Comparisons / Anthropic Agent SDK vs LlamaIndex
Anthropic Agent SDK vs LlamaIndex: Which Agent Framework to Use?
The Anthropic Agent SDK packages Claude Code's agent loop as a library. 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
Anthropic Agent SDK
3.1k
582
Python
MIT
2023-01-17
Anthropic
Google, Spark Capital
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 | Anthropic Agent SDK | LlamaIndex |
|---|---|---|
| Agent | Claude agent with built-in tools, MCP servers, and system prompt | `AgentRunner` with `AgentWorker`, or `ReActAgent` for tool-calling agents |
| Tools | Built-in tools (`bash`, file read/write, web) + MCP server connections | `FunctionTool` for custom tools, `QueryEngineTool` to query an index as a tool |
| Agent Loop | SDK's internal agentic loop with automatic tool dispatch | `AgentRunner.chat()` manages step-by-step execution via `AgentWorker` tasks |
| Sub-Agents | Agents invoke other agents as tools via the SDK | — |
| Lifecycle Hooks | 18 hook events: pre/post tool call, message, error, etc. | — |
| MCP Integration | One-line MCP server config for Playwright, Slack, GitHub, etc. | — |
| 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 |
Anthropic Agent SDK vs LlamaIndex, head to head
Anthropic Agent SDK The Anthropic Agent SDK packages Claude Code's agent loop as a library.
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 Anthropic Agent SDK if
Pick Anthropic Agent SDK if the Anthropic Agent SDK's real value is packaging Claude Code's battle-tested agent loop with built-in tools and MCP integration. If you want a production agent that reads files, runs commands, and connects to services, it saves significant plumbing. For understanding how agents work, the plain version is more instructive. Anthropic Agent 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.
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; Anthropic Agent SDK would force you to translate.
What both add
Both Anthropic Agent SDK 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 Anthropic Agent SDK 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 →