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DSPy vs AutoGen

DSPyAutoGen

Bottom line: DSPy for mL and NLP engineers; AutoGen for teams already running AutoGen in production.

DSPyDSPy

Framework for programming language models instead of prompting them

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AutoGenAutoGen

Microsoft's multi-agent conversation framework (now in maintenance mode).

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Votes00
PricingFreeFree
CategoryAgent FrameworksAgent Frameworks
Tags
prompt-optimizationllm-programmingragopen-sourcestanford
agentsmulti-agentopen-sourcemicrosoftorchestration
Best for
  • ML and NLP engineers
  • Researchers building RAG systems
  • Teams wanting reproducible LLM pipelines
  • Teams already running AutoGen in production
  • Researchers studying multi-agent systems
  • Developers prototyping agent collaboration
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
  • Pioneered accessible multi-agent conversation patterns
  • Free and open source
  • Backed by Microsoft Research with strong documentation
  • 0.4 architecture is asynchronous and more observable
  • Works with many model providers
Cons
  • Steeper conceptual learning curve
  • Python-only
  • Optimization runs can be compute-intensive
  • Less suited to simple single prompts
  • Requires defining good metrics
  • In maintenance mode as of 2026, no new feature focus
  • Microsoft steers new projects to the Agent Framework
  • Multiple version lines (0.2 vs 0.4/0.7) cause confusion
  • Multi-agent loops can be hard to control and cost-predict
  • Less enterprise tooling than the successor framework

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