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DSPy {{ config.extra.stats.release_version }} — {{ config.extra.stats.release_blurb }} ·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.

$ pip install -U dspy Getting Started →

python ≥ 3.10MIT licenseStanford NLPgithub.com/stanfordnlp/dspy

Change LLMAdd a FieldMake it an Agent

extract_events.py

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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)

output

Prediction(

event_name="Team Offsite",

date="Thursday, June 5"

)

monthly downloads

contributors

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.

Learn about Signatures →

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.

Explore Modules →

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.

Try Optimizers →

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")

Define a task. Grow it into a system.

ExtractAgentPipelineMultimodalOptimize

class Extract(dspy.Signature):

"""Extract contact info."""

message: str = dspy.InputField()

name: str = dspy.OutputField()

email: Optional[str] = dspy.OutputField()

intent: Literal[

"meeting", "intro", "follow-up"

] = dspy.OutputField()

extract = dspy.Predict(Extract)

extract(message="I'm Sarah"

"([email protected]). Meet Thursday?")

outputstdout

Prediction(

name="Sarah",

email="[email protected]",

intent="meeting"

)

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?")

outputstdout

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]

outputstdout

>>> 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"))

outputstdout

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")

outputstdout

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

Signatures define tasks and enforce output types Define tools as functions and pass them to a ReAct module Compose multiple Signatures into new modules with plain Python control flow Images are a Signature field types, enabling multimodal tasks Optimizers improve your program against a defined metric Learn more about Signatures → Learn how to add tools → Learn how to compose modules → Learn how to build multimodal programs → Learn how to write metrics and optimize →

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.

Dec 2025

Recursive Language Models

arxiv →

Jul 2025

GEPA: Reflective Prompt Evolution

arxiv →

Jul 2024

BetterTogether: Fine-Tuning + Prompt Opt.

arxiv →

Jun 2024

MIPROv2: Optimizing Instructions & Demos

arxiv →