How does the self-learning feature work?

The skill uses ReasoningBank to track 'trajectories,' recording the success or failure of operation sequences to suggest high-confidence approaches for future tasks.

What is a 'trajectory' in this context?

A trajectory is a recorded sequence of operations performed to achieve a specific task. By finalizing trajectories with success scores, the AI builds a library of best practices.

Can it help with multi-agent projects?

Absolutely. It includes coordination logic that allows multiple agents (such as developers, reviewers, and testers) to learn from each other's operations in real-time.

What makes Agentic Jujutsu different from Git?

It is significantly faster than Git due to its lock-free architecture and is specifically designed for AI agents to work simultaneously without the usual merge conflict bottlenecks.

Is Agentic Jujutsu secure?

Yes, it incorporates quantum-resistant security features including SHA3-512 fingerprints and HQC-128 encryption to protect repository integrity against future computing threats.

Agentic Jujutsu

Name: Agentic Jujutsu
Author: ellisapotheosis

byellisapotheosis

Collaboration Tools

Provides a lock-free, self-learning version control system designed for seamless multi-agent AI coordination and quantum-resistant security.

About

Agentic Jujutsu is a next-generation version control solution specifically engineered for environments where multiple AI agents collaborate on code simultaneously. By moving beyond traditional Git bottlenecks with a lock-free architecture and utilizing ReasoningBank intelligence, it enables agents to learn from past operations, discover successful workflow patterns, and resolve conflicts automatically with high success rates. It bridges the gap between raw versioning and intelligent agentic behavior, offering quantum-resistant security and real-time trajectory tracking to ensure future-proof, collaborative AI development.

Key Features

Quantum-resistant security using SHA3-512 fingerprints and HQC-128 encryption
Advanced pattern discovery to identify and suggest optimized operation sequences
0 GitHub stars
Lock-free version control optimized for simultaneous multi-agent code modification
ReasoningBank intelligence that learns from successful trajectories and failure patterns
Automatic conflict resolution with an 87% success rate for collaborative workflows

Use Cases

Coordinating multiple autonomous AI agents working on a shared repository without merge conflicts
Implementing self-improving CI/CD workflows that learn from deployment success and failure
Securing agentic repositories against future quantum computing threats with HQC-128 encryption

About

Key Features

Quantum-resistant security using SHA3-512 fingerprints and HQC-128 encryption
Advanced pattern discovery to identify and suggest optimized operation sequences
0 GitHub stars
Lock-free version control optimized for simultaneous multi-agent code modification
ReasoningBank intelligence that learns from successful trajectories and failure patterns
Automatic conflict resolution with an 87% success rate for collaborative workflows

Use Cases

Coordinating multiple autonomous AI agents working on a shared repository without merge conflicts
Implementing self-improving CI/CD workflows that learn from deployment success and failure
Securing agentic repositories against future quantum computing threats with HQC-128 encryption