release: 0.5.2 (#178)

* chore: sync repo

* feat(api): manual updates

* codegen metadata

* codegen metadata

* codegen metadata

* codegen metadata

* release: 0.5.2

---------

Co-authored-by: stainless-app[bot] <142633134+stainless-app[bot]@users.noreply.github.com>
Co-authored-by: Daniel Miller <[email protected]>

Author: stainless-app[bot]

.github/workflows/publish-pypi.yml

@@ -1,6 +1,6 @@
 # This workflow is triggered when a GitHub release is created.
 # It can also be run manually to re-publish to PyPI in case it failed for some reason.
-# You can run this workflow by navigating to https://www.github.com/scaleapi/agentex-python/actions/workflows/publish-pypi.yml
+# You can run this workflow by navigating to https://www.github.com/scaleapi/scale-agentex-python/actions/workflows/publish-pypi.yml
 name: Publish PyPI
 on:
   workflow_dispatch:

.github/workflows/release-doctor.yml

@@ -9,7 +9,7 @@ jobs:
   release_doctor:
     name: release doctor
     runs-on: ubuntu-latest
-    if: github.repository == 'scaleapi/agentex-python' && (github.event_name == 'push' || github.event_name == 'workflow_dispatch' || startsWith(github.head_ref, 'release-please') || github.head_ref == 'next')
+    if: github.repository == 'scaleapi/scale-agentex-python' && (github.event_name == 'push' || github.event_name == 'workflow_dispatch' || startsWith(github.head_ref, 'release-please') || github.head_ref == 'next')
 
     steps:
       - uses: actions/checkout@v4

.release-please-manifest.json

@@ -1,3 +1,3 @@
 {
-  ".": "0.5.1"
+  ".": "0.5.2"
 }

.stats.yml

@@ -1,4 +1,4 @@
 configured_endpoints: 34
-openapi_spec_url: https://storage.googleapis.com/stainless-sdk-openapi-specs/sgp%2Fagentex-sdk-2b422fbf02ff3b77795fb8c71cbe784de3a3add48560655ba4fe7f3fcc509995.yml
-openapi_spec_hash: bca5c04d823694c87417dae188480291
-config_hash: 6481ea6b42040f435dedcb00a98f35f8
+openapi_spec_url: https://storage.googleapis.com/stainless-sdk-openapi-specs/sgp%2Fagentex-sdk-2ba36d013a2829080b1003d4fecc30e89447db013f87491dd4d76f728d200b85.yml
+openapi_spec_hash: ff4de1c4bd38d4ff35bcf30755f0d870
+config_hash: 0197f86ba1a4b1b5ce813d0e62138588

CHANGELOG.md

@@ -1,12 +1,18 @@
 # Changelog
 
-## 0.5.1 (2025-10-29)
+## 0.5.2 (2025-10-31)
 
-Full Changelog: [v0.5.0...v0.5.1](https://github.com/scaleapi/agentex-python/compare/v0.5.0...v0.5.1)
+Full Changelog: [v0.5.0...v0.5.2](https://github.com/scaleapi/scale-agentex-python/compare/v0.5.0...v0.5.2)
 
-### Bug Fixes
+### Features
+
+* **api:** manual updates ([dc66b57](https://github.com/scaleapi/scale-agentex-python/commit/dc66b57618525669b3aa15676343ef542675a5f9))
+* bump the helm chart version ([1ffafb0](https://github.com/scaleapi/scale-agentex-python/commit/1ffafb0406138d6abd84254fa394b88c4a28ce70))
+
+
+### Chores
 
-* **client:** close streams without requiring full consumption ([f56acae](https://github.com/scaleapi/agentex-python/commit/f56acae74ee83a116e735ca7bf68f2096aafaf6e))
+* sync repo ([0e05416](https://github.com/scaleapi/scale-agentex-python/commit/0e05416219ca93ae347e6175804bc0f2259a6b44))
 
 ## 0.5.0 (2025-10-28)
 

CONTRIBUTING.md

@@ -62,7 +62,7 @@ If you’d like to use the repository from source, you can either install from g
 To install via git:
 
 ```sh
-$ pip install git+ssh://[email protected]/scaleapi/agentex-python.git
+$ pip install git+ssh://[email protected]/scaleapi/scale-agentex-python.git
 ```
 
 Alternatively, you can build from source and install the wheel file:
@@ -120,7 +120,7 @@ the changes aren't made through the automated pipeline, you may want to make rel
 
 ### Publish with a GitHub workflow
 
-You can release to package managers by using [the `Publish PyPI` GitHub action](https://www.github.com/scaleapi/agentex-python/actions/workflows/publish-pypi.yml). This requires a setup organization or repository secret to be set up.
+You can release to package managers by using [the `Publish PyPI` GitHub action](https://www.github.com/scaleapi/scale-agentex-python/actions/workflows/publish-pypi.yml). This requires a setup organization or repository secret to be set up.
 
 ### Publish manually
 

README.md

@@ -268,9 +268,9 @@ task = response.parse()  # get the object that `tasks.list()` would have returne
 print(task)
 ```
 
-These methods return an [`APIResponse`](https://github.com/scaleapi/agentex-python/tree/main/src/agentex/_response.py) object.
+These methods return an [`APIResponse`](https://github.com/scaleapi/scale-agentex-python/tree/main/src/agentex/_response.py) object.
 
-The async client returns an [`AsyncAPIResponse`](https://github.com/scaleapi/agentex-python/tree/main/src/agentex/_response.py) with the same structure, the only difference being `await`able methods for reading the response content.
+The async client returns an [`AsyncAPIResponse`](https://github.com/scaleapi/scale-agentex-python/tree/main/src/agentex/_response.py) with the same structure, the only difference being `await`able methods for reading the response content.
 
 #### `.with_streaming_response`
 
@@ -374,7 +374,7 @@ This package generally follows [SemVer](https://semver.org/spec/v2.0.0.html) con
 
 We take backwards-compatibility seriously and work hard to ensure you can rely on a smooth upgrade experience.
 
-We are keen for your feedback; please open an [issue](https://www.github.com/scaleapi/agentex-python/issues) with questions, bugs, or suggestions.
+We are keen for your feedback; please open an [issue](https://www.github.com/scaleapi/scale-agentex-python/issues) with questions, bugs, or suggestions.
 
 ### Determining the installed version
 

examples/tutorials/00_sync/000_hello_acp/README.md

@@ -3,10 +3,7 @@
 This is a simple AgentEx agent that just says hello and acknowledges the user's message to show which ACP methods need to be implemented for the sync ACP type.
 The simplest agent type: synchronous request/response pattern with a single `@acp.on_message_send` handler. Best for stateless operations that complete immediately.
 
-## What You'll Learn
-- Building a basic synchronous agent
-- The `@acp.on_message_send` handler pattern
-- When to use sync vs agentic agents
+## Official Documentation
 
 ## Prerequisites
 - Development environment set up (see [main repo README](https://github.com/scaleapi/scale-agentex))
@@ -42,3 +39,4 @@ That's it - one handler, immediate response. No task creation, no state manageme
 Sync agents are the simplest way to get started with AgentEx. They're perfect for learning the basics and building stateless agents. Once you need conversation memory or task tracking, you'll graduate to agentic agents.
 
 **Next:** [010_multiturn](../010_multiturn/) - Add conversation memory to your agent
+[000 Hello ACP](https://dev.agentex.scale.com/docs/tutorials/sync/000_hello_acp)

examples/tutorials/00_sync/010_multiturn/README.md

@@ -1,54 +1,7 @@
 # [Sync] Multiturn
 
-Handle multi-turn conversations in synchronous agents by manually maintaining conversation history and context between messages.
+This tutorial demonstrates how to handle multiturn conversations in AgentEx agents using the Agent 2 Client Protocol (ACP).
 
-## What You'll Learn
-- How to handle conversation history in sync agents
-- Building context from previous messages
-- The limitations of stateless multiturn patterns
+## Official Documentation
 
-## Prerequisites
-- Development environment set up (see [main repo README](https://github.com/scaleapi/scale-agentex))
-- Backend services running: `make dev` from repository root
-- Understanding of basic sync agents (see [000_hello_acp](../000_hello_acp/))
-
-## Quick Start
-
-```bash
-cd examples/tutorials/00_sync/010_multiturn
-uv run agentex agents run --manifest manifest.yaml
-```
-
-## Key Pattern
-
-Sync agents are stateless by default. To handle multi-turn conversations, you need to:
-1. Accept conversation history in the request
-2. Maintain context across messages
-3. Return responses that build on previous exchanges
-
-```python
-@acp.on_message_send
-async def handle_message_send(params: SendMessageParams):
-    # Accept conversation history from client
-    history = params.conversation_history
-
-    # Build context from history
-    context = build_context(history)
-
-    # Generate response considering full context
-    response = generate_response(params.content, context)
-
-    return TextContent(author="agent", content=response)
-```
-
-The handler accepts history, builds context, and returns responses that reference previous exchanges.
-
-## When to Use
-- Simple chatbots that need conversation memory
-- When client can maintain and send conversation history
-- Quick prototypes before building full agentic agents
-
-## Why This Matters
-While sync agents can handle conversations, you're responsible for managing state on the client side. This becomes complex quickly. For production conversational agents, consider agentic agents ([10_agentic/00_base/010_multiturn](../../10_agentic/00_base/010_multiturn/)) where the platform manages state automatically.
-
-**Next:** [020_streaming](../020_streaming/) - Stream responses in real-time
+[010 Multiturn](https://dev.agentex.scale.com/docs/tutorials/sync/010_multiturn)

examples/tutorials/00_sync/020_streaming/README.md

@@ -1,45 +1,9 @@
 # [Sync] Streaming
 
-Stream responses progressively using async generators instead of returning a single message. Enables showing partial results as they're generated.
+This tutorial demonstrates how to implement streaming responses in AgentEx agents using the Agent 2 Client Protocol (ACP).
 
-## What You'll Learn
-- How to stream responses using async generators
-- The `yield` pattern for progressive updates
-- When streaming improves user experience
+## Official Documentation
 
-## Prerequisites
-- Development environment set up (see [main repo README](https://github.com/scaleapi/scale-agentex))
-- Backend services running: `make dev` from repository root
-- Understanding of basic sync agents (see [000_hello_acp](../000_hello_acp/))
+[020 Streaming](https://dev.agentex.scale.com/docs/tutorials/sync/020_streaming)
 
-## Quick Start
 
-```bash
-cd examples/tutorials/00_sync/020_streaming
-uv run agentex agents run --manifest manifest.yaml
-```
-
-## Key Code
-
-```python
-@acp.on_message_send
-async def handle_message_send(params: SendMessageParams):
-    async def stream_response():
-        for chunk in response_chunks:
-            yield TaskMessageUpdate(content=TextContent(...))
-
-    return stream_response()
-```
-
-Return an async generator instead of a single response - each `yield` sends an update to the client.
-
-## When to Use
-- Streaming LLM responses (OpenAI, Anthropic, etc.)
-- Large data processing with progress updates
-- Any operation that takes >1 second to complete
-- Improving perceived responsiveness
-
-## Why This Matters
-Streaming dramatically improves user experience for longer operations. Instead of waiting 10 seconds for a complete response, users see results immediately as they're generated. This is essential for modern AI agents.
-
-**Next:** Ready for task management? → [10_agentic/00_base/000_hello_acp](../../10_agentic/00_base/000_hello_acp/)