---
license: apache-2.0
library_name: nato1000-runtime
tags:
- agentic-ai
- orchestration
- auditability
- data-ingestion
- model-adapters
pipeline_tag: text-generation
---

# NATO-1000 Advanced AGI Runtime

NATO-1000 is a modular orchestration and evaluation runtime for specialized AI adapters. It provides a reproducible execution contract for five model roles, configurable inference parameters, a multi-format ingestion pipeline, structured logs, append-only hash-linked audit events, quarantine handling, deterministic offline execution, and explicit extension points for real model backends.

> **Capability statement.** This repository is a runnable orchestration framework and adapter suite. It is not evidence of AGI, sentience, autonomous general intelligence, or newly trained foundation-model weights. The default backend is deterministic and offline so that every test can run without credentials, network access, or GPU hardware.

## Model roster

| Model ID | Specialty | Default backend | Primary output |
|---|---|---|---|
| NATO1000-SENTINEL | Systems engineering and diagnostics | deterministic-demo | Operational analysis |
| NATO1000-ALCHEMY | Creative synthesis and ideation | deterministic-demo | Candidate concepts |
| Nadose.Daboi | General language and reasoning adapter | deterministic-demo | Text response |
| TWINBRAIN-DELIBERATOR | Multi-perspective comparison | deterministic-demo | Deliberation record |
| SOULDOCTRINE-TRUTHMATRIX | Provenance and evidence organization | deterministic-demo | Evidence record |

## Quick start

```bash
python3 -m venv .venv
. .venv/bin/activate
pip install -r requirements.txt
python run.py "Explain the runtime health protocol"
pytest -q
```

Every adapter is directly executable through the same contract:

```python
from nato1000_runtime.config import RuntimeConfig
from nato1000_runtime.runtime import NATO1000Runtime

runtime = NATO1000Runtime(RuntimeConfig(model_id="NATO1000-ALCHEMY"))
result = runtime.run("Generate three designs for reducing industrial waste")
print(result.text)
print(runtime.health())
```

## Configuration and parameters

Configuration is validated before execution. `temperature` is constrained to 0–2, `top_p` to (0, 1], `max_tokens` to 1–32768, and policy mode to `research`, `standard`, or `restricted`. Network and tool execution are disabled by default. `fail_closed=true` converts adapter failures into an explicit `RuntimeFailure` and records the event. This design separates user-adjustable inference behavior from system permissions.

The default configuration is in `configs/default.json`. Do not place credentials, tokens, private keys, or personally identifiable information in configuration files. Use deployment-secret managers for production credentials.

## Data-ingestion pipeline

`DataIngestionPipeline` accepts UTF-8 TXT, Markdown, JSON, JSONL, and CSV files. It enforces a byte limit, validates extensions, optionally redacts common credential patterns, calculates SHA-256 content identifiers, removes duplicates, records ingestion outcomes, and copies failed sources to `data/quarantine/`. The pipeline is intentionally passive: it does not execute ingested files, follow embedded instructions, or grant network permissions.

```python
from nato1000_runtime.ingestion import DataIngestionPipeline
rows = DataIngestionPipeline().ingest_file("data/example.jsonl")
```

## Logging, debugging, and auditing

Runtime logs are written to `logs/runtime.log`. Audit events are written as JSON Lines to `logs/audit.jsonl`. Each event contains a timestamp, run identifier, component, status, message, previous hash, and event hash. `AuditLedger.verify()` checks the complete chain and reports tampering. Errors include an exception class and traceback in the runtime log while the audit record stores a minimal non-secret summary.

Operational procedure is: validate configuration; run a health check; execute in offline mode; inspect logs; verify the audit chain; review quarantined inputs; and only then enable an explicitly reviewed production backend. Tool and network permissions must remain disabled unless an operator has defined allowlists, authentication, timeouts, and rollback procedures.

## Backend extension points

`deterministic-demo` is dependency-free and is the tested baseline. `transformers` and `openai-compatible` are reserved extension identifiers; they are intentionally not silently activated. A production adapter must implement the `ModelAdapter` protocol, preserve the `ModelResult` schema, validate prompt and parameter types, enforce timeouts, redact secrets from logs, and provide tests for normal, malformed, timeout, and backend-unavailable paths.

## Licensing and model provenance

The original source code and documentation in this repository are licensed under Apache License 2.0 in `LICENSE`. The repository contains no model weights and no training dataset. Any upstream base model, including a Qwen-derived model referenced by Nadose.Daboi, remains subject to its own license, terms, attribution, and acceptable-use requirements. Users must perform a provenance review before redistributing weights or training on third-party data. Dataset contributors retain their rights; ingestion into this runtime does not transfer ownership.

## Safety and operational boundaries

“Uncensored” is not treated as a security control. The runtime exposes adjustable policy labels for research, standard, and restricted operation, but it does not grant arbitrary system access. It executes no shell commands from model output, does not autonomously contact external systems, and does not claim that generated text is factual. Human review, least privilege, input provenance, and rollback are required for consequential use.

## Test and release contract

The test suite covers all five adapters, configuration validation, secret redaction, deduplication, oversized-file quarantine, audit-chain tamper detection, and fail-closed inference. Release evidence must include `pytest -q`, `python3 -m compileall -q .`, an offline smoke run for every model ID, and a verified audit ledger. See `docs/OPERATIONS.md`, `docs/PROTOCOLS.md`, `docs/TESTING.md`, and `docs/DATA_INGESTION.md`.

## References

[1]: https://huggingface.co/docs/hub/en/model-cards "Hugging Face Model Cards"
[2]: https://huggingface.co/docs/datasets/en/loading "Hugging Face Datasets Loading"
[3]: https://spdx.dev/learn/handling-license-info/ "SPDX License Information"

© 2026 iNFINITEAi2025. Apache-2.0 applies to original repository code and documentation unless a file states otherwise.