Comparisons / Google ADK vs Rasa
Google ADK vs Rasa: Which Agent Framework to Use?
Google's Agent Development Kit (ADK) is an open-source framework for building multi-agent systems. Rasa is an open-source framework for building conversational AI — chatbots and virtual assistants. Here is how they compare — paradigm, ecosystem, and the use cases each one is actually built for.
By the numbers
Google ADK
18.7k
3.2k
Python
Apache-2.0
2025-04-01
Google/Alphabet
Vertex AI
Yes
Rasa
21.1k
4.9k
Python
Apache-2.0
2016-10-14
Rasa Technologies
Rasa Pro / Rasa Cloud
Yes
GitHub stats as of April 2026. Stars indicate community interest, not necessarily quality or fit for your use case.
| Concept | Google ADK | Rasa |
|---|---|---|
| Agent | `LlmAgent` class with model, instructions, and `sub_agents` list | Rasa agent with NLU pipeline, dialogue policies, and action server |
| Tools | `FunctionTool`, built-in tools (Search, Code Exec), third-party integrations | Custom actions running on a separate action server via HTTP |
| 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 | — |
| NLU | — | NLU pipeline: tokenizer, featurizer, intent classifier, entity extractor |
| Dialogue | — | Stories/Rules YAML + dialogue policies for conversation flow |
| Slots | — | Typed slots for tracking entities and state across turns |
| CALM | — | LLM for understanding + deterministic `Flows` for business logic |
Google ADK vs Rasa, head to head
Google ADK Google's Agent Development Kit (ADK) is an open-source framework for building multi-agent systems.
Rasa Rasa is an open-source framework for building conversational AI — chatbots and virtual assistants.
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 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; Rasa would force you to translate.
Pick Rasa if
Pick Rasa if rasa is purpose-built for production conversational AI with enterprise requirements — on-premise deployment, regulatory compliance, deterministic business logic. For general-purpose agents or simple chatbots, an LLM with a system prompt and a few tools is faster to build and more flexible. Rasa 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.
What both add
Both Google ADK and Rasa 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 Google ADK and Rasa 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 →