Intro to multi-agent systems with ADK

Key Concepts

• ADK (Agent Development Kit): A Google framework designed to simplify the creation of LLM-based agents, handling boilerplate code, tool integration, and execution loops.
• Agent: An LLM equipped with tools, operating in a loop to complete specific tasks.
• Tool: A function or capability (e.g., Python code, Google Search) that extends an LLM's functionality beyond its training data.
• Multi-Agent System: A framework where specialized agents act as tools for an "orchestrator" agent, allowing for complex, multi-step workflows.
• LLM as a Judge: A pattern where one agent evaluates the output or accuracy of another agent.

---

1. Getting Started with ADK

To begin development, users must generate an API key via Google AI Studio and install the google-adk package within a Python virtual environment.

• Initialization: Use the command adk create [agent_name] to scaffold a new project.
• Configuration: Agents are defined by selecting a model (e.g., Gemini 1.5 Flash), providing a name, and setting a system prompt (instructions).
• Testing:
  • CLI: adk run [agent_name] allows for direct interaction.
  • Web Interface: adk web launches a local server (default 127.0.0.1:8000) providing a GUI to test agents and inspect stack traces for debugging.

2. Equipping Agents with Tools

LLMs are limited by their training data cut-off dates. Tools allow agents to access real-time data.

• Deterministic Tools: Simple Python functions (e.g., get_current_time) can be passed into the tools list within the agent definition.
• Built-in Tools: ADK provides pre-built tools like GoogleSearch.
  • Configuration: When using tools that require multiple steps, set bypass_multiple_tools_limit = True to allow the agent to perform sequential searches.
• Observation: The web interface allows developers to view the specific sources retrieved by the GoogleSearch tool, helping identify potential hallucinations or broken URLs.

3. Building Multi-Agent Systems

Multi-agent systems allow for modular, specialized workflows, which improve accuracy and cost-efficiency.

• The "Verifier" Pattern: To combat LLM hallucinations, a URL verifier agent is created. It uses a fetch_url tool to retrieve page content and determines if the information is "verified," "not verified," or "inconclusive."
• Orchestration: A "Researcher" agent acts as the central controller. It is equipped with both the Search agent and the Verifier agent as tools.
• Workflow:
  1. Researcher triggers the Search Agent to gather data.
  2. Researcher passes the retrieved URLs to the URL Verifier.
  3. Verifier confirms the validity of the content.
  4. Researcher synthesizes the final, verified answer.

4. Strategic Advantages of Multi-Agent Systems

• Task Specialization: By assigning specific tasks to individual agents, developers gain better control over performance and memory usage.
• Model Optimization: Developers can assign cheaper, faster models to simple tasks and more advanced, expensive models to complex reasoning tasks within the same system.
• Reliability: Using an "LLM as a judge" provides a layer of validation that a single-agent system lacks.

Synthesis

The ADK framework shifts the paradigm from building monolithic agents to creating modular, collaborative systems. By treating agents as tools for other agents, developers can overcome the inherent limitations of LLMs—such as data staleness and hallucination—while maintaining granular control over cost and performance. For further documentation and tutorials, visit adk.dev.