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1,272 ★ · sipyourdrink-ltd/bernstein · read from the repository page · (checked 2026-10-16 20:00:20)
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collected from the punkpeye/awesome-mcp-servers list · type: MCP server
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first seen 2026-10-09 · last seen 2026-10-16 · seen 32× · listed by 10 sources
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score 1,003 · 1,272 ★ · 10 sources · mcp
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repository sipyourdrink-ltd/bernstein;first catalogued 2026-10-09;last seen 2026-10-16。There is no official certification or quality rating here — we record sources and dates only.
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> *"To achieve great things, two things are needed: a plan and not quite enough
the open-source governance layer for AI agents website · docs · install · first run · glossary · limitations · name policy · discord · sponsor 简体中文 · 繁體中文 · 日本語 · 한국어 · हिन्दी · বাংলা · Русский · Español · Português · Deutsch · Français · Italiano · Nederlands · Polski · Svenska · Suomi · Українська · Türkçe · العربية · עברית · Bahasa Indonesia · Tiếng Việt · ไทย --- > **Status: beta.** Solo-maintained, under active development. The version number counts releases, not maturity - minor versions may change interfaces. Pin the version for anything you depend on; regressions get fixed fast, file them. Bernstein is the open-source governance layer for AI agents. It runs on policy as code: you write the policy - who may do what, what needs approval, what must be recorded - and Bernstein enforces it and produces the verifiable record. A deterministic scheduler - no model in the coordination loop - runs agents in parallel, gates what they produce, and records every step, so a run can be verified after the fact, offline, from the artifacts alone. CLI coding agents work out of the box (Claude Code, Codex, Gemini CLI, and 50+ more), and the same layer governs any agent workload: the deliverable can be a diff, a research report, a dataset, or an audit evidence pack. Air-gap install profile included. Apache-2.0. at a glance Four things set it apart; everything after is detail. - **No LLM in the coordination loop.** Scheduling is plain Python, so a run is reproducible end to end. Replay yesterday's plan and get yesterday's task graph. - **Checkable after the fact.** The replay journal records every run, and the always-on lineage spine records every lineage-bearing step; the opt-in HMAC-chained audit log (BERNSTEIN_AUDIT=1) adds receipts you verify offline. Non-determinism surfaces as a hash mismatch at the exact step, not a flaky re-run. Non-code deliverables get the same treatment: a task can declare an artifact contract (report, dataset, action log, ops result) and completes on a signed lineage receipt rather than a git commit. - **Isolated by construction.** Each coding task gets its own git worktree behind merge gates; artifact-mode tasks get a working directory under .sdd/workspaces/. Agents share no mutable workspace by default; the only shared state is the task backlog, which is claimed atomically. Stricter filesystem enforcement is opt-in, from the sandbox backends. Disable worktrees and every task runs in the shared checkout. - **Broad and local.** More than 50 selectable CLI agent adapters plus a generic --prompt wrapper, file-based state, no SaaS hop, no third-party data plane. The full list is on the capabilities page; the feature matrix is the exhaustive index. what a run looks like One YAML file declares the run: phases, roles, dependencies, and the conditions under which a node runs at all. The scheduler executes it as plain Python - nothing in the file is a prompt, and no model decides what happens next. This graph produces an audit evidence pack; the full file ships at .bernstein/workflows/audit-evidence-pack.yaml. Each node is claimed by an agent whose role the phase allows; role fences and approval gates hold no matter what the agent does inside the task. A coding node completes behind merge gates in its own git worktree. The nodes above complete differently: an artifact contract names the deliverable (report, dataset, scan, action log), and the node finishes on a signed lineage receipt instead of a commit. Same scheduler, same journal, same offline verification - whether the graph ships code, research, an ops change, or a mix of all three. Ready-made graphs for software, research, docs, enterprise, and contributor workflows live in .bernstein/scenarios/.