All Toolsโ€บAgent State Tracker
๐Ÿ”ง AI Agent FrameworksMay 7, 2026โœ… Tests passing

Agent State Tracker

A lightweight Python library for tracking and managing the state of autonomous AI agents during task execution. It offers state transition management, logging, and checkpointing features for developers building robust AI systems.

What It Does

  • State Transition Management: Validate and manage transitions between states.
  • Checkpointing: Save the current state for recovery.
  • Built-in Logging: Log all state changes for debugging and monitoring.

Installation

pip install pytest==7.4.0

Usage

from agent_state_tracker import State, StateTracker

initial_state = State(name="Idle")
tracker = StateTracker(initial_state)
next_state = State(name="Active")
tracker.transition_to(next_state)
tracker.checkpoint()
print(tracker.get_state_log())

Source Code

import logging
from dataclasses import dataclass, field
from typing import Any, Dict, Callable

@dataclass
class State:
    name: str
    data: Dict[str, Any] = field(default_factory=dict)

class StateTracker:
    def __init__(self, initial_state: State):
        self.current_state = initial_state
        self.state_log = []
        self.state_log.append(f"Initialized with state: {self.current_state.name}")
        self.logger = logging.getLogger("StateTracker")
        self.logger.setLevel(logging.INFO)
        handler = logging.StreamHandler()
        handler.setFormatter(logging.Formatter('%(asctime)s - %(message)s'))
        self.logger.addHandler(handler)
        self.logger.info(f"Initialized with state: {self.current_state.name}")

    def transition_to(self, next_state: State, validator: Callable[[State, State], bool] = None):
        if validator and not validator(self.current_state, next_state):
            raise ValueError(f"Invalid state transition from {self.current_state.name} to {next_state.name}")
        self.logger.info(f"Transitioning from {self.current_state.name} to {next_state.name}")
        self.state_log.append(f"Transitioned from {self.current_state.name} to {next_state.name}")
        self.current_state = next_state

    def checkpoint(self):
        self.logger.info(f"Checkpointing state: {self.current_state.name}")
        self.state_log.append(f"Checkpointed state: {self.current_state.name}")

    def get_state_log(self):
        return self.state_log

if __name__ == "__main__":
    initial = State(name="Idle")
    tracker = StateTracker(initial)
    next_state = State(name="Active")
    tracker.transition_to(next_state)
    tracker.checkpoint()
    print(tracker.get_state_log())

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Details

Tool Name
agent_state_tracker
Category
AI Agent Frameworks
Generated
May 7, 2026
Tests
Passing โœ…

Quick Install

Clone just this tool:

git clone --depth 1 --filter=blob:none --sparse \
  https://github.com/ptulin/autoaiforge.git
cd autoaiforge
git sparse-checkout set generated_tools/2026-05-07/agent_state_tracker
cd generated_tools/2026-05-07/agent_state_tracker
pip install -r requirements.txt 2>/dev/null || true
python agent_state_tracker.py
Agent State Tracker โ€” AI Tools by AutoAIForge