turbyho/mem-context

Overview

A multi-modal RAG engine that stores conversation history, conclusions, diffs, error traces, and other development artifacts in LanceDB. It uses vector search, multi-factor scoring, and an LLM-driven consolidation pipeline to retrieve relevant context for AI assistants.

Best for

Best for
Developers building AI assistants that need persistent, context-aware memory from development artifacts

Use cases

• Retrieving past debugging sessions and error traces for faster issue resolution
• Providing AI assistants with consolidated project context from code diffs and discussions
• Building a persistent memory layer for development-focused chatbots

Notes

A multi-modal RAG engine that stores conversation history, conclusions, diffs, error traces, and other development artifacts in LanceDB. It uses vector search, multi-factor scoring, and an LLM-driven consolidation pipeline to retrieve relevant context for AI assistants.

0 stars on GitHub. Last updated 2026-06-11. Licensed MIT.

Use cases

• Retrieving past debugging sessions and error traces for faster issue resolution
• Providing AI assistants with consolidated project context from code diffs and discussions
• Building a persistent memory layer for development-focused chatbots

Pros

• Supports multiple artifact types beyond plain text, including diffs and error traces
• Uses multi-factor scoring to improve retrieval relevance
• LanceDB backend enables efficient vector search and scaling

Cons

• Requires an LLM for the consolidation pipeline, adding latency and cost
• No active maintenance or community contributions (0 stars)
• Limited documentation and examples for setup and customization

Indexed from awesome-mcp-servers-punkpeye and enriched against its public facts.