Merge pull request #76 from vortezwohl/autono

UPDATE: Rename project ceo-py to autono.

Author: vortezwohl

CITATION.cff

@@ -9,15 +9,15 @@ authors:
       - Fujian University of Technology
     email: vortez.wohl@gmail.com
     github: https://github.com/vortezwohl
-title: CEO-Autonomous-Agent-Framework
-version: 0.13.1
-date-released: 2024-10-25
+title: Autono: A ReAct-Based Highly Robust Autonomous Agent Framework
+version: 1.0.0
+date-released: 2025-04-06
 abstract: >
-    This research proposes a lightweight autonomous agent framework based on the ReAct paradigm, designed to solve complex tasks through adaptive decision making and multi-agent collaboration. Unlike traditional frameworks that rely on fixed workflows generated by an LLM-based scheduler, this framework dynamically generates next actions during agent execution based on prior trajectories, which enhances their robustness. To address potential termination issues caused by adaptive execution paths, I propose a timely abandonment strategy with a probabilistic penalty mechanism. For multi-agent collaboration, I introduce a memory transfer mechanism that enables shared and dynamically updated memory among agents. The framework's innovative timely abandonment strategy dynamically adjusts the probability of task abandonment via probabilistic penalties, allowing developers to balance conservative and exploratory tendencies in agent execution strategies by tuning hyperparameters. This significantly improves adaptability and task efficiency in complex environments. Additionally, agents can be expanded through external tool use, supported by modular design and MCP protocol compatibility for flexible action space expansion. Through explicit division of labor, the multi-agent collaboration mechanism enables agents to focus on specific task components, thereby significantly improving execution efficiency and quality.
+    This paper (project) proposes a highly robust autonomous agent framework based on the ReAct paradigm, designed to solve complex tasks through adaptive decision making and multi-agent collaboration. Unlike traditional frameworks that rely on fixed workflows generated by LLM-based planners, this framework dynamically generates next actions during agent execution based on prior trajectories, thereby enhancing its robustness. To address potential termination issues caused by adaptive execution paths, I propose a timely abandonment strategy incorporating a probabilistic penalty mechanism. For multi-agent collaboration, I introduce a memory transfer mechanism that enables shared and dynamically updated memory among agents. The framework's innovative timely abandonment strategy dynamically adjusts the probability of task abandonment via probabilistic penalties, allowing developers to balance conservative and exploratory tendencies in agent execution strategies by tuning hyperparameters. This significantly improves adaptability and task execution efficiency in complex environments. Additionally, agents can be extended through external tool integration, supported by modular design and MCP protocol compatibility, which enables flexible action space expansion. Through explicit division of labor, the multi-agent collaboration mechanism enables agents to focus on specific task components, thereby significantly improving execution efficiency and quality.
 keywords:
-  - LLM
-  - Autonomous Agent
   - ReAct
+  - Robustness
+  - Autonomous Agent
   - Multi-Agent
 license: GPL-3.0
-url: https://github.com/vortezwohl/CEO-Autonomous-Agent-Framework
+url: https://github.com/vortezwohl/Autono

README.md

@@ -26,21 +26,21 @@
 
 __AUTHOR__ = '吴子豪 / Vortez Wohl'
 __EMAIL__ = 'vortez.wohl@gmail.com'
-__VERSION__ = '0.13.1'
+__VERSION__ = '1.0.0'
 __GITHUB__ = 'https://github.com/vortezwohl'
 __BLOG__ = 'https://vortezwohl.github.io'
 
-logger = logging.getLogger('ceo')
+logger = logging.getLogger('autono')
 logger.setLevel(logging.ERROR)
 console_handler = logging.StreamHandler()
 console_handler.setLevel(logging.DEBUG)
 formatter = logging.Formatter('[%(levelname)s] %(asctime)s %(name)s : %(message)s')
 console_handler.setFormatter(formatter)
 logger.addHandler(console_handler)
 
-logger = logging.getLogger('ceo.prompt')
+logger = logging.getLogger('autono.prompt')
 logger.setLevel(logging.ERROR)
-logger = logging.getLogger('ceo.ability')
+logger = logging.getLogger('autono.ability')
 logger.setLevel(logging.ERROR)
-logger = logging.getLogger('ceo.agent')
+logger = logging.getLogger('autono.agent')
 logger.setLevel(logging.ERROR)

ceo/__init__.py autono/__init__.pyceo/__init__.py renamed to autono/__init__.py

@@ -3,8 +3,8 @@
 
 from typing_extensions import Callable, override
 
-from ceo.util.synchronized_call import synchronized_call
-from ceo.ability.base_ability import BaseAbility
+from autono.util.synchronized_call import synchronized_call
+from autono.ability.base_ability import BaseAbility
 
 
 class Ability(BaseAbility):

ceo/ability/__init__.py autono/ability/__init__.pyceo/ability/__init__.py renamed to autono/ability/__init__.py

@@ -4,23 +4,23 @@
 
 from typing_extensions import override
 
-from ceo.ability.ability import Ability
-from ceo.brain.memory_augment import MemoryAugment
+from autono.ability.ability import Ability
+from autono.brain.memory_augment import MemoryAugment
 
 PREFIX = '__AgenticAbility__'
 
-from ceo.brain.hook.after_action_taken import BaseHook
-from ceo.brain.base_agent import BaseAgent
+from autono.brain.hook.after_action_taken import BaseHook
+from autono.brain.base_agent import BaseAgent
 
-log = logging.getLogger('ceo.ability')
+log = logging.getLogger('autono.ability')
 
 
 class AgenticAbility(Ability):
     def __init__(self, agent: BaseAgent | MemoryAugment):
         try:
             _test_mem = agent.memory
         except AttributeError:
-            raise TypeError("The 'agent' of AgenticAbility should be instance of 'ceo.Agent'.")
+            raise TypeError("The 'agent' of AgenticAbility should be instance of 'autono.Agent'.")
         self._agent = agent
         self.__name__ = f'{PREFIX}talk_to_{agent.name}'
         self.__doc__ = json.dumps({

ceo/ability/ability.py autono/ability/ability.pyceo/ability/ability.py renamed to autono/ability/ability.py

@@ -3,7 +3,7 @@
 from mcp import ClientSession
 from mcp.types import Tool, CallToolResult
 
-from ceo.ability.base_ability import BaseAbility
+from autono.ability.base_ability import BaseAbility
 
 DEFAULT_RETURNS = "<class 'str'>"
 NONE = '/'

ceo/ability/agentic_ability.py autono/ability/agentic_ability.pyceo/ability/agentic_ability.py renamed to autono/ability/agentic_ability.py

@@ -2,8 +2,8 @@
 
 from typing_extensions import override
 
-from ceo.ability.agentic_ability import AgenticAbility
-from ceo.brain.base_agent import BaseAgent
+from autono.ability.agentic_ability import AgenticAbility
+from autono.brain.base_agent import BaseAgent
 
 
 class McpAgenticAbility(AgenticAbility):