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https://github.com/ruvnet/RuView
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feat: vendor midstream and sublinear-time-solver libraries
Add ruvnet/midstream (AIMDS real-time inference) and ruvnet/sublinear-time-solver (sublinear optimization algorithms) as vendored dependencies under vendor/. Co-Authored-By: claude-flow <ruv@ruv.net>
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# Lean Agentic Learning System - TypeScript/JavaScript Client
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A revolutionary learning framework combining formal reasoning, agentic AI, and stream learning for real-time adaptation.
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## Features
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- 🎯 **Formal Reasoning** - Lean-style theorem proving for verified knowledge
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- 🤖 **Agentic AI** - Autonomous decision-making with Plan-Act-Observe-Learn loops
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- 📊 **Stream Learning** - Real-time online adaptation from data streams
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- 🧠 **Knowledge Graph** - Dynamic knowledge representation and evolution
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- ⚡ **Real-Time Processing** - Low-latency stream processing
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- 🔒 **Type Safety** - Full TypeScript support with comprehensive types
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## Installation
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```bash
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npm install @midstream/lean-agentic
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```
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## Quick Start
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```typescript
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import { LeanAgenticClient, StreamProcessor } from '@midstream/lean-agentic';
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// Initialize client
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const client = new LeanAgenticClient('http://localhost:8080', {
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enableFormalVerification: true,
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learningRate: 0.01,
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maxPlanningDepth: 5,
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});
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// Create stream processor
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const processor = new StreamProcessor(client, 'session_001');
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// Process stream chunks
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const chunk = {
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content: 'Hello, I need weather information',
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timestamp: Date.now(),
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};
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const result = await processor.processChunk(chunk);
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console.log('Action:', result.action.description);
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console.log('Reward:', result.reward);
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console.log('Verified:', result.verified);
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```
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## Core Concepts
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### 1. Agentic Loop (Plan-Act-Observe-Learn)
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```typescript
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import { AgenticLoop } from '@midstream/lean-agentic';
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const loop = new AgenticLoop();
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const context = client.createContext('my_session');
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// Plan
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const plan = await loop.plan(context, 'Get weather for Tokyo');
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// The system autonomously:
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// - Generates action candidates
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// - Ranks by expected reward
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// - Executes highest-value action
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// - Observes results
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// - Learns from experience
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```
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### 2. Knowledge Graph
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```typescript
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import { KnowledgeGraph, EntityType } from '@midstream/lean-agentic';
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const kg = new KnowledgeGraph();
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// Extract entities from text
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const entities = kg.extractEntities('Alice works at Google in California');
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// Update knowledge graph
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kg.update(entities);
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// Query entities
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const people = kg.queryEntities(EntityType.Person);
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const orgs = kg.queryEntities(EntityType.Organization);
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// Find related entities
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const related = kg.findRelated('alice_entity_id', 2);
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```
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### 3. Stream Processing
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```typescript
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import { StreamProcessor } from '@midstream/lean-agentic';
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const processor = new StreamProcessor(client, 'session_id');
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// Listen to events
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processor.on('chunk_processed', ({ chunk, result }) => {
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console.log(`Processed: ${chunk.content}`);
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console.log(`Reward: ${result.reward}`);
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});
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processor.on('high_reward', (result) => {
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console.log('High reward action detected!', result);
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});
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// Process stream
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const chunks = [
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{ content: 'chunk 1', timestamp: Date.now() },
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{ content: 'chunk 2', timestamp: Date.now() },
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];
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const results = await processor.processStream(chunks);
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```
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### 4. Batched Processing
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```typescript
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import { BatchedStreamProcessor } from '@midstream/lean-agentic';
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// Process in batches of 10
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const batchProcessor = new BatchedStreamProcessor(
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client,
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'session_id',
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10
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);
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batchProcessor.on('batch_processed', ({ result }) => {
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console.log('Batch processed with reward:', result.reward);
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});
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```
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## Advanced Usage
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### Configuration
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```typescript
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const client = new LeanAgenticClient('http://localhost:8080', {
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// Enable formal verification of all actions
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enableFormalVerification: true,
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// Learning rate for online adaptation (0.0 - 1.0)
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learningRate: 0.01,
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// Maximum depth for action planning
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maxPlanningDepth: 5,
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// Confidence threshold for executing actions
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actionThreshold: 0.7,
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// Enable multi-agent collaboration
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enableMultiAgent: true,
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// Knowledge graph update frequency
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kgUpdateFreq: 100,
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});
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```
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### Context Management
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```typescript
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const context = client.createContext('session_001');
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// Add to history
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context.history.push('Previous message');
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// Set preferences
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context.preferences['preferred_language'] = 0.9;
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context.preferences['detail_level'] = 0.5;
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// Update environment
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context.environment['user_location'] = 'Tokyo';
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context.environment['time_of_day'] = 'morning';
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```
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### Querying System State
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```typescript
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// Get system statistics
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const stats = await client.getStats();
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console.log(`Entities: ${stats.totalEntities}`);
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console.log(`Theorems: ${stats.totalTheorems}`);
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console.log(`Actions: ${stats.totalActions}`);
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console.log(`Avg Reward: ${stats.averageReward}`);
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// Get learning statistics
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const learningStats = await client.getLearningStats();
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console.log(`Iterations: ${learningStats.iterations}`);
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console.log(`Parameters: ${learningStats.modelParameters}`);
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// Query knowledge graph
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const entities = await client.queryEntities({
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entityType: 'Person',
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searchText: 'Alice',
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limit: 10,
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});
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// Get theorems
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const theorems = await client.getTheorems(['safety', 'verified']);
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```
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## Examples
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### Real-time Chat Assistant
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```typescript
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import { LeanAgenticClient, StreamProcessor } from '@midstream/lean-agentic';
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async function chatAssistant() {
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const client = new LeanAgenticClient('http://localhost:8080');
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const processor = new StreamProcessor(client, 'chat_session');
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// Track high-value interactions
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processor.on('high_reward', (result) => {
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console.log('✨ Learned something valuable!');
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});
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// Process user messages
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const messages = [
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'What is the weather like?',
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'Remember I prefer detailed forecasts',
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'How about tomorrow?',
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];
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for (const msg of messages) {
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const result = await processor.processChunk({
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content: msg,
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timestamp: Date.now(),
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});
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console.log(`User: ${msg}`);
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console.log(`Action: ${result.action.description}`);
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console.log(`Verified: ${result.verified ? '✓' : '✗'}`);
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console.log('---');
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}
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// Get final statistics
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const stats = await client.getStats();
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console.log('Session Stats:', stats);
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}
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```
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### Learning from Feedback
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```typescript
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async function learningExample() {
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const client = new LeanAgenticClient('http://localhost:8080', {
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learningRate: 0.05, // Higher learning rate
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});
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const context = client.createContext('learning_session');
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// Process with feedback loop
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for (let i = 0; i < 100; i++) {
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const result = await client.processChunk(
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`Training example ${i}`,
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context
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);
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// System automatically learns from rewards
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// Higher rewards reinforce action selection
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}
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const stats = await client.getLearningStats();
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console.log(`Learned from ${stats.iterations} iterations`);
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console.log(`Average reward: ${stats.averageReward}`);
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}
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```
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## API Reference
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See [API Documentation](./docs/API.md) for complete reference.
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## Architecture
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```
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┌─────────────────────────────────────────────────────────┐
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│ Lean Agentic Learning System │
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├─────────────────────────────────────────────────────────┤
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│ │
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│ ┌──────────────┐ ┌──────────────┐ │
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│ │ Formal │ │ Agentic │ │
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│ │ Reasoning │◄────►│ Loop │ │
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│ │ Engine │ │ (P-A-O-L) │ │
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│ └──────┬───────┘ └──────┬───────┘ │
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│ │ │ │
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│ │ ┌────────────────▼─────┐ │
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│ └───►│ Knowledge Graph & │ │
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│ │ Theorem Store │ │
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│ └────────────┬─────────┘ │
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│ │ │
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│ ┌────────────▼─────────┐ │
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│ │ Stream Learning & │ │
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│ │ Online Adaptation │ │
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│ └──────────────────────┘ │
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└─────────────────────────────────────────────────────────┘
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```
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## License
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MIT
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## Contributing
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Contributions welcome! See [CONTRIBUTING.md](./CONTRIBUTING.md)
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## Support
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- Issues: https://github.com/ruvnet/midstream/issues
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- Discussions: https://github.com/ruvnet/midstream/discussions
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