Can I use MCP tools with this skill?

Absolutely. Mastra supports both connecting to existing MCP servers as a client and exposing Mastra tools via an MCPServer for use in other environments.

Does Mastra support RAG and vector databases?

Yes, Mastra includes built-in support for document processing (MDocument), embedding generation, and integration with vector stores like Pinecone, Qdrant, and pgvector.

What is the difference between Mastra Agents and Workflows?

Agents use LLMs to decide which tools to use autonomously, while Workflows are deterministic pipelines that chain together steps with typed inputs and outputs for predictable execution.

How does Mastra handle agent memory?

Mastra provides three types of memory: working memory, conversation history, and semantic recall, with support for various storage adapters like LibSQL, Postgres, and MongoDB.

What is the Mastra skill for Claude Code?

It is a specialized capability that allows Claude to help you build, debug, and optimize AI agents using the Mastra TypeScript framework, providing domain-specific documentation and implementation patterns.

Mastra AI Agent Framework

Name: Mastra AI Agent Framework
Author: hashintel

byhashintel

•

1,387

데이터 과학 및 ML

Builds sophisticated AI agents with integrated memory, deterministic workflows, RAG, and MCP support using the Mastra TypeScript framework.

소개

The Mastra skill equips Claude with deep knowledge of the Mastra TypeScript framework, enabling it to architect, implement, and debug complex AI agent systems. It provides specific patterns for creating autonomous agents with structured outputs, designing multi-step deterministic workflows, managing persistent conversation memory, and implementing Retrieval-Augmented Generation (RAG). Whether you are connecting to Model Context Protocol (MCP) servers or integrating real-time voice capabilities, this skill ensures Claude follows Mastra's best practices for building production-ready AI applications.

주요 기능

Deterministic workflow design with state sharing and chaining
Multi-layered memory management including semantic recall and history
MCP server and client configuration for extensible toolsets
RAG implementation with document processing and vector store adapters
1,387 GitHub stars
Agent construction with structured outputs and tool integration

사용 사례

Building a multi-agent system with complex, step-by-step business logic
Implementing a RAG-based knowledge assistant with persistent thread memory
Exposing custom internal tools to LLMs using the Model Context Protocol

소개

주요 기능

Deterministic workflow design with state sharing and chaining
Multi-layered memory management including semantic recall and history
MCP server and client configuration for extensible toolsets
RAG implementation with document processing and vector store adapters
1,387 GitHub stars
Agent construction with structured outputs and tool integration

사용 사례

Building a multi-agent system with complex, step-by-step business logic
Implementing a RAG-based knowledge assistant with persistent thread memory
Exposing custom internal tools to LLMs using the Model Context Protocol