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The precipiq.integrations.crewai module wraps CrewAI’s step_callback hook so every AgentAction (a mid-task tool call) and AgentFinish (task complete) is automatically recorded as a Precipiq decision. Pass the callback when you construct a crewai.Agent and all subsequent task executions land in your ledger with full input/output capture and a reasoning trace. Like the LangChain adapter, this module is import-time optional — the base precipiq package never imports anything from crewai.

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Install

pip install precipiq crewai

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Usage

from crewai import Agent, Crew, Task
from precipiq import Precipiq
from precipiq.integrations.crewai import PrecipiqCrewAICallback

pq = Precipiq(api_key="pq_test_demo_key_REPLACE_ME")

callback = PrecipiqCrewAICallback(pq, agent_id="researcher")

researcher = Agent(
    role="Researcher",
    goal="Find three sources on a topic",
    backstory="…",
    step_callback=callback,
)

task = Task(
    description="Research the AI Consequences Ledger",
    agent=researcher,
    expected_output="three bullet points",
)

Crew(agents=[researcher], tasks=[task]).kickoff()

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What gets recorded per step

Every step_callback invocation produces a decision record with the following fields:
  • action_type — "agent_action" for an AgentAction (mid-task tool call), or "agent_finish" for an AgentFinish (task complete).
  • inputs — the tool_input on an action, or the prior output on a finish.
  • outputs — the tool’s result on an action, or the agent’s final answer on a finish.
  • metadata — the CrewAI step log, which contains the agent’s reasoning trace.

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Multi-agent crews

Instantiate one callback per agent so each agent’s decisions are attributed to its own agent_id. All decisions land on the same org-wide ledger — use the dashboard’s agent filter to separate them for reporting. 
researcher_cb = PrecipiqCrewAICallback(pq, agent_id="researcher")
writer_cb = PrecipiqCrewAICallback(pq, agent_id="writer")

researcher = Agent(
    role="Researcher",
    step_callback=researcher_cb,
    goal="Find three primary sources on the topic",
    backstory="A careful reader.",
)
writer = Agent(
    role="Writer",
    step_callback=writer_cb,
    goal="Turn research into a one-page brief",
    backstory="A clear prose stylist.",
)
With multiple agents in the ledger, the Precipiq dashboard’s agent filter lets you drill down to the decisions made by a single agent while still viewing the aggregate P&L across the full crew.