dspy-langchain-bridge 0.4.0

A compatibility bridge between DSPy and LangChain — use DSPy optimizers inside LangChain pipelines and LangChain tools inside DSPy modules.

dspy-langchain-bridge

A production-grade compatibility bridge between DSPy and LangChain.

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The Problem

DSPy and LangChain are the two most widely used LLM frameworks — and they don't talk to each other.

• LangChain has a massive ecosystem: tools, retrievers, agents, memory, LangGraph.
• DSPy has the best prompt optimizers in the ecosystem (BootstrapFewShot, MIPROv2, COPRO).

If you want DSPy's optimization inside a LangChain pipeline — or LangChain's retrievers inside a DSPy RAG module — you're currently on your own. dspy-langchain-bridge solves that.

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Installation

pip install dspy-langchain-bridge

With optional extras:

pip install "dspy-langchain-bridge[openai,langgraph]"   # OpenAI + LangGraph support
pip install "dspy-langchain-bridge[all]"                # Everything

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Quick Start

Use a DSPy module inside a LangChain chain

import dspy
from langchain_openai import ChatOpenAI
from langchain_core.prompts import ChatPromptTemplate
from dspy_lc_bridge import DSPyRunnable, LangChainLM

# Configure DSPy to use any LangChain LLM
llm = ChatOpenAI(model="gpt-4o")
dspy.settings.configure(lm=LangChainLM(llm))

# Build a DSPy module
cot = dspy.ChainOfThought("question -> answer")

# Drop it into an LCEL chain
prompt = ChatPromptTemplate.from_template("{question}")
chain = prompt | DSPyRunnable(cot)

result = chain.invoke({"question": "What is the capital of France?"})
print(result["answer"])

Use a LangChain retriever inside DSPy RAG

from langchain_community.vectorstores import FAISS
from langchain_openai import OpenAIEmbeddings
from dspy_lc_bridge import LangChainRetriever

vectorstore = FAISS.load_local("my_index", OpenAIEmbeddings())
retriever = LangChainRetriever(vectorstore.as_retriever(), k=3)

class RAG(dspy.Module):
    def __init__(self):
        self.retrieve = retriever
        self.generate = dspy.ChainOfThought("context, question -> answer")

    def forward(self, question):
        passages = self.retrieve(question).passages
        context = "\n".join(passages)
        return self.generate(context=context, question=question)

Optimize a LangChain prompt with DSPy

from langchain_core.prompts import ChatPromptTemplate
from dspy.teleprompt import BootstrapFewShot
from dspy_lc_bridge import optimize_langchain_prompt

prompt = ChatPromptTemplate.from_template("Answer the question: {question}")

optimized = optimize_langchain_prompt(
    prompt=prompt,
    optimizer=BootstrapFewShot(metric=my_metric),
    trainset=my_examples,
)
# optimized is a ChatPromptTemplate with few-shot examples injected

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Compatibility Matrix

DSPy Primitive	LangChain Equivalent	Bridge Class / Function
dspy.Module	Runnable	DSPyRunnable
LangChain BaseLLM / BaseChatModel	DSPy LM backend	LangChainLM
dspy.Signature	PromptTemplate	signature_to_prompt / prompt_to_signature
LangChain BaseRetriever	dspy.Retrieve	LangChainRetriever
dspy.Retrieve	LangChain BaseRetriever	DSPyRetriever
dspy.Module	LangChain BaseTool	DSPyTool
LangChain BaseTool	DSPy tool	LangChainTool
dspy.Module	LangGraph node function	DSPyNode
LangChain BaseMemory	DSPy history	LangChainMemoryAdapter
DSPy optimizer	LangChain prompt optimizer	optimize_langchain_prompt

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Public API

from dspy_lc_bridge import (
    # Core
    DSPyRunnable,              # DSPy module → LangChain Runnable
    LangChainLM,               # LangChain LLM → DSPy LM backend

    # Signatures
    signature_to_prompt,       # dspy.Signature → PromptTemplate
    prompt_to_signature,       # PromptTemplate → dspy.Signature

    # Retrievers
    LangChainRetriever,        # LangChain retriever → DSPy Retrieve
    DSPyRetriever,             # DSPy Retrieve → LangChain BaseRetriever

    # Tools & Agents
    DSPyTool,                  # DSPy module → LangChain Tool
    LangChainTool,             # LangChain Tool → DSPy tool
    DSPyNode,                  # DSPy module → LangGraph node

    # Memory
    LangChainMemoryAdapter,    # LangChain memory → DSPy history

    # Optimizers
    optimize_langchain_prompt, # DSPy optimizer → optimized LangChain prompt
)

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Roadmap

Version	Contents	Status
v0.1.0	Core bridge: DSPyRunnable, LangChainLM, signature converters	✅ Released
v0.2.0	Retriever & memory bridges	✅ Released
v0.3.0	Agent & tool interop + LangGraph bridges	✅ Released
v0.4.0	Optimizer bridge	✅ Released
v1.0.0	Stable release, full docs, streaming, mypy compliance	🔜 Planned

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Contributing

See CONTRIBUTING.md. PRs, issues, and discussions are welcome.

License

MIT