Why ElsiumAI

Every framework helps you call an LLM. ElsiumAI helps you trust the result.

ElsiumAI is built on three pillars that the ecosystem hasn't prioritized yet:

It also does everything you'd expect — multi-provider gateway, agents, tools, RAG, workflows, MCP, streaming, cost tracking. But those are table stakes. The three pillars are what make ElsiumAI different.

Who Should Use ElsiumAI

ElsiumAI is for teams building AI systems that must be auditable, reliable, and reproducible in production.

• Enterprise engineering teams shipping AI features where downtime, compliance gaps, or silent model regressions are not acceptable
• Regulated industries (finance, healthcare, legal) where you need to prove what happened, who authorized it, and that outputs haven't been tampered with
• Production-grade AI products where “it works on my machine” isn't good enough — you need deterministic test suites, CI-integrated output pinning, and provider failover

If you're building a weekend hack with a single API key, you probably don't need this. If you're building something that other people depend on, read on.

What You Get

Reproducible AI — you can reproduce results

Nobody is doing this well at the framework level. LLMs are non-deterministic by nature. ElsiumAI gives you the tools to constrain, measure, and track output consistency — and catch regressions in CI before they reach production.

const result = await assertDeterministic(
  (seed) => llm.complete({
    messages: [{ role: 'user', content: [{ type: 'text', text: 'Classify: spam' }] }],
    temperature: 0,
    seed,
  }).then(r => extractText(r.message.content)),
  { runs: 5, seed: 42, tolerance: 0 },
)
// { deterministic: true, variance: 0, uniqueOutputs: 1 }

Model provider ships a silent update and your classifier starts returning different results? Your CI pipeline catches it before production does.

Reliability — your system stays up

Providers go down. Rate limits hit. Costs spiral. ElsiumAI treats failure as a first-class concern.

const mesh = createProviderMesh({
  providers: [
    { name: 'anthropic', config: { apiKey: env('ANTHROPIC_API_KEY') }, priority: 1 },
    { name: 'openai', config: { apiKey: env('OPENAI_API_KEY') }, priority: 2 },
  ],
  strategy: 'fallback',
  circuitBreaker: { failureThreshold: 5, resetTimeoutMs: 30_000 },
})

Anthropic goes down at 3 AM? Traffic reroutes to OpenAI. No pages. No code changes. No downtime.

Governance — you can prove what happened

const policies = createPolicySet([
  modelAccessPolicy(['claude-sonnet-4-6', 'gpt-4o-mini']),
  costLimitPolicy(5.00),
])

const audit = createAuditTrail({ hashChain: true })

const rbac = createRBAC({
  roles: [{ name: 'analyst', permissions: ['model:use:gpt-4o-mini'], inherits: ['viewer'] }],
})

When an auditor asks “who ran this query, which model handled it, and can you prove the log hasn't been modified?” — you have the answer.

What Else It Does

The three pillars are what make ElsiumAI unique. It also delivers everything you'd expect from a modern AI framework:

Use Cases

Fintech & Compliance

Policy engine enforces model allowlists and cost budgets. Hash-chained audit trail provides tamper-proof logs for regulatory review. RBAC ensures only authorized roles access sensitive models.

Healthcare

PII detection auto-redacts patient data before it reaches any model. Approval gates require human sign-off on high-stakes operations. Provenance tracking maintains full lineage for every AI decision.

Enterprise SaaS

Provider mesh with circuit breakers keeps your AI features online during provider outages. Cost intelligence with loop detection prevents runaway spend. RBAC maps to your existing org structure.

AI-Powered Products

Determinism assertions and output pinning integrate into CI/CD — model regressions are caught before they ship. Request-matched fixtures make test suites fast and order-independent.

Internal Tools

Cost budgets prevent a single runaway query from blowing your monthly spend. Loop detection catches infinite agent cycles. X-Ray mode gives full visibility into what's happening under the hood.

When NOT to Use ElsiumAI

Being honest about scope builds trust. ElsiumAI is not the right choice if:

• You're building a quick prototype — if you just need a wrapper around one API and don't care about failover, governance, or reproducibility, a lighter library will get you there faster
• You need a Python framework — ElsiumAI is TypeScript-only
• You don't need the three pillars — if governance, reliability, and reproducibility aren't concerns for your use case, the overhead isn't worth it

Performance

ElsiumAI is designed to add negligible overhead to your LLM calls. Measured with a zero-latency mock provider to isolate framework cost.

Full methodology and reproduction steps in benchmarks/.

Where ElsiumAI Fits

The AI ecosystem has solved foundational problems. LangChain and LlamaIndex made orchestration accessible. Vercel AI SDK brought streaming-first ergonomics to the frontend. LiteLLM, Portkey, and Helicone built powerful LLM gateways for routing, observability, and cost tracking.

ElsiumAI stands on those shoulders. It solves the next problem: what happens when you take AI to production with regulated workloads? When you need to prove what happened, survive provider outages without code changes, and catch model regressions before they ship.

You can use ElsiumAI alongside your existing tools, or as a standalone framework — it works either way.

Your Application
├── Orchestration     → LangChain, LlamaIndex, Vercel AI SDK, or ElsiumAI agents
├── LLM Gateway       → LiteLLM, Portkey, or ElsiumAI gateway
└── Production Layer  → ElsiumAI (reliability, governance, reproducibility)

ElsiumAI is the production layer. Use it with whatever orchestration and gateway you already have, or use ElsiumAI's own — the three pillars work regardless.

What ElsiumAI Adds

Production Reliability

Governance & Compliance

Reproducible AI

Get Started

npm install @elsium-ai/core @elsium-ai/gateway @elsium-ai/agents

import { env } from '@elsium-ai/core'
import { gateway } from '@elsium-ai/gateway'
import { defineAgent } from '@elsium-ai/agents'

const llm = gateway({
  provider: 'anthropic',
  model: 'claude-sonnet-4-6',
  apiKey: env('ANTHROPIC_API_KEY'),
})

const agent = defineAgent(
  { name: 'assistant', system: 'You are a helpful assistant.' },
  { complete: (req) => llm.complete(req) },
)

const result = await agent.run('What is TypeScript?')

Next steps:

• Getting Started — full setup guide with CLI scaffolding
• Fundamentals — comprehensive guide to every feature with examples