DSPy 3.4.0 — PythonInterpreter improvements, faster GEPA, and MCP v2 compatibility ·
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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
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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"
) 5.2M+
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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.
classify = dspy.Predict(Triage)
classify = dspy.ChainOfThought(Triage)
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)
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
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
[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
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
Jul 2025
GEPA: Reflective Prompt Evolution
Jul 2024
BetterTogether: Fine-Tuning + Prompt Opt.
Jun 2024
MIPROv2: Optimizing Instructions & Demos
Feb 2024
STORM: Writing Wikipedia-like Articles Oct 2023
DSPy: Compiling Declarative LM Calls
Dec 2022
Demonstrate-Search-Predict
DSPy in production
Metadata extraction across all shops; ~550× cost reduction
Optimized Dash relevance judge for ranking and evaluation
Prompt migration from larger to smaller models on Amazon Nova
Multiple chatbot use cases on Databricks
Code repair pipeline using code LLMs to synthesize diffs
LM judges, RAG, classification, and customer solutions
Evolutionary self-improvement for the Hermes agent
See all companies using DSPy in production