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Agents 2026 Rank: #2460

Mastra ✓

Build AI agents that reason, remember, and act.

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Agent AI Frameworks Agent Development
More categories (4)
Typescript Workflows AI Reasoning Agents
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Free + from $250/mo

Verdict

A quick read before you dig into the full page.

Best if
Build AI agents that reason, remember, and act
Skip if
Complex setup process
Pick this when
Defines runtime behavior

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Overview

Generated by ChatGPT

Mastra is a TypeScript-based artificial intelligence (AI) framework developed to build and improve AI agents and apps. It equips developers with a set of tools and capabilities to create AI agents that can reason, remember, and act.

The framework consists of several key components: Agents, Workflows, a Harness, Memory, and a Server. Agents in Mastra are built with defined instructions, models, and tools, and are coded to have a specific runtime behavior.

Workflows represent the process sequences that include typed steps, retries, and branches, enabling teams to create, inspect, and ship production workflows.

The Harness is a unique feature that coordinates multi-mode agents with a shared state, threads, and storage. It functions similar to the in-built Mastra code operation.

The Memory provides AI agents with a durable context having observational memory and semantic recall. It also provides thread-aware storage for efficient information retrieval and utilization.

Finally, the Mastra Server is used to register agents and workflows. It can be run locally or deployed via Mastra's cloud platform. Overall, Mastra is suitable for developing both internal and customer-facing AI agents while enabling improved observability through metrics, logs, and traces.

It also accommodates cloud deployment for the developed agents through its server feature.

Mastra website

Access & connections

Where you can use Mastra.

Platforms

Web

Capabilities

What this tool accepts, produces, and is strongest at.

Input

  • Code

Output

  • Code

Pros & cons

Pros

  • Defines runtime behavior
  • Built-in observability
  • Serves internal and customer-facing applications
  • TypeScript-based framework
  • Multi-mode agent coordination
  • Semantic recall
  • Observational memory
  • Thread-aware storage
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  • Cloud deployment
  • Metrics tracking
  • Integrated workflows
  • Provides Harness for shared state
  • Enables registration of agents
  • Can run locally
  • Powerful for prototyping and production
  • Supports both agent and app development
  • Memory and tools included
  • Reasoning capability
  • Shipment of production workflows
  • Pattern defined process sequences
  • Enables observability through logs and traces
  • Accommodates local and cloud server registration
  • Provides thread aware storage for information retrieval
  • Facilitates efficient insights by using trace intelligence
  • Facilitates improving agents by iterative evaluation
  • Provides agentic search capabilities
  • Supports versioned datasets for evaluation
  • Offers detailed observable factors like latency, cost
  • Supports subscribing agent threads to external resources

Cons

  • TypeScript exclusive
  • Complex setup process
  • Missing user interface
  • Limited memory options
  • Difficult thread awareness
  • Limitations in Agent behavior
  • Inadequate data security
  • No multi-language support
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  • Undefined error handling
  • Poor documentation

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Releases 1

Initial release August 21, 2026 Current
Initial release of Mastra.

FAQ 12

What is Mastra?
Mastra is a TypeScript-based artificial intelligence (AI) framework for building and enhancing AI agents and apps. It provides developers with a host of tools and features that enable the creation of AI agents able to reason, remember, and act.
What does the Mastra framework consist of?
The Mastra framework is composed of several key components: Agents, Workflows, a Harness, Memory, and a Server. Agents are built with specific instructions, models, and tools, designed to follow certain runtime behaviours. Workflows are the process sequences that include typed steps, retries, and branches. The Harness feature coordinates multi-mode agents with a shared state, threads, and storage. The Memory section provides AI agents with durable context that retains observational memory and semantic recall. Finally, there's a Server used to register agents and workflows, which can run locally or be deployed via the Mastra's cloud platform.
What is the role of the Harness feature in Mastra?
The Harness feature in Mastra functions as a unique component that coordinates multi-mode agents with a common state, threads, and storage. It works in a similar fashion to the in-built Mastra code operation, aiding in the efficient management and functioning of multi-faceted agents.
How can user information be stored and retrieved in Mastra?
In Mastra, user information is stored and retrieved through its Memory feature. This provides AI agents with a durable context having observational memory and semantic recall, and even offers thread-aware storage for efficient retrieval and use of information.
How does Mastra handle server registration and deployment?
Mastra handles server registration and deployment with its own dedicated Mastra Server. This server is used to register agents and workflows. It can operate locally or be deployed using Mastra's cloud platform, thus catering to both local and cloud-based requirements for agent and workflow registration.
What types of AI agents can be developed using Mastra?
Mastra can be used to develop a wide range of AI agents. These include both internal and customer-facing AI agents. Internal agents can be used for functions within the organisation, while customer-facing agents are designed to interact with clients or users. Both these types of AI agents are designed to reason, remember, and act in accordance to their defined roles.
How does the memory feature in Mastra aid AI agents?
The Memory feature in Mastra aids AI agents by providing a durable context with observational memory and semantic recall. This allows the AI agents to remember past interactions, store and utilize important context information and make inferences based on the stored data, which ultimately results in a coherent and context-aware response.
Can Mastra remember and act upon specific runtime behaviors?
Yes, agents in Mastra are designed to remember and act upon specific runtime behaviors. These behaviors are defined during the agent creation process using the tools and models provided. The agents follow these runtime behaviors when they are deployed and operated.
Can Mastra's cloud platform be used for deploying developed agents?
Yes, Mastra's cloud platform can be used for deploying developed agents. The Mastra Server, a key component of the framework, offers a feature for cloud-based deployment, allowing developers to register and run their AI agents via Mastra's cloud platform.
How does Mastra facilitate efficient information retrieval and utilization?
Mastra facilitates efficient information retrieval and utilization with its memory feature. This feature is designed to provide thread-aware storage, which efficiently stores and retrieves information from past interactions, external APIs, databases, and other sources. As a result, AI agents can quickly access and use the relevant information when it is needed.
What tools does Mastra provide to developers?
Mastra provides developers with an extensive set of tools and capabilities for building AI agents. This includes tools for defining the agent instructions, models, and runtime behavior, the built-in Harness that manages multi-mode agents with shared state, threads, and storage, and finally a Server that allows for both local and cloud deployment of the agents and workflows.
What is the Mastra server used for?
The Mastra Server is used for registering agents and workflows. It has the capability to run locally or be deployed via Mastra's cloud platform. This feature makes it especially useful for both local development and cloud deployment context.
Mastra Free + from $250/mo
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