AI Stack

Atlas OS routes every AI task by cost and complexity. The rule: never burn paid tokens on work a free or local model can handle. Free tier first, escalate only when it can't cope.

Tier 0 — Zero-cost local (`localhost:8888/v1`, RTX A2000)

An OpenAI-compatible router exposes local Ollama models. Used for all non-critical subagent work, copy, summaries, translations, and classification.

Alias	Backing	Use for
`atlas-fast`	LLaMA 1B	Ultra-fast classification, routing, labels
`atlas-free`	Hermes3	Dutch copy, summaries, FAQ, product descriptions
`atlas-code`	Qwen Coder	Code completion, simple fixes, boilerplate
`atlas-qwen`	qwen2.5	General Dutch content, emails
`atlas-phi3`	qwen2.5:7b	Reasoning, analysis, comparisons
`atlas-auto`	smart router	Auto-picks the best free local model

import openai
client = openai.OpenAI(base_url="http://localhost:8888/v1", api_key="atlas")
client.chat.completions.create(model="atlas-auto", messages=[{"role":"user","content":"..."}])

Tier 1 — Free cloud APIs

Provider	Models	Best for
Groq	llama-3.3-70b-versatile, mixtral-8x7b	Fast 70B reasoning, bulk tasks
Cerebras	llama-3.1-70b	Fastest inference, free tier
OpenRouter	50+ free models incl. Gemini	Multi-model fallback
Gemini (via OpenRouter)	gemini-2.5-pro / 3.5-flash	1M context, vision, PDF

Tier 2 — Paid Anthropic (only when free tiers can't cope)

Task	Model
Simple classification, short copy	`claude-haiku-4-5`
Code, multi-step reasoning, planning	`claude-sonnet-4-6`
Deep research, architecture, complex builds	`claude-opus-4-8`

Routing decision tree

copy / text / translation / summary / Dutch content
    → atlas-free (local) or Groq llama-3.3-70b (free cloud)
simple code / boilerplate / fix
    → atlas-code (local) or Cerebras (free cloud)
non-critical workflow subagent
    → atlas-auto or Groq  (never Claude)
multi-step code / planning / complex reasoning
    → claude-sonnet-4-6
deep architecture / research / competitive analysis
    → claude-opus-4-8 or gemini-2.5-pro (free)
1M context / vision / PDF
    → Gemini via OpenRouter

Always-on AI services

Service	What it does
`atlas-brain`	Collects mail + CRM + bank every 15 min → synthesizes the daily brief
`atlas-loop`	Background signal loop — publishes nudges to NATS
`atlas-meridian`	Autonomous decision engine / battle responder
`atlas-litellm`	Unified LLM proxy (OpenAI-compat API for all models above)
NATS mesh	Real-time event bus across all devices on the tailnet
Langfuse	Full LLM observability — every call traced with latency, cost, tokens

All model calls flow through the LiteLLM proxy so cost and latency are traced centrally in Langfuse, regardless of which tier served the request.

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