akshaypachpute-1998andCopybara-Service
0b89f1882d
Merge https://github.com/google/adk-python/pull/2409 Description: This PR Fixes: #2407 The AgentTool in /google/adk/tools/agent_tool.py uses a hardcoded user_id='tmp_user' when creating a new session for the agent it wraps. This happens within the run_async method. code snippet ... @override async def run_async( self, *, args: dict[str, Any], tool_context: ToolContext, ) -> Any: ... session = await runner.session_service.create_session( app_name=self.agent.name, user_id='tmp_user', # <-- This is hardcoded state=tool_context.state.to_dict(), ) ... Why is this a problem? This hardcoding breaks the chain of user identity. When a parent agent calls a sub-agent via the AgentTool, the original user_id is lost. Any tool or logic inside the sub-agent that needs to perform user-specific actions (e.g., accessing user data from a database, retrieving user-specific memory, checking permissions) will fail or operate on the wrong context because it receives 'tmp_user' instead of the actual user's ID. Impact: This prevents the creation of robust, multi-agent applications where user context must be maintained across different agents and tools. It limits the utility of AgentTool to only stateless sub-agents that do not require user-specific information. Suggested Fix: The user_id should be retrieved from the parent context, which is available via the tool_context parameter passed into run_async. The create_session call should be updated to use the dynamic user_id from the parent session.For example, the fix might involve accessing the user ID from the tool_context. code-snippet session = await runner.session_service.create_session( app_name=self.agent.name, user_id=tool_context._invocation_context.user_id, state=tool_context.state.to_dict(), ) To Reproduce Steps to reproduce the behavior: To reproduce this bug, we need to set up a two-agent system: a ParentAgent that calls a ChildAgent using the AgentTool. The ChildAgent will have a tool designed to simply return the user_id it receives from its context. Expected behavior It should return the user_id of the user calling the agent, but, in current situation we are getting tmp_user COPYBARA_INTEGRATE_REVIEW=https://github.com/google/adk-python/pull/2409 from akshaypachpute-1998:fix-issue-2407-agent-tool-context-propogation 0c3e8656fdf11386e3ab13a3a1f2df99a396dbd1 PiperOrigin-RevId: 798315832
Agent Development Kit (ADK)
An open-source, code-first Python toolkit for building, evaluating, and deploying sophisticated AI agents with flexibility and control.
Important Links: Docs, Samples, Java ADK & ADK Web.
</html>Agent Development Kit (ADK) is a flexible and modular framework for developing and deploying AI agents. While optimized for Gemini and the Google ecosystem, ADK is model-agnostic, deployment-agnostic, and is built for compatibility with other frameworks. ADK was designed to make agent development feel more like software development, to make it easier for developers to create, deploy, and orchestrate agentic architectures that range from simple tasks to complex workflows.
✨ What's new
- Agent Config: Build agents without code. Check out the Agent Config feature.
✨ Key Features
-
Rich Tool Ecosystem: Utilize pre-built tools, custom functions, OpenAPI specs, 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.
-
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.
🤖 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.
🚀 Installation
Stable Release (Recommended)
You can install the latest stable version of ADK using pip:
pip install google-adk
The release cadence is 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.
📚 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-2.0-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-2.0-flash", ...)
task_executor = LlmAgent(name="task_executor", model="gemini-2.0-flash", ...)
# Create parent agent and assign children via sub_agents
coordinator = LlmAgent(
name="Coordinator",
model="gemini-2.0-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.
Vibe Coding
If you are 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.
📄 License
This project is licensed under the Apache 2.0 License - see the LICENSE file for details.
Happy Agent Building!