不做了睡大觉
2780ae2892
Merge https://github.com/google/adk-python/pull/4618 ## Summary Fixes #4564 When using `output_key` with a `temp:` prefix (e.g. `output_key='temp:result'`) in a `SequentialAgent`, the output was silently lost. Agent-2 could never read the temp state written by agent-1. ## Root Cause Two issues in `append_event`: 1. `_trim_temp_delta_state()` removed temp keys from the event delta **before** `_update_session_state()` could apply them to the in-memory session 2. `_update_session_state()` also explicitly skipped `temp:`-prefixed keys ```python # Before (broken ordering): async def append_event(self, session, event): event = self._trim_temp_delta_state(event) # temp keys gone! self._update_session_state(session, event) # nothing to apply ``` ## Fix Introduce `_apply_temp_state()` which writes temp-scoped keys to the in-memory `session.state` **before** the event delta is trimmed: ```python # After: async def append_event(self, session, event): self._apply_temp_state(session, event) # temp keys → session.state event = self._trim_temp_delta_state(event) # temp keys removed from delta self._update_session_state(session, event) # non-temp keys applied ``` This ensures: - ✅ Temp state is available to subsequent agents within the same invocation - ✅ Temp state is still stripped from event deltas (not persisted to storage) - ✅ All three session services (InMemory, Database, SQLite) behave consistently ## Files Changed - `src/google/adk/sessions/base_session_service.py`: Added `_apply_temp_state()`, reordered `append_event` logic, removed temp-skip in `_update_session_state` - `src/google/adk/sessions/database_session_service.py`: Added `_apply_temp_state()` call before trim - `src/google/adk/sessions/sqlite_session_service.py`: Added `_apply_temp_state()` call before trim - `tests/unittests/sessions/test_session_service.py`: Updated existing test + added new test for sequential agent scenario ## Testing All 67 session service tests pass across InMemory, Database, and SQLite backends. COPYBARA_INTEGRATE_REVIEW=https://github.com/google/adk-python/pull/4618 from stakeswky:fix/temp-state-output-key b9fc737e7a6dc07e06e99af3271a8fc026acae4a PiperOrigin-RevId: 878499263
Agent Development Kit (ADK)
An open-source, code-first Python framework for building, evaluating, and deploying sophisticated AI agents with flexibility and control.
Important Links: Docs, Samples, Java ADK, Go ADK & ADK Web.
</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.
🔥 What's new
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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)
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Rewind: Add the ability to rewind a session to before a previous invocation (9dce06f).
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New CodeExecutor: Introduces a new AgentEngineSandboxCodeExecutor class that supports executing agent-generated code using the Vertex AI Code Execution Sandbox API (ee39a89)
✨ Key Features
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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.
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Code-First Development: Define agent logic, tools, and orchestration directly in Python for ultimate flexibility, testability, and versioning.
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Agent Config: Build agents without code. Check out the Agent Config feature.
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Tool Confirmation: A tool confirmation flow(HITL) that can guard tool execution with explicit confirmation and custom input.
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Modular Multi-Agent Systems: Design scalable applications by composing multiple specialized agents into flexible hierarchies.
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Deploy Anywhere: Easily containerize and deploy agents on Cloud Run or scale seamlessly with Vertex AI Agent Engine.
🚀 Installation
Stable Release (Recommended)
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.
🤖 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.
📚 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 to work 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
- General contribution guideline and flow.
- Then if you want to contribute code, please read Code Contributing Guidelines to get started.
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.
📄 License
This project is licensed under the Apache 2.0 License - see the LICENSE file for details.
Happy Agent Building!