MyContext — Connects all your work data into a private, local-first AI context for better understanding.
Analyzed by Sai Pavan Gopularam · AI · Desktop App · View on GitHub
- Stars: 4098
- Forks: 144
- Commits last 30 days: 58
- Health: Active (58 commits this month)
- Language: TypeScript
- License: Other
What It Is
MyContext is a desktop application that acts like a personal brain for your work. It pulls information from all your communication apps, documents, and calendars, then organizes it into a private, evolving view of what you know and who you work with.
This matters because it gives AI models a rich, personalized context to draw from, instead of starting from scratch. It solves the problem of scattered information, allowing you to ask questions across all your data and get answers with traceable sources, all while keeping your data private and under your control.
License Verdict
Elastic License 2.0 — Commercial Restrictions Apply — Self-hosting allowed • No SaaS offering to third parties
You are free to use, modify, and self-host MyContext, even within a company. However, you are explicitly prohibited from offering it as a hosted or managed service to third parties. This means you cannot run it on your servers and sell access to others.
How to Use It
MyContext is a desktop application built with Electron and React. It's in developer preview, so expect rapid changes. Setting it up involves cloning the repository, installing dependencies, and running the application locally.
Prerequisites:
- Node.js
- npm (or yarn)
Estimated setup time: 10 minutes.
git clone https://github.com/openTrinity/mycontext.git
cd mycontext
npm install
npm start
What I'd Build With This
Personal Context Setup & Customization Service (micro-saas)
Offer a specialized service to individuals and small teams, helping them deploy and customize their own MyContext instances. This includes setting up data connectors for their specific tools, configuring the knowledge graph, and providing training on how to best leverage their personal AI context. You'd charge for setup and ongoing support.
Effort: 1 Week Build Time · Target: Knowledge Workers, Small Teams · Pricing: $199/setup + $50/hr consulting
Vertical-Specific Self-Hosted AI Assistant Package (saas)
Develop pre-configured MyContext packages tailored for specific industries (e.g., legal research, medical documentation). These packages would include custom-built 'channels' for industry-specific data sources and specialized AI agents. You'd sell this as a license for a self-hosted solution, providing the software, custom integrations, and support, but clients manage their own deployment.
Effort: 2 Months Build Time · Target: Specialized Professionals, Small Agencies · Pricing: $2k-$10k one-time + $200-$500/mo support
Enterprise Internal Knowledge Graph & AI Assistant (enterprise)
Target large enterprises that prioritize data privacy and control. Offer to integrate MyContext into their internal infrastructure, creating custom connectors for proprietary internal systems (CRMs, internal wikis, HR platforms). This provides employees with a secure, local-first AI assistant that leverages the company's entire knowledge base, with a focus on compliance and security.
Effort: 6 Months Build Time · Target: Large Enterprises (Finance, Pharma) · Pricing: $50k+ annual license for custom integration/support
Sai Pavan Gopularam's Take
MyContext is an ambitious project tackling the 'personal context' problem, which is huge for AI. The Elastic License 2.0 is a curveball for traditional SaaS, but it opens doors for consulting and specialized self-hosted solutions. I'd estimate a well-executed niche package could bring in $5k/month.
Watch Out For
- Developer Preview: MyContext is in active 'developer preview,' meaning the project is iterating rapidly and will have compatibility-breaking changes. This isn't a stable release.
- Elastic License 2.0: While you can use and modify the code, you cannot offer MyContext as a hosted or managed service to third parties. This significantly limits traditional SaaS business models.
- Local-First Data: All personal work data and indexes live on the user's machine. This is great for privacy but means you can't build a centralized, cloud-based service directly on top of it.
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