DSPy 3.2 — Improved tool calling and more · 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 ≥ 3.10 MIT license Stanford NLP github.com/stanfordnlp/dspy
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)
output
Prediction(
event_name="Team Offsite",
date="Thursday, June 5"
)
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monthly downloads
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in production at
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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.

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"
"(sarah@acme.co). Meet Thursday?")
outputstdout
Prediction(
name="Sarah",
email="sarah@acme.co",
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 →
Feb 2024
STORM: Writing Wikipedia-like Articles
arxiv →
Oct 2023
DSPy: Compiling Declarative LM Calls
arxiv →
Dec 2022
Demonstrate-Search-Predict
arxiv →

DSPy in production

Shopify
Metadata extraction across all shops; ~550× cost reduction
Dropbox
Optimized Dash relevance judge for ranking and evaluation
AWS
Prompt migration from larger to smaller models on Amazon Nova
JetBlue
Multiple chatbot use cases on Databricks
Replit
Code repair pipeline using code LLMs to synthesize diffs
Databricks
LM judges, RAG, classification, and customer solutions
Nous Research
Evolutionary self-improvement for the Hermes agent
More →
See all companies using DSPy in production
Community
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discord members
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merged PRs / yr
60+
tutorials & recipes
GitHub → Discord →