GitHub - PKU-YuanGroup/OpenAI4S: Open-source AI agent for scientific research. Analyze data in Python/R with Claude, GPT, Gemini, and more.

Open AI for Scientist

💸 Replicating Claude Science in two cuts or less

An open-source hybrid scientific research agent.
_{JSON tools orchestrate; persistent Python/R kernels do the science.}

Launched by the Peking University–YuanKong Intelligence AI Joint Research Laboratory.
_{由北京大学—元空AI联合实验室推出。}

English · 简体中文

Tip

Why "two cuts"? No pricey frontier-model key needed — OpenAI4S runs on Doubao (豆包) via the cheapest "Small" plan on Volcengine Ark (火山方舟): ¥9.9 / month (≈ US$1.4). Pick the ark provider in the UI and you get a Claude-Science-class agent for less than a cup of coffee.

Volcengine Ark · Agent Plan — Small tier at ¥9.9/month

_{Volcengine Ark · Agent Plan (Personal) — the entry Small tier is ¥9.9 / month.}

🧬 JSON orchestration, Code-as-Action science

OpenAI4S deliberately has two action planes. Provider-native JSON tool calls handle deterministic orchestration, permissions, metadata, external services, and human approval. Python/R Code-as-Action handles computation, exploration, analysis, simulation, and long-running scientific work in persistent kernels. Python cells can synchronously call the in-kernel host API while they run; R is an independent persistent analysis channel.

This is not a choice between tools and code: each does the job it is good at. Tool-only and conversational work can finish through the Engine-owned, strictly structured finalize_response action. Scientific cells keep the important host.submit_output(...) completion contract, including structured artifacts and metrics. host.submit_output is the only completion signal that can fire inside a Cell; a later sole finalize_response may still close the Engine after earlier Cells have run.

	JSON control plane	Python/R science plane
Best for	workflow, permissions, metadata, services	computation, analysis, simulation
Action unit	One ordered native-tool batch	One complete code cell
Composition	auditable schemas and resource policy	`for`, `if`, libraries; Python also has mid-cell Host RPC
State	append-only Action Ledger	kernel memory + versioned artifacts
Completion	Engine-owned `finalize_response`	Python: `host.submit_output(...)`; R: no in-cell completion
Extending	named `Tool` subclass	import a library or load a Skill
# ReAct: ~14 round-trips (read → … → filter → sort → plot). OpenAI4S: one code cell. hits = [f for f in files if pattern in host.read_file(f)] top3 = sorted(hits, key=os.path.getsize, reverse=True)[:3] frames = [pd.read_csv(f) for f in top3] # a 100k-row DataFrame stays in the kernel... host.save_artifact(plot(frames)) # ...only "<DataFrame 100000×20>" hits context

📣 News

2026-09 🧭 v0.3.0 — the new workbench, and the first Windows package — the Preact/TypeScript workbench replaces the app.js monolith as the default UI, and the release adds the first Windows/WSL2 zip beside the Linux tarball. Every local daemon now requires its access token: the OPENAI4S_REQUIRE_TOKEN=0 loopback opt-out is gone. Interactive HTML reports run on a separate sandboxed origin, Notebook cells keep the exact artifact versions they produced, long-context compaction actually lands and survives a restart, delegated sub-agents inherit their environment and leave durable, exportable cell records, and Auto Mode admits budget atomically and stops a run that makes no progress. The single-cell RNA analysis Skill brings the total to 604. The Apple Silicon macOS image is an ad-hoc-signed preview, not a notarized one. The database schema moves from 27 to 32, so read Upgrading from 0.2.x before the first start.
2026-08-24 🚀 v0.2.0 — the multi-platform release — one release, two desktop packages: the Apple Silicon .dmg and a relocatable Linux x86_64 tarball carrying the same embedded Python and science stack (the Windows/WSL2 zip is built and under stabilization — it ships in a coming release). Underneath: Auto Mode with a Guardian review boundary, honest completion-evidence reconciliation (a crashed cell can no longer render as a clean success), the MCP Streamable HTTP transport with the Volcengine DataPro connector and Doubao web search, Anthropic Messages SSE streaming, the pinned 561-recipe bioSkills collection (603 Skills in all, installable anywhere via npx), a trajectory-ledger view in the workbench, Docker/Kubernetes deployment, openai4s --version, and the interrupt-signal train that makes a running R cell reliably stoppable on every platform.
2026-08-04 🔭 main — on the way to v0.2.0 — read-only session sharing over an outbound relay tunnel (openai4s share / openai4s relay), seven normalized public-database connectors that carry where a record came from and when, a versioned /api/v1 surface (keyset pagination, one error envelope, a resumable WebSocket cursor), environments as a transaction (openai4s env plan|apply|rollback), a redacted doctor / diagnostics support bundle, consent-gated revocable telemetry, a retrosynthesis-planning Skill, and a 10-workflow / 20-case benchmark that runs against the real Store, kernels, and dispatcher. Linux and Windows desktop packages were built and tested here — the Linux package ships in v0.2.0 above, and the Windows package follows in a coming release.
2026-07-15 🍎 v0.1.0 — macOS app — a one-click, no-toolchain Apple Silicon .dmg with an embedded Python and the full default kernel science stack (rdkit · scanpy · the single-cell stack), plus PyPI packaging (pip install openai4s) and release automation. New here? → Startup guide.
2026-07-06 🎉 Open-sourced — the pure-stdlib Code-as-Action engine, the scientific web app, 24 science Skills, and BYOC remote compute.

😮 Highlights

🧬 Hybrid action engine — class-based native JSON tools orchestrate while persistent Python/R kernels execute science. CLI and Web adapters start foreground language slots lazily, so tool/finalize routing itself does not spawn one; individual tools may still manage dedicated workers.
📒 Ledger-first runtime — action groups/events and terminal facts are append-only; execution attempts, generation lifecycle, usage, and completion records remain durable and reconstructable.
🐍 Pure-stdlib core — the engine and the web server are stdlib-only (http.server + hand-rolled WebSocket, no framework, no deps). The LLM client speaks OpenAI / Anthropic / Gemini over urllib alone.
🔌 One-line multi-provider — ark (doubao · glm · kimi · deepseek · minimax) plus official chatgpt · claude · gemini, behind a single host.llm; switch from the UI.
🖥️ Scientific workbench — live streaming, versioned artifacts, provenance, an Action Timeline surface, and a read-only-by-default Notebook. An explicit developer flag enables multiline Python/R input against the shared kernels.
🔐 Hardened local execution — strict child-environment allowlists, durable approvals, one-shot generation-bound host.bash capabilities, and OS sandbox adapters (Seatbelt on macOS, bubblewrap on Linux) with visible degraded/fail-closed modes.
🔬 606 bundled Skills — 45 curated OpenAI4S recipes for GPU/model science, research workflows, and platform operations, plus all 561 recipes from the pinned MIT-licensed GPTomics/bioSkills collection. Skills are recipes of code, not JSON schemas; the large third-party collection is searched on demand and occupies only one always-on prompt line. User-authored Skills stay under the data directory and cannot shadow bundled trust.
☁️ BYOC remote compute — with a configured, reachable provider, dispatch GPU jobs via ssh:<alias> or the bundled NVIDIA NIM integration. General remote compute remains a Prototype surface; host.fold uses a strict no-fabrication policy.
🔗 Read-only session sharing — publish a session as a snapshot anyone with the link can view and import, through a relay you run. The daemon never binds a public port; it dials out. Memories, permission state, and keys never leave, and residual secrets fail the publish closed. → Web sharing
🔎 Source-attributed retrieval — seven normalized public-database connectors (UniProt · RCSB PDB · Ensembl · ChEMBL · PubChem · arXiv · OpenAlex). Retrieved records carry where they came from and when, without the API key that fetched them.
🧰 Operable, not just runnable — a versioned /api/v1 (keyset pagination, one error envelope, correlation IDs, a resumable WebSocket cursor), a local credential required at startup, a redacted doctor / diagnostics support bundle, and consent-gated telemetry that is off by default and destroys its identity when revoked.

📦 What ships today

A capability map of the current tree — what is implemented and reachable, plane by plane.

plane	what's implemented
Control & orchestration	class-based native `Tool`s · append-only Action Ledger · plan/review with a durable state machine · context compaction that archives the raw slices it summarizes · concurrent sub-agent delegation (fanout 48, depth 4) a user can stop mid-flight · enforced Specialist allowlists a child cannot widen · MCP connectors · cross-session memory
Scientific execution	persistent Python and R kernels · synchronous mid-cell `host` RPC · object-level data lineage · versioned artifacts · environment provenance recorded per kernel generation, never borrowed from the daemon · background execution · 606 Skills (45 curated + 561 pinned bioSkills) · a FIFO execution coordinator with ABA-safe watchdog recovery
Data & retrieval	seven normalized public-database connectors (UniProt · RCSB PDB · Ensembl · ChEMBL · PubChem · arXiv · OpenAlex) whose records carry source and time · a nightly canary over three of them · Agent-Plan-keyed Doubao Search Custom as the primary web search · Tavily and keyless search as backups · managed DataPro professional-dataset search
Workbench	live streaming · Action Timeline · read-only-by-default Notebook · branch fork/activate/revert · verified recovery with an explicit Partial/Failed state · `@file` references pinned to the version they name · 2D chemistry/genome/sequence/MSA/LaTeX renderers · Markdown and `.ipynb` export
Sharing & portability	read-only session shares over an outbound relay you operate · quarantined portable Session packages · an optional Jupyter KernelSpec bridge onto the same kernels
Ops, safety & release	`/api/v1` and a startup credential · Seatbelt/bubblewrap sandbox adapters with visible degraded and fail-closed modes · durable approvals that deny by default when unattended · redacted diagnostics · revocable telemetry · environments as a transaction · a 13-workflow/46-case benchmark against the real Store, kernels, and dispatcher · a staged release pipeline that verifies artifacts before anything becomes public

Experimental features

A default-off semantic judgment layer (TypeSafe Jev) can add Skill suggestions, literature claim checks, text-feature engineering, and recording-only safety / task-mode shadows. It stays off until you enable it, needs your own TypeSafe key, and sends text to a service hosted in the United States. → Experimental semantic judgment

🎬 Demo

Live API workflow — from UniProt / RCSB to a 3D structure & report	Real-data analysis — human insulin INS (P01308): from UniProt / RCSB to a reproducible report
Visual artifact editing — “raise the confidence cutoff to 75” in one line	Annotation-driven chart editing — lasso a region & recolor the legend
Plan-mode research — artemisinin & paclitaxel solubility prediction	Protein engineering — from sequence to ranked mutants & structural rationale

⚡ Quickstart

git clone https://github.com/PKU-YuanGroup/OpenAI4S && cd OpenAI4S
./setup.sh     # one-time: build the environment with uv
./start.sh     # launch the web UI at http://127.0.0.1:8760/

setup.sh creates the lightweight control .venv with uv. For the comprehensive Python + R scientific kernels, install a Conda-family manager (micromamba, mamba, or conda) and run ./setup.sh --with-kernel-envs instead. Existing kernel environments can be synchronized with ./setup.sh --update-kernel-envs; updates do not prune user-installed packages. start.sh launches the daemon + web UI. No API key is needed to boot — set your model in the UI (Customize → Models). One-shot without the UI: uv run openai4s run "Compute the mean of [4,8,15,16,23,42] and submit it." -v.

macOS

Note

v0.3.0 ships a preview macOS image, not a notarized one. OpenAI4S-0.3.0-macos-arm64.dmg on the v0.3.0 release page is Apple Silicon only, ad-hoc signed and not notarized, so Gatekeeper blocks its first launch; the startup guide has the steps. It was built and attached outside the release workflow, which still uploads a .dmg only when it is Developer-ID-signed and notarized, and the credentials for that do not exist yet. Use that pinned page rather than the newest release, because a release the workflow produces carries no image. On an Intel Mac, or if you would rather not run an un-notarized app, install from PyPI as below or run from the source checkout above. Coming from the v0.2.0 app, read Upgrading from 0.2.x first: 0.3.0 upgrades the data directory, and 0.2.0 must not open it afterwards.

Install from PyPI into a virtual environment of its own. This works on Apple Silicon and Intel, with Python 3.10 or newer:

python3 -m venv ~/.venvs/openai4s && source ~/.venvs/openai4s/bin/activate
pip install "openai4s[science]"   # numpy · pandas · matplotlib · scikit-learn; use [science,chemistry] for RDKit
openai4s serve                    # starts the daemon and opens the workbench with its access token

Data lives in ~/.openai4s. If you close the tab, openai4s url prints the authenticated workbench URL again. The R kernel needs a Conda-family manager (micromamba, mamba or conda); run openai4s setup once to build it.

First run — point it at a model, then at search. No key ships, so once the workbench is open:

Model API — open Settings ⚙ → Models, pick a protocol (Ark-compatible for Doubao/GLM/Kimi/DeepSeek/MiniMax, or OpenAI- / Anthropic-compatible), paste your API Key, click Add, then Set active. Cheapest path: the ark protocol on Volcengine Ark's ¥9.9/mo plan.
Search API (optional, recommended) — open Settings ⚙ → Network, keep Allow network access on, and paste your Ark Agent Plan Key into the primary Doubao Search Custom card → Save credential. If the active Ark model already uses that key, OpenAI4S reuses it automatically. Tavily and keyless engines remain backup options; the dedicated Doubao health check never reports a fallback result as Doubao.

Full walkthrough (install → model → search → R kernel, plus the Gatekeeper steps for the v0.2.0 preview image): Startup guide.

Linux app (no toolchain required)

Note

The Linux package ships with v0.2.0 and every later release. The Windows/WSL2 package ships from v0.3.0 on; see its section below. On older releases (v0.1.0 carried the macOS image only, and v0.2.0 had no Windows package), use the source checkout above or pip install openai4s.

Download OpenAI4S-<version>-linux-x86_64.tar.gz from the latest release, unpack it anywhere, and run it. It embeds its own Python and the pre-baked science stack, as a relocatable directory:

tar -xzf OpenAI4S-*-linux-x86_64.tar.gz && cd OpenAI4S-*-linux-x86_64
./OpenAI4S          # starts the daemon and opens http://127.0.0.1:8760/
./install.sh        # optional: `openai4s` on your PATH + an application-menu entry

install.sh is per-user and needs no root — it only writes into $HOME, and ./uninstall.sh undoes it while leaving your data in ~/.openai4s alone. Install bubblewrap (apt install bubblewrap) so cells run sandboxed; without it the default OPENAI4S_KERNEL_SANDBOX=auto reports a visibly degraded, unisolated kernel. Only x86_64 is published — on arm64 Linux, install from PyPI (pip install openai4s).

Windows (via WSL2)

Download OpenAI4S-<version>-windows-x86_64.zip, unzip it, and double-click OpenAI4S.cmd. The first run checks WSL2 and a working bubblewrap 0.8.0+ sandbox, verifies and installs the bundled Linux payload, creates ~/.local/bin/openai4s, starts the daemon there, and opens an authenticated local URL in your Windows browser. No application download, no pip, no toolchain. Ubuntu 24.04 is the supported baseline; mainland PyPI/Conda mirrors and an optional WSL-reachable proxy can be configured by the launcher. See the bilingual Windows/WSL2 guide.

v0.3.0 is the first release that ships this package. Its acceptance evidence covers WSL2 on x86_64 with the tested Ubuntu 24.04 distribution. The last section of the WSL2 parity audit ("Fix verification — 2026-09-07") leaves the following unverified:

A side-by-side macOS run for the parity comparison. The macOS side of that comparison was read from source, not run.
Windows on ARM, other distributions, and WSL network modes other than the tested one.
Real provider sign-in and inference. The scientist flow ran with OPENAI4S_NOTEBOOK_REPL=1 and no live model.
Conda environment provisioning, so R and other named environments on Windows are unverified. R was installed in the test distribution only as a test prerequisite.
Every domain recipe.
A Windows reboot. Only a restart of the WSL distribution was tested; it reopened the saved results and a stored credential.
Cold-start performance. The unmodified full browser smoke did not pass: it exceeded its 20-second queue-admission wait. WSL service connection timeouts were also seen under concurrent load.

Native Windows is not supported, and the program refuses to start a kernel there rather than warning and proceeding — it spawns POSIX subprocesses, the R channel rides file descriptors 3 and 4 through a shell redirection, and the sandbox has no Windows backend. WSL2 reports as Linux, so this package runs the same build every other platform runs. If you do not have WSL2 yet, the launcher stops and tells you the exact command (wsl --install, from an Administrator PowerShell). Details: Supported platforms.

🐳 Docker and Kubernetes

docker compose up -d --build          # http://127.0.0.1:8760/
docker compose exec openai4s openai4s url   # the URL, token included

The image is built from this tree — Debian-slim CPython, the wheel, and the science extra — and runs as an unprivileged user with one volume at /data. Supply the model key as OPENAI4S_SECRET_LLM_LLM_API_KEY (a Secret in the cluster); the image reads credentials from the environment and writes nothing credential-shaped to the volume. For a cluster, kubectl apply -f deploy/kubernetes.yaml gives a single-replica Deployment, a ReadWriteOnce claim and a ClusterIP Service, with probes on /health.

Release images are published to GitHub Packages as ghcr.io/pku-yuangroup/openai4s:<version> and :latest (linux/amd64, from 0.2.0 on) by publish-image.yml, which pushes an image only after it passes the same container_smoke.sh that gates every pull request. If docker pull ghcr.io/pku-yuangroup/openai4s:latest asks you to log in, build the image from the checkout as above. Two things are worth knowing before you expose it. Binding 0.0.0.0 inside the container makes the access token mandatory and switches the DNS-rebind Host allowlist off, so the token becomes the only control in front of endpoints that execute code — which is why the compose file publishes to loopback and the Service is a ClusterIP. And an unprivileged container cannot give bubblewrap the namespaces it needs, so the kernel sandbox degrades visibly and the container becomes the boundary; that is a coarser one, and the container guide says exactly what it stops covering.

🧩 Take the Skills anywhere (`npx`)

The 606 bundled Skills are recipes — prose, code, and the operational knowledge to run them — and nothing about them is OpenAI4S-specific. The npm release @pku-yuangroup/openai4s-skills@0.2.0 contains 603 Skills: 42 curated + 561 pinned bioSkills. Install that fixed release with:

npx @pku-yuangroup/openai4s-skills@0.2.0 install --all                  # v0.2.0: 42 curated Skills
npx @pku-yuangroup/openai4s-skills@0.2.0 install --collection bioskills # v0.2.0: 561 pinned bioinformatics recipes
npx @pku-yuangroup/openai4s-skills@0.2.0 install alphafold2 boltz --target claude
npx @pku-yuangroup/openai4s-skills@0.2.0 list
npx @pku-yuangroup/openai4s-skills@0.2.0 uninstall --all

npx @pku-yuangroup/openai4s-skills <command> selects the latest npm release. To use the current repository catalog instead (606 Skills: 45 curated + 561 bioSkills), run directly from GitHub; this form follows the default branch:

npx github:PKU-YuanGroup/OpenAI4S install --all                  # the 45 curated Skills
npx github:PKU-YuanGroup/OpenAI4S install --collection bioskills # the 561 pinned bioinformatics recipes

Both sources use the same target and overwrite rules. --target claude writes to ~/.claude/skills, --target openai4s to <data_dir>/user-skills, and --dir <path> anywhere you name; the resolved absolute path is printed before anything is written there, and --dry-run stops at that plan. Every installed file's SHA-256 goes into a manifest beside the Skills, so a reinstall refuses to overwrite a Skill you have edited or one it did not install, and an uninstall removes only files it wrote. Every curated Skill page and the collection root under skills/ carry an Install section with their own name already filled in, so you can install from whichever page you landed on.

If you already run OpenAI4S from this checkout, you already have all 606 — a bundled Skill takes precedence over a same-named one in your data directory. The command exists for the other direction.

📚 Documentation

The canonical bilingual documentation is published at openai4s.org/docs. Its public source and issue tracker live in Nobody-Zhang/openai4s-docs; the links below point to the code-adjacent copies kept with this repository.

doc	what's inside
Startup guide	macOS walkthrough: install the v0.3.0 preview image (Apple Silicon, ad-hoc signed, with its Gatekeeper steps) or from PyPI, model setup, and one-key Doubao Search authorization (with Tavily/keyless backups)
Upgrading from 0.2.x	Back up the database before the 27 → 32 schema migration, why going back to 0.2.x is unsupported, and the access token that is now always required
Architecture	the hybrid action router, Action Ledger, `host` RPC, and lazy kernels
Backend extension guide	where new Tool classes, host services, repositories, and session behaviour belong
Model backend bring-up	local/remote GPU selection, checkpoint staging, real-inference canary admission, and connector portability
Skills	45 curated Skills + 561 pinned bioSkills + how to write your own
Remote compute	BYOC GPU jobs, `host.fold`, auto-provisioning
Science connectors	the seven public databases, their filters, and retrieval provenance
Web app	UI features, Action Timeline, read-only Notebook, artifacts, and implementation status
Web sharing	read-only session shares, the trust model, and running your own relay
Jupyter adapter	optional standalone Python/R KernelSpecs, install commands, and compatibility limits
Configuration	model providers, env vars, conda envs, CLI
Docker / Kubernetes	the image, `compose.yaml`, the cluster manifests, and what a wildcard bind actually changes
Supported platforms	the per-OS support tiers and why native Windows refuses to start a kernel
Windows / WSL2	Ubuntu 24.04 installation, sandbox checks, lifecycle commands, mainland mirrors, and localhost proxy behavior
Security	defense-in-depth safety layers & remote-access notes
Experimental semantic judgment	default-off TypeSafe Jev layer: how to enable and disable it, what each capability sends, kill switch, doctor/audit status

🗺️ Roadmap

Delivered

Ship the next-generation workbench foundation: branch activation and append-only Revert/Undo projections, verified recovery with explicit Partial/Failed state, dependency-level stale propagation, durable delegation, quarantined portable Session packages, checkpointed plan/review/memory state, and dedicated 2D chemistry/genome/sequence/MSA/LaTeX renderers. Arbitrary in-memory namespace objects are deliberately not serialized; recovery remains Partial unless a safe recipe can rebuild and verify them, and Fork is offered only on records that carry a proven checkpoint mapping, so older history returns 409.
Read-only session sharing over an outbound relay you operate, with the daemon never binding a public port and residual secrets failing the publish closed.
An executable benchmark of end-to-end scientific workflows — 13 workflows / 46 cases run against the real Store, kernel managers, host dispatcher, and compute manager, where a declared failure / permission_denied / recovered / provenance outcome fails when the run succeeds. Publishing comparable public results is still ahead.
Environments as a transaction (openai4s env plan|apply|rollback): a generation is built fresh, verified, and only then pointed at atomically, so an artifact's provenance can name an immutable one.

A notarized macOS image and arm64 packages. The Linux package ships from v0.2.0 and the Windows/WSL2 package from v0.3.0, but the macOS image is still an ad-hoc-signed preview that Gatekeeper blocks on first launch, and only x86_64 is published for Linux and Windows. A Developer ID-signed, notarized .dmg plus arm64 Linux and Windows-on-ARM packages would let every supported platform install without a toolchain.
NVIDIA scientific computing suites — bring BioNeMo (biomolecular foundation models) and Parabricks (GPU-accelerated genomics pipelines) in as first-class Skills and BYOC backends, beyond today's NVIDIA NIM integration.
Local GPU model serving so structure/design Skills run without remote compute.
More BYOC providers (Modal / SLURM) beyond SSH + NVIDIA NIM.
Stronger Linux isolation beyond bubblewrap where available (for example seccomp), and wider packaged sandbox smoke coverage.
Keyless web_search beyond DuckDuckGo (rate-limit resilience).

💡 Contributing

OpenAI4S is a community effort to keep the Code-as-Action paradigm open.

Before opening a PR, please read .github/CONTRIBUTING.md — it defines branch naming, the PR checklist (.github/pull_request_template.md), code ownership (.github/CODEOWNERS), review & release policy, and the offline-test policy.

Development setup

Requires Python ≥ 3.10 and uv.

git clone https://github.com/PKU-YuanGroup/OpenAI4S && cd OpenAI4S
./setup.sh                          # uv sync --locked --extra science + pre-commit hook
./setup.sh --with-kernel-envs       # optional: full Python + R kernel stacks
uv run pytest                       # offline test suite (LLM mocked)
uv run pre-commit run --all-files   # format + lint everything

Style is enforced by pre-commit — black, isort (--profile black), and ruff, pinned in .pre-commit-config.yaml. Runtime deps: the core is zero-dependency (pure stdlib); the optional science extra pins numpy>=1.24 · pandas>=2.0 · matplotlib>=3.7.

What we welcome

New Skills — a SKILL.md (+ optional kernel.py) under skills/ — recipes of code, not schemas.
New providers — a wire adapter under openai4s/llm/providers/ plus its provider definition and registry entry, or a BYOC compute provider.
Engine & UI — the core is pure stdlib and readable; the web app is framework-free.

Keep the core dependency-free, guard optional science imports behind try/except ImportError, and make sure uv run pytest and uv run pre-commit run --all-files pass before opening a PR.

👍 Acknowledgement & related work

Claude Science (Anthropic) — the closed reference architecture whose Code-as-Action design, persistent kernel, host-RPC protocol, and safety layers OpenAI4S independently reproduces in open source.
CodeAct — "Executable Code Actions Elicit Better LLM Agents" — code as a unified action interface.
ReAct — "Synergizing Reasoning and Acting in Language Models" — the tool_use baseline this project departs from.
The science Skills stand on ColabFold / AlphaFold, ESM, OpenFold, Boltz, Chai, ProteinMPNN, DiffDock, Evo2, Borzoi, scGPT, scVI-tools and open data services (NCBI, UniProt, RCSB PDB, EBI, OpenAlex, Crossref).

🔒 License

Released under the MIT License — see LICENSE.

✨ Star History

✏️ Citing

@misc{zhang2026openai4scodeactionscience,
      title={OpenAI4S: Code as Action, Science as Sessions},
      author={Gongbo Zhang and Hao Li and Yu Wang and Mujie Lin and Liuzhenghao Lv and Yicheng Mao and Yimi Wang and Jun Zhu and Minhan Tang and Zhengxiang Jiang and Yusong Wang and Jiayu Yao and Kunpeng Ning and Dawei Pang and Yonghong Tian and OpenAI4S Community and Yuyang Liu and Li Yuan},
      year={2026},
      eprint={2609.15096},
      archivePrefix={arXiv},
      primaryClass={cs.AI},
      url={https://arxiv.org/abs/2609.15096},
}

🤝 Community contributors

_{Auto-generated daily from the GitHub contributors graph and a maintained public-recognition list by scripts/update_contributors.py.}

_{OpenAI4S · code is the action, the kernel is the environment. · 简体中文 · Friend Link https://linux.do}

Name		Name	Last commit message	Last commit date
.github		.github
assets		assets
deploy		deploy
docs		docs
envs		envs
frontend		frontend
harness		harness
openai4s		openai4s
openai4s_compute_provider		openai4s_compute_provider
openai4s_worker_runtime		openai4s_worker_runtime
scripts		scripts
skills		skills
tests		tests
tools		tools
workflows		workflows
.dockerignore		.dockerignore
.env.example		.env.example
.gitattributes		.gitattributes
.gitignore		.gitignore
.mailmap		.mailmap
.pre-commit-config.yaml		.pre-commit-config.yaml
AGENTS.md		AGENTS.md
CITATION.cff		CITATION.cff
CLAUDE.md		CLAUDE.md
Dockerfile		Dockerfile
LICENSE		LICENSE
MANIFEST.in		MANIFEST.in
README.md		README.md
README_zh.md		README_zh.md
compose.yaml		compose.yaml
package-lock.json		package-lock.json
package.json		package.json
pyproject.toml		pyproject.toml
setup.sh		setup.sh
start.sh		start.sh
uv.lock		uv.lock

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Open AI for Scientist

💸 Replicating Claude Science in two cuts or less

🧬 JSON orchestration, Code-as-Action science

📣 News

😮 Highlights

📦 What ships today

Experimental features

🎬 Demo

⚡ Quickstart

macOS

Linux app (no toolchain required)

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Open AI for Scientist

💸 Replicating Claude Science in two cuts or less

🧬 JSON orchestration, Code-as-Action science

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