Agentic Jujutsu

Agentic Jujutsu — AI Agent Version Control

Lock-free, self-learning version control designed for multiple AI agents working simultaneously without conflicts. Wraps the Jujutsu (jj) VCS with a ReasoningBank trajectory store (learned suggestions), AgentDB operation tracking, and quantum-resistant integrity fingerprints.

When to use

• Multiple AI agents are modifying the same repo simultaneously and you want to avoid Git's lock contention and serialised commits.
• You need conflict-free concurrent worktree isolation across agents.
• You want operations tracked per agent, with pattern discovery and learned suggestions for repeated workflows (deploys, merges, reviews).
• You want fast quantum-resistant (SHA3-512 / HQC-128) integrity checks on commits or trajectories.

When not to use

• Standard single-actor Git operations with no multi-agent coordination — use Git directly.
• GitHub PR workflows, issue tracking, or release management — use the github-code-review / github-release-management skills.
• General agent memory and pattern storage without version control — use agentdb-memory-patterns.
• CI/CD pipeline automation — use github-workflow-automation.

Quick start

npx agentic-jujutsu

const { JjWrapper } = require('agentic-jujutsu');

const jj = new JjWrapper();

// Basic operations
await jj.status();
await jj.newCommit('Add feature');
await jj.log(10);

// Self-learning trajectory
const id = jj.startTrajectory('Implement authentication');
await jj.branchCreate('feature/auth');
await jj.newCommit('Add auth');
jj.addToTrajectory();
jj.finalizeTrajectory(0.9, 'Clean implementation');

// Get an AI suggestion for a similar task
const suggestion = JSON.parse(jj.getSuggestion('Add logout feature'));
console.log(`Confidence: ${suggestion.confidence}`);

The core loop is: startTrajectory(task) → do work (auto-tracked) → addToTrajectory() → finalizeTrajectory(score, critique). Later, getSuggestion(task) returns a learned recommendation. Use honest success scores (never always 1.0) so the model can learn.

Learning memory (ReasoningBank trajectories, patterns, coordination state) is stored in centralized RuVector PostgreSQL, retrieved via pgvector HNSW.

Detailed references

• references/cookbook.md — worked examples per capability (self-learning, pattern discovery, multi-agent coordination, quantum security, operation tracking), advanced multi-agent use cases, best practices, and end-to-end examples.
• references/api.md — full method catalog (Core / ReasoningBank / AgentDB / Quantum), validation rules, performance characteristics, RuVector storage details, troubleshooting, and version history.