The AvaLangStack provides a powerful set of tools to build LLM applications grounded in narrative intelligence and indigenous relational paradigms. This guide will help you get started quickly.
Our libraries are available as scoped npm packages. You can install individual packages using your preferred package manager:
# Using npm
npm install ava-langchain-prompt-decomposition
npm install ava-langchain-inquiry-routing
npm install ava-langchain-relational-intelligence
npm install ava-langchain-narrative-tracing
npm install ava-langchain-state-machine-spec
# Using pnpm
pnpm add ava-langchain-prompt-decomposition
pnpm add ava-langchain-inquiry-routing
pnpm add ava-langchain-relational-intelligence
pnpm add ava-langchain-narrative-tracing
pnpm add ava-langchain-state-machine-spec
# Using yarn
yarn add ava-langchain-prompt-decomposition
yarn add ava-langchain-inquiry-routing
yarn add ava-langchain-relational-intelligence
yarn add ava-langchain-narrative-tracing
yarn add ava-langchain-state-machine-spec
Here’s a quick example demonstrating how to use some of the AvaLangStack components. For more detailed examples, please see the examples/src/avalangstack/ directory in our repository.
import { StructuralTensionChain } from "ava-langchain-relational-intelligence";
import { decompose } from "ava-langchain-prompt-decomposition";
async function runExample() {
// Evaluate structural tension
const chain = new StructuralTensionChain();
const vector = chain.evaluate(
"Monolith with no tests, team works in silos",
"Microservices with full coverage, daily ceremonies"
);
console.log(`Tension magnitude: \${vector.magnitude}`);
console.log(`Direction: \${vector.direction}`);
// Decompose a complex prompt
const result = await decompose("Build a knowledge graph with ceremony gating...");
console.log(result.markdown);
}
runExample();
Explore each of our core packages to understand their unique contributions to the AvaLangStack ecosystem.