by gmickel · MCP Server · ★ 79
Last updated: · Indexed by AgentSkillsHub · Auto-synced every 8h
Local AI-powered document search and editing with first-in-class hybrid retrieval, LLM answers, WebUI, REST API and MCP support for AI clients.
| Stars | 79 |
| Forks | 6 |
| Language | TypeScript |
| Category | MCP Server |
| License | MIT |
| Quality Score | 35.75/100 |
| Open Issues | 3 |
| Last Updated | 2026-05-09 |
| Created | 2025-12-16 |
| Platforms | browser, cli, mcp, node |
| Est. Tokens | ~1555k |
Looking for a gno alternative? If you're comparing gno with other mcp server tools, these 6 projects are the closest alternatives on Agent Skills Hub — ranked by topic overlap, star count, and community traction.
Self-hosted RAG platform for AI document search across GitHub, Notion, Google Drive, local files, and web sour
Local code search combining BM25, vector similarity, and cross-encoder reranking. Parses 60+ languages with tr
Local memory infrastructure for AI agents. Store knowledge and skills in isolated vaults you compose, control
Semantic code searcher and codebase utility
Local-first persistent agentic memory powered by Recursive Memory Harness (RMH). Open source must win.
Structural memory for AI coding agents. Bi-temporal graph, MCP-native, zero LLM calls. Cursor · Claude Code ·
Explore other popular mcp server tools:
gno is Local AI-powered document search and editing with first-in-class hybrid retrieval, LLM answers, WebUI, REST API and MCP support for AI clients.. It is categorized as a MCP Server with 79 GitHub stars.
gno is primarily written in TypeScript. It covers topics such as ai-assistant, bun, cli.
You can find installation instructions and usage details in the gno GitHub repository at github.com/gmickel/gno. The project has 79 stars and 6 forks, indicating an active community.
gno is released under the MIT license, making it free to use and modify according to the license terms.
The top alternatives to gno on Agent Skills Hub include OpenDocuments, Vera, ctxvault. Each offers a different approach to the same problem space — compare them side-by-side by stars, quality score, and community activity.