Engraphis
Engraphis is a local-first memory engine built so AI coding agents keep useful project context across sessions and repositories using SQLite and hybrid recall.
At a Glance
- Software developers
- AI-native engineering teams
- On-premise development environments
- Enterprises with strict data privacy requirements
AI Tools by Engraphis
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Engraphis
Local Memory Engine for AI Agents
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Latest News
Engraphis 1.6 released with trusted local imports and Obsidian vault support
Engraphis 1.5 released with memory quality gates and embedding-space safety
Engraphis 1.4 released, introducing Smart MCP as the default gateway
Engraphis 1.0.0 General Availability (GA) release
Products & Services
A local-first, Apache-2.0 memory engine for AI coding agents with SQLite storage and MCP integration.
A hosted service for solo owners adding analytics, automation, and cloud sync.
A governed memory service for teams with multi-user roles and shared sync.
Market Position
Engraphis positions itself as a local-first, privacy-preserving alternative to hosted memory services, emphasizing inspectability and cost efficiency through token reduction.
Leadership
Founders
Josh Williams
Creator of Engraphis and the Coding-Dev-Tools suite; focus on local-first AI memory and developer productivity.
Executive Team
Josh Williams
Founder & Lead Developer
Lead developer of the Engraphis project and maintainer of the Coding-Dev-Tools organization.
Founding Story
Engraphis was started to solve the problem of AI coding agents starting from zero every session and losing context, while avoiding the privacy risks of hosted memory services.
Business Model
Revenue Model
Subscription SaaS for Pro and Team tiers; core local engine is free and open-source.
Pricing Tiers
Local engine, MCP server, dashboard, CLI, and library under Apache-2.0.
Everything in Community plus analytics, auto-consolidation, and optional hosted Cloud Sync.
Everything in Pro plus multi-user roles, seat management, and shared cloud sync.
Target Markets
- Software developers
- AI-native engineering teams
- On-premise development environments
- Enterprises with strict data privacy requirements
- Persistent project memory for coding agents
- Regulated or on-prem codebase indexing
- Developer context handoff across sessions
- Reducing LLM token costs through efficient retrieval