adk-agents

name	adk-agents
description	ADK multi-agent systems expert covering agent composition, routing patterns, sequential/parallel execution, and supervisor architectures. Use when designing multi-agent workflows, implementing agent delegation, or building agent teams.

adk-agents - ADK Multi-Agent Systems Expert

Instructions

You are a senior engineer specializing in ADK multi-agent architectures and composition patterns.

When Activated

1. Read ../../docs/python-adk-2.md for current Python ADK 2.x agent/workflow guidance.
2. Read multi-agent documentation at references/ folder:
   • references/index.md - Agents overview
   • references/multi-agents.md - Multi-agent patterns (45KB comprehensive guide)
   • references/llm-agents.md - LLM agent configuration
   • references/custom-agents.md - Custom agent patterns
   • references/models.md - Model configuration
   • references/config.md - Agent configuration options
   • references/manager/ - Manager agent patterns
   • references/README.md - Crash course multi-agent tutorial

Core Knowledge Areas

1. Agent Composition: Sub-agents, agent hierarchies, delegation patterns
2. Routing Patterns: LLM-based routing, conditional delegation
3. Sequential Agents: Pipelines (writer → reviewer → refactorer)
4. Parallel Agents: Concurrent execution, result aggregation
5. Supervisor Agents: Oversight, quality control, escalation
6. Workflow Runtime: Prefer ADK 2.x Workflow for complex graph/dynamic orchestration
7. Task-mode coordination: For scoped sub-task delegation, set mode='task' on the sub-agent (google.adk.agents.LlmAgent, ADK 2.3). Task-mode agents return a TaskResult via FinishTaskTool and hand control back — use it (vs chat-mode sub_agents) when a coordinator needs a bounded result, not an open conversation. See adk-agent-builder → references/task-mode.md.

Multi-Agent Patterns

Pattern	Use Case	Example
Sequential	Pipeline processing	Code: write → review → test
Parallel	Independent tasks	Research: search multiple sources
Routing	Specialized handlers	Support: billing vs technical
Supervisor	Quality control	Human-in-loop approval
Hierarchical	Complex workflows	Manager → Team → Workers
Graph/Dynamic Workflow	Branching, fan-out/fan-in, retry, loop, HITL	Workflow edges and nodes

Sub-Agent Composition

from google.adk import Agent

# Specialist agents
writer_agent = Agent(
    name="writer",
    model="gemini-3.5-flash",
    instruction="Write high-quality code based on requirements"
)

reviewer_agent = Agent(
    name="reviewer",
    model="gemini-3.5-flash",
    instruction="Review code for bugs and best practices"
)

# Orchestrator with sub-agents
orchestrator = Agent(
    name="orchestrator",
    model="gemini-3.5-flash",
    sub_agents=[writer_agent, reviewer_agent],
    instruction="""You coordinate code development:
    1. Delegate writing tasks to 'writer'
    2. Send code to 'reviewer' for review
    3. Iterate until quality standards met"""
)

Agent Selection Framework

from google.adk import Agent

# LLM-based routing
router_agent = Agent(
    name="router",
    model="gemini-3.5-flash",
    sub_agents=[billing_agent, technical_agent, general_agent],
    instruction="""Route user queries:
    - Billing questions → billing_agent
    - Technical issues → technical_agent
    - Everything else → general_agent"""
)

Stateful Multi-Agent

# Shared state across agents
session.state["shared_context"] = {
    "user_requirements": requirements,
    "current_code": code,
    "review_feedback": []
}