ruvector/examples/agentic-jujutsu

Agentic-Jujutsu Examples

This directory contains comprehensive examples demonstrating the capabilities of agentic-jujutsu, a quantum-resistant, self-learning version control system designed for AI agents.

Examples Overview

1. Basic Usage (basic-usage.ts)

Fundamental operations for getting started:

• Repository status checks
• Creating commits
• Branch management
• Viewing commit history and diffs

Run: npx ts-node basic-usage.ts

2. Learning Workflow (learning-workflow.ts)

Demonstrates ReasoningBank self-learning capabilities:

• Starting and tracking learning trajectories
• Recording operations and outcomes
• Getting AI-powered suggestions
• Viewing learning statistics and discovered patterns

Run: npx ts-node learning-workflow.ts

3. Multi-Agent Coordination (multi-agent-coordination.ts)

Shows how multiple AI agents work simultaneously:

• Concurrent commits without locks (23x faster than Git)
• Shared learning across agents
• Collaborative code review workflows
• Conflict-free coordination

Run: npx ts-node multi-agent-coordination.ts

4. Quantum Security (quantum-security.ts)

Demonstrates quantum-resistant security features:

• SHA3-512 quantum fingerprints (<1ms)
• HQC-128 encryption
• Data integrity verification
• Secure trajectory storage

Run: npx ts-node quantum-security.ts

Key Features Demonstrated

Performance Benefits

• 23x faster concurrent commits (350 ops/s vs Git's 15 ops/s)
• 10x faster context switching (<100ms vs Git's 500-1000ms)
• 87% automatic conflict resolution
• Zero lock waiting time

Self-Learning Capabilities

• Trajectory tracking for continuous improvement
• Pattern discovery from successful operations
• AI-powered suggestions with confidence scores
• Learning statistics and improvement metrics

Quantum-Resistant Security

• SHA3-512 fingerprints (NIST FIPS 202)
• HQC-128 post-quantum encryption
• <1ms verification performance
• Future-proof against quantum computers

Multi-Agent Features

• Lock-free concurrent operations
• Shared learning between agents
• Collaborative workflows
• Cross-agent pattern recognition

Prerequisites

# Install agentic-jujutsu
npm install agentic-jujutsu

# Or run directly
npx agentic-jujutsu

Running the Examples

Individual Examples

# Basic usage
npx ts-node examples/agentic-jujutsu/basic-usage.ts

# Learning workflow
npx ts-node examples/agentic-jujutsu/learning-workflow.ts

# Multi-agent coordination
npx ts-node examples/agentic-jujutsu/multi-agent-coordination.ts

# Quantum security
npx ts-node examples/agentic-jujutsu/quantum-security.ts

Run All Examples

cd examples/agentic-jujutsu
for file in *.ts; do
  echo "Running $file..."
  npx ts-node "$file"
  echo ""
done

Testing

Comprehensive test suites are available in /tests/agentic-jujutsu/:

# Run all tests
./tests/agentic-jujutsu/run-all-tests.sh

# Run with coverage
./tests/agentic-jujutsu/run-all-tests.sh --coverage

# Run with verbose output
./tests/agentic-jujutsu/run-all-tests.sh --verbose

# Stop on first failure
./tests/agentic-jujutsu/run-all-tests.sh --bail

Integration with Ruvector

Agentic-jujutsu can be integrated with Ruvector for:

• Versioning vector embeddings
• Tracking AI model experiments
• Managing agent memory evolution
• Collaborative AI development

Example integration:

import { VectorDB } from 'ruvector';
import { JjWrapper } from 'agentic-jujutsu';

const db = new VectorDB();
const jj = new JjWrapper();

// Track vector database changes
jj.startTrajectory('Update embeddings');
await db.insert('doc1', [0.1, 0.2, 0.3]);
await jj.newCommit('Add new embeddings');
jj.addToTrajectory();
jj.finalizeTrajectory(0.9, 'Embeddings updated successfully');

Best Practices

1. Trajectory Management

• Use meaningful task descriptions
• Record honest success scores (0.0-1.0)
• Always finalize trajectories
• Add detailed critiques for learning

2. Multi-Agent Coordination

• Let agents work concurrently (no manual locks)
• Share learning through trajectories
• Use suggestions for informed decisions
• Monitor improvement rates

3. Security

• Enable encryption for sensitive operations
• Verify fingerprints regularly
• Use quantum-resistant features for long-term data
• Keep encryption keys secure

4. Performance

• Batch operations when possible
• Use async operations for I/O
• Monitor operation statistics
• Optimize based on learning patterns

Documentation

For complete API documentation and guides:

• Skill Documentation: .claude/skills/agentic-jujutsu/SKILL.md
• NPM Package: https://npmjs.com/package/agentic-jujutsu
• GitHub: https://github.com/ruvnet/agentic-flow/tree/main/packages/agentic-jujutsu

Version

Examples compatible with agentic-jujutsu v2.3.2+

License

MIT License - See project LICENSE file