๐ง AI Agent FrameworksMay 7, 2026โ
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==7.4.0Usage
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())Community
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Details
- Tool Name
- agent_state_tracker
- Category
- AI Agent Frameworks
- Generated
- May 7, 2026
- Tests
- Passing โ
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