webweavex 2.1.0

Deterministic runtime cognition infrastructure for humans and AI agents

Production-grade deterministic runtime cognition infrastructure
for humans and AI agents
Operational runtime substrate · PyPI · replay-safe · Kaalka v5 parity

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Contents

• What is WebWeaveX?
• Humans and AI agents
• Why AI agents need this
• What WebWeaveX is NOT
• Why existing systems fail
• Core capabilities
• Authenticated runtime continuation
• Architecture
• Canonical pipeline
• Quick start
• Code examples
• Determinism
• Validation
• Security
• Architecture guarantees
• Contributing

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What is WebWeaveX?

WebWeaveX is to runtime state what Git is to source code: deterministic, replayable, reconstructable, and auditable.

Modern operational systems generate runtime state that is typically lost, difficult to reproduce, and impossible to validate. WebWeaveX transforms that runtime state into deterministic artifacts that humans and AI agents can continue, reconstruct, replay, and verify.

WebWeaveX is deterministic runtime cognition infrastructure for humans and AI agents operating on authenticated software. It captures how systems actually run—browser DOM, sessions, Electron, native UI, workflows, connectors—and compiles replay-safe runtime graphs with Kaalka-encrypted persistence (webweavex-formula+kaalka@5.0.0).

This is not a scraping library or LLM wrapper. It is an operational runtime substrate for extraction, memory, execution, reconstruction, and replay equivalence.

Ecosystem portal: main · npm sibling: javascript

Why it exists

Modern systems are authenticated, stateful, runtime-driven, SPA-based, Electron-based, synchronized, and operationally dynamic. Operators need continuity across runs, not another HTML snapshot.

Traditional extraction fails because it is:

Failure mode	Consequence
HTML-only parsing	Misses hydration, storage, IPC, native UI
Stateless requests	Loses session and workflow continuity
No authenticated persistence	Re-login and drift between runs
No replay contract	Cannot prove equivalence after rebuild
No reconstruction	Cannot rebuild operational topology from IR
Weak SPA/Electron support	Unstable IDs, routes, and storage break diffs

WebWeaveX exists to deliver deterministic runtime extraction and replay-safe operational reconstruction through one canonical pipeline.

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Universal Runtime Extraction

WebWeaveX is not merely a scraping library — it is a runtime extraction and cognition substrate. It transforms heterogeneous operational sources into deterministic runtime representations through one canonical pipeline.

Source	Runtime Representation
Websites	Runtime graph
SPAs	Stabilized runtime state
Browser sessions	Replay-safe artifacts
APIs	Operational topology
Documents	Unified IR
Repositories	Dependency intelligence
Runtime systems	Memory fabric

Every source converges on the same bounded, hashable, replayable runtime IR.

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Web Extraction Without Fragility

Most extraction systems focus on collecting content. WebWeaveX focuses on preserving runtime state. Traditional scraping breaks when authentication expires, SPA frameworks re-render, runtime identifiers change, workflows span sessions, or replay must be validated later.

Extraction Challenge	Traditional Approach	WebWeaveX
SPA instability	Re-scrape repeatedly	Runtime stabilization
Authenticated workflows	Start over	Runtime continuation
Session portability	Manual export	Encrypted runtime persistence
Validation	Manual inspection	Replay equivalence
Recovery	Re-run workflow	Runtime reconstruction

The result is extraction that can be continued, replayed, reconstructed, and verified.

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Humans and AI agents

WebWeaveX is designed for both humans and AI agents.

Audience	Use
Engineers	Inspect authenticated systems, preserve workflows, audit runtime behavior
AI agents	Maintain continuity, deterministic state, replay-safe memory, environment reconstruction

Same APIs, same determinism contract, same honesty about authorization.

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Why AI Agents Need WebWeaveX

Browser and operational agents interact with systems that change continuously. Without deterministic runtime infrastructure, agents lose context between actions.

Agent Failure Mode	Operational Impact	WebWeaveX Capability
Lost browser state	Re-authentication	Runtime continuation
Lost workflow context	Restart execution	Runtime memory fabric
DOM instability	Broken selectors	DOM stabilization
Replay drift	Non-repeatable behavior	Replay equivalence
Session expiration	Lost progress	Encrypted persistence
Workflow interruption	Incomplete execution	Runtime reconstruction

WebWeaveX provides a deterministic runtime layer beneath agents so operational state becomes persistent, replayable, and auditable.

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Why AI agents need deterministic runtime infrastructure

Problem	Without substrate	With WebWeaveX
LLMs lose state	Re-plan from scratch each turn	Stable runtime memory + graph identity
Browser agents lose auth	Re-login drift	Authorized session continuation (Kaalka)
Workflows go nondeterministic	Unauditable actions	Replay equivalence + fingerprints
Operational systems are opaque	HTML-only views	Runtime cognition IR + reconstruction
Cross-run reasoning breaks	Ephemeral DOM	Stabilized hashes + parity-validated crypto

WebWeaveX provides the deterministic operational runtime layer agents and teams share—not autonomous superintelligence.

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What WebWeaveX is NOT

WebWeaveX is not:

Category	Clarification
Auth bypass tooling	Does not defeat MFA, CAPTCHA, or login controls
Malware or exploit infrastructure	Not designed for unauthorized access
Credential theft tooling	Does not harvest secrets you do not already hold
CAPTCHA bypass software	No circumvention of bot defenses
Browser exploitation tooling	Not a vulnerability framework
AGI or “autonomous hacking”	No probabilistic agent that “figures out” sites
Hacking infrastructure	No unauthorized intrusion features
An LLM wrapper	Core path is deterministic; optional plugins fail safe
A chatbot	Infrastructure library, not conversational AI

WebWeaveX only operates on authorized authenticated runtimes and data you explicitly provide.

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Why existing systems fail

System	Strength	Limitation for operational runtime
BeautifulSoup	Fast static HTML parse	No live session, storage, or runtime graph
Selenium	Browser automation	No unified IR, Kaalka fabric, or replay equivalence layer
Playwright	Reliable browser control	Automation driver—not extraction + memory + reconstruction
Puppeteer	Chromium scripting	Same gap: no federated sync or deterministic checkpoints
Stateless crawlers	Scale on public pages	Poor on authenticated operational systems
Probabilistic-only agents	Flexible tasks	Weak replay, memory, and audit guarantees

Common gaps WebWeaveX addresses:

• Lack of runtime continuity across processes
• Lack of replay and fingerprint equivalence
• Lack of authenticated persistence (encrypted, deterministic)
• Lack of reconstruction from structured IR
• Lack of synchronization between browser, semantic, workflow, and memory layers

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How WebWeaveX Differs

Tool	Primary Focus
Playwright	Browser automation
Scrapy	Crawling
BeautifulSoup	HTML parsing
Firecrawl	Extraction
LangChain	LLM orchestration
CrewAI	Agent orchestration
WebWeaveX	Deterministic runtime cognition infrastructure

WebWeaveX does not replace these systems. It provides deterministic runtime infrastructure that can sit beneath them.

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Runtime Cognition Infrastructure

WebWeaveX introduces a category beyond traditional scraping, browser automation, or agent orchestration.

Infrastructure that captures, stabilizes, fingerprints, reconstructs, and continues operational runtime state through deterministic contracts.

Category	Focus
Browser automation	Execute actions
Web scraping	Extract content
Agent orchestration	Coordinate reasoning
Runtime cognition infrastructure	Preserve operational runtime state

WebWeaveX works alongside existing ecosystems rather than replacing them.

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Core capabilities

Capability	Description
Browser runtime extraction	Bounded Playwright capture, network/session envelopes
SPA stabilization	DOM and route stabilization for framework noise
Electron extraction	Routes, IPC, storage metadata, deterministic Electron hash
Native runtime cognition	Desktop, terminal, VM, remote (graceful OS fallbacks)
Terminal runtime	Shell-oriented cognition fixtures
Distributed extraction	Autonomous workers + Kaalka checkpoints
Runtime causality	Event chains and propagation in extraction fabrics
Semantic cognition	Entities, ontology, semantic graphs
Workflow runtime	Plans, objectives, workflow memory
Synchronization runtime	Multi-source runtime alignment
Reconstruction engine	Replay-safe rebuild from IR
Federated memory	Deterministic merge and stable hashes
Execution sandbox	Allowlisted actions only
Runtime replay	validate_replay_equivalence()
Runtime graph	Normalized universal runtime graph
Deterministic fingerprints	Global and pipeline hashes
Authenticated runtime continuation	Encrypted session reload
Kaalka v5 crypto (cross-language)	webweavex-formula+kaalka@5.0.0 — verified vs javascript branch
Connector runtime fabric	Database, API, container, K8s, telemetry (bounded)

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Authenticated runtime continuation

Modern applications authenticate with cookies, localStorage, sessionStorage, tokens, runtime identity, and cross-navigation continuity. Electron adds IndexedDB metadata, IPC, and route state. Multi-tab products add synchronization state across surfaces.

WebWeaveX supports:

• Encrypted authenticated session persistence (save_encrypted_session, session paths on extract_web)
• Runtime continuation across extractions when you supply the same Kaalka key and session file
• Deterministic replay-safe reconstruction of operational graphs from IR

Persistence uses Kaalka v5 deterministic encryption (algorithm: webweavex-formula+kaalka@5.0.0)—not plaintext JSON checkpoints on disk.

Stored surface	Mechanism
Cookies / headers	Encrypted session store
Browser snapshot	Session + identity engines
Electron storage	Native/Electron cognition (bounded)
Workflow / sync state	Kaalka checkpoint engines

WebWeaveX does not: bypass auth, defeat MFA, bypass security controls, or access systems without authorization.

WebWeaveX only operates on authorized authenticated runtimes explicitly provided by the user.

from webweavex import extract_web

result = extract_web(
    "https://app.example.com/dashboard",
    authenticated=True,
    session_path="./session.kaalka",
    encryption_key="your-kaalka-master-key",
)

---

Runtime Lifecycle

Capture → Normalize → Fingerprint → Graph → Memory → Replay Validation → Reconstruction → Continuation

Every WebWeaveX runtime moves through this bounded lifecycle: captured state is normalized and fingerprinted, compiled into a runtime graph and memory fabric, validated for replay equivalence, then reconstructed and continued.

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Cross-Language Determinism

WebWeaveX ships as two independent products — Python (pip install webweavex) and JavaScript (npm install webweavex) — that conform to one shared specification/. They share byte-identical deterministic contracts:

Contract	Verified
Kaalka hashing	byte-identical Python ⇄ JavaScript
Global runtime fingerprint	byte-identical Python ⇄ JavaScript
Runtime graph structure	structurally equal
Encrypted value persistence	byte-identical Python ⇄ JavaScript

See CROSS_LANGUAGE_PARITY_REPORT.md for the measured per-capability status. Neither implementation invokes the other at runtime; parity is proven against the specification, not by cross-runtime calls.

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Architecture

                              ┌──────────────────┐
                              │      Input       │
                              │  UniversalInput  │
                              └────────┬─────────┘
                                       │
                                       ▼
                              ┌──────────────────┐
                              │ Canonical Pipeline│
                              │ run_canonical_    │
                              │   pipeline()      │
                              └────────┬─────────┘
                                       │
                                       ▼
                              ┌──────────────────┐
                              │ Runtime Cognition │
                              │ web·native·repo   │
                              └────────┬─────────┘
                                       │
           ┌───────────────────────────┼───────────────────────────┐
           ▼                           ▼                           ▼
    ┌─────────────┐            ┌─────────────┐            ┌─────────────┐
    │  Semantic   │            │  Causality  │            │  Workflow   │
    │   Layer     │            │   Layer     │            │  Runtime    │
    └──────┬──────┘            └──────┬──────┘            └──────┬──────┘
           │                          │                          │
           └──────────────────────────┼──────────────────────────┘
                                      ▼
                             ┌─────────────────┐
                             │ Synchronization │
                             │    Runtime      │
                             └────────┬────────┘
                                      ▼
                             ┌─────────────────┐
                             │ Federated Memory│
                             └────────┬────────┘
                                      ▼
                             ┌─────────────────┐
                             │ Execution Fabric│
                             └────────┬────────┘
                                      ▼
                             ┌─────────────────┐
                             │ Reconstruction  │
                             │    Engine       │
                             └────────┬────────┘
                                      ▼
                             ┌─────────────────┐
                             │ Universal Runtime│
                             │     Graph        │
                             └─────────────────┘

Source: core/kernel/runtime_pipeline.py

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Canonical pipeline

Single production execution path—no shadow orchestrators.

from webweavex import UniversalInput, run_canonical_pipeline

result = run_canonical_pipeline(
    UniversalInput(source="https://example.com", source_type="web"),
)

print(result["pipeline_hash"])
print(len(result["unified_runtime_graph"].get("nodes", [])))

Property	Detail
Single execution path	run_canonical_pipeline() only
Deterministic normalization	RuntimeGraphContract.normalize()
Replay-safe runtime	Fingerprint at pipeline boundary
Canonical IR generation	Per-kind extraction → kernel phases

---

Quick start

pip install webweavex
pip install "webweavex[browser]"
pip install "webweavex[full]"

python -c "import webweavex; print(webweavex.__version__)"
# 2.1.0

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Core Capabilities

WebWeaveX exposes one deterministic engine across six cognition domains. Every output is a bounded, hashable, replayable IR.

Domain	Capabilities	Representative public APIs
Extraction	Bounded web/SPA/Electron capture, structured content, runtime envelopes	extract_web, run_canonical_pipeline, universal_extract
Documents	Document IR; structure, citation, and discourse analysis	extract_docs, compile_document, query_documents
Repositories	Repository IR; dependency, build, and topology intelligence	extract_repository, compile_repository, query_repository
Runtime	Runtime graphs, memory fabric, replay equivalence, reconstruction	build_runtime_graph, validate_replay_equivalence, run_reconstruction_runtime
Applications	Application cognition, runtime objectives, session memory	run_application_cognition, execute_runtime_objective
Cognition	Causality, semantic, synchronization, evolution, workflows, execution	run_semantic_runtime, run_causality_runtime, run_autonomous_workflow
Determinism	Canonical normalization, stable serialization, fingerprints, Kaalka v5	compute_global_runtime_fingerprint, encrypt_value
Cross-language parity	Byte-identical hashes across Python · JavaScript · Dart	fingerprint, compute_kaalka_hash

---

Common Workflows

from webweavex import (
    UniversalInput, run_canonical_pipeline,
    extract_web, extract_docs, extract_repository,
    query_semantics, run_application_cognition,
)

# Extract structured content
content = extract_web("https://example.com")

# Analyze documents
doc_ir = extract_docs("./report.pdf")

# Analyze repositories
repo_ir = extract_repository("./my-project")

# Query semantic IR
semantics = query_semantics("entities", repo_ir)

# Runtime reasoning
pipeline = run_canonical_pipeline(
    UniversalInput(source="https://example.com", source_type="web"),
)
print(pipeline["pipeline_hash"])

# Application cognition
app = run_application_cognition(
    url="https://app.example.com",
    html="<html>...</html>",
)

---

Supported Platforms

Aspect	Detail
Runtime	CPython 3.10 – 3.13
Operating systems	Linux · macOS · Windows (OS-independent core)
Install	pip install webweavex (extras: [browser], [full])
Optional	Playwright (browser), tree-sitter (parsers), OCR / ingestion extras

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Versioning

WebWeaveX follows Semantic Versioning — MAJOR.MINOR.PATCH. The version is synchronized across all three implementations: PyPI, npm, and pub.dev share the same 2.1.0, so a given version number denotes the same certified deterministic contract in every language. MAJOR marks a breaking change, MINOR adds backward-compatible capability, PATCH is a fix. The internal engine contract version (v1_phase_14) is independent of the package version and changes only when the deterministic wire format changes.

---

Real code examples

Browser, auth, replay, semantic, reconstruction, distributed, native

Browser extraction

from webweavex import extract_web, compute_global_runtime_fingerprint

out = extract_web("https://example.com")
print(out.get("bounded"), compute_global_runtime_fingerprint(out))

Authenticated runtime persistence

from webweavex import save_encrypted_session, extract_web

save_encrypted_session(
    "./session.kaalka",
    {"cookies": [], "headers": {}, "auth_tokens": []},
    "your-kaalka-master-key",
)

out = extract_web(
    "https://app.example.com",
    authenticated=True,
    session_path="./session.kaalka",
    encryption_key="your-kaalka-master-key",
)

Runnable: examples/authenticated_extraction.py

Replay equivalence

from webweavex import validate_replay_equivalence

assert validate_replay_equivalence(original, replayed)["equivalent"]

Semantic runtime

out = extract_web("https://example.com", semantic_runtime=True)

Reconstruction

from webweavex import run_reconstruction_runtime

rebuilt = run_reconstruction_runtime(
    sources={"extraction": prior},
    runtime_type="browser",
)

Distributed extraction

from webweavex import run_autonomous_extraction

out = run_autonomous_extraction(
    tasks=[{"task_id": "t1", "url": "https://example.com", "priority": 0}],
)

Native extraction

from webweavex import extract_native

out = extract_native(runtime="desktop", application="notepad")

---

Determinism

Mechanism	Role
compute_global_runtime_fingerprint()	Cross-run runtime digest
validate_replay_equivalence()	Graph + fingerprint + topology checks
compute_stable_dom_hash()	DOM meaning stable under attribute noise
SPA stabilizer	Framework route/state freeze
stable_memory_hash()	Ordered federated memory merge
Kaalka encrypt_value	UTF-8 → derive_kaalka_time_key → Kaalka v5 _proc → base64

Cross-language parity (verified): validation/parity/javascript_vectors.json vs Python output — normalization, serialization, SHA-256 hash, and ciphertext match the javascript branch. Spec: docs/architecture/CROSS_LANGUAGE_PARITY.md.

PYTHONPATH=. python validation/validate_cross_language_parity.py

Honest limitations: live SPA fetches may differ run-to-run; parity applies to the canonical formula, not wall-clock Kaalka CLI encryption without a fixed derived time key.

---

Reconstruction engine

WebWeaveX reconstructs operational structure from runtime IR:

• Runtime topology and unified graphs
• Workflow and application memory views
• Browser/application state envelopes
• Semantic operational graphs

Property	Meaning
Runtime reconstruction	IR → bounded runtime view
Operational graph rebuilding	Normalized nodes/edges
Replay-safe reconstruction	Tested equivalence paths
Deterministic recreation	Sorted, canonical structures

This is not full machine cloning or sci-fi simulation—it is auditable operational recreation for engineering workflows.

---

Real validation

Validation commands and CI gates

Metric	Value
Tests	760+ passing (pytest -q)
Scoped coverage	≥ 90% (production packages in pyproject.toml)
Wheel	webweavex-2.1.0-py3-none-any.whl
Replay	validate_replay_equivalence suite
Determinism	Kaalka cross-language + fingerprint tests
Playwright	Browser extraction paths (optional extra)
Native	Orchestrator + platform fallbacks
Distributed	Autonomous extraction tests

pytest -q
python -m build
python validation/final_production_master.py

---

Security model

Control	Implementation
Allowlisted execution	core/execution/ sandbox
No arbitrary eval/exec	Forbidden in production paths
Sandboxed runtime	Bounded simulate/rollback
Deterministic persistence	Kaalka-only checkpoints
Encrypted memory/session	encrypt_value, session wrappers
Replay-safe recovery	Deterministic reload envelopes

See SECURITY.md. Report issues responsibly.

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Architecture guarantees

Guarantee	How
Deterministic outputs	Canonical ordering, stable hashes
Replay-safe persistence	Kaalka + equivalence validation
Bounded execution	Explicit bounded: True contracts
Graceful degradation	Playwright/native/connectors fail soft
Canonical normalization	Graph and DOM contracts
Stable graph generation	build_runtime_graph + normalize
Cross-language consistency	Kaalka reference vectors

Contract document: WEBWEAVEX_v2_ARCHITECTURE_LOCK_REPORT.md

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Repository structure

WebWeaveX/
├── core/           # Runtime infrastructure (kernel, browser, memory, sync, …)
├── webweavex/      # Public Python package
├── tests/          # 760+ tests
├── docs/           # Architecture, API, security, Kaalka, replay, validation
├── examples/       # Runnable scripts
├── validation/     # Production and real-world validators
└── .github/        # CI, templates, code of conduct, funding

Package	Role
core/kernel/	Canonical pipeline, RuntimeKernel
core/browser/	Web extraction, DOM/SPA stabilization
core/crypto/	Kaalka engines
core/memory/	Federated memory fabric
core/synchronization/	Sync runtime
core/reconstruction/	Reconstruction orchestrator
core/replay/	Replay equivalence
webweavex/	Stable public API

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Contributing

See CONTRIBUTING.md and .github/CODE_OF_CONDUCT.md.

Rule	Requirement
Determinism	No random / uuid4 in runtime paths
Replay safety	Preserve graph normalization semantics
Canonical pipeline	No parallel mega-orchestrators
Persistence	Kaalka for new checkpoints
Tests	pytest -q must pass; coverage gate ≥ 90% scoped

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Long-Term Vision

WebWeaveX aims to be a deterministic runtime substrate — a shared operational layer that runtime state can be captured into, reasoned over, and continued from. The goal is a common foundation for humans, AI agents, workflows, operational systems, and distributed cognition systems. It is infrastructure, not an application: the same deterministic contract serves every consumer.

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Future Direction

WebWeaveX is evolving toward a shared runtime substrate where operational state can move between humans, workflows, services, and AI agents without losing determinism.

Future areas include:

• broader language parity
• deeper runtime graph intelligence
• expanded connector ecosystems
• stronger replay guarantees
• larger runtime memory fabrics
• distributed operational cognition

Runtime state should be as reproducible, portable, and verifiable as source code.

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Roadmap

See ROADMAP.md.

v2.1 focus:

• Deeper native bindings (UIA, AX, AT-SPI)
• Distributed runtime infrastructure hardening
• Stronger SPA normalization
• Real connector runtimes (live Postgres, Redis, K8s validation)
• Native OS integrations behind optional extras

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API Reference

The complete public API surface — every function, its parameters, and its cross-language parity classification (Complete / Partial / Deferred) — is in API_REFERENCE.md, generated from PARITY_MANIFEST.json (portable-API parity gap: 0).

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Cross-language parity & certification

Three implementations — Python (canonical, PyPI), JavaScript (npm), Dart (pub.dev) — byte-identical on the certified surface. Every claim is regenerated by execution; nothing passes on the strength of a report:

Proof	Scale	Result
Core determinism	10k vectors × 3 runs × 3 languages	60,001/60,001 byte-identical
Extraction	10k synthetic + 1,006 real pages + 14 torture	3-way PASS
Semantic IR (layers A–O + parsers + repository + application)	667 fixtures, ~300 engines	3-way hash + deep equality
Million-vector battery	1,000,000 vectors across 5 IR families	single aggregate digest, identical in all 3

The full model, reproduction commands, and current verdict: CERTIFICATION.md and final_certification.json. Per-API status: API_REFERENCE.md (generated from PARITY_MANIFEST.json).

AI-agent usage

WebWeaveX is built to be operated by AI agents as much as by humans: every output is a bounded, deterministic, evidence-carrying IR that an agent can hash, diff, replay, and reason over without screenshots or DOM diffing. Agents contributing to the codebase should start at AI_AGENT_GUIDE.md — architecture map, determinism rules, the cross-language pitfalls catalogue, and the certification workflow.

Limitations

• Network/live-browser APIs are Partial by design — the extract* / crawl* family and five bounded APIs have certified deterministic cores; their live side effects are out of certification scope.
• Platform-bound APIs are Deferred — live-page capture and OS-coupled native cognition (extract_native, run_native_cognition) branch on the host OS even in Python and cannot be made cross-platform-deterministic.
• Valid-Python AST enrichment is Python-only — the certified JS/Dart contract takes the parse-error fallback for parse_ast on valid Python (see ARCHITECTURE.md, "The AST contract").

License

Apache 2.0 — see LICENSE.

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Final positioning

WebWeaveX is deterministic runtime cognition infrastructure for humans and AI agents—operational runtime substrate for the authenticated web, not a crawler, not an LLM wrapper, not AGI hype.

If this work helps your team, consider supporting it:

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_{Documentation · docs/ · examples/ · release report}