- Python
- TypeScript
Installation
pip install zespan crewai
Usage
Wrap yourCrew instance with wrap_crew before calling kickoff. Both sync and async kickoff are supported.from crewai import Agent, Task, Crew
from zespan.integrations.crewai_handler import wrap_crew
import zespan
zespan.init(api_key="zsp_your_api_key_here")
researcher = Agent(
role="Researcher",
goal="Research the topic thoroughly",
backstory="Expert researcher with broad knowledge",
llm="gpt-4o",
)
task = Task(
description="Research the impact of LLMs on software development",
expected_output="A detailed summary",
agent=researcher,
)
crew = Crew(agents=[researcher], tasks=[task])
crew = wrap_crew(crew)
result = crew.kickoff(inputs={"topic": "LLMs in software development"})
print(result.raw)
Async usage
import asyncio
crew = wrap_crew(crew)
result = asyncio.run(crew.kickoff_async(inputs={"topic": "AI agents"}))
With guardrails
Passguardrails=True to run pre/post content checks on crew inputs and outputs.from zespan.integrations.crewai_handler import wrap_crew
from zespan import GuardrailBlockedError
crew = wrap_crew(crew, guardrails=True)
try:
result = crew.kickoff(inputs={"topic": "..."})
except GuardrailBlockedError as e:
print(f"Blocked at {e.phase}:", e.results)
crew = wrap_crew(crew, guardrails={
"pre": True,
"post": True,
"fail_closed": False,
})
CrewAI is a Python framework. For Node.js services that receive HTTP calls from a Python CrewAI agent, use context extraction to continue the trace across the boundary.
import { extractAgentContext, zespan } from "@zespan/sdk";
import { type NextRequest } from "next/server";
zespan.init({ apiKey: process.env.ZESPAN_API_KEY! });
export async function POST(req: NextRequest) {
// Restore trace context injected by the upstream CrewAI agent
extractAgentContext(Object.fromEntries(req.headers));
// All LLM calls made here now link back to the CrewAI trace
const result = await processRequest(req);
return Response.json(result);
}