feat: Add adk project overview and architecture

This explains the high-level architecture and philosophy of the ADK project.
It can also be feed into LLMs for vibe-coding.

PiperOrigin-RevId: 780178125

Author: hangfei

AGENTS.md

@@ -6,6 +6,10 @@ This document provides context for the Gemini CLI and Gemini Code Assist to unde
 
 The Agent Development Kit (ADK) is an open-source, code-first Python toolkit for building, evaluating, and deploying sophisticated AI agents with flexibility and control. 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.
 
+## Project Architecture
+
+Please refer to [ADK Project Overview and Architecture](https://github.com/google/adk-python/blob/main/contributing/adk_project_overview_and_architecture.md) for details.
+
 ## ADK: Style Guides
 
 ### Python Style Guide

contributing/README.md

@@ -7,3 +7,10 @@ This folder host resources for ADK contributors, for example, testing samples et
 Samples folder host samples to test different features. The samples are usually minimal and simplistic to test one or a few scenarios.
 
 **Note**: This is different from the [google/adk-samples](https://github.com/google/adk-samples) repo, which hosts more complex e2e samples for customers to use or modify directly.
+
+## ADK project and architecture overview
+
+The [adk_project_overview_and_architecture.md](adk_project_overview_and_architecture.md) describes the ADK project overview and its technical architecture from high-level.
+
+This is helpful for contributors to understand the project and design philosophy.
+ It can also be feed into LLMs for vibe-coding.

contributing/adk_project_overview_and_architecture.md

@@ -0,0 +1,111 @@
+# ADK Project Overview and Architecture
+
+Google Agent Development Kit (ADK) for Python
+
+## Core Philosophy & Architecture
+
+- Code-First: Everything is defined in Python code for versioning, testing, and IDE support. Avoid GUI-based logic.
+
+- Modularity & Composition: We build complex multi-agent systems by composing multiple, smaller, specialized agents.
+
+- Deployment-Agnostic: The agent's core logic is separate from its deployment environment. The same agent.py can be run locally for testing, served via an API, or deployed to the cloud.
+
+## Foundational Abstractions (Our Vocabulary)
+
+- Agent: The blueprint. It defines an agent's identity, instructions, and tools. It's a declarative configuration object.
+
+- Tool: A capability. A Python function an agent can call to interact with the world (e.g., search, API call).
+
+- Runner: The engine. It orchestrates the "Reason-Act" loop, manages LLM calls, and executes tools.
+
+- Session: The conversation state. It holds the history for a single, continuous dialogue.
+
+- Memory: Long-term recall across different sessions.
+
+- Artifact Service: Manages non-textual data like files.
+
+## Canonical Project Structure
+
+Adhere to this structure for compatibility with ADK tooling.
+
+my_adk_project/ \
+└── src/ \
+    └── my_app/ \
+        ├── agents/ \
+        │   ├── my_agent/ \
+        │   │   ├── __init__.py   # Must contain: from. import agent \
+        │   │   └── agent.py      # Must contain: root_agent = Agent(...) \
+        │   └── another_agent/ \
+        │       ├── __init__.py \
+        │       └── agent.py\
+
+agent.py: Must define the agent and assign it to a variable named root_agent. This is how ADK's tools find it.
+
+__init__.py: In each agent directory, it must contain from. import agent to make the agent discoverable.
+
+## Local Development & Debugging
+
+Interactive UI (adk web): This is our primary debugging tool. It's a decoupled system:
+
+Backend: A FastAPI server started with adk api_server.
+
+Frontend: An Angular app that connects to the backend.
+
+Use the "Events" tab to inspect the full execution trace (prompts, tool calls, responses).
+
+CLI (adk run): For quick, stateless functional checks in the terminal.
+
+Programmatic (pytest): For writing automated unit and integration tests.
+
+## The API Layer (FastAPI)
+
+We expose agents as production APIs using FastAPI.
+
+- get_fast_api_app: This is the key helper function from google.adk.cli.fast_api that creates a FastAPI app from our agent directory.
+
+- Standard Endpoints: The generated app includes standard routes like /list-apps and /run_sse for streaming responses. The wire format is camelCase.
+
+- Custom Endpoints: We can add our own routes (e.g., /health) to the app object returned by the helper.
+
+Python
+
+from google.adk.cli.fast_api import get_fast_api_app
+app = get_fast_api_app(agent_dir="./agents")
+
+@app.get("/health")
+async def health_check():
+    return {"status": "ok"}
+
+
+## Deployment to Production
+
+The adk cli provides the "adk deploy" command to deploy to Google Vertex Agent Engine, Google CloudRun, Google GKE.
+
+## Testing & Evaluation Strategy
+
+Testing is layered, like a pyramid.
+
+### Layer 1: Unit Tests (Base)
+
+What: Test individual Tool functions in isolation.
+
+How: Use pytest in tests/test_tools.py. Verify deterministic logic.
+
+### Layer 2: Integration Tests (Middle)
+
+What: Test the agent's internal logic and interaction with tools.
+
+How: Use pytest in tests/test_agent.py, often with mocked LLMs or services.
+
+### Layer 3: Evaluation Tests (Top)
+
+What: Assess end-to-end performance with a live LLM. This is about quality, not just pass/fail.
+
+How: Use the ADK Evaluation Framework.
+
+Test Cases: Create JSON files with input and a reference (expected tool calls and final response).
+
+Metrics: tool_trajectory_avg_score (does it use tools correctly?) and response_match_score (is the final answer good?).
+
+Run via: adk web (UI), pytest (for CI/CD), or adk eval (CLI).
+