AI :: Agent :: Framework :: mastra :: About :: Build Agentic Workflows with Mastra - The TypeScript Agent Framework
⪼ Made with 💜 by Polyglot.
Good for research tasks, less good for discreet tasks that need high accuracy
This video is an interview-style live stream with the founders of MOSTRA, a modern open-source AI agent framework for TypeScript and JavaScript developers. The conversation explores why MOSTRA was created, how it compares to Python-first AI frameworks, and what unique features it brings to building and deploying agentic AI applications. The session includes a detailed walkthrough of the framework, live coding and demo, discussion of workflows, memory, RAG, and vector search, as well as practical Q&A about architecture, best practices, and use cases.
The intent is to educate developers about MOSTRA, inspire confidence in TypeScript/JavaScript as a first-class citizen for AI development, and showcase how MOSTRA can help ship production AI products faster and more flexibly, especially for web developers.
- Founded by former Gatsby team after frustrations building AI-powered CRMs with existing Python-centric tools
- Purpose-built for TypeScript/JavaScript devs to make agentic AI accessible beyond Python/data science
- Strong focus on developer experience, flexibility, and plug-and-play modularity
- Mastra wraps the "agentic spectrum": from deterministic workflows to full agent networks
- Framework adds structure, memory, prompt tuning, tool calling, and workflow orchestration
- Agents are granted "tools" (functions with schemas), and workflows provide playbook-like, step-by-step logic
- Built on top of the
ai-sdk(not reinventing the wheel), tightly integrated for model routing/tool calling - Compatible with all modern JS/TS backends (Next.js, stand-alone, microservices) and frontend frameworks via AGUI/CopilotKit
- Pluggable memory and vector store providers (libSQL by default, MongoDB, key-value stores, etc.)
- Out-of-the-box dev playground with live tracing, debug spans, and workflow step visualization
- "Create tool" API abstracts most boilerplate—devs define schemas, provide functions, and MOSTRA handles the rest
- Clear best practices for tool descriptions to maximize LLM usability
- Detailed walkthrough of using MOSTRA with MongoDB Vector for RAG (retrieval-augmented generation) over 20,000 movie records
- Chunking, embedding, and metadata filtering explained for enterprise/production search use cases
- Agent networks (experimental) allow multi-agent systems with supervisor/subagent orchestration
- Storing and resuming workflow state, human-in-the-loop patterns, and suspending/resuming for async user input
- Streaming workflow results to the frontend for interactive UX
- Memory persistence, context threading, and ongoing improvements as best practices evolve
- Upcoming Mastra course teased ("unlike any course you've taken")
- Encouragement to follow Mastra for updates, join live streams, and contribute to docs/testing