Comparisons / LlamaIndex vs n8n AI
LlamaIndex vs n8n AI: Which Agent Framework to Use?
LlamaIndex started as a RAG framework — connect your data, query it with an LLM. n8n is a workflow automation platform that added AI agent capabilities with native LangChain integration. Here is how they compare — paradigm, ecosystem, and the use cases each one is actually built for.
By the numbers
LlamaIndex
48.3k
7.2k
Python
MIT
2022-11-02
Jerry Liu
n8n AI
182.4k
56.5k
TypeScript
Sustainable Use License
2019-06-22
Jan Oberhauser
71.8k
n8n Cloud
Yes
GitHub stats as of April 2026. Stars indicate community interest, not necessarily quality or fit for your use case.
| Concept | LlamaIndex | n8n AI |
|---|---|---|
| Agent | `AgentRunner` with `AgentWorker`, or `ReActAgent` for tool-calling agents | AI Agent node with model, tools, and memory connected via canvas wires |
| Tools | `FunctionTool` for custom tools, `QueryEngineTool` to query an index as a tool | Tool nodes (HTTP Request, Code, database) wired into the agent node |
| Agent Loop | `AgentRunner.chat()` manages step-by-step execution via `AgentWorker` tasks | Agent node internally loops: call LLM → detect tool use → run tool → repeat |
| RAG Integration | `VectorStoreIndex` + `QueryEngineTool` — the agent can query your data as a tool call | — |
| Memory | `ChatMemoryBuffer` with token limit, or custom memory modules | Memory node (window buffer, vector store) connected to agent node |
| Orchestration | `AgentRunner` step API for custom control flow, or multi-agent pipelines | Visual workflow canvas with triggers, conditionals, and parallel branches |
| Integrations | — | 500+ pre-built nodes for Slack, Gmail, Notion, databases, APIs |
LlamaIndex vs n8n AI, head to head
LlamaIndex LlamaIndex started as a RAG framework — connect your data, query it with an LLM.
n8n AI n8n is a workflow automation platform that added AI agent capabilities with native LangChain integration.
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 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; n8n AI would force you to translate.
Pick n8n AI if
Pick n8n AI if n8n AI is the right choice when your team builds automations visually, needs 500+ integrations out of the box, and wants to self-host. But the AI agent logic inside each node is the same loop you would write in Python — the value is in the integration catalog and visual builder, not the agent pattern. n8n AI 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.
What both add
Both LlamaIndex and n8n AI 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 LlamaIndex and n8n AI 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 →