George Weale
fabf0fd552
Both cross-loop startup tests entered mock.patch.object on the shared plugin instance from inside each worker thread. patch.object swaps and restores one attribute on one object and is not thread safe: when two threads read the original before either installs its mock, both record that the attribute was absent from the instance, and both delete it on exit. The second delete raises, so the test failed with AttributeError: object has no attribute "_lazy_setup" which is the mock unwinding itself, not anything about coalescing. Install the mock once from the test thread and let the worker threads race only on _ensure_started, which is what these tests are for. The behaviour under test is unchanged: both loops still call in concurrently and setup still has to coalesce to a single run. Co-authored-by: George Weale <gweale@google.com> PiperOrigin-RevId: 953746627
Agent Development Kit (ADK) 2.0
An open-source, code-first Python framework for building, evaluating, and deploying sophisticated AI agents with flexibility and control.
Important Links: Docs, Samples & ADK Web.
⚠️ BREAKING CHANGES FROM 1.x
This release includes breaking changes to the agent API, event model, and session schema. Sessions generated by ADK 2.0 are readable by ADK 1.28+ (extra fields will be ignored), but are incompatible with older 1.x versions.
🔥 What's New in 2.0
-
Workflow Runtime: A graph-based execution engine for composing deterministic execution flows for agentic apps, with support for routing, fan-out/fan-in, loops, retry, state management, dynamic nodes, human-in-the-loop, and nested workflows.
-
Task API: Structured agent-to-agent delegation with multi-turn task mode, single-turn controlled output, mixed delegation patterns, human-in-the-loop, and task agents as workflow nodes.
🚀 Installation
pip install google-adk
Requirements: Python 3.10+.
To install optional integrations, you can use the following command:
pip install "google-adk[extensions]"
The release cadence is roughly bi-weekly.
Quick Start
Beginner Note: ADK applications are built using two main classes:
Agent(defines an AI's instructions, tools, and behavior) andWorkflow(orchestrates agents and tasks in a graph-based flow).
Agent
from google.adk import Agent
root_agent = Agent(
name="greeting_agent",
model="gemini-2.5-flash",
instruction="You are a helpful assistant. Greet the user warmly.",
)
Workflow
from google.adk import Agent, Workflow
generate_fruit_agent = Agent(
name="generate_fruit_agent",
instruction="Return the name of a random fruit. Return only the name.",
)
generate_benefit_agent = Agent(
name="generate_benefit_agent",
instruction="Tell me a health benefit about the specified fruit.",
)
root_agent = Workflow(
name="root_agent",
edges=[("START", generate_fruit_agent, generate_benefit_agent)],
)
Run Locally
# Interactive CLI
adk run path/to/my_agent
# Web UI (supports multi-agent directories or pointing directly to a single agent folder)
adk web path/to/agents_dir
📚 Documentation
- Getting Started: https://google.github.io/adk-docs/
- Samples: See
contributing/workflow_samples/andcontributing/task_samples/for workflow and task API examples.
🤝 Contributing
See CONTRIBUTING.md for details.
📄 License
This project is licensed under the Apache 2.0 License — see the LICENSE file for details.