DSPy
FreeThe framework for programming—not prompting—language models.
DSPy is an open-source Python framework from Stanford NLP for building modular AI systems. Instead of hand-crafting brittle prompt strings, developers declare typed signatures, compose reusable modules, and use optimizers to automatically tune prompts and weights against a metric. It is designed for Python teams that need reliable, optimizable LLM components in production pipelines.
What is it
A Python framework that replaces ad-hoc prompt engineering with typed signatures, composable modules, and metric-driven optimizers.
What it can do
Define tasks as input/output signatures, chain modules like Predict and ChainOfThought, add tool use with ReAct, compile programs with optimizers such as BootstrapFewShot and MIPROv2, and swap language models without rewriting prompts.
Who is it for
AI engineers, Python developers, and research-to-production teams building classification, extraction, RAG, and agent systems that must be measurable and maintainable.
Key Features
Typed Signatures — Declarative Task Definitions
Define tasks as typed inputs and outputs instead of managing long, brittle prompts. Signatures enforce output types and make programs portable across models.
Composable Modules — Swap Execution Strategies
Use Predict, ChainOfThought, ReAct, and other modules with the same signature to add reasoning, tool use, or ensembles without rewriting the task.
Automatic Optimizers — Compile Against Metrics
Give DSPy examples and a scoring function, and optimizers like BootstrapFewShot, MIPROv2, and GEPA tune prompts, demonstrations, and weights until quality converges.
Tool Use and Agents — ReAct with Python Functions
Pass ordinary Python functions as tools to a ReAct module to build agents that reason, act, and call external APIs or calculators in a loop.
Use Cases
Entity Extraction from Documents
Legal, finance, and operations teams parse emails, contracts, and reports to extract names, dates, amounts, and intents into structured records without hand-tuned prompts.
Text Classification at Scale
Support and content teams route tickets, flag toxicity, or categorize articles by defining a signature and letting optimizers improve accuracy on a labeled dataset.
RAG Pipeline Optimization
Engineers build retrieval-augmented generation systems where DSPy tunes the answer-generation module against a relevance or F1 metric for better, cheaper responses.
AI Agents with Tool Use
Developers create agents that search knowledge bases, run calculations, and call APIs through ReAct modules, keeping reasoning loops explicit and testable.
Pricing plans
Free to use
This tool is listed as free. There is no paid pricing page to show here—visit the official site for any usage limits, quotas, or terms of service.
Frequently Asked Questions
Discussion
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