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Observed since 20 Oct, 23:12 UTC
No superseded metadata states collected. Unchanged observations do not create new revisions.
A simple decorator for tracking and reverting agent actions
A lightweight package to govern and track agent actions. It allows you to register agents, track their actions via decorators, and monitor affected applications.
pip install agent-revert
@track_action to monitor execution and affected apps.from agent_revert import register_agent, track_action
# Register the agent
register_agent("MyAwesomeAgent")
@track_action(agent_name="MyAwesomeAgent", affected_apps=["Database"])
def my_function():
# Do something
pass
Here is an example of how to integrate agent-revert with a basic LangChain agent that fetches weather data and updates a Google Document.
Prerequisites:
import os
from langchain.agents import initialize_agent, Tool, AgentType
from langchain.llms import OpenAI
from agent_revert import register_agent, track_action
# Ensure you have OPENAI_API_KEY set
# os.environ["OPENAI_API_KEY"] = "..."
# 1. Register the Agent
AGENT_NAME = "DailyReporter"
register_agent(AGENT_NAME)
# 2. Define Tools with Tracking
@track_action(agent_name=AGENT_NAME, affected_apps=["Weather API"])
def get_weather(location: str) -> str:
"""Mock weather function."""
# In a real app, call a weather API here
return f"The weather in {location} is Sunny, 25°C."
@track_action(agent_name=AGENT_NAME, affected_apps=["Google Docs"])
def update_google_doc(text: str) -> str:
"""Mock Google Doc update."""
# In a real app, use Google Docs API here
# See: https://developers.google.com/docs/api/quickstart/python
print(f"Updating Google Doc with: {text}")
return "Document updated successfully."
tools = [
Tool(
name="GetWeather",
func=get_weather,
description="Useful for getting the weather for a specific location."
),
Tool(
name="UpdateGoogleDoc",
func=update_google_doc,
description="Useful for updating the daily report Google Document."
)
]
# 3. Initialize LangChain Agent
llm = OpenAI(temperature=0)
agent = initialize_agent(
tools,
llm,
agent=AgentType.ZERO_SHOT_REACT_DESCRIPTION,
verbose=True
)
# 4. Run the Agent
query = "Check the weather in San Francisco and update the daily report document with the forecast."
agent.run(query)
Download counts and popularity will appear here as the collector receives them.
| Version | Published | Direct dependencies | Open version |
|---|---|---|---|
| 0.1.5 | 24 Nov 2025 | 6 |
This describes our collected data; it is not a security assessment.
Observed since 20 Oct, 23:12 UTC
No superseded metadata states collected. Unchanged observations do not create new revisions.
package:fadcda4f0e0134885c69d813fbceb7a528025609609ad1c1b30083013d90f162