A side-by-side of two agent frameworks for building AI agents — live GitHub data, languages, and what each is best at.
Short answer: LangGraph leads LangGraph vs DSPy by community traction (★ 40k vs ★ 37k). Pick LangGraph for stateful multi-agent workflows; pick DSPy for prompt optimization.
✓ Live data verified
| LangGraph | DSPy | |
|---|---|---|
| GitHub stars | ★ 40k | ★ 37k |
| Language | Python | Python |
| Category | Agent frameworks | Agent frameworks |
| Best for | stateful multi-agent workflows | prompt optimization |
| Repository | langchain-ai/langgraph | stanfordnlp/dspy |
LangGraph and DSPy are both credible choices. By community traction, LangGraph leads (★ 40k). Pick LangGraph for stateful multi-agent workflows; pick DSPy for prompt optimization.
Both are credible agent frameworks. By community traction LangGraph leads (★ 40k). Pick LangGraph for stateful multi-agent workflows; pick DSPy for prompt optimization.
LangGraph is Graph-based orchestration for stateful, multi-actor agent workflows with explicit control flow and checkpointing.. DSPy is Programming — not prompting — language models: compile declarative pipelines into optimized prompts/weights..
LangGraph has more — ★ 40k vs ★ 37k (live counts).
Often yes — many teams combine agent frameworks. Check each tool's docs for interop; they solve overlapping but not identical problems.
LangGraph is primarily Python; DSPy is primarily Python.
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