Run it under a durable workflow, so every tool call is saved as it completes. For CrewAI durable execution, that's one wrapper class around the agent you already have: DaprWorkflowAgentRunner turns each tool call into a Dapr workflow activity, and after a crash the run continues from the last completed one instead of starting over.
What happens without it
crew.kickoff() runs the whole task inside your Python process. The tool results the agent has gathered and its place in the reasoning loop live in that process's memory. If a deploy or an OOM kill takes the process out halfway, the task starts again from the top, and every tool it already called gets called again.
The change
pip install "diagrid[crewai]"
dapr init
python
from crewai import Agent, Task
from crewai.tools import tool
import dapr.ext.workflow as wf
from diagrid.agent.crewai import DaprWorkflowAgentRunner
@tool("Search the web for information")
def search_web(query: str) -> str:
return f"Results for: {query}" # swap in your real search tool
agent = Agent(
role="Research Analyst",
goal="Analyse market trends",
backstory="A careful analyst who cites sources",
tools=[search_web],
llm="openai/gpt-4o-mini",
)
task = Task(
description="Research Q4 trends in durable execution for AI agents",
expected_output="Five bullet points with sources",
agent=agent,
)
runner = DaprWorkflowAgentRunner(agent=agent, name="market-research", max_iterations=10)
runner.start()
run_id = "q4-trends"
client = wf.DaprWorkflowClient()
if client.get_workflow_state(run_id) is None:
out = runner.run_sync(task=task, session_id=run_id, workflow_id=run_id, timeout=600)
print(out.final_response)
else:
state = client.wait_for_workflow_completion(run_id, timeout_in_seconds=600)
print(state.serialized_output)
runner.shutdown()
dapr run --app-id market-research -- python crew_app.py
The Agent and Task are the same objects you'd hand to a Crew. The runner takes the place of kickoff().
What gets saved
Each tool call runs as a workflow activity. When it finishes, its input and output go into the workflow history in your state store. If the process dies, the Dapr runtime reactivates the workflow and replays it. Completed activities return their saved results, and execution continues from the first one that hadn't finished, with the agent's local state restored.
The fixed run_id is there so a restarted script finds the unfinished run and waits on it, rather than starting a second copy of the same task.
Where it stops
An activity that was mid-flight when the process died runs again from its start, because nothing recorded its result. For a search tool that costs you one extra search. For a tool that sends an email or writes a record, pass an idempotency key made from the run ID and the step, and let the receiving system drop the repeat.
Takeaway
If a CrewAI agent runs longer than the gap between your deploys, wrap it, give each run a stable ID, and make a list of which of its tools write to something. The CrewAI page has the rest: https://www.diagrid.io/learn/crewai-durable-execution
Give it a spin and lmk what breaks 👀