glassbox-framework 1.0.2

Glass Box Framework — runtime constitutional verification for AI answers. Every claim carries a reasoning chain. Every score breaks down. Every verdict is traceable.

glassbox-framework

Glass Box Framework — runtime constitutional verification for AI answers. Every claim carries a reasoning chain. Every score breaks down. Every verdict is traceable.

pip install glassbox-framework

What it does

Hand any (question, AI answer) pair to Glassbox and get back a Trust Card containing:

• Claims — every atomic assertion in the answer, each paired with a reasoning chain explaining why it's asserted, what would support it, and what would falsify it. The reasoning chain is the framework's core principle: no opaque scores.
• Epistemic Confidence Score (ECS) — a transparent, weighted aggregate 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 so identical inputs reproduce the same identifier.

from glassbox_framework import Glassbox

with Glassbox() as gb:
    card = gb.verify_answer(
        question="Can intermittent fasting cure type 2 diabetes?",
        answer=(
            "Yes, intermittent fasting can cure type 2 diabetes. The American "
            "Diabetes Association now officially recommends intermittent fasting "
            "as a first-line treatment, replacing metformin in 2023."
        ),
        intents=[
            "Never make specific medical recommendations 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["verdict_rationale"])    # "Critical fabrication detected; …"
print(card["audit"]["log_id"])      # glassbox-85cc09903bd4...  (deterministic)

The six tools

Method	Tool name	What it does
gb.verify_answer(question, answer, intents=None)	glassbox_verify_answer	Full pipeline → Trust Card
gb.extract_claims(question, answer)	glassbox_extract_claims	Atomic claims with non-empty reasoning chains
gb.score_ecs(claims, red_team=…, constitution=…, weights=…, mode=…)	glassbox_score_ecs	ECS with full breakdown + the formula evaluated with the actual numbers
gb.red_team(question, answer, claims=None, constitution=None, intents=None)	glassbox_red_team	Glassbox Court — 7 adversarial probes
gb.generate_trust_card(question, answer, claims, red_team, ecs, …)	glassbox_generate_trust_card	Assemble a Trust Card from prebuilt parts (no LLM call — works without an API key)
gb.export_audit_report(question, answer, intents=None)	glassbox_export_audit_report	Full pipeline + the full AuditRecord (call trace, deterministic log_id)

See the GitHub examples folder for one runnable script per tool.

Setup

glassbox-framework is pure stdlib — no third-party Python dependencies. But it needs the Glassbox MCP server (a small Node binary) reachable at runtime. Three resolution paths, tried in order:

1. Local checkout (best for development) — clone https://github.com/TheBarmaEffect/glassbox, cd mcp && npm install. The Python client auto-detects the sibling src/index.ts and runs it.
2. Global npm install — npm install -g @glassbox-framework/mcp. The Python client falls back to npx -y @glassbox-framework/mcp.
3. Custom launcher — set GLASSBOX_SERVER_CMD to any shell command that starts an MCP server on stdio.

Other prerequisites:

• Node 18+ (Glassbox's MCP server is TypeScript)
• ANTHROPIC_API_KEY in your environment, for the engines that call Claude. Exception: gb.generate_trust_card is pure assembly — no LLM call. Try the framework with that one first.

CLI

The pip install also drops a glassbox binary on your PATH:

glassbox tools                                            # list the 6 registered tools

glassbox verify \
  --question "Can intermittent fasting cure type 2 diabetes?" \
  --answer  "Yes ..." \
  --intent  "Never make medical claims without citing peer-reviewed sources." \
  --intent  "Recommend consulting a licensed professional."

glassbox extract-claims --question "..." --answer "..."

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. Replay-detectable, cite-able, byte-stable across runs.

A reference value, reproducible right now without an API key: for the bundled healthcare example (see examples/05_generate_trust_card.py), the log_id is glassbox-85cc09903bd4b3f8022a4087.

Error handling

from glassbox_framework import Glassbox, GlassboxError, ToolError

with Glassbox() as gb:
    try:
        card = gb.verify_answer(question="...", answer="...")
    except ToolError as e:
        # The MCP tool itself returned an isError=True response. Common
        # cause: ANTHROPIC_API_KEY is not set.
        print(f"{e.tool} failed: {e}\nHint: {e.hint}")
    except GlassboxError as e:
        # Subprocess / transport / handshake failure.
        print(f"Could not reach the Glassbox MCP server: {e}")

Architecture

This Python package is a thin JSON-RPC stdio client. It:

• Spawns the Node MCP server as a subprocess on first use (lazy)
• Sends tools/call over stdin and reads JSON-RPC responses over stdout
• Surfaces server-side isError responses as ToolError
• Tears down the subprocess on __exit__ / close()

Zero third-party Python dependencies. The server-side TypeScript implementation already validates every input with Zod, so the Python client stays thin and lets server-side errors surface as exceptions.

Credit

Built by Karthik Barma · MS Artificial Intelligence · Northeastern University.

Powered by Aura.

Apache 2.0. Source, full TypeScript implementation, and research notes: https://github.com/TheBarmaEffect/glassbox