DSPy

DSPy 3.4.0 — PythonInterpreter improvements, faster GEPA, and MCP v2 compatibility · learn more → Program, don’t prompt,your LLMs. DSPy is a Python framework for building AI systems. Express your tasks as structured signatures, not prompts, to produce maintainable, modular, and optimizable programs. extract_events.py 12345 678910 lm = dspy.LM("openai/gpt-5.4-nano") class ExtractEvent(dspy.Signature): """Extract event details from an email.""" email: str = dspy.InputField() event_name: str = dspy.OutputField() date: str = dspy.OutputField() extract = dspy.Predict(ExtractEvent) extract(email=inbox_message) Prediction( event_name="Team Offsite", date="Thursday, June 5" ) 5.2M+ monthly downloads 461+ contributors 38k github stars in production at Compose programs with reusable primitives. Signatures Declare your task. Define your task as typed inputs and outputs instead of managing messy prompts. Portable, maintainable, and easy to iterate on. class Triage(dspy.Signature): """Route a support ticket.""" ticket: str = dspy.InputField() urgency: Literal["low", "high"] = dspy.OutputField() team: str = dspy.OutputField() Modules Same interface, different strategy. Modules control how your signature executes. Reason, run ensembles, use tools, add a REPL, and more without rewriting your task. # Direct completion classify = dspy.Predict(Triage) # Add step-by-step reasoning classify = dspy.ChainOfThought(Triage) # Add tools and a reasoning loop classify = dspy.ReAct(Triage, tools=[search]) Optimizers Compile your program against a metric. Give DSPy examples and a scoring function. It tunes your prompts automatically until quality converges. tp = dspy.GEPA( metric=semantic_f1, auto="medium") opt = tp.compile(rag, trainset) # Before: 0.41 F1 # After: 0.63 F1 opt.save("rag.v2.json") Extract Agent Pipeline Multimodal Optimize def search(query: str) -> list[str]: """Search a knowledge base.""" return kb.query(query, k=3) def calc(expr: str) -> float: """Evaluate a math expression.""" return dspy.PythonInterpreter({}).execute(expr) agent = dspy.ReAct( "question -> answer", tools=[search, calc]) agent(question="GDP per capita of France?") # thought 1: I need France's GDP and population. # action 1: search("France GDP") → ... # thought 2: Now divide GDP by population. # action 2: calc("3.13e12 / 68e6") → 46029.4 Prediction(answer="$46,029") class FactCheck(dspy.Module): def __init__(self): self.find = dspy.ChainOfThought( "article -> claims: list[str]") self.verify = dspy.ChainOfThought( "claim, source -> verdict") def forward(self, article): found = self.find(article=article) return [ self.verify(claim=c, source=article) for c in found.claims] # >>> FactCheck()(article=news_article) [Prediction(verdict="supported"), Prediction(verdict="unsupported"), Prediction(verdict="supported")] class AnalyzeChart(dspy.Signature): """Describe the trend and key data points in a chart.""" chart: dspy.Image = dspy.InputField() title: str = dspy.OutputField() trend: str = dspy.OutputField() data_points: list[dict] = dspy.OutputField() analyze = dspy.Predict(AnalyzeChart) analyze(chart=dspy.Image("quarterly_revenue.png")) Prediction( title="Quarterly Revenue (2024)", trend="Steady growth, Q3 dip, strong Q4 recovery", data_points=[{"q": "Q1", "rev": "$4.2M"}, ...] ) optimizer = dspy.GEPA( metric=accuracy, auto="medium") optimized = optimizer.compile( extract, trainset=labeled_emails) optimized.save("extract_v2.json") # Baseline 62% (gpt-5.4-mini, zero-shot) # Optimized 89% (gpt-5.4-mini + GEPA compile) # Cost $2.18 · 200 examples # Saved to → extract_v2.json Built in the open, since Dec 2022. DSPy started at Stanford NLP and grew into a research community. New optimizers and module types land here first — then show up in production systems at companies you’ve heard of.