---

name: dspy-configuration description: DSPy configuration, LM setup, caching, and version management. Use when configuring language models, enabling caching, managing dependencies, or setting up multi-provider configurations.

DSPy Configuration

DSPy configuration, LM setup, caching, and version management.

Quick Start

Configure LM Globally

Copyimport dspy

# Configure LM globally
dspy.configure(lm=dspy.LM('openai/gpt-4o-mini'))
response = qa(question="How many floors are in the castle?")
print('GPT-4o-mini:', response.answer)

Switch LM Locally

Copy# Change LM within a context block
with dspy.context(lm=dspy.LM('openai/gpt-3.5-turbo')):
    response = qa(question="How many floors are in the castle?")
    print('GPT-3.5-turbo:', response.answer)

Enable Caching

Copy# Configure caching
dspy.configure_cache(
    enable_disk_cache=True,
    enable_memory_cache=True,
)

Configure Responses API

Copydspy.configure(
    lm=dspy.LM(
        "openai/gpt-5-mini",
        model_type="responses",
        temperature=1.0,
        max_tokens=16000,
    ),
)

When to Use This Skill

Use this skill when:

• Configuring language models for DSPy
• Setting up LM switching (global/local)
• Enabling and customizing caching
• Configuring multi-provider LMs
• Managing version compatibility
• Setting up Responses API for advanced models

Core Concepts

LM Configuration

Configure which language models to use and how to switch between them.

Key features:

• Global configuration: dspy.configure(lm=...) for entire session
• Local overrides: dspy.context(lm=...) for code blocks
• Multi-provider: Support for OpenAI, Anthropic, Together AI, and more
• Responses API: Enable advanced model features

See: references/lm-config.md for:

• LM configuration patterns
• Multi-provider setup
• Responses API configuration
• Best practices

Caching

Manage cache behavior to improve performance and control costs.

Cache layers:

• In-memory cache: Fast access using cachetools.LRUCache
• On-disk cache: Persistent storage using diskcache.FanoutCache
• Server-side cache: Managed by LLM provider (OpenAI, Anthropic)

See: references/caching.md for:

• Cache architecture details
• Custom cache key implementation
• Cache debugging techniques
• Configuration options

Version Management

Track and manage dependency versions to ensure compatibility.

Key features:

• Automatic versioning: save() captures dependency versions
• Version checking: Alerts on version mismatches
• Compatibility: Prevents issues from outdated dependencies

See: references/versioning.md for:

• Dependency versioning patterns
• Version mismatch detection
• Compatibility best practices

Scripts

The scripts/ directory provides reusable tools:

• compile-dspy.py: Compile DSPy modules with proper caching
• clear-cache.py: Clear DSPy cache safely

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-advanced: ReAct agents, tool calling, output refinement