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.

Completed research sessions can also be exported as portable .science packages for review, handoff, and archiving, with selected conversation branches, file versions, Notebook records, and verification evidence.

💡 AIPOCH Open-Science v0.37.2 released (last updated October 2026). AIPOCH Open-Science v0.37.2 recovers more gracefully: startup repairs backend, settings and network failures by itself, a runtime disconnect no longer traps the app — Quit stays available and a manual reconnect is offered — and large session and notebook snapshots transfer reliably. Scientific PDF structure extraction keeps improving, the Linux CLI also ships as standalone .deb and .rpm packages, and background Windows subprocesses stay console-free. See the latest release notes for full details.

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

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

Download from the official download page; see download verification if needed.

On macOS, you can also install with Homebrew:

brew install --cask open-science

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

brew install aipoch/tap/open-science

The CLI includes Node and runs without the desktop app. See the standalone runtime guide for startup, prerequisites, shared profiles and upgrades.

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

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

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.

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.

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.

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.

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.

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.

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 → 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; use Create a copy to continue research to run follow-up experiments. Re-executing a result still requires a complete recipe, required inputs, and an available runtime.

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.

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.

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. There are four ways to connect a model:

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.

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.

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.

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

Install the 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 that service. Quitting a desktop stops a server it started; quitting a desktop attached to a CLI-started server only disconnects the desktop.

The same localhost web UI can be reached from a phone or tablet through Remote.It pairing. Pair a browser with a six-digit AIPOCH Open-Science code, approve it once on the desktop, and the workspace stays reachable without exposing the loopback server directly. Browser trust is revocable, and mode changes or service shutdown immediately invalidate active remote sessions.

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

A: Check the API Key for missing characters or spaces, verify the Base URL and region, use the provider's exact model ID, and confirm network access and account balance. For a Claude subscription, retry the shared browser login or refresh the isolated claude setup-token credential, depending on the selected mode.

A: The current step has not met its required condition. Fix any environment row marked Action needed, install or repair the selected agent runtime, or validate the model provider, depending on the active step. Notebook setup is optional and only affects Notebook execution.

A: Remote Compute (SSH) is always enabled and does not need to be enabled in Settings. Register an SSH compute host under Settings → Compute, make it available to the current session, and then use natural language or /remote-compute-ssh. You need a reachable SSH host, valid authentication, permission for the required directories, and the software, dependencies, and compute resources required by the workload. Direct SSH does not require a scheduler; Slurm mode requires a working Slurm environment and permission to submit jobs. “Always enabled” refers to the skill, not to the availability of every registered host.

A: Yes. Install the standalone Node package, or use Settings → General → Command line tool → Install command to control an already running desktop instance. For independent startup, use the standalone package and ordinary Node. The Debian package-owned launcher is an exception: it starts the bundled backend with the bundled Node runtime. Initialize the local profile, then control the service and submit research tasks without opening a browser:

# Initialize the local CLI profile and start the service in the background
open-science init
open-science start --no-open

# Create a project and run a task by its exact name
open-science project create "Systematic review"
open-science run --project "Systematic review" \
  --prompt-file ./task.md \
  --approval-profile auto \
  --skill literature-review \
  --wait --json

# Download a generated artifact
open-science artifacts list <session-id> --json
open-science artifacts download <artifact-id> --output ./report.md

See the CLI guide for the full command reference, JSON/JSONL output formats, exit codes, and headless service options.

A: Open the generated artifact and choose Provenance. Select a version to inspect the content identity and the available producer code, execution history, inputs, environment inventory, producing conversation context, and reviewer evidence. Evidence AIPOCH Open-Science could not verify is marked unavailable.

A: Yes. Edit a completed user message and resend it to create a new branch from that point. The original later turns remain available, and the revision arrows beside the message switch between the alternative paths.

AIPOCH Open-Science welcomes bug reports, feature proposals, design discussions, community questions, and contributions through GitHub, Discord, X, and the AIPOCH website. Choose the channel that best matches your goal, then follow the linked contribution guidance and public-posting safety reminder before sharing project details.

Before opening a public issue, remove API Keys, tokens, private file paths, unpublished data, patient identifiers, and other sensitive material from logs and screenshots. See CONTRIBUTING.md for the development workflow.

⭐ Star the repo: If this project has been helpful, we'd greatly appreciate a star on GitHub. Starring the repository encourages continued development. It only takes a second, but it has a meaningful impact on the project.

For remote access setup, see the Remote control guide.

For shipped, partial, and planned capabilities, see the Capability Map.

Apache License 2.0 — see LICENSE.