mcp-local-rag vs cortex-memory

Common questions

What is the difference between mcp-local-rag and cortex-memory?

mcp-local-rag: Local-first RAG server for developers with semantic and keyword search capabilities.. cortex-memory: Persistent AI memory for coding sessions, auto-capturing decisions and context.. See the comparison table for live GitHub stats and shared categories.

When should I choose mcp-local-rag over cortex-memory?

Choose mcp-local-rag over cortex-memory when Tags unique to mcp-local-rag: agent-skills, hybrid-search, local-first, privacy-first; mcp-local-rag ships an MCP server manifest; When you need to perform both semantic and keyword-based search operations within a local, privacy-focused environment tailored specifically for handling codebases and technical documentation.

When should I choose cortex-memory over mcp-local-rag?

Choose cortex-memory over mcp-local-rag when Pricing: The tool is open-source under the MIT License, making it free to use, but additional features or services may come at a cost.; Requirements: Min 2 GB RAM; Requires a compatible AI coding assistant like Claude Code, Cursor, or Cline.; Works best with VSCode, but also supports CLI and MCP server.; Tags unique to cortex-memory: ai, ai-coding-assistant, ai-memory, claude; When you are working with specific AI coding assistants like Claude Code, Cursor, or Cline and need persistent memory across coding sessions.

When should I avoid mcp-local-rag?

When you do not require a privacy-first approach and are willing to rely on cloud-based solutions that might offer more extensive feature sets beyond just keyword and semantic searches. If your use case involves scaling search capabilities across multiple remote databases or servers, as mcp-local-rag is best suited for local data only.

When should I avoid cortex-memory?

If you are not using AI coding assistants like Claude Code, Cursor, or Cline, as cortex-memory is specifically designed to work with these tools. When you prefer a tool that does not automatically capture decisions and context, and you want full control over what is stored and retrieved. If you are working in an environment where zero-configuration tools are not suitable, and you require extensive customization options.

Is mcp-local-rag or cortex-memory more popular on GitHub?

mcp-local-rag has more GitHub stars (400 vs 8). Stars measure visibility, not whether either tool fits your constraints.

Are mcp-local-rag and cortex-memory open source?

Yes - both are open-source projects on GitHub (mcp-local-rag: MIT, cortex-memory: MIT).

Where can I find alternatives to mcp-local-rag or cortex-memory?

GraphCanon lists graph-backed alternatives at mcp-local-rag alternatives and cortex-memory alternatives (mcp-local-rag markdown twin, cortex-memory markdown twin), ranked by typed relationship edges rather than popularity votes.

Is there a machine-readable version of this comparison?

Yes. The markdown twin at this comparison mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers.

Which is better maintained, mcp-local-rag or cortex-memory?

mcp-local-rag: Very active. cortex-memory: Slowing. Compare maintenance labels, days since push, and release cadence in the trust section below - stars alone do not measure maintenance.

Where are the full trust reports for mcp-local-rag and cortex-memory?

GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: mcp-local-rag trust report; cortex-memory trust report.