CrewAI

pip install opentelemetry-api opentelemetry-sdk

# Install OpenInference instrumentor for CrewAI
pip install openinference-instrumentation-crewai

# If CrewAI uses LiteLLM (depends on your setup):
pip install openinference-instrumentation-litellm

# Install Deepchecks client
pip install deepchecks-llm-client

from openinference.instrumentation.crewai import CrewAIInstrumentor
from openinference.instrumentation.litellm import LiteLLMInstrumentor
from deepchecks_llm_client.otel import register_dc_exporter

# 1. Register the Deepchecks exporter
tracer_provider = register_dc_exporter(
    host="https://app.llm.deepchecks.com/",       # Deepchecks endpoint
    api_key="YOUR_API_KEY",                       # API key from your Deepchecks workspace
    app_name="app_name",                          # Application name in Deepchecks
    version_name="version_name",                  # Version name for this run
    env_type="EVAL",                              # Environment: EVAL, PROD, etc.
    log_to_console=True,                          # Optional: also log spans to console
)

# 2. Instrument CrewAI and LiteLLM
CrewAIInstrumentor().instrument(tracer_provider=tracer_provider)
LiteLLMInstrumentor().instrument(tracer_provider=tracer_provider)

from openinference.instrumentation.crewai import CrewAIInstrumentor
from openinference.instrumentation.litellm import LiteLLMInstrumentor
from deepchecks_llm_client.otel import register_dc_exporter

os.environ["OPENAI_API_KEY"] = "Your LLM API key"
SEMANTIC_SCHOLAR_API_URL = "https://api.semanticscholar.org/graph/v1/paper/search"

# 1. Register the Deepchecks exporter
tracer_provider = register_dc_exporter(
    host="https://app.llm.deepchecks.com/",   # Deepchecks endpoint
    api_key="DC_API_KEY",                     # API key from your Deepchecks workspace
    app_name="DC_APP_NAME",                   # Application name in Deepchecks
    version_name="DC_VERSION_NAME",           # Version name for this run
    env_type="EVAL",                          # Environment: EVAL, PROD, etc.
    log_to_console=True,                      # Optional: also log spans to console
)

# 2. Instrument CrewAI and LiteLLM
CrewAIInstrumentor().instrument(tracer_provider=tracer_provider)
LiteLLMInstrumentor().instrument(tracer_provider=tracer_provider)


@tool("academic_papers_lookup")
def search_academic_papers(query: str) -> str:
    """Search for academic papers related to the given query and return the titles and abstracts from Semantic Scholar."""
    params = {
        "query": query,
        "fields": "title,abstract",
        "limit": 3  # Get top 3 papers
    }
    try:
        response = requests.get(SEMANTIC_SCHOLAR_API_URL, params=params)
        response.raise_for_status()
        data = response.json()

        if "data" not in data or not data["data"]:
            return "No relevant academic papers found."

        results = []
        for paper in data["data"]:
            title = paper.get("title", "No title available")
            abstract = paper.get("abstract", "No abstract available")
            results.append(f"Title: {title}\nAbstract: {abstract}")

        return "\n\n".join(results)

    except requests.exceptions.RequestException as e:
        return f"Error fetching papers: {e}"


@tool("wikipedia_lookup")
def wikipedia_lookup(query: str) -> str:
    """Search Wikipedia for the given query and return the summary."""
    try:
        summary = wikipedia.summary(query, sentences=2)
        return summary
    except wikipedia.exceptions.DisambiguationError as e:
        return f"Disambiguation error: {e.options}"
    except wikipedia.exceptions.PageError:
        return "Page not found."
    except Exception as e:
        return f"An error occurred: {e}"


# Define the agent
agent = Agent(
    role='Research Assistant',
    goal='Provide accurate information on various topics by searching Wikipedia and verifying in academic papers.',
    backstory='Proficient in retrieving and summarizing information from Wikipedia and Academic resources.',
    tools=[wikipedia_lookup, search_academic_papers],
    llm='gpt-4.1-mini',
    verbose=True  # Enable detailed logging
)

# Define a task for the agent
task1 = Task(
    description='How old was George Washington when he died and when did he become a general',
    expected_output='The age at which George Washington died and became a general.',
    agent=agent
)

# Create a crew with the agent and the task
crew = Crew(
    agents=[agent],
    tasks=[task1],
    verbose=True,  # Enable detailed logging for the crew
    process=Process.sequential,  # Use sequential process for simplicity
)

if __name__ == "__main__":
    # Run your CrewAI crew
    result = crew.kickoff()