langchain-chaos-middleware 0.1.4

Chaos testing middleware for LangChain agents

LangChain Chaos Middleware ( Chaos Monkey )

A middleware for LangChain agents that intentionally injects failures (exceptions) into tool and model calls to test agent resilience.

Table of Contents

• Features
• Installation
• Usage
  • Basic Example
• Configuration
• Default Exception Profiles
• Custom Exception Profiles
• Understanding Failure Rates
• Important Note for Production
• For Developers
  • Setting Up the Development Environment
  • Running Tests
  • Test Coverage
  • Contributing

Features

• Failure Injection: Randomly raises exceptions based on a configurable failure rate.
• Targeted Chaos: Include or exclude specific tools from chaos.
• Safety Mechanism: Requires an environment variable (ENABLE_CHAOS=true) to be active, preventing accidental production issues.
• Customizable Exceptions: Choose which exceptions to raise (e.g., network errors, rate limits).

Installation

pip install langchain-chaos-middleware

Usage

Basic Example

import os
from langchain.agents import create_agent
from chaos_middleware import ChaosMiddleware, NETWORK_ERRORS

# 1. Enable Chaos (Safety Check)
os.environ["ENABLE_CHAOS"] = "true"

# 2. Configure Middleware
chaos_config = {
    "failure_rate": 0.2,  # 20% chance of failure
    "exception_types": NETWORK_ERRORS,
    "exclude_tools": ["save_memory"], # Don't break critical tools
}
chaos = ChaosMiddleware(chaos_config)

# 3. Inject into Agent
agent = create_agent(
    model="gpt-4o",
    tools=[...],
    middleware=[chaos]
)

# 4. Run Agent
# The agent will now experience random network errors!

Configuration

The ChaosMiddleware is initialized with a ChaosConfig dictionary:

Key	Type	Default	Description
failure_rate	float	0.1	Probability of failure (0.0 to 1.0).
exception_types	List[Exception]	[]	List of exceptions to randomly choose from.
include_tools	List[str]	None	If set, only these tools will be targeted.
exclude_tools	List[str]	[]	These tools will NEVER fail.
seed	int	None	Random seed for reproducible chaos.
safety_key	str	"ENABLE_CHAOS"	Env var name required to enable the middleware.

Default Exception Profiles

The library provides pre-built lists of exceptions for common scenarios:

• NETWORK_ERRORS: [TimeoutError, ConnectionError]
• LLM_ERRORS: [RateLimitError, ServiceUnavailableError]
• CRITICAL_ERRORS: [ValueError, KeyError]

Custom Exception Profiles

You can define your own lists of exceptions to simulate specific scenarios (e.g., database failures, custom API errors). Just create a list of exception classes and pass it to the exception_types configuration:

class DatabaseError(Exception):
    pass

MY_DB_ERRORS = [DatabaseError]

config = {
    "exception_types": MY_DB_ERRORS,
    # ...
}

Understanding Failure Rates

The failure_rate applies to each individual tool call, not to the entire agent task. If your agent needs to call multiple tools to complete a task, the overall success rate will be lower than you might expect.

Example: With a 20% failure rate per tool:

• Single tool call: 80% chance of success
• Two tool calls: 80% × 80% = 64% chance of success
• Three tool calls: 80% × 80% × 80% = 51% chance of success

This means that even with a "low" 20% failure rate, an agent that needs to call 3 tools has nearly a 50% chance of experiencing at least one failure during the task.

Tip: Start with a low failure rate (e.g., 10-20%) and observe how it affects your agent's overall success rate before increasing it.

Important Note for Production

To prevent accidental chaos in production, the middleware checks for an environment variable (default: ENABLE_CHAOS). If this variable is not set to "true", the middleware acts as a pass-through and does nothing.

For Developers

Setting Up the Development Environment

1. Clone the repository:

   git clone https://github.com/iroy2000/langchain-chaos-middleware.git
   cd langchain-chaos-middleware
   

2. Create a virtual environment:

   python3 -m venv .venv
   source .venv/bin/activate  # On Windows: .venv\Scripts\activate
   

3. Install dependencies:

   # Install the package dependencies
   .venv/bin/pip install langchain langgraph
   
   # Install test dependencies
   .venv/bin/pip install python-dotenv langchain-openai
   

Running Tests

The project includes unit tests to verify the middleware functionality.

Run all tests:

.venv/bin/python -m unittest discover tests

Run only unit tests:

.venv/bin/python -m unittest tests.test_unit

Run integration tests (requires OpenAI API key):

# Create a .env file with your OpenAI API key
echo "OPENAI_API_KEY=your-key-here" > .env

# Run the integration test
.venv/bin/python tests/test_integration.py

Test Coverage

The test suite includes:

• Safety mechanism tests: Verifies chaos is disabled without the safety key
• Failure injection tests: Confirms failures are injected at the configured rate
• Tool filtering tests: Validates include/exclude tool lists work correctly
• Model call tests: Ensures chaos applies to model calls as well

Contributing

Contributions are welcome! Please ensure all tests pass before submitting a pull request:

.venv/bin/python -m unittest discover tests