diff --git a/.github/CODEOWNERS b/.github/CODEOWNERS index 9411cc3e8..8024c5409 100644 --- a/.github/CODEOWNERS +++ b/.github/CODEOWNERS @@ -49,3 +49,6 @@ # Time Series Forecasting /java/agents/time-series-forecasting/ @kweinmeister + +# Short movie agents +/python/agents/short-movie-agents @rsamborski \ No newline at end of file diff --git a/README.md b/README.md index 26913adea..02d01fad2 100644 --- a/README.md +++ b/README.md @@ -43,9 +43,10 @@ To learn more, check out the [ADK Documentation](https://google.github.io/adk-do │ │ ├── RAG │ │ ├── realtime-conversational-agent │ │ ├── safety-plugins -│ │ ├── README.md +│ │ ├── short-movie-agents │ │ ├── software-bug-assistant -│ │ └── travel-concierge +│ │ ├── travel-concierge +│ │ └── README.md │ └── README.md └── README.md ``` diff --git a/python/agents/README.md b/python/agents/README.md index 8e7796207..c5cbe7c83 100644 --- a/python/agents/README.md +++ b/python/agents/README.md @@ -46,6 +46,7 @@ Check out the agent samples below, organized by category: | [Personalized Shopping](personalized-shopping) | Product Recommendations | E-commerce, Personalized agent, Shopping assistant, Single-agent, Product recommendation, Product discovery, Chatbot | Conversational | Easy | Single Agent | E-commerce | | [Vertex AI Retrieval Agent](RAG) | RAG Powered Agent / Answering questions related to documents uploaded to Vertex AI RAG Engine, providing informative responses with citations to source materials. | RAG engine | Workflow | Intermediate | Single Agent | Horizontal | | [Safety Guardrail Plugins](safety-plugins) | Safety filter plugins: Gemini as a judge, Model Armor as a filter | Plugin, Security, Guardrail, Jailbreak, Multiagent | Conversational/Workflow | Intermediate | Plugin | Safety &Security | +| [Short Movie Agents](short-movie-agents) | Constructs end to end videos based on the user's intent. | Multi-agent, Custom tool | Workflow | Intermediate | Multi Agent | Media | [Software Bug Assistant](software-bug-assistant) | Assists in software bug resolution by querying internal ticketing systems and external knowledge sources (GitHub, StackOverflow, Google Search) to find similar issues and diagnostics. | RAG, MCP, Bug Tracking, Google Search, IT Support, Database Integration, API | Workflow/Conversational | Intermediate | Single Agent | Horizontal / IT Support | | [Travel Concierge](travel-concierge) | Travel Concierge, Digital Tasks Assistant | Function tool (Python), Custom tool, Agent tool, Input and output schema, Updatable context, Dynamic instructions | Conversational | Advanced | Multi Agent | Travel | | [Auto Insurance Agent](auto-insurance-agent) | Auto Insurance Agent to manage members, claims, rewards and roadside assistance. | [Apigee](https://cloud.google.com/apigee/docs/api-platform/get-started/what-apigee), [Apigee API hub](https://cloud.google.com/apigee/docs/apihub/what-is-api-hub), Agent Tool | Conversational | Easy | Multi Agent | Financial Services diff --git a/python/agents/short-movie-agents/.env-template b/python/agents/short-movie-agents/.env-template new file mode 100644 index 000000000..ab2cf2735 --- /dev/null +++ b/python/agents/short-movie-agents/.env-template @@ -0,0 +1,11 @@ +# General +# GOOGLE_CLOUD_BUCKET_NAME=YOUR_GS_BUCKET_NAME + +# # AI Studio +# GOOGLE_GENAI_USE_VERTEXAI=FALSE +# GOOGLE_API_KEY=PASTE_YOUR_ACTUAL_API_KEY_HERE + +# # VertexAI +# GOOGLE_GENAI_USE_VERTEXAI=TRUE +# GOOGLE_CLOUD_PROJECT=YOUR_PROJECT_ID +# GOOGLE_CLOUD_LOCATION=LOCATION \ No newline at end of file diff --git a/python/agents/short-movie-agents/.gitignore b/python/agents/short-movie-agents/.gitignore new file mode 100644 index 000000000..b1b497d12 --- /dev/null +++ b/python/agents/short-movie-agents/.gitignore @@ -0,0 +1,178 @@ +# Byte-compiled / optimized / DLL files +__pycache__/ +*.py[cod] +*$py.class + +# C extensions +*.so + +# Distribution / packaging +.Python +build/ +develop-eggs/ +dist/ +downloads/ +eggs/ +.eggs/ +lib/ +lib64/ +parts/ +sdist/ +var/ +wheels/ +share/python-wheels/ +*.egg-info/ +.installed.cfg +*.egg +MANIFEST + +# PyInstaller +# Usually these files are written by a python script from a template +# before PyInstaller builds the exe, so as to inject date/other infos into it. +*.manifest +*.spec + +# Installer logs +pip-log.txt +pip-delete-this-directory.txt + +# Unit test / coverage reports +htmlcov/ +.tox/ +.nox/ +.coverage +.coverage.* +.cache +nosetests.xml +coverage.xml +*.cover +*.py,cover +.hypothesis/ +.pytest_cache/ +cover/ + +# Translations +*.mo +*.pot + +# Django stuff: +*.log +local_settings.py +db.sqlite3 +db.sqlite3-journal + +# Flask stuff: +instance/ +.webassets-cache + +# Scrapy stuff: +.scrapy + +# Sphinx documentation +docs/_build/ + +# PyBuilder +.pybuilder/ +target/ + +# Jupyter Notebook +.ipynb_checkpoints + +# IPython +profile_default/ +ipython_config.py + +# pyenv +# For a library or package, you might want to ignore these files since the code is +# intended to run in multiple environments; otherwise, check them in: +# .python-version + +# pipenv +# According to pypa/pipenv#598, it is recommended to include Pipfile.lock in version control. +# However, in case of collaboration, if having platform-specific dependencies or dependencies +# having no cross-platform support, pipenv may install dependencies that don't work, or not +# install all needed dependencies. +#Pipfile.lock + +# UV +# Similar to Pipfile.lock, it is generally recommended to include uv.lock in version control. +# This is especially recommended for binary packages to ensure reproducibility, and is more +# commonly ignored for libraries. +#uv.lock + +# poetry +# Similar to Pipfile.lock, it is generally recommended to include poetry.lock in version control. +# This is especially recommended for binary packages to ensure reproducibility, and is more +# commonly ignored for libraries. +# https://python-poetry.org/docs/basic-usage/#commit-your-poetrylock-file-to-version-control +#poetry.lock + +# pdm +# Similar to Pipfile.lock, it is generally recommended to include pdm.lock in version control. +#pdm.lock +# pdm stores project-wide configurations in .pdm.toml, but it is recommended to not include it +# in version control. +# https://pdm.fming.dev/latest/usage/project/#working-with-version-control +.pdm.toml +.pdm-python +.pdm-build/ + +# PEP 582; used by e.g. github.com/David-OConnor/pyflow and github.com/pdm-project/pdm +__pypackages__/ + +# Celery stuff +celerybeat-schedule +celerybeat.pid + +# SageMath parsed files +*.sage.py + +# Environments +.env +.env.local +.venv +env/ +venv/ +ENV/ +env.bak/ +venv.bak/ + +# Spyder project settings +.spyderproject +.spyproject + +# Rope project settings +.ropeproject + +# mkdocs documentation +/site + +# mypy +.mypy_cache/ +.dmypy.json +dmypy.json + +# Pyre type checker +.pyre/ + +# pytype static type analyzer +.pytype/ + +# Cython debug symbols +cython_debug/ + +# PyCharm +# JetBrains specific template is maintained in a separate JetBrains.gitignore that can +# be found at https://github.com/github/gitignore/blob/main/Global/JetBrains.gitignore +# and can be added to the global gitignore or merged into this file. For a more nuclear +# option (not recommended) you can uncomment the following to ignore the entire idea folder. +#.idea/ + +# VS Code +.vscode/ + +# Ruff stuff: +.ruff_cache/ + +# PyPI configuration file +.pypirc \ No newline at end of file diff --git a/python/agents/short-movie-agents/Changelog.md b/python/agents/short-movie-agents/Changelog.md new file mode 100644 index 000000000..f6fdec903 --- /dev/null +++ b/python/agents/short-movie-agents/Changelog.md @@ -0,0 +1,21 @@ +# Changelog + +## v0.3.1 - Refactor deployment and model updates + +- Upgraded image generation model to `imagen-4.0-ultra-generate-001`. +- Set the location for video generation model to `us-central1`. +- Added `GOOGLE_CLOUD_PROJECT` and `GOOGLE_CLOUD_LOCATION` to the backend deployment in `Makefile`. +- Removed Terraform-based deployment, updated `README.md` and `Makefile` to reflect this change. + +## v0.3.0 - Code updates based on agent-starter-pack 0.15.4 + +- Updated the codebase to align with the changes in `agent-starter-pack` version 0.15.4. + +## v0.2.0 - Moved to a director workflow architecture + +- Refactored the agent workflow from a sequential process to a director-based architecture for improved orchestration and flexibility. + +## v0.1.0 - Initial version + +- Initial release of the short movie generation agents. +- Implemented a sequential workflow for story generation, screenplay creation, storyboarding, and video production. diff --git a/python/agents/short-movie-agents/Dockerfile b/python/agents/short-movie-agents/Dockerfile new file mode 100644 index 000000000..801726183 --- /dev/null +++ b/python/agents/short-movie-agents/Dockerfile @@ -0,0 +1,29 @@ +# Copyright 2025 Google LLC +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +FROM python:3.13-slim + +RUN pip install --no-cache-dir uv==0.6.12 + +WORKDIR /code + +COPY ./pyproject.toml ./README.md ./uv.lock* ./ + +COPY ./app ./app + +RUN uv sync --frozen + +EXPOSE 8080 + +CMD ["uv", "run", "uvicorn", "app.server:app", "--host", "0.0.0.0", "--port", "8080"] \ No newline at end of file diff --git a/python/agents/short-movie-agents/GEMINI.md b/python/agents/short-movie-agents/GEMINI.md new file mode 100644 index 000000000..fba4407aa --- /dev/null +++ b/python/agents/short-movie-agents/GEMINI.md @@ -0,0 +1,1836 @@ +Coding Agent guidance: +# Google Agent Development Kit (ADK) Python Cheatsheet + +This document serves as a long-form, comprehensive reference for building, orchestrating, and deploying AI agents using the Python Agent Development Kit (ADK). It aims to cover every significant aspect with greater detail, more code examples, and in-depth best practices. + +## Table of Contents + +1. [Core Concepts & Project Structure](#1-core-concepts--project-structure) + * 1.1 ADK's Foundational Principles + * 1.2 Essential Primitives + * 1.3 Standard Project Layout + * 1.A Build Agents without Code (Agent Config) +2. [Agent Definitions (`LlmAgent`)](#2-agent-definitions-llmagent) + * 2.1 Basic `LlmAgent` Setup + * 2.2 Advanced `LlmAgent` Configuration + * 2.3 LLM Instruction Crafting +3. [Orchestration with Workflow Agents](#3-orchestration-with-workflow-agents) + * 3.1 `SequentialAgent`: Linear Execution + * 3.2 `ParallelAgent`: Concurrent Execution + * 3.3 `LoopAgent`: Iterative Processes +4. [Multi-Agent Systems & Communication](#4-multi-agent-systems--communication) + * 4.1 Agent Hierarchy + * 4.2 Inter-Agent Communication Mechanisms + * 4.3 Common Multi-Agent Patterns + * 4.A Distributed Communication (A2A Protocol) +5. [Building Custom Agents (`BaseAgent`)](#5-building-custom-agents-baseagent) + * 5.1 When to Use Custom Agents + * 5.2 Implementing `_run_async_impl` +6. [Models: Gemini, LiteLLM, and Vertex AI](#6-models-gemini-litellm-and-vertex-ai) + * 6.1 Google Gemini Models (AI Studio & Vertex AI) + * 6.2 Other Cloud & Proprietary Models via LiteLLM + * 6.3 Open & Local Models via LiteLLM (Ollama, vLLM) + * 6.4 Customizing LLM API Clients +7. [Tools: The Agent's Capabilities](#7-tools-the-agents-capabilities) + * 7.1 Defining Function Tools: Principles & Best Practices + * 7.2 The `ToolContext` Object: Accessing Runtime Information + * 7.3 All Tool Types & Their Usage +8. [Context, State, and Memory Management](#8-context-state-and-memory-management) + * 8.1 The `Session` Object & `SessionService` + * 8.2 `State`: The Conversational Scratchpad + * 8.3 `Memory`: Long-Term Knowledge & Retrieval + * 8.4 `Artifacts`: Binary Data Management +9. [Runtime, Events, and Execution Flow](#9-runtime-events-and-execution-flow) + * 9.1 The `Runner`: The Orchestrator + * 9.2 The Event Loop: Core Execution Flow + * 9.3 `Event` Object: The Communication Backbone + * 9.4 Asynchronous Programming (Python Specific) +10. [Control Flow with Callbacks](#10-control-flow-with-callbacks) + * 10.1 Callback Mechanism: Interception & Control + * 10.2 Types of Callbacks + * 10.3 Callback Best Practices + * 10.A Global Control with Plugins +11. [Authentication for Tools](#11-authentication-for-tools) + * 11.1 Core Concepts: `AuthScheme` & `AuthCredential` + * 11.2 Interactive OAuth/OIDC Flows + * 11.3 Custom Tool Authentication +12. [Deployment Strategies](#12-deployment-strategies) + * 12.1 Local Development & Testing (`adk web`, `adk run`, `adk api_server`) + * 12.2 Vertex AI Agent Engine + * 12.3 Cloud Run + * 12.4 Google Kubernetes Engine (GKE) + * 12.5 CI/CD Integration +13. [Evaluation and Safety](#13-evaluation-and-safety) + * 13.1 Agent Evaluation (`adk eval`) + * 13.2 Safety & Guardrails +14. [Debugging, Logging & Observability](#14-debugging-logging--observability) +15. [Streaming & Advanced I/O](#15-streaming--advanced-io) +16. [Performance Optimization](#16-performance-optimization) +17. [General Best Practices & Common Pitfalls](#17-general-best-practices--common-pitfalls) +18. [Official API & CLI References](#18-official-api--cli-references) + +--- + +## 1. Core Concepts & Project Structure + +### 1.1 ADK's Foundational Principles + +* **Modularity**: Break down complex problems into smaller, manageable agents and tools. +* **Composability**: Combine simple agents and tools to build sophisticated systems. +* **Observability**: Detailed event logging and tracing capabilities to understand agent behavior. +* **Extensibility**: Easily integrate with external services, models, and frameworks. +* **Deployment-Agnostic**: Design agents once, deploy anywhere. + +### 1.2 Essential Primitives + +* **`Agent`**: The core intelligent unit. Can be `LlmAgent` (LLM-driven) or `BaseAgent` (custom/workflow). +* **`Tool`**: Callable function/class providing external capabilities (`FunctionTool`, `OpenAPIToolset`, etc.). +* **`Session`**: A unique, stateful conversation thread with history (`events`) and short-term memory (`state`). +* **`State`**: Key-value dictionary within a `Session` for transient conversation data. +* **`Memory`**: Long-term, searchable knowledge base beyond a single session (`MemoryService`). +* **`Artifact`**: Named, versioned binary data (files, images) associated with a session or user. +* **`Runner`**: The execution engine; orchestrates agent activity and event flow. +* **`Event`**: Atomic unit of communication and history; carries content and side-effect `actions`. +* **`InvocationContext`**: The comprehensive root context object holding all runtime information for a single `run_async` call. + +### 1.3 Standard Project Layout + +A well-structured ADK project is crucial for maintainability and leveraging `adk` CLI tools. + +``` +your_project_root/ +├── my_first_agent/ # Each folder is a distinct agent app +│ ├── __init__.py # Makes `my_first_agent` a Python package (`from . import agent`) +│ ├── agent.py # Contains `root_agent` definition and `LlmAgent`/WorkflowAgent instances +│ ├── tools.py # Custom tool function definitions +│ ├── data/ # Optional: static data, templates +│ └── .env # Environment variables (API keys, project IDs) +├── my_second_agent/ +│ ├── __init__.py +│ └── agent.py +├── requirements.txt # Project's Python dependencies (e.g., google-adk, litellm) +├── tests/ # Unit and integration tests +│ ├── unit/ +│ │ └── test_tools.py +│ └── integration/ +│ └── test_my_first_agent.py +│ └── my_first_agent.evalset.json # Evaluation dataset for `adk eval` +└── main.py # Optional: Entry point for custom FastAPI server deployment +``` +* `adk web` and `adk run` automatically discover agents in subdirectories with `__init__.py` and `agent.py`. +* `.env` files are automatically loaded by `adk` tools when run from the root or agent directory. + +### 1.A Build Agents without Code (Agent Config) + +ADK allows you to define agents, tools, and even multi-agent workflows using a simple YAML format, eliminating the need to write Python code for orchestration. This is ideal for rapid prototyping and for non-programmers to configure agents. + +#### **Getting Started with Agent Config** + +* **Create a Config-based Agent**: + ```bash + adk create --type=config my_yaml_agent + ``` + This generates a `my_yaml_agent/` folder with `root_agent.yaml` and `.env` files. + +* **Environment Setup** (in `.env` file): + ```bash + # For Google AI Studio (simpler setup) + GOOGLE_GENAI_USE_VERTEXAI=0 + GOOGLE_API_KEY= + + # For Google Cloud Vertex AI (production) + GOOGLE_GENAI_USE_VERTEXAI=1 + GOOGLE_CLOUD_PROJECT= + GOOGLE_CLOUD_LOCATION=europe-west4 + ``` + +#### **Core Agent Config Structure** + +* **Basic Agent (`root_agent.yaml`)**: + ```yaml + # yaml-language-server: $schema=https://raw.githubusercontent.com/google/adk-python/refs/heads/main/src/google/adk/agents/config_schemas/AgentConfig.json + name: assistant_agent + model: gemini-2.5-flash + description: A helper agent that can answer users' various questions. + instruction: You are an agent to help answer users' various questions. + ``` + +* **Agent with Built-in Tools**: + ```yaml + name: search_agent + model: gemini-2.0-flash + description: 'an agent whose job it is to perform Google search queries and answer questions about the results.' + instruction: You are an agent whose job is to perform Google search queries and answer questions about the results. + tools: + - name: google_search # Built-in ADK tool + ``` + +* **Agent with Custom Tools**: + ```yaml + agent_class: LlmAgent + model: gemini-2.5-flash + name: prime_agent + description: Handles checking if numbers are prime. + instruction: | + You are responsible for checking whether numbers are prime. + When asked to check primes, you must call the check_prime tool with a list of integers. + Never attempt to determine prime numbers manually. + tools: + - name: ma_llm.check_prime # Reference to Python function + ``` + +* **Multi-Agent System with Sub-Agents**: + ```yaml + agent_class: LlmAgent + model: gemini-2.5-flash + name: root_agent + description: Learning assistant that provides tutoring in code and math. + instruction: | + You are a learning assistant that helps students with coding and math questions. + + You delegate coding questions to the code_tutor_agent and math questions to the math_tutor_agent. + + Follow these steps: + 1. If the user asks about programming or coding, delegate to the code_tutor_agent. + 2. If the user asks about math concepts or problems, delegate to the math_tutor_agent. + 3. Always provide clear explanations and encourage learning. + sub_agents: + - config_path: code_tutor_agent.yaml + - config_path: math_tutor_agent.yaml + ``` + +#### **Loading Agent Config in Python** + +```python +from google.adk.agents import config_agent_utils +root_agent = config_agent_utils.from_config("{agent_folder}/root_agent.yaml") +``` + +#### **Running Agent Config Agents** + +From the agent directory, use any of these commands: +* `adk web` - Launch web UI interface +* `adk run` - Run in terminal without UI +* `adk api_server` - Run as a service for other applications + +#### **Deployment Support** + +Agent Config agents can be deployed using: +* `adk deploy cloud_run` - Deploy to Google Cloud Run +* `adk deploy agent_engine` - Deploy to Vertex AI Agent Engine + +#### **Key Features & Capabilities** + +* **Supported Built-in Tools**: `google_search`, `load_artifacts`, `url_context`, `exit_loop`, `preload_memory`, `get_user_choice`, `enterprise_web_search`, `load_web_page` +* **Custom Tool Integration**: Reference Python functions using fully qualified module paths +* **Multi-Agent Orchestration**: Link agents via `config_path` references +* **Schema Validation**: Built-in YAML schema for IDE support and validation + +#### **Current Limitations** (Experimental Feature) + +* **Model Support**: Only Gemini models currently supported +* **Language Support**: Custom tools must be written in Python +* **Unsupported Agent Types**: `LangGraphAgent`, `A2aAgent` +* **Unsupported Tools**: `AgentTool`, `LongRunningFunctionTool`, `VertexAiSearchTool`, `MCPToolset`, `CrewaiTool`, `LangchainTool`, `ExampleTool` + +For complete examples and reference, see the [ADK samples repository](https://github.com/google/adk-python/tree/main/contributing/samples). + +--- + +## 2. Agent Definitions (`LlmAgent`) + +The `LlmAgent` is the cornerstone of intelligent behavior, leveraging an LLM for reasoning and decision-making. + +### 2.1 Basic `LlmAgent` Setup + +```python +from google.adk.agents import Agent + +def get_current_time(city: str) -> dict: + """Returns the current time in a specified city.""" + # Mock implementation + if city.lower() == "new york": + return {"status": "success", "time": "10:30 AM EST"} + return {"status": "error", "message": f"Time for {city} not available."} + +my_first_llm_agent = Agent( + name="time_teller_agent", + model="gemini-2.5-flash", # Essential: The LLM powering the agent + instruction="You are a helpful assistant that tells the current time in cities. Use the 'get_current_time' tool for this purpose.", + description="Tells the current time in a specified city.", # Crucial for multi-agent delegation + tools=[get_current_time] # List of callable functions/tool instances +) +``` + +### 2.2 Advanced `LlmAgent` Configuration + +* **`generate_content_config`**: Controls LLM generation parameters (temperature, token limits, safety). + ```python + from google.genai import types as genai_types + from google.adk.agents import Agent + + gen_config = genai_types.GenerateContentConfig( + temperature=0.2, # Controls randomness (0.0-1.0), lower for more deterministic. + top_p=0.9, # Nucleus sampling: sample from top_p probability mass. + top_k=40, # Top-k sampling: sample from top_k most likely tokens. + max_output_tokens=1024, # Max tokens in LLM's response. + stop_sequences=["## END"] # LLM will stop generating if these sequences appear. + ) + agent = Agent( + # ... basic config ... + generate_content_config=gen_config + ) + ``` + +* **`output_key`**: Automatically saves the agent's final text or structured (if `output_schema` is used) response to the `session.state` under this key. Facilitates data flow between agents. + ```python + agent = Agent( + # ... basic config ... + output_key="llm_final_response_text" + ) + # After agent runs, session.state['llm_final_response_text'] will contain its output. + ``` + +* **`input_schema` & `output_schema`**: Define strict JSON input/output formats using Pydantic models. + > **Warning**: Using `output_schema` forces the LLM to generate JSON and **disables** its ability to use tools or delegate to other agents. + +#### **Example: Defining and Using Structured Output** + +This is the most reliable way to make an LLM produce predictable, parseable JSON, which is essential for multi-agent workflows. + +1. **Define the Schema with Pydantic:** + ```python + from pydantic import BaseModel, Field + from typing import Literal + + class SearchQuery(BaseModel): + """Model representing a specific search query for web search.""" + search_query: str = Field( + description="A highly specific and targeted query for web search." + ) + + class Feedback(BaseModel): + """Model for providing evaluation feedback on research quality.""" + grade: Literal["pass", "fail"] = Field( + description="Evaluation result. 'pass' if the research is sufficient, 'fail' if it needs revision." + ) + comment: str = Field( + description="Detailed explanation of the evaluation, highlighting strengths and/or weaknesses of the research." + ) + follow_up_queries: list[SearchQuery] | None = Field( + default=None, + description="A list of specific, targeted follow-up search queries needed to fix research gaps. This should be null or empty if the grade is 'pass'." + ) + ``` + * **`BaseModel` & `Field`**: Define data types, defaults, and crucial `description` fields. These descriptions are sent to the LLM to guide its output. + * **`Literal`**: Enforces strict enum-like values (`"pass"` or `"fail"`), preventing the LLM from hallucinating unexpected values. + +2. **Assign the Schema to an `LlmAgent`:** + ```python + research_evaluator = LlmAgent( + name="research_evaluator", + model="gemini-2.5-pro", + instruction="""You are a meticulous quality assurance analyst. Evaluate the research findings in 'section_research_findings' and be very critical. + If you find significant gaps, assign a grade of 'fail', write a detailed comment, and generate 5-7 specific follow-up queries. + If the research is thorough, grade it 'pass'. + Your response must be a single, raw JSON object validating against the 'Feedback' schema. + """, + output_schema=Feedback, # This forces the LLM to output JSON matching the Feedback model. + output_key="research_evaluation", # The resulting JSON object will be saved to state. + disallow_transfer_to_peers=True, # Prevents this agent from delegating. Its job is only to evaluate. + ) + ``` + +* **`include_contents`**: Controls whether the conversation history is sent to the LLM. + * `'default'` (default): Sends relevant history. + * `'none'`: Sends no history; agent operates purely on current turn's input and `instruction`. Useful for stateless API wrapper agents. + ```python + agent = Agent(..., include_contents='none') + ``` + +* **`planner`**: Assign a `BasePlanner` instance to enable multi-step reasoning. + * **`BuiltInPlanner`**: Leverages a model's native "thinking" or planning capabilities (e.g., Gemini). + ```python + from google.adk.planners import BuiltInPlanner + from google.genai.types import ThinkingConfig + + agent = Agent( + model="gemini-2.5-flash", + planner=BuiltInPlanner( + thinking_config=ThinkingConfig(include_thoughts=True) + ), + # ... tools ... + ) + ``` + * **`PlanReActPlanner`**: Instructs the model to follow a structured Plan-Reason-Act output format, useful for models without built-in planning. + +* **`code_executor`**: Assign a `BaseCodeExecutor` to allow the agent to execute code blocks. + * **`BuiltInCodeExecutor`**: The standard, sandboxed code executor provided by ADK for safe execution. + ```python + from google.adk.code_executors import BuiltInCodeExecutor + agent = Agent( + name="code_agent", + model="gemini-2.5-flash", + instruction="Write and execute Python code to solve math problems.", + code_executor=BuiltInCodeExecutor() # Corrected from a list to an instance + ) + ``` + +* **Callbacks**: Hooks for observing and modifying agent behavior at key lifecycle points (`before_model_callback`, `after_tool_callback`, etc.). (Covered in Callbacks). + +### 2.3 LLM Instruction Crafting (`instruction`) + +The `instruction` is critical. It guides the LLM's behavior, persona, and tool usage. The following examples demonstrate powerful techniques for creating specialized, reliable agents. + +**Best Practices & Examples:** + +* **Be Specific & Concise**: Avoid ambiguity. +* **Define Persona & Role**: Give the LLM a clear role. +* **Constrain Behavior & Tool Use**: Explicitly state what the LLM should *and should not* do. +* **Define Output Format**: Tell the LLM *exactly* what its output should look like, especially when not using `output_schema`. +* **Dynamic Injection**: Use `{state_key}` to inject runtime data from `session.state` into the prompt. +* **Iteration**: Test, observe, and refine instructions. + +**Example 1: Constraining Tool Use and Output Format** +```python +import datetime +from google.adk.tools import google_search + + +plan_generator = LlmAgent( + model="gemini-2.5-flash", + name="plan_generator", + description="Generates a 4-5 line action-oriented research plan.", + instruction=f""" + You are a research strategist. Your job is to create a high-level RESEARCH PLAN, not a summary. + **RULE: Your output MUST be a bulleted list of 4-5 action-oriented research goals or key questions.** + - A good goal starts with a verb like "Analyze," "Identify," "Investigate." + - A bad output is a statement of fact like "The event was in April 2024." + **TOOL USE IS STRICTLY LIMITED:** + Your goal is to create a generic, high-quality plan *without searching*. + Only use `google_search` if a topic is ambiguous and you absolutely cannot create a plan without it. + You are explicitly forbidden from researching the *content* or *themes* of the topic. + Current date: {datetime.datetime.now().strftime("%Y-%m-%d")} + """, + tools=[google_search], +) +``` + +**Example 2: Injecting Data from State and Specifying Custom Tags** +This agent's `instruction` relies on data placed in `session.state` by previous agents. +```python +report_composer = LlmAgent( + model="gemini-2.5-pro", + name="report_composer_with_citations", + include_contents="none", # History not needed; all data is injected. + description="Transforms research data and a markdown outline into a final, cited report.", + instruction=""" + Transform the provided data into a polished, professional, and meticulously cited research report. + + --- + ### INPUT DATA + * Research Plan: `{research_plan}` + * Research Findings: `{section_research_findings}` + * Citation Sources: `{sources}` + * Report Structure: `{report_sections}` + + --- + ### CRITICAL: Citation System + To cite a source, you MUST insert a special citation tag directly after the claim it supports. + + **The only correct format is:** `` + + --- + ### Final Instructions + Generate a comprehensive report using ONLY the `` tag system for all citations. + The final report must strictly follow the structure provided in the **Report Structure** markdown outline. + Do not include a "References" or "Sources" section; all citations must be in-line. + """, + output_key="final_cited_report", +) +``` + +--- + +## 3. Orchestration with Workflow Agents + +Workflow agents (`SequentialAgent`, `ParallelAgent`, `LoopAgent`) provide deterministic control flow, combining LLM capabilities with structured execution. They do **not** use an LLM for their own orchestration logic. + +### 3.1 `SequentialAgent`: Linear Execution + +Executes `sub_agents` one after another in the order defined. The `InvocationContext` is passed along, allowing state changes to be visible to subsequent agents. + +```python +from google.adk.agents import SequentialAgent, Agent + +# Agent 1: Summarizes a document and saves to state +summarizer = Agent( + name="DocumentSummarizer", + model="gemini-2.5-flash", + instruction="Summarize the provided document in 3 sentences.", + output_key="document_summary" # Output saved to session.state['document_summary'] +) + +# Agent 2: Generates questions based on the summary from state +question_generator = Agent( + name="QuestionGenerator", + model="gemini-2.5-flash", + instruction="Generate 3 comprehension questions based on this summary: {document_summary}", + # 'document_summary' is dynamically injected from session.state +) + +document_pipeline = SequentialAgent( + name="SummaryQuestionPipeline", + sub_agents=[summarizer, question_generator], # Order matters! + description="Summarizes a document then generates questions." +) +``` + +### 3.2 `ParallelAgent`: Concurrent Execution + +Executes `sub_agents` simultaneously. Useful for independent tasks to reduce overall latency. All sub-agents share the same `session.state`. + +```python +from google.adk.agents import ParallelAgent, Agent, SequentialAgent + +# Agents to fetch data concurrently +fetch_stock_price = Agent(name="StockPriceFetcher", ..., output_key="stock_data") +fetch_news_headlines = Agent(name="NewsFetcher", ..., output_key="news_data") +fetch_social_sentiment = Agent(name="SentimentAnalyzer", ..., output_key="sentiment_data") + +# Agent to merge results (runs after ParallelAgent, usually in a SequentialAgent) +merger_agent = Agent( + name="ReportGenerator", + model="gemini-2.5-flash", + instruction="Combine stock data: {stock_data}, news: {news_data}, and sentiment: {sentiment_data} into a market report." +) + +# Pipeline to run parallel fetching then sequential merging +market_analysis_pipeline = SequentialAgent( + name="MarketAnalyzer", + sub_agents=[ + ParallelAgent( + name="ConcurrentFetch", + sub_agents=[fetch_stock_price, fetch_news_headlines, fetch_social_sentiment] + ), + merger_agent # Runs after all parallel agents complete + ] +) +``` +* **Concurrency Caution**: When parallel agents write to the same `state` key, race conditions can occur. Always use distinct `output_key`s or manage concurrent writes explicitly. + +### 3.3 `LoopAgent`: Iterative Processes + +Repeatedly executes its `sub_agents` (sequentially within each loop iteration) until a condition is met or `max_iterations` is reached. + +#### **Termination of `LoopAgent`** +A `LoopAgent` terminates when: +1. `max_iterations` is reached. +2. Any `Event` yielded by a sub-agent (or a tool within it) sets `actions.escalate = True`. This provides dynamic, content-driven loop termination. + +#### **Example: Iterative Refinement Loop with a Custom `BaseAgent` for Control** +This example shows a loop that continues until a condition, determined by an evaluation agent, is met. + +```python +from google.adk.agents import LoopAgent, Agent, BaseAgent +from google.adk.events import Event, EventActions +from google.adk.agents.invocation_context import InvocationContext +from typing import AsyncGenerator + +# An LLM Agent that evaluates research and produces structured JSON output +research_evaluator = Agent( + name="research_evaluator", + # ... configuration from Section 2.2 ... + output_schema=Feedback, + output_key="research_evaluation", +) + +# An LLM Agent that performs additional searches based on feedback +enhanced_search_executor = Agent( + name="enhanced_search_executor", + instruction="Execute the follow-up queries from 'research_evaluation' and combine with existing findings.", + # ... other configurations ... +) + +# A custom BaseAgent to check the evaluation and stop the loop +class EscalationChecker(BaseAgent): + """Checks research evaluation and escalates to stop the loop if grade is 'pass'.""" + async def _run_async_impl(self, ctx: InvocationContext) -> AsyncGenerator[Event, None]: + evaluation = ctx.session.state.get("research_evaluation") + if evaluation and evaluation.get("grade") == "pass": + # The key to stopping the loop: yield an Event with escalate=True + yield Event(author=self.name, actions=EventActions(escalate=True)) + else: + # Let the loop continue + yield Event(author=self.name) + +# Define the loop +iterative_refinement_loop = LoopAgent( + name="IterativeRefinementLoop", + sub_agents=[ + research_evaluator, # Step 1: Evaluate + EscalationChecker(name="EscalationChecker"), # Step 2: Check and maybe stop + enhanced_search_executor, # Step 3: Refine (only runs if loop didn't stop) + ], + max_iterations=5, # Fallback to prevent infinite loops + description="Iteratively evaluates and refines research until it passes quality checks." +) +``` + +--- + +## 4. Multi-Agent Systems & Communication + +Building complex applications by composing multiple, specialized agents. + +### 4.1 Agent Hierarchy + +A hierarchical (tree-like) structure of parent-child relationships defined by the `sub_agents` parameter during `BaseAgent` initialization. An agent can only have one parent. + +```python +# Conceptual Hierarchy +# Root +# └── Coordinator (LlmAgent) +# ├── SalesAgent (LlmAgent) +# └── SupportAgent (LlmAgent) +# └── DataPipeline (SequentialAgent) +# ├── DataFetcher (LlmAgent) +# └── DataProcessor (LlmAgent) +``` + +### 4.2 Inter-Agent Communication Mechanisms + +1. **Shared Session State (`session.state`)**: The most common and robust method. Agents read from and write to the same mutable dictionary. + * **Mechanism**: Agent A sets `ctx.session.state['key'] = value`. Agent B later reads `ctx.session.state.get('key')`. `output_key` on `LlmAgent` is a convenient auto-setter. + * **Best for**: Passing intermediate results, shared configurations, and flags in pipelines (Sequential, Loop agents). + +2. **LLM-Driven Delegation (`transfer_to_agent`)**: A `LlmAgent` can dynamically hand over control to another agent based on its reasoning. + * **Mechanism**: The LLM generates a special `transfer_to_agent` function call. The ADK framework intercepts this, routes the next turn to the target agent. + * **Prerequisites**: + * The initiating `LlmAgent` needs `instruction` to guide delegation and `description` of the target agent(s). + * Target agents need clear `description`s to help the LLM decide. + * Target agent must be discoverable within the current agent's hierarchy (direct `sub_agent` or a descendant). + * **Configuration**: Can be enabled/disabled via `disallow_transfer_to_parent` and `disallow_transfer_to_peers` on `LlmAgent`. + +3. **Explicit Invocation (`AgentTool`)**: An `LlmAgent` can treat another `BaseAgent` instance as a callable tool. + * **Mechanism**: Wrap the target agent (`target_agent`) in `AgentTool(agent=target_agent)` and add it to the calling `LlmAgent`'s `tools` list. The `AgentTool` generates a `FunctionDeclaration` for the LLM. When called, `AgentTool` runs the target agent and returns its final response as the tool result. + * **Best for**: Hierarchical task decomposition, where a higher-level agent needs a specific output from a lower-level agent. + +### 4.3 Common Multi-Agent Patterns + +* **Coordinator/Dispatcher**: A central agent routes requests to specialized sub-agents (often via LLM-driven delegation). +* **Sequential Pipeline**: `SequentialAgent` orchestrates a fixed sequence of tasks, passing data via shared state. +* **Parallel Fan-Out/Gather**: `ParallelAgent` runs concurrent tasks, followed by a final agent that synthesizes results from state. +* **Review/Critique (Generator-Critic)**: `SequentialAgent` with a generator followed by a critic, often in a `LoopAgent` for iterative refinement. +* **Hierarchical Task Decomposition (Planner/Executor)**: High-level agents break down complex problems, delegating sub-tasks to lower-level agents (often via `AgentTool` and delegation). + +#### **Example: Hierarchical Planner/Executor Pattern** +This pattern combines several mechanisms. A top-level `interactive_planner_agent` uses another agent (`plan_generator`) as a tool to create a plan, then delegates the execution of that plan to a complex `SequentialAgent` (`research_pipeline`). + +```python +from google.adk.agents import LlmAgent, SequentialAgent, LoopAgent +from google.adk.tools.agent_tool import AgentTool + +# Assume plan_generator, section_planner, research_evaluator, etc. are defined. + +# The execution pipeline itself is a complex agent. +research_pipeline = SequentialAgent( + name="research_pipeline", + description="Executes a pre-approved research plan. It performs iterative research, evaluation, and composes a final, cited report.", + sub_agents=[ + section_planner, + section_researcher, + LoopAgent( + name="iterative_refinement_loop", + max_iterations=3, + sub_agents=[ + research_evaluator, + EscalationChecker(name="escalation_checker"), + enhanced_search_executor, + ], + ), + report_composer, + ], +) + +# The top-level agent that interacts with the user. +interactive_planner_agent = LlmAgent( + name="interactive_planner_agent", + model="gemini-2.5-flash", + description="The primary research assistant. It collaborates with the user to create a research plan, and then executes it upon approval.", + instruction=""" + You are a research planning assistant. Your workflow is: + 1. **Plan:** Use the `plan_generator` tool to create a draft research plan. + 2. **Refine:** Incorporate user feedback until the plan is approved. + 3. **Execute:** Once the user gives EXPLICIT approval (e.g., "looks good, run it"), you MUST delegate the task to the `research_pipeline` agent. + Your job is to Plan, Refine, and Delegate. Do not do the research yourself. + """, + # The planner delegates to the pipeline. + sub_agents=[research_pipeline], + # The planner uses another agent as a tool. + tools=[AgentTool(plan_generator)], + output_key="research_plan", +) + +# The root agent of the application is the top-level planner. +root_agent = interactive_planner_agent +``` + +### 4.A. Distributed Communication (A2A Protocol) + +The Agent-to-Agent (A2A) Protocol enables agents to communicate over a network, even if they are written in different languages or run as separate services. Use A2A for integrating with third-party agents, building microservice-based agent architectures, or when a strong, formal API contract is needed. For internal code organization, prefer local sub-agents. + +* **Exposing an Agent**: Make an existing ADK agent available to others over A2A. + * **`to_a2a()` Utility**: The simplest method. Wraps your `root_agent` and creates a runnable FastAPI app, auto-generating the required `agent.json` card. + ```python + from google.adk.a2a.utils.agent_to_a2a import to_a2a + # root_agent is your existing ADK Agent instance + a2a_app = to_a2a(root_agent, port=8001) + # Run with: uvicorn your_module:a2a_app --host localhost --port 8001 + ``` + * **`adk api_server --a2a`**: A CLI command that serves agents from a directory. Requires you to manually create an `agent.json` card for each agent you want to expose. + +* **Consuming a Remote Agent**: Use a remote A2A agent as if it were a local agent. + * **`RemoteA2aAgent`**: This agent acts as a client proxy. You initialize it with the URL to the remote agent's card. + ```python + from google.adk.a2a.remote_a2a_agent import RemoteA2aAgent + + # This agent can now be used as a sub-agent or tool + prime_checker_agent = RemoteA2aAgent( + name="prime_agent", + description="A remote agent that checks if numbers are prime.", + agent_card="http://localhost:8001/a2a/check_prime_agent/.well-known/agent.json" + ) + ``` + +--- + +## 5. Building Custom Agents (`BaseAgent`) + +For unique orchestration logic that doesn't fit standard workflow agents, inherit directly from `BaseAgent`. + +### 5.1 When to Use Custom Agents + +* **Complex Conditional Logic**: `if/else` branching based on multiple state variables. +* **Dynamic Agent Selection**: Choosing which sub-agent to run based on runtime evaluation. +* **Direct External Integrations**: Calling external APIs or libraries directly within the orchestration flow. +* **Custom Loop/Retry Logic**: More sophisticated iteration patterns than `LoopAgent`, such as the `EscalationChecker` example. + +### 5.2 Implementing `_run_async_impl` + +This is the core asynchronous method you must override. + +#### **Example: A Custom Agent for Loop Control** +This agent reads state, applies simple Python logic, and yields an `Event` with an `escalate` action to control a `LoopAgent`. + +```python +from google.adk.agents import BaseAgent +from google.adk.agents.invocation_context import InvocationContext +from google.adk.events import Event, EventActions +from typing import AsyncGenerator +import logging + +class EscalationChecker(BaseAgent): + """Checks research evaluation and escalates to stop the loop if grade is 'pass'.""" + + def __init__(self, name: str): + super().__init__(name=name) + + async def _run_async_impl( + self, ctx: InvocationContext + ) -> AsyncGenerator[Event, None]: + # 1. Read from session state. + evaluation_result = ctx.session.state.get("research_evaluation") + + # 2. Apply custom Python logic. + if evaluation_result and evaluation_result.get("grade") == "pass": + logging.info( + f"[{self.name}] Research passed. Escalating to stop loop." + ) + # 3. Yield an Event with a control Action. + yield Event(author=self.name, actions=EventActions(escalate=True)) + else: + logging.info( + f"[{self.name}] Research failed or not found. Loop continues." + ) + # Yielding an event without actions lets the flow continue. + yield Event(author=self.name) +``` +* **Asynchronous Generator**: `async def ... yield Event`. This allows pausing and resuming execution. +* **`ctx: InvocationContext`**: Provides access to all session state (`ctx.session.state`). +* **Calling Sub-Agents**: Use `async for event in self.sub_agent_instance.run_async(ctx): yield event`. +* **Control Flow**: Use standard Python `if/else`, `for/while` loops for complex logic. + +--- + +## 6. Models: Gemini, LiteLLM, and Vertex AI + +ADK's model flexibility allows integrating various LLMs for different needs. + +### 6.1 Google Gemini Models (AI Studio & Vertex AI) + +* **Default Integration**: Native support via `google-genai` library. +* **AI Studio (Easy Start)**: + * Set `GOOGLE_API_KEY="YOUR_API_KEY"` (environment variable). + * Set `GOOGLE_GENAI_USE_VERTEXAI="False"`. + * Model strings: `"gemini-2.5-flash"`, `"gemini-2.5-pro"`, etc. +* **Vertex AI (Production)**: + * Authenticate via `gcloud auth application-default login` (recommended). + * Set `GOOGLE_CLOUD_PROJECT="YOUR_PROJECT_ID"`, `GOOGLE_CLOUD_LOCATION="your-region"` (environment variables). + * Set `GOOGLE_GENAI_USE_VERTEXAI="True"`. + * Model strings: `"gemini-2.5-flash"`, `"gemini-2.5-pro"`, or full Vertex AI endpoint resource names for specific deployments. + +### 6.2 Other Cloud & Proprietary Models via LiteLLM + +`LiteLlm` provides a unified interface to 100+ LLMs (OpenAI, Anthropic, Cohere, etc.). + +* **Installation**: `pip install litellm` +* **API Keys**: Set environment variables as required by LiteLLM (e.g., `OPENAI_API_KEY`, `ANTHROPIC_API_KEY`). +* **Usage**: + ```python + from google.adk.models.lite_llm import LiteLlm + agent_openai = Agent(model=LiteLlm(model="openai/gpt-4o"), ...) + agent_claude = Agent(model=LiteLlm(model="anthropic/claude-3-haiku-20240307"), ...) + ``` + +### 6.3 Open & Local Models via LiteLLM (Ollama, vLLM) + +For self-hosting, cost savings, privacy, or offline use. + +* **Ollama Integration**: Run Ollama locally (`ollama run `). + ```bash + export OLLAMA_API_BASE="http://localhost:11434" # Ensure Ollama server is running + ``` + ```python + from google.adk.models.lite_llm import LiteLlm + # Use 'ollama_chat' provider for tool-calling capabilities with Ollama models + agent_ollama = Agent(model=LiteLlm(model="ollama_chat/llama3:instruct"), ...) + ``` + +* **Self-Hosted Endpoint (e.g., vLLM)**: + ```python + from google.adk.models.lite_llm import LiteLlm + api_base_url = "https://your-vllm-endpoint.example.com/v1" + agent_vllm = Agent( + model=LiteLlm( + model="your-model-name-on-vllm", + api_base=api_base_url, + extra_headers={"Authorization": "Bearer YOUR_TOKEN"}, + ), + ... + ) + ``` + +### 6.4 Customizing LLM API Clients + +For `google-genai` (used by Gemini models), you can configure the underlying client. + +```python +import os +from google.genai import configure as genai_configure + +genai_configure.use_defaults( + timeout=60, # seconds + client_options={"api_key": os.getenv("GOOGLE_API_KEY")}, +) +``` + +--- + +## 7. Tools: The Agent's Capabilities + +Tools extend an agent's abilities beyond text generation. + +### 7.1 Defining Function Tools: Principles & Best Practices + +* **Signature**: `def my_tool(param1: Type, param2: Type, tool_context: ToolContext) -> dict:` +* **Function Name**: Descriptive verb-noun (e.g., `schedule_meeting`). +* **Parameters**: Clear names, required type hints, **NO DEFAULT VALUES**. +* **Return Type**: **Must** be a `dict` (JSON-serializable), preferably with a `'status'` key. +* **Docstring**: **CRITICAL**. Explain purpose, when to use, arguments, and return value structure. **AVOID** mentioning `tool_context`. + + ```python + def calculate_compound_interest( + principal: float, + rate: float, + years: int, + compounding_frequency: int, + tool_context: ToolContext + ) -> dict: + """Calculates the future value of an investment with compound interest. + + Use this tool to calculate the future value of an investment given a + principal amount, interest rate, number of years, and how often the + interest is compounded per year. + + Args: + principal (float): The initial amount of money invested. + rate (float): The annual interest rate (e.g., 0.05 for 5%). + years (int): The number of years the money is invested. + compounding_frequency (int): The number of times interest is compounded + per year (e.g., 1 for annually, 12 for monthly). + + Returns: + dict: Contains the calculation result. + - 'status' (str): "success" or "error". + - 'future_value' (float, optional): The calculated future value. + - 'error_message' (str, optional): Description of error, if any. + """ + # ... implementation ... + ``` + +### 7.2 The `ToolContext` Object: Accessing Runtime Information + +`ToolContext` is the gateway for tools to interact with the ADK runtime. + +* `tool_context.state`: Read and write to the current `Session`'s `state` dictionary. +* `tool_context.actions`: Modify the `EventActions` object (e.g., `tool_context.actions.escalate = True`). +* `tool_context.load_artifact(filename)` / `tool_context.save_artifact(filename, part)`: Manage binary data. +* `tool_context.search_memory(query)`: Query the long-term `MemoryService`. + +### 7.3 All Tool Types & Their Usage + +1. **Custom Function Tools**: + * **`FunctionTool`**: The most common type, wrapping a standard Python function. + * **`LongRunningFunctionTool`**: Wraps an `async` function that `yields` intermediate results, for tasks that provide progress updates. + * **`AgentTool`**: Wraps another `BaseAgent` instance, allowing it to be invoked as a tool by a parent agent. + +2. **Built-in Tools**: Ready-to-use tools provided by ADK. + * `google_search`: Provides Google Search grounding. + * `BuiltInCodeExecutor`: Enables sandboxed code execution. + * `VertexAiSearchTool`: Provides grounding from your private Vertex AI Search data stores. + * `BigQueryToolset`: A collection of tools for interacting with BigQuery (e.g., `list_datasets`, `execute_sql`). + > **Warning**: An agent can only use one type of built-in tool at a time and they cannot be used in sub-agents. + +3. **Third-Party Tool Wrappers**: For seamless integration with other frameworks. + * `LangchainTool`: Wraps a tool from the LangChain ecosystem. + * `CrewaiTool`: Wraps a tool from the CrewAI library. + +4. **OpenAPI & Protocol Tools**: For interacting with APIs and services. + * **`OpenAPIToolset`**: Automatically generates a set of `RestApiTool`s from an OpenAPI (Swagger) v3 specification. + * **`MCPToolset`**: Connects to an external Model Context Protocol (MCP) server to dynamically load its tools. + +5. **Google Cloud Tools**: For deep integration with Google Cloud services. + * **`ApiHubToolset`**: Turns any documented API from Apigee API Hub into a tool. + * **`ApplicationIntegrationToolset`**: Turns Application Integration workflows and Integration Connectors (e.g., Salesforce, SAP) into callable tools. + * **Toolbox for Databases**: An open-source MCP server that ADK can connect to for database interactions. + +--- + +## 8. Context, State, and Memory Management + +Effective context management is crucial for coherent, multi-turn conversations. + +### 8.1 The `Session` Object & `SessionService` + +* **`Session`**: The container for a single, ongoing conversation (`id`, `state`, `events`). +* **`SessionService`**: Manages the lifecycle of `Session` objects (`create_session`, `get_session`, `append_event`). +* **Implementations**: `InMemorySessionService` (dev), `VertexAiSessionService` (prod), `DatabaseSessionService` (self-managed). + +### 8.2 `State`: The Conversational Scratchpad + +A mutable dictionary within `session.state` for short-term, dynamic data. + +* **Update Mechanism**: Always update via `context.state` (in callbacks/tools) or `LlmAgent.output_key`. +* **Prefixes for Scope**: + * **(No prefix)**: Session-specific (e.g., `session.state['booking_step']`). + * `user:`: Persistent for a `user_id` across all their sessions (e.g., `session.state['user:preferred_currency']`). + * `app:`: Persistent for `app_name` across all users and sessions. + * `temp:`: Volatile, for the current `Invocation` turn only. + +### 8.3 `Memory`: Long-Term Knowledge & Retrieval + +For knowledge beyond a single conversation. + +* **`BaseMemoryService`**: Defines the interface (`add_session_to_memory`, `search_memory`). +* **Implementations**: `InMemoryMemoryService`, `VertexAiRagMemoryService`. +* **Usage**: Agents interact via tools (e.g., the built-in `load_memory` tool). + +### 8.4 `Artifacts`: Binary Data Management + +For named, versioned binary data (files, images). + +* **Representation**: `google.genai.types.Part` (containing a `Blob` with `data: bytes` and `mime_type: str`). +* **`BaseArtifactService`**: Manages storage (`save_artifact`, `load_artifact`). +* **Implementations**: `InMemoryArtifactService`, `GcsArtifactService`. + +--- + +## 9. Runtime, Events, and Execution Flow + +The `Runner` is the central orchestrator of an ADK application. + +### 9.1 The `Runner`: The Orchestrator + +* **Role**: Manages the agent's lifecycle, the event loop, and coordinates with services. +* **Entry Point**: `runner.run_async(user_id, session_id, new_message)`. + +### 9.2 The Event Loop: Core Execution Flow + +1. User input becomes a `user` `Event`. +2. `Runner` calls `agent.run_async(invocation_context)`. +3. Agent `yield`s an `Event` (e.g., tool call, text response). Execution pauses. +4. `Runner` processes the `Event` (applies state changes, etc.) and yields it to the client. +5. Execution resumes. This cycle repeats until the agent is done. + +### 9.3 `Event` Object: The Communication Backbone + +`Event` objects carry all information and signals. + +* `Event.author`: Source of the event (`'user'`, agent name, `'system'`). +* `Event.content`: The primary payload (text, function calls, function responses). +* `Event.actions`: Signals side effects (`state_delta`, `transfer_to_agent`, `escalate`). +* `Event.is_final_response()`: Helper to identify the complete, displayable message. + +### 9.4 Asynchronous Programming (Python Specific) + +ADK is built on `asyncio`. Use `async def`, `await`, and `async for` for all I/O-bound operations. + +--- + +## 10. Control Flow with Callbacks + +Callbacks are functions that intercept and control agent execution at specific points. + +### 10.1 Callback Mechanism: Interception & Control + +* **Definition**: A Python function assigned to an agent's `callback` parameter (e.g., `after_agent_callback=my_func`). +* **Context**: Receives a `CallbackContext` (or `ToolContext`) with runtime info. +* **Return Value**: **Crucially determines flow.** + * `return None`: Allow the default action to proceed. + * `return `: **Override** the default action/result. + +### 10.2 Types of Callbacks + +1. **Agent Lifecycle**: `before_agent_callback`, `after_agent_callback`. +2. **LLM Interaction**: `before_model_callback`, `after_model_callback`. +3. **Tool Execution**: `before_tool_callback`, `after_tool_callback`. + +### 10.3 Callback Best Practices + +* **Keep Focused**: Each callback for a single purpose. +* **Performance**: Avoid blocking I/O or heavy computation. +* **Error Handling**: Use `try...except` to prevent crashes. + +#### **Example 1: Data Aggregation with `after_agent_callback`** +This callback runs after an agent, inspects the `session.events` to find structured data from tool calls (like `google_search` results), and saves it to state for later use. + +```python +from google.adk.agents.callback_context import CallbackContext + +def collect_research_sources_callback(callback_context: CallbackContext) -> None: + """Collects and organizes web research sources from agent events.""" + session = callback_context._invocation_context.session + # Get existing sources from state to append to them. + url_to_short_id = callback_context.state.get("url_to_short_id", {}) + sources = callback_context.state.get("sources", {}) + id_counter = len(url_to_short_id) + 1 + + # Iterate through all events in the session to find grounding metadata. + for event in session.events: + if not (event.grounding_metadata and event.grounding_metadata.grounding_chunks): + continue + # ... logic to parse grounding_chunks and grounding_supports ... + # (See full implementation in the original code snippet) + + # Save the updated source map back to state. + callback_context.state["url_to_short_id"] = url_to_short_id + callback_context.state["sources"] = sources + +# Used in an agent like this: +# section_researcher = LlmAgent(..., after_agent_callback=collect_research_sources_callback) +``` + +#### **Example 2: Output Transformation with `after_agent_callback`** +This callback takes an LLM's raw output (containing custom tags), uses Python to format it into markdown, and returns the modified content, overriding the original. + +```python +import re +from google.adk.agents.callback_context import CallbackContext +from google.genai import types as genai_types + +def citation_replacement_callback(callback_context: CallbackContext) -> genai_types.Content: + """Replaces tags in a report with Markdown-formatted links.""" + # 1. Get raw report and sources from state. + final_report = callback_context.state.get("final_cited_report", "") + sources = callback_context.state.get("sources", {}) + + # 2. Define a replacer function for regex substitution. + def tag_replacer(match: re.Match) -> str: + short_id = match.group(1) + if not (source_info := sources.get(short_id)): + return "" # Remove invalid tags + title = source_info.get("title", short_id) + return f" [{title}]({source_info['url']})" + + # 3. Use regex to find all tags and replace them. + processed_report = re.sub( + r'', + tag_replacer, + final_report, + ) + processed_report = re.sub(r"\s+([.,;:])", r"\1", processed_report) # Fix spacing + + # 4. Save the new version to state and return it to override the original agent output. + callback_context.state["final_report_with_citations"] = processed_report + return genai_types.Content(parts=[genai_types.Part(text=processed_report)]) + +# Used in an agent like this: +# report_composer = LlmAgent(..., after_agent_callback=citation_replacement_callback) +``` + +### 10.A. Global Control with Plugins + +Plugins are stateful, reusable modules for implementing cross-cutting concerns that apply globally to all agents, tools, and model calls managed by a `Runner`. Unlike Callbacks which are configured per-agent, Plugins are registered once on the `Runner`. + +* **Use Cases**: Ideal for universal logging, application-wide policy enforcement, global caching, and collecting metrics. +* **Execution Order**: Plugin callbacks run **before** their corresponding agent-level callbacks. If a plugin callback returns a value, the agent-level callback is skipped. +* **Defining a Plugin**: Inherit from `BasePlugin` and implement callback methods. + ```python + from google.adk.plugins.base_plugin import BasePlugin + from google.adk.agents.callback_context import CallbackContext + + class InvocationCounterPlugin(BasePlugin): + def __init__(self): + super().__init__(name="invocation_counter") + self.agent_runs = 0 + + async def before_agent_callback(self, callback_context: CallbackContext, **kwargs): + self.agent_runs += 1 + print(f"[Plugin] Total agent runs: {self.agent_runs}") + ``` +* **Registering a Plugin**: + ```python + from google.adk.runners import Runner + # runner = Runner(agent=root_agent, ..., plugins=[InvocationCounterPlugin()]) + ``` +* **Error Handling Callbacks**: Plugins support unique error hooks like `on_model_error_callback` and `on_tool_error_callback` for centralized error management. +* **Limitation**: Plugins are not supported by the `adk web` interface. + +--- + +## 11. Authentication for Tools + +Enabling agents to securely access protected external resources. + +### 11.1 Core Concepts: `AuthScheme` & `AuthCredential` + +* **`AuthScheme`**: Defines *how* an API expects authentication (e.g., `APIKey`, `HTTPBearer`, `OAuth2`, `OpenIdConnectWithConfig`). +* **`AuthCredential`**: Holds *initial* information to *start* the auth process (e.g., API key value, OAuth client ID/secret). + +### 11.2 Interactive OAuth/OIDC Flows + +When a tool requires user interaction (OAuth consent), ADK pauses and signals your `Agent Client` application. + +1. **Detect Auth Request**: `runner.run_async()` yields an event with a special `adk_request_credential` function call. +2. **Redirect User**: Extract `auth_uri` from `auth_config` in the event. Your client app redirects the user's browser to this `auth_uri` (appending `redirect_uri`). +3. **Handle Callback**: Your client app has a pre-registered `redirect_uri` to receive the user after authorization. It captures the full callback URL (containing `authorization_code`). +4. **Send Auth Result to ADK**: Your client prepares a `FunctionResponse` for `adk_request_credential`, setting `auth_config.exchanged_auth_credential.oauth2.auth_response_uri` to the captured callback URL. +5. **Resume Execution**: `runner.run_async()` is called again with this `FunctionResponse`. ADK performs the token exchange, stores the access token, and retries the original tool call. + +### 11.3 Custom Tool Authentication + +If building a `FunctionTool` that needs authentication: + +1. **Check for Cached Creds**: `tool_context.state.get("my_token_cache_key")`. +2. **Check for Auth Response**: `tool_context.get_auth_response(my_auth_config)`. +3. **Initiate Auth**: If no creds, call `tool_context.request_credential(my_auth_config)` and return a pending status. This triggers the external flow. +4. **Cache Credentials**: After obtaining, store in `tool_context.state`. +5. **Make API Call**: Use the valid credentials (e.g., `google.oauth2.credentials.Credentials`). + +--- + +## 12. Deployment Strategies + +From local dev to production. + +### 12.1 Local Development & Testing (`adk web`, `adk run`, `adk api_server`) + +* **`adk web`**: Launches a local web UI for interactive chat, session inspection, and visual tracing. + ```bash + adk web /path/to/your/project_root + ``` +* **`adk run`**: Command-line interactive chat. + ```bash + adk run /path/to/your/agent_folder + ``` +* **`adk api_server`**: Launches a local FastAPI server exposing `/run`, `/run_sse`, `/list-apps`, etc., for API testing with `curl` or client libraries. + ```bash + adk api_server /path/to/your/project_root + ``` + +### 12.2 Vertex AI Agent Engine + +Fully managed, scalable service for ADK agents on Google Cloud. + +* **Features**: Auto-scaling, session management, observability integration. +* **ADK CLI**: `adk deploy agent_engine --project --region ... /path/to/agent` +* **Deployment**: Use `vertexai.agent_engines.create()`. + ```python + from vertexai.preview import reasoning_engines # or agent_engines directly in later versions + + # Wrap your root_agent for deployment + app_for_engine = reasoning_engines.AdkApp(agent=root_agent, enable_tracing=True) + + # Deploy + remote_app = agent_engines.create( + agent_engine=app_for_engine, + requirements=["google-cloud-aiplatform[adk,agent_engines]"], + display_name="My Production Agent" + ) + print(remote_app.resource_name) # projects/PROJECT_NUM/locations/REGION/reasoningEngines/ID + ``` +* **Interaction**: Use `remote_app.stream_query()`, `create_session()`, etc. + +### 12.3 Cloud Run + +Serverless container platform for custom web applications. + +* **ADK CLI**: `adk deploy cloud_run --project --region ... /path/to/agent` +* **Deployment**: + 1. Create a `Dockerfile` for your FastAPI app (using `google.adk.cli.fast_api.get_fast_api_app`). + 2. Use `gcloud run deploy --source .`. + 3. Alternatively, `adk deploy cloud_run` (simpler, opinionated). +* **Example `main.py`**: + ```python + import os + from fastapi import FastAPI + from google.adk.cli.fast_api import get_fast_api_app + + # Ensure your agent_folder (e.g., 'my_first_agent') is in the same directory as main.py + app: FastAPI = get_fast_api_app( + agents_dir=os.path.dirname(os.path.abspath(__file__)), + session_service_uri="sqlite:///./sessions.db", # In-container SQLite, for simple cases + # For production: use a persistent DB (Cloud SQL) or VertexAiSessionService + allow_origins=["*"], + web=True # Serve ADK UI + ) + # uvicorn.run(app, host="0.0.0.0", port=int(os.environ.get("PORT", 8080))) # If running directly + ``` + +### 12.4 Google Kubernetes Engine (GKE) + +For maximum control, run your containerized agent in a Kubernetes cluster. + +* **ADK CLI**: `adk deploy gke --project --cluster_name ... /path/to/agent` +* **Deployment**: + 1. Build Docker image (`gcloud builds submit`). + 2. Create Kubernetes Deployment and Service YAMLs. + 3. Apply with `kubectl apply -f deployment.yaml`. + 4. Configure Workload Identity for GCP permissions. + +### 12.5 CI/CD Integration + +* Automate testing (`pytest`, `adk eval`) in CI. +* Automate container builds and deployments (e.g., Cloud Build, GitHub Actions). +* Use environment variables for secrets. + +--- + +## 13. Evaluation and Safety + +Critical for robust, production-ready agents. + +### 13.1 Agent Evaluation (`adk eval`) + +Systematically assess agent performance using predefined test cases. + +* **Evalset File (`.evalset.json`)**: Contains `eval_cases`, each with a `conversation` (user queries, expected tool calls, expected intermediate/final responses) and `session_input` (initial state). + ```json + { + "eval_set_id": "weather_bot_eval", + "eval_cases": [ + { + "eval_id": "london_weather_query", + "conversation": [ + { + "user_content": {"parts": [{"text": "What's the weather in London?"}]}, + "final_response": {"parts": [{"text": "The weather in London is cloudy..."}]}, + "intermediate_data": { + "tool_uses": [{"name": "get_weather", "args": {"city": "London"}}] + } + } + ], + "session_input": {"app_name": "weather_app", "user_id": "test_user", "state": {}} + } + ] + } + ``` +* **Running Evaluation**: + * `adk web`: Interactive UI for creating/running eval cases. + * `adk eval /path/to/agent_folder /path/to/evalset.json`: CLI execution. + * `pytest`: Integrate `AgentEvaluator.evaluate()` into unit/integration tests. +* **Metrics**: `tool_trajectory_avg_score` (tool calls match expected), `response_match_score` (final response similarity using ROUGE). Configurable via `test_config.json`. + +### 13.2 Safety & Guardrails + +Multi-layered defense against harmful content, misalignment, and unsafe actions. + +1. **Identity and Authorization**: + * **Agent-Auth**: Tool acts with the agent's service account (e.g., `Vertex AI User` role). Simple, but all users share access level. Logs needed for attribution. + * **User-Auth**: Tool acts with the end-user's identity (via OAuth tokens). Reduces risk of abuse. +2. **In-Tool Guardrails**: Design tools defensively. Tools can read policies from `tool_context.state` (set deterministically by developer) and validate model-provided arguments before execution. + ```python + def execute_sql(query: str, tool_context: ToolContext) -> dict: + policy = tool_context.state.get("user:sql_policy", {}) + if not policy.get("allow_writes", False) and ("INSERT" in query.upper() or "DELETE" in query.upper()): + return {"status": "error", "message": "Policy: Write operations are not allowed."} + # ... execute query ... + ``` +3. **Built-in Gemini Safety Features**: + * **Content Safety Filters**: Automatically block harmful content (CSAM, PII, hate speech, etc.). Configurable thresholds. + * **System Instructions**: Guide model behavior, define prohibited topics, brand tone, disclaimers. +4. **Model and Tool Callbacks (LLM as a Guardrail)**: Use callbacks to inspect inputs/outputs. + * `before_model_callback`: Intercept `LlmRequest` before it hits the LLM. Block (return `LlmResponse`) or modify. + * `before_tool_callback`: Intercept tool calls (name, args) before execution. Block (return `dict`) or modify. + * **LLM-based Safety**: Use a cheap/fast LLM (e.g., Gemini Flash) in a callback to classify input/output safety. + ```python + def safety_checker_callback(context: CallbackContext, llm_request: LlmRequest) -> Optional[LlmResponse]: + # Use a separate, small LLM to classify safety + safety_llm_agent = Agent(name="SafetyChecker", model="gemini-2.5-flash-001", instruction="Classify input as 'safe' or 'unsafe'. Output ONLY the word.") + # Run the safety agent (might need a new runner instance or direct model call) + # For simplicity, a mock: + user_input = llm_request.contents[-1].parts[0].text + if "dangerous_phrase" in user_input.lower(): + context.state["safety_violation"] = True + return LlmResponse(content=genai_types.Content(parts=[genai_types.Part(text="I cannot process this request due to safety concerns.")])) + return None + ``` +5. **Sandboxed Code Execution**: + * `BuiltInCodeExecutor`: Uses secure, sandboxed execution environments. + * Vertex AI Code Interpreter Extension. + * If custom, ensure hermetic environments (no network, isolated). +6. **Network Controls & VPC-SC**: Confine agent activity within secure perimeters (VPC Service Controls) to prevent data exfiltration. +7. **Output Escaping in UIs**: Always properly escape LLM-generated content in web UIs to prevent XSS attacks and indirect prompt injections. + +**Grounding**: A key safety and reliability feature that connects agent responses to verifiable information. +* **Mechanism**: Uses tools like `google_search` or `VertexAiSearchTool` to fetch real-time or private data. +* **Benefit**: Reduces model hallucination by basing responses on retrieved facts. +* **Requirement**: When using `google_search`, your application UI **must** display the provided search suggestions and citations to comply with terms of service. + +--- + +## 14. Debugging, Logging & Observability + +* **`adk web` UI**: Best first step. Provides visual trace, session history, and state inspection. +* **Event Stream Logging**: Iterate `runner.run_async()` events and print relevant fields. + ```python + async for event in runner.run_async(...): + print(f"[{event.author}] Event ID: {event.id}, Invocation: {event.invocation_id}") + if event.content and event.content.parts: + if event.content.parts[0].text: + print(f" Text: {event.content.parts[0].text[:100]}...") + if event.get_function_calls(): + print(f" Tool Call: {event.get_function_calls()[0].name} with {event.get_function_calls()[0].args}") + if event.get_function_responses(): + print(f" Tool Response: {event.get_function_responses()[0].response}") + if event.actions: + if event.actions.state_delta: + print(f" State Delta: {event.actions.state_delta}") + if event.actions.transfer_to_agent: + print(f" TRANSFER TO: {event.actions.transfer_to_agent}") + if event.error_message: + print(f" ERROR: {event.error_message}") + ``` +* **Tool/Callback `print` statements**: Simple logging directly within your functions. +* **Logging**: Use Python's standard `logging` module. Control verbosity with `adk web --log_level DEBUG` or `adk web -v`. +* **Observability Integrations**: ADK supports OpenTelemetry, enabling integration with platforms like: + * Google Cloud Trace + * AgentOps + * Arize AX + * Phoenix + * Weave by WandB + ```python + # Example using Comet Opik integration (conceptual) + # pip install comet_opik_adk + # from comet_opik_adk import enable_opik_tracing + # enable_opik_tracing() # Call at app startup + # Then run your ADK app, traces appear in Comet workspace. + ``` +* **Session History (`session.events`)**: Persisted for detailed post-mortem analysis. + +--- + +## 15. Streaming & Advanced I/O + +ADK supports real-time, bidirectional communication for interactive experiences like live voice conversations. + +* **Bidirectional Streaming**: Enables low-latency, two-way data flow (text, audio, video) between the client and agent, allowing for interruptions. +* **Core Components**: + * **`Runner.run_live()`**: The entry point for starting a streaming session. + * **`LiveRequestQueue`**: A queue for sending data (e.g., audio chunks) from the client to the agent during a live session. + * **`RunConfig`**: A configuration object passed to `run_live()` to specify modalities (`['TEXT', 'AUDIO']`), speech synthesis options, etc. +* **Streaming Tools**: A special type of `FunctionTool` that can stream intermediate results back to the agent. + * **Definition**: Must be an `async` function with a return type of `AsyncGenerator`. + ```python + from typing import AsyncGenerator + + async def monitor_stock_price(symbol: str) -> AsyncGenerator[str, None]: + """Yields stock price updates as they occur.""" + while True: + price = await get_live_price(symbol) + yield f"Update for {symbol}: ${price}" + await asyncio.sleep(5) + ``` + +* **Advanced I/O Modalities**: ADK (especially with Gemini Live API models) supports richer interactions. + * **Audio**: Input via `Blob(mime_type="audio/pcm", data=bytes)`, Output via `genai_types.SpeechConfig` in `RunConfig`. + * **Vision (Images/Video)**: Input via `Blob(mime_type="image/jpeg", data=bytes)` or `Blob(mime_type="video/mp4", data=bytes)`. Models like `gemini-2.5-flash-exp` can process these. + * **Multimodal Input in `Content`**: + ```python + multimodal_content = genai_types.Content( + parts=[ + genai_types.Part(text="Describe this image:"), + genai_types.Part(inline_data=genai_types.Blob(mime_type="image/jpeg", data=image_bytes)) + ] + ) + ``` + * **Streaming Modalities**: `RunConfig.response_modalities=['TEXT', 'AUDIO']`. + +--- + +## 16. Performance Optimization + +* **Model Selection**: Choose the smallest model that meets requirements (e.g., `gemini-2.5-flash` for simple tasks). +* **Instruction Prompt Engineering**: Concise, clear instructions reduce tokens and improve accuracy. +* **Tool Use Optimization**: + * Design efficient tools (fast API calls, optimize database queries). + * Cache tool results (e.g., using `before_tool_callback` or `tool_context.state`). +* **State Management**: Store only necessary data in state to avoid large context windows. +* **`include_contents='none'`**: For stateless utility agents, saves LLM context window. +* **Parallelization**: Use `ParallelAgent` for independent tasks. +* **Streaming**: Use `StreamingMode.SSE` or `BIDI` for perceived latency reduction. +* **`max_llm_calls`**: Limit LLM calls to prevent runaway agents and control costs. + +--- + +## 17. General Best Practices & Common Pitfalls + +* **Start Simple**: Begin with `LlmAgent`, mock tools, and `InMemorySessionService`. Gradually add complexity. +* **Iterative Development**: Build small features, test, debug, refine. +* **Modular Design**: Use agents and tools to encapsulate logic. +* **Clear Naming**: Descriptive names for agents, tools, state keys. +* **Error Handling**: Implement robust `try...except` blocks in tools and callbacks. Guide LLMs on how to handle tool errors. +* **Testing**: Write unit tests for tools/callbacks, integration tests for agent flows (`pytest`, `adk eval`). +* **Dependency Management**: Use virtual environments (`venv`) and `requirements.txt`. +* **Secrets Management**: Never hardcode API keys. Use `.env` for local dev, environment variables or secret managers (Google Cloud Secret Manager) for production. +* **Avoid Infinite Loops**: Especially with `LoopAgent` or complex LLM tool-calling chains. Use `max_iterations`, `max_llm_calls`, and strong instructions. +* **Handle `None` & `Optional`**: Always check for `None` or `Optional` values when accessing nested properties (e.g., `event.content and event.content.parts and event.content.parts[0].text`). +* **Immutability of Events**: Events are immutable records. If you need to change something *before* it's processed, do so in a `before_*` callback and return a *new* modified object. +* **Understand `output_key` vs. direct `state` writes**: `output_key` is for the agent's *final conversational* output. Direct `tool_context.state['key'] = value` is for *any other* data you want to save. +* **Example Agents**: Find practical examples and reference implementations in the [ADK Samples repository](https://github.com/google/adk-samples). + + +### Testing the output of an agent + +The following script demonstrates how to programmatically test an agent's output. This approach is extremely useful when an LLM or coding agent needs to interact with a work-in-progress agent, as well as for automated testing, debugging, or when you need to integrate agent execution into other workflows: +``` +import asyncio + +from google.adk.runners import Runner +from google.adk.sessions import InMemorySessionService +from app.agent import root_agent +from google.genai import types as genai_types + + +async def main(): + """Runs the agent with a sample query.""" + session_service = InMemorySessionService() + await session_service.create_session( + app_name="app", user_id="test_user", session_id="test_session" + ) + runner = Runner( + agent=root_agent, app_name="app", session_service=session_service + ) + query = "I want a recipe for pancakes" + async for event in runner.run_async( + user_id="test_user", + session_id="test_session", + new_message=genai_types.Content( + role="user", + parts=[genai_types.Part.from_text(text=query)] + ), + ): + if event.is_final_response(): + print(event.content.parts[0].text) + + +if __name__ == "__main__": + asyncio.run(main()) +``` + +--- + +## 18. Official API & CLI References + +For detailed specifications of all classes, methods, and commands, refer to the official reference documentation. + +* [Python API Reference](./api-reference/python/index.html) +* [Java API Reference](./api-reference/java/index.html) +* [CLI Reference](./api-reference/cli/index.html) +* [REST API Reference](./api-reference/rest/index.md) +* [Agent Config YAML Reference](./api-reference/agentconfig/index.html) + +--- +**llm.txt** documents the "Agent Starter Pack" repository, providing a source of truth on its purpose, features, and usage. +--- + +### Section 1: Project Overview + +* **Project Name:** Agent Starter Pack +* **Purpose:** Accelerate development of production-ready GenAI Agents on Google Cloud. +* **Tagline:** Production-Ready Agents on Google Cloud, faster. + +**The "Production Gap":** +While prototyping GenAI agents is quick, production deployment often takes 3-9 months. + +**Key Challenges Addressed:** +* **Customization:** Business logic, data grounding, security/compliance. +* **Evaluation:** Metrics, quality assessment, test datasets. +* **Deployment:** Cloud infrastructure, CI/CD, UI integration. +* **Observability:** Performance tracking, user feedback. + +**Solution: Agent Starter Pack** +Provides MLOps and infrastructure templates so developers focus on agent logic. + +* **You Build:** Prompts, LLM interactions, business logic, agent orchestration. +* **We Provide:** + * Deployment infrastructure, CI/CD, testing + * Logging, monitoring + * Evaluation tools + * Data connections, UI playground + * Security best practices + +Establishes production patterns from day one, saving setup time. + +--- +### Section 2: Creating & Enhancing Agent Projects + +Start by creating a new agent project from a predefined template, or enhance an existing project with agent capabilities. Both processes support interactive and fully automated setup. + +**Prerequisites:** +Before you begin, ensure you have `uv`/`uvx`, `gcloud` CLI, `terraform`, `git`, and `gh` CLI (for automated CI/CD setup) installed and authenticated. + +**Installing the `agent-starter-pack` CLI:** +Choose one method to get the `agent-starter-pack` command: + +1. **`uvx` (Recommended for Zero-Install/Automation):** Run directly without prior installation. + ```bash + uvx agent-starter-pack create ... + ``` +2. **Virtual Environment (`pip` or `uv`):** + ```bash + pip install agent-starter-pack + ``` +3. **Persistent CLI Install (`pipx` or `uv tool`):** Installs globally in an isolated environment. + +--- +### `agent-starter-pack create` Command + +Generates a new agent project directory based on a chosen template and configuration. + +**Usage:** +```bash +agent-starter-pack create PROJECT_NAME [OPTIONS] +``` + +**Arguments:** +* `PROJECT_NAME`: Name for your new project directory and base for GCP resource naming (max 26 chars, converted to lowercase). + +**Template Selection:** +* `-a, --agent`: Agent template - built-in agents (e.g., `adk_base`, `agentic_rag`), remote templates (`adk@gemini-fullstack`, `github.com/user/repo@branch`), or local projects (`local@./path`). + +**Deployment Options:** +* `-d, --deployment-target`: Target environment (`cloud_run` or `agent_engine`). +* `--cicd-runner`: CI/CD runner (`google_cloud_build` or `github_actions`). +* `--region`: GCP region (default: `europe-west4`). + +**Data & Storage:** +* `-i, --include-data-ingestion`: Include data ingestion pipeline. +* `-ds, --datastore`: Datastore type (`vertex_ai_search`, `vertex_ai_vector_search`, `alloydb`). +* `--session-type`: Session storage (`in_memory`, `alloydb`, `agent_engine`). + +**Project Creation:** +* `-o, --output-dir`: Output directory (default: current directory). +* `--agent-directory, -dir`: Agent code directory name (default: `app`). +* `--in-folder`: Create files in current directory instead of new subdirectory. + +**Automation:** +* `--auto-approve`: **Skip all interactive prompts (crucial for automation).** +* `--skip-checks`: Skip GCP/Vertex AI verification checks. +* `--debug`: Enable debug logging. + +**Automated Creation Example:** +```bash +uvx agent-starter-pack create my-automated-agent \ + -a adk_base \ + -d cloud_run \ + --region europe-west4 \ + --auto-approve +``` + +--- + +### `agent-starter-pack enhance` Command + +Enhance your existing project with AI agent capabilities by adding agent-starter-pack features in-place. This command supports all the same options as `create` but templates directly into the current directory instead of creating a new project directory. + +**Usage:** +```bash +agent-starter-pack enhance [TEMPLATE_PATH] [OPTIONS] +``` + +**Key Differences from `create`:** +* Templates into current directory (equivalent to `create --in-folder`) +* `TEMPLATE_PATH` defaults to current directory (`.`) +* Project name defaults to current directory name +* Additional `--base-template` option to override template inheritance + +**Enhanced Project Example:** +```bash +# Enhance current directory with agent capabilities +uvx agent-starter-pack enhance . \ + --base-template adk_base \ + -d cloud_run \ + --region europe-west4 \ + --auto-approve +``` + +**Project Structure:** Expects agent code in `app/` directory (configurable via `--agent-directory`). + +--- + +### Available Agent Templates + +Templates for the `create` command (via `-a` or `--agent`): + +| Agent Name | Description | +| :--------------------- | :------------------------------------------- | +| `adk_base` | Base ReAct agent (ADK) | +| `adk_gemini_fullstack` | Production-ready fullstack research agent | +| `agentic_rag` | RAG agent for document retrieval & Q&A | +| `langgraph_base_react` | Base ReAct agent (LangGraph) | +| `crewai_coding_crew` | Multi-agent collaborative coding assistance | +| `live_api` | Real-time multimodal RAG agent | + +--- + +### Including a Data Ingestion Pipeline (for RAG agents) + +For RAG agents needing custom document search, enabling this option automates loading, chunking, embedding documents with Vertex AI, and storing them in a vector database. + +**How to enable:** +```bash +uvx agent-starter-pack create my-rag-agent \ + -a agentic_rag \ + -d cloud_run \ + -i \ + -ds vertex_ai_search \ + --auto-approve +``` +**Post-creation:** Follow your new project's `data_ingestion/README.md` to deploy the necessary infrastructure. + +--- +### Section 3: Development & Automated Deployment Workflow +--- + +This section describes the end-to-end lifecycle of an agent, with emphasis on automation. + + +### 1. Local Development & Iteration + +Once your project is created, navigate into its directory to begin development. + +**First, install dependencies (run once):** +```bash +make install +``` + +**Next, test your agent. The recommended method is to use a programmatic script.** + +#### Programmatic Testing (Recommended Workflow) + +This method allows for quick, automated validation of your agent's logic. + +1. **Create a script:** In the project's root directory, create a Python script named `run_agent.py`. +2. **Invoke the agent:** In the script, write code to programmatically call your agent with sample input and `print()` the output for inspection. + * **Guidance:** If you're unsure or no guidance exists, you can look at files in the `tests/` directory for examples of how to import and call the agent's main function. + * **Important:** This script is for simple validation. **Assertions are not required**, and you should not create a formal `pytest` file. +3. **Run the test:** Execute your script from the terminal using `uv`. + ```bash + uv run python run_agent.py + ``` +You can keep the test file for future testing. + +#### Manual Testing with the UI Playground (Optional) + +If the user needs to interact with your agent manually in a chat interface for debugging: + +1. Run the following command to start the local web UI: + ```bash + make playground + ``` + This is useful for human-in-the-loop testing and features hot-reloading. + +### 2. Deploying to a Cloud Development Environment +Before setting up full CI/CD, you can deploy to a personal cloud dev environment. + +1. **Set Project:** `gcloud config set project YOUR_DEV_PROJECT_ID` +2. **Provision Resources:** `make setup-dev-env` (uses Terraform). +3. **Deploy Backend:** `make backend` (builds and deploys the agent). + +### 3. Automated Production-Ready Deployment with CI/CD +For reliable deployments, the `setup-cicd` command streamlines the entire process. It creates a GitHub repo, connects it to your chosen CI/CD runner (Google Cloud Build or GitHub Actions), provisions staging/prod infrastructure, and configures deployment triggers. + +**Automated CI/CD Setup Example (Recommended):** +```bash +# Run from the project root. This command will guide you or can be automated with flags. +uvx agent-starter-pack setup-cicd +``` + +**CI/CD Workflow Logic:** +* **On Pull Request:** CI pipeline runs tests. +* **On Merge to `main`:** CD pipeline deploys to staging. +* **Manual Approval:** A manual approval step triggers the production deployment. + +--- +### Section 4: Key Features & Customization +--- + +### Deploying with a User Interface (UI) +* **Unified Deployment (for Dev/Test):** The backend and frontend can be packaged and served from a single Cloud Run service, secured with Identity-Aware Proxy (IAP). +* **Deploying with UI:** `make backend IAP=true` +* **Access Control:** After deploying with IAP, grant users the `IAP-secured Web App User` role in IAM to give them access. + +### Session Management + +For stateful agents, the starter pack supports persistent sessions. +* **Cloud Run:** Choose between `in_memory` (for testing) and durable `alloydb` sessions using the `--session-type` flag. +* **Agent Engine:** Provides session management automatically. + +### Monitoring & Observability +* **Technology:** Uses OpenTelemetry to emit events to Google Cloud Trace and Logging. +* **Custom Tracer:** A custom tracer in `app/utils/tracing.py` (or a different agent directory instead of app) handles large payloads by linking to GCS, overcoming default service limits. +* **Infrastructure:** A Log Router to sink data to BigQuery is provisioned by Terraform. + +--- +### Section 5: CLI Reference for CI/CD Setup +--- + +### `agent-starter-pack setup-cicd` +Automates the complete CI/CD infrastructure setup for GitHub-based deployments. Intelligently detects your CI/CD runner (Google Cloud Build or GitHub Actions) and configures everything automatically. + +**Usage:** +```bash +uvx agent-starter-pack setup-cicd [OPTIONS] +``` + +**Prerequisites:** +- Run from the project root (directory with `pyproject.toml`) +- Required tools: `gh` CLI (authenticated), `gcloud` CLI (authenticated), `terraform` +- `Owner` role on GCP projects +- GitHub token with `repo` and `workflow` scopes + +**Key Options:** +* `--staging-project`, `--prod-project`: GCP project IDs (will prompt if omitted). +* `--repository-name`, `--repository-owner`: GitHub repo details (will prompt if omitted). +* `--cicd-project`: CI/CD resources project (defaults to prod project). +* `--dev-project`: Development project ID (optional). +* `--region`: GCP region (default: `europe-west4`). +* `--auto-approve`: Skip all interactive prompts. +* `--local-state`: Use local Terraform state instead of GCS backend. +* `--debug`: Enable debug logging. + +**What it does:** +1. Creates/connects GitHub repository +2. Sets up Terraform infrastructure with remote state +3. Configures CI/CD runner connection (Cloud Build or GitHub Actions with WIF) +4. Provisions staging/prod environments +5. Sets up local Git repository with origin remote + +**Automated Example:** +```bash +uvx agent-starter-pack setup-cicd \ + --staging-project your-staging-project \ + --prod-project your-prod-project \ + --repository-name your-repo-name \ + --repository-owner your-username \ + --auto-approve +``` + +**After setup, push to trigger pipeline:** +```bash +git add . && git commit -m "Initial commit" && git push -u origin main +``` + +* Note: For coding agents - ask user for required project IDs and repo details before running with `--auto-approve`. +* Note: If user prefers different git provider, refer to `deployment/README.md` for manual deployment. +--- +### Section 6: Operational Guidelines for Coding Agents + +These guidelines are essential for interacting with the Agent Starter Pack project effectively. + +--- + +### Principle 1: Code Preservation & Isolation + +When executing code modifications using tools like `replace` or `write_file`, your paramount objective is surgical precision. You **must alter only the code segments directly targeted** by the user's request, while **strictly preserving all surrounding and unrelated code.** + +**Mandatory Pre-Execution Verification:** + +Before finalizing any `new_string` for a `replace` operation, meticulously verify the following: + +1. **Target Identification:** Clearly define the exact lines or expressions to be changed, based *solely* on the user's explicit instructions. +2. **Preservation Check:** Compare your proposed `new_string` against the `old_string`. Ensure all code, configuration values (e.g., `model`, `version`, `api_key`), comments, and formatting *outside* the identified target remain identical and verbatim. + +**Example: Adhering to Preservation** + +* **User Request:** "Change the agent's instruction to be a recipe suggester." +* **Original Code Snippet:** + ```python + root_agent = Agent( + name="root_agent", + model="gemini-2.5-flash", + instruction="You are a helpful AI assistant." + ) + ``` +* **Incorrect Modification (VIOLATION):** + ```python + root_agent = Agent( + name="recipe_suggester", + model="gemini-1.5-flash", # UNINTENDED MUTATION - model was not requested to change + instruction="You are a recipe suggester." + ) + ``` +* **Correct Modification (COMPLIANT):** + ```python + root_agent = Agent( + name="recipe_suggester", # OK, related to new purpose + model="gemini-2.5-flash", # MUST be preserved + instruction="You are a recipe suggester." # OK, the direct target + ) + ``` + +**Critical Error:** Failure to adhere to this preservation principle is a critical error. Always prioritize the integrity of existing, unchanged code over the convenience of rewriting entire blocks. + +--- + +### Principle 2: Workflow & Execution Best Practices + +* **Standard Workflow:** + The validated end-to-end process is: `create` → `test` → `setup-cicd` → push to deploy. Trust this high-level workflow as the default for developing and shipping agents. + +* **Agent Testing:** + * **Avoid `make playground`** unless specifically instructed; it is designed for human interaction. Focus on programmatic testing. + +* **Model Selection:** + * **When using Gemini, prefer the 2.5 model family** for optimal performance and capabilities: "gemini-2.5-pro" and "gemini-2.5-flash" + +* **Running Python Commands:** + * Always use `uv` to execute Python commands within this repository (e.g., `uv run run_agent.py`). + * Ensure project dependencies are installed by running `make install` before executing scripts. + * Consult the project's `Makefile` and `README.md` for other useful development commands. + +* **Further Reading & Troubleshooting:** + * For questions about specific frameworks (e.g., LangGraph) or Google Cloud products (e.g., Cloud Run), their official documentation and online resources are the best source of truth. + * **When encountering persistent errors or if you're unsure how to proceed after initial troubleshooting, a targeted Google Search is strongly recommended.** It is often the fastest way to find relevant documentation, community discussions, or direct solutions to your problem. diff --git a/python/agents/short-movie-agents/Makefile b/python/agents/short-movie-agents/Makefile new file mode 100644 index 000000000..2cb11266a --- /dev/null +++ b/python/agents/short-movie-agents/Makefile @@ -0,0 +1,43 @@ +# Install dependencies using uv package manager +install: + @command -v uv >/dev/null 2>&1 || { echo "uv is not installed. Installing uv..."; curl -LsSf https://astral.sh/uv/0.6.12/install.sh | sh; source $HOME/.local/bin/env; } + uv sync --dev +# Launch local dev playground +playground: + @echo "===============================================================================" + @echo "| 🚀 Starting your agent playground... |" + @echo "| |" + @echo "| 🔍 IMPORTANT: Select the 'app' folder to interact with your agent. |" + @echo "===============================================================================" + uv run adk web . --port 8501 --reload_agents + +# Deploy the agent remotely +# Usage: make backend [PORT=8080] - Set IAP=true to enable Identity-Aware Proxy, PORT to specify container port +backend: + PROJECT_ID=$$(gcloud config get-value project) && \ + gcloud beta run deploy short-movie-agents \ + --source . \ + --memory "4Gi" \ + --project $$PROJECT_ID \ + --region "europe-west4" \ + --no-cpu-throttling \ + --labels "created-by=adk,dev-tutorial=sample-short-movie-agents" \ + --allow-unauthenticated \ + --env-vars-file .env \ + $(if $(PORT),--port=$(PORT)) + +# Launch local development server with hot-reload +local-backend: + uv run uvicorn app.server:app --host 0.0.0.0 --port 8000 --reload + +# Run unit and integration tests +test: + uv run pytest tests/unit && uv run pytest tests/integration + +# Run code quality checks (codespell, ruff, mypy) +lint: + uv sync --dev --extra lint + uv run codespell + uv run ruff check . --diff + uv run ruff format . --check --diff + uv run mypy . \ No newline at end of file diff --git a/python/agents/short-movie-agents/README.md b/python/agents/short-movie-agents/README.md new file mode 100644 index 000000000..92f6c235e --- /dev/null +++ b/python/agents/short-movie-agents/README.md @@ -0,0 +1,105 @@ +# Short Movie Agents +ADK version: 1.14.1, Owner: @rsamborski + +Short Movie Agents demo is an ADK example showcasing a multi-agent architecture to construct end to end videos based on the user's intent. It includes agents which have a different role each: + +- [director agent](app/agent.py) - main coordinator +- [story agent](app/story_agent.py) - creates the story +- [screenplay agent](app/screenplay_agent.py) - generates screenplay based on the story +- [storyboard agent](app/storyboard_agent.py) - uses context from previous agents and Imagen4 model to generate storyboards +- [video agent](app/video_agent.py) - produces final video using Veo3 + +Diagram: + +![Agent Diagram](assets/agent_diagram.png) + +## Changelog + +See [Changelog.md](Changelog.md). + +## Project Structure + +This project is organized as follows: + +``` +short-movie-agents/ +├── app/ # Core application code +│ ├── agent.py # Main agent logic +│ ├── server.py # FastAPI Backend server +│ └── utils/ # Utility functions and helpers +├── Makefile # Makefile for common commands +├── GEMINI.md # AI-assisted development guide +└── pyproject.toml # Project dependencies and configuration +``` + +## Requirements + +Before you begin, ensure you have: +- **uv**: Python package manager (used for all dependency management in this project) - [Install](https://docs.astral.sh/uv/getting-started/installation/) ([add packages](https://docs.astral.sh/uv/concepts/dependencies/) with `uv add `) +- **Google Cloud SDK**: For GCP services - [Install](https://cloud.google.com/sdk/docs/install) +- **make**: Build automation tool - [Install](https://www.gnu.org/software/make/) (pre-installed on most Unix-based systems) + + +## Quick Start (Local Testing) + +Install required packages and launch the local development environment: + +```bash +cp .env-template .env +vim .env # Uncomment and update the environment variables +make install && make playground +``` + +## Commands + +| Command | Description | +| -------------------- | ------------------------------------------------------------------------------------------- | +| `make install` | Install all required dependencies using uv | +| `make playground` | Launch local development environment with backend and frontend - leveraging `adk web` command.| +| `make backend` | Deploy agent to Cloud Run | +| `make local-backend` | Launch local development server | +| `make test` | Run unit and integration tests | +| `make lint` | Run code quality checks (codespell, ruff, mypy) | +| `uv run jupyter lab` | Launch Jupyter notebook | + +For full command options and usage, refer to the [Makefile](Makefile). + + +## Usage + +This template follows a "bring your own agent" approach - you focus on your business logic, and the template handles everything else (UI, infrastructure, deployment, monitoring). + +1. **Integrate:** Update the agent by editing files in `app` folder. +1. **Test:** Explore your agent functionality using the Streamlit playground with `make playground`. The playground offers features like chat history, user feedback, and various input types, and automatically reloads your agent on code changes. +1. **Deploy:** Refer to the [deployment section](#deployment) for comprehensive instructions. +5. **Monitor:** Track performance and gather insights using Cloud Logging, Tracing, and the Looker Studio dashboard to iterate in your application. + +The project includes a `GEMINI.md` file that provides context for AI tools like Gemini CLI when asking questions about the project. + + +## Deployment + +### Dev Environment + +You can test deployment towards a Dev Environment using the following command: + +```bash +cp .env-template .env +vim .env # Uncomment and update the environment variables +gcloud config set project +make backend +``` + + +## Monitoring and Observability +> You can use [this Looker Studio dashboard](https://lookerstudio.google.com/reporting/46b35167-b38b-4e44-bd37-701ef4307418/page/tEnnC +) template for visualizing events being logged in BigQuery. See the "Setup Instructions" tab to getting started. + +The application uses OpenTelemetry for comprehensive observability with all events being sent to Google Cloud Trace and Logging for monitoring and to BigQuery for long term storage. + +### Disclaimer + +This list is not an official Google product. Links on this list also are not necessarily to official Google products. + +Initial agent structure was generated with [`googleCloudPlatform/agent-starter-pack`](https://github.com/GoogleCloudPlatform/agent-starter-pack) version `0.15.4`. + diff --git a/python/agents/short-movie-agents/app/__init__.py b/python/agents/short-movie-agents/app/__init__.py new file mode 100644 index 000000000..7b5f13a09 --- /dev/null +++ b/python/agents/short-movie-agents/app/__init__.py @@ -0,0 +1,17 @@ +# Copyright 2025 Google LLC +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +from app.agent import root_agent + +__all__ = ["root_agent"] diff --git a/python/agents/short-movie-agents/app/agent.py b/python/agents/short-movie-agents/app/agent.py new file mode 100644 index 000000000..97f71baa8 --- /dev/null +++ b/python/agents/short-movie-agents/app/agent.py @@ -0,0 +1,54 @@ +# Copyright 2025 Google LLC +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import logging + +from google.adk.agents import Agent + +from .screenplay_agent import screenplay_agent +from .story_agent import story_agent +from .storyboard_agent import storyboard_agent +from .utils.utils import load_prompt_from_file +from .video_agent import video_agent + +# Set logging +logger = logging.getLogger(__name__) + +# Configuration constants +MODEL = "gemini-2.5-flash" +DESCRIPTION = "Orchestrates the creation of a short, animated campfire story based on user input, utilizing specialized sub-agents for story generation, storyboard creation, and video generation." + +# --- Director Agent (root agent) --- + +if story_agent and screenplay_agent and storyboard_agent and video_agent: + root_agent = Agent( + name="director_agent", + model=MODEL, + description=(DESCRIPTION), + instruction=load_prompt_from_file("director_agent.txt"), + sub_agents=[story_agent, screenplay_agent, storyboard_agent, video_agent], + ) + logger.info(f"✅ Agent '{root_agent.name}' created using model '{MODEL}'.") +else: + logger.error( + "❌ Cannot create root agent because one or more sub-agents failed to initialize or a tool is missing." + ) + if not story_agent: + logger.error(" - Story Agent is missing.") + if not screenplay_agent: + logger.error(" - Screenplay Agent is missing.") + if not storyboard_agent: + logger.error(" - Storyboard Agent is missing.") + if not video_agent: + logger.error(" - Video Agent is missing.") diff --git a/python/agents/short-movie-agents/app/prompts/director_agent.txt b/python/agents/short-movie-agents/app/prompts/director_agent.txt new file mode 100644 index 000000000..46d81933f --- /dev/null +++ b/python/agents/short-movie-agents/app/prompts/director_agent.txt @@ -0,0 +1,46 @@ +**Role:** Director Agent + +**Primary Objective:** To guide the user step-by-step through the creation of a short, animated campfire story. You will orchestrate a team of specialized sub-agents for story generation, screenplay writing, storyboard creation, and video generation, seeking user approval at each stage. + +**Core Tasks and Conversational Workflow:** + +1. **Welcome and Gather Initial Idea:** + * Start by welcoming the user and asking for their initial concept for a campfire story. + * Ask for key elements like setting, characters, and tone. + +2. **Step 1: Story Generation** + * Inform the user that the first step is to create the story. + * Call the `story_agent` with the user's concept to generate a story. + * Present the generated story to the user and ask for their approval. + * **If the user is not satisfied,** ask for feedback and work with them to refine the story by calling the `story_agent` again with updated prompts. + * **Once the user approves,** proceed to the next step. + +3. **Step 2: Screenplay Creation** + * Inform the user that you will now write the screenplay based on the approved story. + * Call the `screenplay_agent` with the story as input. + * Present the screenplay to the user for their review and approval. + * Allow for revisions by calling the `screenplay_agent` again if needed. + * **Once the user approves,** move on. + +4. **Step 3: Storyboard Visualization** + * Explain that the next step is to create storyboards for each scene. + * Call the `storyboard_agent` with the screenplay and story. + * Show the generated storyboards to the user. + * Ask for their feedback and allow for regeneration of specific storyboards. + * **Once the user is happy with the storyboards,** proceed to the final step. + +5. **Step 4: Video Generation** + * Inform the user that you will now generate the final video scenes. + * Call the `video_agent` with the screenplay and storyboards. + * Present the final video to the user. + * Allow the user to request regeneration of specific scenes if they are not satisfied. + +6. **Conclusion:** + * Once the user is happy with the final video, congratulate them on creating their movie. + +**Important Considerations:** + +* **Be a Guide:** Your primary role is to be a helpful guide for the user. Keep your responses clear, concise, and friendly. +* **One Step at a Time:** Do not move to the next step until the user has approved the current one. +* **Manage State:** Keep track of the approved assets (story, screenplay, etc.) to use them as input for subsequent steps. +* **Be Patient:** The user may want to make many changes. Be patient and accommodating. diff --git a/python/agents/short-movie-agents/app/prompts/screenplay_agent.txt b/python/agents/short-movie-agents/app/prompts/screenplay_agent.txt new file mode 100644 index 000000000..365ed3bb4 --- /dev/null +++ b/python/agents/short-movie-agents/app/prompts/screenplay_agent.txt @@ -0,0 +1,30 @@ +**Role:** Screenplay Generation Agent + +**Primary Objective:** To transform a campfire story, provided by the story agent and stored in the session state under the key 'story', into a standard screenplay format suitable for video production. + +**Core Tasks:** + +1. **Retrieve Story and Character Descriptions:** Access the session state and retrieve the story and the character descriptions stored under the key 'story'. + +2. **Exclude Framing Narrative:** The story is told by a scout at a campfire. Your task is to extract the core narrative and write it as a screenplay. Do NOT include the campfire setting or the scout telling the story in the screenplay itself, unless the story is about a scout at a campfire. + +3. **Interpret and Translate:** Carefully read and interpret the story. Preserve the intended meaning, tone, and any specific nuances of the narrative. + +4. **Structure the Screenplay:** Organize the translated story into a standard screenplay format. This involves: + * **Scene Headings (Sluglines):** Create clear and concise scene headings that indicate the location (INT. or EXT.), the specific place, and the time of day (DAY or NIGHT). Ensure logical progression between scenes based on the story. + * **Action Lines:** Describe the visual elements of each scene, including character actions, movements, expressions, and the environment. Write these in the present tense and be descriptive enough for visualization but concise. + * **Character Names:** Clearly identify each character speaking in a scene, centered and in all caps. + **Dialogue:** Present the dialogue spoken by each character directly below their name, in standard sentence case. + * **Parentheticals (Optional):** Use sparingly to indicate specific emotional states or delivery instructions for the actors (e.g., (sadly), (with a twinkle in his eye)). + +5. **Maintain Character Consistency:** Use the provided character descriptions to ensure that all character actions and dialogue are consistent with their defined personalities. + +6. **Cover All Parts of the Story:** Ensure that the screenplay comprehensively covers the entire narrative provided in the story, including the beginning, rising action, climax, and resolution. Do not omit any significant plot points, character interactions, or descriptive elements. + +7. **Prepare for Video Production:** Format the screenplay in a way that is easily readable and understandable for all stages of video production, including: + * Clear and consistent formatting. + * Logical scene breaks. + * Sufficient visual and action descriptions for the director and camera crew. + * Dialogue that is clear for actors. + +8. **Output:** Output the generated screenplay in a standard markdown format. diff --git a/python/agents/short-movie-agents/app/prompts/story_agent.txt b/python/agents/short-movie-agents/app/prompts/story_agent.txt new file mode 100644 index 000000000..23454c826 --- /dev/null +++ b/python/agents/short-movie-agents/app/prompts/story_agent.txt @@ -0,0 +1,18 @@ +**Name:** Scout Leader + +You are now speaking to young scouts gathered around a crackling fire. They are eager to hear one of your famous campfire stories. + +Therefore, when responding to any query, you must fully embody the persona of a friendly and engaging Scout Leader. Your tone should be warm, enthusiastic, and a little bit mysterious to draw the listeners in. + +Crucially, your answers must always be presented as a longer story or anecdote, one with a clear and discernible structure. Each tale must have a distinct **beginning** that sets the scene and introduces characters or a situation. The narrative should then build through **rising action**, presenting challenges, developing conflicts, and increasing tension. This should lead to a **climax**, the most significant turning point or moment of truth in the story. Finally, the story must conclude with a clear **resolution**, showing the outcome of the events and often imparting a lesson or piece of wisdom related to the initial query. + +Even if the question is direct, weave your response into this more extended narrative format. The story should clearly contain the answer to the user's query within its unfolding events or the wisdom gleaned from its resolution. Aim for stories that are engaging and impactful, allowing the young scouts (and the user) to fully grasp the lesson through the experience shared. + +**Character Descriptions:** +At the end of the story, you MUST provide a list of all characters with a brief, one-sentence description of their appearance. For example: +- Sammy the Squirrel: A small, fluffy squirrel with a bushy tail and a mischievous glint in his eye. +- Herbert the Hedgehog: A stout hedgehog with brown spines and a friendly, twitching nose. + +Remember, always prioritize the Scout Leader voice and this more detailed storytelling approach with a clear beginning, rising action, climax, and resolution in every response." + +After providing the initial story transfer back to the root agent. diff --git a/python/agents/short-movie-agents/app/prompts/story_agent_desc.txt b/python/agents/short-movie-agents/app/prompts/story_agent_desc.txt new file mode 100644 index 000000000..544af529f --- /dev/null +++ b/python/agents/short-movie-agents/app/prompts/story_agent_desc.txt @@ -0,0 +1,29 @@ +**Name:** Scout Leader + +**Function:** Provides answers and guidance in the style of a friendly and engaging Scout Leader. All responses are delivered as a short, illustrative story or anecdote that subtly conveys the requested information or wisdom. + +**Key Characteristics for Routing:** + +* **Persona:** Consistently adopts the Scout Leader persona. +* **Output Format:** Always delivers answers as part of a brief narrative. +* **Language Style:** Warm, enthusiastic, and a little bit mysterious. +* **Knowledge Domain:** While capable of addressing a wide range of topics, the responses will always be filtered through the lens of campfire stories and presented within a story. +* **Intended Use:** Suitable for users seeking guidance, or creative and engaging answers presented in a unique and memorable way. Not ideal for users requiring direct, concise factual information without narrative framing or stylistic alteration. +* **Potential Strengths:** Engaging user experience, memorable responses, ability to convey abstract concepts through storytelling. +* **Potential Weaknesses:** May not be suitable for time-sensitive queries requiring immediate, direct answers. The indirect, story-based approach might not be preferred by all users. + +**Routing Instructions:** + +Route user queries to this agent if: + +* The user expresses interest in or explicitly requests a response in the style of a Scout Leader. +* The query is open-ended and could benefit from an insightful answer presented through a story. +* The user is likely to appreciate a creative and engaging interaction over a purely factual one. +* The query is not urgent and allows for a more narrative and stylistically unique response. + +**Do NOT route user queries to this agent if:** + +* The user requires a direct, factual answer without any narrative or stylistic embellishment. +* The query is time-sensitive and demands an immediate response. +* The user explicitly requests a formal or non-fictional tone. +* The query involves highly technical or domain-specific information where accuracy and conciseness are paramount over creative storytelling. \ No newline at end of file diff --git a/python/agents/short-movie-agents/app/prompts/storyboard_agent.txt b/python/agents/short-movie-agents/app/prompts/storyboard_agent.txt new file mode 100644 index 000000000..7e27b2fb4 --- /dev/null +++ b/python/agents/short-movie-agents/app/prompts/storyboard_agent.txt @@ -0,0 +1,51 @@ +**Role:** Storyboard Generation Agent + +**Primary Objective:** To generate storyboards for each scene of a campfire story, utilizing the 'storyboard_generate' tool to create black and white comic-like drawings and returning the Google Cloud Storage (GCS) links to the generated images. The `storyboard_generate` tool requires the scene number as the second parameter. + +**Core Tasks:** + +1. **Retrieve Screenplay and Character Descriptions:** + * Access the session state and retrieve the screenplay under the key 'screenplay' and the character descriptions under the key 'story'. + +2. **Process Each Scene:** Iterate through each scene defined in the screenplay. For each scene, keep track of the current scene number (starting from 1). + + * **Identify Key Visual Elements:** Analyze the scene heading and action lines in the screenplay to identify the key visual elements that need to be represented in the storyboards. Consider: + * Location and environment. + * Characters present and their actions. + * Camera angles and framing implied by the action. + * Mood and atmosphere. + + * **Generate Storyboard Image:** Call the 'storyboard_generate' tool **once** for each scene. When calling the tool, provide **two** parameters: + * **Parameter 1 (Prompt):** A detailed text prompt that incorporates: + * The scene heading (e.g., "INT. TENT - NIGHT"). + * Relevant action lines from the screenplay describing what is happening visually. + * The characters involved and their current actions or expressions, **using the provided character descriptions to ensure visual consistency.** + * The overall mood or tone of the scene. + * Specific keywords or visual cues from the story. + * **Crucially, explicitly include the desired image style:** "**in the style of a black and white comic book drawing**, **a storyboard**, **sketch**, or **line art**". + * **Parameter 2 (Scene Number):** The current scene number being processed. + + * **Store Image Link:** The 'storyboard_generate' tool will return a link to the generated storyboard image saved in a Google Cloud Storage (GCS) bucket. Store this link, maintaining the order corresponding to the scene number. + +3. **Maintain Scene Order:** Ensure that the generated GCS links for the storyboard images are stored in an ordered list based on the scene number they correspond to. + +4. **Return Ordered List of Image Links:** Once all scenes in the screenplay have been processed and a storyboard image link has been generated and stored for each, return an ordered list (formatted in markdown as bullet list with active links) containing all the GCS links. This list represents the sequence of storyboards for the short movie, in the order of the scenes. + +**Tool Usage:** + +* **Tool Name:** `storyboard_generate` +* **Input:** + * **Parameter 1:** A text prompt describing the visual elements of a scene, drawing information from the screenplay, **including the desired black and white comic/storyboard style and character descriptions for visual consistency.** + * **Parameter 2:** An integer representing the **scene number**. +* **Output:** A **single GCS link** to the generated storyboard image. + +**Example Workflow (for the first scene):** + +1. Read the first scene in the screenplay (e.g., "EXT. FOREST - NIGHT"). Action lines describe a scout telling a story. +2. Call `storyboard_generate` with: + * **Prompt:** "**in the style of a black and white comic book drawing**, a storyboard, Exterior shot of a dark forest at night. A young scout is sitting by a campfire, telling a story to their friends. The scout is wearing a uniform and has a friendly expression. One of the friends is a small, fluffy squirrel with a bushy tail." + * **Scene Number:** `1` +3. Receive a single GCS link (e.g., `gs://your-bucket/scene_1_storyboard.png`). +4. Add this GCS link to the first position in the ordered list of storyboard image links. + +Continue this process for each subsequent scene, incrementing the scene number for each call to `storyboard_generate`. diff --git a/python/agents/short-movie-agents/app/prompts/video_agent.txt b/python/agents/short-movie-agents/app/prompts/video_agent.txt new file mode 100644 index 000000000..cd81681ce --- /dev/null +++ b/python/agents/short-movie-agents/app/prompts/video_agent.txt @@ -0,0 +1,54 @@ +**Role:** Video Generation Agent + +**Primary Objective:** To generate video clips for each scene of a campfire story, utilizing the `video_generate` tool with information extracted from the screenplay (stored in the session state) and the corresponding storyboard image links (provided as a list). + +**Core Tasks:** + +1. **Retrieve Screenplay, Storyboard Links, and Character Descriptions:** + * Access the session state and retrieve the screenplay under the key 'screenplay', the character descriptions under the key 'story', and the ordered list of storyboard links. + +2. **Process Each Scene:** Iterate through each scene in the screenplay, keeping track of the current scene number (starting from 1) and accessing the corresponding storyboard image link from the provided list. + +3. **Generate Video for Each Scene:** For each scene in the screenplay, call the `video_generate` tool **once**, passing the following arguments: + + * **Prompt:** Construct a detailed text prompt that instructs the video generation tool on what the video should present for the current scene. This prompt should be based on: + * **Scene Heading:** Include the scene location and time (e.g., "INT. TENT - NIGHT"). + * **Action Lines:** Describe the visual actions, character movements, and environment as detailed in the screenplay for the current scene. Be specific about what should be visible and how it should move. + * **Character Consistency:** Use the character descriptions to ensure all characters are rendered consistently with their defined appearance. + * **Overall Mood and Tone:** Convey the intended atmosphere of the scene (e.g., "tense and urgent," "peaceful and reflective," "action-packed"). + * **Reference to Storyboard (Optional but Recommended):** You can explicitly instruct the tool to adhere closely to the composition and framing of the provided `image_link`. For example: "The video should follow the composition of the provided storyboard image, showing..." + * **Desired Video Style (Implicit):** While the storyboard provides a visual guide, you can subtly influence the video style through your prompt (e.g., "realistic rendering," "cinematic shot," "dynamic camera movement"). + + * **Scene Number:** Pass the current scene number. + + * **Image Link:** Pass the GCS link to the storyboard image corresponding to the current scene. + + * **Screenplay:** Pass the full text of the current scene's screenplay. This will be used to extract dialogue for the audio track. + +4. **Store Video Links:** The `video_generate` tool will return a link to the generated video clip (likely a GCS link). Store each returned video link in a list, maintaining the order corresponding to the scene number. + +5. **Present List of Video Links:** After processing all the scenes in the screenplay and generating a video for each, return the ordered list containing the links to all the generated video clips. This list represents the complete sequence of video clips for the short movie, in the correct scene order. + +**Tool Usage:** + +* **Tool Name:** `video_generate` +* **Input:** + * **Prompt:** A detailed text description of the video content for the current scene, based on the screenplay and including character descriptions for visual consistency. + * **Scene Number:** An integer representing the current scene number. + * **Image Link:** A GCS link to the storyboard image for the current scene. + * **Screenplay:** The full text of the current scene's screenplay. +* **Output:** A GCS link to the generated video clip for that scene. + +**Example Workflow (for the first scene):** + +1. Access the first scene of the screenplay (Scene 1: EXT. FOREST - NIGHT - A scout tells a story). +2. Retrieve the first storyboard image link from the provided list (e.g., `gs://your-bucket/scene_1_storyboard.png`). +3. Call `video_generate` with: + * **Prompt:** "Exterior shot of a dark forest at night. A young scout is sitting by a campfire, telling a story to their friends. The scout is wearing a uniform and has a friendly expression. One of the friends is a small, fluffy squirrel with a bushy tail. The scene should convey a sense of mystery and adventure, following the composition of the provided storyboard image." + * **Scene Number:** `1` + * **Image Link:** `gs://your-bucket/scene_1_storyboard.png` + * **Screenplay:** "EXT. FOREST - NIGHT\nA young SCOUT sits by a campfire with friends.\nSCOUT\n(whispering)\nAnd then... the squirrel stole the golden acorn." +4. Receive the GCS link to the generated video clip for Scene 1 (e.g., `gs://your-bucket/scene_1_video.mp4`). +5. Add this video link to the first position in the list of generated video links. + +Continue this process for all scenes in the screenplay, using the corresponding storyboard image link for each scene. Finally, return the complete ordered list of video clip links. diff --git a/python/agents/short-movie-agents/app/screenplay_agent.py b/python/agents/short-movie-agents/app/screenplay_agent.py new file mode 100644 index 000000000..b7edbc0e2 --- /dev/null +++ b/python/agents/short-movie-agents/app/screenplay_agent.py @@ -0,0 +1,43 @@ +# Copyright 2025 Google LLC +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import logging + +from google.adk.agents import Agent + +from .utils.utils import load_prompt_from_file + +# Set logging +logger = logging.getLogger(__name__) + +# Configuration constants +MODEL = "gemini-2.5-flash" +DESCRIPTION = "Agent responsible for writing a screenplay based on a story" + +# --- Screenplay Agent --- +screenplay_agent = None +try: + screenplay_agent = Agent( + # Using a potentially different/cheaper model for a simple task + model=MODEL, + name="screenplay_agent", + description=(DESCRIPTION), + instruction=load_prompt_from_file("screenplay_agent.txt"), + output_key="screenplay", + ) + logger.info(f"✅ Agent '{screenplay_agent.name}' created using model '{MODEL}'.") +except Exception as e: + logger.error( + f"❌ Could not create Screenplay agent. Check API Key ({MODEL}). Error: {e}" + ) diff --git a/python/agents/short-movie-agents/app/server.py b/python/agents/short-movie-agents/app/server.py new file mode 100644 index 000000000..6a1bade2b --- /dev/null +++ b/python/agents/short-movie-agents/app/server.py @@ -0,0 +1,78 @@ +# Copyright 2025 Google LLC +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import os + +import google.auth +from fastapi import FastAPI +from google.adk.cli.fast_api import get_fast_api_app +from google.cloud import logging as google_cloud_logging +from opentelemetry import trace +from opentelemetry.sdk.trace import TracerProvider, export + +from app.utils.gcs import create_bucket_if_not_exists +from app.utils.tracing import CloudTraceLoggingSpanExporter +from app.utils.typing import Feedback + +_, project_id = google.auth.default() +logging_client = google_cloud_logging.Client() +logger = logging_client.logger(__name__) +allow_origins = ( + os.getenv("ALLOW_ORIGINS", "").split(",") if os.getenv("ALLOW_ORIGINS") else None +) + +bucket_name = f"gs://{project_id}-short-movie-agents-logs-data" +create_bucket_if_not_exists( + bucket_name=bucket_name, project=project_id, location="europe-west4" +) + +provider = TracerProvider() +processor = export.BatchSpanProcessor(CloudTraceLoggingSpanExporter()) +provider.add_span_processor(processor) +trace.set_tracer_provider(provider) + +AGENT_DIR = os.path.dirname(os.path.dirname(os.path.abspath(__file__))) +# In-memory session configuration - no persistent storage +session_service_uri = None + +app: FastAPI = get_fast_api_app( + agents_dir=AGENT_DIR, + web=True, + artifact_service_uri=bucket_name, + allow_origins=allow_origins, + session_service_uri=session_service_uri, +) +app.title = "short-movie-agents" +app.description = "API for interacting with the short-movie-agents" + + +@app.post("/feedback") +def collect_feedback(feedback: Feedback) -> dict[str, str]: + """Collect and log feedback. + + Args: + feedback: The feedback data to log + + Returns: + Success message + """ + logger.log_struct(feedback.model_dump(), severity="INFO") + return {"status": "success"} + + +# Main execution +if __name__ == "__main__": + import uvicorn + + uvicorn.run(app, host="0.0.0.0", port=8000) diff --git a/python/agents/short-movie-agents/app/story_agent.py b/python/agents/short-movie-agents/app/story_agent.py new file mode 100644 index 000000000..a720703a0 --- /dev/null +++ b/python/agents/short-movie-agents/app/story_agent.py @@ -0,0 +1,41 @@ +# Copyright 2025 Google LLC +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import logging + +from app.utils.utils import load_prompt_from_file + +try: + from google.adk.agents import Agent +except ImportError: + from adk.agents import Agent + +# --- Story Agent --- +story_agent = None + +try: + MODEL = "gemini-2.5-flash" + DESCRIPTION = "An agent that generates a short, engaging campfire story for scouts." + story_agent = Agent( + model=MODEL, + name="story_agent", + description=load_prompt_from_file("story_agent_desc.txt"), + instruction=load_prompt_from_file("story_agent.txt"), + output_key="story", + ) + logging.info(f"✅ Agent '{story_agent.name}' created using model '{MODEL}'.") +except Exception as e: + logging.error( + f"❌ Could not create Story agent. Check API Key ({MODEL}). Error: {e}" + ) diff --git a/python/agents/short-movie-agents/app/storyboard_agent.py b/python/agents/short-movie-agents/app/storyboard_agent.py new file mode 100644 index 000000000..996435251 --- /dev/null +++ b/python/agents/short-movie-agents/app/storyboard_agent.py @@ -0,0 +1,115 @@ +# Copyright 2025 Google LLC +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import logging +import os + +import vertexai +from google.adk.agents import Agent +from google.adk.tools import ToolContext +from vertexai.preview.vision_models import ImageGenerationModel + +from .utils.utils import load_prompt_from_file + +# Set logging +logger = logging.getLogger(__name__) +logger.setLevel(logging.DEBUG) + +# Configuration constants +MODEL = "gemini-2.5-flash" +DESCRIPTION = ( + "Agent responsible for creating storyboards based on a screenplay and story" +) + +# Initialize VertexAI and image generation model +IMAGEN_MODEL = "imagen-4.0-ultra-generate-001" + +logger.debug(f"ProjectID: {os.getenv('GOOGLE_CLOUD_PROJECT')}") +logger.debug(f"Location: {os.getenv('GOOGLE_CLOUD_LOCATION')}") + +vertexai.init( + project=os.getenv("GOOGLE_CLOUD_PROJECT"), + location=os.getenv("GOOGLE_CLOUD_LOCATION"), +) +generation_model = ImageGenerationModel.from_pretrained(IMAGEN_MODEL) + + +# Storyboard generate tool +def storyboard_generate( + prompt: str, scene_number: int, tool_context: ToolContext +) -> list[str]: + """ + Generate storyboard image representing the passed prompt. + + Args: + prompt (str): A text prompt describing the storyboard image that should be generated and returned by the tool. + scene_number (int): Scene number + tool_context (): ToolContext needed by the tool + + Returns: + str: Link to the image stored in GCS bucket. + """ + try: + # Get session_id for the GCS_PATH + session_id = tool_context._invocation_context.session.id + bucket_name = os.getenv("GOOGLE_CLOUD_BUCKET_NAME") + GCS_PATH = f"gs://{bucket_name}/{session_id}" + AUTHORIZED_URI = "https://storage.mtls.cloud.google.com/" + + # Actual image generation + logger.info(f"Generating image for scene {scene_number} with prompt: {prompt}") + response = generation_model.generate_images( + prompt=prompt, + number_of_images=1, + output_gcs_uri=f"{GCS_PATH}/scene_{scene_number}", + aspect_ratio="1:1", + negative_prompt="", + person_generation="allow_adult", + safety_filter_level="block_few", + add_watermark=True, + ) + + if response.images: + logger.info( + f"Generated {len(response.images)} image(s) for prompt: {prompt}" + ) + return [ + image._gcs_uri.replace("gs://", AUTHORIZED_URI) + for image in response.images + ] + else: + logger.info(f"Generated no (0) images for prompt: {prompt}") + return [] # Return an empty list if no images + except Exception as e: + logger.error(f"Error generating an image for {prompt}: {e}") + return [] + + +# --- Storyboard Agent --- +storyboard_agent = None +try: + storyboard_agent = Agent( + # Using a potentially different/cheaper model for a simple task + model=MODEL, + name="storyboard_agent", + description=(DESCRIPTION), + instruction=load_prompt_from_file("storyboard_agent.txt"), + output_key="storyboard", + tools=[storyboard_generate], + ) + logger.info(f"✅ Agent '{storyboard_agent.name}' created using model '{MODEL}'.") +except Exception as e: + logger.error( + f"❌ Could not create Storyboard agent. Check API Key ({MODEL}). Error: {e}" + ) diff --git a/python/agents/short-movie-agents/app/utils/gcs.py b/python/agents/short-movie-agents/app/utils/gcs.py new file mode 100644 index 000000000..208dcdcb3 --- /dev/null +++ b/python/agents/short-movie-agents/app/utils/gcs.py @@ -0,0 +1,42 @@ +# Copyright 2025 Google LLC +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import logging + +import google.cloud.storage as storage +from google.api_core import exceptions + + +def create_bucket_if_not_exists(bucket_name: str, project: str, location: str) -> None: + """Creates a new bucket if it doesn't already exist. + + Args: + bucket_name: Name of the bucket to create + project: Google Cloud project ID + location: Location to create the bucket in (defaults to europe-west4) + """ + storage_client = storage.Client(project=project) + + if bucket_name.startswith("gs://"): + bucket_name = bucket_name[5:] + try: + storage_client.get_bucket(bucket_name) + logging.info(f"Bucket {bucket_name} already exists") + except exceptions.NotFound: + bucket = storage_client.create_bucket( + bucket_name, + location=location, + project=project, + ) + logging.info(f"Created bucket {bucket.name} in {bucket.location}") diff --git a/python/agents/short-movie-agents/app/utils/tracing.py b/python/agents/short-movie-agents/app/utils/tracing.py new file mode 100644 index 000000000..5ab016f3f --- /dev/null +++ b/python/agents/short-movie-agents/app/utils/tracing.py @@ -0,0 +1,149 @@ +# Copyright 2025 Google LLC +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import json +import logging +from collections.abc import Sequence +from typing import Any + +import google.cloud.storage as storage +from google.cloud import logging as google_cloud_logging +from opentelemetry.exporter.cloud_trace import CloudTraceSpanExporter +from opentelemetry.sdk.trace import ReadableSpan +from opentelemetry.sdk.trace.export import SpanExportResult + + +class CloudTraceLoggingSpanExporter(CloudTraceSpanExporter): + """ + An extended version of CloudTraceSpanExporter that logs span data to Google Cloud Logging + and handles large attribute values by storing them in Google Cloud Storage. + + This class helps bypass the 256 character limit of Cloud Trace for attribute values + by leveraging Cloud Logging (which has a 256KB limit) and Cloud Storage for larger payloads. + """ + + def __init__( + self, + logging_client: google_cloud_logging.Client | None = None, + storage_client: storage.Client | None = None, + bucket_name: str | None = None, + debug: bool = False, + **kwargs: Any, + ) -> None: + """ + Initialize the exporter with Google Cloud clients and configuration. + + :param logging_client: Google Cloud Logging client + :param storage_client: Google Cloud Storage client + :param bucket_name: Name of the GCS bucket to store large payloads + :param debug: Enable debug mode for additional logging + :param kwargs: Additional arguments to pass to the parent class + """ + super().__init__(**kwargs) + self.debug = debug + self.logging_client = logging_client or google_cloud_logging.Client( + project=self.project_id + ) + self.logger = self.logging_client.logger(__name__) + self.storage_client = storage_client or storage.Client(project=self.project_id) + self.bucket_name = bucket_name or f"{self.project_id}-test-agent-logs-data" + self.bucket = self.storage_client.bucket(self.bucket_name) + + def export(self, spans: Sequence[ReadableSpan]) -> SpanExportResult: + """ + Export the spans to Google Cloud Logging and Cloud Trace. + + :param spans: A sequence of spans to export + :return: The result of the export operation + """ + for span in spans: + span_context = span.get_span_context() + trace_id = format(span_context.trace_id, "x") + span_id = format(span_context.span_id, "x") + span_dict = json.loads(span.to_json()) + + span_dict["trace"] = f"projects/{self.project_id}/traces/{trace_id}" + span_dict["span_id"] = span_id + + span_dict = self._process_large_attributes( + span_dict=span_dict, span_id=span_id + ) + + if self.debug: + print(span_dict) + + # Log the span data to Google Cloud Logging + self.logger.log_struct( + span_dict, + labels={ + "type": "agent_telemetry", + "service_name": "test-agent", + }, + severity="INFO", + ) + # Export spans to Google Cloud Trace using the parent class method + return super().export(spans) + + def store_in_gcs(self, content: str, span_id: str) -> str: + """ + Initiate storing large content in Google Cloud Storage/ + + :param content: The content to store + :param span_id: The ID of the span + :return: The GCS URI of the stored content + """ + if not self.storage_client.bucket(self.bucket_name).exists(): + logging.warning( + f"Bucket {self.bucket_name} not found. " + "Unable to store span attributes in GCS." + ) + return "GCS bucket not found" + + blob_name = f"spans/{span_id}.json" + blob = self.bucket.blob(blob_name) + + blob.upload_from_string(content, "application/json") + return f"gs://{self.bucket_name}/{blob_name}" + + def _process_large_attributes(self, span_dict: dict, span_id: str) -> dict: + """ + Process large attribute values by storing them in GCS if they exceed the size + limit of Google Cloud Logging. + + :param span_dict: The span data dictionary + :param trace_id: The trace ID + :param span_id: The span ID + :return: The updated span dictionary + """ + attributes = span_dict["attributes"] + if len(json.dumps(attributes).encode()) > 255 * 1024: # 250 KB + # Separate large payload from other attributes + attributes_payload = dict(attributes.items()) + attributes_retain = dict(attributes.items()) + + # Store large payload in GCS + gcs_uri = self.store_in_gcs(json.dumps(attributes_payload), span_id) + attributes_retain["uri_payload"] = gcs_uri + attributes_retain["url_payload"] = ( + f"https://storage.mtls.cloud.google.com/" + f"{self.bucket_name}/spans/{span_id}.json" + ) + + span_dict["attributes"] = attributes_retain + logging.info( + "Length of payload span above 250 KB, storing attributes in GCS " + "to avoid large log entry errors" + ) + + return span_dict diff --git a/python/agents/short-movie-agents/app/utils/typing.py b/python/agents/short-movie-agents/app/utils/typing.py new file mode 100644 index 000000000..d9e39218e --- /dev/null +++ b/python/agents/short-movie-agents/app/utils/typing.py @@ -0,0 +1,46 @@ +# Copyright 2025 Google LLC +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +import uuid +from typing import ( + Literal, +) + +from google.adk.events.event import Event +from google.genai.types import Content +from pydantic import ( + BaseModel, + Field, +) + + +class Request(BaseModel): + """Represents the input for a chat request with optional configuration.""" + + message: Content + events: list[Event] + user_id: str = Field(default_factory=lambda: str(uuid.uuid4())) + session_id: str = Field(default_factory=lambda: str(uuid.uuid4())) + + model_config = {"extra": "allow"} + + +class Feedback(BaseModel): + """Represents feedback for a conversation.""" + + score: int | float + text: str | None = "" + invocation_id: str + log_type: Literal["feedback"] = "feedback" + service_name: Literal["test-agent"] = "test-agent" + user_id: str = "" diff --git a/python/agents/short-movie-agents/app/utils/utils.py b/python/agents/short-movie-agents/app/utils/utils.py new file mode 100644 index 000000000..bb2959341 --- /dev/null +++ b/python/agents/short-movie-agents/app/utils/utils.py @@ -0,0 +1,40 @@ +# Copyright 2025 Google LLC +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import logging +import os + +# Set logging +logger = logging.getLogger(__name__) +PROMPTS_PATH = "../prompts/" + + +def load_prompt_from_file( + filename: str, default_instruction: str = "Default instruction." +) -> str: + """Reads instruction text from a file relative to this script.""" + instruction = default_instruction + try: + # Construct path relative to the current script file (__file__) + filepath = os.path.join(os.path.dirname(__file__), PROMPTS_PATH, filename) + with open(filepath, encoding="utf-8") as f: + instruction = f.read() + logger.info(f"Successfully loaded instruction from {filename}") + except FileNotFoundError: + logger.warning( + f"WARNING: Instruction file not found: {filepath}. Using default." + ) + except Exception as e: + logger.error(f"ERROR loading instruction file {filepath}: {e}. Using default.") + return instruction diff --git a/python/agents/short-movie-agents/app/video_agent.py b/python/agents/short-movie-agents/app/video_agent.py new file mode 100644 index 000000000..eb39dc4e7 --- /dev/null +++ b/python/agents/short-movie-agents/app/video_agent.py @@ -0,0 +1,134 @@ +# Copyright 2025 Google LLC +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import logging +import os +import re +import time + +from google import genai +from google.adk.agents import Agent +from google.adk.tools import ToolContext +from google.genai import types + +from .utils.utils import load_prompt_from_file + +# Set logging +logger = logging.getLogger(__name__) +logger.setLevel(logging.DEBUG) + +# Configuration constants +MODEL = "gemini-2.5-flash" +VIDEO_MODEL = "veo-3.0-generate-preview" +VIDEO_MODEL_LOCATION = "us-central1" +DESCRIPTION = ( + "Agent responsible for creating videos based on a screenplay and storyboards" +) +ASPECT_RATIO = "16:9" + +client = genai.Client( + vertexai=True, + project=os.getenv("GOOGLE_CLOUD_PROJECT"), + location=VIDEO_MODEL_LOCATION, +) + + +# Video generate tool +def video_generate( + prompt: str, + scene_number: int, + image_link: str, + screenplay: str, + tool_context: ToolContext, +) -> list[str]: + """ + Generate video based on the passed prompt and storyboard image. + + Args: + prompt (str): A text prompt describing the video that should be generated and returned by the tool. + scene_number (int): Scene number + image_link (str): Link to the image stored in GCS bucket + screenplay (str): Screenplay for the scene + tool_context (): ToolContext needed by the tool + + Returns: + str: Link to the video stored in GCS bucket. + """ + try: + # Get session_id for the GCS_PATH + session_id = tool_context._invocation_context.session.id + bucket_name = os.getenv("GOOGLE_CLOUD_BUCKET_NAME") + GCS_PATH = f"gs://{bucket_name}/{session_id}" + AUTHORIZED_URI = "https://storage.mtls.cloud.google.com/" + + # Extract dialogue from screenplay + dialogue = "\n".join(re.findall(r"^\w+\s*\(.+\)\s*$", screenplay, re.MULTILINE)) + dialogue += "\n".join(re.findall(r"^\s{2,}.+$", screenplay, re.MULTILINE)) + + if dialogue: + prompt += f"\n\nAudio:\n{dialogue}" + + # Actual video generation + logger.info(f"Generating video for prompt '{prompt}' and image '{image_link}'") + + operation = client.models.generate_videos( + model=VIDEO_MODEL, + prompt=prompt, + config=types.GenerateVideosConfig( + aspect_ratio=ASPECT_RATIO, + output_gcs_uri=f"{GCS_PATH}/scene_{scene_number}", + number_of_videos=1, + duration_seconds=8, + person_generation="allow_adult", + ), + ) + + while not operation.done: + time.sleep(15) + operation = client.operations.get(operation) + logger.info(f"Video generation operation: {operation}") + + if operation.response: + logger.info( + f"Generated {len(operation.result.generated_videos)} video(s) for prompt: {prompt}" + ) + return [ + video.video.uri.replace("gs://", AUTHORIZED_URI) + for video in operation.result.generated_videos + ] + else: + logger.info(f"Generated no (0) video for prompt: {prompt}") + return [] # Return an empty list if no video + except Exception as e: + logger.error(f"Error generating a video for {prompt}: {e}") + return [] + + +# --- Video Agent --- +video_agent = None +try: + video_agent = Agent( + # Using a potentially different/cheaper model for a simple task + model=MODEL, + name="video_agent", + description=(DESCRIPTION), + instruction=load_prompt_from_file("video_agent.txt"), + output_key="video", + tools=[video_generate], + ) + logger.info(f"✅ Agent '{video_agent.name}' created using model '{MODEL}'.") +except Exception as e: + logger.error( + f"❌ Could not create Storyboard agent. Check API Key ({MODEL}). Error: {e}" + ) diff --git a/python/agents/short-movie-agents/assets/agent_diagram.png b/python/agents/short-movie-agents/assets/agent_diagram.png new file mode 100644 index 000000000..656b0cb5c Binary files /dev/null and b/python/agents/short-movie-agents/assets/agent_diagram.png differ diff --git a/python/agents/short-movie-agents/pyproject.toml b/python/agents/short-movie-agents/pyproject.toml new file mode 100644 index 000000000..f796e02b1 --- /dev/null +++ b/python/agents/short-movie-agents/pyproject.toml @@ -0,0 +1,108 @@ +# Copyright 2025 Google LLC +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +[project] +name = "short-movie-agents" +version = "0.3.1" +description = "Demo showcasing how agents can collaborate to build short movies based on user requests." +authors = [ + {name = "Remigiusz Samborski"}, +] +dependencies = [ + "google-adk~=1.14.0", + "opentelemetry-exporter-gcp-trace~=1.9.0", + "google-cloud-logging>=3.12.0", + "google-cloud-aiplatform[evaluation]~=1.116.0", + "fastapi~=0.115.8", + "uvicorn~=0.34.0", + "psycopg2-binary>=2.9.10", +] + +requires-python = ">=3.13,<3.14" + + +[dependency-groups] +dev = [ + "pytest>=8.3.4", + "pytest-asyncio>=0.23.8", + "nest-asyncio>=1.6.0", +] + +[project.optional-dependencies] + +jupyter = [ + "jupyter~=1.0.0", +] +lint = [ + "ruff>=0.4.6", + "mypy~=1.15.0", + "codespell~=2.2.0", + "types-pyyaml~=6.0.12.20240917", + "types-requests~=2.32.0.20240914", +] + +[tool.ruff] +line-length = 88 +target-version = "py313" + +[tool.ruff.lint] +select = [ + "E", # pycodestyle + "F", # pyflakes + "W", # pycodestyle warnings + "I", # isort + "C", # flake8-comprehensions + "B", # flake8-bugbear + "UP", # pyupgrade + "RUF", # ruff specific rules +] +ignore = ["E501", "C901", "B006"] # ignore line too long, too complex + +[tool.ruff.lint.isort] +known-first-party = ["app", "frontend"] + +[tool.mypy] +disallow_untyped_calls = true +disallow_untyped_defs = true +disallow_incomplete_defs = true +no_implicit_optional = true +check_untyped_defs = true +disallow_subclassing_any = true +warn_incomplete_stub = true +warn_redundant_casts = true +warn_unused_ignores = true +warn_unreachable = true +follow_imports = "silent" +ignore_missing_imports = true +explicit_package_bases = true +disable_error_code = ["misc", "no-untyped-call", "no-any-return"] + +exclude = [".venv"] + +[tool.codespell] +ignore-words-list = "rouge" +skip = "./locust_env/*,uv.lock,.venv,./frontend,**/*.ipynb" + + +[build-system] +requires = 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