VelesDB — MCP Server by cyberlife-coder

by cyberlife-coder · MCP Server · ★ 74

Last updated: · Indexed by AgentSkillsHub · Auto-synced every 8h

About VelesDB

Your AI agents forget everything. VelesDB fixes that. One 9 MB binary. Three engines. One query language. Zero cloud dependency. Vector + Graph + ColumnStore — unified under VelesQL <a

aiai-memoryall-in-one-databseavx512columnstore-databaseembeddingsgraph-databasehnswlocal-firstmachine-learning

Quick Facts

Stars74
Forks8
LanguageRust
CategoryMCP Server
Quality Score60.7118110515203/100
Last Updated2026-07-02
Created2025-12-17
Platformsbrowser, rust
Est. Tokens~32k

Compatible Skills

These tools work well together with VelesDB for enhanced workflows:

  • AIMAXXING — semantic(0.22)+complementary+same_lang+similar_pop+shared_platform (58%)
  • samyama-graph — semantic(0.37)+rare_topics+same_lang+similar_pop+shared_platform (57%)

VelesDB alternative? Top 6 similar tools

Looking for a VelesDB alternative? If you're comparing VelesDB 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.

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Frequently Asked Questions

What is VelesDB?

VelesDB is The local-first memory engine for AI agents. One offline Rust binary fuses vector + graph + columnar under SQL — remember / recall / why over the Model Context Protocol. why() reconnects a decision to. It is categorized as a MCP Server with 74 GitHub stars.

What programming language is VelesDB written in?

VelesDB is primarily written in Rust. It covers topics such as ai, ai-memory, all-in-one-databse.

How do I install or use VelesDB?

You can find installation instructions and usage details in the VelesDB GitHub repository at github.com/cyberlife-coder/VelesDB. The project has 74 stars and 8 forks, indicating an active community.

What are the best alternatives to VelesDB?

The top alternatives to VelesDB on Agent Skills Hub include rust-docs-mcp-server, yantrikdb-server, relevanceai. Each offers a different approach to the same problem space — compare them side-by-side by stars, quality score, and community activity.

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