Market Research Fan-Out – Parallel FanOut¶

Demonstrates a ParallelAgent that runs branches concurrently. The scenario: a market research system that simultaneously gathers intelligence from web sources, academic papers, and social media to produce a comprehensive competitive analysis.

Real-world use case: Competitive intelligence system that simultaneously gathers data from web, academic, and social media sources. Used by market research teams to produce comprehensive analysis in minutes instead of days.

In other frameworks: LangGraph requires a StateGraph with fan-out nodes and edge wiring (~30 lines). CrewAI supports parallel via Crew(process=”parallel”) but lacks explicit fan-out composition. adk-fluent uses the | operator for declarative parallel execution.

Pipeline topology: ( web_analyst | academic_analyst | social_analyst )

Tip

What you’ll learn How to compose agents into a sequential pipeline.

Source: 05_parallel_fanout.py

adk-fluent

from adk_fluent import Agent, FanOut

fanout_fluent = (
    FanOut("market_research")
    .branch(
        Agent("web_analyst")
        .model("gemini-2.5-flash")
        .instruct(
            "Search the web for recent news articles, press releases, "
            "and blog posts about competitors in this market segment."
        )
    )
    .branch(
        Agent("academic_analyst")
        .model("gemini-2.5-flash")
        .instruct("Search academic databases for recent research papers and industry reports relevant to this market.")
    )
    .branch(
        Agent("social_analyst")
        .model("gemini-2.5-flash")
        .instruct("Analyze social media sentiment and trending discussions about products and brands in this market.")
    )
    .build()
)

Native ADK

from google.adk.agents.llm_agent import LlmAgent
from google.adk.agents.parallel_agent import ParallelAgent

fanout_native = ParallelAgent(
    name="market_research",
    sub_agents=[
        LlmAgent(
            name="web_analyst",
            model="gemini-2.5-flash",
            instruction=(
                "Search the web for recent news articles, press releases, "
                "and blog posts about competitors in this market segment."
            ),
        ),
        LlmAgent(
            name="academic_analyst",
            model="gemini-2.5-flash",
            instruction=(
                "Search academic databases for recent research papers and industry reports relevant to this market."
            ),
        ),
        LlmAgent(
            name="social_analyst",
            model="gemini-2.5-flash",
            instruction=(
                "Analyze social media sentiment and trending discussions about products and brands in this market."
            ),
        ),
    ],
)

Architecture

        graph TD
    n1{"market_research (parallel)"}
    n2["web_analyst"]
    n3["academic_analyst"]
    n4["social_analyst"]
    n1 --> n2
    n1 --> n3
    n1 --> n4

Equivalence¶

assert type(fanout_native) == type(fanout_fluent)
assert len(fanout_fluent.sub_agents) == 3
assert fanout_fluent.sub_agents[0].name == "web_analyst"
assert fanout_fluent.sub_agents[1].name == "academic_analyst"
assert fanout_fluent.sub_agents[2].name == "social_analyst"