Skip to main content

Documentation Index

Fetch the complete documentation index at: https://docs.benchspan.com/llms.txt

Use this file to discover all available pages before exploring further.

CrewAI uses LangChain’s callback protocol under the hood, so the LangChain integration applies directly. Pass BenchGuard as a callback on the Crew and every tool output flowing through any agent gets scanned.
Python only. CrewAI doesn’t publish an official TypeScript SDK.

​
Install

pip install benchspan crewai

​
Usage

crew.py
from benchspan import BenchGuard, InjectionDetectedError
from crewai import Agent, Crew, Task

guard = BenchGuard(api_key="ag_live_...", agent="research-crew", mode="block")

researcher = Agent(
    role="Senior Researcher",
    goal="Find accurate information on the topic",
    tools=[web_search_tool, document_reader],
)

writer = Agent(
    role="Technical Writer",
    goal="Write clear summaries",
)

tasks = [
    Task(description="Research {topic}", agent=researcher),
    Task(description="Summarize findings", agent=writer),
]

crew = Crew(
    agents=[researcher, writer],
    tasks=tasks,
    callbacks=[guard],  # every tool output scanned before the LLM sees it
)

try:
    result = crew.kickoff(inputs={"topic": "indirect prompt injection"})
except InjectionDetectedError as e:
    print(f"Crew blocked an injection: score={e.result.score:.4f} id={e.result.id}")

​
What gets scanned

Every tool result returned to the LLM inside any agent of the crew. System prompts and agent-to-agent messages (the assistant role) are not scanned; they’re inside your trust boundary.

​
Common pitfalls

BenchGuard only scans string content. If your tool returns a dict, convert to JSON before returning, or scan the relevant string field(s) manually with guard.scan(text, role="tool").
Construct one BenchGuard per agent and attach each crew-by-crew. You can have some crews in warn mode for evaluation and others in block for production.