Bernstein

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Bernstein takes a goal, breaks it into tasks, assigns them to AI coding agents running in parallel, verifies the output, and merges the results. You come back to working code, passing tests, and a clean git history.

No framework to learn. No vendor lock-in. Agents are interchangeable workers — swap any agent, any model, any provider. The orchestrator itself is deterministic Python code. Zero LLM tokens on scheduling.

pip install bernstein
bernstein -g "Add JWT auth with refresh tokens, tests, and API docs"

Also available via pipx, uv tool install, brew, dnf copr, and npx bernstein-orchestrator. See install options.

Supported agents

Bernstein auto-discovers installed CLI agents. Mix them in the same run — cheap local models for boilerplate, heavy cloud models for architecture.

Agent	Models	Install
Claude Code	opus 4.6, sonnet 4.6, haiku 4.5	npm install -g @anthropic-ai/claude-code
Codex CLI	gpt-5.4, gpt-5.4-mini	npm install -g @openai/codex
Gemini CLI	gemini-3.1-pro, gemini-3-flash	npm install -g @google/gemini-cli
Cursor	sonnet 4.6, opus 4.6, gpt-5.4	Cursor app
Aider	Any OpenAI/Anthropic-compatible	pip install aider-chat
Ollama + Aider	Local models (offline)	brew install ollama
Cloudflare Agents	Workers AI models	bernstein cloud init
Codex on Cloudflare	gpt-5.4 via CF gateway	bernstein cloud init
Amp, Cody, Continue.dev, Goose, IaC (Terraform/Pulumi), Kilo, Kiro, OpenCode, Qwen, Roo Code, Tabby	Various	See docs
Generic	Any CLI with --prompt	Built-in

Any adapter also works as the internal scheduler LLM — run the entire stack without any specific provider:

internal_llm_provider: gemini            # or qwen, ollama, codex, goose, ...
internal_llm_model: gemini-3.1-pro-preview

[!TIP] Run bernstein --headless for CI pipelines — no TUI, structured JSON output, non-zero exit on failure.

Quick start

cd your-project
bernstein init                    # creates .sdd/ workspace + bernstein.yaml
bernstein -g "Add rate limiting"  # agents spawn, work in parallel, verify, exit
bernstein live                    # watch progress in the TUI dashboard
bernstein stop                    # graceful shutdown with drain

For multi-stage projects, define a YAML plan:

bernstein run plan.yaml           # skips LLM planning, goes straight to execution
bernstein run --dry-run plan.yaml # preview tasks and estimated cost

How it works

1. Decompose — the manager breaks your goal into tasks with roles, owned files, and completion signals
2. Spawn — agents start in isolated git worktrees, one per task. Main branch stays clean.
3. Verify — the janitor checks concrete signals: tests pass, files exist, lint clean, types correct
4. Merge — verified work lands in main. Failed tasks get retried or routed to a different model.

The orchestrator is a Python scheduler, not an LLM. Scheduling decisions are deterministic, auditable, and reproducible.

Cloud execution (Cloudflare)

Bernstein can run agents on Cloudflare Workers instead of locally. The bernstein cloud CLI handles deployment and lifecycle.

• Workers — agent execution on Cloudflare's edge, with Durable Workflows for multi-step tasks and automatic retry
• V8 sandbox isolation — each agent runs in its own isolate, no container overhead
• R2 workspace sync — local worktree state syncs to R2 object storage so cloud agents see the same files
• Workers AI — use Cloudflare-hosted models as the LLM provider (no external API keys required)
• D1 analytics — task metrics and cost data stored in D1 for querying
• Vectorize — semantic cache backed by Cloudflare's vector database
• Browser rendering — headless Chrome on Workers for agents that need to inspect web output
• MCP remote transport — expose or consume MCP servers over Cloudflare's network

bernstein cloud init       # scaffold wrangler.toml + bindings
bernstein cloud deploy     # push agent workers
bernstein cloud run plan.yaml  # execute a plan on Cloudflare

Capabilities

Core orchestration — parallel execution, git worktree isolation, janitor verification, quality gates (lint + types + PII scan), cross-model code review, circuit breaker for misbehaving agents, token growth monitoring with auto-intervention.

Intelligence — contextual bandit router learns optimal model/effort pairs over time. Knowledge graph for codebase impact analysis. Semantic caching saves tokens on repeated patterns. Cost anomaly detection with Z-score flagging.

Enterprise — HMAC-chained tamper-evident audit logs. Policy limits with fail-open defaults and multi-tenant isolation. PII output gating. OAuth 2.0 PKCE. SSO/SAML/OIDC auth. WAL crash recovery — no silent data loss.

Observability — Prometheus /metrics, OTel exporter presets, Grafana dashboards. Per-model cost tracking (bernstein cost). Terminal TUI and web dashboard. Agent process visibility in ps.

Ecosystem — MCP server mode, A2A protocol support, GitHub App integration, pluggy-based plugin system, multi-repo workspaces, cluster mode for distributed execution, self-evolution via --evolve.

Full feature matrix: FEATURE_MATRIX.md

How it compares

Feature	Bernstein	CrewAI	AutoGen	LangGraph
Orchestrator	Deterministic code	LLM-driven	LLM-driven	Graph + LLM
Works with	Any CLI agent (20 adapters)	Python SDK classes	Python agents	LangChain nodes
Git isolation	Worktrees per agent	No	No	No
Verification	Janitor + quality gates	No	No	Conditional edges
Cost tracking	Built-in	No	No	No
State model	File-based (.sdd/)	In-memory	In-memory	Checkpointer
Self-evolution	Built-in	No	No	No
Declarative plans (YAML)	Yes	Partial	No	Yes
Model routing per task	Yes	No	No	Manual
MCP support	Yes	No	No	No
Agent-to-agent chat	No	Yes	Yes	No
Web UI	No	Yes	Yes	Partial
Cloud hosted option	Yes (Cloudflare)	Yes	No	Yes
Built-in RAG/retrieval	No	Yes	Yes	Yes

Last verified: 2026-04-14. See full comparison pages for detailed feature matrices.

Monitoring

bernstein live       # TUI dashboard
bernstein dashboard  # web dashboard
bernstein status     # task summary
bernstein ps         # running agents
bernstein cost       # spend by model/task
bernstein doctor     # pre-flight checks
bernstein recap      # post-run summary
bernstein trace <ID> # agent decision trace
bernstein run-changelog --hours 48  # changelog from agent-produced diffs
bernstein explain <cmd>  # detailed help with examples
bernstein dry-run    # preview tasks without executing
bernstein dep-impact # API breakage + downstream caller impact
bernstein aliases    # show command shortcuts
bernstein config-path    # show config file locations
bernstein init-wizard    # interactive project setup
bernstein debug-bundle   # collect logs, config, and state for bug reports

bernstein fingerprint build --corpus-dir ~/oss-corpus  # build local similarity index
bernstein fingerprint check src/foo.py                 # check generated code against the index

Install

Method	Command
pip	pip install bernstein
pipx	pipx install bernstein
uv	uv tool install bernstein
Homebrew	brew tap chernistry/bernstein && brew install bernstein
Fedora / RHEL	sudo dnf copr enable alexchernysh/bernstein && sudo dnf install bernstein
npm (wrapper)	npx bernstein-orchestrator

Editor extensions: VS Marketplace · Open VSX

Contributing

PRs welcome. See CONTRIBUTING.md for setup and code style.

Support

If Bernstein saves you time: GitHub Sponsors · Open Collective

License

Apache License 2.0

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"To achieve great things, two things are needed: a plan and not quite enough time." — Leonard Bernstein