Accelerating AI Inference Workflows with the Atomic Inference Boilerplate

#ai #llm #showdev #tooling

An opinionated foundation for reliable, composable LLM inference

Large language model (LLM) applications grow complex fast. Prompt logic, schema validation, multi-provider setups, and execution patterns become scattered. What if you could standardize how individual inference steps are written, validated, and executed — leaving orchestration, pipelines, and workflows to higher-level layers?

That’s the problem the atomic-inference-boilerplate aims to solve: provide a production-ready foundation for building robust inference units that are:

Atomic: Each unit performs one focused step — rendering a prompt, calling an LLM, validating structured output
Composable: Easily integrated into larger workflows such as LangGraph, Prefect, or custom orchestration layers
Type-safe: Outputs are never raw strings; results conform strictly to Pydantic schemas
Provider-agnostic: Works with OpenAI, Anthropic, Ollama, LM Studio via LiteLLM routing — switch models without rewriting logic

Let’s unpack what this boilerplate brings to your AI toolkit.

🧱 Project Philosophy: Atomic Execution Units

At the heart is a simple but powerful design principle:

“Complex reasoning should be broken down into atomic units — single, focused inference steps.”

An Atomic Unit encapsulates:

A Prompt Template (Jinja2) – separates text generation templates from business logic
A Schema (Pydantic) – defines strong typing expectations on outputs
A Runner (LiteLLM + Instructor) – resolves the model provider, generates completions, and validates output

This structure ensures your inference logic is modular, testable, and predictable.

📂 Repository Structure

Here’s how the repo’s main components are organized:

src/
├── core/           # Boilerplate core classes (AtomicUnit, renderer, client)
├── modules/        # Shared utilities (vector store helpers, validation utils)
├── prompts/        # Jinja2 prompt template files
└── schemas/        # Pydantic schema definitions
examples/           # Usage samples (basic, LangGraph, Prefect pipelines)
tests/              # Unit and integration tests
docs/ specs/        # Extended specifications and docs

The core, prompts, and schemas folders embody the atomic execution pattern. The examples/ folder contains concrete patterns you can use in real projects — from basic extraction tasks to multi-agent LangGraph configurations.

⚙️ Getting Started (Quickstart)

Clone the repo and install dependencies:

git clone <repo-url>
cd atomic-inference-boilerplate
conda activate atomic      # or your Python env
pip install -r requirements.txt
cp .env.example .env       # configure API keys
python examples/basic.py   # run a basic example

This bootstraps the boilerplate and executes a simple inference unit from the examples/ directory.

🧪 Example: Define & Run an Inference Unit

Each atomic unit is defined with:

a template,
an output schema, and
optional model choice.

A simple example:

from src.core import AtomicUnit
from pydantic import BaseModel

class ExtractedEntity(BaseModel):
    name: str
    entity_type: str

extractor = AtomicUnit(
    template_name="extraction.j2",
    output_schema=ExtractedEntity,
    model="gpt-4o-mini"
)

result = extractor.run({"text": "Apple Inc. is a technology company."})
print(result)  # ExtractedEntity(name='Apple Inc.', entity_type='company')

Here, the unit receives a text prompt, formats the Jinja2 template, executes the LLM call via LiteLLM, and validates the structured output against the ExtractedEntity schema. No loose strings — everything is typed and predictable.

🤖 Scaling to Real Workflows

Rather than replacing a workflow or orchestration framework, this boilerplate plugs into them. For instance:

📌 LangGraph Integration

Examples like langgraph_single_agent.py and langgraph_multi_agent.py demonstrate how atomic units become the execution layer behind orchestration decisions made by LangGraph. Higher layers decide what to do next, while atomic units decide how to perform each inference step.

📌 Prefect Pipelines

In extract-transform-load style pipelines (e.g., document processing), atomic units can extract metadata, detect structure, and chunk content — each step isolated, typed, and testable.

This separation of concerns improves maintainability and accelerates development. Instead of ad-hoc prompts scattered across your codebase, you get a clear, reusable pattern for every LLM interaction.

🧠 Why Atomic Inference Matters

In modern LLM applications, teams rapidly face challenges like:

Prompt logic tangled with business logic
Dirty text outputs requiring fragile parsing
Changing LLM providers or models
Hard-to-test inference steps

The atomic-inference-boilerplate tackles these by:

enforcing template + schema separation
imbuing type safety by design
enabling provider abstraction
fostering modularity and reuse

This approach mirrors best practices seen in software architecture (like atomic design in UI or modular microservices), but applied to the inference layer of AI systems.

🏁 Conclusion

If you’re building AI applications with anything beyond throwaway prototypes — where inference must be reliable, validated, maintainable, and scalable — then structuring your inference logic matters.

This boilerplate is a strong candidate for the core execution layer of your LLM pipelines. Whether you embed it inside workflow frameworks like Prefect, orchestrators like LangGraph, or custom pipelines, you get: