DSPy vs Google ADK
Updated March 10, 2026
Overview
DSPy is a framework from Stanford for programming—not prompting—foundation models. It replaces manual prompt engineering with composable, optimizable modules. DSPy compilers automatically tune prompts and weights for your specific pipeline and dataset, enabling more reliable LLM applications.
Google's Agent Development Kit (ADK) is a modular framework for building AI agents that integrates natively with Gemini models and Vertex AI. It supports multi-agent architectures, tool use, memory, and deployment to Google Cloud, providing an end-to-end solution for building agents in the Google ecosystem.
Strengths and tradeoffs
What each tool does well, and the limitations to keep in mind.
Pros
- Free and open-source (MIT license)
- Algorithmic optimization beats manual prompt engineering
- Stanford research-backed methodology
- Makes LM pipelines more maintainable
Cons
- Steeper learning curve than manual prompting
- Requires understanding of optimization concepts
- Academic focus may lack enterprise polish
Pros
- Code-first approach enables testable, maintainable agent development
- Comprehensive evaluation framework builds trustworthy agents
- Modular design supports scalable multi-agent architectures
- Open source with active community contributions
Cons
- Optimized primarily for Google Cloud ecosystem
- May be overly structured for simple use cases
- Less flexibility for multi-cloud or non-Google deployments
Compare DSPy and Google ADK on your own traffic
Respan lets you trace LLM and agent calls across any model or framework, A/B test prompts on production traffic, and route requests across 500+ models through one gateway.
