"Learning NestJS Framework Knowledge" matching MCP servers:

An MCP server that enforces a pedagogical workflow to teach topics step by step, using research, decomposition, and spaced retrieval practice.

A local MCP server that helps you maintain a personal Japanese learning knowledge base, including vocabulary, confusion relations, mistakes, and spaced-repetition reviews. It provides tools and prompts for managing and reviewing your Japanese learning data without calling external LLM APIs.

Enables developers to record feature-based development processes and turn Git changes, AI conversations, token usage, technical decisions, and debugging attempts into structured Obsidian learning reviews.

Extends the official Obsidian MCP server with intelligent learning and second-brain features, including AI-generated challenges, progress analytics, spaced repetition, and knowledge gap analysis.

Implements cognitive science-backed interleaved learning techniques to create study plans, generate mixed-topic quizzes, manage flashcard decks, and track learning progress for optimal knowledge retention.

This MCP server provides an AI-driven learning and knowledge management platform that automatically summarizes conversations, manages knowledge graphs, tracks project progress, explores new technologies, and generates study plans through specialized agents.

Local RAG system for Claude Code with hybrid search (semantic + BM25), cross-encoder reranking, markdown-aware chunking, and 12 MCP tools. Zero external servers, pure ONNX in-process.

Provides centralized knowledge management for projects, allowing users to store, search, and maintain project-specific knowledge that persists across sessions.

Enables cross-session memory and recall for AI coding assistants through a git-synced knowledge base, hybrid semantic and TF-IDF search across tool transcripts, and automatic distillation of insights with secrets scrubbing.

Local knowledge engine for codebases with hybrid search, knowledge graph, and interaction tracking, enabling Claude Code to search and interact with project knowledge locally.

Long-term memory for AI agents. Compiles conversations into a structured knowledge base with Claim/Evidence model, source provenance, append-only timeline, and contradiction detection. Multi-path retrieval (Exact + BM25 + Graph + weighted RRF + reranker) — 96.6% R@5 on LongMemEval-S, zero vector dependencies.

A local knowledge graph MCP server that provides AI agents with permanent, structured memory about codebases, enabling semantic search, blast radius analysis, and convention enforcement.

An MCP server that acts as a knowledge engine for software projects, delivering relevant context at the start of a task and accumulating knowledge at its end through tools like start_task, context, finish_task, remember, and search, with a file-based source of truth and optional semantic retrieval via Graphiti/Neo4j.

Transforms YouTube into a queryable knowledge source with search, video details, transcript analysis, and AI-powered tools for summaries, learning paths, and knowledge graphs. Features quota-aware API access with caching and optional OpenAI/Anthropic integration for advanced content analysis.

A sovereign, AI-driven pedagogical and spaced-repetition skills development MCP server supporting progressive syllabi, Bloom's Taxonomy, and secure LMS handshakes.

An MCP server that enables semantic search over a private knowledge base via pgvector, with GitHub OAuth authentication and a RAG pipeline.

A knowledge base MCP server that aggregates team knowledge from multiple sources into Postgres. It provides hybrid search (full-text + vector + RRF) via MCP tools, and enables direct recording of decisions, learnings, and pitfalls.

语义搜索知识库，支持文档获取和索引刷新，使AI Agent能够检索团队知识。

Enables AI agents to learn from their work by recording tasks, extracting patterns, detecting mistakes, and proactively surfacing insights, all using the agent's own model through a cooperative intelligence pattern.

An MCP server that enables AI agents to query specialized, domain-specific knowledge bases built using the LightRAG framework for enhanced retrieval-augmented generation. It allows for managing and searching knowledge graphs and vector embeddings to provide accurate, context-aware information during an AI assistant's reasoning process.