Comparisons / CrewAI vs Mastra
CrewAI vs Mastra: Which Agent Framework to Use?
CrewAI crewai organizes work into agents, tasks, and crews. Mastra mastra is a typescript-first framework for building ai agents, from the team behind gatsby. Here is how they compare — and what the same patterns look like in plain Python.
By the numbers
CrewAI
48.0k
6.5k
Python
MIT
2023-10-27
João Moura
Mastra
22.7k
1.8k
TypeScript
MIT
2024-08-06
Mastra AI
244.0k
GitHub stats as of April 2026. Stars indicate community interest, not necessarily quality or fit for your use case.
| Concept | CrewAI | Mastra | Plain Python |
|---|---|---|---|
| Agent | Agent(role, goal, backstory, tools, llm) | new Agent({ model, instructions, tools }) with automatic tool dispatch | A function with a system prompt and a tools dict |
| Tools | Tool registration with @tool decorator, custom Tool classes | createTool({ name, schema, execute }) with Zod validation | A dict: tools[name](**args) |
| Agent Loop | Internal to Agent execution, hidden from user | — | A while loop over messages with tool_calls check |
| Task Delegation | Crew(agents, tasks, process=sequential/hierarchical) | — | A task queue processed in a while loop with a budget cap |
| Memory | ShortTermMemory, LongTermMemory, EntityMemory | Short-term thread memory + long-term vector memory across sessions | A dict injected into the system prompt |
| State | Task output passed between agents via Crew orchestration | — | A dict tracking tool calls and results |
| Workflows | — | Workflow class with .step(), .then(), .branch() for orchestration | Async function calls in sequence with if/else branching |
| RAG | — | Built-in document syncing, chunking, embedding, and vector search | fetch() to embedding API, store in array, cosine similarity search |
| Studio | — | Mastra Studio: local GUI for testing agents, viewing traces, debugging | console.log() statements and a test script you run from the terminal |
What both do in plain Python
Every concept in the table above — agent, tools, loop, memory, state — maps to a handful of Python primitives: a function, a dict, a list, and a while loop. Both CrewAI and Mastra wrap these primitives in their own class hierarchies and APIs. The underlying pattern is the same ~60 lines of code. The difference is how much ceremony each framework adds on top.
When to use CrewAI
CrewAI shines for multi-agent setups where you want named roles ("researcher", "writer"). But the core mechanics — tool dispatch, the agent loop, task scheduling — are the same patterns you can build in plain Python.
What CrewAI does
CrewAI models multi-agent systems as a crew of specialists. Each Agent has a role ("Senior Researcher"), a goal ("Find the best data sources"), a backstory that shapes its behavior, and a set of tools it can use. Tasks define discrete units of work with expected outputs. The Crew orchestrates execution — sequentially, hierarchically, or with a custom process. CrewAI also provides memory systems (short-term, long-term, entity) and delegation, where one agent can hand off subtasks to another. The mental model is a team of people collaborating on a project. For prototyping multi-agent workflows where you want to reason about roles and responsibilities, it provides a clean vocabulary.
The plain Python equivalent
An Agent in CrewAI is a function with a system prompt that includes the role, goal, and backstory. The tools dict maps names to callables. Task delegation is a list of tasks processed in order — each task calls the assigned agent function with the task description appended to the messages. Hierarchical execution is a manager agent that decides which sub-agent to call next (just another tool choice). Memory is a dict injected into the system prompt. The entire crew pattern — multiple agents, task queue, delegation — is a for-loop over tasks, where each iteration calls the right agent function. No Crew class, no process kwarg. Just functions calling functions with a shared state dict passed between them.
When to use Mastra
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.
What Mastra does
Mastra provides a full-stack TypeScript framework for building AI agents. You define agents with a model, system prompt, and tools — the framework handles the agent loop, tool dispatch, and response parsing. The workflow engine lets you compose multi-step processes with explicit steps, conditions, and error handling. Built-in RAG support covers the full pipeline: document loading, chunking, embedding, vector storage, and retrieval. Memory spans both short-term (thread-scoped message history) and long-term (vector-based recall across sessions). Mastra Studio gives you a local browser-based GUI to test agents, inspect traces, and debug workflows visually. Created by the Gatsby team, it targets TypeScript developers who want a productive, type-safe agent development experience.
The plain TypeScript equivalent
An agent is an async function that POSTs to the LLM API, checks for tool_calls in the response, executes matching functions from a tools object, and loops. Workflows are async functions that call other async functions with if/else branching — no framework needed to run step A, then step B, then branch on a condition. RAG is three operations: call an embedding API, store vectors in an array (or database), and find the closest match with cosine similarity. Memory is a messages array you persist to a file or database. Studio is console.log and a test file. The entire agent — tools, memory, RAG retrieval — fits in about 60 lines of TypeScript. No classes, no decorators, no build step. Just functions, objects, and fetch calls.
Or build your own in 60 lines
Both CrewAI and Mastra 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.
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