🎯 AutoGen vs DSPy
Community Vote — Which tool wins?
AutoGen
Tool AOpen-source framework for creating multi-agent AI systems where multiple AI agents collaborate to solve complex problems through structured conversations, role-based interactions, and autonomous task execution.
Starting Price
Open-source
Key Strengths
- ✓GroupChat and speaker selection patterns enable sophisticated multi-agent debates and collaborative problem-solving
- ✓Built-in code execution with Docker sandboxing lets agents write, run, and iterate on code safely
- ✓AutoGen 0.4's event-driven runtime supports distributed multi-process agent deployments via gRPC
DSPy
Tool BDSPy is a framework from Stanford NLP that programmatically optimizes AI prompts and model pipelines rather than relying on manual prompt engineering. Instead of hand-crafting prompts, you define your AI pipeline as modular Python code with input/output signatures, and DSPy automatically finds the best prompts, few-shot examples, and fine-tuning configurations through optimization algorithms. It treats prompt engineering as a machine learning problem — define your metric, provide training examples, and let the optimizer find what works. DSPy supports major LLM providers and produces reproducible, testable AI systems.
Starting Price
Open-source
Key Strengths
- ✓Automatic prompt optimization eliminates manual prompt engineering — define metrics and let optimizers find the best prompts
- ✓Model-portable programs: switch from GPT-4 to Claude to Llama and re-optimize without rewriting any prompts
- ✓Modular architecture lets you compose ChainOfThought, ReAct, and custom modules using standard Python control flow
Which would you choose for...
Vote in each scenario below