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

Orchestrate any AI coding agent. Any model. One command.

Documentation · Getting Started · Glossary · Limitations

Bernstein takes a goal, breaks it into tasks, assigns them to AI coding agents running in parallel, verifies the output, and merges the results. When agents succeed, the janitor merges verified work into main. Failed tasks retry or route to a different model.

LLMs write code well. They schedule work across other LLMs badly. Most agent orchestrators use an LLM as the coordinator and hit the same failure modes: non-reproducible plans, silent coordination drift, token burn on meta-decisions a 200-line event loop does reliably. Bernstein inverts that. One LLM call upfront decomposes the goal; after that, scheduling, worktree isolation, quality gates, and HMAC-chained audit replay are all deterministic Python. Every run is bit-identically replayable.

No framework to learn. No vendor lock-in. Agents are interchangeable workers. Swap any agent, any model, any provider.

pipx install bernstein
cd your-project && bernstein init
bernstein -g "Add JWT auth with refresh tokens, tests, and API docs"

$ bernstein -g "Add JWT auth"
[manager] decomposed into 4 tasks
[agent-1] claude-sonnet: src/auth/middleware.py  (done, 2m 14s)
[agent-2] codex:         tests/test_auth.py      (done, 1m 58s)
[verify]  all gates pass. merging to main.

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

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

18 CLI agent adapters: 17 third-party wrappers plus a generic wrapper for anything with --prompt.

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-2.5-pro

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

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.

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 (experimental). 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 login      # authenticate with Bernstein Cloud
bernstein cloud deploy     # push agent workers
bernstein cloud run plan.yaml  # execute a plan on Cloudflare

A bernstein cloud init scaffold for wrangler.toml and bindings is planned.

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 for model/effort selection. Knowledge graph for codebase impact analysis. Semantic caching saves tokens on repeated patterns. Cost anomaly detection (burn-rate alerts). Behavior anomaly detection with Z-score flagging.

Sandboxing. Pluggable SandboxBackend protocol — run agents in local git worktrees (default), Docker containers, E2B Firecracker microVMs, or Modal serverless containers (with optional GPU). Plugin authors can register custom backends through the bernstein.sandbox_backends entry-point group. Inspect installed backends with bernstein agents sandbox-backends.

Artifact storage. .sdd/ state can stream to pluggable ArtifactSink backends: local filesystem (default), S3, Google Cloud Storage, Azure Blob, or Cloudflare R2. BufferedSink keeps the WAL crash-safety contract by writing locally with fsync first and mirroring to the remote asynchronously.

Skill packs. Progressive-disclosure skills (OpenAI Agents SDK pattern): only a compact skill index ships in every spawn's system prompt, agents pull full bodies via the load_skill MCP tool on demand. 17 built-in role packs plus third-party bernstein.skill_sources entry-points.

Controls. HMAC-chained audit logs, policy engine, PII output gating, WAL-backed crash recovery (experimental multi-worker safety), OAuth 2.0 PKCE. SSO/SAML/OIDC support is in progress.

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 (experimental).

Full feature matrix: FEATURE_MATRIX.md · Recent features: What's New

Last verified: 2026-04-19. See full comparison pages for detailed feature matrices.

The table above compares Bernstein against LLM-orchestration frameworks (they orchestrate LLM calls). The table below covers the closer category — other tools that orchestrate CLI coding agents:

Bernstein's wedge in this category: Python-native, MCP-server-first, widest adapter coverage. If your stack is TypeScript and you want a product with a dashboard, Composio's @aoagents/ao is a better fit; if you want a polished desktop ADE, emdash is. If you want a primitive that imports into Python, exposes itself over MCP to any client, and covers the full agent breadth (including Qwen, Goose, Ollama, OpenAI Agents SDK, Cloudflare Agents, and more) — Bernstein.

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 skills list    # discoverable skill packs (progressive disclosure)
bernstein skills show <name>  # print a skill body with its references

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

Provider SDKs are optional so the base install stays lean. Pick what you need:

Combine extras with brackets, e.g. pip install 'bernstein[openai,docker,s3]'.

Editor extensions: VS Marketplace · Open VSX

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

If Bernstein saves you time: GitHub Sponsors

Contact: forte@bernstein.run

Apache License 2.0