RAIL is a local-first Tauri desktop app for graph workflows, task threads, research collection, and workspace knowledge management.
It combines:
- a DAG canvas for structured multi-step execution
- a task thread surface for agent-driven work
- a research pipeline that can collect, normalize, and visualize evidence
- a local knowledge/database layer for stored runs, artifacts, and documents
RAIL is designed for fast iteration inside a single workspace without depending on a hosted orchestration backend.
RAIL supports three main working styles:
- Graph workflows Build node-based flows that combine role nodes, transforms, gates, and data collection.
- Task threads
Ask for work in a chat-like thread, tag role agents such as
@researcher, and review artifacts as they are produced. - Research monitoring Run research-oriented collection jobs, inspect structured evidence, and view question-aware charts in the Visualize tab.
The Graph tab is the canvas-first workflow editor.
Typical use cases:
- build multi-node pipelines
- branch on pass / fail decisions
- combine role outputs into a final document
- inject files and grounded evidence into later nodes
The graph runtime is oriented around DAG execution and explicit node-to-node handoff.
The Tasks tab is the fastest way to ask for work.
Key behavior:
- create a thread
- tag one or more role agents such as
@researcher - stream status, logs, and artifacts into the thread
- stop a running request from the composer
- inspect related files and generated outputs in context
Tasks are useful when you want the system to choose the execution path for you instead of hand-building a graph.
The Visualize tab is the research monitor.
It is intended for questions such as:
- “What are the best-rated genres on Steam right now?”
- “Compare community sentiment for these games.”
- “Show the strongest evidence behind this research report.”
Visualize reads normalized research outputs and renders:
- question-aware charts
- timeline or aggregate tables when appropriate
- evidence streams
- research history / prior sessions
The Database tab is the local knowledge browser.
Use it to:
- inspect stored run artifacts
- open grouped documents
- review previously generated outputs
- manage saved research and knowledge entries
The Settings tab contains operational controls for the app.
Current settings areas include:
- appearance and base preferences
- Web Connect / bridge status
- account and Codex-related controls
- memory and retention management
- locale selection
RAIL includes a research-oriented collection path used by @researcher.
That flow can:
- interpret the user question
- choose a collection mode such as genre ranking or comparison
- collect relevant evidence
- normalize collected items into local storage
- generate a report spec for Visualize
The current system is built so that new research runs can be viewed later instead of being lost after a single answer.
RAIL can expose a local bridge for browser-connected flows.
The bridge surface shows:
- local bridge URL
- full connection code for the extension or external client
- restart and refresh controls
The bridge is intended for local workflow integration, not public deployment.
RAIL is local-first.
Important data is kept in the workspace, including:
- task runs
- studio role runs
- collected research artifacts
- normalized research storage
- knowledge/database entries
This makes it possible to inspect or reuse prior work without depending on a remote service.
The current user-facing locale selector supports:
- Korean
- English
Some additional locale assets may still exist internally, but the current settings surface is intentionally limited to the actively supported options.
- Tauri
- React
- TypeScript
- Vite
Install dependencies:
npm installRun the app in development:
npm run tauri:devType-check:
./node_modules/.bin/tsc --noEmitRAIL is actively evolving around:
- reliable role-agent orchestration
- research collection quality
- better visualize/report generation
- local knowledge and artifact management
- low-noise desktop UX for everyday use