Adding tests for tutorials

.github/workflows/main.yml

@@ -2,7 +2,5 @@ name: Test AgentEx Tutorials
 
 on:
   workflow_dispatch:
-
-  workflow_call:
-
 
+  workflow_call:

.github/workflows/publish-pypi.yml

@@ -21,8 +21,8 @@ jobs:
           curl -sSf https://rye.astral.sh/get | bash
           echo "$HOME/.rye/shims" >> $GITHUB_PATH
         env:
-          RYE_VERSION: '0.44.0'
-          RYE_INSTALL_OPTION: '--yes'
+          RYE_VERSION: "0.44.0"
+          RYE_INSTALL_OPTION: "--yes"
 
       - name: Publish to PyPI
         run: |

.gitignore

@@ -14,4 +14,6 @@ dist
 codegen.log
 Brewfile.lock.json
 
-.DS_Store
+.DS_Store
+
+examples/**/uv.lock

examples/tutorials/00_sync/000_hello_acp/Dockerfile

@@ -22,16 +22,17 @@ RUN uv pip install --system --upgrade pip setuptools wheel
 
 ENV UV_HTTP_TIMEOUT=1000
 
-# Copy just the requirements file to optimize caching
-COPY 000_hello_acp/requirements.txt /app/requirements.txt
+# Copy pyproject.toml and README.md to install dependencies
+COPY 000_hello_acp/pyproject.toml /app/000_hello_acp/pyproject.toml
+COPY 000_hello_acp/README.md /app/000_hello_acp/README.md
 
-WORKDIR /app/
-
-# Install the required Python packages
-RUN uv pip install --system -r requirements.txt
+WORKDIR /app/000_hello_acp
 
 # Copy the project code
-COPY 000_hello_acp/project /app/project
+COPY 000_hello_acp/project /app/000_hello_acp/project
+
+# Install the required Python packages from pyproject.toml
+RUN uv pip install --system .
 
 # Set environment variables
 ENV PYTHONPATH=/app

examples/tutorials/00_sync/000_hello_acp/manifest.yaml

@@ -15,7 +15,7 @@
 build:
   context:
     # Root directory for the build context
-    root: ../  # Keep this as the default root
+    root: ../ # Keep this as the default root
 
     # Paths to include in the Docker build context
     # Must include:
@@ -34,14 +34,13 @@ build:
     # Helps keep build context small and builds fast
     dockerignore: 000_hello_acp/.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) 
+    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:
@@ -53,7 +52,6 @@ local_development:
     #   /absolute/path/acp.py   (absolute path)
     acp: project/acp.py
 
-
 # Agent Configuration
 # -----------------
 agent:
@@ -83,39 +81,39 @@ agent:
   #     secret_name: openai-api-key
   #     secret_key: api-key
 
-  # Optional: Set Environment variables for running your agent locally as well 
+  # Optional: Set Environment variables for running your agent locally as well
   # as for deployment later on
   # env:
   #   - name: OPENAI_BASE_URL
   #     value: "https://api.openai.com/v1"
   #   - name: ACCOUNT_ID
   #     value: "your_account_id_here"
 
-
 # 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
+    tag: "latest" # Default tag, should be versioned in production
 
   # Global deployment settings that apply to all clusters
   # These can be overridden in cluster-specific files (deploy/*.yaml)
   global:
     agent:
       name: "s000-hello-acp"
       description: "An AgentEx agent that just says hello and acknowledges the user's message"
-    
+
     # Default replica count
     replicaCount: 1
-    
+
     # Default resource requirements
     resources:
       requests:
         cpu: "500m"
         memory: "1Gi"
       limits:
         cpu: "1000m"
-        memory: "2Gi" 
+        memory: "2Gi"
+

examples/tutorials/00_sync/000_hello_acp/pyproject.toml

@@ -11,6 +11,8 @@ requires-python = ">=3.12"
 dependencies = [
     "agentex-sdk",
     "scale-gp",
+    "pytest",
+    "pytest-xdist"
 ]
 
 [project.optional-dependencies]
@@ -30,4 +32,4 @@ target-version = ['py312']
 
 [tool.isort]
 profile = "black"
-line_length = 88 
+line_length = 88

examples/tutorials/00_sync/000_hello_acp/tests/test_agent.py

@@ -0,0 +1,128 @@
+"""
+Sample tests for AgentEx ACP agent.
+
+This test suite demonstrates how to test the main AgentEx API functions:
+- Non-streaming message sending
+- Streaming message sending
+- Task creation via RPC
+
+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: hello-acp)
+"""
+
+import os
+from agentex.types import TextContentParam, TextDelta, TextContent
+from agentex.types.agent_rpc_params import ParamsSendMessageRequest
+from agentex.types.task_message_update import StreamTaskMessageDelta, StreamTaskMessageFull
+import pytest
+from agentex import Agentex
+
+
+# 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", "s000-hello-acp")
+
+
+@pytest.fixture
+def client():
+    """Create an AgentEx client instance for testing."""
+    client = Agentex(base_url=AGENTEX_API_BASE_URL)
+    yield client
+    # Clean up: close the client connection
+    client.close()
+
+
+@pytest.fixture
+def agent_name():
+    """Return the agent name for testing."""
+    return AGENT_NAME
+
+
+class TestNonStreamingMessages:
+    """Test non-streaming message sending."""
+
+    def test_send_simple_message(self, client: Agentex, agent_name: str):
+        """Test sending a simple message and receiving a response."""
+
+        message_content = "Hello, Agent! How are you?"
+        response = client.agents.send_message(
+            agent_name=agent_name,
+            params=ParamsSendMessageRequest(
+                content=TextContentParam(
+                    author="user",
+                    content=message_content,
+                    type="text",
+                )
+            ),
+        )
+        result = response.result
+        assert result is not None
+        assert len(result) == 1
+        message = result[0]
+        assert isinstance(message.content, TextContent)
+        assert (
+            message.content.content
+            == f"Hello! I've received your message. Here's a generic response, but in future tutorials we'll see how you can get me to intelligently respond to your message. This is what I heard you say: {message_content}"
+        )
+
+
+class TestStreamingMessages:
+    """Test streaming message sending."""
+
+    def test_stream_simple_message(self, client: Agentex, agent_name: str):
+        """Test streaming a simple message and aggregating deltas."""
+
+        message_content = "Hello, Agent! Can you stream your response?"
+        aggregated_content = ""
+        full_content = ""
+        received_chunks = False
+
+        for chunk in client.agents.send_message_stream(
+            agent_name=agent_name,
+            params=ParamsSendMessageRequest(
+                content=TextContentParam(
+                    author="user",
+                    content=message_content,
+                    type="text",
+                )
+            ),
+        ):
+            received_chunks = True
+            task_message_update = chunk.result
+            # Collect text deltas as they arrive or check full messages
+            if isinstance(task_message_update, StreamTaskMessageDelta) and task_message_update.delta is not None:
+                delta = task_message_update.delta
+                if isinstance(delta, TextDelta) and delta.text_delta is not None:
+                    aggregated_content += delta.text_delta
+
+            elif isinstance(task_message_update, StreamTaskMessageFull):
+                content = task_message_update.content
+                if isinstance(content, TextContent):
+                    full_content = content.content
+
+        if not full_content and not aggregated_content:
+            raise AssertionError("No content was received in the streaming response.")
+        if not received_chunks:
+            raise AssertionError("No streaming chunks were received, when at least 1 was expected.")
+
+        if full_content:
+            assert (
+                full_content
+                == f"Hello! I've received your message. Here's a generic response, but in future tutorials we'll see how you can get me to intelligently respond to your message. This is what I heard you say: {message_content}"
+            )
+
+        if aggregated_content:
+            assert (
+                aggregated_content
+                == f"Hello! I've received your message. Here's a generic response, but in future tutorials we'll see how you can get me to intelligently respond to your message. This is what I heard you say: {message_content}"
+            )
+
+
+if __name__ == "__main__":
+    pytest.main([__file__, "-v"])

examples/tutorials/00_sync/010_multiturn/Dockerfile

@@ -22,19 +22,21 @@ RUN uv pip install --system --upgrade pip setuptools wheel
 
 ENV UV_HTTP_TIMEOUT=1000
 
-# Copy just the requirements file to optimize caching
-COPY 010_multiturn/requirements.txt /app/requirements.txt
+# Copy pyproject.toml and README.md to install dependencies
+COPY 010_multiturn/pyproject.toml /app/010_multiturn/pyproject.toml
+COPY 010_multiturn/README.md /app/010_multiturn/README.md
 
-WORKDIR /app/
-
-# Install the required Python packages
-RUN uv pip install --system -r requirements.txt
+WORKDIR /app/010_multiturn
 
 # Copy the project code
-COPY 010_multiturn/project /app/project
+COPY 010_multiturn/project /app/010_multiturn/project
+
+# Install the required Python packages from pyproject.toml
+RUN uv pip install --system .
 
+WORKDIR /app/010_multiturn
 # Set environment variables
 ENV PYTHONPATH=/app
 
 # Run the agent using uvicorn
-CMD ["uvicorn", "project.acp:acp", "--host", "0.0.0.0", "--port", "8000"] 
+CMD ["uvicorn", "project.acp:acp", "--host", "0.0.0.0", "--port", "8000"] 

examples/tutorials/00_sync/010_multiturn/project/acp.py

@@ -3,12 +3,12 @@
 
 from agentex.lib import adk
 from agentex.lib.types.acp import SendMessageParams
-from agentex.types.task_message import TaskMessageContent
 from agentex.lib.utils.model_utils import BaseModel
 from agentex.lib.types.llm_messages import LLMConfig, UserMessage, SystemMessage, AssistantMessage
 from agentex.lib.sdk.fastacp.fastacp import FastACP
 from agentex.types.task_message_update import TaskMessageUpdate
-from agentex.types.task_message_content import TextContent
+from agentex.types.task_message_content import TaskMessageContent
+from agentex.types import TextContent
 
 # Create an ACP server
 acp = FastACP.create(
@@ -24,7 +24,7 @@ class StateModel(BaseModel):
 # Note: The return of this handler is required to be persisted by the Agentex Server
 @acp.on_message_send
 async def handle_message_send(
-    params: SendMessageParams
+    params: SendMessageParams,
 ) -> Union[TaskMessageContent, AsyncGenerator[TaskMessageUpdate, None]]:
     """
     In this tutorial, we'll see how to handle a basic multi-turn conversation without streaming.
@@ -33,12 +33,12 @@ async def handle_message_send(
     # 0. Validate the message.
     #########################################################
 
-    if not hasattr(params.content, 'type') or params.content.type != "text":
+    if not hasattr(params.content, "type") or params.content.type != "text":
         raise ValueError(f"Expected text message, got {getattr(params.content, 'type', 'unknown')}")
 
-    if not hasattr(params.content, 'author') or params.content.author != "user":
+    if not hasattr(params.content, "author") or params.content.author != "user":
         raise ValueError(f"Expected user message, got {getattr(params.content, 'author', 'unknown')}")
-    
+
     if not os.environ.get("OPENAI_API_KEY"):
         return TextContent(
             author="agent",
@@ -74,12 +74,14 @@ async def handle_message_send(
     llm_messages = [
         SystemMessage(content=state.system_prompt),
         *[
-            UserMessage(content=getattr(message.content, 'content', '')) if getattr(message.content, 'author', None) == "user" else AssistantMessage(content=getattr(message.content, 'content', ''))
+            UserMessage(content=getattr(message.content, "content", ""))
+            if getattr(message.content, "author", None) == "user"
+            else AssistantMessage(content=getattr(message.content, "content", ""))
             for message in task_messages
-            if getattr(message.content, 'type', None) == "text"
-        ]
+            if getattr(message.content, "type", None) == "text"
+        ],
     ]
-    
+
     # TaskMessages are messages that are sent between an Agent and a Client. They are fundamentally decoupled from messages sent to the LLM. This is because you may want to send additional metadata to allow the client to render the message on the UI differently.
 
     # LLMMessages are OpenAI-compatible messages that are sent to the LLM, and are used to track the state of a conversation with a model.
@@ -90,7 +92,7 @@ async def handle_message_send(
     #   - Taking a markdown document output by an LLM, postprocessing it into a JSON object to clearly denote title, content, and footers. This can be sent as a DataContent TaskMessage to the client and converted back to markdown here to send back to the LLM.
     #   - If using multiple LLMs (like in an actor-critic framework), you may want to send DataContent that denotes which LLM generated which part of the output and write conversion logic to split the TaskMessagehistory into multiple LLM conversations.
     #   - If using multiple LLMs, but one LLM's output should not be sent to the user (i.e. a critic model), you can leverage the State as an internal storage mechanism to store the critic model's conversation history. This i s a powerful and flexible way to handle complex scenarios.
-    
+
     #########################################################
     # 4. Call an LLM to respond to the user's message.
     #########################################################
@@ -113,7 +115,4 @@ async def handle_message_send(
     else:
         content_str = ""
 
-    return TextContent(
-        author="agent",
-        content=content_str
-    )
+    return TextContent(author="agent", content=content_str)