Comparisons / LlamaIndex vs OpenAI Agents SDK
LlamaIndex vs OpenAI Agents SDK: Which Agent Framework to Use?
LlamaIndex started as a RAG framework — connect your data, query it with an LLM. OpenAI's Agents SDK (evolved from Swarm) provides Agent, Runner, handoffs, and guardrails. 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
OpenAI Agents SDK
20.6k
3.4k
Python
MIT
2025-03-11
OpenAI
GitHub stats as of April 2026. Stars indicate community interest, not necessarily quality or fit for your use case.
| Concept | LlamaIndex | OpenAI Agents SDK |
|---|---|---|
| Agent | `AgentRunner` with `AgentWorker`, or `ReActAgent` for tool-calling agents | `Agent(name, instructions, model, tools)` |
| Tools | `FunctionTool` for custom tools, `QueryEngineTool` to query an index as a tool | Python functions with type hints, auto-converted to schemas |
| Agent Loop | `AgentRunner.chat()` manages step-by-step execution via `AgentWorker` tasks | `Runner.run()` handles the loop internally |
| 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 | — |
| Handoffs | — | `Handoff` between `Agent` objects for multi-agent routing |
| Guardrails | — | `InputGuardrail` and `OutputGuardrail` with tripwire pattern |
| Context | — | Typed context object passed through the agent lifecycle |
LlamaIndex vs OpenAI Agents SDK, head to head
LlamaIndex LlamaIndex started as a RAG framework — connect your data, query it with an LLM.
OpenAI Agents SDK OpenAI's Agents SDK (evolved from Swarm) provides Agent, Runner, handoffs, and guardrails.
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; OpenAI Agents SDK would force you to translate.
Pick OpenAI Agents SDK if
Pick OpenAI Agents SDK if the Agents SDK is the thinnest framework on this list — it barely abstracts beyond what you'd write yourself. Use it when you want OpenAI's conventions and auto-schema generation. Skip it when you want full control or use non-OpenAI models. OpenAI Agents SDK 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 OpenAI Agents SDK 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 OpenAI Agents SDK 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 →