Comparisons / ControlFlow vs LlamaIndex
ControlFlow vs LlamaIndex: Which Agent Framework to Use?
ControlFlow by Prefect flips the typical agent framework: instead of defining agents that choose tasks, you define tasks and assign agents to them. 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
ControlFlow
1.5k
120
Python
Apache-2.0
2024-05-01
Prefect
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 | ControlFlow | LlamaIndex |
|---|---|---|
| Agent | `cf.Agent()` with name, model, instructions, and tool access | `AgentRunner` with `AgentWorker`, or `ReActAgent` for tool-calling agents |
| Tools | Python functions passed to `Task()` or `Agent()` as tool lists | `FunctionTool` for custom tools, `QueryEngineTool` to query an index as a tool |
| Task | `cf.Task()` with `result_type`, `instructions`, `agents`, and `dependencies` | — |
| Flow | `@cf.flow` decorator composing tasks with dependency resolution | — |
| Multi-Agent | Multiple `cf.Agent()` instances assigned to different tasks in one flow | — |
| Observability | Built-in Prefect integration for logging, retries, and monitoring | — |
| Agent Loop | — | `AgentRunner.chat()` manages step-by-step execution via `AgentWorker` tasks |
| 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 |
ControlFlow vs LlamaIndex, head to head
ControlFlow ControlFlow by Prefect flips the typical agent framework: instead of defining agents that choose tasks, you define tasks and assign agents to them.
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 ControlFlow if
Pick ControlFlow if controlFlow's task-centric model is a genuinely different way to think about agent orchestration — define what you want, not how to get it. The Prefect integration adds real production value. But if your workflow is linear and your tasks are simple, plain function composition does the same job with less ceremony. ControlFlow 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; ControlFlow would force you to translate.
What both add
Both ControlFlow 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 ControlFlow 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 →