Comparisons / DSPy vs Google ADK
DSPy vs Google ADK: Which Agent Framework to Use?
DSPy replaces hand-written prompts with compiled modules. Google's Agent Development Kit (ADK) is an open-source framework for building multi-agent systems. Here is how they compare — paradigm, ecosystem, and the use cases each one is actually built for.
By the numbers
DSPy
33.4k
2.8k
Python
MIT
2023-01-09
Stanford NLP (Omar Khattab)
Google ADK
18.7k
3.2k
Python
Apache-2.0
2025-04-01
Google/Alphabet
Vertex AI
Yes
GitHub stats as of April 2026. Stars indicate community interest, not necessarily quality or fit for your use case.
| Concept | DSPy | Google ADK |
|---|---|---|
| Agent | `dspy.ReAct` module with signature and tools | `LlmAgent` class with model, instructions, and `sub_agents` list |
| Prompts | `dspy.Signature` defines input/output fields, compiled to optimized prompts | — |
| Optimization | `dspy.BootstrapFewShot`, `MIPROv2` auto-tune prompts against a metric | — |
| Tools | Tools passed to `ReAct` module as callable list | `FunctionTool`, built-in tools (Search, Code Exec), third-party integrations |
| Chaining | `dspy.ChainOfThought`, `dspy.Module` with `forward()` composition | — |
| Evaluation | `dspy.Evaluate` with metric functions and dev sets | — |
| Agent Loop | — | `Runner.run()` with automatic tool dispatch and sub-agent delegation |
| Multi-Agent | — | Hierarchical agent tree with root agent delegating to specialized sub-agents |
| Workflows | — | `SequentialAgent`, `ParallelAgent`, `LoopAgent` workflow primitives |
| Session | — | Session and State service with typed channels and persistence |
DSPy vs Google ADK, head to head
DSPy DSPy replaces hand-written prompts with compiled modules.
Google ADK Google's Agent Development Kit (ADK) is an open-source framework for building multi-agent systems.
Both wrap the same underlying agent pattern — an LLM call, a tool dispatch, a loop — in different abstractions. The choice between them is mostly about which mental model and ecosystem fits the team you have, not which one is technically more capable.
Pick DSPy if
Pick DSPy if dSPy's real innovation is automated prompt optimization — replacing manual prompt engineering with algorithmic tuning. This is genuinely novel and valuable for production systems where prompt quality matters at scale. For simple agents or learning, hand-written prompts are easier to understand and modify. DSPy is the right fit when the tradeoffs in its intro line up with how your team actually wants to work day-to-day; Google ADK would force you to translate.
Pick Google ADK if
Pick Google ADK if aDK earns its complexity when you need multi-agent orchestration on Google Cloud with Vertex AI deployment. If you're using Gemini and need production-grade agent infrastructure, it's well-designed. For single-agent use cases or non-Google stacks, plain Python keeps things simpler. Google ADK is the right fit when the tradeoffs in its intro line up with how your team actually wants to work day-to-day; DSPy would force you to translate.
What both add
Both DSPy and Google ADK pull in a class hierarchy and a dependency tree to wrap what is, at the core, an HTTP POST in a while loop. If your use case is straightforward — one provider, a handful of tools, a single agent — the framework cost may exceed the framework benefit. The lesson below shows the same pattern in ~60 lines without either dependency.
Or build your own in 60 lines
Both DSPy and Google ADK implement the same 8 patterns. An agent is a function. Tools are a dict. The loop is a while loop. The whole thing composes in ~60 lines of Python.
No framework. No dependencies. No opinions. Just the code.
Build it from scratch →