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stanfordnlp/dspy

dspy

DSPy: The framework for programming—not prompting—language models

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stanfordnlp

stanfordnlp

stanfordnlp • individual

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Created

Jan 9, 2023

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README Summary

DSPy is a framework that shifts from prompting to programming language models by providing composable and declarative modules for building LM pipelines. It introduces a compile-time optimization approach that can tune prompts and weights automatically to maximize specified metrics. The framework enables systematic development of language model applications through structured programming paradigms rather than manual prompt engineering.

AI Dev Skills

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Prompt EngineeringLanguage Model OptimizationPipeline OrchestrationFew-shot LearningChain-of-Thought ReasoningRetrieval-Augmented GenerationMulti-step ReasoningLarge Language Model IntegrationAutomated Prompt TuningCompositional AI Systems

Tags

Prompt EngineeringLanguage Model OptimizationPipeline OrchestrationFew-shot LearningChain-of-Thought ReasoningRetrieval-Augmented GenerationMulti-step ReasoningLarge Language Model IntegrationAutomated Prompt TuningCompositional AI SystemsText ClassificationCloud APIModular AI ArchitectureResearchMulti-hop ReasoningAgentic AIContent CreationOn-premiseEducational Content GenerationAutomated Content ProcessingTextComplex Reasoning PipelinesSystematic AI DevelopmentQuestion Answering SystemsResearch Paper AnalysisPrompt OptimizationSelf-hostedCompound AI SystemsEducationInformation RetrievalDeveloper ToolsPython

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Recent Activity

Updated 27 days ago

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Quality

beta
Quality
high
Maturity
beta

Categories

Evals & BenchmarkingPrimaryNLP & TextSearch & KnowledgeOther AI / MLInference & ServingML Platform & InfrastructureMLOps & InfrastructureDev Tools & AutomationLearning ResourcesRAG & RetrievalFoundation ModelsAI Agents

PM Skills

Scale & ReliabilityDeveloper Platform

Languages

Python100.0%

Timeline

Project created
Jan 9, 2023
Forked
Mar 12, 2026
Your last push
27 days ago
Upstream last push
11 days ago
Tracked since
Mar 17, 2026

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