Which is more popular, Agent or Mastra?

Mastra has more GitHub stars (28,525 vs 472).

Which is more actively developed, Agent or Mastra?

Mastra had more commits in the last 90 days (4,109 vs 310).

Compare their capabilities, limitations and "best for" notes above. Trying each on a small task is the fastest way to decide.

Side-by-side comparison of two AI agent tools

Mastra is growing faster: +968 GitHub stars in the last 30 days vs +21 for Agent.
Pick Agent for: create state-machine-powered LLM agents using XState. Pick Mastra for: from the team behind Gatsby, Mastra is a framework for building AI-powered applications and agents.

From GitHub data refreshed daily.

Agentopen-source

Create state-machine-powered LLM agents using XState

From the team behind Gatsby, Mastra is a framework for building AI-powered applications and agents with a modern TypeScript stack.

+State machine structure provides predictable, auditable agent behavior with clear transition logic
+Learning capabilities through observations and feedback enable agents to improve performance over time
+Flexible model provider support via Vercel AI SDK integration allows switching between different LLMs

-Higher complexity compared to simple prompt-based agents, requiring knowledge of both XState and AI concepts
-Documentation appears incomplete with placeholder sections for key setup instructions
-State machine approach may be overkill for simple conversational agents or basic AI tasks

•Customer service chatbots that need to follow specific escalation workflows and remember interaction history
•Game AI characters that must exhibit consistent behavior patterns while adapting to player actions
•Automated support systems requiring structured decision trees with learning from resolution outcomes

Which is more popular, Agent or Mastra?: Mastra has more GitHub stars (28,525 vs 472).
Which is more actively developed, Agent or Mastra?: Mastra had more commits in the last 90 days (4,109 vs 310).
Should I use Agent or Mastra?: Compare their capabilities, limitations and "best for" notes above. Trying each on a small task is the fastest way to decide.