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DSPy vs Mastra

DSPyMastra

Bottom line: DSPy for mL and NLP engineers; Mastra for typeScript and full-stack JavaScript teams.

DSPyDSPy

Framework for programming language models instead of prompting them

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MastraMastra

The TypeScript framework for building AI agents and workflows.

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Votes00
PricingFreeFreemium
CategoryAgent FrameworksAgent Frameworks
Tags
prompt-optimizationllm-programmingragopen-sourcestanford
agentstypescriptopen-sourceworkflowrag
Best for
  • ML and NLP engineers
  • Researchers building RAG systems
  • Teams wanting reproducible LLM pipelines
  • TypeScript and full-stack JavaScript teams
  • Startups shipping AI features fast
  • Developers wanting agents without Python
Pros
  • Removes brittle manual prompt engineering
  • Automatic prompt and few-shot optimization
  • Modular, composable and testable
  • Backed by Stanford NLP research
  • Free and MIT-licensed
  • TypeScript-native, rare among serious agent frameworks
  • Open source and free to self-host
  • Ergonomic agents, workflows, memory, and evals in one toolkit
  • Strong funding and momentum (22k+ GitHub stars)
  • Fits full-stack and front-end teams
Cons
  • Steeper conceptual learning curve
  • Python-only
  • Optimization runs can be compute-intensive
  • Less suited to simple single prompts
  • Requires defining good metrics
  • Young project with a still-maturing ecosystem
  • APIs may change as it evolves toward stability
  • Fewer integrations than older Python frameworks
  • TypeScript-only, no Python option
  • Managed cloud pricing not fully transparent

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