Comparisons / LlamaIndex vs Pydantic AI
LlamaIndex vs Pydantic AI: Which Agent Framework to Use?
LlamaIndex started as a RAG framework — connect your data, query it with an LLM. Pydantic AI is a type-safe agent framework built by the Pydantic team. 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
Pydantic AI
16.1k
1.9k
Python
MIT
2024-06-21
Pydantic (Samuel Colvin)
GitHub stats as of April 2026. Stars indicate community interest, not necessarily quality or fit for your use case.
| Concept | LlamaIndex | Pydantic AI |
|---|---|---|
| Agent | `AgentRunner` with `AgentWorker`, or `ReActAgent` for tool-calling agents | `Agent()` class with typed `result_type`, system prompt, and `model` parameter |
| Tools | `FunctionTool` for custom tools, `QueryEngineTool` to query an index as a tool | `@agent.tool` decorator with typed parameters and Pydantic validation |
| Agent Loop | `AgentRunner.chat()` manages step-by-step execution via `AgentWorker` tasks | `agent.run()` handles the tool-call loop internally with typed dispatch |
| 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 | — |
| Structured Output | — | `result_type=MyModel` enforces Pydantic model on final LLM response |
| Model Switching | — | Swap `model='openai:gpt-4o'` to `model='anthropic:claude-sonnet'` in one line |
| Dependencies | — | `RunContext[DepsType]` injects typed dependencies into tools at runtime |
LlamaIndex vs Pydantic AI, head to head
LlamaIndex LlamaIndex started as a RAG framework — connect your data, query it with an LLM.
Pydantic AI Pydantic AI is a type-safe agent framework built by the Pydantic team.
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; Pydantic AI would force you to translate.
Pick Pydantic AI if
Pick Pydantic AI if pydantic AI adds genuine value if you want compile-time type checking across your agent's tools, outputs, and dependencies. If you already use Pydantic in your stack, it fits naturally. But the core agent logic — loop, dispatch, validate — is still ~60 lines of Python you can own entirely. Pydantic 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 Pydantic 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 Pydantic 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 →