Document Processing Pipeline – Sequential Pipeline¶

Real-world use case: Contract review system used by legal teams to process vendor agreements at scale. Extracts key terms, identifies legal risks, and produces executive summaries – replacing hours of manual review.

In other frameworks: LangGraph requires a StateGraph with TypedDict state, 3 node functions, and 5 edge declarations (~35 lines). CrewAI needs 3 Agent objects with role/goal/backstory plus 3 Task objects (~30 lines). Native ADK needs 3 LlmAgent + 1 SequentialAgent (~20 lines). adk-fluent composes the same pipeline in a single expression.

Pipeline topology: extractor >> risk_analyst >> summarizer

Tip

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

Source: 04_sequential_pipeline.py

adk-fluent

from adk_fluent import Agent, Pipeline

pipeline_fluent = (
    Pipeline("contract_review")
    .describe("Extract, analyze, and summarize contracts")
    .step(
        Agent("extractor")
        .model("gemini-2.5-flash")
        .instruct(
            "Extract key terms from the contract: parties involved, "
            "effective dates, payment terms, and termination clauses."
        )
    )
    .step(
        Agent("risk_analyst")
        .model("gemini-2.5-flash")
        .instruct(
            "Analyze the extracted terms for legal risks. Flag any "
            "unusual clauses, missing protections, or liability concerns."
        )
    )
    .step(
        Agent("summarizer")
        .model("gemini-2.5-flash")
        .instruct(
            "Produce a one-page executive summary combining the extracted "
            "terms and risk analysis. Use clear, non-legal language."
        )
    )
    .build()
)

Native ADK

from google.adk.agents.llm_agent import LlmAgent
from google.adk.agents.sequential_agent import SequentialAgent

extractor = LlmAgent(
    name="extractor",
    model="gemini-2.5-flash",
    instruction=(
        "Extract key terms from the contract: parties involved, "
        "effective dates, payment terms, and termination clauses."
    ),
)
analyst = LlmAgent(
    name="risk_analyst",
    model="gemini-2.5-flash",
    instruction=(
        "Analyze the extracted terms for legal risks. Flag any "
        "unusual clauses, missing protections, or liability concerns."
    ),
)
summarizer = LlmAgent(
    name="summarizer",
    model="gemini-2.5-flash",
    instruction=(
        "Produce a one-page executive summary combining the extracted "
        "terms and risk analysis. Use clear, non-legal language."
    ),
)
pipeline_native = SequentialAgent(
    name="contract_review",
    description="Extract, analyze, and summarize contracts",
    sub_agents=[extractor, analyst, summarizer],
)

Architecture

        graph TD
    n1[["contract_review (sequence)"]]
    n2["extractor"]
    n3["risk_analyst"]
    n4["summarizer"]
    n2 --> n3
    n3 --> n4

Equivalence¶

assert type(pipeline_native) == type(pipeline_fluent)
assert len(pipeline_fluent.sub_agents) == 3
assert pipeline_fluent.sub_agents[0].name == "extractor"
assert pipeline_fluent.sub_agents[1].name == "risk_analyst"
assert pipeline_fluent.sub_agents[2].name == "summarizer"