Glass Box Framework

Runtime constitutional verification for AI answers. Every claim carries a reasoning chain. Every score breaks down. Every verdict is traceable.

⭐️ Star this repo if you want runtime AI verification to become the default. Every star moves Glassbox up the search ranking on GitHub, the MCP Registry, and Smithery — which means more developers find this before they ship an AI feature without a Trust Card.

pip install glassbox-framework         # Python
npm install -g @glassbox-framework/mcp # Node / MCP
brew install thebarmaeffect/glassbox/glassbox-mcp   # macOS

What it is

The Glass Box Framework hands an (question, answer) pair to a runtime verification pipeline and returns a structured Trust Card containing:

• Claims — every atomic assertion in the answer, paired with a reasoning chain explaining why it's asserted, what would support it, and what would falsify it.
• Epistemic Confidence Score (ECS) — a transparent, weighted aggregate over five dimensions with a published formula and an always-visible per-dimension breakdown.
• Glassbox Court — seven adversarial probes (fabrication, source manipulation, bias injection, context attack, overconfidence, underspecification, constitutional violation).
• Constitution — your natural-language deployer intents compiled into structured runtime rules and evaluated against the answer.
• Verdict — trust / caution / reject, with the exact reasoning that derived it.
• Audit reference — a deterministic SHA-256 log_id; identical inputs reproduce the same identifier across runs and languages.

It is intentionally not a wrapper around a single LLM call — the reasoning chain on every claim, the formula on the ECS, and the determinism of the audit hash together form the "Glass Box" principle: no opaque scores.

Quick start (Python)

from glassbox_framework import Glassbox

with Glassbox() as gb:
    card = gb.verify_answer(
        question="Can intermittent fasting cure type 2 diabetes?",
        answer="Yes ...",
        intents=[
            "Never make specific medical claims without citing peer-reviewed sources.",
            "Always recommend consultation with a licensed healthcare professional.",
        ],
    )

print(card["verdict"])              # "reject"
print(card["ecs"]["total"])         # 0.6032
print(card["audit"]["log_id"])      # glassbox-85cc09903bd4...  (deterministic)

The six tools

Full schemas, examples, and configuration: mcp/README.md. Python pip-specific docs: mcp/python/README.md.

Architecture (two-layer)

┌──────────────────────────────────────────────────────────┐
│ glassbox-framework (PyPI)         Python client          │
│   thin JSON-RPC stdio wrapper                            │
│   spawns ↓                                               │
├──────────────────────────────────────────────────────────┤
│ @glassbox-framework/mcp (npm)     Node MCP server        │
│   6 tools, Zod-validated I/O                             │
│   ↳ verify_answer  ↳ extract_claims  ↳ score_ecs         │
│   ↳ red_team       ↳ generate_trust_card                 │
│   ↳ export_audit_report                                  │
└──────────────────────────────────────────────────────────┘

The Python client makes zero LLM calls itself; it forwards arguments to the MCP server over stdio and renders the returned JSON. Set ANTHROPIC_API_KEY once and both layers use it.

Use with Claude Desktop

{
  "mcpServers": {
    "glass-box": {
      "command": "npx",
      "args": ["-y", "@glassbox-framework/mcp"],
      "env": { "ANTHROPIC_API_KEY": "sk-ant-..." }
    }
  }
}

~/Library/Application Support/Claude/claude_desktop_config.json on macOS.

Determinism

Audit log_ids are SHA-256 over canonicalised JSON of (inputs_hash, claims, ECS dimensions, red-team probe verdicts, constitution evaluations). Timestamps are recorded but never enter the hash, so identical inputs and identical engine outputs always produce the same log_id — across runs, machines, and even languages (the Python client → Node server → JSON canonicalisation produces byte-identical hashes).

Verifiable example, no API key needed:

pip install glassbox-framework
python -c "
import json
from glassbox_framework import Glassbox
with open('mcp/demo/raw-inputs.json') as f: i = json.load(f)
with Glassbox() as gb:
    c = gb.generate_trust_card(
        question=i['question'], answer=i['answer'],
        claims=i['claims'], red_team=i['red_team'], ecs=i['ecs'],
        constitution=i['constitution'])
print(c['audit']['log_id'])   # glassbox-85cc09903bd4b3f8022a4087
"

Project layout

mcp/                       — the MCP server + Python client (this release)
  ├── src/                 — TypeScript MCP server (6 tools)
  ├── python/              — Python pip package (glassbox-framework)
  ├── homebrew/            — Homebrew formula
  ├── assets/              — Launch video + reveal + title cards
  ├── demo/                — Live terminal demo with prebuilt Trust Card
  ├── Dockerfile           — Container image
  ├── server.json          — MCP Registry manifest
  ├── smithery.yaml        — Smithery.ai manifest
  ├── LAUNCH.md            — Launch kit
  └── DISTRIBUTION.md      — Every channel's status + commands
LICENSE                    — Apache 2.0
ROADMAP.md                 — Phase 5 (governor) plans for the broader framework
CONTRIBUTING.md
CHANGELOG.md

Contributing

Glassbox is open source under Apache 2.0 and actively wants forks and PRs. A few specific places we'd love help:

• More red-team probes — mcp/src/engines/redteam.ts has // v2: placeholders for alignment_faking, reasoning_trace_deception, eval_awareness_gaming, agentic_misalignment, and sustained_jailbreak. Each is a tractable PR — same shape as the existing 7 probes, just a different angle. See .github/ISSUE_TEMPLATE/good_first_issue.md.
• More language clients — currently Python (glassbox-framework) and Node (@glassbox-framework/mcp). Go, Rust, Ruby, Swift, Kotlin would all be welcome as thin JSON-RPC clients that spawn the existing MCP server.
• More integrations — Cursor / Cline / Continue / Roo Cline / Zed / Neovim — wherever MCP is read, Glassbox should be one paste away.
• Real-world Trust Card examples — submit (Q, A) pairs from your own AI workflows so the test suite covers more terrain.

Process:

1. Pick a good first issue or open one with your idea
2. Fork, branch, work — the PR template walks you through verification
3. CI must pass (.github/workflows/ci.yml) — TS strict mode, Python wheel build, cross-language determinism on the canonical audit hash
4. Open the PR; we aim for review within 48 hours

Code of conduct: Contributor Covenant 2.1. Be kind, stay on substance, no harassment, contact thebarmaeffect@gmail.com for anything off-public-channel.

Star ⭐ this repo

The fastest way to help right now is to star the repo. Every star:

• Surfaces Glassbox higher in GitHub's MCP topic listings
• Pushes the project up on the MCP Registry and Smithery rankings
• Tells the next developer evaluating AI-safety tooling that this is the one with eyes on it

⭐ Star Glassbox

Author

Karthik Barma · MS Artificial Intelligence · Northeastern University.

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Issues + PRs: https://github.com/TheBarmaEffect/glassbox/issues