If you've spent any time trying to build reliable LLM-powered tools, you know the pain: Most startups and teams re-invent the wheel every time they want to build a chatbot, automation, or AI-powered workflow that's both smart and deterministic.

LLMs are powerful-but they're unpredictable, hard to constrain, and most libraries either go "all in" on black-box AI or fall back to brittle, rule-based matching. When you want composability, reliability, and auditability (especially in a product you're delivering to someone else), you're left cobbling together hacks. That's why I built intent-kit.

What is intent-kit?

intent-kit is a universal Python framework for building intent-driven classification and execution systems-chatbots, automation tools, or custom workflow apps-using any combination of rule-based logic, LLMs, or custom classifiers. It's designed for developers and product teams who need to:

Define all capabilities, constraints, and dependencies up front
Mix and match classic and AI-driven routing in the same workflow
Build systems that are predictable, testable, and production-safe
Escape both "spaghetti rules" and "LLM guesswork"

intent-kit lets you wire up hierarchical "intent graphs," route inputs through any number of classifiers (from simple keywords to the latest LLMs), and always know exactly what's happening at every step.

Why does intent-kit exist?

After seeing team after team run into the same problems-

Overly complex, one-off intent routers
Rule-based systems that don't scale
Messy, untestable parameter extraction
Product demos that never become maintainable products

-I realized there was a missing piece: A framework that's flexible enough for AI, reliable enough for enterprise, and open-ended enough to be used for any workflow. Not just another chatbot framework or hacky script, but something composable, deterministic, and actually production-ready.

How is intent-kit different?

intent-kit isn't just another "prompt router" or chatbot SDK. It's:

Universal: Works with any classification method-keyword, regex, custom ML, or LLM-zero dependencies by default.
Deterministic & Composable: You define all valid intents, parameters, and context up front. No surprises, no hallucinations, no "magic."
Extensible: Add LLMs only if/when you need them. Plug in your own business logic, APIs, or classifiers.
Testable & Debuggable: Parameter extraction, routing, and context flow are all inspectable, unit-testable, and production-ready.
Production Focused: Built for building tools for other companies-not just for demos, but for products you need to maintain.

Most importantly: you're always in control. intent-kit doesn't do anything you didn't define-so you can deliver products that are both "AI-powered" and robust enough for real users.

Eval API: Test Your Workflows with Real Data

A powerful feature of intent-kit is its built-in Eval API. You can supply your own datasets-structured as YAML or JSON examples (see repo examples)-to automatically test and validate your entire workflow against a wide range of user inputs and edge cases.

No more hand-testing: Evaluate how your graph/classifiers perform on real data, not just your best guesses.
Customize your evals: Bring your own domain-specific cases and regression tests.
Actionable reporting: Quickly spot gaps, errors, or unintended behaviors before you ship.

This means you can confidently ship deterministic, AI-powered workflows-knowing exactly how your system will respond in the wild.

Learn More / Get Started

Read the docs: docs.intentkit.io
Get the code: github.com/Stephen-Collins-tech/intent-kit

See examples and demos in the docs, or try the package locally with:

  pip install intent-kit
  pip install intent-kit[openai] # to use OpenAI's SDK

Questions or feedback? Open a GitHub issue or DM me on LinkedIn.

Stop gluing together broken workflows.
Start building with intent.

Introducing intent-kit: Universal, Deterministic Intent Workflows for Python