Mastra 博客 Foundations 专栏：43 篇 AI 智能体基础文章汇总

43 posts on Foundations from the Mastra blog.

Data residency, PII controls, and what GDPR means for the Mastra framework vs. the Mastra platform.

HIPAA, PHI redaction, access controls, and auditability with the Mastra framework and platform.

This guide compares eleven leading AI agent integration platforms, explains where each one excels, and helps you choose the right integration infrastructure for your next AI application.

This guide compares six leading web data platforms for AI agents, explains where each one excels, and helps you choose the right web retrieval infrastructure for your next AI application.

This guide compares six leading frameworks, explains where each one excels, and helps you choose the right foundation for your next AI application.

Multi-agent orchestration and how it works

What multi-agent orchestration is, the five workflows Anthropic names, and Mastra code examples for each, with an interactive explainer.

This guide compares six leading browser platforms for AI agents, explains where each one excels, and helps you choose the right browser infrastructure for your next AI application.

This guide compares eight leading AI agent email providers, explains where each one excels, and helps you choose the right email infrastructure for your next AI application.

What is LLM observability? A span-by-span breakdown

LLM observability records what an agent did at the level of individual model calls and tool executions. This reads the trace of a live agent run span by span from storage, shows what a hallucination and a failed tool call look like, covers where the OpenTelemetry GenAI conventions stand in mid-2026, and walks the four dials that control tracing cost in production.

What are durable AI agents? How state persistence and checkpoints survive a restart

A long-running agent is a durable record that short-lived processes take turns with. This walks through why a context window is not a persistence layer, the three granularities of persistence, how Mastra checkpoints a run to JSON and resumes it on a fresh machine, why a worker can't judge its own work, and the two kinds of state a durable agent has to keep.

What is the Agent2Agent (A2A) protocol? How AI agents delegate work

A2A is a shared standard for one agent to hand work to another on a remote server. This walks through its five-object data model, the agent card, the eight task states, the three ways to watch a task, how trust is decided before work is sent, and how Mastra implements both sides on the official @a2a-js/sdk.

Internal agents help us keep information out of silos, automate work in public, and scale how we listen to users.

Anatomy of a harness: building a coding agent that can run for hours

How Mastra Code's harness wraps the agent loop: conversation-as-channel, event-to-display-state translation, human-in-the-loop tools, observational memory, subagents, modes, and an ordered tool-approval chain.

The engineering and context principles Micro uses to deliver sub-3s answers on live calls with Mastra.

A look at how Mastra solves the multi-user agent problem: concurrency, memory scoping, tool approval, and per-thread sandboxes.

Compare the best AI workflow automation tools in 2026, from developer-native frameworks like Mastra to no-code platforms like Gumloop, and more.

The Agent Prototype Playbook

A 7-step playbook for scoping, building, and shipping AI agent prototypes.

A guide to the different deployment models for Mastra Server and Studio with real-world examples of each.

The top AI personal assistants in 2026 are moving beyond chat into real execution, from Lindy's cross-app workflows to Vellum's long-term memory.

How to Structure Projects for AI Agents and LLMs

Learn how Mastra is optimized for modern AI systems, and how to implement a similar approach.

Human-in-the-Loop: Where to Put Approval in Agents and Workflows

Agents can pause before tool calls. Workflows can suspend at steps. But when they're working together, where's the right place for a human-in-the-loop?

Human-in-the-Loop: When to Use Agent Approval

When should an agent ask for permission? How to think about approval, suspension, and the trust spectrum in your agent design.

You can now use multiple storage providers in a single Mastra project, all configured from one place.

Workflow state lets you share values across steps without threading them through every `inputSchema` and `outputSchema`.

Building agentic copilots with CopilotKit and Mastra

Learn how to build interactive, real-time agentic apps using CopilotKit as a frontend for Mastra agents.

Introducing Mastra's improved agent orchestration with AI SDK v5 support

Mastra now controls the agent loop and tool calling with increased orchestration capabilities—while maintaining backward compatibility with AI SDK v4 and v5.

Reducing tool calling error rates from 15% to 3% for OpenAI, Anthropic, and Google Gemini models

We recently built a tool compatibility layer that reduced tool calling error rates from 15% to 3% for 12 OpenAI, Anthropic, and Google Gemini models, across a set of 30 property types and constraints.

Typescript leader Matt Pocock builds, breaks, and reacts to Mastra in real time.

AI prompting techniques

Learn about effective AI prompting techniques, including types of prompts, handling their output, and evals.

AI with JavaScript in 2025

A dive into four leading open-source JS AI libraries: Vercel AI SDK for model routing, Mastra for agents and workflows, Langfuse for observability, and Stagehand for web automation.

Choosing a JavaScript Agent Framework

When choosing a JavaScript agent framework, consider the needs of your project and the team's expertise. Mastra and LangGraph.js are two leading options.

Mastra Storage: a flexible storage system for AI Applications

A look at how Mastra's storage system simplifies building AI applications by managing workflows, memory, traces, and eval data with flexible storage backends.

Exploring a recent handful of Mastra optimizations that help it run smoothly on edge functions

A deep dive into why Mastra is adopting Model Context Protocol (MCP) for AI tool integration, comparing it with Agents.json and Composio, addressing current limitations in discovery, quality, and configuration, and providing a framework-friendly proposal with practical code examples for implementation.

Giving Agents a Voice

Mastra Voice lets developers add speech-to-text and text-to-speech capabilities to AI agents using a straightforward API

Benchmarking pgvector RAG performance across different dataset sizes

Benchmark tests for pgvector, showing how IVFFlat, adaptive lists, flat search, and HNSW perform at different dataset sizes in recall and latency.

Solving the hardest problem in computer science with LLMs

How we use LLMs at Mastra to design clean, intuitive APIs by improving naming, structure, and developer workflow.

How Mastra enhances Vercel's AI SDK with agent primitives, RAG pipelines, and evaluation capabilities

A technical guide to implementing agent memory in Mastra, covering storage, vector search, and context management

Baby steps towards AI Ops

How do we apply the lessons learned from the last decade of DevOps to manage, deploy, and operate AI agents at scale?

Every API needs a natural language endpoint

Building a world where every API has a chat endpoint

RAG tutorial: Setting up a RAG pipeline

A guide to setting up a simple RAG pipeline with Mastra

Building workflows with Mastra

A few patterns for building workflows

Ship better agents