RAG Frameworks · FutureUniant

WorkShadow

WorkShadow is a local-first desktop work journal application built with TypeScript, React, Rust, and Tauri. It provides rich-text logging, semantic search via embeddings, AI-powered summarization and Q&A against your own logs, and requires users to bring their own LLM/embedding models.

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Source: GitHub — github.com/FutureUniant/WorkShadow

817

GitHub stars

Forks

TypeScript

Primary language

AGPL-3.0

License (OSI-approved)

Key facts

Objective fields from the source. Values we can't verify are shown as “Unknown” rather than guessed.

Field	Value
Repository	FutureUniant/WorkShadow
Owner	FutureUniant
Primary language	TypeScript
License	AGPL-3.0 — OSI-approved
Stars	817
Forks	36
Open issues	0
Latest release	v0.2.0 (2026-06-17)
Last updated	2026-06-29
Source	https://github.com/FutureUniant/WorkShadow

What WorkShadow is

Desktop app (Windows; Tauri + React frontend, Rust backend) storing logs in SQLite with LanceDB for vector search. Supports markdown import, optional local model inference (paid tier), and exports data as .ws packages. Integrates external LLM/embedding APIs via user configuration.

Quickstart

Get the WorkShadow source

Clone the repository and explore it locally.

terminalbash

1git clone https://github.com/FutureUniant/WorkShadow.git2cd WorkShadow3# follow the project's README for install & configuration

Need it deployed, integrated, or customized instead? DEV.co ships production installs.

Best use cases

Knowledge Worker Daily Logging & Retrieval

Teams or individuals who record work decisions, problems, and context daily and need to quickly surface past decisions or details via semantic search without platform lock-in.

Automated Report & Summary Generation

Use the 'Workbench' feature to batch-generate daily/weekly/monthly reports or email drafts from selected logs using your configured LLM, reducing manual compilation.

Privacy-First, Self-Hosted Alternative to Cloud Note Services

Organizations or individuals uncomfortable storing work logs on third-party cloud platforms; all data remains local unless user explicitly configures external AI models.

Implementation considerations

User must configure external LLM (large language model), embedding model, and optionally multimodal model endpoints in settings; no defaults provided in free tier beyond paid local inference.
Data export/import via .ws file format; no built-in cloud sync or backup automation—user responsible for version control and disaster recovery.
Semantic search requires embedding model configuration; keyword search works offline but is less powerful than vector retrieval.
Code runs on Windows only; evaluate platform availability before committing to team adoption.
Latest release (v0.2.0, June 2026) is recent; production stability and long-term maintenance cadence Unknown from available data.

When to avoid it — and what to weigh

Require Cross-Platform (macOS/Linux) Native Support — Current release targets Windows only. macOS and Linux support not mentioned in available documentation or release artifacts.
Need Real-Time Collaboration & Conflict Resolution — WorkShadow is single-user, local-first. No mention of multi-user sync, conflict resolution, or real-time co-editing capabilities.
Expect Production-Grade AI without External Model Integration — Free 'dev' tier has no local model inference; 'paid' tier (still free to install) offers local inference but requires separate model setup. Summary/QA both require external LLM or paid tier local model.
Running in Headless/Server Environment — Desktop-only Tauri app; no web server, API, or CLI-based server deployment mode documented.

License & commercial use

Licensed under AGPL-3.0 (GNU Affero General Public License v3.0). This is a copyleft license: any modifications to the software and distribution of the application or service over a network requires you to provide source code to users. AGPL is stricter than GPL in that network use triggers copyleft obligations.

AGPL-3.0 is not a permissive license suitable for proprietary commercial deployment without significant legal review. If you plan to run a modified version as a service or embed it in a commercial product, you must provide source code to users. For using the unmodified open-source build internally within your organization, consult legal counsel. The README states the open-source builds are 'free,' but commercial licensing terms (if any) are not documented; requires direct contact with maintainers ([email protected]).

DEV.co evaluation signals

Editorial assessment — not user reviews. Directional, with an explicit confidence level.

Signal	Assessment
Maintenance	Active
Documentation	Adequate
License clarity	Clear
Deployment complexity	Low
DEV.co fit	Possible
Assessment confidence	Medium

Security considerations

Data stored locally in SQLite and user's designated folder; no encryption at rest mentioned. External AI model calls depend on user-configured endpoints and keys—no encryption/audit trail documented. No mention of code signing for Windows installer. Relies on user responsibility for backup, key management, and isolation of external API credentials. No security audit or vulnerability disclosure policy documented.

Alternatives to consider

Notion

Cloud-based note/project management with AI integration; no local-first guarantee and vendor lock-in, but offers collaboration, templates, and maturity.

Obsidian

Local-first markdown note-taking with community plugins; lacks built-in semantic search, AI summary, and workbench-style report generation out-of-box.

LogSeq

Open-source local-first outliner; web/desktop available; limited native AI and semantic search; stronger community plugin ecosystem than WorkShadow.

Software development agency

Build on WorkShadow with DEV.co software developers

If local-first data privacy and AI-powered work logging align with your needs, start with the free Windows installer or source build. Ensure AGPL-3.0 compliance and external LLM availability before production rollout. Contact maintainers for commercial licensing questions.

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WorkShadow FAQ

Can I use WorkShadow without an external LLM?

Yes. Core features (logging, rich editing, keyword search, organization) work offline. Semantic search, summarization, and Q&A require either an external LLM API or the paid 'installation' tier with local model inference. Paid tier is free to download but offers additional features.

Is my data encrypted?

Data is stored locally in SQLite on your machine and in your designated folder; encryption at rest is not mentioned in documentation. External API calls (for AI features) depend on your configured endpoint security.

Can I use WorkShadow on macOS or Linux?

Not currently. Released binaries are Windows-only (.exe). Building from source for other platforms Unknown; likely requires Tauri platform support and untested.

Can I self-host a modified version as a service?

AGPL-3.0 requires you to offer source code to users if you modify and deploy it as a network service. Contact maintainers ([email protected]) for commercial licensing options or clarification.

Software developers & web developers for hire

Need help beyond evaluating WorkShadow? DEV.co is a software development agency offering software development services and web development for teams of every size. Our software developers and web developers build custom software, web applications, APIs, and rag frameworks integrations — and maintain them long-term.

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