DSPy
Declarative framework for programming and optimizing LLM pipelines
DSPy is profiled here as a Prompt Management tool for engineering teams. Read about features, pricing, and how it compares to related options in the tools directory.
Description
DSPy is an open-source framework from Stanford NLP for building LLM pipelines as declarative Python programs. Omar Khattab and collaborators released it in 2023, and Databricks now supports its development. Developers describe each step with typed signatures, compose the steps into modules, and let DSPy's optimizers tune prompts and few-shot examples against a metric the team defines, which replaces rounds of manual prompt editing. Research teams and production groups run the same programs across classification, RAG, and agent pipelines, swapping models without rewriting prompts.
Key Capabilities:
Typed signatures and composable modules for LLM programs
Automatic prompt optimization with MIPROv2 and GEPA optimizers
Bootstrapped few-shot example selection from labeled data
Built-in evaluation harness with custom metrics
Provider-agnostic model access through LiteLLM
MIT-licensed Python library with an active research community
Alternative tools
- Google AI Studio
Free browser workspace for prototyping with Gemini models
- Basalt
Collaborative prompt management and deployment for AI teams
- Promptmetheus
Prompt engineering IDE for composing and testing LLM prompts
- BAML
A domain-specific language for typed LLM functions
- Langtail
Collaborative prompt playground with testing and deployment
- Lunary
Open-source prompt management and observability for LLM apps