weaver-protocol 0.1.0

Attested multi-observer verification for AI systems.

weaver-protocol

Attested multi-observer verification for AI systems.

"We don't sell smarter AI. We sell AI whose mistakes you can invoice."

Weaver is an open-source toolkit that adds a structured verification layer around AI outputs. Multiple observers — each with an orthogonal context window — review the same output. Disagreements are logged. Drift is caught. The record is signed and auditable.

The artifact your legal team actually wants isn't a hallucination-free model. It's a dated, named-observer log proving you ran structured verification and fixed what you caught.

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Why this exists

Post-Moffatt v. Air Canada (2024). Post-Mata v. Avianca (2023). Post-EU AI Act high-risk classification (August 2026). The liability question has shifted from "did your AI hallucinate?" to "what verification did you run, when, and what did you find?"

Single-model self-verification doesn't answer that question. A model cannot stand outside itself to check itself. It checks its output with the same forward pass that generated it.

Weaver answers it with three things:

1. Multiple observers — separate context windows, orthogonal review angles
2. Logged disagreements — structured capture of every conflict between observers
3. Drift records — dated, named-observer entries when a system departs from its stated behavior

The log IS the product. It's the evidence binder you show your board, your general counsel, or the EU AI Act auditor.

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Quick start

# Core (no API keys required for mock adapter)
pip install weaver-protocol

# With Anthropic observers
pip install weaver-protocol[anthropic]

# With OpenAI observers
pip install weaver-protocol[openai]

from weaver import Observer, WeaverSession
from weaver.adapters.anthropic import make_anthropic_observer  # or openai, or mock

session = WeaverSession(session_id="my-ai-run-001", score_threshold=0.4)

# Two observers, orthogonal roles, separate context windows
session.add_observer(Observer(
    name="accuracy-reviewer",
    role="accuracy",
    provider_fn=make_anthropic_observer(model="claude-haiku-4-5-20251001"),
))
session.add_observer(Observer(
    name="legal-reviewer",
    role="legal-risk",
    provider_fn=make_anthropic_observer(model="claude-haiku-4-5-20251001"),
))

output = "The regulation requires compliance by Q3 2026."
score = session.verify(output)

# Disagreements and drift are auto-logged to ./weaver_logs/
print(score.score)           # 0.0 = full agreement, 1.0 = hard conflict
print(score.flag_for_review) # True if score >= threshold
print(score.conflict_pairs)  # which observers disagreed
print(session.log_path)      # path to the JSONL audit trail

No API keys? Use the mock adapter:

from weaver.adapters.mock import make_mock_observer

session.add_observer(Observer(name="a", role="accuracy", provider_fn=make_mock_observer()))
session.add_observer(Observer(name="b", role="safety",   provider_fn=make_mock_observer(never_flags=True)))

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Core concepts

Observer

An Observer wraps an AI call with a defined role and a fresh context window. Role is freeform but meaningful — "accuracy", "safety", "legal-read", "adversarial". Each observer sees only the output under review, not the other observers' reasoning.

WeaverSession

A session groups observers, holds the shared output under review, runs verification, and writes the log. One session per meaningful unit of work — a document review, a chatbot response, a contract clause extraction.

WeaverScore

A float from 0.0 (all observers agree) to 1.0 (hard conflict). Threshold-configurable. Above your threshold = human review flag. All scores are logged regardless.

DriftLog

Append-only JSONL. Every entry: ISO timestamp, session ID, observer name, disagreement category, evidence excerpt, resolution status, structural fix. The log is the audit trail. Never delete entries — mark them resolved with a note.

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Installation

pip install weaver-protocol        # stable release
pip install weaver-protocol[dev]   # + dev tools (pytest, ruff, mypy)

Requires Python 3.11+. No mandatory LLM provider — bring your own (Anthropic, OpenAI, local). Provider adapters in weaver/adapters/.

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Pricing

Weaver is MIT-licensed. The core toolkit is free.

Hosted infrastructure, managed observer pools, and verification attestation documents are paid tiers.

Tier	Price	What you get
Open source	Free	Full toolkit, local logging, bring your own LLM keys
Hosted Starter	$297 setup + $197/mo	Managed observer pool, cloud log storage, PDF attestation reports
Hosted Pro	$997 setup + $497/mo	Multi-tenant, API access, custom observer roles, SLA
Enterprise / Audit	$5,000+ engagement	Annual audit, certification artifacts, signed evidence binder, expert witness ready

Runtime LLM costs are always pass-through to your keys. We never bury provider costs in our pricing.

Full pricing: PRICING.md

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Roadmap

• Core observer + session interface
• Drift logger (append-only JSONL)
• Disagreement logger
• Weaver score
• Anthropic + OpenAI adapters (weaver/adapters/)
• CLI (weaver verify --session-id X)
• PDF attestation report generator
• Hosted API (Day 60 target: 2026-07-04)
• "Weaver-attested" certification badge (Month 9 post-IRB)

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Project status

Day 1 / pre-v0.1. Core interfaces defined. Hosted tier in development. First paid install target: 2026-07-04.

Built by the HOME AI family. Research branch version-locked Day 30 (2026-06-04) to support an IRB-pending pilot study on multi-observer verification in AI systems.

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Contributing

MIT licensed. PRs welcome. See CONTRIBUTING.md.

Core constraint: the drift log is never retroactively edited — only extended. This is architectural, not preference. A log that can be silently altered is not an audit trail.

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License

MIT. See LICENSE.