Comparisons / Google ADK vs LlamaIndex
Google ADK vs LlamaIndex: Which Agent Framework to Use?
Google's Agent Development Kit (ADK) is an open-source framework for building multi-agent systems. LlamaIndex started as a RAG framework — connect your data, query it with an LLM. 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
LlamaIndex
48.3k
7.2k
Python
MIT
2022-11-02
Jerry Liu
GitHub stats as of April 2026. Stars indicate community interest, not necessarily quality or fit for your use case.
| Concept | Google ADK | LlamaIndex |
|---|---|---|
| Agent | `LlmAgent` class with model, instructions, and `sub_agents` list | `AgentRunner` with `AgentWorker`, or `ReActAgent` for tool-calling agents |
| Tools | `FunctionTool`, built-in tools (Search, Code Exec), third-party integrations | `FunctionTool` for custom tools, `QueryEngineTool` to query an index as a tool |
| Agent Loop | `Runner.run()` with automatic tool dispatch and sub-agent delegation | `AgentRunner.chat()` manages step-by-step execution via `AgentWorker` tasks |
| 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 | — |
| RAG Integration | — | `VectorStoreIndex` + `QueryEngineTool` — the agent can query your data as a tool call |
| Memory | — | `ChatMemoryBuffer` with token limit, or custom memory modules |
| Orchestration | — | `AgentRunner` step API for custom control flow, or multi-agent pipelines |
Google ADK vs LlamaIndex, head to head
Google ADK Google's Agent Development Kit (ADK) is an open-source framework for building multi-agent systems.
LlamaIndex LlamaIndex started as a RAG framework — connect your data, query it with an LLM.
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; LlamaIndex would force you to translate.
Pick LlamaIndex if
Pick LlamaIndex if llamaIndex adds genuine value when your agent needs to query structured or unstructured data as part of its reasoning — that's the index-as-tool pattern, and it's well-executed. But if you're building a general-purpose agent that doesn't need RAG, the agent framework is overhead. The plain Python version of the agent loop is the same 60 lines either way. LlamaIndex 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 LlamaIndex 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 LlamaIndex 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 →