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name: dspy-advanced description: Advanced DSPy modules including ReAct agents, tool calling, output refinement (BestOfN, Refine), and adapters. Use when building agent systems, integrating external tools, or implementing multi-step reasoning.

DSPy Advanced

Advanced DSPy modules for agents, tools, and output refinement.

Quick Start

ReAct Agent with Tools

Copyimport dspy

# Define tools
def get_weather(city: str) -> str:
    return f"The weather in {city} is sunny."

def search_web(query: str) -> str:
    return f"Search results for '{query}'"

# Create ReAct agent
react = dspy.ReAct(
    signature="question -> answer",
    tools=[get_weather, search_web],
    max_iters=5
)

# Use agent
result = react(question="What's the weather in Tokyo?")
print(result.answer)
print("Tool calls:", result.trajectory)

BestOfN for Multiple Attempts

Copydef one_word_answer(args, pred: dspy.Prediction) -> float:
    return 1.0 if len(pred.answer.split()) == 1 else 0.0

best_of_3 = dspy.BestOfN(
    module=dspy.ChainOfThought("question -> answer"),
    N=3,
    reward_fn=one_word_answer,
    threshold=1.0
)

result = best_of_3(question="What is the capital of Belgium?")
print(result.answer)  # Brussels

Refine for Iterative Improvement

Copyrefine = dspy.Refine(
    module=dspy.ChainOfThought("question -> answer"),
    N=3,
    reward_fn=one_word_answer,
    threshold=1.0
)

result = refine(question="What is the capital of Belgium?")
print(result.answer)  # Brussels

When to Use This Skill

Use this skill when:

• Building ReAct agents with tool calling
• Implementing output refinement strategies
• Integrating external tools and APIs
• Using custom adapters for LM integration
• Implementing multi-step reasoning with feedback loops

Core Concepts

ReAct Agents

ReAct combines reasoning and action, enabling agents to use tools to answer questions.

Key features:

• Automatic reasoning before tool calls
• Tool trajectory tracking
• Multi-iteration tool usage
• Dynamic tool selection

See: references/react-tools.md for:

• ReAct agent patterns
• Tool definition best practices
• Multi-tool coordination

Output Refinement

Improve output quality through multiple attempts or iterative refinement.

Strategies:

• BestOfN: Generate N candidates and select best
• Refine: Iteratively improve with feedback loop

See: references/output-refinement.md for:

• BestOfN vs Refine comparison
• Reward function patterns
• Use cases and examples

Adapters

Adapters bridge DSPy modules with Language Models, handling prompt formatting and output parsing.

Adapter types:

• ChatAdapter: For chat-based LMs
• JSONAdapter: For structured output
• Custom adapters: For specialized use cases

See: references/adapters.md for:

• Adapter architecture
• Custom adapter implementation
• Multi-provider configuration

Progressive Disclosure

This skill uses progressive disclosure:

1. SKILL.md (this file): Quick reference and navigation
2. references/: Detailed technical docs loaded as needed

Load reference files only when you need detailed information on a specific topic.

Related Skills

• dspy-basics: Signature design, basic modules, program composition
• dspy-optimization: Teleprompters, metrics, optimization workflows
• dspy-configuration: LM setup, caching, and version management