OpenSpec

OpenScience is an open-source AI workbench for scientific research developed by Synthetic Sciences. It runs as a browser-based workspace where a single research agent takes a goal through literature review, hypothesis formation, code writing and execution, experiments, analysis, and a written report. The agent can load domain skills, delegate bounded exploratory or execution work, query scientific databases, and preserve sessions as observable research traces. The local server hosts the workspace UI, agent runtime, skill library, and tool layer. The agent plans with a research harness and calls shell, editor, LSP, MCP, scientific database, and skill tools; sessions, skills, artifacts, and provenance are stored on disk. The workbench includes a file tree, code editor, terminal, session history, and inline rendering for molecules, structures, genomes, and plots. Its bundled skills cover training, evaluation, datasets, molecular and clinical biology, cheminformatics, papers and LaTeX, figures, and scientific runtimes, while database connectors provide direct access to services including UniProt, PDB, Ensembl, ChEMBL, PubChem, arXiv, OpenAlex, and Semantic Scholar. It supports frontier and open-weight models through user-provided keys, eligible ChatGPT or Codex access, local models, and optional Ace-managed models. Models are routed per request, allowing providers to be switched without changing the workspace. Extensibility includes LSP integration, MCP servers, plugins, custom agents and commands, experimental Python environments and BioNeMo adapters, and a TypeScript SDK. It is installed with the @synsci/openscience npm package or launched through npx; platform binaries and desktop installers are distributed through GitHub Releases. A free Synthetic Sciences account links an installation and can provide credential synchronization, private research graphs, enhanced search, and optional credit-backed models, while BYOK and local-model usage remain separate from Ace.

GitHub Spec Kit is an open-source, community-driven toolkit from GitHub for spec-driven software development with AI coding agents. It provides the Specify CLI to initialize projects and create feature branches, establishes project principles in a constitution, defines specifications, plans implementations, breaks plans into actionable tasks, implements them through an agent, and checks the result against the specification, plan, and tasks. The toolkit supports integrations with AI coding agents and includes extensions, presets, role-based bundles, and an opt-in bug-fixing extension with an assess–fix–test workflow.

OpenResearch is a local-first workspace for research agents and autoresearch, available as a desktop application and a macOS/Linux CLI. It turns Claude Code, Codex, or OpenCode into agents that can review literature, develop hypotheses, run experiments, and produce research artifacts. The workspace assigns independent agent sessions and isolated Git worktrees to parallel research directions. It tracks experiment variants in a Git-native experiment tree, archives each run against its recorded commit, and keeps logs, diffs, files, results, and artifacts tied to the work that produced them. Its autoresearch loop can propose an idea, modify code, launch an experiment, inspect evidence, and choose the next direction. The same committed source snapshot can run locally or through SSH, Slurm, Kubernetes, Ray, Hugging Face Jobs, Modal, Tinker, or managed OpenResearch compute. By default, projects and run data remain on the user's machine in a local SQLite store, with a browser dashboard served on localhost. An OpenResearch account is used for service-owned capabilities such as organizations and managed compute. The CLI also installs an OpenResearch skill into supported coding agents and provides commands for projects, runs, logs, experiments, discovery, and paper lookup.

Open Science is an open-source, local-first, model-agnostic AI research workbench developed by AIPOCH for scientists and researchers. It runs on macOS, Windows, and Linux and supports computational and data-intensive research in fields including machine learning, statistics, life sciences, chemistry, materials science, physics, and environmental science. Users create projects and sessions, describe research goals in plain language, attach source files, select a model and approval mode, and inspect the agent’s tool activity. Scientific AI agents can read files, search the web, execute Python and R code, query scientific data sources, and produce reports, tables, figures, and other research artifacts. The workbench supports literature review, hypothesis development, code execution, data analysis, simulation, visualization, and related reproducible research workflows. Its first-run setup checks the environment, configures model-provider credentials, optionally prepares Python and R runtimes, and prepares an agent runtime such as Claude Code, OpenCode, or Codex; app-managed runtimes can be installed without Node.js, npm, or administrator privileges. Projects keep sessions, uploads, generated files, and preview state together. Conversations record agent answers and the commands, file reads, edits, searches, and connector calls behind them. Generated artifacts are stored as immutable, checksummed versions; their Provenance view can show producer code and execution history, referenced inputs, the observed environment inventory, the producing conversation branch, and version-scoped reviewer findings, marking unavailable evidence as unavailable rather than guessing.