DriftDetector

Behavioral Drift Detection for LLM Agents — LangChain · CrewAI · Any Python Agent

"My LangChain agent is stuck in a loop." "My CrewAI crew keeps repeating the same output." "My AI agent started using weird vocabulary after step 3." "How do I detect when my LLM agent changes behavior mid-session?"

DriftDetector solves exactly this. It monitors your LLM agents for behavioral drift — vocabulary shrinkage, tool pattern changes, repetitive loops, and output stagnation — and alerts you before they break production.

5 detection signals · <10ms overhead · 3-line integration · MIT License

Install

pip install drift-detector-agent

Quick Start (3 lines)

from drift_detector.core import DriftDetectorAgent

detector = DriftDetectorAgent()
report = detector.measure_drift(snapshot_before, snapshot_after)
print(f"Is drifting? {report.is_drifting} (score: {report.combined_drift_score:.3f})")

Setup (Interactive)

Guided Installation with Setup Wizard:

python setup_wizard.py

Wizard guides you through:

1. Choose your setup: Core, Core+UI, Core+LangChain, Core+CrewAI, All, or Custom
2. Generate .env: Auto-creates config with all API providers listed
3. Health check: Verifies installation

Or skip wizard:

pip install drift-detector-agent[ui]          # Core + Web Dashboard
pip install drift-detector-agent[langchain]   # Core + LangChain
pip install drift-detector-agent[all]         # Everything

Web Dashboard (UI)

Monitor drift in real-time with web interface:

python -m drift_detector.ui.server
# Visit: http://127.0.0.1:8000

Features:

• ✅ Measure drift interactively
• ✅ View real-time trends (moving average, slope)
• ✅ Historical reports chart
• ✅ Session-level analytics
• ✅ REST API: /api/health, /api/config, /api/drift, /api/chain

What It Detects

5 Drift Signals

1. Ghost Lexicon - Vocabulary shrinkage (lost precision)
2. Behavioral Shift - Tool usage changes
3. Agreement Score - Multi-model consensus divergence
4. Loop Detection - Repetitive action sequences
5. Stagnation - Identical output repetition

LangChain Integration

from drift_detector.integrations import DriftDetectionCallback
from langchain.agents import AgentExecutor

callback = DriftDetectionCallback()
result = executor.run(
    "Your task here",
    callbacks=[callback]
)

report = callback.get_drift_report()
if report['total_drifts'] > 5:
    print("⚠️ Agent behavior unstable!")

CrewAI Integration

from drift_detector.integrations import DriftDetectorEventListener
from crewai import Crew

listener = DriftDetectorEventListener()
crew.add_event_listener(listener)

result = crew.kickoff()
report = listener.get_drift_report()

for entry in report['history']:
    if entry['is_drifting']:
        print(f"Drift detected at {entry['timestamp']}")

Multi-LLM Support

Works with 5 LLM providers (all tested & verified Apr 2026):

Just add your API keys to .env - done!

Setup

Requirements

• Python 3.8 or higher
• pip (package installer)
• At least one LLM API key (Groq free tier recommended)

1. Install Package

pip install drift-detector-agent

Verify installation:

python3 -c "from drift_detector.core import DriftDetectorAgent; print('✅ Installed')"

2. Get API Key

Groq (Recommended - Free)

1. Go to https://console.groq.com/keys
2. Create API key
3. Copy the key (starts with gsk_)

3. Configure API Key

Option A: Environment variable (recommended)

export GROQ_API_KEY="your_key_here"
python3 your_script.py

Option B: .env file

cp .env.example .env
# Edit .env and add: GROQ_API_KEY=your_key_here
python3 your_script.py

4. Run Your First Detection

from drift_detector.core import DriftDetectorAgent

# Initialize detector
detector = DriftDetectorAgent(agent_id="my_agent")

# Create snapshots
snap1 = detector.snapshot(
    agent_id="step1",
    response_text="This is detailed output with many tokens",
    tool_calls=["search", "analyze"]
)

snap2 = detector.snapshot(
    agent_id="step2",
    response_text="Brief output",
    tool_calls=["summarize"]
)

# Measure drift
report = detector.measure_drift(snap1, snap2)
print(f"Drift: {report.combined_drift_score:.3f}")
print(f"Is drifting: {report.is_drifting}")

5. Use with LangChain or CrewAI

See examples/ for working code with real LLMs.

Documentation

• API & Signals Reference - Detection theory, signal math, API surface
• Integration Guides - LangChain & CrewAI patterns
• Examples - Working code samples
• Security Policy - Deployment hardening & vulnerability reporting
• Changelog - Release history

Performance

Overhead: <15ms per detection cycle. Safe for production.

Real Example

Task: Multi-step research pipeline (4 steps)

Step 1: Research detailed analysis
  └─ Vocabulary: 1,500+ tokens, detailed
  
Step 2: Analyze findings
  └─ Vocabulary: 800+ tokens, synthesis
  └─ Drift detected: 0.304 (Ghost Lexicon 0.180)

Step 3: Summarize
  └─ Vocabulary: 260 tokens, brief
  └─ Drift detected: 0.433 (Ghost Lexicon 0.780) ⚠️ STRONG

Step 4: Final report
  └─ Vocabulary: 300 tokens, recovery
  └─ Drift detected: 0.267 (normal)

✅ System correctly identified vocabulary collapse at step 3

Features

✅ 5 Drift Signals - Detects vocabulary loss, tool changes, loops, stagnation, consensus divergence

✅ LangChain Integration - One callback for auto-detection

✅ CrewAI Integration - Event listener for multi-agent tasks

✅ Multi-LLM Support - Groq, Cerebras, Gemini, OpenRouter, Ollama

✅ SQLite Persistence - Auto-saves all reports

✅ Real-Time Monitoring - FastAPI dashboard included

✅ Production Ready - 159/159 tests pass, security audited

✅ <10ms Overhead - Safe for real-time systems

Testing

# Install dev dependencies
pip install -e ".[dev]"

# Run all tests
pytest tests/ -v

# Run with coverage
pytest tests/ --cov=drift_detector

Status: 159/159 tests PASS ✅

Common Issues

"No API key found"

cp .env.example .env
# Add your GROQ_API_KEY to .env

"Model not available"

• Use the optional LLMRouter (auto-selects the fastest provider that has an API key set)
• Requires the langchain extra: pip install drift-detector-agent[langchain]

"Drift not detected"

• Lower drift_threshold in config (default: 0.4)
• See docs/README.md for signal interpretation

Status & Roadmap

v2.0.0 (Current) - Open-source release

• ✅ 5 drift signals
• ✅ LangChain + CrewAI integrations
• ✅ Multi-LLM support (Groq, Cerebras, Gemini, OpenRouter, Ollama)
• ✅ SQLite persistence
• ✅ FastAPI dashboard with pinned SRI-integrity Chart.js
• ✅ 159/159 tests passing
• ✅ Security-audited (see SECURITY.md and SECURITY_AUDIT_REPORT.md)

v2.1 (Planned)

• Distributed persistence (PostgreSQL)
• Custom signal weights
• Advanced loop detection
• WebSocket alerts
• Multi-detector orchestration

License

MIT License - See LICENSE

Support & Issues

Found a bug? → GitHub Issues

Question? → GitHub Discussions

Need support? → Open an issue or discussion on GitHub

Contributing

We welcome contributions! Please:

1. Fork the repository
2. Create a feature branch
3. Add tests
4. Submit a pull request

Troubleshooting

"ModuleNotFoundError: No module named 'drift_detector'"

# Install the package
pip install drift-detector-agent

# Or verify installation
pip list | grep drift-detector-agent

"ImportError: No module named 'langchain'"

LangChain is optional. Install if using LangChain:

pip install langchain langchain-openai

"API key not found"

Make sure your API key is set:

# Check if GROQ_API_KEY is set
echo $GROQ_API_KEY

# If empty, set it
export GROQ_API_KEY="your_key_here"

"No module named 'groq'"

This is handled gracefully - DriftDetector works without Groq. But for examples, install:

pip install langchain-groq

".env file contains my API key - is it safe?"

⚠️ IMPORTANT: Never commit .env to version control!

The .gitignore already excludes .env files. Verify:

git status .env
# Should show: .env is ignored

Ready to detect agent drift?

pip install drift-detector-agent

Quick Start Guide • View Examples • Read Docs • GitHub