Comparisons / Mastra vs Rasa
Mastra vs Rasa: Which Agent Framework to Use?
Mastra vs Rasa, head to head
Mastra and Rasa both let you build an agent, but they sit in different parts of the stack and they assume different things about who's writing the code.
Mastra is a TypeScript-first framework for building AI agents, from the team behind Gatsby.
Rasa is an open-source framework for building conversational AI — chatbots and virtual assistants.
Underneath, both wrap the same thing: a model call, a tool dispatch, a loop. The decision is about which abstraction your team wants to think in day to day, and which ecosystem you're willing to inherit along with it. There's an honest, framework-free version of the same pattern in about 60 lines of Python in the lesson at the bottom of this page — useful as a baseline regardless of which framework wins.
Pick Mastra if
Pick Mastra if mastra is the best option for TypeScript teams that want a batteries-included agent framework without leaving the Node.js ecosystem. The workflow engine and Studio are genuinely productive. For simple agents or Python teams, the plain approach avoids an unnecessary dependency. The tradeoffs in its intro should match how your team already thinks about agents; Rasa will feel like translation if they don't.
Pick Rasa if
Pick Rasa if rasa is purpose-built for production conversational AI with enterprise requirements — on-premise deployment, regulatory compliance, deterministic business logic. For general-purpose agents or simple chatbots, an LLM with a system prompt and a few tools is faster to build and more flexible. The tradeoffs in its intro should match how your team already thinks about agents; Mastra will feel like translation if they don't.
By the numbers
By the numbers
Mastra
22.7k
1.8k
TypeScript
Apache-2.0
2024-08-06
Mastra AI
Spark Capital, Y Combinator
Series A ($22M, Apr 2026 — $35M total)
244.0k
Rasa
21.1k
4.9k
Python
Apache-2.0
2016-10-14
Rasa Technologies
Rasa Pro / Rasa Cloud
Yes
GitHub stats as of April 2026. Stars indicate community interest, not necessarily quality or fit for your use case.
| Concept | Mastra | Rasa |
|---|---|---|
| Agent | `new Agent({ model, instructions, tools })` with automatic tool dispatch | Rasa agent with NLU pipeline, dialogue policies, and action server |
| Tools | `createTool({ name, schema, execute })` with Zod validation | Custom actions running on a separate action server via HTTP |
| Workflows | `Workflow` class with `.step()`, `.then()`, `.branch()` for orchestration | — |
| RAG | Built-in document syncing, chunking, embedding, and vector search | — |
| Memory | Short-term thread memory + long-term vector memory across sessions | — |
| Studio | Mastra Studio: local GUI for testing agents, viewing traces, debugging | — |
| NLU | — | NLU pipeline: tokenizer, featurizer, intent classifier, entity extractor |
| Dialogue | — | Stories/Rules YAML + dialogue policies for conversation flow |
| Slots | — | Typed slots for tracking entities and state across turns |
| CALM | — | LLM for understanding + deterministic `Flows` for business logic |
Or build your own in 60 lines
Both Mastra and Rasa implement the same 8 patterns. An agent is a function. Tools are a dict. The loop is a while loop. The whole thing composes in ~60 lines of Python.
No framework. No dependencies. No opinions. Just the code.
Build it from scratch →