Which is more popular, FastAgency or Mastra?

Mastra has more GitHub stars (28,498 vs 548).

Which is more actively developed, FastAgency or Mastra?

Mastra had more commits in the last 90 days (4,044 vs 0).

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

FastAgency has had no commit in 9 months; Mastra is actively maintained (4,044 commits in the last 90 days).
Mastra is growing faster: +969 GitHub stars in the last 30 days vs +3 for FastAgency.
Pick FastAgency for: the fastest way to bring multi-agent workflows to production. Pick Mastra for: from the team behind Gatsby, Mastra is a framework for building AI-powered applications and agents.

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FastAgencyopen-source

The fastest way to bring multi-agent workflows to production.

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

+Unified interface for deploying AG2 workflows to production with minimal code changes
+Supports both web chat applications and REST API services from the same codebase
+Built-in scaling capabilities with distributed architecture and message broker coordination

-Dependent on AG2 framework, limiting flexibility to other agent frameworks
-Relatively small community with 532 GitHub stars compared to major frameworks
-Limited documentation available in the provided materials for advanced features

•Deploying AG2 multi-agent chatbots as web applications for customer service or support
•Creating REST API services that expose agent workflows for integration with existing systems
•Building scalable distributed agent systems that coordinate across multiple servers or datacenters

Which is more popular, FastAgency or Mastra?: Mastra has more GitHub stars (28,498 vs 548).
Which is more actively developed, FastAgency or Mastra?: Mastra had more commits in the last 90 days (4,044 vs 0).
Should I use FastAgency or Mastra?: Compare their capabilities, limitations and "best for" notes above. Trying each on a small task is the fastest way to decide.