CorvinOS — The Operating System for AI Organizations
Apache-2.0 · The self-hosted agentic OS
The agentic OS<br>you actually own.<br>EU-compliant by design.
A self-hosted operating system for AI agents: it runs the engines, personas, workflows and runtime-forged tools, meets your users on every messenger, drives a real browser, and keeps a hash-chained audit of it all — with compliance built into the architecture, not bolted on.
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Messaging bridges
Engines detected
Compliance frameworks
0%
Self-hosted
The command center<br>The chat runs the whole OS
The chat isn't a demo window — it's the shell. Personas, engines, workflows, the Forge, RAG, agentic compute and the A2A mesh are all reachable from one prompt. And you can drive it end‑to‑end by voice: speak a request, CorvinOS transcribes it, routes it, does the work, and speaks the answer back — tuned to how you listen. And you don't even need the console open: every messaging bridge is a full control surface, so you can run the whole OS straight from a Telegram, WhatsApp or Signal thread.
Voice on
Voice in · voice out<br>Run it hands‑free
Hit the mic and talk. Speech is transcribed locally by default — the audio is deleted the moment it returns, and only metadata ever touches the audit chain.
🎙️
Speak “Rank Q2 cohorts by retention.” — captured from any bridge.
Transcribe Local Whisper by default, OpenAI as fallback. Runs before the engine even wakes.
Route A persona and engine are picked for the turn — your overrides always win.
Act One turn can reach every layer: Forge, workflows, RAG, agentic compute, A2A.
🔊
Speak back Answer is spoken through a listener‑tuned voice — jargon and depth to taste.
One prompt reaches every layer
🎭
Personas<br>Many minds, one runtime — routed per turn.
⚙️
Engines<br>Claude, Codex, OpenCode & local models.
🔀
Workflows<br>Multi-step AWP pipelines, on demand or scheduled.
🔨
Forge<br>Schema-bound tools & skills, built at runtime.
📚
RAG<br>Grounded answers over your own documents.
🧮
Agentic Compute<br>Sweeps & tuning without spending tokens.
🔗
A2A mesh<br>Signed task envelopes across teams.
🛡️
Audit<br>Every step on a hash-chained spine.
Regulatory compliance<br>Built in, not bolted on.
Every compliance mechanism is a structural design constraint. There is no "compliance mode" you can accidentally leave off.
EU AI Act Art. 50
Bot Disclosure
One-time AI-nature disclosure per user, structurally locked. Cannot be disabled via configuration.
GDPR Art. 6 & 7
Consent Gate
Deny-by-default consent for transcript sharing. Per-user, TTL-capped, re-validated at consume time.
GDPR Art. 30 & 32
Hash-Chained Audit
Every event appends to a SHA-256-linked chain. Tampering invalidates it. Offline-verifiable.
EU AI Act Art. 14
Data Residency
Compliance-zone routing and egress lockdown structurally prevent data from leaving the permitted zone.
EU AI Act 2026<br>GDPR<br>ISO/IEC 42001<br>NIST AI RMF
Agentic Compute<br>Compute that adapts<br>to any problem
That's Agentic Compute : the worker owns the loop, the model owns the framing. The agent says what to optimise and when to stop; a sandboxed worker picks the strategy that fits the problem and turns the crank — from a quick tune to a Bayesian search over an 8 GB dataset, and never a raw byte in the context window.
01
Frame it The model submits one job — what to optimise, the parameter space, and the stopping rule.
02
Run it A sandboxed worker evaluates combinations in parallel and streams Top-K progress back.
03
Read it Three model calls total — submit, poll, read. Zero tokens spent while the loop turns.
3 model calls / sweep<br>up to 16 parallel<br>SHA-256 audited
compute_run<br>bayesian · GP + EI
sweep · learning_rate × n_estimators · 4 parallel
w0
w1
w2
w3
4/100<br>iterations
↓ 0.900<br>best loss
● sha-256
N iterations run in the worker — the model is idle until the result returns.
grid
Exhaustive coverage
Evaluates every combination in the grid. Pre-generated at submit — deterministic and fully reproducible.
random
Broad sampling
Uniform samples per batch from continuous ranges. One seed per run, stored for reproducibility.
bayesian
Fewest evaluations
A Gaussian-Process surrogate picks the highest Expected-Improvement points — finds a good region in the fewest runs.
Recursive delegation · the ACS run<br>It keeps working until the answer is good enough
A manager engine plans the task, delegates pieces to parallel worker runs, scores every result, and iterates toward a loss target. The point of the recursion is adaptation : where a capability is missing, a worker forges the tool or skill it needs at runtime — so the same flow can tune a model, run a backtest, or analyse an 8 GB dataset straight from a prompt.
★ MANAGER · claude-sonnet-4-6
loss — · idle
ITER 1<br>queued<br>loss 0.82
W-A★ sub-mgr↳ spawnsSW-B1 · hermesSW-B2 · hermesW-C ⚒ forge.create_tool()
ITER 2<br>queued<br>loss 0.51
W-DW-EW-F ✦ skill.create()
ITER 3<br>queued<br>loss...