feat: Add parallel node groups with join strategies

contributing/samples/graph_agent_basic/README.md

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+# GraphAgent Basic Example — Conditional Routing
+
+This example demonstrates a data validation pipeline using **conditional routing** based on runtime
+state. The validator checks input quality and branches to either a processor (success path) or an
+error handler (failure path), showing how GraphAgent enables state-dependent decision logic that
+sequential or parallel agent composition alone cannot achieve.
+
+## When to Use This Pattern
+
+- Any workflow requiring "if X then A, else B" branching on agent output
+- Input validation before expensive downstream processing
+- Quality-gate patterns where the next step depends on a score or classification
+
+## How to Run
+
+```bash
+adk run contributing/samples/graph_agent_basic
+```
+
+## Graph Structure
+
+```
+validate ──(valid=True)──▶ process
+         ──(valid=False)─▶ error
+```
+
+## Key Code Walkthrough
+
+- **`GraphNode(name="validate", agent=validator_agent)`** — wraps an `LlmAgent` as a graph node
+- **`add_edge("validate", "process", condition=lambda s: s.data["valid"] == True)`** — conditional
+  edge that only fires when the validation flag is set
+- **Two end nodes** (`process` and `error`) — GraphAgent can have multiple terminal nodes
+- **State propagation** — each node's output is written to `state.data[node_name]` and read by
+  downstream condition functions
+- **No cycles** — this is a simple directed acyclic graph; for loops see `graph_agent_dynamic_queue`
+

contributing/samples/graph_agent_basic/agent.py

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+"""Basic GraphAgent example demonstrating conditional routing.
+
+This example shows how GraphAgent enables conditional workflow routing
+based on runtime state, which cannot be achieved with SequentialAgent
+or ParallelAgent composition.
+
+Use case: Data validation pipeline with retry logic.
+- If validation passes -> process data
+- If validation fails -> retry validation
+- After max retries -> route to error handler
+"""
+
+import asyncio
+import os
+
+from google.adk.agents import GraphAgent
+from google.adk.agents import LlmAgent
+from google.adk.agents.graph import GraphState
+from google.adk.runners import Runner
+from google.adk.sessions import InMemorySessionService
+from google.genai import types
+from pydantic import BaseModel
+
+_MODEL = os.getenv("LLM_MODEL_NAME", "gemini-2.5-flash")
+
+
+# --- Validation Result Schema ---
+class ValidationResult(BaseModel):
+  """Validation result structure."""
+
+  valid: bool
+  error: str | None = None
+
+
+# --- Validator Agent ---
+validator = LlmAgent(
+    name="validator",
+    model=_MODEL,
+    instruction="""
+    You validate input data quality.
+    Check if the input contains valid JSON.
+    Return {"valid": true} if valid, {"valid": false, "error": "reason"} if invalid.
+    """,
+    output_schema=ValidationResult,  # Ensures structured JSON output
+    # output_key auto-defaults to "validator" (agent name)
+)
+
+# --- Processor Agent ---
+processor = LlmAgent(
+    name="processor",
+    model=_MODEL,
+    instruction="""
+    You process validated data.
+    Transform the input JSON and return processed results.
+    """,
+)
+
+# --- Error Handler Agent ---
+error_handler = LlmAgent(
+    name="error_handler",
+    model=_MODEL,
+    instruction="""
+    You handle validation errors.
+    Provide helpful error messages and suggestions for fixing invalid data.
+    """,
+)
+
+
+# --- Edge Condition Functions ---
+def is_valid_json(state: GraphState) -> bool:
+  """Check if JSON is valid from structured output."""
+  result = state.get_parsed("validator", ValidationResult)
+  return result.valid if result else False
+
+
+# --- Create GraphAgent with Conditional Routing ---
+def build_validation_graph() -> GraphAgent:
+  """Build the validation pipeline graph."""
+  g = GraphAgent(name="validation_pipeline")
+
+  # Add nodes
+  g.add_node("validate", agent=validator)
+  g.add_node("process", agent=processor)
+  g.add_node("error", agent=error_handler)
+
+  # Add conditional edges
+  # If validation passes (state.data["validator"]["valid"] == True) -> process
+  g.add_edge(
+      "validate",
+      "process",
+      condition=is_valid_json,
+  )
+
+  # If validation fails (state.data["validator"]["valid"] == False) -> error handler
+  g.add_edge(
+      "validate",
+      "error",
+      condition=lambda state: not is_valid_json(state),
+  )
+
+  # Define workflow
+  g.set_start("validate")
+  g.set_end("process")  # Success path ends at process
+  g.set_end("error")  # Error path ends at error handler
+
+  return g
+
+
+# --- Run the workflow ---
+
+
+async def main():
+  graph = build_validation_graph()
+  runner = Runner(
+      app_name="validation_pipeline",
+      agent=graph,
+      session_service=InMemorySessionService(),
+      auto_create_session=True,
+  )
+
+  # Example: Valid input
+  print("=== Testing with valid JSON ===")
+  async for event in runner.run_async(
+      user_id="user_1",
+      session_id="session_1",
+      new_message=types.Content(
+          role="user",
+          parts=[types.Part(text='{"name": "John", "age": 30}')],
+      ),
+  ):
+    if event.content and event.content.parts:
+      print(f"{event.author}: {event.content.parts[0].text}")
+
+  # Example: Invalid input
+  print("\n=== Testing with invalid JSON ===")
+  async for event in runner.run_async(
+      user_id="user_1",
+      session_id="session_2",
+      new_message=types.Content(
+          role="user",
+          parts=[types.Part(text='{"name": "Invalid data')],
+      ),
+  ):
+    if event.content and event.content.parts:
+      print(f"{event.author}: {event.content.parts[0].text}")
+
+
+if __name__ == "__main__":
+  asyncio.run(main())

contributing/samples/graph_agent_basic/root_agent.yaml

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+# yaml-language-server: $schema=https://raw.githubusercontent.com/google/adk-python/refs/heads/main/src/google/adk/agents/config_schemas/AgentConfig.json
+
+agent_class: GraphAgent
+name: validation_pipeline
+description: Data validation pipeline with conditional routing
+
+# Define start and end nodes
+start_node: validate
+end_nodes:
+  - process
+  - error
+
+# Maximum iterations for cyclic graphs (default: 20)
+max_iterations: 10
+
+# Node definitions
+nodes:
+  - name: validate
+    sub_agents:
+      - code: agents.validator
+
+  - name: process
+    sub_agents:
+      - code: agents.processor
+
+  - name: error
+    sub_agents:
+      - code: agents.error_handler
+
+# Edge definitions with conditional routing
+edges:
+  # If validation passes -> process
+  - source_node: validate
+    target_node: process
+    condition: "data.get('valid', False) is True"
+    priority: 1
+
+  # If validation fails -> error handler
+  - source_node: validate
+    target_node: error
+    condition: "data.get('valid', False) is False"
+    priority: 1

contributing/samples/graph_agent_dynamic_queue/README.md

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+# GraphAgent Dynamic Task Queue Example
+
+This example demonstrates the **Dynamic Task Queue** pattern for GraphAgent, enabling AI Co-Scientist and similar workflows where tasks are generated and processed dynamically at runtime.
+
+## Pattern Overview
+
+The dynamic task queue pattern uses a function node with runtime agent dispatch:
+- **Task Queue**: Maintained in GraphState, grows/shrinks dynamically
+- **Agent Dispatch**: Different agents selected based on task type
+- **Dynamic Task Generation**: Agents generate new tasks from their outputs
+- **State-Based Loop**: Continues until queue is empty
+
+## What This Example Shows
+
+1. **Mock Agents**: Three agents (generation, review, experiment) for demonstration
+2. **Task Parsing**: Extract TODO items from agent outputs to create new tasks
+3. **Dynamic Dispatch**: Select agent based on task type at runtime
+4. **Queue Management**: Process tasks until queue is empty
+
+## Architecture Support
+
+This pattern enables **95%+ architecture support** for:
+- AI Co-Scientist (dynamic hypothesis generation and testing)
+- Research paper writing (dynamic outline → research → writing loops)
+- Multi-agent task orchestration
+
+## Running the Example
+
+```bash
+cd /path/to/adk-python
+source venv/bin/activate
+python contributing/samples/graph_agent_dynamic_queue/agent.py
+```
+
+The example will:
+1. Start with 2 initial tasks (generate hypothesis 1 and 2)
+2. Process each task with appropriate agent
+3. Parse agent outputs for new tasks (TODO: review X, TODO: experiment Y)
+4. Add new tasks to queue dynamically
+5. Continue until queue is empty
+
+## Adapting This Pattern
+
+Replace the mock agents with real agents:
+```python
+from your_agents import GenerationAgent, ReviewAgent, ExperimentAgent
+
+generation_agent = GenerationAgent(name="generation")
+review_agent = ReviewAgent(name="review")
+experiment_agent = ExperimentAgent(name="experiment")
+```
+
+Customize task parsing logic in `parse_new_tasks_from_result()` to match your agent outputs.