Iron Frame
Infrastructure reliability layer for LLM-powered systems.
Reliable. Trustworthy. Accurate. Diligent.
Iron Frame sits between raw LLMs and domain applications -- trading, healthcare, compliance, research -- and handles every fundamental limitation of LLMs: hallucinations, reasoning errors, context drift, bias accumulation, and incomplete process execution.
It is infrastructure, not a domain system. Your application sits on top of Iron Frame and trusts the foundation beneath it.
Why Iron Frame
Most "AI reliability" products try to fix hallucination with more AI. Iron Frame takes a different approach: deterministic enforcement outside the LLM context window. A model cannot rationalize around hooks it never sees.
- Hooks execute outside the LLM. Deterministic gates at the platform level, not in the prompt.
- Self-auditing from day 1. Confidence scoring, self-consistency, and cross-model verification on every output.
- Compliance-ready audit from day 1. HIPAA, FINRA, SOC2, SEC, GDPR fields captured by the audit schema natively -- not bolted on.
- Spend caps are mandatory. Per-request, per-session, and per-day budget ceilings prevent runaway cost.
- Immutable audit trail. Write-before-release. If logging fails, the operation does not complete.
- Model-agnostic. Swappable providers with capability-based routing (fast / smart / cheap / verification / long-context).
Install
pip install ironframe
Optional extras:
pip install "ironframe[openai]" # OpenAI / Perplexity adapter
pip install "ironframe[z3]" # Symbolic verification (Tier 4)
pip install "ironframe[all]" # Everything
Quickstart
from ironframe import IronFrameConfig, IronFrameClient
config = IronFrameConfig.from_env()
client = IronFrameClient(config)
response = client.complete(
prompt="Summarize the key risks in this contract.",
preference="smart",
)
print(response.content)
print(f"Cost: ${response.cost:.4f}")
print(f"Model: {response.model} Tokens: {response.tokens_in}/{response.tokens_out}")
response is an IronFrameResponse — a dict subclass that supports
both attribute access (response.content, response.cost, response.model)
and dict access (response["text"], response.get("cost_usd")). Every
call is audited and budget-checked. Confidence scoring is a separate
pass — run sae.verify(response) to populate response.confidence; raw
MAL calls leave it as None. See examples/ for more.
Architecture
Iron Frame has 18 components organized around four pillars:
| Pillar | Components |
|---|---|
| Model Access | Model Abstraction Layer (MAL), Budget Manager, Error Recovery |
| Verification | Self-Audit Engine (SAE), Logic Skills, Eval & Regression, KB Grounding |
| Enforcement | Hook Engine, State Machine, Tool Governance, Security, Agent Trust, I/O Schema |
| Observability | Immutable Audit Log, Compliance Adapters, Conformance & Drift Engine, Context Budget |
See SPEC.md for the canonical specification and docs/architecture.md for a walkthrough.
Compliance Adapters
Iron Frame ships adapters for HIPAA, FINRA, SOC2, SEC, and GDPR compliance requirements. The base classes are Apache 2.0 -- you can write your own adapters for any protocol.
The pre-built regulatory adapters in src/ironframe/compliance/adapters/ are source-available under PolyForm Noncommercial. They are free for research, education, nonprofits, and personal projects. Commercial use in a for-profit production system requires a commercial license.
See src/ironframe/compliance/adapters/LICENSE_COMMERCIAL and docs/compliance-adapters.md.
Licensing (TL;DR)
- Core framework: Apache License 2.0 -- use it freely, including commercially. Includes an explicit patent grant.
- Compliance adapters (
compliance/adapters/): PolyForm Noncommercial. Free for noncommercial use. Commercial license available. - Base classes (
compliance/base_v1_0.py,compliance/audit_requirements_v1_0.py): Apache 2.0. Write your own adapters.
See LICENSE, NOTICE, and the open-core ADR.
Status
Iron Frame is in beta. The API surface is stable (every module is explicitly versioned with a _v1_0 suffix), but expect refinement before v1.0.
- 18 components implemented
- 5 compliance adapters
- Requirements Traceability Matrix with 22 tracked requirements
- Unit + integration test coverage
Contributing
See CONTRIBUTING.md.
Documentation
SPEC.md-- Canonical specificationconstitution.md-- Non-negotiable framework lawsdocs/getting-started.md-- First usedocs/architecture.md-- Architecture overviewdocs/compliance-adapters.md-- Writing and using compliance adaptersdocs/decisions/-- Architecture Decision Records
Metadata
Release files for ironframe 0.1.2
For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.
Source distribution (sdist)
| File | Size | Uploaded | |
|---|---|---|---|
| ironframe-0.1.2.tar.gz | 225.7 kB | Details |
Built distribution (wheel)
| File | Interpreter | ABI | Platform | Reset |
|---|---|---|---|---|
| ironframe-0.1.2-py3-none-any.whl | Python 3 | none | any | Details |
Total release size: 505.4 kB
Release files / ironframe-0.1.2.tar.gz
| Download URL | ironframe-0.1.2.tar.gz |
|---|---|
| Size | 225.7 kB |
| Tags | Source |
|
SHA-256 checksum How to use checksums |
eac06fb1c7a6987a1a6c4cba679f6f66fb457f69f1b807859f22898ccb553d9c
|
|
BLAKE2b-256 checksum How to use checksums |
064b69a3278c3aa0a8016e1c17a98ce23f30f958bd845a03ae3f9178aaf6aa35
|
| Upload date | |
|
Uploaded using Trusted Publishing? What is trusted publishing? |
Yes |
| Uploaded via |
twine/6.1.0 CPython/3.13.12
|
Provenance
Provenance describes where a file came from. On PyPI, provenance is shared via attestations, which provide a verifiable record of the build or publishing details. View details, limitations and caveats.
PyPI Publish Attestation
PyPI verified that this artifact, at this checksum, originated from the publisher listed below.
Signed by GitHub Actions, verified by PyPI on Apr 20, 2026.
Transparency logRelease files / ironframe-0.1.2-py3-none-any.whl
| Download URL | ironframe-0.1.2-py3-none-any.whl |
|---|---|
| Size | 279.6 kB |
| Tags | Python 3 |
|
SHA-256 checksum How to use checksums |
d07675554afa1effdd2d964750cfd57ada8bc5f0efe135462f9a6992326f47ce
|
|
BLAKE2b-256 checksum How to use checksums |
da88271c063e53228f85dbbd85b2e863a3412b462facb89cb65d255db22accb9
|
| Upload date | |
|
Uploaded using Trusted Publishing? What is trusted publishing? |
Yes |
| Uploaded via |
twine/6.1.0 CPython/3.13.12
|
Provenance
Provenance describes where a file came from. On PyPI, provenance is shared via attestations, which provide a verifiable record of the build or publishing details. View details, limitations and caveats.
PyPI Publish Attestation
PyPI verified that this artifact, at this checksum, originated from the publisher listed below.
Signed by GitHub Actions, verified by PyPI on Apr 20, 2026.
Transparency log