Agent Frameworks
🎯 DSPy vs LangGraph
Community Vote — Which tool wins?
DSPy
Tool AStanford NLP's framework for programming language models with declarative Python modules instead of prompts, featuring automatic optimizers that compile programs into effective prompts and fine-tuned weights.
Starting Price
Free (MIT open-source)
Key Strengths
- ✓Automatic prompt optimization eliminates the fragile, manual prompt engineering cycle — you define metrics, DSPy finds the best prompts
- ✓Model portability means switching from GPT-4 to Claude to Llama requires re-optimization, not prompt rewriting — programs transfer across providers
- ✓Small model optimization routinely achieves competitive accuracy on Llama/Mistral models, reducing inference costs by 10-50x versus large commercial models
LangGraph
Tool BGraph-based stateful orchestration runtime for agent loops.
Starting Price
Open-source + Cloud
Key Strengths
- ✓Graph-based state machine gives precise control over execution flow with conditional branching, loops, and cycles
- ✓Built-in checkpointing enables time-travel debugging, human-in-the-loop approval, and fault-tolerant resume from any step
- ✓Subgraph composition lets you build complex multi-agent systems from reusable, independently testable graph components
Which would you choose for...
Vote in each scenario below