Observed since 20 Oct, 21:35 UTC
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Chaos Monkey for AI agents
Automated adversarial security testing for LangGraph-based AI agents. Catch vulnerabilities in LLM-powered applications before they reach production.
agent-breaker init, agent-breaker run, with options for debug/full outputpip install agent-breaker
# Optional: for ML judge (recommended)
pip install agent-breaker[ml]
Initialize config:
agent-breaker init
Run tests:
agent-breaker run
Optional environment variables (default: off): AGENT_BREAKER_DEBUG=1 Enable debug mode AGENT_BREAKER_FULL_OUTPUT=1 Enable full output
agent-breaker init
# Edit breaker.yaml to point to your agent
agent-breaker run
version: "0.2"
target:
type: "langgraph"
path: "my_agent.py"
attr: "graph"
prompt_variable: "SYSTEM_PROMPT"
input_key: "user_query"
output_key: "response"
state_class: "AgentState"
generator:
strategy: "template"
domain: "finance"
attacks:
- name: "prompt_injection"
enabled: true
max_api_calls: 10
judge:
model: "ml" # or "behaviour"
# In your agent file (my_agent.py):
graph = workflow.compile()
# breaker.yaml should reference this file and variable
Documentation: https://github.com/GokulAIx/Agent-Breaker#readme
MIT License
P. Gokul Sree Chandra
Download counts and popularity will appear here as the collector receives them.
| Version | Published | Direct dependencies | Open version |
|---|---|---|---|
| 0.2.1 | 22 Mar 2026 | 11 |
This describes our collected data; it is not a security assessment.
Observed since 20 Oct, 21:35 UTC
No superseded metadata states collected. Unchanged observations do not create new revisions.
package:c49f6450aa40c3598c937b88c1b456dad1843c2d73e8dd1d3b09c990052bfc61