> ## Documentation Index > Fetch the complete documentation index at: https://daily-docs-flows-declarative.mintlify.site/llms.txt > Use this file to discover all available pages before exploring further. # Pipecat Flows Examples > Pipecat Flows examples grouped by form: declarative flow configs with handlers, and flows written in Python where code is needed. Each example exists to show one thing. The **declarative** ones are a folder of three files — `bot.py` runs the pipeline, `flow.yaml` is the graph, and `handlers.py` is the Python the graph names. The **programmatic** ones are a single file, and each is in Python for a specific reason. ## Declarative Start here. `hello_world` exists in both forms, so the two can be read side by side. The smallest possible flow: two nodes and one tool. Read this first. The full-featured flow: branching between pizza and sushi, a pre-action, a global function, and transition-only entries. A branch table keyed on a tool's result, and the shim pattern: flow-agnostic business logic under a thin tool that reports its outcome as a `status` field. Data-capture tools, a branch on a boolean result, and session facts such as the practice and patient names read from state. Prompts built from computed values: the handlers store each quote in state, the results node reads it with a placeholder, and adjusting the coverage re-enters the node with the new figures. A node that transitions back to itself. ## Programmatic Each of these is in Python because something about it needs code. See [when to write a flow in code](/pipecat/flows/introduction#choosing). The smallest possible flow, in code; the pair of the declarative `hello_world`. The food-ordering flow in code, showing node and edge functions; the base for the schema variant below. The same flow with `FlowsFunctionSchema`s: an `enum` on the pizza size and type and a numeric range on the sushi count, which a direct function can only hint at in prose. A flow driven by transport events: the bot transfers the caller to a human agent, briefs the agent while the caller hears hold music from a parallel pipeline, then drops out (DailyTransport only). A flow living inside a worker: a structured reservation worker hands off to and from a free-form `LLMWorker` router over the bus, sharing one conversation context. Switching between LLM providers during a conversation; the flow is incidental to the `LLMSwitcher`. All examples are available in the [Pipecat repository on GitHub](https://github.com/pipecat-ai/pipecat/tree/main/examples/flows), whose README covers setup and how to run them.