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Core concepts

  • State — a flat, JSON-serializable dict. Nodes transform state, nothing else.
  • Nodes — pure async def fn(ctx, state) -> dict functions (or built-ins: LLM, Transform, ReActAgent, ToolExec).
  • Graph — nodes + edges, including conditional edges, branches, and __error__ fallbacks. The graph owns routing and resilience.
  • Tools — implement Tool or use the @tool decorator; shareable across nodes. A set of built-in tools ships in teff[tools].
  • RAG — RAGTool over pluggable vector stores (InMemoryVectorStore, SQLite, Chroma, Qdrant, PGVector, …).

State

State is just a dict. Each node is a pure async function returning the changes it wants to apply:

async def my_node(ctx, state):
    state["greeting"] = f"Hello {state.get('name', 'world')}"
    return state

Nodes

A node in YAML is an entry in steps: with an id, a registered type, and a config. In the Flow API you instantiate node objects and chain them. Beyond plain functions, teff ships built-in node types: transform, llm_chat (LLM), react_agent (ReActAgent/Harness), tool_exec, interrupt, parallel, map.

Graph & edges

The Graph ties nodes together. Plain edges route unconditionally; conditional edges route on a state key. Branches, cycles (e.g. ReAct loops) and __error__ fallbacks all live here.

Next: the guide