tandem
Your AI coworker for any folder: local-first, secure by design, cross-platform, and built for supervised automation.
Stars: 59
Tandem is a local-first, privacy-focused AI workspace that runs entirely on your machine. It is inspired by early AI coworking research previews, open source, and provider-agnostic. Tandem offers privacy-first operation, provider agnosticism, zero trust model, true cross-platform support, open-source licensing, modern stack, and developer superpowers for everyone. It provides folder-wide intelligence, multi-step automation, visual change review, complete undo, zero telemetry, provider freedom, secure design, cross-platform support, visual permissions, full undo, long-term memory, skills system, document text extraction, workspace Python venv, rich themes, execution planning, auto-updates, multiple specialized agent modes, multi-agent orchestration, project management, and various artifacts and outputs.
README:
A local-first, privacy-focused AI workspace. Your AI coworker that runs entirely on your machine.
Inspired by early AI coworking research previews, but open source and provider-agnostic.
π Privacy First: Unlike cloud-based AI tools, Tandem runs on your machine. Your code, documents, and API keys never touch our servers because we don't have any.
π° Provider Agnostic: Use any LLM provider - don't get locked into one vendor. Switch between OpenRouter, Anthropic, OpenAI, or run models locally with Ollama.
π‘οΈ Zero Trust: Every file operation requires explicit approval. AI agents are powerful but Tandem treats them as "untrusted contractors" with supervised access.
π True Cross-Platform: Native apps for Windows, macOS (Intel & Apple Silicon), and Linux. No Electron bloat - built on Tauri for fast, lightweight performance.
π Open Source: MIT licensed. Review the code, contribute features, or fork it for your needs.
π οΈ Modern Stack: Built with Rust, Tauri, React, and sqlite-vec β designed for high performance and low memory footprint on consumer hardware.
In 2024, AI coding tools like Cursor transformed how developers work - letting them interact with entire codebases, automate complex tasks, and review changes before they happen.
But why should only programmers have these capabilities?
- Researchers need to synthesize hundreds of papers
- Writers need consistency across sprawling manuscripts
- Analysts need to cross-reference quarterly reports
- Administrators need to organize mountains of documents
Tandem brings the same transformative capabilities to everyone. Point it at any folder of files, and you get:
- Folder-wide intelligence - AI that understands your entire collection, not just one file
- Multi-step automation - Complex tasks broken into reviewable steps
- Visual change review - See exactly what will change before it happens
- Complete undo - Roll back any operation with one click
What Cursor did for developers, Tandem does for everyone.
- π Zero telemetry - No data leaves your machine except to your chosen LLM provider
- π Provider freedom - Use OpenRouter, Anthropic, OpenAI, Ollama, or any OpenAI-compatible API
- π‘οΈ Secure by design - API keys stored in encrypted vault using AES-256-GCM, never in plaintext
- π Cross-platform - Native installers for Windows, macOS (Intel & Apple Silicon), and Linux
- ποΈ Visual permissions - Approve every file access and action with granular control
- βͺ Full undo - Rollback any AI operation with comprehensive operation journaling
- π§ Long-Term Memory - Vector database stores codebase context and history for smarter answers
- π§© Skills System - Import and manage custom AI capabilities and instructions
- π·οΈ Skill runtime hints - Starter skill cards show optional runtime requirements (Python/Node/Bash)
- π Document text extraction - Extract text from PDF/DOCX/PPTX/XLSX/RTF for skills and chat context
-
π Workspace Python venv - Guided setup creates
.tandem/.venvand enforces venv-only tools - π¨ Rich themes - Enhanced background art and consistent gradient rendering across the app
- π Execution Planning - Review and batch-approve multi-step AI operations before execution
- π Auto-updates - Seamless updates with code-signed releases (when using installers)
Tandem supports multiple specialized agent modes powered by the native Tandem engine:
- π¬ Chat Mode - Interactive conversation with context-aware file operations
-
π Plan Mode - Draft comprehensive implementation plans (
.md) before executing changes - βΎοΈ Ralph Loop - Autonomous iterative loop that works until a task is verifiably complete
- π Ask Mode - Read-only exploration and analysis without making changes
- π Debug Mode - Systematic debugging with runtime evidence
New in v0.2.0: Tandem now features a powerful Orchestration Mode that coordinates specialized sub-agents to solve complex problems.
Instead of a single AI trying to do everything, Tandem builds a dependency graph of tasks and delegates them to:
- Planner: Architect your solution
- Builder: Write the code
- Validator: Verify the results
This supervised loop ensures complex features are implemented correctly with human-in-the-loop approval at every critical step.
- π Multi-project support - Manage multiple workspaces with separate contexts
- π Per-project permissions - Fine-grained file access control
- π Project switching - Quick navigation between different codebases
- πΎ Persistent history - Chat history saved per project
- π HTML/Canvas - Generate secure, interactive HTML dashboards and reports
- π½οΈ Presentation Engine - Export high-fidelity PPTX slides with theme support
- π Markdown Reports - Clean, formatted documents and plans
Platform-specific:
| Platform | Additional Requirements |
|---|---|
| Windows | Build Tools for Visual Studio |
| macOS | Xcode Command Line Tools: xcode-select --install
|
| Linux |
libwebkit2gtk-4.1-dev, libappindicator3-dev, librsvg2-dev, build-essential, pkg-config
|
-
Clone the repository
git clone https://github.com/frumu-ai/tandem.git cd tandem -
Install dependencies
pnpm install
-
Build the engine binary
cargo build -p tandem-engine
This builds the native Rust
tandem-enginebinary for your platform. -
Run in development mode
pnpm tauri dev
If you want to build a distributable installer, run:
# Build for current platform
pnpm tauri buildNote on Code Signing: Tandem uses Tauri's secure updater. If you are building the app for yourself, you will need to generate your own signing keys:
- Generate keys:
pnpm tauri signer generate -w ./src-tauri/tandem.key - Set environment variables:
-
TAURI_SIGNING_PRIVATE_KEY: The content of the.keyfile -
TAURI_SIGNING_PASSWORD: The password you set during generation
-
- Update the
pubkeyinsrc-tauri/tauri.conf.jsonwith your new public key.
For more details, see the Tauri signing documentation.
If a macOS user downloads the .dmg from GitHub Releases and macOS says the app is "damaged" or "corrupted", this is usually Gatekeeper rejecting an app bundle/DMG that is not Developer ID signed and notarized.
Things to check:
- Download the correct DMG for the Mac:
- Apple Silicon (M1/M2/M3):
aarch64-apple-darwin/arm64 - Intel:
x86_64-apple-darwin/x64
- Apple Silicon (M1/M2/M3):
- Try opening via Finder:
- Right click the app ->
Open(orSystem Settings -> Privacy & Security->Open Anyway)
- Right click the app ->
For distribution to non-technical users, the real fix is to ship macOS artifacts that are signed + notarized. The release workflow (.github/workflows/release.yml) supports this once the Apple signing/notarization secrets are configured.
# Output locations:
# Windows: src-tauri/target/release/bundle/msi/
# macOS: src-tauri/target/release/bundle/dmg/
# Linux: src-tauri/target/release/bundle/appimage/Tandem supports multiple LLM providers. Configure them in Settings:
- Launch Tandem
- Click the Settings icon (gear) in the sidebar
- Choose and configure your provider:
Supported Providers:
| Provider | Description | Get API Key |
|---|---|---|
| OpenRouter β | Access 100+ models through one API (recommended) | openrouter.ai/keys |
| OpenCode Zen | Fast, cost-effective models optimized for coding | opencode.ai/zen |
| Anthropic | Anthropic models (Sonnet, Opus, Haiku) | console.anthropic.com |
| OpenAI | GPT-4, GPT-3.5 and other OpenAI models | platform.openai.com |
| Ollama | Run models locally (no API key needed) | Setup Guide |
| Custom | Any OpenAI-compatible API endpoint | Configure endpoint URL |
- Enter your API key - it's encrypted with AES-256-GCM and stored securely in the local vault
- (Optional) Configure model preferences and endpoints
Tandem operates on a zero-trust model - it can only access folders you explicitly grant permission to:
- Click Projects in the sidebar
- Click + New Project or Select Workspace
- Choose a folder via the native file picker
- Tandem can now read/write files in that folder (with your approval)
You can manage multiple projects and switch between them easily. Each project maintains its own:
- Chat history
- Permission settings
- File access scope
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β Tandem Desktop App β
βββββββββββββββββββ¬ββββββββββββββββββββ¬ββββββββββββββββββββββββ€
β React Frontend β Tauri Core β Tandem Engine Sidecarβ
β (TypeScript) β (Rust) β (AI Agent Runtime) β
β - Modern UI β - Security β - Multi-mode agents β
β - File browser β - Permissions β - Tool execution β
β - Chat interfaceβ - State mgmt β - Context awareness β
βββββββββββββββββββ΄ββββββββββββββββββββ΄ββββββββββββββββββββββββ€
β SecureKeyStore (AES-256-GCM) β
β Encrypted API keys β’ Secure vault β
βββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββ
Tech Stack:
- Frontend: React 18, TypeScript, Tailwind CSS, Framer Motion
- Backend: Rust, Tauri 2.0
- Agent Runtime: Tandem Engine (Rust, HTTP + SSE)
- Encryption: AES-256-GCM for API key storage
- IPC: Tauri's secure command system
Tandem treats the AI as an "untrusted contractor":
- All operations go through a Tool Proxy
- Write operations require user approval
- Full operation journal with undo capability
- Circuit breaker for resilience
- Execution Planning - Review all changes as a batch before applying
Tandem offers two modes for handling AI operations:
Immediate Mode (default):
- Approve each file change individually via toast notifications
- Good for quick, small changes
- Traditional AI assistant experience
Plan Mode (recommended for complex tasks):
- Toggle with the "Plan Mode" button in the chat header
- Uses Tandem's native Plan mode runtime
- AI proposes file operations that are staged for review
- All changes appear in the Execution Plan panel (bottom-right)
- Review diffs side-by-side before applying
- Remove unwanted operations
- Execute all approved changes with one click
How to use Plan Mode:
- Click "Immediate" β "Plan Mode" toggle in header
- Ask AI to make changes (e.g., "Refactor the auth system")
- AI proposes operations β they appear in Execution Plan panel
- Review diffs and operations
- Click "Execute Plan" button in panel
- All changes applied together + AI continues
The Execution Plan panel appears automatically when the AI proposes file changes in Plan Mode.
- Full undo support for the entire batch
Toggle between modes using the button in the chat header.
Tandem is built with security and privacy as core principles:
- π API keys: Encrypted with AES-256-GCM in SecureKeyStore, never stored in plaintext
- π File access: Scoped to user-selected directories only - zero-trust model
- π Network: Only connects to localhost (sidecar) + user-configured LLM endpoints
- π« No telemetry: Zero analytics, zero tracking, zero call home
- β Code-signed releases: All installers are signed for security (Windows, macOS)
- π Sandboxed: Tauri security model with CSP and permission system
- πΎ Local-first: All data stays on your machine unless sent to your LLM provider
Denied by default:
-
.envfiles and environment variables -
.pem,.keyfiles - SSH keys (
.ssh/*) - Secrets folders
- Password databases
See SECURITY.md for our complete security model and threat analysis.
We welcome contributions! Please see CONTRIBUTING.md.
# Run lints
pnpm lint
# Run tests
pnpm test
cargo test
# Format code
pnpm format
cargo fmtEngine-specific build/run/smoke instructions (including pnpm tauri dev sidecar setup): see docs/ENGINE_TESTING.md.
Engine CLI usage reference (commands, flags, examples): see docs/ENGINE_CLI.md.
Engine runtime communication contract (desktop/TUI <-> engine): see docs/ENGINE_COMMUNICATION.md.
tandem/
βββ src/ # React frontend
β βββ components/ # UI components
β βββ hooks/ # React hooks
β βββ lib/ # Utilities
βββ src-tauri/ # Rust backend
β βββ src/ # Rust source
β βββ capabilities/ # Permission config
β βββ binaries/ # Sidecar (gitignored)
βββ scripts/ # Build scripts
βββ docs/ # Documentation
- [x] Phase 1: Security Foundation - Encrypted vault, permission system
- [x] Phase 2: Sidecar Integration - Tandem agent runtime
- [x] Phase 3: Glass UI - Modern, polished interface
- [x] Phase 4: Provider Routing - Multi-provider support
- [x] Phase 5: Agent Capabilities - Multi-mode agents, execution planning
- [x] Phase 6: Project Management - Multi-workspace support
- [x] Phase 7: Advanced Presentations - PPTX export engine, theme mapping, explicit positioning
- [x] Phase 8: Brand Evolution - Rubik 900 typography, polished boot sequence
- [x] Phase 9: Memory & Context - Vector database integration (
sqlite-vec) - [x] Phase 10: Skills System - Importable agent skills and custom instructions
- [ ] Phase 11: Browser Integration - Web content access
- [ ] Phase 12: Team Features - Collaboration tools
- [ ] Phase 13: Mobile Companion - iOS/Android apps
See docs/todo_specialists.md for ideas on specialized AI assistants for non-technical users.
For developers and teams who want:
- Control: Your data, your keys, your rules
- Flexibility: Any LLM provider, any model
- Security: Encrypted storage, sandboxed execution, zero telemetry
- Transparency: Open source, auditable code
For a deeper dive into Tandem's philosophy and how it compares to other tools, see our Marketing Guide.
If Tandem saves you time or helps you keep your data private while using AI, consider sponsoring development. Your support helps with:
- Cross-platform packaging and code signing
- Security hardening and privacy features
- Quality-of-life improvements and bug fixes
- Documentation and examples
MIT - Use it however you want.
- Anthropic for the Cowork inspiration
- Tauri for the secure desktop framework
- The open source community
Tandem - Your local-first AI coworker.
Note: This codebase communicates with the native tandem-engine sidecar binary for AI agent capabilities and routes to various LLM providers (OpenRouter, Anthropic, OpenAI, Ollama, or custom APIs). All communication stays local except for LLM provider API calls.
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RAGFlow is an open-source Retrieval-Augmented Generation (RAG) engine that combines deep document understanding with Large Language Models (LLMs) to provide accurate question-answering capabilities. It offers a streamlined RAG workflow for businesses of all sizes, enabling them to extract knowledge from unstructured data in various formats, including Word documents, slides, Excel files, images, and more. RAGFlow's key features include deep document understanding, template-based chunking, grounded citations with reduced hallucinations, compatibility with heterogeneous data sources, and an automated and effortless RAG workflow. It supports multiple recall paired with fused re-ranking, configurable LLMs and embedding models, and intuitive APIs for seamless integration with business applications.
Dot
Dot is a standalone, open-source application designed for seamless interaction with documents and files using local LLMs and Retrieval Augmented Generation (RAG). It is inspired by solutions like Nvidia's Chat with RTX, providing a user-friendly interface for those without a programming background. Pre-packaged with Mistral 7B, Dot ensures accessibility and simplicity right out of the box. Dot allows you to load multiple documents into an LLM and interact with them in a fully local environment. Supported document types include PDF, DOCX, PPTX, XLSX, and Markdown. Users can also engage with Big Dot for inquiries not directly related to their documents, similar to interacting with ChatGPT. Built with Electron JS, Dot encapsulates a comprehensive Python environment that includes all necessary libraries. The application leverages libraries such as FAISS for creating local vector stores, Langchain, llama.cpp & Huggingface for setting up conversation chains, and additional tools for document management and interaction.
emerging-trajectories
Emerging Trajectories is an open source library for tracking and saving forecasts of political, economic, and social events. It provides a way to organize and store forecasts, as well as track their accuracy over time. This can be useful for researchers, analysts, and anyone else who wants to keep track of their predictions.
reor
Reor is an AI-powered desktop note-taking app that automatically links related notes, answers questions on your notes, and provides semantic search. Everything is stored locally and you can edit your notes with an Obsidian-like markdown editor. The hypothesis of the project is that AI tools for thought should run models locally by default. Reor stands on the shoulders of the giants Ollama, Transformers.js & LanceDB to enable both LLMs and embedding models to run locally. Connecting to OpenAI or OpenAI-compatible APIs like Oobabooga is also supported.
swirl-search
Swirl is an open-source software that allows users to simultaneously search multiple content sources and receive AI-ranked results. It connects to various data sources, including databases, public data services, and enterprise sources, and utilizes AI and LLMs to generate insights and answers based on the user's data. Swirl is easy to use, requiring only the download of a YML file, starting in Docker, and searching with Swirl. Users can add credentials to preloaded SearchProviders to access more sources. Swirl also offers integration with ChatGPT as a configured AI model. It adapts and distributes user queries to anything with a search API, re-ranking the unified results using Large Language Models without extracting or indexing anything. Swirl includes five Google Programmable Search Engines (PSEs) to get users up and running quickly. Key features of Swirl include Microsoft 365 integration, SearchProvider configurations, query adaptation, synchronous or asynchronous search federation, optional subscribe feature, pipelining of Processor stages, results stored in SQLite3 or PostgreSQL, built-in Query Transformation support, matching on word stems and handling of stopwords, duplicate detection, re-ranking of unified results using Cosine Vector Similarity, result mixers, page through all results requested, sample data sets, optional spell correction, optional search/result expiration service, easily extensible Connector and Mixer objects, and a welcoming community for collaboration and support.


