aipoch/open-science

AIPOCH Open-Science AI research workbench for reproducible science — open-source, local-first, and model-agnostic. AIPOCH Open-Science is an AI research workbench for scientists and researchers, developed by AIPOCH with an open-source, local-first, model-agnostic approach. It enables reproducible, inspectable research with scientific AI agents, Python and R execution, scientific data connectors, and cross-platform support for macOS, Windows, and Linux. Create a project, describe your research goal in plain language, and let the agents read files, search the web, run code, query scientific data sources, and produce reports, tables, and figures with traceable provenance—all in one workspace. AIPOCH Open-Science supports computational and data-intensive research across disciplines, including machine learning, statistics, life sciences, chemistry, materials science, physics and environmental science. It supports the research process from literature review and hypothesis development to code execution, data analysis, simulation, visualization, and the production of traceable research outputs. 💡 AIPOCH Open-Science v0.32.0 released (last updated September 2026) . AIPOCH Open-Science v0.32.0 makes PDF evidence durable and artifact exports interoperable: persistent annotations and a per-file document notebook keep text styles, area marks, notes, comments, colors, and tags with the file version they belong to, exporting to annotated PDFs or Markdown/CSV without touching source bytes, and verified artifact versions now package as complete RO-Crate archives with their exact inputs. Sequence and omics work gains asynchronous NCBI BLAST searches, ENA run discovery with original submitted files, PRIDE project file listings, and UniProt protein discovery by gene or organism. Installing local PDF parsing models no longer depends on a single download source thanks to verified mirrors, capability selection can point at a custom self-hosted classification service, and unattended CLI runs can be barred from waiting on humans. Startup and long conversations run faster through batched recovery, deferred Markdown work, and reduced observer overhead. See the latest release notes for full details. Table of Contents Quick Start Product Tour Benchmark Performance Core Capabilities Model Providers Data, Permissions, and Trust Development & Packaging Frequently Asked Questions Get Involved License Star History 🚀 Quick Start 1. Download the app Open the latest release , expand Assets , and choose the installer for your computer: Your computer Choose macOS 12+ — Apple Silicon (M1 or newer) The macOS DMG for Apple Silicon / ARM64 macOS 12+ — Intel The macOS DMG for Intel / x64 Windows x64 The Windows x64 installer Linux x64 The Linux x64 AppImage or Debian package Download from the official release page; see download verification if needed. On macOS, you can also install with Homebrew : brew install --cask open-science 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. Host compatibility, storage, and network checks Provider, API Key, endpoint, and model validation 3. Start a research project Click New project , open a session, and describe your research goal, inputs, and expected outputs. Attach files, select a model and approval mode, then send the task. Use @ to reference project files or / to choose a skill. 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. Product Tour From a research request to a traceable result Consider a representative bioinformatics task: reproduce a published differential-expression analysis, compare the regenerated results with the paper, and deliver the report, tables, and figures needed for review. The screenshots below are representative views from documented AIPOCH Open-Science workflows; they illustrate each stage rather than one continuous session. 1. Define the research task and evidence Describe the research question, source paper and datasets, required methods or thresholds, expected outputs, and acceptance criteria. Upload supporting files or reference an existing project artifact with @ , so the agent starts from explicit inputs instead of hidden context. 2. Execute with inspectable scientific tools The agent can combine scientific skills, permissioned research connectors, searches, file operations, and Python or R code in the shared Notebook. Generated figures can be reviewed beside the research summary, while the artifact record exposes captured producer code and execution evidence for inspection. 3. Review reports, tables, and figures in place The final response summarizes what reproduced, what differed, and which limitations matter. Generated Markdown reports, CSV tables, images, and other research artifacts remain attached to the session and are collected in the project file library, where they can be previewed beside the conversation and reused in follow-up work. 4. Trace every artifact back to its evidence Each generated artifact is stored as an immutable, checksummed version. Its Provenance view can expose the producing code and execution history, referenced inputs, observed environment inventory, producing conversation branch, and version-scoped Reviewer findings. Evidence that could not be verified is marked unavailable rather than inferred. Benchmark Performance 🏆 #1 on BiomniBench-DA Public 50 AIPOCH Open-Science achieved the highest ranking score in the compiled BiomniBench-DA Public 50 comparison, earning 79.05 with gpt-5.6-sol (xhigh) . The result combines a Gemini 3.1 Pro judge score of 81.04 and a DeepSeek v4-pro judge score of 77.06 through an equal-weight mean, placing AIPOCH Open-Science #1 among the collected Public 50 results. Explore the BiomniBench-DA dataset . 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. Area Core capability Scientific skills Extend research workflows with 23 built-in skills and 525 skills available from the Skills Marketplace , with one-click installation and updates. Create skills through conversation or completed work, and import packages or GitHub sources. Marketplace contributions are published after review; local imports do not publish skills. Connectors Access scientific resources through 24 built-in connectors , or add custom local and remote MCP connectors. Manage tool-level permissions and import or export connector configurations. Specialists and delegation Install 10 Specialists from the Specialist Marketplace , or create and customize personal specialists for delegation from the main agent. Specialist packages support import and export; marketplace contributions are reviewed before publication, and local imports do not publish them. Models and agent backends Use cloud models, compatible custom gateways, or Claude and Codex subscription logins. Choose Claude Code, OpenCode, Codex, or CodeBuddy as the agent backend, with model connection checks, image input, and reasoning controls. Projects, sessions, and research packages Organize projects with pinned sessions, message branches, side chats, and recoverable history.