sipyourdrink-ltd/bernstein

The open-source governance layer for AI agents: write the rules declaratively, Bernstein enforces them and produces the verifiable, replayable record. Free,...

View on GitHub ↗Jump to charts ↓Open shareable report →

sipyourdrink-ltd/bernstein has 1k+ stars and 150+ forks on GitHub, is written in Python, and was last pushed on Oct 17, 2026. Data as of . Signed-in members get hourly updates — create a free account.

Summary Information

Updated 1 hour ago
Added to GitGenius on August 31st, 2026
Created on March 22nd, 2026
Open Issues & Pull Requests: 274 (+2)
GitHub issues: Enabled
Number of forks: 184
Total Stargazers: 1,290 (+0)
Total Subscribers: 11 (+0)

Repository Insights (GitGenius)

Median issue/PR response: 21.2 hours
Mean response time: 3.5 days
90th percentile: 8.4 days
Tracked items: 2,252

Maintainer activity

69 people did triage or write work on this repository in the last 12 months.

Counts unlabeled, assigned, unassigned, milestoned, demilestoned, locked, unlocked over the last 12 months. These are issue and pull request events that require triage or write permission. Commits and code review are not counted. labeled and renamed are excluded because GitHub issue forms record the issue author as the actor. Figures from October 11, 2026. This count is not comparable across projects: each project's automation decides which of these events a person emits. How this is counted.

How this project is maintained

About 5% of issues opened in the past year have never received a reply. Only 10% of open issues come from outside the core team — the tracker reads mainly as internal planning. Work labelled "bug" is answered fastest, typically in about 9 hours, while "python" waits about 4 days. Almost all tracked open issues have seen activity in the last three months. 92% of issues opened in the past year have since been closed.

Charts & Analytics

Fetching additional details & charts...

Issue Activity (beta)

Open issues: 197
New in 7 days: 32
Closed in 7 days: 24
Avg open age: 19 days
Stale 30+ days: 63
Stale 90+ days: 0

Recent activity

Opened in 7 days: 31
Closed in 7 days: 17
Comments in 7 days: 3
Events in 7 days: 20

Top labels

  • enhancement (805)
  • help wanted (737)
  • size/m (539)
  • size/s (489)
  • bug (484)
  • python (410)
  • up-for-grabs (394)
  • P1 (375)

Most active issues this week

Sign in to see which issues are moving.
Sign in

Detailed Description

Bernstein is a deterministic orchestrator for CLI coding agents that coordinates parallel execution of AI-driven code generation tools without placing a language model in the coordination loop itself.

The problem Bernstein addresses is the need to run multiple CLI coding agents in parallel while maintaining reproducibility and auditability. Its approach isolates each task in separate git worktrees, allowing parallel runs to replay byte-identically. This deterministic execution model removes the model from the orchestration layer, replacing it with a coordination system that can be audited and verified. The tool provides signed lineage tracking and an optional HMAC audit chain that reviewers can verify offline, creating a tamper-evident record of how code was generated and modified.

Bernstein suits teams deploying autonomous coding agents at scale who need reproducible results and strong auditability guarantees. It works with over forty CLI coding agents including Claude Code, Codex, and Gemini CLI. The project supports both cluster mode for distributed execution and air-gap deployment for isolated environments, making it applicable to security-conscious organizations that cannot route agent execution through external services. Teams should adopt this tool if deterministic replay and offline audit verification are requirements; those prioritizing simplicity over reproducibility may find the additional infrastructure overhead unnecessary.

The project maintains active development with regular commits addressing core functionality and bug fixes. Work focuses on expanding agent compatibility and refining the deterministic execution model. The maintainers respond to issues and pull requests, indicating ongoing engagement with users. Development activity shows attention to the orchestration layer and audit mechanisms that distinguish the tool, with less frequent but meaningful updates to documentation and deployment examples.