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Give AI agents semantic memory and web search for stronger retrieval and reasoning.
Copy the install command and let the AI configure it · recommended for beginners
No copy-paste install info for "mcp-knowledge-assistant" yet — see the docs or source repo.
Design how to connect mcp-knowledge-assistant to my LangGraph ReAct agent so it can store user preferences, past tasks, and key facts as semantic notes, and explain the retrieval and write flow.
An integration plan describing memory structure, retrieval timing, write strategy, and agent call flow.
Help me plan an agent tool strategy: query the semantic note memory first, and if local memory is insufficient, call Tavily web search for fresh information, then define tool selection rules.
A retrieval priority and routing policy that lets the agent switch between memory and external search.
Design a QA assistant architecture based on mcp-knowledge-assistant that supports note vector retrieval, source citation, and web search when needed, and explain suitable prompts and tool orchestration.
A knowledge-augmented QA plan including architecture, prompting approach, and tool orchestration guidance.
Turn local notes into a private searchable knowledge base for AI assistants.
Build a project knowledge graph for code search, traversal, and Q&A.
Give AI agents persistent memory and semantic retrieval across conversations.
Lets AI assistants manage notes, tasks, and ideas in a personal knowledge base.
Search markdown knowledge bases with hybrid ranking and intelligent reranking.
Production-ready MCP server for query normalization, retrieval, and RAG prompt building.