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.
| Concept | Operational Meaning |
|---|---|
| Runtime Cognition | Captures live behavior (graphs, events, execution state) rather than transient markup. |
| Operational Substrate | Stable node UUIDs, structural fingerprints, and tick-indexed execution history. |
| Session Continuation | Resumes authenticated sessions using authorized tokens, cookies, or credentials. |
| Deterministic Normalization | Canonical sorting, float rounding, whitespace stabilization, and UTF-8 encoding. |
| Federated Memory Fabric | Merges multi-turn execution histories into a tick-indexed, deterministic memory graph. |
| Cross-Language Parity | Shared 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.
| Challenge | Traditional Scrapers | WebWeaveX |
|---|---|---|
| Surface Capture | Returns static HTML strings | Captures multi-layered runtime graphs with stable node identities |
| Auth Continuity | Session collapses after login | Persists authenticated sessions with Kaalka v5 encryption |
| Operational Context | No memory of previous state | Maintains tick-indexed memory fabric and workflow state machines |
| Replay Verification | Fails on dynamic class names | Proves equivalence via normalized graph hashes |
| Reconstruction | Requires manual mock coding | Automatically rebuilds topology from unified IR payloads |
| Determinism | Probabilistic outputs | Strictly deterministic SHA-256 graph digests |
| AI Integration | Overflowing raw HTML prompts | Compact, 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.
pip install webweavex
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.
npm install webweavex
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.
dart pub add webweavex
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.
implementation 'io.github.piyush-mishra-00:webweavex:3.0.0'
<dependency>
<groupId>io.github.piyush-mishra-00</groupId>
<artifactId>webweavex</artifactId>
<version>3.0.0</version>
</dependency>
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.
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
dependencies {
implementation(files("libs/webweavex-kotlin-3.0.0.jar"))
}
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 →
| Language | Registry | Coordinates | Version | Status |
|---|---|---|---|---|
| Python | PyPI | webweavex | 3.0.1 | Published |
| JavaScript / TS | npm | webweavex | 3.0.0 | Published |
| Dart / Flutter | pub.dev | webweavex | 3.0.0 | Published |
| Java | Maven Central | io.github.piyush-mishra-00:webweavex | 3.0.0 | Published |
| Kotlin | GitHub (branch) | webweavex-kotlin-3.0.0.jar | 3.0.0 | Direct 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.
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
[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.
| Metric | Python | JS/TS | Dart | Java | Kotlin |
|---|---|---|---|---|---|
| Graph Normalization | 1.2 ms | 0.8 ms | 0.9 ms | 0.6 ms | 0.7 ms |
| Kaalka Encrypt (10KB) | 0.4 ms | 0.2 ms | 0.3 ms | 0.1 ms | 0.2 ms |
| Hash Rate | 85K/s | 120K/s | 95K/s | 150K/s | 140K/s |
| Memory Overhead | 4.2 MB | 3.8 MB | 3.5 MB | 2.9 MB | 3.1 MB |
| Code Coverage | 94.8% | 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.
@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}
}