balagan-agent 0.1.1

Chaos engineering framework for AI agents - stress-test agent reliability

BalaganAgent

Chaos Engineering for AI Agents

Everyone demos agents. Nobody stress-tests them.

BalaganAgent is a reliability testing framework that stress-tests AI agents through controlled fault injection—because your agent will fail in production, and you should know how it handles it.

Why BalaganAgent?

AI agents are entering production environments, but there's zero reliability discipline. BalaganAgent brings the battle-tested principles of chaos engineering (think Chaos Monkey, Gremlin) to the world of AI agents.

The Problem:

• Agents fail silently in production
• Tool calls time out, return garbage, or hallucinate
• Context gets corrupted, budgets get exhausted
• Nobody knows until users complain

The Solution:

• Inject failures in development, not production
• Measure recovery time (MTTR)
• Score reliability like we score SLAs
• Find breaking points before users do

Features

Fault Injection

• Tool Failures: Exceptions, timeouts, empty responses, malformed data, rate limits
• Delays: Fixed, random, spike patterns, degrading latency
• Hallucinations: Wrong values, fabricated data, contradictions, fake references
• Context Corruption: Truncation, reordering, noise injection, encoding issues
• Budget Exhaustion: Token limits, cost caps, rate limiting, call quotas

Metrics & Analysis

• MTTR (Mean Time To Recovery): How fast does your agent recover?
• Recovery Quality: Did it recover correctly or just fail gracefully?
• Reliability Score: SRE-grade scoring (five nines to one nine)
• Error Budget Tracking: Know when to freeze changes

Reports

• Terminal output with colors
• JSON for programmatic analysis
• Markdown for documentation
• HTML dashboards

Quick Start

Installation

pip install balaganagent

CrewAI Integration

Using CrewAI? Check out our CrewAI Integration Guide for a complete step-by-step walkthrough!

Quick example:

from balaganagent.wrappers.crewai import CrewAIWrapper

# Your existing CrewAI setup
crew = Crew(agents=[agent], tasks=[task])

# Add chaos testing (3 lines!)
wrapper = CrewAIWrapper(crew, chaos_level=0.5)
wrapper.configure_chaos(enable_tool_failures=True, enable_delays=True)
result = wrapper.kickoff()

# Check metrics
metrics = wrapper.get_metrics()
print(f"Success rate: {metrics['aggregate']['operations']['success_rate']:.1%}")

→ Full CrewAI Integration Guide

Basic Usage

from balaganagent import ChaosEngine, AgentWrapper

# Your agent with tools
class MyAgent:
    def search(self, query: str) -> dict:
        return {"results": [...]}

    def calculate(self, expr: str) -> float:
        return eval(expr)

# Wrap it with chaos
agent = MyAgent()
wrapper = AgentWrapper(agent)
wrapper.configure_chaos(chaos_level=0.5)  # 50% chaos intensity

# Now calls might fail randomly!
result = wrapper.call_tool("search", "test query")

Run an Experiment

from balaganagent import ChaosEngine
from balaganagent.runner import scenario, ExperimentRunner

# Define a test scenario
test = (
    scenario("search-reliability")
    .description("Test search under chaos")
    .call("search", "AI safety")
    .call("search", "machine learning")
    .call("calculate", "2 + 2")
    .with_chaos(level=0.75)
    .build()
)

# Run it
runner = ExperimentRunner()
runner.set_agent(MyAgent())
result = runner.run_scenario(test)

print(f"Success Rate: {result.experiment_result.success_rate:.1%}")
print(f"MTTR: {result.mttr_stats['mttr_seconds']:.2f}s")

CLI Usage

# Run a demo
balaganagent demo --chaos-level 0.5

# Initialize a new project
balaganagent init my-chaos-tests

# Run a scenario file
balaganagent run scenarios/search_test.json --chaos-level 0.75

# Run stress tests
balaganagent stress scenarios/critical_path.json --iterations 100

Chaos Levels

The chaos_level parameter controls fault injection probability:

Level	Base Failure Rate	Use Case
0.0	0%	Baseline (no chaos)
0.25	2.5%	Light testing
0.5	5%	Moderate chaos
1.0	10%	Standard chaos
2.0	20%	Stress testing

Fault Injectors

Tool Failure Injector

Simulates various tool failure modes:

from balaganagent.injectors import ToolFailureInjector
from balaganagent.injectors.tool_failure import ToolFailureConfig, FailureMode

injector = ToolFailureInjector(ToolFailureConfig(
    probability=0.1,
    failure_modes=[
        FailureMode.TIMEOUT,
        FailureMode.RATE_LIMIT,
        FailureMode.SERVICE_UNAVAILABLE,
    ]
))

Delay Injector

Simulates network latency patterns:

from balaganagent.injectors import DelayInjector
from balaganagent.injectors.delay import DelayConfig, DelayPattern, LatencySimulator

# Use presets
injector = LatencySimulator.create("poor")  # High latency, high jitter

# Or configure manually
injector = DelayInjector(DelayConfig(
    pattern=DelayPattern.SPIKE,
    min_delay_ms=50,
    max_delay_ms=200,
    spike_probability=0.1,
    spike_multiplier=10,
))

Hallucination Injector

Corrupts data to test agent's ability to detect bad information:

from balaganagent.injectors import HallucinationInjector
from balaganagent.injectors.hallucination import HallucinationConfig, HallucinationType

injector = HallucinationInjector(HallucinationConfig(
    probability=0.05,
    severity=0.5,  # 0=subtle, 1=obvious
    hallucination_types=[
        HallucinationType.WRONG_VALUE,
        HallucinationType.FABRICATED_DATA,
        HallucinationType.NONEXISTENT_REFERENCE,
    ]
))

Budget Exhaustion Injector

Tests behavior when resources run out:

from balaganagent.injectors import BudgetExhaustionInjector
from balaganagent.injectors.budget import BudgetExhaustionConfig

injector = BudgetExhaustionInjector(BudgetExhaustionConfig(
    token_limit=10000,
    cost_limit_dollars=1.00,
    rate_limit_per_minute=60,
    fail_hard=True,  # Raise exception vs return error
))

Metrics

MTTR Calculator

from balaganagent.metrics import MTTRCalculator

calc = MTTRCalculator()

# Record failure and recovery
calc.record_failure("search", "timeout")
# ... agent recovers ...
calc.record_recovery("search", "timeout", retries=2)

stats = calc.get_recovery_stats()
print(f"MTTR: {stats['mttr_seconds']:.2f}s")
print(f"Recovery Rate: {stats['recovery_rate']:.1%}")

Reliability Scorer

from balaganagent.metrics import ReliabilityScorer

scorer = ReliabilityScorer(slos={
    "availability": 0.99,
    "latency_p99_ms": 2000,
})

# Record operations
for result in agent_results:
    scorer.record_operation(
        success=result.success,
        latency_ms=result.latency,
    )

report = scorer.calculate_score()
print(f"Grade: {report.grade.value}")
print(f"Availability: {report.availability:.3%}")
print(f"Error Budget Remaining: {report.error_budget_remaining:.1%}")

Scenarios

Scenarios can be defined in code or JSON:

{
  "name": "critical-path-test",
  "description": "Test the critical user journey",
  "operations": [
    {"tool": "authenticate", "args": ["user123"]},
    {"tool": "fetch_profile", "args": ["user123"]},
    {"tool": "search", "args": ["recent orders"]},
    {"tool": "process_request", "kwargs": {"action": "refund"}}
  ],
  "chaos_config": {
    "chaos_level": 0.5,
    "enable_tool_failures": true,
    "enable_delays": true,
    "enable_budget_exhaustion": true
  }
}

Stress Testing

Find your agent's breaking point:

runner = ExperimentRunner()
runner.set_agent(agent)

results = runner.run_stress_test(
    scenario,
    iterations=100,
    chaos_levels=[0.1, 0.25, 0.5, 0.75, 1.0, 1.5, 2.0],
)

for level, data in results["levels"].items():
    print(f"Chaos {level}: {data['pass_rate']:.1%} pass rate")

Reports

Generate reports in multiple formats:

from balaganagent.reporting import ReportGenerator

gen = ReportGenerator()
report = gen.generate_from_results(results, metrics)

# Terminal (with colors)
print(gen.to_terminal(report))

# Save as files
gen.save(report, "report.json", format="json")
gen.save(report, "report.md", format="markdown")
gen.save(report, "report.html", format="html")

Example Output

============================================================
  BALAGANAGENT EXPERIMENT REPORT
============================================================

  Generated: 2024-01-15T10:30:00
  Status: WARNING

SUMMARY
  Experiments: 5 (Completed: 4, Failed: 1)
  Operations:  150 (Success Rate: 87.3%)
  Faults:      23 injected

RELIABILITY
  Score: 0.82
  Grade: 99%
  MTTR:  1.3s

EXPERIMENTS
  search-reliability [completed]
    Duration: 12.34s | Success: 90.0% | Recovery: 85.0%

  calculate-stress [completed]
    Duration: 8.21s | Success: 95.0% | Recovery: 100.0%

RECOMMENDATIONS
  1. Recovery rate is 85.0%. Agents should implement better recovery mechanisms.
  2. Most frequent fault type: tool_failure. Focus testing on this failure mode.

============================================================

Project Structure

balaganagent/
├── __init__.py          # Main exports
├── engine.py            # Chaos engine core
├── experiment.py        # Experiment definitions
├── wrapper.py           # Agent wrapping
├── runner.py            # Experiment runner
├── reporting.py         # Report generation
├── cli.py               # Command-line interface
├── injectors/           # Fault injectors
│   ├── base.py          # Base injector class
│   ├── tool_failure.py  # Tool failure injection
│   ├── delay.py         # Latency injection
│   ├── hallucination.py # Data corruption
│   ├── context.py       # Context corruption
│   └── budget.py        # Budget exhaustion
└── metrics/             # Metrics collection
    ├── collector.py     # General metrics
    ├── mttr.py          # MTTR calculation
    ├── recovery.py      # Recovery quality
    └── reliability.py   # Reliability scoring

License

MIT

Contributing

Contributions welcome! Please read the contributing guidelines first.

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"Hope is not a strategy. Test your agents."