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Sequential API

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The Sequential API

You've learned two ways to build programs: the Functional API (Lesson 1a) and Subclassing (Lesson 1b). Now, let's explore the simplest approach: the Sequential API.

When to Use Sequential

The Sequential API is perfect when your program is a simple pipeline - data flows through modules one after another, like water through pipes:

graph LR
    Input --> A[Module A] --> B[Module B] --> C[Module C] --> Output

No branching, no conditionals, just a straight line of transformations.

Comparison: Three Ways to Build Programs

API Use Case Complexity
Sequential Simple linear pipelines Easiest
Functional Graphs with branches/merges Medium
Subclassing Custom logic, full control Advanced

The Sequential Pattern

Building with Sequential is as simple as making a list:

program = synalinks.Sequential(
    [
        synalinks.Input(data_model=InputType),     # First: where data enters
        synalinks.SomeModule(...),                  # Middle: transformations
        synalinks.Generator(data_model=OutputType), # Last: final output
    ],
    name="my_program",
)

Think of it like a recipe:

  1. Start with ingredients (Input)
  2. Apply steps in order (Modules)
  3. Get the final dish (Output)

Complete Example

import asyncio
from dotenv import load_dotenv
import synalinks

class Query(synalinks.DataModel):
    query: str = synalinks.Field(description="The user query")

class AnswerWithThinking(synalinks.DataModel):
    thinking: str = synalinks.Field(description="Your step by step thinking")
    answer: str = synalinks.Field(description="The correct answer")

async def main():
    load_dotenv()
    language_model = synalinks.LanguageModel(model="gemini/gemini-3.1-flash-lite-preview")

    # Create a sequential program - just a list of modules!
    program = synalinks.Sequential(
        [
            synalinks.Input(data_model=Query),
            synalinks.Generator(
                data_model=AnswerWithThinking,
                language_model=language_model,
            ),
        ],
        name="chain_of_thought",
    )

    result = await program(Query(query="What is the capital of France?"))
    print(f"Answer: {result['answer']}")

asyncio.run(main())

Key Takeaways

  • Sequential API: The simplest way to build programs - just provide a list of modules that execute in order.
  • Linear Pipelines: Best for simple data flows with no branching or conditional logic.
  • Minimal Boilerplate: No need to manually connect inputs/outputs - Sequential handles the wiring automatically.
  • Quick Prototyping: Great for testing ideas quickly before moving to more complex architectures.

Program Visualization

chain_of_thought

API References

AnswerWithThinking

Bases: DataModel

The output from our program - reasoning + final answer.

Source code in examples/1c_sequential.py 
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class AnswerWithThinking(synalinks.DataModel):
    """The output from our program - reasoning + final answer."""

    thinking: str = synalinks.Field(
        description="Your step by step thinking",
    )
    answer: str = synalinks.Field(
        description="The correct answer",
    )

Query

Bases: DataModel

The input to our program - a user's question.

Source code in examples/1c_sequential.py 
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class Query(synalinks.DataModel):
    """The input to our program - a user's question."""

    query: str = synalinks.Field(
        description="The user query",
    )

Source

import asyncio

from dotenv import load_dotenv

import synalinks

# =============================================================================
# STEP 1: Define Your Data Models
# =============================================================================
# Same as previous lessons - structure of inputs and outputs


class Query(synalinks.DataModel):
    """The input to our program - a user's question."""

    query: str = synalinks.Field(
        description="The user query",
    )


class AnswerWithThinking(synalinks.DataModel):
    """The output from our program - reasoning + final answer."""

    thinking: str = synalinks.Field(
        description="Your step by step thinking",
    )
    answer: str = synalinks.Field(
        description="The correct answer",
    )


# =============================================================================
# STEP 2: Build and Run the Program
# =============================================================================


async def main():
    load_dotenv()

    # Enable observability for tracing (view traces at http://localhost:5000)
    synalinks.enable_observability(
        tracking_uri="http://localhost:5000",
        experiment_name="lesson_1c_sequential",
    )

    # Initialize the language model
    language_model = synalinks.LanguageModel(
        model="gemini/gemini-3.1-flash-lite-preview",
    )

    # -------------------------------------------------------------------------
    # 2.1: Build the Program with Sequential API
    # -------------------------------------------------------------------------
    print("=" * 60)
    print("Building a Chain-of-Thought Program (Sequential API)")
    print("=" * 60)

    # Create a sequential program - just a list of modules!
    # Data flows through each module in order.
    program = synalinks.Sequential(
        [
            # First module: defines the input type
            synalinks.Input(data_model=Query),
            # Second module: transforms Query into AnswerWithThinking
            synalinks.Generator(
                data_model=AnswerWithThinking,
                language_model=language_model,
            ),
        ],
        name="chain_of_thought",
        description="Useful to answer in a step by step manner.",
    )

    # Generate a visualization
    synalinks.utils.plot_program(
        program,
        to_folder="examples",
        show_module_names=True,
        show_trainable=True,
        show_schemas=True,
    )

    # -------------------------------------------------------------------------
    # 2.2: Run the Program
    # -------------------------------------------------------------------------
    print("\nRunning the program...")
    print("-" * 60)

    result = await program(
        Query(query="What are the key aspects of human cognition?"),
    )

    print("\nResult:")
    print(result.prettify_json())

    # Access individual fields directly
    print("\n" + "=" * 60)
    print("Accessing individual fields:")
    print("=" * 60)
    print(f"\nThinking: {result['thinking'][:100]}...")
    print(f"\nAnswer: {result['answer'][:100]}...")


if __name__ == "__main__":
    asyncio.run(main())