Open Science

AIPOCH Open-Science

AI research workbench for reproducible science — open-source, local-first, and model-agnostic.

AIPOCH Open-Science is an open-source AI research workbench. Start with a question, a paper, or a data file. Describe what you want to find out in your own words, and work with an AI agent to explore the evidence, analyze data, or create a chart.

When you want to see how a result was produced, inspect the available inputs, code, and execution history. To share the work, export a .science package with the conversation branches, file versions, Notebook records, and verification evidence you choose.

💡 AIPOCH Open-Science v1.1.0 updates

November 6, 2026

• Research films: Export completed sessions as portable narrated films with terminal playback. Voice narration requires a locally installed voice model.
• Mobile pairing: Scan a QR code to open the current workspace on a phone or tablet over local WiFi or a Pinggy tunnel; internet traffic passes through Pinggy’s servers.
• Literature in CLI and MCP: Search and manage references from the command line and MCP clients, with read-only browsing plus write tools.
• Resumable overviews: Batch paper overviews resume after interruptions without regenerating completed results.
• Resource center: A renamed resource center with an adapted case library layout, more compact external links, and compute settings under connections.
• Diagnostics: Exports preserve Notebook execution evidence, allow selecting historical backend logs, and can include opt-in resource recordings (Windows).
• Reliability fixes: Recover conversations after interruptions, restore PDF figures and tables from searchable scans, and improve startup, Windows update checks, and memory use.

View the full v1.1.0 release notes

Table of Contents

• Quick Start
• AIPOCH Open-Science .science Research Packages
• AIPOCH Open-Science Paper and Citation
• Benchmark Results
• Core Capabilities
• Model Providers
• Data, Permissions, and Trust
• Development & Packaging
• Frequently Asked Questions
• Contributors
• Get Involved
• License
• Star History

🚀 Quick Start

1. Download the app

Open the official download page and choose the installer for your computer:

See download verification for checks before installation.

On macOS, you can also install with Homebrew:

brew install --cask open-science

The release assets include standalone CLI archives for macOS, Linux, and Windows. They bundle Node.js, so you can run the CLI without installing Node.js, npm, or Homebrew.

For the standalone CLI on macOS or Linux, use the separate AIPOCH tap:

brew install aipoch/tap/open-science

This Formula includes Node and runs without the desktop app; its release may differ from the desktop Cask. See the standalone runtime guide for prerequisites and upgrades.

For the standalone CLI and Web UI, install the published npm package with Node.js 22.13.0 or later:

npm install --global @aipoch/open-science
open-science start --no-open
open-science url

Open the printed URL to complete setup, or follow the terminal setup guide. npm supports macOS ARM64/x64, glibc Linux ARM64/x64 and Windows x64; the desktop app is not required. Keep npm optional dependencies enabled and review the Linux prerequisites and upgrade instructions. The URL contains an authentication token; keep it private.

Windows reinstalls preserve research data. For a full cleanup, see the data reset tool, which permanently deletes local data after confirmation.

2. Complete first-time setup

Follow the setup wizard: Environment → Data location → Agent runtime → Model provider → Notebook runtime.

Complete the required environment and agent-runtime checks and test your model connection. Python/R Notebook setup is optional; Notebook and data-location settings can be changed later.

After setup, open System settings to change appearance or notifications. In General, you can find diagnostics, command-line tools, and the guide to manual installation and updates.

Host compatibility, storage, and network checks	Provider, API Key, endpoint, and model validation

3. Start a research project

1. Click New project, open a session, and describe your research goal, inputs, and expected outputs.
2. Attach files, select a model and approval mode, then send the task. Use @ to reference project files or / to choose a skill.
3. Review tool activity and any approval requests, preview the results, and check their available evidence in Provenance.

Screenshots in this README illustrate the workflow. Labels, catalogs, and other interface details may differ from the version you install.

AIPOCH Open-Science .science Research Packages

Export a completed research session as a portable .science package for review, handoff, and archiving. Choose the conversation branches, file versions, Notebook records, and verification evidence to include, then import the package into another project or computer.

Export and import are available in the desktop app, local Web sessions, and the headless CLI. Remote Web sessions cannot read or write arbitrary local package paths.

Export → transfer → inspect

1. Export a completed session and choose the contents to include.
2. Move the .science file to a collaborator or another computer.
3. Import it into a project. The right-hand Research replay shows the recorded process; the left-hand conversation lets you ask about it and follow fixed step references.

Imported records remain read-only. Watching a replay does not execute code, call a model, or restore credentials. Your first question creates an associated writable discussion. Choose Fork to continue to create a writable copy for follow-up work. Re-executing a result still requires a complete recipe, required inputs, and an available runtime.

Side Chat conversations, private reading bookmarks, and credentials are excluded; included files depend on export selection. Received verification records describe the sender’s checks, not a rerun on this computer. See the .science package guide.

AIPOCH Open-Science Paper and Citation

AIPOCH Open-Science: An Open-Source AI Research Workbench with Reproducible .science Packages

arXiv preprint · v1 · Submitted October 12, 2026.

Our preprint introduces AIPOCH Open-Science, an open-source AI research workbench with portable .science packages for sharing research context, artifact versions, and verification records. Read the paper for the package exchange design and controlled evaluation.

If you use AIPOCH Open-Science in your research, you can cite the preprint:

@misc{ouyang2026aipochopenscienceopensourceai,
      title={AIPOCH Open-Science: An Open-Source AI Research Workbench with Reproducible .science Packages},
      author={Wen Ouyang and Bocheng Huang and Qianyu Yao and Fei Sun and Minying Deng and Haoyue Cheng and Wenwen Zhou and Lei Mou and Yusi Wei and Jiang Li and Kun Zhu and Lei Wang and Yi Feng and Jiaheng Dai and Ke Qian and Dan Wang and Yahui Wang and Chenyi Wang and Mingsheng Lyu and Yaqiang Zhang and Ping Zheng and Chunhua Tang and Xinge Ma and Xu Zhang and Youjia Fan and Can Lu and Zhijun Dai and Man Hu and Qiqi Xie and Beinan Liu and Yuanning Guo and Yunze Liu and Jinhong Jiang and Xianjun Fu and Mingwei Zhang and Yuanji Xu and Shilin Xia and Bowen Yang and Mingjun Yu and Jingdun Xie and Xuwei Chen and Xia Zou and Yuzhou Xue and Jianguo Zhou and Chunhao Cao and Donghao Tian and Lei Huang and Jinxiao Li and Shengrong Long and Zefeng Song and Honglin Li and Jiang Luo and Wenying Jiang and Shanshan Li and Lei Dai and Ning Jiang and Ying Cai and Chaohui Li and Chunyan Peng and Hetong Zhao and Angqing Li and Chunyan Yang and Chongyang Sun and Yong Li and Xinyu Shi and Wei Dai and Ming Peng and Chen Sang and Yuanyuan Han and Desong Qiu and Haixin Miao and Jian Xu and Yi Chen and Xiaoliang Shi and Tian Shen and Teng Hu and Shuhao Wang and Yingyong Hou and Huimei Wang},
      year={2026},
      eprint={2610.17110},
      archivePrefix={arXiv},
      primaryClass={cs.SE},
      url={https://arxiv.org/abs/2610.17110},
}

Benchmark Results

AIPOCH evaluated AIPOCH Open-Science on tasks ranging from finding a paper to answering questions about its contents and carrying out a research plan. The results below come from AIPOCH's own runs. Each score describes a particular task and evaluation setup.

AstaBench — Literature tasks

ASI-Bench — Following a plan and choosing methods

The four conditions ask the agent to execute a specified plan (B1), organize a workflow (B2), choose methods (B3), or work through distracting information (B4). The first two rows of the chart compare local runs using the same GPT-6 Astra model.

Open-Science scored 79.35, 56.11, 59.67, and 59.46 on B1–B4; the local Codex reference scored 74.78, 56.09, 58.12, and 55.45. The B2 scores were nearly identical. Other published runs differ in task instances, configurations, or judging.

The local B1 score covers 59 tasks after one scorer-related exclusion. Three local repeats were reported for B1/B2. B3/B4 used Open-Science v0.32.0 and a substitute judge; each score averages three 60-task runs.

BiomniBench-DA — Biomedical data analysis

On the Public 50 task set, the Gemini 3.1 Pro judge scored the outputs 81.04 and the DeepSeek v4-pro judge scored them 77.06. AIPOCH reports their equal-weight mean, 79.05, for the gpt-5.6-sol (xhigh) configuration.

Evaluation scope: These reports cover local/internal runs from August to November 2026 with the stated models and splits; the evaluations were not rerun for the current release. Reference scores are historical snapshots, not live rankings. Scores from different benchmark families are not directly comparable, and numerical gaps do not establish statistical significance.

Read the AstaBench and ASI-Bench report and the BiomniBench-DA report for evaluation details.

Core Capabilities

AIPOCH Open-Science combines project management, multi-model agent execution, Python and R notebooks, scientific data connectors, immutable artifact versions with provenance, and permissioned human-in-the-loop control in one local workspace. The installed app and latest release notes are the source of truth for changing catalogs, packaging details, and newly added options.

When querying the eQTL Catalogue by genomic coordinates, use the GRCh38 assembly. The connector checks the assembly before looking up matches.

Import references, tags, collections, and source links from Zotero 10 or later running on the same computer, with the option to copy local PDF attachments. This is a one-way import into Open-Science and does not modify Zotero.

Add a reference using a DOI, arXiv ID, PMID, PMCID, or supported article link, or paste up to 100 references with one per line. Review and edit a single reference before saving, or select which references to import in a batch. Full-text PDFs can be added separately.

Persistent PDF annotations and document notes are available. With a screening model configured, smart collections evaluate references against your inclusion/exclusion criteria; AI decisions remain separate from manual decisions and support human review.

In reading mode, agents can list, locate, and create notes and marks on linked PDFs. PDF annotations are tied to the annotated file version and do not sync across computers.

Open a paper's detail view to generate an AI paper overview with your configured main model. The card identifies its source (full text or abstract) and summarizes the research question, study population, methods, main findings, and limitations.

Optional OCR recognizes scanned PDF pages after the component and required language packs have been downloaded. Nine language packs are available: English, Simplified Chinese, Traditional Chinese, German, Spanish, French, Japanese, Korean, and Russian. Page-level coverage indicators show which pages still lack text.

Translated PDFs can optionally reflow body text and captions into a single-column, content-first layout while retaining images, tables, formulas, and the bibliography in their original form, with source-to-output page mappings for comparison. On new setups, Notebook and Shell folder restrictions are enabled by default and confine those runtimes to the workspace, approved folders, and files required by tools; this setting does not control agent-native tools or remote computers.

In the desktop app, artifact provenance can also be exported as RO-Crate 1.1. A version-bound environment bundle can create a managed environment only when the captured bundle is complete and compatible with the target platform; this is separate from a full machine backup.

Browse research cases in the resource center, filter by category, search by keyword, and download packages from case details.

Model Providers

AIPOCH Open-Science is model-agnostic at the product level: connect it to major cloud LLM providers, a custom gateway, or reuse an existing Claude or Codex subscription. Provider availability currently depends on the selected agent backend and the API protocols it supports. The following connection options are available:

There are 25 preset provider options, including OpenAI, Anthropic, DeepSeek, NVIDIA Build, and others; available models and regional endpoints depend on the installed version and selected agent backend. Check the provider selector and connection test in the app.

Data, Permissions, and Trust

AIPOCH Open-Science stores project data, settings, artifact versions, and provenance evidence on the local computer. API Keys are kept locally and use the operating system's secure credential storage when it is available. Logs are local and are not uploaded automatically.

External data flow is still possible and should be reviewed:

• Model requests send the prompt and necessary context to the selected model provider.
• Web searches and remote connectors send their displayed parameters to external services.
• Local connectors may execute trusted commands on the computer.
• The application can also contact update servers, marketplace catalogs, and runtime or model download services.
• Attachments, @ references, logs, and generated reports may contain sensitive research data.

Choose the narrowest permission profile that fits the task:

The effective profile depends on the selected backend and existing grants. Connector, tool, and compute-network policies also apply; check the effective mode shown by the app.

Review connector parameters and tool activity before approving them. Never include API Keys, access tokens, patient identifiers, unpublished data, or sensitive local paths in screenshots or public issue logs.

Development & Packaging

AIPOCH Open-Science has an ordinary Node backend and an Electron desktop client, built with React, TypeScript, Prisma/SQLite, and an ACP-based agent runtime.

Prerequisites for source development:

• Node.js 24 (see .nvmrc) with npm
• Git
• Notebook execution optionally uses app-managed Python/R environments or a compatible interpreter you configure.

git clone https://github.com/aipoch/open-science.git
cd open-science
npm install
npm run dev

See the development command and packaging reference and contribution guide for build commands and the development workflow.

Localhost Web and standalone Node runtime

The same Web UI can run on an ordinary Node backend without Electron or a graphical display. Electron starts or attaches to that same Node service. One runtime owns each data directory.

npm run dev:web       # Build and run the Node backend + Web UI
npm run pack:backend  # Build a target-specific standalone npm tarball

For a published installation, use npm install --global @aipoch/open-science; the commands above are for source development. Install the package or locally built tarball, then run open-science start --no-open and open-science url to open the existing UI. See the standalone runtime guide for installation, secure credential storage, host capability boundaries and data compatibility.

The desktop app and browser share the same service. App connections and an explicit open-science start hold keep it running. Quitting the last App stops the service only when no explicit hold remains.

Mobile remote access

Scan a QR code to open the current workspace on a phone or tablet over local WiFi or a Pinggy tunnel. Local WiFi pairing stays on your network; internet tunnels route traffic through Pinggy’s servers, as disclosed in the connection dialog.

Headless CLI and SDK

The headless CLI and zero-dependency Node.js SDK use the same local daemon, projects, sessions, credentials, and permissions as the desktop and web interfaces. Detailed usage lives with the publishable package so there is one command reference to maintain:

• CLI guide - installation, service lifecycle, task automation, artifacts, output formats, and exit codes
• SDK package overview - Node.js quick start and package entry point

The standalone CLI can expose its research tools to external MCP clients through open-science mcp. Start and authenticate the selected profile's service first. The adapter uses that profile's existing permissions; it is not a separate sandbox or read-only view, so connected clients can persist changes and incur model costs. It does not expose arbitrary shell or RPC access, credential management, file import/export, download-to-path operations, or a remote HTTP MCP endpoint.

Frequently Asked Questions

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