Comparisons / AutoGen vs Mastra
AutoGen vs Mastra: Which Agent Framework to Use?
AutoGen autogen by microsoft models agents as conversableagents that chat with each other. 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
AutoGen
56.7k
8.5k
Python
CC-BY-4.0
2023-08-18
Microsoft Research
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 | AutoGen | Mastra | Plain Python |
|---|---|---|---|
| Agent | ConversableAgent with system_message, llm_config | new Agent({ model, instructions, tools }) with automatic tool dispatch | A function with a system prompt that POSTs to the LLM API |
| Tools | register_for_llm() and register_for_execution() | createTool({ name, schema, execute }) with Zod validation | A dict of callables + JSON schema descriptions |
| Conversation | Two-agent chat with initiate_chat(), message history | — | A messages array that grows with each turn |
| Multi-Agent | GroupChat with GroupChatManager, speaker selection | — | Multiple agent functions called in sequence on shared messages |
| Nested Chats | register_nested_chats() for sub-task handling | — | A task queue (BFS) — agent schedules follow-ups via a tool |
| Termination | is_termination_msg callback, max_consecutive_auto_reply | — | The while loop exits when no tool_calls or max_turns reached |
| 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 |
| Memory | — | Short-term thread memory + long-term vector memory across sessions | A messages array for short-term, a JSON file or DB query for long-term |
| 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 AutoGen 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 AutoGen
AutoGen excels at complex multi-agent workflows where agents need to debate or collaborate. For single-agent use cases or simple tool-calling agents, the plain Python version is significantly simpler.
What AutoGen does
AutoGen's core abstraction is the ConversableAgent — an agent that can send and receive messages. Two agents chat by alternating turns on a shared message history. GroupChat extends this to N agents, with a GroupChatManager that selects the next speaker (round-robin, random, or LLM-based selection). Nested chats allow an agent to spin up a sub-conversation to handle a complex subtask before returning to the main thread. AutoGen also provides code execution sandboxes, letting agents write and run code as part of their conversation. The framework thinks in terms of conversations, not chains or graphs. This makes it natural for workflows where agents need to debate, critique, or iteratively refine outputs together.
The plain Python equivalent
A ConversableAgent is a function that takes a messages array, calls the LLM with a system prompt, and returns the assistant message. Two-agent chat is a while loop where you alternate between calling agent_a(messages) and agent_b(messages), appending each response. GroupChat is the same loop but with a speaker selection step — either rotate through a list or ask the LLM "who should speak next?" and call that agent function. Nested chats are a function call within the loop: pause the main conversation, run a sub-loop with different agents, and inject the result back. Tool registration is adding functions to a tools dict with their JSON schemas. The conversation-as-primitive model is just messages arrays passed between functions.
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 AutoGen 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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