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DSPy vs Agno

DSPyAgno

Bottom line: DSPy for mL and NLP engineers; Agno for python teams building agents.

DSPyDSPy

Framework for programming language models instead of prompting them

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AgnoAgno

High-performance Python framework for building multi-agent systems and AgentOS

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Votes00
PricingFreeFreemium
CategoryAgent FrameworksAgent Frameworks
Tags
prompt-optimizationllm-programmingragopen-sourcestanford
multi-agentpythonagentopsragopen-source
Best for
  • ML and NLP engineers
  • Researchers building RAG systems
  • Teams wanting reproducible LLM pipelines
  • Python teams building agents
  • Teams wanting predictable flat pricing
  • Multi-agent system builders
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
  • Performance-focused, Python-first design
  • Full local control plane free of charge
  • Flat pricing with no token or egress fees
  • Built-in knowledge, memory, and evals
  • Model-agnostic across major providers
Cons
  • Steeper conceptual learning curve
  • Python-only
  • Optimization runs can be compute-intensive
  • Less suited to simple single prompts
  • Requires defining good metrics
  • Python-only framework
  • Pro plan starts relatively high at $150/month
  • Additional connections and seats add up
  • Rebrand from Phidata may cause some confusion
  • Ecosystem younger than the largest frameworks

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