LlamaIndex

LlamaIndex lets you build function agents that call multiple tools and often run asynchronously; integrating it with ZenML executes those agents inside reproducible pipelines with artifact lineage, observability, and a clean path from local development to production.

Features with ZenML

• Async-friendly orchestration. Run LlamaIndex function agents that require awaiting inside ZenML steps without changing agent code.
• ‍Tool call lineage. Track queries, intermediate tool outputs, and final responses as versioned artifacts.
• ‍Composable pipelines. Chain agents with retrieval, evals, and deployment in one DAG.
• ‍Evaluation ready. Add post-run checks to score response quality, latency, and tool accuracy.
• ‍Portable execution. Move the same pipeline from local runs to Kubernetes or Airflow via ZenML stacks.

Main Features

• Function agents. Define agents that call Python tools to solve tasks.
• ‍Async execution. Properly await agent.run(...) for non-blocking workflows.
• ‍Multiple tools. Plug in weather, tip calculator, and custom utilities.

How to use ZenML with LlamaIndex

from zenml import ExternalArtifact, pipeline, step
from agent import agent  # LlamaIndex function agent with tools

@step
def run_llamaindex(query: str) -> str:
   # LlamaIndex agent.run is async; await it inside the step
   import asyncio
   async def _run():
       return await agent.run(query)
   resp = asyncio.run(_run())
   return str(getattr(resp, "response", resp))

@pipeline
def llamaindex_agent_pipeline() -> str:
   q = ExternalArtifact(
       value="What's the weather in New York and calculate a 15% tip for $50?"
   )
   return run_llamaindex(q.value)

if __name__ == "__main__":
   print(llamaindex_agent_pipeline())

Additional Resources

• LlamaIndex Documentation