$ -weight: 500;">pip -weight: 500;">install clevagent
-weight: 500;">pip -weight: 500;">install clevagent
-weight: 500;">pip -weight: 500;">install clevagent
import clevagent
clevagent.init(api_key="YOUR_KEY", agent="my-crew")
import clevagent
clevagent.init(api_key="YOUR_KEY", agent="my-crew")
import clevagent
clevagent.init(api_key="YOUR_KEY", agent="my-crew")
-weight: 500;">pip -weight: 500;">install clevagent
-weight: 500;">pip -weight: 500;">install clevagent
-weight: 500;">pip -weight: 500;">install clevagent
import os
import clevagent clevagent.init( api_key=os.environ["CLEVAGENT_API_KEY"], agent="my-research-crew",
)
import os
import clevagent clevagent.init( api_key=os.environ["CLEVAGENT_API_KEY"], agent="my-research-crew",
)
import os
import clevagent clevagent.init( api_key=os.environ["CLEVAGENT_API_KEY"], agent="my-research-crew",
)
def track_step(step_output): clevagent.ping( -weight: 500;">status="step_complete", meta={ "agent": step_output.agent, "output_length": len(str(step_output.output)), }, )
def track_step(step_output): clevagent.ping( -weight: 500;">status="step_complete", meta={ "agent": step_output.agent, "output_length": len(str(step_output.output)), }, )
def track_step(step_output): clevagent.ping( -weight: 500;">status="step_complete", meta={ "agent": step_output.agent, "output_length": len(str(step_output.output)), }, )
researcher = Agent( role="Research Analyst", goal="Find the latest market data", backstory="You are a senior research analyst...", llm=llm, step_callback=track_step,
)
researcher = Agent( role="Research Analyst", goal="Find the latest market data", backstory="You are a senior research analyst...", llm=llm, step_callback=track_step,
)
researcher = Agent( role="Research Analyst", goal="Find the latest market data", backstory="You are a senior research analyst...", llm=llm, step_callback=track_step,
)
import os
from crewai import Agent, Task, Crew, Process
import clevagent # Initialize monitoring
clevagent.init( api_key=os.environ["CLEVAGENT_API_KEY"], agent="daily-research-crew",
) def track_step(step_output): clevagent.ping( -weight: 500;">status="step_complete", meta={ "agent": step_output.agent, "output_length": len(str(step_output.output)), }, ) # Define agents
researcher = Agent( role="Research Analyst", goal="Find the 3 most important tech news stories today", backstory="You are a senior research analyst who reads dozens of sources daily.", verbose=True, step_callback=track_step,
) writer = Agent( role="Report Writer", goal="Write a concise morning briefing from the research", backstory="You are a technical writer who distills complex topics into clear summaries.", verbose=True, step_callback=track_step,
) # Define tasks
research_task = Task( description="Search for today's top 3 tech news stories. Include source URLs.", expected_output="A list of 3 news items with title, summary, and source URL.", agent=researcher,
) writing_task = Task( description="Write a 200-word morning briefing based on the research.", expected_output="A formatted briefing email ready to send.", agent=writer,
) # Assemble and run
crew = Crew( agents=[researcher, writer], tasks=[research_task, writing_task], process=Process.sequential, verbose=True,
) result = crew.kickoff() # Report completion with output metadata
clevagent.ping( -weight: 500;">status="crew_complete", meta={ "output_length": len(str(result)), "agents_used": 2, },
) print(result)
import os
from crewai import Agent, Task, Crew, Process
import clevagent # Initialize monitoring
clevagent.init( api_key=os.environ["CLEVAGENT_API_KEY"], agent="daily-research-crew",
) def track_step(step_output): clevagent.ping( -weight: 500;">status="step_complete", meta={ "agent": step_output.agent, "output_length": len(str(step_output.output)), }, ) # Define agents
researcher = Agent( role="Research Analyst", goal="Find the 3 most important tech news stories today", backstory="You are a senior research analyst who reads dozens of sources daily.", verbose=True, step_callback=track_step,
) writer = Agent( role="Report Writer", goal="Write a concise morning briefing from the research", backstory="You are a technical writer who distills complex topics into clear summaries.", verbose=True, step_callback=track_step,
) # Define tasks
research_task = Task( description="Search for today's top 3 tech news stories. Include source URLs.", expected_output="A list of 3 news items with title, summary, and source URL.", agent=researcher,
) writing_task = Task( description="Write a 200-word morning briefing based on the research.", expected_output="A formatted briefing email ready to send.", agent=writer,
) # Assemble and run
crew = Crew( agents=[researcher, writer], tasks=[research_task, writing_task], process=Process.sequential, verbose=True,
) result = crew.kickoff() # Report completion with output metadata
clevagent.ping( -weight: 500;">status="crew_complete", meta={ "output_length": len(str(result)), "agents_used": 2, },
) print(result)
import os
from crewai import Agent, Task, Crew, Process
import clevagent # Initialize monitoring
clevagent.init( api_key=os.environ["CLEVAGENT_API_KEY"], agent="daily-research-crew",
) def track_step(step_output): clevagent.ping( -weight: 500;">status="step_complete", meta={ "agent": step_output.agent, "output_length": len(str(step_output.output)), }, ) # Define agents
researcher = Agent( role="Research Analyst", goal="Find the 3 most important tech news stories today", backstory="You are a senior research analyst who reads dozens of sources daily.", verbose=True, step_callback=track_step,
) writer = Agent( role="Report Writer", goal="Write a concise morning briefing from the research", backstory="You are a technical writer who distills complex topics into clear summaries.", verbose=True, step_callback=track_step,
) # Define tasks
research_task = Task( description="Search for today's top 3 tech news stories. Include source URLs.", expected_output="A list of 3 news items with title, summary, and source URL.", agent=researcher,
) writing_task = Task( description="Write a 200-word morning briefing based on the research.", expected_output="A formatted briefing email ready to send.", agent=writer,
) # Assemble and run
crew = Crew( agents=[researcher, writer], tasks=[research_task, writing_task], process=Process.sequential, verbose=True,
) result = crew.kickoff() # Report completion with output metadata
clevagent.ping( -weight: 500;">status="crew_complete", meta={ "output_length": len(str(result)), "agents_used": 2, },
) print(result)
result = crew.kickoff() clevagent.ping( -weight: 500;">status="crew_complete", meta={ "report_date": today, "stories_found": len(stories), "word_count": len(result.split()), },
)
result = crew.kickoff() clevagent.ping( -weight: 500;">status="crew_complete", meta={ "report_date": today, "stories_found": len(stories), "word_count": len(result.split()), },
)
result = crew.kickoff() clevagent.ping( -weight: 500;">status="crew_complete", meta={ "report_date": today, "stories_found": len(stories), "word_count": len(result.split()), },
) - Agent hangs: One agent waits indefinitely for an LLM response. The crew stalls, but the process stays alive.
- Infinite loops: An agent retries a failed tool call endlessly. Your token meter spins, but no useful output appears.
- Silent quality degradation: The LLM returns garbage, the next agent processes it anyway, and the final output is subtly wrong. No error thrown.
- Cost spikes: A single crew run normally costs $0.15. One bad run costs $12 because an agent kept rephrasing the same request. - Did today's 6 AM run actually complete?
- How many stories did it find compared to yesterday?
- Is the output length consistent, or did something degrade? - Why Your AI Agent Health Check Is Lying to You — The hidden gap between "process alive" and "agent working."
- Three AI Agent Failure Modes That Traditional Monitoring Will Never Catch — Silent exits, zombie agents, and runaway loops with real examples.
- How to Monitor LangChain Agents in Production — LangChain callback handler and LangGraph node monitoring.
- How to Monitor AI Agents in Production — The complete guide to heartbeat-based monitoring for any AI agent framework.
