AI Pipeline DAG Builder

A DAG (Directed Acyclic Graph) is a graph where nodes represent tasks or LLM calls, and edges represent data dependencies. 'Directed' means data flows one way; 'acyclic' means no circular dependencies. DAGs enable parallel execution of independent steps, making AI pipelines faster and more efficient than purely sequential chains.

A chain executes steps sequentially: A → B → C. Each step waits for the previous. A DAG allows parallel branches: A → {B, C} → D, where B and C run simultaneously. For AI workflows, this means you can call multiple LLMs in parallel, dramatically reducing wall-clock latency.

LangGraph (LangChain) uses graph-based state machines. Prefect and Airflow support Python DAGs with AI integrations. Ray and Dask enable distributed parallel execution. n8n has a visual DAG editor. For lightweight Python, you can use asyncio.gather() for parallel LLM calls without a framework.

Critical path = the longest path through the graph. If A takes 2s and B takes 3s in parallel, the critical path is 3s (not 5s). Total elapsed time = sum of critical path steps, not sum of all steps. Identify your critical path to focus optimization efforts — speeding up non-critical steps doesn't reduce overall latency.

Use parallel when steps are independent (no data dependency between them). Use sequential when step B needs output from step A. Common parallel patterns: fan-out (one → many), map-reduce (parallel processing + aggregation), ensemble (multiple models → vote). Always check for shared state that could cause race conditions.