Mastra vs Agno: two agent frameworks with very different centers of gravity

Agent frameworks are starting to separate into two camps.

One camp wants to live inside the product stack. It cares about frontend integration, typed tools, local dev loops, deployment to the same places your web app already runs, and enough observability that a product team can debug what the agent did.

The other camp wants to become the runtime for agents as infrastructure. It cares about sessions, traces, RBAC, scheduling, audit logs, persistent state, teams of agents, and control planes.

That is the cleanest way I found to think about Mastra and Agno.

Mastra is the more natural fit for TypeScript product teams. Agno is the more natural fit for Python teams building a self hosted agent platform. They overlap, but they are not trying to win the same developer by taking the same route.

The short version

If your app is already a Next.js, Node, React, or TypeScript product, start with Mastra. It gives you agents, workflows, RAG, memory, evals, observability, Studio, and MCP in a stack that feels native to web developers.

If you are building a Python based agent service, especially one with multiple specialized agents, persistent sessions, RBAC, tracing, scheduling, and a control plane, start with Agno. Its AgentOS runtime is the part that changes the shape of the system.

What Mastra is good at

Mastra describes itself as a TypeScript framework for building AI agents and applications. The quickstart is exactly what you would expect from a Node project:

npm create mastra@latest

That framing matters. Mastra is not a Python framework with a JavaScript wrapper bolted on later. It is aimed at the people who build web products in TypeScript and want an agent layer that fits the same mental model.

The core pieces are familiar if you have built AI apps before:

• Agents for open ended tool use.
• Tools with typed inputs and outputs.
• Workflows for predictable multi-step execution.
• RAG for retrieval over documents and vector stores.
• Memory for conversation history, working memory, semantic recall, and observational memory.
• Evals through scorers that can run live or in experiments.
• Observability for traces, logs, metrics, token usage, and cost.
• MCP support as both a client and a server.

The interesting part is not that Mastra has these features. Most frameworks have a version of them now. The interesting part is that Mastra packages them for a TypeScript team that wants to build a product, not maintain a separate AI research service.

Studio is a serious advantage

Mastra Studio is easy to underrate until you imagine debugging an agent without it.

Studio gives you a local interface for chatting with agents, running workflows, inspecting tool calls, viewing traces and logs, testing tools independently, exploring MCP servers, and running scorers. That shortens the edit-test-debug loop. A lot.

For product teams, this matters more than a benchmark. The bottleneck is often not “can I call the model?” It is “why did the agent call that tool, with those arguments, after that prompt change?”

Mastra has a good answer there.

Mastra workflows are for control

Mastra’s workflow model is explicit and typed. You define steps, give them input and output schemas, and compose them with patterns like .then(), .branch(), and .parallel().

That makes Mastra a good fit when the process is known ahead of time:

• enrich a lead,
• validate the input,
• call an agent,
• run a deterministic check,
• ask for approval,
• write the result somewhere,
• emit a trace.

An agent can be part of the workflow, but the workflow owns the shape of the process. That is usually what you want in a production app. Agents are useful, but not every step should be left to the model.

The memory model is practical

Mastra’s memory docs are more thoughtful than the usual “store the chat history” story.

It supports message history, working memory, semantic recall, multi-user threads, and observational memory. Observational memory uses background agents to turn older raw messages into dense observations, so long conversations do not keep dragging the entire transcript into context.

That is the right direction. Long running agents need memory that gets smaller and more useful over time, not memory that grows until it becomes expensive noise.

Where Mastra can be awkward

Mastra’s strengths are also its constraints.

If your team is Python-first, Mastra may feel like adding another runtime. If you want a backend agent platform with RBAC, audit logs, isolated multi-user sessions, scheduling, and a control plane, Mastra has pieces of the story, but Agno is more directly aimed at that shape.

There are also deployment details to watch. For example, Mastra’s docs explicitly warn that local filesystem backed LibSQLStore does not work in serverless environments like Vercel, Netlify, or Cloudflare. You need to swap storage appropriately before shipping.

What Agno is good at

Agno is a Python SDK and runtime for building your own agent platform. Its SDK gives you three main primitives:

• Agent: one autonomous model-driven worker with tools and instructions.
• Team: multiple agents coordinated on a task.
• Workflow: a deterministic pipeline over agents, teams, functions, and nested workflows.

That middle primitive, Team, is the big tell. Agno treats multi-agent coordination as a first class design problem, not something you improvise with a few tool calls.

A team can contain agents or sub-teams. The team leader can route, coordinate, or broadcast work depending on the mode. Agno’s docs are honest about the tradeoff: teams add specialization and parallelism, but they also add coordination overhead. That is exactly the right warning.

AgentOS is the real differentiator

The most important part of Agno is not just the SDK. It is AgentOS.

AgentOS turns agents, teams, and workflows into an operational service. It generates APIs for runs, sessions, memory, knowledge, evals, traces, metrics, schedules, approvals, components, and database migrations. It supports REST and SSE by default, with other interface options around the runtime.

That is a different ambition from “agent framework” in the narrow sense. Agno is trying to be the runtime layer for an agent platform.

This is useful when agents are no longer a feature tucked inside one product screen. Once agents have users, sessions, state, permissions, traces, approvals, and background jobs, you need something closer to an operating layer.

Agno is built around that idea.

Data ownership is a strong theme

Agno repeatedly emphasizes that your sessions, memory, traces, and audit logs live in your own database. AgentOS tracing stores spans in tables like agno_traces and agno_spans, and you can query them directly.

That matters for enterprise use cases. Agent traces often contain prompts, tool arguments, model responses, customer data, and internal system behavior. Sending all of that to a hosted observability service is not always acceptable.

Agno’s answer is simple: keep it in your database, use OpenTelemetry-style traces, and inspect it through the control plane.

Storage and RAG are broad

Agno has a broad persistence story. Its storage docs cover PostgreSQL, SQLite, MongoDB, Redis, DynamoDB, Firestore, MySQL, SingleStore, SurrealDB, GCS, JSON, and in-memory storage.

The knowledge layer is similarly broad. Agno documents support for 25+ vector databases through a unified interface, including PgVector, Pinecone, Qdrant, Weaviate, Milvus, ChromaDB, LanceDB, MongoDB, Redis, Cassandra, ClickHouse, SingleStore, Upstash, Couchbase, and SurrealDB.

If your agent platform needs to sit near a bunch of Python data systems, Agno will probably feel more natural than Mastra.

Where Agno can be too much

Agno can be more infrastructure than you need.

If the job is “add an AI assistant to my TypeScript app,” standing up a Python AgentOS runtime may be the wrong first move. You get power, but also another service boundary, another deployment unit, another auth surface, and another operational layer.

Teams can also be overused. Multi-agent systems sound elegant in diagrams, but a single well-instructed agent with good tools is often cheaper, easier to debug, and good enough. Agno’s team abstraction is useful when the task actually needs specialization. It should not be the default just because it exists.

Side-by-side comparison

The decision framework

I would choose Mastra when most of these are true:

• The main app is TypeScript, Node, Next.js, React, SvelteKit, Express, or Hono.
• The agent is part of a product experience, not a standalone platform.
• You want typed tools and workflows in the same language as the app.
• You want Studio for local testing and debugging.
• You care about evals and observability, but do not need a full agent control plane on day one.
• MCP integration matters in a TypeScript environment.

I would choose Agno when most of these are true:

• The team is Python-first.
• You are building a service or platform around agents, not just embedding one agent into an app.
• You need multi-agent teams as a central abstraction.
• You need persistent sessions, memory, trace storage, schedules, approvals, RBAC, and audit logs.
• You want traces and agent data to stay in your own database.
• You are already close to Python data systems, vector databases, and backend automation.

A hybrid architecture is reasonable

This does not have to be a religious choice.

A TypeScript product could use Mastra for the product-facing AI layer, especially if the UI and app logic already live in Next.js. The same company could run Agno behind the scenes for heavier Python agents, data workflows, multi-agent research, or internal automation.

In that setup:

• Mastra owns the in-product agent experience.
• Agno owns the backend agent platform.
• Both expose APIs.
• The product decides which runtime to call based on the task.

That sounds messy, but it can be cleaner than forcing one framework to do work outside its natural shape.

My take

Mastra feels like the agent framework for teams that want AI to fit into the product stack they already have. It is pragmatic, typed, and web-native. Studio, workflows, evals, memory, observability, and MCP make it feel like a serious TypeScript AI application framework rather than a thin wrapper around model calls.

Agno feels like the agent framework for teams that want agents to become infrastructure. Teams, AgentOS, persistent storage, trace ownership, RBAC, scheduling, approvals, and the control plane are the point. It is less about sprinkling an agent into an app and more about operating agents as a system.

So the practical answer is simple:

• Pick Mastra if your center of gravity is a TypeScript product.
• Pick Agno if your center of gravity is a Python agent platform.

The harder part is being honest about which one you are actually building.

Sources

• Mastra docs
• Mastra GitHub
• Mastra memory
• Mastra workflows
• Mastra evals
• Mastra observability
• Mastra MCP
• Mastra RAG
• Agno docs
• Agno SDK introduction
• Agno GitHub
• Agno teams
• Agno workflows
• Agno Agent API
• Agno storage
• Agno vector databases
• Agno observability
• Agno security and auth