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README.md

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:

Version

Examples compatible with agentic-jujutsu v2.3.2+

License

MIT License - See project LICENSE file