OpenResearch

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.

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.

Hyperresearch is an MIT-licensed Python command-line research harness that extends Claude Code with an agent-driven, persistent research knowledge base. It classifies a query into light, full, or opt-in dissertation work and runs a tier-adaptive pipeline covering query decomposition, source search and fetching, contradiction and locus analysis, depth investigation, evidence digestion, multi-angle drafting, synthesis, adversarial criticism, targeted gap fetching, surgical patching, citation checks, and readability review. The pipeline stores fetched sources as Markdown notes with a rebuildable SQLite index, provenance links, lifecycle statuses, full-text or optional semantic search, source-quality and retraction metadata, and resumable per-run manifests. It can fetch PDFs, seek disclosed open-access replacements for thin or blocked academic sources through Unpaywall and Europe PMC, and apply structural checks for citation bindings, quote integrity, provenance, retractions, and patch-only editing. The vault can also be accessed through an MCP server or a dependency-free local web UI. It is installed with pip for Python 3.11–3.13 and is intended to run with Claude Code; model assignments and research scale can be configured per project.

OpenClaude is an open-source, terminal-first coding-agent CLI for cloud and local model providers. It connects to OpenAI-compatible APIs, Gemini, GitHub Models, Codex, Ollama, Atomic Chat, and other supported backends, providing prompts, streaming output, Bash and file tools, grep, glob, agents, tasks, MCP, slash commands, web search and fetch, and image inputs for compatible providers. The CLI supports guided provider setup with saved profiles, conversation continuation and forking, detached local background sessions, model-specific agent routing, repository maps based on PageRank-ranked code structure, and a headless bidirectional-streaming gRPC server for integrations, CI/CD pipelines, and custom interfaces. A bundled VS Code extension provides launch integration, in-editor chat, provider-aware controls, and theme support. OpenClaude runs on Node.js 22 or newer, is distributed through npm and an Arch Linux AUR package, and can use local inference or remote APIs. Its repository is licensed MIT for the project's modifications and states that it is an independent community project derived from and substantially modified from the Claude Code codebase, without Anthropic affiliation.

OpenWorker is an open-source desktop AI coworker that produces finished deliverables from everyday tasks, including code-security reviews with proposed fixes, cloud-configuration audits, incident reports, documents, spreadsheets, drafted messages, and scheduled briefs. It runs on the user's machine through a native desktop app and a local Python agent server built on aisuite, working across local files, the terminal, connected applications, and more than 25 connectors. Specialist coworkers cover security review, cloud posture, incident triage, everyday work, and recurring automations; security review combines deterministic scanners such as Semgrep with model reasoning, then re-scans and diff-reviews proposed fixes before approval. The agent breaks requested outcomes into steps and uses models from providers such as OpenAI, Anthropic, and Google, open-weight providers, or a local Ollama deployment, with the user supplying model access. Consequential actions—including sending messages, changing calendars, and running commands—are approval-gated. Its governance design includes human-only floors for dangerous or irreversible operations, explicitly granted and revocable autonomy rules, circuit-breaker escalation for uncertain or repeatedly denied actions, and an audit trail recording tool calls, approval provenance, and reviewer reasoning; unattended runs cannot self-approve. The project is in open beta and provides downloads for macOS on Apple Silicon and Windows on x64.