Merge branch 'main' into jason/document-examples

Author: jasonyang101

.release-please-manifest.json

@@ -1,3 +1,3 @@
 {
-  ".": "0.4.26"
+  ".": "0.5.0"
 }

.stats.yml

@@ -1,4 +1,4 @@
 configured_endpoints: 34
-openapi_spec_url: https://storage.googleapis.com/stainless-sdk-openapi-specs/sgp%2Fagentex-sdk-c34c0577d9716c22554633ecb41d091675248d1385e9d1cc7c19479d55b56cb1.yml
-openapi_spec_hash: 3daaeab87b12b271ba2ee39e7dc166f2
+openapi_spec_url: https://storage.googleapis.com/stainless-sdk-openapi-specs/sgp%2Fagentex-sdk-2b422fbf02ff3b77795fb8c71cbe784de3a3add48560655ba4fe7f3fcc509995.yml
+openapi_spec_hash: bca5c04d823694c87417dae188480291
 config_hash: 6481ea6b42040f435dedcb00a98f35f8

CHANGELOG.md

@@ -1,5 +1,25 @@
 # Changelog
 
+## 0.5.0 (2025-10-28)
+
+Full Changelog: [v0.4.28...v0.5.0](https://github.com/scaleapi/agentex-python/compare/v0.4.28...v0.5.0)
+
+### Features
+
+* **api:** api update ([129fae6](https://github.com/scaleapi/agentex-python/commit/129fae69844e655b5dd02b6f67c44d15f5dbfa93))
+
+## 0.4.28 (2025-10-28)
+
+Full Changelog: [v0.4.27...v0.4.28](https://github.com/scaleapi/agentex-python/compare/v0.4.27...v0.4.28)
+
+## 0.4.27 (2025-10-27)
+
+Full Changelog: [v0.4.26...v0.4.27](https://github.com/scaleapi/agentex-python/compare/v0.4.26...v0.4.27)
+
+### Features
+
+* **api:** api update ([f5e4fd2](https://github.com/scaleapi/agentex-python/commit/f5e4fd2f2fbb2c7e67e51795fba1f0b2e13048de))
+
 ## 0.4.26 (2025-10-21)
 
 Full Changelog: [v0.4.25...v0.4.26](https://github.com/scaleapi/agentex-python/compare/v0.4.25...v0.4.26)

api.md

@@ -43,16 +43,16 @@ Methods:
 Types:
 
 ```python
-from agentex.types import Task, TaskListResponse
+from agentex.types import Task, TaskRetrieveResponse, TaskListResponse, TaskRetrieveByNameResponse
 ```
 
 Methods:
 
-- <code title="get /tasks/{task_id}">client.tasks.<a href="./src/agentex/resources/tasks.py">retrieve</a>(task_id) -> <a href="./src/agentex/types/task.py">Task</a></code>
+- <code title="get /tasks/{task_id}">client.tasks.<a href="./src/agentex/resources/tasks.py">retrieve</a>(task_id, \*\*<a href="src/agentex/types/task_retrieve_params.py">params</a>) -> <a href="./src/agentex/types/task_retrieve_response.py">TaskRetrieveResponse</a></code>
 - <code title="get /tasks">client.tasks.<a href="./src/agentex/resources/tasks.py">list</a>(\*\*<a href="src/agentex/types/task_list_params.py">params</a>) -> <a href="./src/agentex/types/task_list_response.py">TaskListResponse</a></code>
 - <code title="delete /tasks/{task_id}">client.tasks.<a href="./src/agentex/resources/tasks.py">delete</a>(task_id) -> <a href="./src/agentex/types/shared/delete_response.py">DeleteResponse</a></code>
 - <code title="delete /tasks/name/{task_name}">client.tasks.<a href="./src/agentex/resources/tasks.py">delete_by_name</a>(task_name) -> <a href="./src/agentex/types/shared/delete_response.py">DeleteResponse</a></code>
-- <code title="get /tasks/name/{task_name}">client.tasks.<a href="./src/agentex/resources/tasks.py">retrieve_by_name</a>(task_name) -> <a href="./src/agentex/types/task.py">Task</a></code>
+- <code title="get /tasks/name/{task_name}">client.tasks.<a href="./src/agentex/resources/tasks.py">retrieve_by_name</a>(task_name, \*\*<a href="src/agentex/types/task_retrieve_by_name_params.py">params</a>) -> <a href="./src/agentex/types/task_retrieve_by_name_response.py">TaskRetrieveByNameResponse</a></code>
 - <code title="get /tasks/{task_id}/stream">client.tasks.<a href="./src/agentex/resources/tasks.py">stream_events</a>(task_id) -> object</code>
 - <code title="get /tasks/name/{task_name}/stream">client.tasks.<a href="./src/agentex/resources/tasks.py">stream_events_by_name</a>(task_name) -> object</code>
 

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

@@ -17,12 +17,13 @@
 """
 
 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
 
+from agentex import Agentex
+from agentex.types import TextDelta, TextContent, TextContentParam
+from agentex.types.agent_rpc_params import ParamsSendMessageRequest
+from agentex.types.task_message_update import StreamTaskMessageFull, StreamTaskMessageDelta
 
 # Configuration from environment variables
 AGENTEX_API_BASE_URL = os.environ.get("AGENTEX_API_BASE_URL", "http://localhost:5003")

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

@@ -1,14 +1,17 @@
 import os
 from typing import Union, AsyncGenerator
 
+from agents import Agent, Runner, RunConfig
+
 from agentex.lib import adk
+from agentex.types import TextContent
 from agentex.lib.types.acp import SendMessageParams
+from agentex.lib.types.converters import convert_task_messages_to_oai_agents_inputs
 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 TaskMessageContent
-from agentex.types import TextContent
+from agentex.lib.adk.providers._modules.sync_provider import SyncStreamingProvider
 
 # Create an ACP server
 acp = FastACP.create(
@@ -66,21 +69,27 @@ async def handle_message_send(
     task_messages = await adk.messages.list(task_id=params.task.id)
 
     #########################################################
-    # 3. Convert task messages to LLM messages.
+    # 3. Run the agent with OpenAI Agents SDK
     #########################################################
 
-    # This might seem duplicative, but the split between TaskMessage and LLMMessage is intentional and important.
+    # Initialize the provider and run config to allow for tracing
+    provider = SyncStreamingProvider(
+        trace_id=params.task.id,
+    )
+
+    run_config = RunConfig(
+        model_provider=provider,
+    )
+
+    # Initialize the agent
+    test_agent = Agent(name="assistant", instructions=state.system_prompt, model=state.model)
+
+    # Convert task messages to OpenAI Agents SDK format
+    input_list = convert_task_messages_to_oai_agents_inputs(task_messages)
+
+    # Run the agent
+    result = await Runner.run(test_agent, input_list, run_config=run_config)
 
-    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", ""))
-            for message in task_messages
-            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.
 
@@ -94,25 +103,7 @@ async def handle_message_send(
     #   - 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.
+    # 4. Return the agent response to the client.
     #########################################################
 
-    # Call an LLM to respond to the user's message
-    chat_completion = await adk.providers.litellm.chat_completion(
-        llm_config=LLMConfig(model=state.model, messages=llm_messages),
-        trace_id=params.task.id,
-    )
-
-    #########################################################
-    # 5. Return the agent response to the client.
-    #########################################################
-
-    # The Agentex server automatically commits input and output messages to the database so you don't need to do this yourself, simply process the input content and return the output content.
-
-    # Return the agent response to the client
-    if chat_completion.choices[0].message:
-        content_str = chat_completion.choices[0].message.content or ""
-    else:
-        content_str = ""
-
-    return TextContent(author="agent", content=content_str)
+    return TextContent(author="agent", content=result.final_output)

examples/tutorials/00_sync/010_multiturn/pyproject.toml

@@ -9,7 +9,7 @@ description = "An AgentEx agent"
 readme = "README.md"
 requires-python = ">=3.12"
 dependencies = [
-    "agentex-sdk",
+    "agentex-sdk==0.4.28",
     "scale-gp",
 ]
 

examples/tutorials/00_sync/010_multiturn/tests/test_agent.py

@@ -16,19 +16,15 @@
 - AGENT_NAME: Name of the agent to test (default: s010-multiturn)
 """
 
-import enum
 import os
 
-from agentex.lib.sdk.fastacp.base.base_acp_server import uuid
-from agentex.lib.types.acp import SendMessageParams
-
-from test_utils.sync import collect_streaming_response, validate_text_in_string
-
-from agentex.types import TaskMessageContentParam, TextContent, TextContentParam, task_list_params
-from agentex.types.agent_rpc_params import ParamsCreateTaskRequest, ParamsSendMessageRequest
 import pytest
-from agentex import Agentex
+from test_utils.sync import validate_text_in_string, collect_streaming_response
 
+from agentex import Agentex
+from agentex.types import TextContent, TextContentParam
+from agentex.types.agent_rpc_params import ParamsCreateTaskRequest, ParamsSendMessageRequest
+from agentex.lib.sdk.fastacp.base.base_acp_server import uuid
 
 # Configuration from environment variables
 AGENTEX_API_BASE_URL = os.environ.get("AGENTEX_API_BASE_URL", "http://localhost:5003")

examples/tutorials/00_sync/020_streaming/project/acp.py

@@ -1,19 +1,19 @@
 import os
 from typing import Union, AsyncGenerator
 
+from agents import Agent, Runner, RunConfig
+
 from agentex.lib import adk
 from agentex.lib.types.acp import SendMessageParams
+from agentex.lib.types.converters import convert_task_messages_to_oai_agents_inputs
 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_delta import TextDelta
-from agentex.types.task_message_update import (
-    TaskMessageUpdate,
-    StreamTaskMessageDone,
-    StreamTaskMessageFull,
-    StreamTaskMessageDelta,
-)
+from agentex.types.task_message_update import TaskMessageUpdate, StreamTaskMessageFull
 from agentex.types.task_message_content import TextContent, TaskMessageContent
+from agentex.lib.adk.providers._modules.sync_provider import (
+    SyncStreamingProvider,
+    convert_openai_to_agentex_events,
+)
 
 # Create an ACP server
 acp = FastACP.create(
@@ -69,40 +69,35 @@ async def handle_message_send(
 
     task_messages = await adk.messages.list(task_id=params.task.id)
 
-    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", ""))
-            for message in task_messages
-            if message.content and getattr(message.content, "type", None) == "text"
-        ],
-    ]
 
-    #########################################################
-    # 4. Call an LLM to respond to the user's message and stream the response to the client.
-    #########################################################
+    # Initialize the provider and run config to allow for tracing
+    provider = SyncStreamingProvider(
+        trace_id=params.task.id,
+    )
 
-    # Call an LLM to respond to the user's message
+    # Initialize the run config to allow for tracing and streaming
+    run_config = RunConfig(
+        model_provider=provider,
+    )
 
-    print(f"Calling LLM with model {state.model} and messages {llm_messages}")
 
-    # The Agentex server automatically commits input and output messages to the database so you don't need to do this yourself, simply process the input content and return the output content.
+    test_agent = Agent(name="assistant", instructions=state.system_prompt, model=state.model)
+
+    # Convert task messages to OpenAI Agents SDK format
+    input_list = convert_task_messages_to_oai_agents_inputs(task_messages)
+
+    # Run the agent and stream the events
+    result = Runner.run_streamed(test_agent, input_list, run_config=run_config)
+
+
+    #########################################################
+    # 4. Stream the events to the client.
+    #########################################################
+    # Convert the OpenAI events to Agentex events
+    # This is done by converting the OpenAI events to Agentex events and yielding them to the client
+    stream = result.stream_events()
+
+    # Yield the Agentex events to the client
+    async for agentex_event in convert_openai_to_agentex_events(stream):
+        yield agentex_event
 
-    message_index = 0
-    async for chunk in adk.providers.litellm.chat_completion_stream(
-        llm_config=LLMConfig(model=state.model, messages=llm_messages, stream=True),
-        trace_id=params.task.id,
-    ):
-        if chunk and chunk.choices and chunk.choices[0].delta and chunk.choices[0].delta.content:
-            yield StreamTaskMessageDelta(
-                type="delta",
-                index=message_index,
-                delta=TextDelta(type="text", text_delta=chunk.choices[0].delta.content or ""),
-            )
-
-    yield StreamTaskMessageDone(
-        type="done",
-        index=message_index,
-    )

examples/tutorials/00_sync/020_streaming/pyproject.toml

@@ -9,7 +9,7 @@ description = "An AgentEx agent that does multiturn streaming chat"
 readme = "README.md"
 requires-python = ">=3.12"
 dependencies = [
-    "agentex-sdk",
+    "agentex-sdk==0.4.28",
     "scale-gp",
 ]