Mastra
TypeScript framework for building production AI agents with built-in observability.
Mastra's durable execution, built-in observability, and TypeScript-native design make it a strong choice for production agent workflows. Recent additions like Durable Agents, inbox, task lists, and A2A protocol expand its capabilities significantly. It's overkill for simple chatbots and lacks Python support. Pick it if you need multi-step agents with reliability and monitoring.
Verified 18d ago · liveness 95/100 · cite: rightaichoice.com/tools/mastra
- Building multi-step AI agent workflows in TypeScript with durable execution
- Internal automation agents that live in Slack, Discord, or Telegram
- Customer-facing conversational agents requiring handoff and tool approval
- AI SREs or developer platform agents needing robust observability
- Simple single-turn LLM chatbots (overkill)
- Python-first development teams (TypeScript only)
- Quick prototypes with minimal orchestration
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Skip Mastra if you need Python support, a large community with abundant examples, or a simple chatbot that doesn't require durable workflows and observability.
Going past 100k observability events on Starter adds $10 per 100k events, which can add up at high volume.
Mastra's free-tier (Apache 2.0) is generous for self-hosted use, but the cloud platform's metered pricing on observability events and CPU time means costs scale with usage. For teams with predictable low usage, the Starter tier is cheap; high-volume teams may find LangSmith's flat-tier pricing more predictable.
In short
Mastra — TypeScript framework for building production AI agents with built-in observability. Best for Building multi-step AI agent workflows in TypeScript with durable execution, Internal automation agents that live in Slack, Discord, or Telegram, Customer-facing conversational agents requiring handoff and tool approval. Free to start; paid plans from $250/mo.
What's new in Mastra
Checked 17 days agoAcross the latest 9 updates: 9 feature updates.
Introducing File-Based Agents for Mastra
Mastra agents can now be defined using folders and files.
Introducing Browser Recording for Mastra Agents
Mastra agents can record and replay browser sessions.
Introducing Webhook Signals for Mastra Agents
Mastra agents react to webhook events from GitHub, Slack, Stripe, or custom APIs.
What are durable AI agents?
Explains state persistence, checkpointing, and resumability in Mastra agents.
Build Claude Code for X with Agent Controller
Mastra Agent Controller enables long-running agent experiences with sessions, modes, permissions, and subagents.
Give a Mastra agent persistent file storage with Archil
Mastra integrates with Archil for persistent S3-backed file storage accessible by agents.
Introducing Durable Agents
Mastra agents survive client disconnects and network blips with cacheable resumable streams.
Publish and Subscribe with Mastra's Built-In Event System
Mastra agents can send/receive events via Redis Streams and Google Cloud Pub/Sub.
Your Mastra Agent Now Has an Inbox With Priority Notifications
Mastra agents can be triggered by external sources like GitHub and Slack via an inbox.
Viability Score
How likely is Mastra to still be operational in 12 months? Based on 4 signals — momentum (how recently it shipped), wrapper dependency, revenue model, and web presence.
Last calculated: July 2026
How we score →Key Features
- Define typed agents with instructions, models, and tools in one file
- Graph-based workflow engine with .then(), .branch(), .parallel()
- Durable execution via Temporal for fault-tolerant agents
- Human-in-the-loop workflow suspension and tool approval
- Built-in observability: evals, metrics, traces, logs
- Memory system with conversation history, working, and semantic memory
- AI Gateway with response caching and 90+ provider support
- Agent Client Protocol (ACP) for agent-to-agent delegation
- Agent-to-Agent (A2A) communication across frameworks
- Durable Agents with cacheable resumable streams
- Task lists for long-running complex work
- Inbox with priority notifications from external sources
- Built-in event system with Redis Streams and Google Cloud Pub/Sub
- Realtime voice integration with Inworld
- MCP server authoring support
About Mastra
Mastra is an open-source TypeScript framework for building AI agents and AI-powered applications. It provides a unified platform to define typed agents with instructions, models, tools, and runtime behavior in one file. The framework includes built-in observability—evals, metrics, traces, logs—so you can monitor agent behavior in production. Recent additions include Durable Agents that survive client disconnects, a task list system, an inbox with priority notifications, an event system with Redis Streams and Google Cloud Pub/Sub, and Agent-to-Agent (A2A) communication. Mastra's graph-based workflow engine orchestrates multi-step processes with .then(), .branch(), and .parallel() methods, supports human-in-the-loop workflows, and uses durable execution via Temporal. It integrates with 90+ model providers through its AI Gateway and supports MCP server authoring. Compared to LangChain or CrewAI, Mastra is TypeScript-first, opinionated, and batteries-included, making it ideal for production-grade agent workflows that need reliability and transparency.
Behind the Verdict
Mastra is worth serious consideration if your team is all-in on TypeScript and needs to ship multi-step, production-grade AI agents. The framework's durable execution via Temporal is a genuine differentiator—your agents can survive network blips, browser refreshes, and server restarts without losing state. The built-in observability (evals, metrics, traces, logs) means you can debug agent behavior without bolting on external tooling. Recent features like Durable Agents, task lists, and an inbox with priority notifications address real-world pain points for long-running agents. The A2A protocol is forward-looking, enabling agent-to-agent delegation across frameworks. However, Mastra is overkill for simple single-turn chatbots—if you just need an LLM wrapper, there are simpler options. Python teams will be out of luck; this is TypeScript only. The ecosystem is younger than LangChain's, so community plugins are sparse. Pricing is competitive: a generous free tier (100K observability events, 24 CPU hours) and Teams at $250/mo with 1M events and 250 CPU hours. Enterprise custom pricing is available. In practice, we'd reach for Mastra when building internal automation agents (Slack, Discord, Telegram), customer-facing conversational agents with handoff, or AI SREs that need robust monitoring. If you need Python or rapid prototyping, consider LangChain or CrewAI instead.
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Real-world workflow fit
Concrete scenarios for the personas Mastra actually fits — and what changes day-one when you adopt it.
You need an agent that handles inquiries, pauses for human approval on refunds, and survives network blips.
Outcome: Set up a Mastra agent with durable workflows via Temporal, add human-in-the-loop approval, and deploy to production with built-in observability.
Build a Slack-integrated agent that queries databases and summarizes incidents.
Outcome: Use Mastra's Slack integration, database tools, and memory system to create a copilot that responds in Slack channels with real-time data.
You want two agents from different frameworks to collaborate on a task.
Outcome: Implement A2A protocol in Mastra to delegate sub-tasks to a remote agent, with full observability and traceability.
Use Cases
- Build a customer-support agent with durable workflows that pause and resume
- Add LLM-as-judge evals to existing TypeScript agents
- Create a multi-agent system with A2A across frameworks
- Deploy a Slack-integrated agent with channels
- Analyze and edit Google Sheets via natural language
- Let agents browse the web to perform actions
- Query databases using natural language via a natural language interface
- Automate code reviews with a GitHub PR agent
Models Under the Hood
as of 2026-07-06
Limitations
- Newer project than Python incumbents — smaller community, fewer examples, shorter track record.
- Evals and observability are good but not as battle-tested as LangSmith.
- Cloud tier is newer and feature-scoped.
- Some advanced features (e.g., persistent server for 24/7 uptime) cost extra even on paid plans.
- Limited pre-built integrations for enterprise tools like Salesforce or SAP.
as of 2026-07-01
12-month cost
Project the real annual outlay, including the implied monthly cost when only an annual tier is published.
Vendor list price only. Add-on usage, seat overages, and contract minimums are surfaced under Hidden costs & gotchas.
Plans compared
For each published Mastra tier: who it actually fits, and what it adds vs. the previous tier. Cross-reference the cost calculator above for projected annual outlay.
Starter
$0/mo
Ideal for
Small teams or individual developers prototyping on the cloud platform with low to moderate usage.
What this tier adds
100k observability events, 24 CPU hours, and 15-day data retention with metered overages.
Teams
$250/mo
Ideal for
Growing teams needing more capacity, SSO, and longer retention for production deployments.
What this tier adds
1M events, 250 CPU hours, 6-month retention, SSO, and SOC 2 docs.
Enterprise
Custom
Ideal for
Large organizations requiring custom retention, RBAC, audit logs, and dedicated support.
What this tier adds
Custom observability events and retention, RBAC, audit logs, uptime SLAs, and self-hosted VPC option with flat annual fee.
Where the pricing makes sense
The company stage and team size where Mastra's pricing actually pencils out — and where peers do it cheaper.
Mastra's free-tier (Apache 2.0) is generous for self-hosted use, but the cloud platform's metered pricing on observability events and CPU time means costs scale with usage. For teams with predictable low usage, the Starter tier is cheap; high-volume teams may find LangSmith's flat-tier pricing more predictable.
Setup time & first value
How long it actually takes to get something useful out of Mastra — broken out by persona, not the marketing-page minute.
For a developer familiar with TypeScript, you can create a new project and have a basic agent running in under 5 minutes with 'npm create mastra'. More complex setups with integrations and workflows may take 30–60 minutes.
Switching to or from Mastra
How to bring data in from common predecessors and how to get it back out — written for the switcher, not the buyer.
- →From LangChain TypeScript: Port agent definitions to Mastra's typed Agent class, replace LangChain chains with Mastra's workflow engine, and migrate observability to Mastra's built-in evals and traces.
- →From CrewAI: Rewrite agent roles and tasks as Mastra agents and workflows, and adopt Mastra's memory and tool definitions.
- →From custom implementations: Adopt Mastra's framework to standardize agent definitions, observability, and durable execution.
Integrations
Resources & Guides
- Documentationmastra.ai
Get started with Mastra
Full product docs from mastra.ai
- Learnmastra.ai
Build Your First AI Agent in TypeScript
Educational content from mastra.ai
- Resourcemastra.ai
Mastra Resources: Workshops, Books, and Research for AI Agents
Master the skills to build and launch AI agents with guided instruction, practical tools, and real-world examples.
- Guidemastra.ai
Overview
In-depth how-to from mastra.ai
- Resourcemastra.ai
Mastra Blog: AI Agents, Workflows, and TypeScript Engineering
Mastra.ai blog - Latest updates, tutorials, and insights on building AI agents.
- Resourcemastra.ai
AI Agent Builder Templates and Starter Kits
Spin up working examples of AI agents, workflows and MCP servers. Mastra Templates are starter kits for AI agent builders.
Official links
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Common stack mates teams adopt alongside Mastra, with the specific reason each pairing earns its keep.
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