Wisp Science — Local-first AI research workbench

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Wisp Science is an open-source, local-first desktop AI research assistant and scientific computing workbench. It connects to OpenAI-compatible and Anthropic models, runs persistent Python and R environments on local, SSH, WSL, and GPU compute, loads reusable Agent Skills (SKILL.md), and reaches ~80 bioinformatics and computational biology databases through bundled Model Context Protocol (MCP) servers.

Built with Rust, Tauri v2, and Leptos, Wisp Science runs as a cross-platform desktop app or a headless CLI.

Our manifesto: Wisp Science is open source and borderless. We are building a scientific workbench that anyone, anywhere can use, study, improve, and share.

Status: MVP vertical slice. The agent loop, streaming providers, tools, Python/R REPLs, SQLite store, MCP client, and Leptos UI all build and run. See Roadmap for what is deferred.

Layout

wisp-science/
├─ crates/
│  ├─ wisp-llm/     Provider trait + OpenAI-compatible + Anthropic + SSE + RoutedProvider
│  ├─ wisp-core/    ContextManager (3-tier compaction), SystemPrompt, agent_loop, memory
│  ├─ wisp-tools/   read/write/edit/search/grep/shell/attempt_completion + Windows safety
│  ├─ wisp-store/   sqlx SQLite (projects/frames/messages/artifacts/settings) + OS keyring
│  ├─ wisp-skills/  SKILL.md discovery + search_skills/use_skill progressive loading
│  ├─ wisp-runtime/ project-scoped Python/R runtime manager + REPL tools
│  ├─ wisp-mcp/     stdio JSON-RPC MCP client + McpTool adapter (bundled bio-tools)
│  ├─ wisp-acp/     ACP v1 stdio client for external coding agents
│  ├─ wisp-sync/    Encrypted snapshot protocol + self-hosted relay server
│  └─ wisp-cli/     `wisp-science` headless binary
├─ src-tauri/       Tauri v2 desktop shell (commands + agent event stream)
├─ ui/              Leptos CSR frontend (built by Trunk, loaded in WebView2)
├─ python/          kernel_worker.py + mock MCP server (uv-managed)
├─ r/               optional system-R kernel worker (requires jsonlite)
├─ skills/          Bundled SKILL.md catalog (29 science workflows)
├─ mcp-servers/     Bundled MCP servers (bio-tools: ~80 DB clients)
└─ seed/            Bundled demo session recordings (CRISPR / enzyme / extremophile / immunotherapy)

Prerequisites

• Rust (stable, 1.88+) with wasm32-unknown-unknown: rustup target add wasm32-unknown-unknown
• uv (Python environment manager): https://docs.astral.sh/uv/
• Optional: R with Rscript on PATH and the jsonlite package for the persistent r tool. Wisp never installs R packages automatically.
• Trunk (WASM frontend bundler): cargo install --locked trunk
• Tauri CLI v2: cargo install tauri-cli --version "^2"
• WebView2 Runtime (Windows only) — Windows 10/11 usually has the Evergreen Runtime, and the installer acquires it when missing. An outdated or damaged Runtime can still prevent the main window from appearing; see Windows installation and startup troubleshooting.
• Xcode Command Line Tools (macOS only): xcode-select --install — macOS uses the system WebKit, so no extra runtime is needed.

Build & run

Headless CLI

$env:WISP_API_KEY = "<your provider key>"
$env:WISP_PROVIDER = "openai"           # openai=OpenAI-compatible Chat Completions; or openai_responses / anthropic
$env:WISP_MODEL     = "deepseek-v4-pro" # openai_responses: gpt-5.5; anthropic: claude-sonnet-5
cargo run -p wisp-cli

The CLI auto-loads the bundled skills/ catalog and wires the bundled Python and optional system-R REPLs. Python provisions a uv venv at .wisp/python/.venv on first run; R uses Rscript from PATH and requires jsonlite in that R environment. In the desktop app, Python and R interpreter paths are saved per execution context from Settings → Environments or the agent's set_runtime_interpreter tool, so local, WSL, and each SSH server can use different environments without host environment variables. Each runtime card's Configure path action opens these per-context Python and R settings. The tool restarts the current project's matching REPL when needed, so a failed runtime can recover without restarting the Wisp app; restarting clears that REPL's in-memory state. Clicking a runtime's Python or R label opens its RStudio-style in-memory environment table beside the runtime cards with bounded object names, types, values/shapes, and sizes. Pinning that table moves it onto the conversation as a draggable window, where it remains available after the runtime dialog closes. The composer's agent-options menu groups Auto-review, Reviewer model, Memory, Specialist, and Compute controls in one place. Local compute is always available; the searchable Compute menu only lists configured remote servers. A server must be explicitly selected for the current conversation before the agent can use it, and selected servers are preferred for suitable work. The selection is isolated per conversation. Settings → Environments always shows local compute and owns adding, importing, removing, configuring, and probing environments; it does not control conversation resource selection. Probe uses the bundled probe-compute-environment skill to persist hardware, scheduler, runtime, and privilege facts. An SSH probe performs all checks through one authenticated connection. Batch SSH/SCP always enables OpenSSH IdentitiesOnly; configure an IdentityFile in Wisp or SSH config when using a non-default agent key. SSH hosts must be successfully probed with the configured connection settings before the agent can use them. Hosts can authenticate with an SSH key/agent (recommended) or a password stored only in the OS keyring (never SQLite). Enabling a host without a known-good probe opens a dialog that asks you to check the server and Probe first. Free-form shell ssh/scp is disabled so remote work always uses the registered alias, user, port, and key/password. A failed SSH probe, upload, or launch is not retried automatically: Wisp opens a connectivity gate for that host, fails further managed attempts immediately (without contacting the server), shows a warning, and requires a manual probe after the connection is fixed. The Environment side panel always shows local compute plus the remote servers selected for the current conversation. Each Python or R cell is limited to 1 MiB of source so a malformed request cannot exhaust the persistent worker before execution begins.

Settings → Credentials includes built-in service fields and user-defined credentials. A custom entry maps a display name to the exact environment variable expected by a skill or client (for example METASO_API_KEY). Its metadata is stored in local settings, but the secret value is stored only in the OS keyring and is injected into newly launched local Python and bundled MCP processes. Custom credentials are never copied to SSH/WSL hosts.

Desktop app

cargo tauri dev      # hot-reload: Trunk serves UI, Tauri opens WebView2
cargo tauri build    # produce an MSI/NSIS installer under target/release/bundle

Windows installation and startup troubleshooting

• If Microsoft Defender SmartScreen blocks the unsigned MSI/NSIS installer, verify that it came from this repository's GitHub Releases, then choose More info → Run anyway.
• If installation completes but the main window flashes and disappears, remains invisible, or leaves only the system-tray icon, first choose Quit from the tray menu. Download the latest architecture-matched Evergreen Standalone Installer from Microsoft's official WebView2 download page and run it as administrator to update or repair Microsoft Edge WebView2 Runtime. This asks for a current, supported Evergreen Runtime; it does not mean that Wisp Science depends on one fixed major version.
• Reopen Wisp Science after the WebView2 installer completes. If the window is still missing, restart Windows and try again. If the problem persists, include the winver result, WebView2 Runtime version, installer filename, and reproduction steps in an issue. Never post API keys, tokens, passwords, or private keys.

Desktop development uses port 1421. UI tests use 1422, and their Trunk outputs are isolated in ui/dist-dev and ui/dist-test; release packaging continues to use ui/dist. This prevents a running dev/test server from racing with cargo tauri build while it copies the optimized WASM bundle.

Image and PDF previews can be zoomed and drag-panned whenever their content extends beyond the visible viewport, including at 100% zoom.

On macOS, run the same commands from a shell (cargo tauri build emits a .app and .dmg under target/release/bundle). src-tauri/tauri.macos.conf.json is auto-merged by Tauri to replace the PowerShell beforeBuildCommand with a cross-platform vendor-runtime generation plus trunk build. For a universal binary (Apple Silicon + Intel):

rustup target add x86_64-apple-darwin
cargo tauri build --target universal-apple-darwin

The .app/.dmg are unsigned — first launch needs right-click → Open (or allow it in System Settings → Privacy & Security).

The desktop app stores API keys in the OS keyring and model profiles in .wisp/wisp.sqlite (Settings -> Models). Profiles can point at remote API providers. See Model configuration for the provider fields. The per-turn model/tool loop limit is configurable under Settings → General → Maximum agent iterations per turn (default: 100; 0 disables the limit). Conversations persist to that SQLite database — each turn's messages are appended to the active session frame, so restarting the app restores the full history. The headless CLI keeps using .wisp/session.json for portability.

Projects can be moved between Windows and macOS from the Projects screen. Use the download action on a project card to export a versioned ZIP, then Import project on the other computer. The importer asks for a parent folder and creates a new project directory there; Windows drive letters are never reused. See Project transfer for contents and limitations.

Projects can also be synchronized explicitly between devices. Configure either a self-hosted relay or a folder managed by the Baidu Netdisk/Nutstore desktop client in Settings → General, then press Sync now on a project card. Synchronization never runs in the background and refuses to start while a task, approval, review, or run is active. Project contents are encrypted before they reach either backend; workspace files are uploaded incrementally by content. See Manual project sync or the Chinese sync guide for setup, device codes, conflicts, path behavior, relay deployment, and limitations.

Local ACP Agents

Wisp can launch any already-installed local agent that speaks stable ACP v1 over stdio. This is separate from Settings → Models (HTTP API profiles).

Quick path:

1. Install an ACP adapter, for example Codex: npm install -g @agentclientprotocol/codex-acp
2. Open Settings → Models → ACP Agents, or from the chat model picker click Add ACP Agent. Do not put ACP launch commands in the HTTP “Add model” form.
3. Set Label, Command (codex-acp or npx / npx.cmd), and Arguments (one per line; for npx use -y then @agentclientprotocol/codex-acp).
4. Save Agent → Test Connection → authenticate if offered.
5. Select the agent and send a prompt. If the current conversation already has messages, Wisp starts a new empty session automatically because ACP cannot rebind existing transcript history. The selection locks after the first message.

Do not use plain codex / claude here — they are not ACP. Use an adapter such as codex-acp or claude-agent-acp.

Full setup, Claude example, Windows notes, and troubleshooting: docs/acp-agents.md.

Controlled Agent workflows

Enable Delegation for the current conversation from the composer Agent menu, then open the right panel's Agents tab—or ask the main Agent to propose a plan—to create a persisted multi-Agent workflow. Manual mode uses an explicit ordered team, Assisted mode asks the active model for a reviewable draft, and Automatic mode starts only low-risk local read-only plans without a second confirmation. Review capability limits, then run, cancel, retry, or take over a child conversation. See Controlled Agent delegation for the lifecycle, safety boundary, and current limits.

Composer references and search

In a desktop conversation, type @ to attach a saved artifact, an uploaded file, an execution context, or a language runtime; # to attach a saved session (including another project) or choose #project to search every other saved session in the current project (the open conversation is already in the main context); or / to apply an enabled skill to the next turn. Attachments are explicit, removable chips; cross-project artifacts stay at their original local path and are never copied automatically. The same references work with ACP Agents: selected skills and Reader evidence are sent as ACP text blocks, while artifacts are sent as file links.

Session references are retrieved by the built-in, read-only Reader instead of copying complete transcripts into the main model context. Reader first fans out one task per session. If one session exceeds Reader's configured model context window, only that session is split again at user-turn boundaries; all chunks run under one bounded parallelism limit and merge back into a cited session result. Configure Reader's lower-cost model under Settings → Specialists → Reader, and set that model's context capacity under Settings → Models → Context window. Reader returns compact summaries plus excerpts grounded to saved message sequence numbers; it does not create child sessions or use tools.

Files you uploaded are artifacts too, so they already appear under @; they carry an Upload badge that separates them from files the agent produced.

Image previews include a region-selection tool. After you drag a region, Wisp keeps it highlighted and asks whether to add the crop to chat while staying in the preview, or add it and jump back to the conversation. The crop is not attached until you choose an action.

@ also reaches compute. Naming an execution context (@CPU1) points the turn at that server and turns it on for the conversation, so you do not have to enable it in the compute menu first — local compute needs no toggle and is always available. Naming a runtime (@runtime_R) points the turn at the persistent Python or R session on that context, which keeps its variables between calls, so the agent inspects live state instead of re-running earlier work. Runtime entries appear only for contexts where that interpreter is configured or probed. Referencing a runtime does not start it.

Use Ctrl+K on Windows/Linux or Cmd+K on macOS to search projects, artifacts, sessions, and common commands. Enter opens the selected result; Shift+Enter attaches an artifact or session to the composer.

The composer sends with Enter and inserts a newline with Shift+Enter by default. Under Settings → General → Send and newline shortcuts, you can instead use Ctrl+Enter on Windows/Linux or Cmd+Enter on macOS to send, leaving Enter for a newline.

Saved conversations and conversation folders expose visible action buttons in the sidebar on macOS, Windows, and Linux. Use them to rename or delete folders, or to rename, organize, copy, move, export, or delete a conversation. The sidebar loads the newest 100 conversations first; use Load earlier sessions to fetch older pages. Opening a conversation initially loads its newest 20 user turns; use Load earlier messages at the top of the transcript to fetch older complete turns without splitting tool calls from their results. The chat mounts at most 40 complete user turns at once; use the earlier/newer controls to move through already loaded history without growing the DOM unboundedly. Remote file rows also expose a visible download action, while secondary-click remains available as an alternate path. Cross-project transfer copies the saved transcript only. Project files and runs remain in their source project; conversation-linked artifact records are not transferred, and the underlying workspace files are never deleted.

During an agent turn, assistant progress notes, reasoning, and ordinary tool calls are grouped into one collapsible steps panel. The final answer remains a normal message, and reopening the conversation preserves the same separation.

On macOS, the native app menu mirrors the global desktop command surface, including project navigation, new-session commands, edit shortcuts, and Check for Updates…. Row-specific conversation and folder actions stay beside their rows. The same update check is also available from the Settings page and the Windows in-window Help menu. It now reports the result in an in-app dialog, including whether you are already up to date or a newer release is available on GitHub Releases.

Bundled demos

Configuration

All optional; sensible defaults are bundled.

Bundled bio-tools MCP

WISP_MCP_PKG=mcp_pubmed launches mcp-servers/bio-tools/run_server.py mcp_pubmed inside the uv venv. The venv must have the server's dependencies installed first:

uv pip install mcp requests
# plus any server-specific deps (httpx, xmltodict, etc.) the package imports

The agent first discovers matching tools with search_mcp_tools, then calls the selected tool through use_mcp_tool. The full server catalog is never copied into every model request.

Notion MCP

Notion uses the same remote-URL workflow as other hosted MCP services. Open Settings → Connections → Add connection, choose Remote URL, enter https://mcp.notion.com/mcp, set Authentication to OAuth, and select Test or Save. Either action opens Notion's authorization page in your browser. Test validates the connection and lists its tools without saving it; Save stores the connection only after authorization succeeds. Startup never creates or authorizes a Notion connection.

OAuth access and refresh tokens are kept in the OS keyring rather than the project database. Deleting the saved connection removes its credential. Its detail page reports the service URL, enabled status, and OAuth authentication method.

The connection applies to newly created agent sessions. Notion controls the workspace permissions exposed to the agent, so review write actions before approving them.

File previews

The Files panel previews Jupyter .ipynb notebooks without executing them. Markdown and source cells are rendered alongside outputs that were saved in the notebook: text and tracebacks, raster images, SVG, static HTML, and LaTeX. HTML outputs run in a script-free sandbox with external references removed, SVG is loaded through an isolated image context, and large individual or aggregate outputs are omitted to keep the desktop WebView responsive.

PDF and modern Office files are also previewed fully offline. DOCX keeps its document layout, XLSX opens as a read-only virtualized workbook with sheet tabs and a formula display, and PPTX uses lazy, windowed slide rendering. PDF and OOXML bytes cross the Tauri IPC boundary as raw ArrayBuffers instead of Base64. Office previews are bounded to 32 MiB compressed input and reject unsafe or excessively expanding ZIP packages; they do not execute macros, ActiveX, OLE, formulas, or external relationships. Complex Excel styling and PowerPoint animations may not match Microsoft Office exactly.

Bundled demos

seed/ ships four pre-baked example sessions (CRISPR screen, enzyme engineering, extremophile, immunotherapy) recorded from the upstream agent. In the desktop app, Open demo lists them and opens one as a read-only User + Assistant transcript. Bundled assets_*.tar.gz archives are extracted into the workspace on open so figures and data files in the right panel preview correctly.

Testing

• Rust unit tests — cargo test --workspace (covers wisp-store SQLite round-trips, the seed demo loader, etc.).

• MCP client smoke — cargo run -p wisp-mcp --example smoke launches the bundled mock MCP server via uv and round-trips tools/list + tools/call.

• UI E2E (Playwright + Tauri mock) — ui-tests/ runs the Leptos UI in a headless browser against trunk serve, with a mocked window.__TAURI__ so no Rust backend or API key is needed:

  cd ui-tests
  npm install
  npx playwright install chromium      # one-time browser download
  npx playwright test                  # serve UI + run the full mocked desktop flow suite
  

  The mock (tests/mock-tauri.ts) stubs invoke/listen with canned data and even simulates a streamed assistant turn, so the tests exercise the real Leptos rendering and event handling without touching the network.

Architecture

• Agent loop (wisp-core::agent): read → think → tool-call → verify, streaming tokens to an Output sink. Stops on attempt_completion or when the model returns no tool calls.
• Context compaction (wisp-core::context): three tiers fire before each model call at 80% of the context budget — micro-compact oversized tool output, drop old turns, then an LLM-driven full summary as a last resort. Skill catalogs and MCP schemas use progressive disclosure instead of living in the baseline prompt.
• Providers (wisp-llm): one trait, two wire formats (OpenAI /chat/completions and Anthropic /v1/messages), both with SSE streaming. RoutedProvider picks a low/medium/high tier per turn from the last user message.
• Tools (wisp-tools): filesystem + shell tools with Windows-aware dangerous-command gating and a dunce-canonicalized path sandbox rooted at the project directory.
• Python/R REPLs (wisp-runtime): one manager-owned process per project/execution context/language keeps its namespace across cells and conversations; local, WSL, and SSH contexts use the same versioned protocol. R is optional and uses an existing Rscript plus jsonlite. The Contexts panel probes interpreter capabilities; selecting a local, WSL, or SSH server reveals only that context's runtimes and runs in the detail pane. Runtime details include status, memory, last activity, destructive Stop/Restart controls, and a read-only environment table opened from the Python/R label with object names, types, shapes/sizes, and bounded metadata. The table can be pinned over the conversation and dragged without interrupting the runtime.
• MCP (wisp-mcp): a minimal newline-JSON-RPC client launches any stdio MCP server. Remote schemas stay behind the fixed search_mcp_tools / use_mcp_tool pair until a task needs them.

Acknowledgements

Special thanks to these community members for their hands-on testing and valuable suggestions:

• Claude Science (Operon) is referenced in product comparison and compatibility research.
• The agent core is based on w4n9H/mangopi-cli (Apache-2.0).
• skills/ and mcp-servers/bio-tools/ vendored from the upstream wisp-science asset bundle (Apache-2.0).
• skills/bear-* from bear-research-skills (CC BY-NC-SA 4.0); requires scimaster-cli for live retrieval.
• kernels/kernel_worker.py protocol adapted from the upstream operon kernel worker, with POSIX-only resource//proc/SIGINT machinery dropped for Windows.

See LICENSE (Apache-2.0). Upstream notices are preserved in their respective directories.

Citation

If you use wisp-science in your research, please cite:

@software{xu2026wisp,
  author    = {Xu, Zhougeng and hoptop},
  title     = {wisp-science: A local-first scientific computing agent},
  version   = {v0.4.1},
  year      = {2026},
  publisher = {Zenodo},
  doi       = {10.5281/zenodo.21193742},
  url       = {https://doi.org/10.5281/zenodo.21193742}
}

Roadmap (post-MVP)

• FlashThinking — phase-aware structured thinking-framework injection.
• loop_engine — deeper Implementer / Verifier / Updater workflows beyond the bounded automatic Reviewer pass shipped today.
• Artifact management + inline Mol* 3D structure viewer in the UI.
• RoutedProvider LLM-score tier selection (keyword tier is already wired).
• Bundling skills/ and mcp-servers/ into the Tauri installer so releases are fully self-contained without the source tree.

Star History