DSPy vs. LangChain
The choice between DSPy and LangChain often comes down to philosophy:
- LangChain focuses on orchestration. It connects LLMs to thousands of tools, APIs, and data sources. It is great for building agents that need to browse the web or query databases out-of-the-box.
- DSPy focuses on optimization. It treats prompts as compiled bytecode. It is great for building high-quality pipelines where accuracy and reliability are paramount.
When to Choose What
| Scenario | Recommended Framework |
|---|---|
| Rapid Prototyping | LangChain (due to rich tooling) |
| Complex Reasoning | DSPy (automatic optimization) |
| Production Optimization | DSPy (metric-driven improvement) |
| Data Integrations | LangChain (lots of loaders) |
Hybrid Architecture
You don't have to choose! A powerful pattern is to use LangChain for data loading and DSPy for core logic.
# 1. Use LangChain to load data
loader = PyPDFLoader("data.pdf")
docs = loader.load()
# 2. Use DSPy to process it intelligently
class Summarizer(dspy.Module):
# ... optimized logic ...
optimizer = dspy.BootstrapFewShot(...)
program = optimizer.compile(Summarizer(), trainset=docs)