agency-agents vs Mastra: Features, Pricing & Which Is Better (2026)
A side-by-side comparison of agency-agents and Mastra — features, pricing, and ideal use cases — to help you decide which AI tool fits your workflow.
a
agency-agents
msitarzewski
Open-source collection of specialized AI agent personalities installable into Claude Code, Cursor, Codex and more.
Key features
- Specialized Agent Roster: Role-specific agents with deep domain expertise rather than generic prompt templates.
- Personality-Driven Design: Each agent has its own voice, communication style and approach.
- Deliverable Focus: Agents define real code, processes and measurable outcomes.
- Desktop Installer App: A native app for macOS, Linux and Windows installs agents with a click and auto-updates.
- Multi-Tool Support: Works with Claude Code, Cursor, Codex, Gemini CLI, OpenCode, Qwen, Windsurf and others.
- Selective Installation: Install only the divisions or teams you need.
Best for
- A developer installs a frontend specialist agent into Claude Code for UI work.
- A founder assembles a virtual agency of marketing and product agents.
- A team uses agent files as reference for writing its own agent prompts.
- A user installs the same roster across Cursor and Codex with the desktop app.
- A community manager adapts a community-focused agent for social outreach.
- A contributor submits a new specialized agent through a pull request.
Mastra
Mastra (team behind Gatsby)
A TypeScript-first agent framework with workflows, memory, streaming, playground, evals, and tracing for building AI apps.
Key features
- Unified Model Interface: Provides a single API to access hundreds of models from dozens of providers (documented access to 1113 models from 53 providers) so developers can switch or compare models without changing application logic.
- Workflows and Orchestration: First-class workflow primitives to compose multi-step agent behaviors and pipelines, enabling complex task decomposition, tool invocation, and sequential processing.
- Long-term Memory: Built-in memory abstractions to persist and recall conversational or agent state across sessions, improving continuity and personalized behavior.
- Streaming Outputs: Support for streaming model responses to enable low-latency progressive output and responsive UX in interactive applications.
- Interactive Playground: A development playground for iterating on prompts, agent strategies, and tool integrations with live testing and debugging.
- Evals and Tracing: Integrated evaluation tooling and tracing to measure agent performance, run automated evaluations, and inspect decision traces for observability and improvement.
- Templates and Example Agents: Ready-made templates (e.g., an AI web search assistant) and sample projects to accelerate building real-world applications.
- Multi-provider Tooling: Facilities to equip agents with external tools, connectors, and integrations while managing provider-specific details through Mastra abstractions.
- TypeScript-first agent framework optimized for modern TypeScript stacks
- Workflow orchestration for multi-step agent behaviors
- Persistent memory management for agents
- Streaming response support for real-time output
- Interactive playground for developing and testing agents
- Evaluation tooling (evals) for measuring agent performance
- Tracing and observability for agent executions
- Unified model interface providing access to 1,113 models from 53 providers via a single API
- Templates and example applications (including a web search assistant)
- Open-source repository and community resources (mastra-ai/mastra on GitHub)
- Course and learning materials for building and deploying agents
Best for
- Building autonomous TypeScript agents that coordinate tools, perform multi-step reasoning, and maintain state with memory across interactions.
- Creating an AI-powered web search assistant that crawls, extracts, and sources open-web information using Mastra templates and connectors.
- Comparing and switching LLM providers easily during development by leveraging Mastra's unified model interface to test dozens of models without rewriting code.
- Developing production workflows that stream partial model outputs to users for real-time feedback while tracing and evaluating agent decisions.
- Prototyping and evaluating agent strategies using the interactive playground and built-in evals to iterate on prompts and measure performance.
- Teaching and onboarding teams through the Mastra course to learn how to equip agents with tools, memory, and MCP patterns in a TypeScript environment.
- Packaging TypeScript-based AI applications with reproducible workflows, templates, and observability for deployment and maintenance.
- Building tool-enabled conversational agents with memory and multi-step workflows
- Creating web search and information retrieval assistants with sourced answers
- Rapidly prototyping and testing agent behavior in an interactive playground
- Integrating many LLM providers through a single unified API for model experimentation
- Deploying production agents with tracing, evals, and observability