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
- Freeplay
Prompt management, evals, and observability for product teams
- MLflow
Track experiments, manage models, and evaluate LLM applications across the full ML lifecycle
- Langtrace
Trace LLM application calls with OpenTelemetry and route data to any observability backend
- Opik by Comet
Trace, evaluate, and monitor LLM applications across the full development lifecycle
- Orq.ai
European enterprise AI agent platform with EU AI Act compliance and agent runtime orchestration.
- Klu.ai
Collaborative prompt engineering platform with multi-LLM evaluation and fine-tuning.