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Chaos engineering for AI agents — Chaos Monkey, but for your agents and tools.

Project description

chaos-agents

Chaos Monkey, but for AI agents.

chaos-agents injects controlled faults — latency, errors, corrupted / truncated / empty outputs — into your agent and tool functions, so you can find out how your system behaves before the real world does it for you.

⚠️ Early alpha (v0.1.0). The API is small on purpose and will grow.

Install

pip install chaos-agents

Usage

from chaos_agents import ChaosMonkey

# Roll the dice on every call.
monkey = ChaosMonkey(
    latency=0.3,      # 30% chance of an artificial delay
    errors=0.1,       # 10% chance of raising ChaosError
    corruption=0.1,   # 10% chance of scrambling the string output
    truncation=0.05,  # 5%  chance of truncating the output
    seed=42,          # reproducible chaos
)

@monkey.wrap
def my_agent(prompt: str) -> str:
    return call_llm(prompt)

my_agent("summarize this document")
print(monkey.report())   # {'latency': 1, 'errors': 0, ...}

You can also wrap inline:

result = monkey.run(call_llm, prompt)

Disable chaos in production without touching call sites:

monkey = ChaosMonkey(errors=0.1, enabled=False)

Built-in faults

Fault What it simulates
latency slow / hanging agents and tools
errors crashes and tool failures (raises ChaosError)
corruption hallucinated / garbled responses
truncation token-limit cutoffs and interrupted streams
empty silent / dropped responses

License

MIT

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