docs: comprehensive README, LICENSE, community-ready

- Full README with: what it does, quick start, config reference,
  architecture overview, extraction pipeline diagram, fact lifecycle,
  hook table, testing instructions, plugin suite roadmap
- MIT License
- Repository URL fixed in package.json
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MIT License
Copyright (c) 2026 Vainplex
Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
in the Software without restriction, including without limitation the rights
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
copies of the Software, and to permit persons to whom the Software is
furnished to do so, subject to the following conditions:
The above copyright notice and this permission notice shall be included in all
copies or substantial portions of the Software.
THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
SOFTWARE.

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# @vainplex/openclaw-knowledge-engine
A real-time knowledge extraction plugin for [OpenClaw](https://github.com/openclaw/openclaw). Automatically extracts entities, facts, and relationships from conversations — building a persistent, queryable knowledge base that grows with every message.
## What it does
Every message your OpenClaw agent processes flows through the Knowledge Engine:
1. **Regex Extraction** (instant, zero cost) — Detects people, organizations, technologies, URLs, emails, and other entities using pattern matching
2. **LLM Enhancement** (optional, batched) — Groups messages and sends them to a local LLM for deeper entity and fact extraction
3. **Fact Storage** — Stores extracted knowledge as structured subject-predicate-object triples with relevance scoring
4. **Relevance Decay** — Automatically decays old facts so recent knowledge surfaces first
5. **Vector Sync** — Optionally syncs facts to ChromaDB for semantic search
6. **Background Maintenance** — Prunes low-relevance facts, compacts storage, runs cleanup
```
User: "We're meeting with Sebastian from Mondo Gate next Tuesday"
├─ Regex → entities: [Sebastian (person), Mondo Gate (organization)]
└─ LLM → facts: [Sebastian — works-at — Mondo Gate]
[Meeting — scheduled-with — Mondo Gate]
```
## Quick Start
### 1. Install
```bash
cd ~/.openclaw
npm install @vainplex/openclaw-knowledge-engine
```
### 2. Sync to extensions
OpenClaw loads plugins from the `extensions/` directory:
```bash
mkdir -p extensions/openclaw-knowledge-engine
cp -r node_modules/@vainplex/openclaw-knowledge-engine/{dist,package.json,openclaw.plugin.json} extensions/openclaw-knowledge-engine/
```
### 3. Configure
Add to your `openclaw.json`:
```json
{
"plugins": {
"entries": {
"openclaw-knowledge-engine": {
"enabled": true,
"config": {
"workspace": "/path/to/your/workspace",
"extraction": {
"regex": { "enabled": true },
"llm": {
"enabled": true,
"endpoint": "http://localhost:11434/api/generate",
"model": "mistral:7b",
"batchSize": 10,
"cooldownMs": 30000
}
}
}
}
}
}
}
```
### 4. Restart gateway
```bash
openclaw gateway restart
```
## Configuration
| Key | Type | Default | Description |
|-----|------|---------|-------------|
| `enabled` | boolean | `true` | Enable/disable the plugin |
| `workspace` | string | `~/.clawd/plugins/knowledge-engine` | Storage directory for knowledge files |
| `extraction.regex.enabled` | boolean | `true` | High-speed regex entity extraction |
| `extraction.llm.enabled` | boolean | `true` | LLM-based deep extraction |
| `extraction.llm.model` | string | `"mistral:7b"` | Ollama/OpenAI-compatible model |
| `extraction.llm.endpoint` | string | `"http://localhost:11434/api/generate"` | LLM API endpoint (HTTP or HTTPS) |
| `extraction.llm.batchSize` | number | `10` | Messages per LLM batch |
| `extraction.llm.cooldownMs` | number | `30000` | Wait time before sending batch |
| `decay.enabled` | boolean | `true` | Periodic relevance decay |
| `decay.intervalHours` | number | `24` | Hours between decay cycles |
| `decay.rate` | number | `0.02` | Decay rate per interval (2%) |
| `embeddings.enabled` | boolean | `false` | Sync facts to ChromaDB |
| `embeddings.endpoint` | string | `"http://localhost:8000/..."` | ChromaDB API endpoint |
| `embeddings.collectionName` | string | `"openclaw-facts"` | Vector collection name |
| `embeddings.syncIntervalMinutes` | number | `15` | Minutes between vector syncs |
| `storage.maxEntities` | number | `5000` | Max entities before pruning |
| `storage.maxFacts` | number | `10000` | Max facts before pruning |
| `storage.writeDebounceMs` | number | `15000` | Debounce delay for disk writes |
### Minimal config (regex only, no LLM)
```json
{
"openclaw-knowledge-engine": {
"enabled": true,
"config": {
"extraction": {
"llm": { "enabled": false }
}
}
}
}
```
This gives you zero-cost entity extraction with no external dependencies.
### Full config (LLM + ChromaDB)
```json
{
"openclaw-knowledge-engine": {
"enabled": true,
"config": {
"workspace": "~/my-agent/knowledge",
"extraction": {
"llm": {
"enabled": true,
"endpoint": "http://localhost:11434/api/generate",
"model": "mistral:7b"
}
},
"embeddings": {
"enabled": true,
"endpoint": "http://localhost:8000/api/v1/collections/facts/add"
},
"decay": {
"intervalHours": 12,
"rate": 0.03
}
}
}
}
```
## How it works
### Extraction Pipeline
```
Message received
├──▶ Regex Engine (sync, <1ms)
│ └─ Extracts: proper nouns, organizations, tech terms,
│ URLs, emails, monetary amounts, dates
└──▶ LLM Batch Queue (async, batched)
└─ Every N messages or after cooldown:
└─ Sends batch to local LLM
└─ Extracts: entities + fact triples
└─ Stores in FactStore
```
### Fact Lifecycle
Facts are stored as structured triples:
```json
{
"id": "f-abc123",
"subject": "Sebastian",
"predicate": "works-at",
"object": "Mondo Gate",
"source": "extracted-llm",
"relevance": 0.95,
"createdAt": 1707123456789,
"lastAccessedAt": 1707123456789
}
```
- **Relevance** starts at 1.0 and decays over time
- **Accessed facts** get a relevance boost (LRU-style)
- **Pruning** removes facts below the relevance floor when storage limits are hit
- **Minimum floor** (0.1) prevents complete decay — old facts never fully disappear
### Storage
All data is persisted as JSON files in your workspace:
```
workspace/
├── entities.json # Extracted entities with types and counts
└── facts.json # Fact triples with relevance scores
```
Writes use atomic file operations (write to `.tmp`, then rename) to prevent corruption.
## Architecture
```
index.ts → Plugin entry point
src/
├── types.ts → All TypeScript interfaces
├── config.ts → Config resolution + validation
├── patterns.ts → Regex factories (Proxy-based, no /g state bleed)
├── entity-extractor.ts → Regex-based entity extraction
├── llm-enhancer.ts → Batched LLM extraction with cooldown
├── fact-store.ts → In-memory fact store with decay + pruning
├── hooks.ts → OpenClaw hook registration + orchestration
├── http-client.ts → Shared HTTP/HTTPS transport
├── embeddings.ts → ChromaDB vector sync
├── storage.ts → Atomic JSON I/O with debounce
└── maintenance.ts → Scheduled background tasks
```
- **12 modules**, each with a single responsibility
- **Zero runtime dependencies** — Node.js built-ins only
- **TypeScript strict** — no `any` in source code
- **All functions ≤40 lines**
## Hooks
| Hook | Priority | Description |
|------|----------|-------------|
| `session_start` | 200 | Loads fact store from disk |
| `message_received` | 100 | Extracts entities + queues LLM batch |
| `message_sent` | 100 | Same extraction on outbound messages |
| `gateway_stop` | 50 | Flushes writes, stops timers |
## Testing
```bash
npm test
# Runs 83 tests across 10 test files
```
Tests cover: config validation, entity extraction, fact CRUD, decay, pruning, LLM batching, HTTP client, embeddings, storage atomicity, maintenance scheduling, hook orchestration.
## Part of the Darkplex Plugin Suite
| # | Plugin | Status | Description |
|---|--------|--------|-------------|
| 1 | [@vainplex/nats-eventstore](https://github.com/alberthild/openclaw-nats-eventstore) | ✅ Published | NATS JetStream event persistence |
| 2 | [@vainplex/openclaw-cortex](https://github.com/alberthild/openclaw-cortex) | ✅ Published | Conversation intelligence (threads, decisions, boot context) |
| 3 | **@vainplex/openclaw-knowledge-engine** | ✅ Published | Real-time knowledge extraction (this plugin) |
| 4 | @vainplex/openclaw-governance | 📋 Planned | Policy enforcement + guardrails |
| 5 | @vainplex/openclaw-memory-engine | 📋 Planned | Unified memory layer |
| 6 | @vainplex/openclaw-health-monitor | 📋 Planned | System health + auto-healing |
## License
MIT

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"license": "MIT",
"repository": {
"type": "git",
"url": "https://github.com/your-repo/openclaw-knowledge-engine.git"
"url": "https://github.com/alberthild/openclaw-knowledge-engine.git"
},
"openclaw": {
"id": "@vainplex/openclaw-knowledge-engine"