations -- **[020_state_machine](10_agentic/10_temporal/020_state_machine/)** - Structured state management -- **[030_custom_activities](10_agentic/10_temporal/030_custom_activities/)** - Custom Temporal activities -- **[050_agent_chat_guardrails](10_agentic/10_temporal/050_agent_chat_guardrails/)** - Safety & validation - -**OpenAI Agents SDK Series:** -- **[060_openai_hello_world](10_agentic/10_temporal/060_open_ai_agents_sdk_hello_world/)** - Plugin-based agents -- **[070_openai_tools](10_agentic/10_temporal/070_open_ai_agents_sdk_tools/)** - Tool integration patterns -- **[080_openai_hitl](10_agentic/10_temporal/080_open_ai_agents_sdk_human_in_the_loop/)** - Human oversight workflows +- **[010_openai_hello_world](10_agentic/10_temporal/010_open_ai_agents_sdk_hello_world/)** - OpenAI Agents SDK integration +- **[020_openai_tools](10_agentic/10_temporal/020_open_ai_agents_sdk_tools/)** - Durable tool patterns +- **[030_openai_hitl](10_agentic/10_temporal/030_open_ai_agents_sdk_human_in_the_loop/)** - Human-in-the-loop workflows **When to use:** Production systems requiring durability, fault tolerance, long-running workflows, or complex state management +**Note:** Older tutorials using the `run_agent_streamed_auto_send` pattern have been archived to `10_temporal/_archived/`. See the [archive README](10_agentic/10_temporal/_archived/README.md) for migration guidance. + --- ## Quick Start diff --git a/examples/tutorials/run_all_agentic_tests.sh b/examples/tutorials/run_all_agentic_tests.sh index 73883237..165997a2 100755 --- a/examples/tutorials/run_all_agentic_tests.sh +++ b/examples/tutorials/run_all_agentic_tests.sh @@ -45,21 +45,24 @@ done # Find all agentic tutorial directories ALL_TUTORIALS=( # sync tutorials - "00_sync/000_hello_acp" - "00_sync/010_multiturn" - "00_sync/020_streaming" + # "00_sync/000_hello_acp" + # "00_sync/010_multiturn" + # "00_sync/020_streaming" # base tutorials - "10_agentic/00_base/000_hello_acp" - "10_agentic/00_base/010_multiturn" - "10_agentic/00_base/020_streaming" - "10_agentic/00_base/030_tracing" - "10_agentic/00_base/040_other_sdks" - "10_agentic/00_base/080_batch_events" + # "10_agentic/00_base/000_hello_acp" + # "10_agentic/00_base/010_multiturn" + # "10_agentic/00_base/020_streaming" + # "10_agentic/00_base/030_tracing" + # "10_agentic/00_base/040_other_sdks" + # "10_agentic/00_base/080_batch_events" # "10_agentic/00_base/090_multi_agent_non_temporal" This will require its own version of this # temporal tutorials - "10_agentic/10_temporal/000_hello_acp" - "10_agentic/10_temporal/010_agent_chat" - "10_agentic/10_temporal/020_state_machine" + # "10_agentic/10_temporal/000_hello_acp" + # "10_agentic/10_temporal/010_open_ai_agents_sdk_hello_world" + # "10_agentic/10_temporal/020_open_ai_agents_sdk_tools" + # "10_agentic/10_temporal/030_open_ai_agents_sdk_human_in_the_loop" + "10_agentic/10_temporal/040_workflow_activities" + ) PASSED=0 diff --git a/examples/tutorials/test_utils/agentic.py b/examples/tutorials/test_utils/agentic.py index fcee04ea..0c7d30cb 100644 --- a/examples/tutorials/test_utils/agentic.py +++ b/examples/tutorials/test_utils/agentic.py @@ -25,6 +25,7 @@ async def send_event_and_poll_yielding( user_message: str, timeout: int = 30, sleep_interval: float = 1.0, + yield_updates: bool = False, ) -> AsyncGenerator[TaskMessage, None]: """ Send an event to an agent and poll for responses, yielding messages as they arrive. @@ -38,6 +39,7 @@ async def send_event_and_poll_yielding( user_message: The message content to send timeout: Maximum seconds to wait for a response (default: 30) sleep_interval: Seconds to sleep between polls (default: 1.0) + yield_updates: If True, yield messages again when their content changes (for streaming) Yields: TaskMessage objects as they are discovered during polling @@ -60,6 +62,7 @@ async def send_event_and_poll_yielding( timeout=timeout, sleep_interval=sleep_interval, messages_created_after=messages_created_after, + yield_updates=yield_updates, ): yield message @@ -70,9 +73,27 @@ async def poll_messages( timeout: int = 30, sleep_interval: float = 1.0, messages_created_after: Optional[float] = None, + yield_updates: bool = False, ) -> AsyncGenerator[TaskMessage, None]: + """ + Poll for messages continuously until timeout. + + Args: + client: AgentEx client instance + task_id: The task ID to poll messages for + timeout: Maximum seconds to poll (default: 30) + sleep_interval: Seconds to sleep between polls (default: 1.0) + messages_created_after: Optional timestamp to filter messages (Unix timestamp) + yield_updates: If True, yield messages again when their content changes (for streaming) + If False, only yield each message ID once (default: False) + + Yields: + TaskMessage objects as they are discovered or updated + """ # Keep track of messages we've already yielded seen_message_ids = set() + # Track message content hashes to detect updates (for streaming) + message_content_hashes = {} start_time = datetime.now() # Poll continuously until timeout @@ -81,10 +102,6 @@ async def poll_messages( # print("DEBGUG: Messages found: ", messages) new_messages_found = 0 for message in messages: - # Skip if we've already yielded this message - if message.id in seen_message_ids: - continue - # Check if message passes timestamp filter if messages_created_after and message.created_at: # If message.created_at is timezone-naive, assume it's UTC @@ -95,14 +112,27 @@ async def poll_messages( if msg_timestamp < messages_created_after: continue - # Yield new messages that pass the filter - seen_message_ids.add(message.id) - new_messages_found += 1 - - # This yield should transfer control back to the caller - yield message + # Check if this is a new message or an update to existing message + is_new_message = message.id not in seen_message_ids + + if yield_updates: + # For streaming: track content changes + content_str = message.content.content if message.content and hasattr(message.content, 'content') else "" + content_hash = hash(content_str + str(message.streaming_status)) + is_updated = message.id in message_content_hashes and message_content_hashes[message.id] != content_hash + + if is_new_message or is_updated: + message_content_hashes[message.id] = content_hash + seen_message_ids.add(message.id) + new_messages_found += 1 + yield message + else: + # Original behavior: only yield each message ID once + if is_new_message: + seen_message_ids.add(message.id) + new_messages_found += 1 + yield message - # If we see this print, it means the caller consumed the message and we resumed # Sleep before next poll await asyncio.sleep(sleep_interval) @@ -210,6 +240,66 @@ async def stream_task_messages( +async def send_event_and_poll_with_streaming( + client: AsyncAgentex, + agent_id: str, + task_id: str, + user_message: str, + timeout: int = 30, + sleep_interval: float = 1.0, + streaming_wait_time: float = 5.0, +) -> TaskMessage: + """ + Send an event to a temporal agent and poll for the complete response. + + This function is specifically designed for temporal agents that use streaming. + It handles the case where streaming updates the same message ID with chunks, + so it captures the initial message and re-fetches it after waiting for streaming to complete. + + Args: + client: AgentEx client instance + agent_id: The agent ID + task_id: The task ID + user_message: The message content to send + timeout: Maximum seconds to wait for initial response (default: 30) + sleep_interval: Seconds to sleep between polls (default: 1.0) + streaming_wait_time: Seconds to wait for streaming to complete before re-fetching (default: 5.0) + + Returns: + The final complete TaskMessage with streamed content + + Raises: + AssertionError: If no agent message is received + """ + # Send the event and poll for the initial message + agent_message = None + + async for message in send_event_and_poll_yielding( + client=client, + agent_id=agent_id, + task_id=task_id, + user_message=user_message, + timeout=timeout, + sleep_interval=sleep_interval, + ): + if message.content and message.content.type == "text" and message.content.author == "agent": + agent_message = message + break + + if agent_message is None: + raise AssertionError("No agent message received") + + # If the message is still in progress, wait and re-fetch + # Otherwise, we can use it directly since streaming already completed + if agent_message.streaming_status == "IN_PROGRESS": + await asyncio.sleep(streaming_wait_time) + final_message = await client.messages.retrieve(agent_message.id) + return final_message + else: + # Already complete + return agent_message + + def validate_text_in_response(expected_text: str, message: TaskMessage) -> bool: """ Validate that expected text appears in any of the messages. diff --git a/pyproject.toml b/pyproject.toml index ab86a5fd..bc4a3fb6 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -24,7 +24,7 @@ dependencies = [ "pyyaml>=6.0.2,<7", "jsonschema>=4.23.0,<5", "jsonref>=1.1.0,<2", - "temporalio>=1.10.0,<2", + "temporalio>=1.18.2,<2", "aiohttp>=3.10.10,<4", "redis>=5.2.0,<6", "litellm>=1.66.0,<2", @@ -32,7 +32,7 @@ dependencies = [ "jinja2>=3.1.3,<4", "mcp[cli]>=1.4.1", "scale-gp>=0.1.0a59", - "openai-agents==0.2.7", # 0.2.3 bug - https://github.com/openai/openai-agents-python/issues/1276 + "openai-agents>=0.4.2", # Latest version with OpenAI v2.x support "tzlocal>=5.3.1", "tzdata>=2025.2", "pytest>=8.4.0", @@ -40,7 +40,7 @@ dependencies = [ "pytest-asyncio>=1.0.0", "scale-gp-beta==0.1.0a20", "ipykernel>=6.29.5", - "openai==1.99.9", # anything higher than 1.99.9 breaks litellm - https://github.com/BerriAI/litellm/issues/13711 + "openai>=2.6.1,<3", # Updated to v2.x to support openai-agents 0.4.2 "cloudpickle>=3.1.1", "datadog>=0.52.1", "ddtrace>=3.13.0" diff --git a/requirements-dev.lock b/requirements-dev.lock index a282ef7f..e7006557 100644 --- a/requirements-dev.lock +++ b/requirements-dev.lock @@ -113,9 +113,9 @@ httpx==0.27.2 # via mcp # via openai # via respx -httpx-aiohttp==0.1.9 # via scale-gp # via scale-gp-beta +httpx-aiohttp==0.1.9 # via agentex-sdk httpx-sse==0.4.1 # via mcp @@ -191,11 +191,11 @@ nox==2023.4.22 oauthlib==3.3.1 # via kubernetes # via requests-oauthlib -openai==1.99.9 +openai==2.6.1 # via agentex-sdk # via litellm # via openai-agents -openai-agents==0.2.7 +openai-agents==0.4.2 # via agentex-sdk opentelemetry-api==1.37.0 # via ddtrace @@ -219,20 +219,6 @@ prompt-toolkit==3.0.51 propcache==0.3.1 # via aiohttp # via yarl -pydantic==2.11.9 - # via agentex-sdk - # via agentex-sdk - # via fastapi - # via litellm - # via mcp - # via openai - # via openai-agents - # via pydantic-settings - # via python-on-whales - # via scale-gp - # via scale-gp-beta -pydantic-core==2.33.2 - # via pydantic protobuf==5.29.5 # via ddtrace # via temporalio @@ -247,6 +233,19 @@ pyasn1==0.6.1 # via rsa pyasn1-modules==0.4.2 # via google-auth +pydantic==2.11.9 + # via agentex-sdk + # via fastapi + # via litellm + # via mcp + # via openai + # via openai-agents + # via pydantic-settings + # via python-on-whales + # via scale-gp + # via scale-gp-beta +pydantic-core==2.33.2 + # via pydantic pydantic-settings==2.10.1 # via mcp pygments==2.18.0 @@ -340,7 +339,7 @@ stack-data==0.6.3 starlette==0.46.2 # via fastapi # via mcp -temporalio==1.15.0 +temporalio==1.18.2 # via agentex-sdk tiktoken==0.11.0 # via litellm @@ -383,9 +382,6 @@ typing-extensions==4.12.2 # via pydantic # via pydantic-core # via pyright - # via typing-inspection -typing-inspection==0.4.1 - # via pydantic # via python-on-whales # via referencing # via scale-gp @@ -393,6 +389,8 @@ typing-inspection==0.4.1 # via temporalio # via typer # via typing-inspection +typing-inspection==0.4.1 + # via pydantic # via pydantic-settings tzdata==2025.2 # via agentex-sdk diff --git a/requirements.lock b/requirements.lock index 81d91576..73a85a8d 100644 --- a/requirements.lock +++ b/requirements.lock @@ -99,12 +99,12 @@ httpcore==1.0.9 httpx==0.27.2 # via agentex-sdk # via httpx-aiohttp -httpx-aiohttp==0.1.9 # via litellm # via mcp # via openai # via scale-gp # via scale-gp-beta +httpx-aiohttp==0.1.9 # via agentex-sdk httpx-sse==0.4.1 # via mcp @@ -174,11 +174,11 @@ nexus-rpc==1.1.0 oauthlib==3.3.1 # via kubernetes # via requests-oauthlib -openai==1.99.9 +openai==2.6.1 # via agentex-sdk # via litellm # via openai-agents -openai-agents==0.2.7 +openai-agents==0.4.2 # via agentex-sdk opentelemetry-api==1.37.0 # via ddtrace @@ -200,19 +200,6 @@ prompt-toolkit==3.0.51 propcache==0.3.1 # via aiohttp # via yarl -pydantic==2.11.9 - # via agentex-sdk - # via fastapi - # via litellm - # via mcp - # via openai - # via openai-agents - # via pydantic-settings - # via python-on-whales - # via scale-gp - # via scale-gp-beta -pydantic-core==2.33.2 - # via pydantic protobuf==5.29.5 # via ddtrace # via temporalio @@ -227,6 +214,19 @@ pyasn1==0.6.1 # via rsa pyasn1-modules==0.4.2 # via google-auth +pydantic==2.11.9 + # via agentex-sdk + # via fastapi + # via litellm + # via mcp + # via openai + # via openai-agents + # via pydantic-settings + # via python-on-whales + # via scale-gp + # via scale-gp-beta +pydantic-core==2.33.2 + # via pydantic pydantic-settings==2.10.1 # via mcp pygments==2.19.2 @@ -311,7 +311,7 @@ stack-data==0.6.3 starlette==0.46.2 # via fastapi # via mcp -temporalio==1.15.0 +temporalio==1.18.2 # via agentex-sdk tiktoken==0.11.0 # via litellm @@ -351,9 +351,6 @@ typing-extensions==4.12.2 # via opentelemetry-api # via pydantic # via pydantic-core - # via typing-inspection -typing-inspection==0.4.1 - # via pydantic # via python-on-whales # via referencing # via scale-gp @@ -361,6 +358,8 @@ typing-inspection==0.4.1 # via temporalio # via typer # via typing-inspection +typing-inspection==0.4.1 + # via pydantic # via pydantic-settings tzdata==2025.2 # via agentex-sdk diff --git a/src/agentex/lib/adk/providers/_modules/openai.py b/src/agentex/lib/adk/providers/_modules/openai.py index ac9f7907..0ad2eee7 100644 --- a/src/agentex/lib/adk/providers/_modules/openai.py +++ b/src/agentex/lib/adk/providers/_modules/openai.py @@ -1,5 +1,6 @@ from __future__ import annotations +import sys from typing import Any, Literal from datetime import timedelta @@ -12,6 +13,12 @@ from agents.agent_output import AgentOutputSchemaBase from agents.model_settings import ModelSettings +# Use warnings.deprecated in Python 3.13+, typing_extensions.deprecated for older versions +if sys.version_info >= (3, 13): + from warnings import deprecated +else: + from typing_extensions import deprecated + from agentex.lib.utils.logging import make_logger from agentex.lib.utils.temporal import in_temporal_workflow from agentex.lib.core.tracing.tracer import AsyncTracer @@ -383,6 +390,10 @@ async def run_agent_streamed( previous_response_id=previous_response_id, ) + @deprecated( + "Use the OpenAI Agents SDK integration with Temporal instead. " + "See examples in tutorials/10_agentic/10_temporal/ for migration guidance." + ) async def run_agent_streamed_auto_send( self, task_id: str, @@ -413,6 +424,10 @@ async def run_agent_streamed_auto_send( """ Run an agent with streaming enabled and automatic TaskMessage creation. + .. deprecated:: + Use the OpenAI Agents SDK integration with Temporal instead. + See examples in tutorials/10_agentic/10_temporal/ for migration guidance. + Args: task_id: The ID of the task to run the agent for. input_list: List of input data for the agent. diff --git a/src/agentex/lib/cli/templates/temporal/project/acp.py.j2 b/src/agentex/lib/cli/templates/temporal/project/acp.py.j2 index 5bcc7cc3..9e258dbd 100644 --- a/src/agentex/lib/cli/templates/temporal/project/acp.py.j2 +++ b/src/agentex/lib/cli/templates/temporal/project/acp.py.j2 @@ -1,6 +1,8 @@ import os import sys +from temporalio.contrib.openai_agents import OpenAIAgentsPlugin + # === DEBUG SETUP (AgentEx CLI Debug Support) === if os.getenv("AGENTEX_DEBUG_ENABLED") == "true": try: @@ -33,9 +35,15 @@ if os.getenv("AGENTEX_DEBUG_ENABLED") == "true": sys.exit(1) # === END DEBUG SETUP === -from agentex.lib.sdk.fastacp.fastacp import FastACP from agentex.lib.types.fastacp import TemporalACPConfig +from agentex.lib.sdk.fastacp.fastacp import FastACP +from agentex.lib.core.temporal.plugins.openai_agents.models.temporal_streaming_model import ( + TemporalStreamingModelProvider, +) +from agentex.lib.core.temporal.plugins.openai_agents.interceptors.context_interceptor import ContextInterceptor +context_interceptor = ContextInterceptor() +temporal_streaming_model_provider = TemporalStreamingModelProvider() # Create the ACP server acp = FastACP.create( @@ -43,8 +51,11 @@ acp = FastACP.create( config=TemporalACPConfig( # When deployed to the cluster, the Temporal address will automatically be set to the cluster address # For local development, we set the address manually to talk to the local Temporal service set up via docker compose + # We are also adding the Open AI Agents SDK plugin to the ACP. type="temporal", - temporal_address=os.getenv("TEMPORAL_ADDRESS", "localhost:7233") + temporal_address=os.getenv("TEMPORAL_ADDRESS", "localhost:7233"), + plugins=[OpenAIAgentsPlugin(model_provider=temporal_streaming_model_provider)], + interceptors=[context_interceptor] ) ) diff --git a/src/agentex/lib/cli/templates/temporal/project/run_worker.py.j2 b/src/agentex/lib/cli/templates/temporal/project/run_worker.py.j2 index 1721abac..90fe4e83 100644 --- a/src/agentex/lib/cli/templates/temporal/project/run_worker.py.j2 +++ b/src/agentex/lib/cli/templates/temporal/project/run_worker.py.j2 @@ -1,13 +1,18 @@ import asyncio -from agentex.lib.core.temporal.activities import get_all_activities -from agentex.lib.core.temporal.workers.worker import AgentexWorker -from agentex.lib.utils.logging import make_logger -from agentex.lib.utils.debug import setup_debug_if_enabled -from agentex.lib.environment_variables import EnvironmentVariables +from temporalio.contrib.openai_agents import OpenAIAgentsPlugin from project.workflow import {{ workflow_class }} - +from agentex.lib.utils.debug import setup_debug_if_enabled +from agentex.lib.utils.logging import make_logger +from agentex.lib.environment_variables import EnvironmentVariables +from agentex.lib.core.temporal.activities import get_all_activities +from agentex.lib.core.temporal.workers.worker import AgentexWorker +from agentex.lib.core.temporal.plugins.openai_agents.interceptors.context_interceptor import ContextInterceptor +from agentex.lib.core.temporal.plugins.openai_agents.models.temporal_streaming_model import ( + TemporalStreamingModelProvider, +) +from agentex.lib.core.temporal.plugins.openai_agents.hooks.activities import stream_lifecycle_content environment_variables = EnvironmentVariables.refresh() @@ -17,16 +22,44 @@ logger = make_logger(__name__) async def main(): # Setup debug mode if enabled setup_debug_if_enabled() - + task_queue_name = environment_variables.WORKFLOW_TASK_QUEUE if task_queue_name is None: raise ValueError("WORKFLOW_TASK_QUEUE is not set") - all_activities = get_all_activities() + [] # add your own activities here - + # Add activities to the worker + # stream_lifecycle_content is required for hooks to work (creates tool_request/tool_response messages) + all_activities = get_all_activities() + [stream_lifecycle_content] # add your own activities here + + # ============================================================================ + # STREAMING SETUP: Interceptor + Model Provider + # ============================================================================ + # This is where the streaming magic is configured! Two key components: + # + # 1. ContextInterceptor (StreamingInterceptor) + # - Threads task_id through activity headers using Temporal's interceptor pattern + # - Outbound: Reads _task_id from workflow instance, injects into activity headers + # - Inbound: Extracts task_id from headers, sets streaming_task_id ContextVar + # - This enables runtime context without forking the Temporal plugin! + # + # 2. TemporalStreamingModelProvider + # - Returns StreamingModel instances that read task_id from ContextVar + # - StreamingModel.get_response() streams tokens to Redis in real-time + # - Still returns complete response to Temporal for determinism/replay safety + # - Uses AgentEx ADK streaming infrastructure (Redis XADD to stream:{task_id}) + # + # Together, these enable real-time LLM streaming while maintaining Temporal's + # durability guarantees. No forked components - uses STANDARD OpenAIAgentsPlugin! + context_interceptor = ContextInterceptor() + temporal_streaming_model_provider = TemporalStreamingModelProvider() + # Create a worker with automatic tracing + # IMPORTANT: We use the STANDARD temporalio.contrib.openai_agents.OpenAIAgentsPlugin + # No forking needed! The interceptor + model provider handle all streaming logic. worker = AgentexWorker( task_queue=task_queue_name, + plugins=[OpenAIAgentsPlugin(model_provider=temporal_streaming_model_provider)], + interceptors=[context_interceptor], ) await worker.run( diff --git a/src/agentex/lib/cli/templates/temporal/project/workflow.py.j2 b/src/agentex/lib/cli/templates/temporal/project/workflow.py.j2 index ad756eb1..66f2bbb4 100644 --- a/src/agentex/lib/cli/templates/temporal/project/workflow.py.j2 +++ b/src/agentex/lib/cli/templates/temporal/project/workflow.py.j2 @@ -28,21 +28,47 @@ class {{ workflow_class }}(BaseWorkflow): def __init__(self): super().__init__(display_name=environment_variables.AGENT_NAME) self._complete_task = False + self._task_id = None + self._trace_id = None + self._parent_span_id = None @workflow.signal(name=SignalName.RECEIVE_EVENT) async def on_task_event_send(self, params: SendEventParams) -> None: logger.info(f"Received task message instruction: {params}") - - # 2. Echo back the client's message to show it in the UI. This is not done by default so the agent developer has full control over what is shown to the user. + + # Echo back the client's message to show it in the UI. This is not done by default + # so the agent developer has full control over what is shown to the user. await adk.messages.create(task_id=params.task.id, content=params.event.content) - # 3. Send a simple response message. - # In future tutorials, this is where we'll add more sophisticated response logic. + # ============================================================================ + # NEXT STEPS: Using OpenAI Agents SDK with Temporal + # ============================================================================ + # This project is configured with the OpenAI Agents SDK integration including: + # - TemporalStreamingModelProvider for real-time token streaming + # - ContextInterceptor for threading task_id through activities + # - TemporalStreamingHooks (via stream_lifecycle_content activity) + # + # To use the OpenAI Agents SDK in your workflow: + # 1. Add: from agents import Agent, Runner + # 2. Add: from agentex.lib.core.temporal.plugins.openai_agents.hooks.hooks import TemporalStreamingHooks + # 3. Initialize streaming context variables: + # self._task_id = params.task.id + # self._trace_id = params.task.id + # self._parent_span_id = params.task.id + # 4. Create hooks: hooks = TemporalStreamingHooks(task_id=params.task.id) + # 5. Create your agent and run: result = await Runner.run(agent, input, hooks=hooks) + # + # For complete examples with tools, see: + # https://github.com/scaleapi/agentex-python/tree/main/examples/tutorials/10_agentic/10_temporal/070_open_ai_agents_sdk_tools + # ============================================================================ + + # Send a simple response message. + # Replace this with your OpenAI Agents SDK logic following the pattern above. await adk.messages.create( task_id=params.task.id, content=TextContent( author="agent", - content=f"Hello! I've received your message. I can't respond right now, but in future tutorials we'll see how you can get me to intelligently respond to your message.", + content=f"Hello! I've received your message. See the comments above for how to integrate the OpenAI Agents SDK.", ), ) diff --git a/src/agentex/lib/core/temporal/workers/worker.py b/src/agentex/lib/core/temporal/workers/worker.py index 28cab2e1..fe169387 100644 --- a/src/agentex/lib/core/temporal/workers/worker.py +++ b/src/agentex/lib/core/temporal/workers/worker.py @@ -128,6 +128,7 @@ def __init__( health_check_port: int | None = None, plugins: list = [], interceptors: list = [], + mcp_server_providers: list = [], ): self.task_queue = task_queue self.activity_handles = [] @@ -138,6 +139,7 @@ def __init__( self.health_check_port = health_check_port if health_check_port is not None else EnvironmentVariables.refresh().HEALTH_CHECK_PORT self.plugins = plugins self.interceptors = interceptors + self.mcp_server_providers = mcp_server_providers @overload async def run( @@ -169,9 +171,57 @@ async def run( if self.interceptors: _validate_interceptors(self.interceptors) + # Prepare plugins list - handle OpenAI Agents plugin and MCP servers + plugins = [] + + # Find existing OpenAI Agents plugin if any + openai_plugin = None + for p in self.plugins: + if isinstance(p, OpenAIAgentsPlugin): + openai_plugin = p + else: + plugins.append(p) + + if self.mcp_server_providers: + # If there's an existing OpenAI plugin, we need to recreate it with MCP servers + if openai_plugin: + # Recreate the plugin with MCP servers added + import dataclasses + + # Get existing config from the plugin + plugin_dict = {} + if hasattr(openai_plugin, '_model_params'): + plugin_dict['model_params'] = openai_plugin._model_params + if hasattr(openai_plugin, '_model_provider'): + plugin_dict['model_provider'] = openai_plugin._model_provider + + # Add MCP servers + plugin_dict['mcp_server_providers'] = self.mcp_server_providers + + plugins.append(OpenAIAgentsPlugin(**plugin_dict)) + logger.info(f"Added {len(self.mcp_server_providers)} MCP server providers to existing OpenAI Agents plugin") + else: + # No existing plugin, create one with MCP servers + from datetime import timedelta + from temporalio.contrib.openai_agents import ModelActivityParameters + + plugins.append( + OpenAIAgentsPlugin( + model_params=ModelActivityParameters( + start_to_close_timeout=timedelta(seconds=180) + ), + mcp_server_providers=self.mcp_server_providers, + ) + ) + logger.info(f"Created OpenAI Agents plugin with {len(self.mcp_server_providers)} MCP server providers") + else: + # No MCP servers, just add back the OpenAI plugin if it exists + if openai_plugin: + plugins.append(openai_plugin) + temporal_client = await get_temporal_client( temporal_address=os.environ.get("TEMPORAL_ADDRESS", "localhost:7233"), - plugins=self.plugins, + plugins=plugins, ) # Enable debug mode if AgentEx debug is enabled (disables deadlock detection)