trustandverify 0.1.0

Agentic knowledge verification using Subjective Logic confidence algebra

🔍 trustandverify

Agentic knowledge verification using Subjective Logic confidence algebra.

trustandverify decomposes research questions into verifiable claims, gathers evidence from multiple search backends, scores confidence using formal Subjective Logic mathematics (Jøsang 2016), and produces provenance-rich reports with per-claim uncertainty quantification.

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The Problem

Every major AI system treats confidence as a single scalar — or ignores it entirely. A confidence = 0.5 is meaningless: it could mean "strong evidence that the probability is 50%", or "we have literally no evidence and are guessing." These require fundamentally different downstream decisions.

trustandverify uses Subjective Logic opinion tuples (belief, disbelief, uncertainty, base_rate) so you always know not just what the AI thinks, but how much evidence it has.

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Quick Start

pip install trustandverify[tavily,gemini]

import asyncio
from trustandverify import verify

report = asyncio.run(verify("Is remote work more productive than office work?"))
print(report.summary)
for claim in report.claims:
    op = claim.opinion
    print(f"  [{claim.verdict}] {claim.text}")
    print(f"    P={op.projected_probability():.3f}  b={op.belief:.3f} d={op.disbelief:.3f} u={op.uncertainty:.3f}")

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Configured Usage

from trustandverify import TrustAgent, TrustConfig
from trustandverify.search import TavilySearch
from trustandverify.llm import GeminiBackend
from trustandverify.storage import SQLiteStorage
from trustandverify.export import JsonLdExporter, MarkdownExporter

config = TrustConfig(num_claims=5, max_sources_per_claim=3)

agent = TrustAgent(
    config=config,
    search=TavilySearch(),
    llm=GeminiBackend(),
    storage=SQLiteStorage("reports.db"),
)

report = await agent.verify("Is nuclear energy safer than solar?")

JsonLdExporter().render_to_file(report, "report.jsonld")
MarkdownExporter().render_to_file(report, "report.md")

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CLI

trustandverify "Is coffee healthy?"
trustandverify "Is coffee healthy?" --claims 5 --llm gemini --format markdown
trustandverify ui   # Launch Streamlit dashboard

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Why Subjective Logic?

Scenario	Scalar Confidence	trustandverify Opinion
Strong evidence it's 50/50	0.5	b=0.45, d=0.45, u=0.10
No evidence at all	0.5	b=0.00, d=0.00, u=1.00
Sources violently disagree	0.5	b=0.40, d=0.40, u=0.20

Confidence algebra (from jsonld-ex):

• Cumulative fusion — more independent agreeing sources → lower uncertainty
• Trust discount — .gov/.edu sources weighted higher than Reddit
• Pairwise conflict detection — surfaces where sources disagree, quantified

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Install Options

pip install trustandverify                              # core only
pip install trustandverify[tavily,gemini]               # minimal working setup
pip install trustandverify[tavily,brave,openai,sqlite]  # typical setup
pip install trustandverify[all]                         # everything

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Architecture

trustandverify/
├── core/        — TrustAgent, pipeline, models, config
├── scoring/     — Subjective Logic algebra (wraps jsonld-ex)
├── search/      — SearchBackend protocol + Tavily, Brave, Bing, SerpAPI
├── llm/         — LLMBackend protocol + Gemini, OpenAI, Anthropic, Ollama
├── storage/     — StorageBackend protocol + memory, SQLite, Postgres, Neo4j
├── cache/       — CacheBackend protocol + file cache, Redis
├── export/      — ExportBackend protocol + JSON-LD, Markdown, HTML, PDF
├── cli/         — Typer CLI
└── ui/          — Streamlit dashboard

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References

• Jøsang, A. (2016). Subjective Logic: A Formalism for Reasoning Under Uncertainty. Springer.
• jsonld-ex — JSON-LD 1.2 extensions with Subjective Logic confidence algebra
• W3C PROV-O — Provenance Ontology

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License

MIT — see LICENSE.