chernistry

chernistry / bernstein

Public

Declarative Agent Orchestration. Ship while you sleep.

chernistry.github.iobernstein a2a agent-orchestration agentic-ai agentic-engineering agentic-workflow
16
2
100% credibility
Found Mar 30, 2026 at 16 stars -- GitGems finds repos before they trend. Get early access to the next one.
Sign Up Free
AI Analysis
Python
AI Summary

Bernstein is a tool that coordinates multiple AI coding assistants to break down development goals into tasks, execute them in parallel, verify the results, and commit improvements to your codebase.

How It Works

1
🔍 Discover Bernstein

You find a helpful tool on GitHub that promises to let smart helpers improve your code while you relax.

2
📦 Install simply

With one easy command, you add it to your project folder and it's ready to go.

3
🎯 Share your goal

You describe what you want in simple words, like 'Make login safer with tests'.

4
👥 Watch the team work

A group of clever assistants jumps in together, splitting the work and checking each other.

5
📊 See live updates

A dashboard shows progress, who's doing what, and how much it's costing in real time.

6
✅ Review perfect results

Everything passes checks, code is clean, and changes are safely saved.

🚀 Ship better code

Your project is stronger, tests pass, and you saved hours of work.

Sign up to see the full architecture

5 more

Sign Up Free

Star Growth

See how this repo grew from 16 to 16 stars Sign Up Free
Repurpose This Repo

Repurpose is a Pro feature

Generate ready-to-use prompts for X threads, LinkedIn posts, blog posts, YouTube scripts, and more -- with full repo context baked in.

Unlock Repurpose
AI-Generated Review

What is bernstein?

Bernstein is declarative agent orchestration for Python developers, letting you define coding goals in a single YAML file or CLI flag like `bernstein -g "Add JWT auth and tests"`. It decomposes tasks, spins up parallel short-lived agents from CLI tools like Claude Code, Codex, Cursor, or Gemini, runs them in isolated git worktrees, verifies output with tests/linting, and commits clean changes. Think Kubernetes for AI agents: ship features overnight without context drift or babysitting.

Why is it gaining traction?

It stands out with deterministic Python scheduling—no LLM tokens wasted on coordination—plus model mixing for 23% cost savings and 1.78x speedups over single agents, per benchmarks. Portable declarative agent instructions work across providers without lock-in, unlike declarative agents in Copilot Studio or Microsoft Teams toolkit. CLI commands like `bernstein ci fix` auto-heal failing pipelines, and self-evolution tweaks prompts automatically.

Who should use this?

Backend teams building agentic workflows for features, refactors, or security audits. Solo devs running `--headless` overnight on Flask/Django repos. DevOps folks integrating declarative agent actions into GitHub Actions for CI autofix, or comparing declarative agents vs custom engine agents.

Verdict

Promising for declarative agent teams avoiding vendor lock-in, with solid docs, benchmarks, and VS Code extension—but only 16 stars and 1.0% credibility signal early maturity; test in a side project first.

(187 words)

Sign up to read the full AI review Sign Up Free

Similar repos coming soon.