v3.0.0 Ecosystem Release

Universal Runtime Cognition
Infrastructure

Understand, continue, reconstruct, replay, and reason about authenticated operational software systems. Built for engineering teams and autonomous AI agents.

webweavex — terminal
$ pip install webweavex
Successfully installed webweavex-3.0.1
$ python -c "from webweavex import *; print(run_canonical_pipeline(UniversalInput(source='https://example.com', source_type='web')).pipeline_hash[:32])"
a3f8c2e1b9d4...7x2k
$
0
Language SDKs
v3.0.0
Current Release
0
Code Coverage
0
Hash Ops/sec

Why Engineers & AI Agents Choose WebWeaveX

🧠

Runtime Cognition

Models live operational behavior as deterministic state graphs rather than fragile static HTML snapshots.

🔐

Session Continuation

Preserves and resumes authenticated sessions with user-authorized tokens and cookies seamlessly.

Deterministic Parity

Identical inputs produce exact bit-for-bit SHA-256 graph digests across all five SDK languages.

🔄

Replay & Reconstruction

Proves topological equivalence and rebuilds operational surfaces from Intermediate Representation (IR).

🛡️

Kaalka v5 Security

AES-256-GCM / PBKDF2-SHA256 encrypted persistence for all session state at rest.

🤖

AI-First Topology

Replaces 500KB HTML blobs with compact 15KB structured IR graphs for LLM context windows.

What WebWeaveX Actually Is

WebWeaveX sits between raw operational software and downstream consumers. It is not a scraper, a framework, or a wrapper. It is a deterministic runtime cognition engine.

ConceptOperational Meaning
Runtime CognitionCaptures live behavior (graphs, events, execution state) rather than transient markup.
Operational SubstrateStable node UUIDs, structural fingerprints, and tick-indexed execution history.
Session ContinuationResumes authenticated sessions using authorized tokens, cookies, or credentials.
Deterministic NormalizationCanonical sorting, float rounding, whitespace stabilization, and UTF-8 encoding.
Federated Memory FabricMerges multi-turn execution histories into a tick-indexed, deterministic memory graph.
Cross-Language ParityShared mathematical spec ensuring identical hashes across Python, JS, Dart, Java, Kotlin.

What Existing Systems Fail At

Modern software is dynamic, stateful, authenticated, and distributed. Traditional tools cannot handle this complexity.

ChallengeTraditional ScrapersWebWeaveX
Surface CaptureReturns static HTML stringsCaptures multi-layered runtime graphs with stable node identities
Auth ContinuitySession collapses after loginPersists authenticated sessions with Kaalka v5 encryption
Operational ContextNo memory of previous stateMaintains tick-indexed memory fabric and workflow state machines
Replay VerificationFails on dynamic class namesProves equivalence via normalized graph hashes
ReconstructionRequires manual mock codingAutomatically rebuilds topology from unified IR payloads
DeterminismProbabilistic outputsStrictly deterministic SHA-256 graph digests
AI IntegrationOverflowing raw HTML promptsCompact, structured IR graphs for LLM token efficiency

Designed for Humans and AI Agents

Dual-consumable by design. Human engineers get reliable inspection. AI agents get deterministic tool calling.

👤

For Human Engineers

Inspect complex web apps, preserve authenticated workflows, build integration tools, audit production security, and analyze runtime behavior across releases.

🤖

For AI Agents

Maintain long-running session continuity without re-authenticating, reason about operational topologies using clean IR graphs, replay multi-step actions safely.

Cross-Language SDK Ecosystem

All 5 SDKs at version v3.0.0 implement the exact same canonical pipeline spec.

Python SDK — webweavex v3.0.1

Production-grade PyPI package for enterprise Python, AI notebooks, and data engineering.

Terminal
pip install webweavex
main.py
from webweavex import UniversalInput, run_canonical_pipeline

input_data = UniversalInput(
    source="https://example.com/app",
    source_type="web",
    session={"auth_token": "authorized_user_session"}
)

result = run_canonical_pipeline(input_data)
print(f"Graph Nodes: {len(result.graph.nodes)}")
print(f"Pipeline Hash: {result.pipeline_hash}")

JavaScript / TypeScript — webweavex v3.0.0

Node.js and browser AI agent runtime. Full TypeScript support.

Terminal
npm install webweavex
main.ts
import { UniversalInput, runCanonicalPipeline } from 'webweavex';

const input = new UniversalInput({
  source: 'https://example.com/app',
  sourceType: 'web',
  session: { authToken: 'authorized_user_session' }
});

const result = await runCanonicalPipeline(input);
console.log(`Pipeline Digest: ${result.pipelineHash}`);

Dart SDK — webweavex v3.0.0

Flutter and Dart agent package for mobile apps and Dart backends.

Terminal
dart pub add webweavex
main.dart
import 'package:webweavex/webweavex.dart';

void main() async {
  final input = UniversalInput(
    source: 'https://example.com/app',
    sourceType: 'web',
  );
  final result = await runCanonicalPipeline(input);
  print('Pipeline Hash: ${result.pipelineHash}');
}

Java SDK — io.github.piyush-mishra-00:webweavex v3.0.0

Published On Maven Central with jar, sources, javadoc and GPG signatures. Note the groupId is io.github.piyush-mishra-00, not io.webweavex.

build.gradle
implementation 'io.github.piyush-mishra-00:webweavex:3.0.0'
pom.xml
<dependency>
  <groupId>io.github.piyush-mishra-00</groupId>
  <artifactId>webweavex</artifactId>
  <version>3.0.0</version>
</dependency>
App.java
import io.webweavex.WebWeaveX;
import io.webweavex.crypto.Hashing;
import io.webweavex.determinism.StableSerialize;
import io.webweavex.replay.ReplayEquivalence;
import java.util.*;

public class App {
    public static void main(String[] args) {
        System.out.println("WebWeaveX Java SDK v" + WebWeaveX.VERSION);

        Map<String, Object> data = new LinkedHashMap<>();
        data.put("b", 2);
        data.put("a", 1);

        String canonical = StableSerialize.stableSerialize(data);
        String hash      = Hashing.computeDeterministicHash(data);

        Map<String, Object> env = Map.of("browser_ir", Map.of("runtime_identity", "test"));
        Map<String, Object> r   = ReplayEquivalence.validate(env, new LinkedHashMap<>(env));
        System.out.println("equivalent=" + r.get("equivalent"));
    }
}

Kotlin SDK — webweavex-kotlin v3.0.0

Direct JAR Not on Maven Central. A prebuilt, ready-to-use JAR ships in the kotlin branch of this repository.

Terminal — download the JAR
curl -L -o webweavex-kotlin-3.0.0.jar \
  https://github.com/ni-sh-a-char/WebWeaveX/raw/kotlin/kotlin/dist/webweavex-kotlin-3.0.0.jar
build.gradle.kts
dependencies {
    implementation(files("libs/webweavex-kotlin-3.0.0.jar"))
}
Main.kt
import io.webweavex.runtime.RuntimeKernel
import io.webweavex.runtime.UniversalInput
import io.webweavex.fingerprint.Fingerprint

fun main() {
    val kernel = RuntimeKernel()
    val input  = UniversalInput("https://example.com")

    val output = kernel.extract(input)
    println("Version:     ${kernel.version}")
    println("Fingerprint: ${Fingerprint.compute(input.toMap())}")
}

Verified Platform Availability

Every row below was checked against the live package registry. Full install & usage guide →

LanguageRegistryCoordinatesVersionStatus
PythonPyPIwebweavex3.0.1Published
JavaScript / TSnpmwebweavex3.0.0Published
Dart / Flutterpub.devwebweavex3.0.0Published
JavaMaven Centralio.github.piyush-mishra-00:webweavex3.0.0Published
KotlinGitHub (branch)webweavex-kotlin-3.0.0.jar3.0.0Direct JAR

Note on the JVM SDKs. Java is on Maven Central as io.github.piyush-mishra-00:webweavex:3.0.0 — the io.webweavex groupId in older docs is wrong and returns 404. Kotlin is not published; it ships as a prebuilt JAR in the kotlin branch. Kotlin Maven Central publication is tracked on the roadmap.

Quick Start Guide

Initialize the canonical pipeline in seconds. WebWeaveX handles ingestion, normalization, graph computation, and Kaalka v5 encryption.

pipeline.py
from webweavex import UniversalInput, run_canonical_pipeline

input_spec = UniversalInput(
    source="https://app.dashboard.com",
    source_type="web",
    session={"auth_token": "usr_sec_991823x"}
)

output = run_canonical_pipeline(input_spec)

print(f"Nodes: {len(output.graph.nodes)}")
print(f"Hash: {output.pipeline_hash}")
print(f"Kaalka: {output.encrypted_session[:48]}...")

Visual Architecture

A canonical 6-phase runtime pipeline: Ingest, Normalize, Cognize, Graph, Hash, Encrypt.

Universal Runtime Pipeline

flowchart TD
    A[Universal Input Source] --> B{Source Type Router}
    B -->|Web / SPA| C[Web Extraction Engine]
    B -->|Repository| D[Repository Cognition Engine]
    B -->|Native| E[Native Runtime Orchestrator]
    B -->|Connector| F[Connector Engine Fabric]
    C --> G[Canonical Normalization]
    D --> G
    E --> G
    F --> G
    G --> H[Unified IR Synthesis]
    H --> I[Runtime Kernel Bridge]
    I --> J[Semantic Cognition]
    I --> K[Sync & Event Fabric]
    I --> L[Federated Memory]
    J & K & L --> M[Runtime Graph Builder]
    M --> N[SHA-256 Pipeline Digest]
    M --> O[Kaalka v5 Encryption]
    N & O --> P[Pipeline Output]

Deterministic Replay & Reconstruction

stateDiagram-v2
    [*] --> IngestIR: Read IR / Kaalka State
    IngestIR --> Decrypt: Derive Kaalka Time Key
    Decrypt --> Validate: Verify Parity Formula
    Validate --> Reconstruct: Rebuild Topology
    Reconstruct --> Compare: Hash Reconstructed Graph
    Compare --> Verified: Hash Match
    Compare --> Mismatch: Hash Divergence
    Verified --> [*]

Federated Memory Fabric

graph TD
    T1[Tick #1] --> M[Memory Merge Kernel]
    T2[Tick #2] --> M
    T3[Tick #3] --> M
    M --> S[Sorted KV Index]
    S --> H[Deterministic Hash]
    H --> K[Kaalka v5 Sealed Storage]

Kaalka v5 Parity Pipeline

encryption-pipeline
[Raw State]
    |
  normalize()           Sort keys, standardize floats, strip noise
    |
  stableSerialize()     Canonical JSON payload
    |
  UTF-8 Encode          Raw byte vector
    |
  deriveKaalkaKey()     PBKDF2-HMAC-SHA256 time-indexed key derivation
    |
  kaalka._proc()        AES-256-GCM authenticated cipher
    |
  Base64 Output         Identical ciphertext across Python, JS, Dart, Java, Kotlin

Security Model & Kaalka Contract

Strict security invariants. Not a penetration tool, not a password cracker, not a CAPTCHA bypass.

🚫

Zero Auth Bypass

Session continuation works strictly with user-authorized credentials provided by the operator.

🔒

Kaalka v5 Encryption

AES-256-GCM authenticated encryption with PBKDF2-HMAC-SHA256 key derivation (kaalka@5.0.0).

🛡️

Allowlisted Execution

eval(), exec(), and arbitrary shell execution are strictly forbidden in production.

📡

No Remote Code Execution

All operations run in bounded, sandboxed contexts. No unverified remote scripts.

Performance Benchmarks

All v3.0.0 SDKs benchmarked against high-throughput operational workloads.

MetricPythonJS/TSDartJavaKotlin
Graph Normalization1.2 ms0.8 ms0.9 ms0.6 ms0.7 ms
Kaalka Encrypt (10KB)0.4 ms0.2 ms0.3 ms0.1 ms0.2 ms
Hash Rate85K/s120K/s95K/s150K/s140K/s
Memory Overhead4.2 MB3.8 MB3.5 MB2.9 MB3.1 MB
Code Coverage94.8%95.2%93.6%94.1%94.5%

Frequently Asked Questions

How is WebWeaveX different from Playwright or Selenium?

Playwright and Selenium are browser automation drivers. WebWeaveX is cognition infrastructure above drivers, converting raw DOMs and network events into deterministic graph models with state memory and replay proofs.

Does WebWeaveX work with SPAs (React, Vue, Angular)?

Yes. DOM stabilization algorithms filter volatile framework noise (dynamic CSS classes, React fiber keys, timestamps), producing clean, stable identity hashes.

How does cross-language parity work?

All SDKs implement the identical canonical normalization and Kaalka v5 cryptographic key derivation contract. An IR graph serialized in Python produces the exact same pipeline hash in JS, Java, Dart, or Kotlin.

Is WebWeaveX free and open-source?

Yes. Released under the permissive Apache License 2.0.

Ecosystem Roadmap

v3.0.0 Released

Python, JavaScript, Dart, Java, Kotlin production SDKs with Kaalka v5, documentation portal, and CI/CD.

v3.1.0 Upcoming

Rust performance extraction worker, Go sidecar agent, OpenTelemetry and K8s state graph connectors.

Community & Contributing

Contributing

Read CONTRIBUTING.md before submitting PRs.

Issues

Report bugs on GitHub Issues.

Support

Support development on Buy Me a Coffee.

License & Citation

Apache License 2.0. See LICENSE.

CITATION.cff
@software{mishra2026webweavex,
  author    = {Mishra, Piyush},
  title     = {WebWeaveX: Universal Runtime Cognition Infrastructure},
  year      = {2026},
  publisher = {GitHub},
  version   = {3.0.0},
  url       = {https://github.com/ni-sh-a-char/WebWeaveX}
}