Connect CrewAI to Airblackbox: 3-Command Integration

Your CrewAI agents are making decisions in a black box. When something goes wrong (and it will), you're debugging with prayer and print statements. That's not engineering. That's wishful thinking.

Airblackbox gives your CrewAI agents a flight recorder. Every LLM call, every decision, every failure — captured, indexed, and queryable. Three commands, zero configuration changes to your existing crew.

The Problem

CrewAI agents fail silently. They hallucinate confidently. They forget context mysteriously. When your crew goes sideways, you get an error message and a shrug. Good luck explaining that to your product manager.

Without observability:

• Agent failures look like "it just stopped working"
• Performance optimization is guesswork
• Debugging requires rebuilding the entire conversation history
• Compliance audits become archaeological expeditions

The Solution: 3-Command Integration

Install Airblackbox, start the gateway, point your crew at it. That's it. No code changes. No refactoring. No "migration story."

Command 1: Install Airblackbox

pip install airblackbox

Command 2: Start the Gateway

airblackbox gateway start --port 8000

This launches an OpenAI-compatible proxy that records everything while staying invisible to your crew.

Command 3: Point CrewAI at the Gateway

import os
from crewai import Agent, Task, Crew
from langchain_openai import ChatOpenAI

# Only change: point base_url at localhost
os.environ['OPENAI_API_KEY'] = 'your-actual-openai-key'

llm = ChatOpenAI(
    model="gpt-4",
    base_url="http://localhost:8000/v1"  # <-- This line
)

researcher = Agent(
    role='Research Analyst',
    goal='Find actionable insights about AI governance',
    backstory="You're a meticulous researcher who digs deeper than headlines",
    llm=llm,
    verbose=True
)

writer = Agent(
    role='Technical Writer',
    goal='Transform research into clear, actionable content',
    backstory="You turn complex topics into tutorials developers actually bookmark",
    llm=llm,
    verbose=True
)

research_task = Task(
    description='Research the latest developments in AI agent observability',
    agent=researcher,
    expected_output="A detailed report with specific examples and use cases"
)

writing_task = Task(
    description='Write a technical tutorial based on the research',
    agent=writer,
    expected_output="A 1000-word tutorial with code examples"
)

crew = Crew(
    agents=[researcher, writer],
    tasks=[research_task, writing_task],
    verbose=2
)

result = crew.kickoff()

That's it. Your crew now has a flight recorder. Every LLM call goes through the gateway, gets recorded, and your crew never knows the difference.

What You Get Immediately

Real-time monitoring: Watch your agents think in the Airblackbox dashboard at http://localhost:3000

Conversation trees: See how tasks flow between agents, where they branch, where they fail

Token tracking: Know exactly what each agent costs, which tasks burn budget

Error correlation: When something breaks, see the full context that led to failure

Architecture: How It Works

CrewAI Agent → Airblackbox Gateway → OpenAI API
     ↓
Dashboard (localhost:3000)
     ↓
SQLite Database (conversations, tokens, compliance)

The gateway is a transparent proxy. Your crew thinks it's talking directly to OpenAI. The gateway intercepts, records, forwards, and responds. Zero latency overhead. Zero behavior changes.

Edge Cases That Will Bite You

Rate limiting: The gateway inherits OpenAI's rate limits. Your crew might hit them faster now that calls are logged. Solution: The gateway respects Retry-After headers automatically.

API key rotation: If you rotate OpenAI keys, restart the gateway. The proxy caches authentication. Solution: airblackbox gateway restart

Port conflicts: Default port 8000 might be taken. Solution: airblackbox gateway start --port 8001

Measuring Success

Your CrewAI integration is working when:

1. Dashboard shows conversation threads: http://localhost:3000/conversations
2. Token costs are tracked per agent: Check the "Analytics" tab
3. EU AI Act compliance scans are running: 6/6 technical checks should show green

Test it:

curl http://localhost:8000/health
# Should return: {"status": "healthy", "gateway": "running", "database": "connected"}

Next Step

Your crew is now observable. Clone the CrewAI integration demo to see advanced patterns: multi-crew orchestration, custom compliance rules, and agent performance optimization.

The black box is open. Time to see what your agents are actually thinking.

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Three commands. Zero refactoring. Full observability. Sometimes the best solutions are boringly simple.