DSPy Skill for Hermes Agent: Declarative LM Programming & Auto-Optimization

DSPy is a framework from Stanford NLP that replaces manual prompt engineering with declarative language model programming. Instead of tweaking prompt strings, you define the input/output structure of your task as a signature, pick a module (Predict, ChainOfThought, ReAct), and optionally optimize the whole pipeline with a data-driven optimizer. This skill installs DSPy into Hermes Agent so you can build, evaluate, and improve multi-stage AI systems from within the agent runtime.

What it does

DSPy treats an LM call as a typed, composable module. You describe what the LM should do, not how to prompt it. The framework handles prompt construction, few-shot example selection, and even prompt optimization across multiple LM providers. In practice, this means you can:

• Build a question-answering system in a few lines of code.
• Add chain-of-thought reasoning without writing a reasoning prompt.
• Create a multi-hop RAG pipeline where each stage is a separate module.
• Automatically improve your system's accuracy by feeding it a small set of labeled examples.
• Swap between OpenAI, Anthropic Claude, or local models (Ollama) without rewriting your pipeline.

Before you start

This skill is optional and installed on demand. It runs on Linux, macOS, and Windows. You need Python and pip available in your Hermes Agent environment.

Install the core library:

# Stable release
pip install dspy

# Latest development version
pip install git+https://github.com/stanfordnlp/dspy.git

# With specific LM providers
pip install dspy[openai]        # OpenAI
pip install dspy[anthropic]     # Anthropic Claude
pip install dspy[all]           # All providers

You will also need an API key for whichever LM provider you plan to use. Set it as an environment variable (e.g., ANTHROPIC_API_KEY) or pass it directly when configuring the LM.

Quick start

Basic example: question answering

import dspy

# Configure your language model
lm = dspy.Claude(model="claude-sonnet-4-5-20250929")
dspy.settings.configure(lm=lm)

# Define a signature (input → output)
class QA(dspy.Signature):
    """Answer questions with short factual answers."""
    question = dspy.InputField()
    answer = dspy.OutputField(desc="often between 1 and 5 words")

# Create a module
qa = dspy.Predict(QA)

# Use it
response = qa(question="What is the capital of France?")
print(response.answer)  # "Paris"

Chain of thought reasoning

import dspy

lm = dspy.Claude(model="claude-sonnet-4-5-20250929")
dspy.settings.configure(lm=lm)

# Use ChainOfThought for better reasoning
class MathProblem(dspy.Signature):
    """Solve math word problems."""
    problem = dspy.InputField()
    answer = dspy.OutputField(desc="numerical answer")

# ChainOfThought generates reasoning steps automatically
cot = dspy.ChainOfThought(MathProblem)

response = cot(problem="If John has 5 apples and gives 2 to Mary, how many does he have?")
print(response.rationale)  # Shows reasoning steps
print(response.answer)     # "3"

Core concepts

1. Signatures

Signatures define the structure of your AI task (inputs → outputs):

# Inline signature (simple)
qa = dspy.Predict("question -> answer")

# Class signature (detailed)
class Summarize(dspy.Signature):
    """Summarize text into key points."""
    text = dspy.InputField()
    summary = dspy.OutputField(desc="bullet points, 3-5 items")

summarizer = dspy.ChainOfThought(Summarize)

When to use each:

• Inline: Quick prototyping, simple tasks
• Class: Complex tasks, type hints, better documentation

2. Modules

Modules are reusable components that transform inputs to outputs:

dspy.Predict

Basic prediction module:

predictor = dspy.Predict("context, question -> answer")
result = predictor(context="Paris is the capital of France",
                   question="What is the capital?")

dspy.ChainOfThought

Generates reasoning steps before answering:

cot = dspy.ChainOfThought("question -> answer")
result = cot(question="Why is the sky blue?")
print(result.rationale)  # Reasoning steps
print(result.answer)     # Final answer

dspy.ReAct

Agent-like reasoning with tools:

from dspy.predict import ReAct

class SearchQA(dspy.Signature):
    """Answer questions using search."""
    question = dspy.InputField()
    answer = dspy.OutputField()

def search_tool(query: str) -> str:
    """Search Wikipedia."""
    # Your search implementation
    return results

react = ReAct(SearchQA, tools=[search_tool])
result = react(question="When was Python created?")

dspy.ProgramOfThought

Generates and executes code for reasoning:

pot = dspy.ProgramOfThought("question -> answer")
result = pot(question="What is 15% of 240?")
# Generates: answer = 240 * 0.15

3. Optimizers

Optimizers improve your modules automatically using training data:

BootstrapFewShot

Learns from examples:

from dspy.teleprompt import BootstrapFewShot

# Training data
trainset = [
    dspy.Example(question="What is 2+2?", answer="4").with_inputs("question"),
    dspy.Example(question="What is 3+5?", answer="8").with_inputs("question"),
]

# Define metric
def validate_answer(example, pred, trace=None):
    return example.answer == pred.answer

# Optimize
optimizer = BootstrapFewShot(metric=validate_answer, max_bootstrapped_demos=3)
optimized_qa = optimizer.compile(qa, trainset=trainset)

# Now optimized_qa performs better!

MIPRO (Most Important Prompt Optimization)

Iteratively improves prompts:

from dspy.teleprompt import MIPRO

optimizer = MIPRO(
    metric=validate_answer,
    num_candidates=10,
    init_temperature=1.0
)

optimized_cot = optimizer.compile(
    cot,
    trainset=trainset,
    num_trials=100
)

BootstrapFinetune

Creates datasets for model fine-tuning:

from dspy.teleprompt import BootstrapFinetune

optimizer = BootstrapFinetune(metric=validate_answer)
optimized_module = optimizer.compile(qa, trainset=trainset)

# Exports training data for fine-tuning

4. Building complex systems

Multi-stage pipeline

import dspy

class MultiHopQA(dspy.Module):
    def __init__(self):
        super().__init__()
        self.retrieve = dspy.Retrieve(k=3)
        self.generate_query = dspy.ChainOfThought("question -> search_query")
        self.generate_answer = dspy.ChainOfThought("context, question -> answer")

    def forward(self, question):
        # Stage 1: Generate search query
        search_query = self.generate_query(question=question).search_query

        # Stage 2: Retrieve context
        passages = self.retrieve(search_query).passages
        context = "\n".join(passages)

        # Stage 3: Generate answer
        answer = self.generate_answer(context=context, question=question).answer
        return dspy.Prediction(answer=answer, context=context)

# Use the pipeline
qa_system = MultiHopQA()
result = qa_system(question="Who wrote the book that inspired the movie Blade Runner?")

RAG system with optimization

import dspy
from dspy.retrieve.chromadb_rm import ChromadbRM

# Configure retriever
retriever = ChromadbRM(
    collection_name="documents",
    persist_directory="./chroma_db"
)

class RAG(dspy.Module):
    def __init__(self, num_passages=3):
        super().__init__()
        self.retrieve = dspy.Retrieve(k=num_passages)
        self.generate = dspy.ChainOfThought("context, question -> answer")

    def forward(self, question):
        context = self.retrieve(question).passages
        return self.generate(context=context, question=question)

# Create and optimize
rag = RAG()

# Optimize with training data
from dspy.teleprompt import BootstrapFewShot

optimizer = BootstrapFewShot(metric=validate_answer)
optimized_rag = optimizer.compile(rag, trainset=trainset)

LM provider configuration

Anthropic Claude

import dspy

lm = dspy.Claude(
    model="claude-sonnet-4-5-20250929",
    api_key="your-api-key",  # Or set ANTHROPIC_API_KEY env var
    max_tokens=1000,
    temperature=0.7
)
dspy.settings.configure(lm=lm)

OpenAI

lm = dspy.OpenAI(
    model="gpt-4",
    api_key="your-api-key",
    max_tokens=1000
)
dspy.settings.configure(lm=lm)

Local models (Ollama)

lm = dspy.OllamaLocal(
    model="llama3.1",
    base_url="http://localhost:11434"
)
dspy.settings.configure(lm=lm)

Multiple models

# Different models for different tasks
cheap_lm = dspy.OpenAI(model="gpt-3.5-turbo")
strong_lm = dspy.Claude(model="claude-sonnet-4-5-20250929")

# Use cheap model for retrieval, strong model for reasoning
with dspy.settings.context(lm=cheap_lm):
    context = retriever(question)

with dspy.settings.context(lm=strong_lm):
    answer = generator(context=context, question=question)

Common patterns

Pattern 1: Structured output

from pydantic import BaseModel, Field

class PersonInfo(BaseModel):
    name: str = Field(description="Full name")
    age: int = Field(description="Age in years")
    occupation: str = Field(description="Current job")

class ExtractPerson(dspy.Signature):
    """Extract person information from text."""
    text = dspy.InputField()
    person: PersonInfo = dspy.OutputField()

extractor = dspy.TypedPredictor(ExtractPerson)
result = extractor(text="John Doe is a 35-year-old software engineer.")
print(result.person.name)  # "John Doe"
print(result.person.age)   # 35

Pattern 2: Assertion-driven optimization

import dspy
from dspy.primitives.assertions import assert_transform_module, backtrack_handler

class MathQA(dspy.Module):
    def __init__(self):
        super().__init__()
        self.solve = dspy.ChainOfThought("problem -> solution: float")

    def forward(self, problem):
        solution = self.solve(problem=problem).solution

        # Assert solution is numeric
        dspy.Assert(
            isinstance(float(solution), float),
            "Solution must be a number",
            backtrack=backtrack_handler
        )

        return dspy.Prediction(solution=solution)

Pattern 3: Self-consistency

import dspy
from collections import Counter

class ConsistentQA(dspy.Module):
    def __init__(self, num_samples=5):
        super().__init__()
        self.qa = dspy.ChainOfThought("question -> answer")
        self.num_samples = num_samples

    def forward(self, question):
        # Generate multiple answers
        answers = []
        for _ in range(self.num_samples):
            result = self.qa(question=question)
            answers.append(result.answer)

        # Return most common answer
        most_common = Counter(answers).most_common(1)[0][0]
        return dspy.Prediction(answer=most_common)

Pattern 4: Retrieval with reranking

class RerankedRAG(dspy.Module):
    def __init__(self):
        super().__init__()
        self.retrieve = dspy.Retrieve(k=10)
        self.rerank = dspy.Predict("question, passage -> relevance_score: float")
        self.answer = dspy.ChainOfThought("context, question -> answer")

    def forward(self, question):
        # Retrieve candidates
        passages = self.retrieve(question).passages

        # Rerank passages
        scored = []
        for passage in passages:
            score = float(self.rerank(question=question, passage=passage).relevance_score)
            scored.append((score, passage))

        # Take top 3
        top_passages = [p for _, p in sorted(scored, reverse=True)[:3]]
        context = "\n\n".join(top_passages)

        # Generate answer
        return self.answer(context=context, question=question)

Evaluation and metrics

Custom metrics

def exact_match(example, pred, trace=None):
    """Exact match metric."""
    return example.answer.lower() == pred.answer.lower()

def f1_score(example, pred, trace=None):
    """F1 score for text overlap."""
    pred_tokens = set(pred.answer.lower().split())
    gold_tokens = set(example.answer.lower().split())

    if not pred_tokens:
        return 0.0

    precision = len(pred_tokens & gold_tokens) / len(pred_tokens)
    recall = len(pred_tokens & gold_tokens) / len(gold_tokens)

    if precision + recall == 0:
        return 0.0

    return 2 * (precision * recall) / (precision + recall)

Evaluation

from dspy.evaluate import Evaluate

# Create evaluator
evaluator = Evaluate(
    devset=testset,
    metric=exact_match,
    num_threads=4,
    display_progress=True
)

# Evaluate model
score = evaluator(qa_system)
print(f"Accuracy: {score}")

# Compare optimized vs unoptimized
score_before = evaluator(qa)
score_after = evaluator(optimized_qa)
print(f"Improvement: {score_after - score_before:.2%}")

Best practices

1. Start simple, iterate

# Start with Predict
qa = dspy.Predict("question -> answer")

# Add reasoning if needed
qa = dspy.ChainOfThought("question -> answer")

# Add optimization when you have data
optimized_qa = optimizer.compile(qa, trainset=data)

2. Use descriptive signatures

# ❌ Bad: Vague
class Task(dspy.Signature):
    input = dspy.InputField()
    output = dspy.OutputField()

# ✅ Good: Descriptive
class SummarizeArticle(dspy.Signature):
    """Summarize news articles into 3-5 key points."""
    article = dspy.InputField(desc="full article text")
    summary = dspy.OutputField(desc="bullet points, 3-5 items")

3. Optimize with representative data

# Create diverse training examples
trainset = [
    dspy.Example(question="factual", answer="...).with_inputs("question"),
    dspy.Example(question="reasoning", answer="...").with_inputs("question"),
    dspy.Example(question="calculation", answer="...").with_inputs("question"),
]

# Use validation set for metric
def metric(example, pred, trace=None):
    return example.answer in pred.answer

4. Save and load optimized models

# Save
optimized_qa.save("models/qa_v1.json")

# Load
loaded_qa = dspy.ChainOfThought("question -> answer")
loaded_qa.load("models/qa_v1.json")

5. Monitor and debug

# Enable tracing
dspy.settings.configure(lm=lm, trace=[])

# Run prediction
result = qa(question="...")

# Inspect trace
for call in dspy.settings.trace:
    print(f"Prompt: {call['prompt']}")
    print(f"Response: {call['response']}")

Comparison to other approaches

Feature	Manual Prompting	LangChain	DSPy
Prompt Engineering	Manual	Manual	Automatic
Optimization	Trial & error	None	Data-driven
Modularity	Low	Medium	High
Type Safety	No	Limited	Yes (Signatures)
Portability	Low	Medium	High
Learning Curve	Low	Medium	Medium-High

When to choose DSPy:

• You have training data or can generate it
• You need systematic prompt improvement
• You're building complex multi-stage systems
• You want to optimize across different LMs

When to choose alternatives:

• Quick prototypes (manual prompting)
• Simple chains with existing tools (LangChain)
• Custom optimization logic needed

Limits and gotchas

• DSPy's optimizers require a metric function and a training set. Without data, you cannot use BootstrapFewShot, MIPRO, or BootstrapFinetune.
• The learning curve is medium-high. You need to understand signatures, modules, and the compile step before you can debug a failing pipeline.
• Not every LM provider is equally supported. The source shows OpenAI, Anthropic, and Ollama; other providers may need extra configuration.
• The dspy.Retrieve module expects a configured retriever backend (e.g., ChromadbRM). It does not include a built-in search engine.

Resources

• Documentation: https://dspy.ai
• GitHub: https://github.com/stanfordnlp/dspy (22k+ stars)
• Discord: https://discord.gg/XCGy2WDCQB
• Twitter: @DSPyOSS
• Paper: "DSPy: Compiling Declarative Language Model Calls into Self-Improving Pipelines"

See also

• references/modules.md - Detailed module guide (Predict, ChainOfThought, ReAct, ProgramOfThought)
• references/optimizers.md - Optimization algorithms (BootstrapFewShot, MIPRO, BootstrapFinetune)
• references/examples.md - Real-world examples (RAG, agents, classifiers)