Alexis MarasiganandCopybara-Service b725045e5a fix: fix httpx client closure during event pagination
Merge https://github.com/google/adk-python/pull/3756

move event iteration inside api_client context in get_session

Move event iteration inside the api_client context manager in VertexAiSessionService.get_session() to prevent client closure during multi-page event fetching.

**Please ensure you have read the [contribution guide](https://github.com/google/adk-python/blob/main/CONTRIBUTING.md) before creating a pull request.**

### Link to Issue or Description of Change

**1. Link to an existing issue (if applicable):**

- Closes: #3757

**2. Or, if no issue exists, describe the change:**

**Problem:**

When a session contains more than 100 events (requiring pagination), `VertexAiSessionService.get_session()` fails with:

```
RuntimeError: Cannot send a request, as the client has been closed.
```

The root cause is that the `events_iterator` is consumed **outside** the `async with self._get_api_client() as api_client:` context block. When the iterator needs to fetch page 2, 3, etc., the API client has already been closed because the `async with` block has exited.

```python
# Current buggy flow:
async with self._get_api_client() as api_client:
    get_session_response, events_iterator = await asyncio.gather(...)
# ← Client closed here

async for event in events_iterator:  # ← Fails on page 2+ (client closed)
    session.events.append(...)
```

**Solution:**

Move the session creation, user validation, and event iteration **inside** the `async with` block so the API client remains open during the entire pagination process:

```python
async with self._get_api_client() as api_client:
    get_session_response, events_iterator = await asyncio.gather(...)
    # Validation and session creation...
    async for event in events_iterator:  # ← Now works for all pages
        session.events.append(...)
# Client closed after all events are fetched
```

### Testing Plan

**Unit Tests:**

- [x] I have added or updated unit tests for my change.
- [x] All unit tests pass locally.

```bash
pytest tests/unittests/sessions/test_vertex_ai_session_service.py -v
```

**Added regression test:** `test_get_session_pagination_keeps_client_open`
- Creates a `MockAsyncClientWithPagination` that tracks whether it's inside the `async with` context
- Raises `RuntimeError` if iteration happens outside the context (matching real httpx behavior)
- Simulates 3 pages of events (100 + 100 + 50 = 250 events)
- Verifies all 250 events are successfully retrieved

**Manual End-to-End (E2E) Tests:**

1. Deploy an ADK agent to Vertex AI Agent Engine
2. Create a session and send 100+ messages to accumulate >100 events
3. Verify `get_session()` successfully retrieves all events without error

**Before fix:**
```
RuntimeError: Cannot send a request, as the client has been closed.
```

**After fix:**
- Session with 201 events (3 pages) loads successfully
- All events are retrieved and appended to the session

### Checklist

- [x] I have read the [CONTRIBUTING.md](https://github.com/google/adk-python/blob/main/CONTRIBUTING.md) document.
- [x] I have performed a self-review of my own code.
- [x] I have commented my code, particularly in hard-to-understand areas.
- [x] I have added tests that prove my fix is effective or that my feature works.
- [x] New and existing unit tests pass locally with my changes.
- [x] I have manually tested my changes end-to-end.
- [x] Any dependent changes have been merged and published in downstream modules.

### Additional context

This bug affects any production deployment where users have extended conversations. Sessions accumulating >100 events (which triggers pagination) become completely unusable as the agent cannot load the session to process new messages.

The fix is minimal and maintains backward compatibility - it only changes the scope of the `async with` block without altering any logic or return values.

**Affected versions:** Tested on google-adk 1.19.0, but the bug exists in earlier versions as well.

COPYBARA_INTEGRATE_REVIEW=https://github.com/google/adk-python/pull/3756 from AlexisMarasigan:fix/vertex-ai-session-service-paginatio 01fbafa6524312f24f7c9feaffb07bff0ad49b77
PiperOrigin-RevId: 855451813
2026-01-12 17:27:29 -08:00
2025-12-04 13:54:17 -08:00
…
2025-11-03 13:33:53 -08:00

Agent Development Kit (ADK)

License PyPI Python Unit Tests r/agentdevelopmentkit Ask Code Wiki

<html>

An open-source, code-first Python framework for building, evaluating, and deploying sophisticated AI agents with flexibility and control.

</html>

Agent Development Kit (ADK) is a flexible and modular framework that applies software development principles to AI agent creation. It is designed to simplify building, deploying, and orchestrating agent workflows, from simple tasks to complex systems. While optimized for Gemini, ADK is model-agnostic, deployment-agnostic, and compatible with other frameworks.


πŸ”₯ What's new

  • Custom Service Registration: Add a service registry to provide a generic way to register custom service implementations to be used in FastAPI server. See short instruction. (391628f)

  • Rewind: Add the ability to rewind a session to before a previous invocation (9dce06f).

  • New CodeExecutor: Introduces a new AgentEngineSandboxCodeExecutor class that supports executing agent-generated code using the Vertex AI Code Execution Sandbox API (ee39a89)

✨ Key Features

  • Rich Tool Ecosystem: Utilize pre-built tools, custom functions, OpenAPI specs, MCP tools or integrate existing tools to give agents diverse capabilities, all for tight integration with the Google ecosystem.

  • Code-First Development: Define agent logic, tools, and orchestration directly in Python for ultimate flexibility, testability, and versioning.

  • Agent Config: Build agents without code. Check out the Agent Config feature.

  • Tool Confirmation: A tool confirmation flow(HITL) that can guard tool execution with explicit confirmation and custom input.

  • Modular Multi-Agent Systems: Design scalable applications by composing multiple specialized agents into flexible hierarchies.

  • Deploy Anywhere: Easily containerize and deploy agents on Cloud Run or scale seamlessly with Vertex AI Agent Engine.

πŸš€ Installation

You can install the latest stable version of ADK using pip:

pip install google-adk

The release cadence is roughly bi-weekly.

This version is recommended for most users as it represents the most recent official release.

Development Version

Bug fixes and new features are merged into the main branch on GitHub first. If you need access to changes that haven't been included in an official PyPI release yet, you can install directly from the main branch:

pip install git+https://github.com/google/adk-python.git@main

Note: The development version is built directly from the latest code commits. While it includes the newest fixes and features, it may also contain experimental changes or bugs not present in the stable release. Use it primarily for testing upcoming changes or accessing critical fixes before they are officially released.

πŸ€– Agent2Agent (A2A) Protocol and ADK Integration

For remote agent-to-agent communication, ADK integrates with the A2A protocol. See this example for how they can work together.

πŸ“š Documentation

Explore the full documentation for detailed guides on building, evaluating, and deploying agents:

🏁 Feature Highlight

Define a single agent:

from google.adk.agents import Agent
from google.adk.tools import google_search

root_agent = Agent(
    name="search_assistant",
    model="gemini-2.5-flash", # Or your preferred Gemini model
    instruction="You are a helpful assistant. Answer user questions using Google Search when needed.",
    description="An assistant that can search the web.",
    tools=[google_search]
)

Define a multi-agent system:

Define a multi-agent system with coordinator agent, greeter agent, and task execution agent. Then ADK engine and the model will guide the agents works together to accomplish the task.

from google.adk.agents import LlmAgent, BaseAgent

# Define individual agents
greeter = LlmAgent(name="greeter", model="gemini-2.5-flash", ...)
task_executor = LlmAgent(name="task_executor", model="gemini-2.5-flash", ...)

# Create parent agent and assign children via sub_agents
coordinator = LlmAgent(
    name="Coordinator",
    model="gemini-2.5-flash",
    description="I coordinate greetings and tasks.",
    sub_agents=[ # Assign sub_agents here
        greeter,
        task_executor
    ]
)

Development UI

A built-in development UI to help you test, evaluate, debug, and showcase your agent(s).

Evaluate Agents

adk eval \
    samples_for_testing/hello_world \
    samples_for_testing/hello_world/hello_world_eval_set_001.evalset.json

🀝 Contributing

We welcome contributions from the community! Whether it's bug reports, feature requests, documentation improvements, or code contributions, please see our

Community Repo

We have adk-python-community repo that is home to a growing ecosystem of community-contributed tools, third-party service integrations, and deployment scripts that extend the core capabilities of the ADK.

Vibe Coding

If you want to develop agent via vibe coding the llms.txt and the llms-full.txt can be used as context to LLM. While the former one is a summarized one and the later one has the full information in case your LLM has big enough context window.

Community Events

  • [Completed] ADK's 1st community meeting on Wednesday, October 15, 2025. Remember to join our group to get access to the recording, and deck.

πŸ“„ License

This project is licensed under the Apache 2.0 License - see the LICENSE file for details.


Happy Agent Building!

S
Description
No description provided
Readme Apache-2.0
45 MiB
Languages
Python 64.2%
JavaScript 32.9%
Jupyter Notebook 2.4%
HTML 0.4%