CrewAI - Sakshi Documentation

CrewAI emits events across a crew’s lifecycle (kickoff, agent, task, LLM call, tool usage) on a global event bus. Sakshi ships a listener for it. Instantiate SakshiCrewListener once before crew.kickoff() and every model call and tool call inside a witness session is recorded on the chain. The listener does not import crewai at module load, so it degrades cleanly and carries no version lock.

Install

pip install sakshi-sdk crewai

Register the listener

Create the listener once (it registers on CrewAI’s event bus), then run your crew inside a witness session.

from crewai import Crew

crew = Crew(agents=[...], tasks=[...])
result = crew.kickoff(inputs={"application_id": "APP-2026-04471"})

from crewai import Crew
from sakshi import SakshiClient
from sakshi.middleware import SakshiCrewListener

client = SakshiClient("https://sakshi.your-company.internal", api_key="sks_...")
SakshiCrewListener()  # registers on the CrewAI event bus (once)

crew = Crew(agents=[...], tasks=[...])

with client.witness("loan-crew", client_ref="APP-2026-04471") as decision:
    result = crew.kickoff(inputs={"application_id": "APP-2026-04471"})
    decision.action(outcome="approved")

What gets witnessed

As the crew runs, the listener records onto the active witness session:

Model calls become llm_call steps with the served model, token usage, and finish reason.
Tool calls become tool_call steps with the tool name, the argument keys (never the values, which are tokenized at ingest anyway), the latency, and the role of the agent that called it. A tool that errors is recorded as such.
Crew, agent, and task boundaries are bracketed, so a multi-agent crew reads as structured control flow on the chain.

Because inputs are tokenized at ingest, a PAN or Aadhaar that flows through a task is tokenized before it is stored, exactly as with every other integration.

Enforcement in CrewAI

CrewAI’s event bus is observe-only, so this middleware witnesses; it does not block a tool the way the ADK or MCP tool-path enforcement does. To gate a consequential action inside CrewAI, call enforce at the top of the tool’s own function and act on the outcome.

from crewai.tools import tool
from sakshi import SakshiBlocked, SakshiSyncReviewRequired

@tool("approve_loan")
def approve_loan(application_id: str, amount: int) -> str:
    try:
        client.enforce("loan-crew", "approve_loan", stakes=amount)
    except (SakshiBlocked, SakshiSyncReviewRequired):
        return "Parked for review: this action needs a human."
    ...  # only runs when the envelope returns auto

Recording is fail-open: a problem writing a step never breaks the crew, and with no active witness session the listener is a no-op. CrewAI dispatches event handlers asynchronously and flushes them before finishing kickoff, so every model and tool call is recorded; the final crew close-bracket is best-effort. Call crewai_event_bus.flush() after kickoff if you want it guaranteed before the witness session closes.

Full SDK reference

The client, the witness session, and enforcement.

​Install

​Register the listener

​What gets witnessed

​Enforcement in CrewAI

Full SDK reference

Install

Register the listener

What gets witnessed

Enforcement in CrewAI