Graph Workflows

Build stateful workflows as graphs — conditional routing, fan-out/fan-in, checkpointing, and resumable pipelines.

📄️ Graph Workflows — SynapseKit Python LLM Framework

Define async LLM pipelines as directed graphs in Python. Parallel execution, conditional routing, cycles, and checkpointing. LangGraph alternative.

StateGraph is the fluent builder used to define your graph before compiling it.

A node is any callable that takes the current graph state and returns a partial state to be merged in.

Edges connect nodes and determine the execution order.

CompiledGraph is the runnable object returned by StateGraph.compile(). It exposes three execution modes and a Mermaid diagram helper.

By default, StateGraph.compile() raises GraphConfigError if it detects a cycle in static edges. This protects against accidental infinite loops.

Checkpointing lets you persist graph state after each execution wave. This enables resuming interrupted runs and inspecting intermediate state.

CompiledGraph.get_mermaid() returns a Mermaid flowchart string for your graph. Use it to visualize pipelines in Markdown, Notion, GitHub, or Jupyter.