Merge pull request #14 from HKUDS/autoagent

Autoagent

autoagent/__init__.py

@@ -3,7 +3,7 @@ from .types import Agent, Response
 # from .workflow import Graph, meta_workflow, FlowEngine
 from .flow import default_drive
 
-import metachain.workflows
-import metachain.tools
-import metachain.agents
+import autoagent.workflows
+import autoagent.tools
+import autoagent.agents
 __all__ = ["MetaChain", "Agent", "Response", "default_drive", ]

autoagent/agents/__init__.py

@@ -1,21 +1,21 @@
-# from metachain.agents.programming_agent import get_programming_agent
-# from metachain.agents.tool_retriver_agent import get_tool_retriver_agent
-# from metachain.agents.agent_check_agent import get_agent_check_agent
-# from metachain.agents.tool_check_agent import get_tool_check_agent
-# from metachain.agents.github_agent import get_github_agent
-# from metachain.agents.programming_triage_agent import get_programming_triage_agent
-# from metachain.agents.plan_agent import get_plan_agent
+# from autoagent.agents.programming_agent import get_programming_agent
+# from autoagent.agents.tool_retriver_agent import get_tool_retriver_agent
+# from autoagent.agents.agent_check_agent import get_agent_check_agent
+# from autoagent.agents.tool_check_agent import get_tool_check_agent
+# from autoagent.agents.github_agent import get_github_agent
+# from autoagent.agents.programming_triage_agent import get_programming_triage_agent
+# from autoagent.agents.plan_agent import get_plan_agent
 
 # import os
 # import importlib
-# from metachain.registry import registry
+# from autoagent.registry import registry
 
 # # 获取当前目录下的所有 .py 文件
 # current_dir = os.path.dirname(__file__)
 # for file in os.listdir(current_dir):
 #     if file.endswith('.py') and not file.startswith('__'):
 #         module_name = file[:-3]
-#         importlib.import_module(f'metachain.agents.{module_name}')
+#         importlib.import_module(f'autoagent.agents.{module_name}')
 
 # # 导出所有注册的 agent 创建函数
 # globals().update(registry.agents)
@@ -24,7 +24,7 @@
 
 import os
 import importlib
-from metachain.registry import registry
+from autoagent.registry import registry
 
 def import_agents_recursively(base_dir: str, base_package: str):
     """Recursively import all agents in .py files
@@ -55,7 +55,7 @@ def import_agents_recursively(base_dir: str, base_package: str):
 
 # get the current directory and import all agents
 current_dir = os.path.dirname(__file__)
-import_agents_recursively(current_dir, 'metachain.agents')
+import_agents_recursively(current_dir, 'autoagent.agents')
 
 # export all agent creation functions
 globals().update(registry.agents)

autoagent/agents/dummy_agent.py

@@ -1,12 +1,12 @@
-from metachain.types import Agent
-from metachain.tools import tool_dummy
+from autoagent.types import Agent
+from autoagent.tools import tool_dummy
 from typing import Union
-from metachain.registry import register_plugin_agent # import the register_agent function from the registry
+from autoagent.registry import register_plugin_agent # import the register_agent function from the registry
 
 @register_plugin_agent(name = "Dummy Agent", func_name="get_dummy_agent") # You must register the agent in the registry, otherwise the agent will not be loaded. The name of register_agent is get_xxx_agent.
 def get_dummy_agent(model: str):
     """
-    This is a dummy agent, it's used for demonstrating the usage of the metachain.
+    This is a dummy agent, it's used for demonstrating the usage of the autoagent.
     Args:
         model: The model to be used for the agent.
     Returns:
@@ -30,7 +30,7 @@ def get_dummy_agent(model: str):
 Form to create an agent: 
 
 agent_name = "Dummy Agent"
-agent_description = "This is a dummy agent, it's used for demonstrating the usage of the metachain."
+agent_description = "This is a dummy agent, it's used for demonstrating the usage of the autoagent."
 agent_instructions = "..." | "...{global_variables}..."
 agent_tools = [tool_dummy]
 """

autoagent/agents/github_agent.py

@@ -1,15 +1,15 @@
-from metachain.types import Agent
-from metachain.tools import (
+from autoagent.types import Agent
+from autoagent.tools import (
     push_changes, submit_pull_request
 )
-from metachain.registry import register_agent
+from autoagent.registry import register_agent
 
 
 @register_agent(name = "Github Agent", func_name="get_github_agent")
 def get_github_agent(model: str):
     def instructions(context_variables):
         return \
-f"""You are an agent that helps user to manage the GitHub repository named 'metachain'. 
+f"""You are an agent that helps user to manage the GitHub repository named 'autoagent'. 
 The user will give you the suggestion of the changes to be pushed to the repository.
 Follow the following routine with the user:
 1. First, use `push_changes` to push the changes to the repository. (If the user want to push all the changes, use `push_changes` with `file_paths=None` as the argument.)

autoagent/agents/math/math_solver_agent.py

@@ -1,6 +1,6 @@
-from metachain.types import Agent
+from autoagent.types import Agent
 
-from metachain.registry import register_plugin_agent
+from autoagent.registry import register_plugin_agent
 
 @register_plugin_agent(name="Math Solver Agent", func_name="get_math_solver_agent")
 def get_math_solver_agent(model: str):

autoagent/agents/math/vote_aggregator_agent.py

@@ -1,6 +1,6 @@
-from metachain.types import Agent
+from autoagent.types import Agent
 
-from metachain.registry import register_plugin_agent
+from autoagent.registry import register_plugin_agent
 
 @register_plugin_agent(name="Vote Aggregator Agent", func_name="get_vote_aggregator_agent")
 def get_vote_aggregator_agent(model: str):

autoagent/agents/meta_agent/agent_creator.py

@@ -1,9 +1,9 @@
-from metachain.registry import register_agent
-from metachain.tools.meta.edit_agents import list_agents, create_agent, delete_agent, run_agent, read_agent, create_orchestrator_agent
-from metachain.tools.meta.edit_tools import list_tools, create_tool, delete_tool, run_tool
-from metachain.tools.terminal_tools import execute_command, terminal_page_down, terminal_page_up, terminal_page_to
-from metachain.types import Agent
-from metachain.io_utils import read_file
+from autoagent.registry import register_agent
+from autoagent.tools.meta.edit_agents import list_agents, create_agent, delete_agent, run_agent, read_agent, create_orchestrator_agent
+from autoagent.tools.meta.edit_tools import list_tools, create_tool, delete_tool, run_tool
+from autoagent.tools.terminal_tools import execute_command, terminal_page_down, terminal_page_up, terminal_page_to
+from autoagent.types import Agent
+from autoagent.io_utils import read_file
 
 
 @register_agent(name = "Agent Creator Agent", func_name="get_agent_creator_agent")

autoagent/agents/meta_agent/agent_editor.py

@@ -1,8 +1,8 @@
-from metachain.registry import register_agent
-from metachain.tools.meta.edit_agents import list_agents, create_agent, delete_agent, run_agent
-from metachain.tools.terminal_tools import execute_command
-from metachain.types import Agent
-from metachain.io_utils import read_file
+from autoagent.registry import register_agent
+from autoagent.tools.meta.edit_agents import list_agents, create_agent, delete_agent, run_agent
+from autoagent.tools.terminal_tools import execute_command
+from autoagent.types import Agent
+from autoagent.io_utils import read_file
 
 @register_agent(name = "Agent Editor Agent", func_name="get_agent_editor_agent")
 def get_agent_editor_agent(model: str) -> str:
@@ -19,7 +19,7 @@ The existing agents are shown below:
 If you want to create a new agent, you should: 
 1. follow the format of the `get_dummy_agent` below: 
 ```python
-{read_file('metachain/agents/dummy_agent.py')}
+{read_file('autoagent/agents/dummy_agent.py')}
 ```
 2. you successfully create the agent only after you have successfully run the agent with the `run_agent` function to satisfy the user's request.
 

autoagent/agents/meta_agent/agent_former.py

@@ -1,9 +1,9 @@
-from metachain.registry import register_agent
-from metachain.tools.meta.edit_agents import list_agents, create_agent, delete_agent, run_agent, read_agent
-from metachain.tools.meta.edit_tools import list_tools, create_tool, delete_tool, run_tool
-from metachain.tools.terminal_tools import execute_command
-from metachain.types import Agent
-from metachain.io_utils import read_file
+from autoagent.registry import register_agent
+from autoagent.tools.meta.edit_agents import list_agents, create_agent, delete_agent, run_agent, read_agent
+from autoagent.tools.meta.edit_tools import list_tools, create_tool, delete_tool, run_tool
+from autoagent.tools.terminal_tools import execute_command
+from autoagent.types import Agent
+from autoagent.io_utils import read_file
 from pydantic import BaseModel, Field
 from typing import List
 
@@ -238,7 +238,7 @@ Follow these examples and guidelines to create appropriate agent forms based on
     )
 
 if __name__ == "__main__":
-    from metachain import MetaChain
+    from autoagent import MetaChain
     agent = get_agent_former_agent("claude-3-5-sonnet-20241022")
     client = MetaChain()
     task_yaml = """\

autoagent/agents/meta_agent/form_complie.py

@@ -137,26 +137,3 @@ def parse_agent_form(xml_content: str) -> Optional[AgentForm]:
     except Exception as e:
         print(f"Unexpected error: {e}")
         return None
-
-# 使用示例
-if __name__ == "__main__":
-    # 读取和解析XML文件
-    import json
-    with open("/Users/tangjiabin/Documents/reasoning/metachain/metachain/agents/meta_agent/agent_form/financial_agent_2.xml", 'r', encoding='utf-8') as f:
-        xml_content = f.read()
-    form = parse_agent_form(xml_content)
-    with open("/Users/tangjiabin/Documents/reasoning/metachain/metachain/agents/meta_agent/agent_form/financial_agent_2.json", 'w', encoding='utf-8') as f:
-        json.dump(form.model_dump(), f, indent=4)
-    if form:
-        # 访问数据（现在有类型提示和验证）
-        print(f"System input: {form.system_input}")
-        print(f"Number of agents: {len(form.agents)}")
-        
-        # 访问第一个agent的信息
-        first_agent = form.agents[0]
-        print(f"First agent name: {first_agent.name}")
-        print(f"First agent tools: {first_agent.tools}")
-        
-        # 转换为字典
-        form_dict = form.model_dump()
-        print(json.dumps(form_dict, indent=4))

autoagent/agents/meta_agent/tool_editor.py

@@ -1,11 +1,11 @@
-from metachain.registry import register_agent
-from metachain.tools.meta.edit_tools import list_tools, create_tool, delete_tool, run_tool, get_metachain_path
-from metachain.tools.meta.tool_retriever import get_api_plugin_tools_doc
-from metachain.tools.meta.search_tools import search_trending_models_on_huggingface, get_hf_model_tools_doc
+from autoagent.registry import register_agent
+from autoagent.tools.meta.edit_tools import list_tools, create_tool, delete_tool, run_tool, get_metachain_path
+from autoagent.tools.meta.tool_retriever import get_api_plugin_tools_doc
+from autoagent.tools.meta.search_tools import search_trending_models_on_huggingface, get_hf_model_tools_doc
 
-from metachain.types import Agent
-from metachain.io_utils import read_file
-from metachain.tools.terminal_tools import execute_command, terminal_page_down, terminal_page_up, terminal_page_to
+from autoagent.types import Agent
+from autoagent.io_utils import read_file
+from autoagent.tools.terminal_tools import execute_command, terminal_page_down, terminal_page_up, terminal_page_to
 @register_agent(name = "Tool Editor Agent", func_name="get_tool_editor_agent")
 def get_tool_editor_agent(model: str) -> Agent:
     """
@@ -19,7 +19,7 @@ You are a tool editor agent responsible for managing plugin tools in the MetaCha
 - Plugin tools are the building blocks of MetaChain
 - All available plugin tools are as follows:
 {list_tools(context_variables)}
-- Plugin tools can ONLY be executed using `run_tool(tool_name, run_code)`. You should import `run_tool` by `from metachain.tools import run_tool`.
+- Plugin tools can ONLY be executed using `run_tool(tool_name, run_code)`. You should import `run_tool` by `from autoagent.tools import run_tool`.
 - NEVER try to import and run plugin tools directly - always use `run_tool`
 
 [TOOL CREATION WORKFLOW]
@@ -38,15 +38,15 @@ You are a tool editor agent responsible for managing plugin tools in the MetaCha
       - Only use internal knowledge if no suitable models are found
    
    c. For visual analysis tasks (images/videos):
-      - MUST use the existing `visual_question_answering` plugin tool by `run_tool("visual_question_answering", "from metachain.tools import visual_question_answering; ...")`. DO NOT use it directly without `run_tool`.
+      - MUST use the existing `visual_question_answering` plugin tool by `run_tool("visual_question_answering", "from autoagent.tools import visual_question_answering; ...")`. DO NOT use it directly without `run_tool`.
       - NO direct implementation of visual processing
       - Chain with other tools as needed
 
 3. Plugin Tool Implementation Requirements:
-   - Use @register_plugin_tool decorator (REQUIRED). You should import `register_plugin_tool` by `from metachain.registry import register_plugin_tool`.
+   - Use @register_plugin_tool decorator (REQUIRED). You should import `register_plugin_tool` by `from autoagent.registry import register_plugin_tool`.
    - Follow this template:
 ```python
-{read_file('metachain/tools/dummy_tool.py')}
+{read_file('autoagent/tools/dummy_tool.py')}
 ```
    - Include clear type hints
    - Make tools abstract and reusable
@@ -103,7 +103,7 @@ You are a tool editor agent responsible for managing plugin tools in the MetaCha
 
 [TESTING]
 Test new tools using `run_tool`:
-`run_tool(tool_name="your_tool", run_code="from metachain.tools import your_tool; print(your_tool(param1='value1'))")`
+`run_tool(tool_name="your_tool", run_code="from autoagent.tools import your_tool; print(your_tool(param1='value1'))")`
 """
     tool_list = [list_tools, create_tool, run_tool, delete_tool, get_api_plugin_tools_doc, execute_command, terminal_page_down, terminal_page_up, terminal_page_to, search_trending_models_on_huggingface, get_hf_model_tools_doc]
     return Agent(
@@ -131,16 +131,16 @@ The existing tools are shown below:
 If you want to create a new tool, you should: 
 1. follow the format of the `tool_dummy` below. Note that if the tool should be used with third-part api key, you should write the api key inside the definition of the tool: 
 ```python
-{read_file('metachain/tools/dummy_tool.py')}
+{read_file('autoagent/tools/dummy_tool.py')}
 ```
 
 2. you successfully create the tool only after you have successfully run the tool with the `run_tool` function, and an example of testing the tool is shown below.:
 ```python
-from metachain.tools import tool_dummy
+from autoagent.tools import tool_dummy
 
 if __name__ == "__main__":
     ... # some pre-operations
-    print(run_tool(tool_name="tool_dummy", run_code="from metachain.tools import tool_dummy; print(tool_dummy(args1=args1, args2=args1, ...))"))
+    print(run_tool(tool_name="tool_dummy", run_code="from autoagent.tools import tool_dummy; print(tool_dummy(args1=args1, args2=args1, ...))"))
 ```
 
 3. If you encounter any error while creating and running the tool, like dependency missing, you should use the `execute_command` function to install the dependency.
@@ -177,11 +177,11 @@ You are a tool editor agent responsible for managing plugin tools in the MetaCha
 
 2. create_tool(tool_name: str, tool_code: str):
    - Creates new plugin tools
-   - Requires proper registration using @register_plugin_tool, and you MUST import `register_plugin_tool` by `from metachain.registry import register_plugin_tool`
+   - Requires proper registration using @register_plugin_tool, and you MUST import `register_plugin_tool` by `from autoagent.registry import register_plugin_tool`
 
 3. run_tool(tool_name: str, run_code: str,):
    - REQUIRED method to execute any plugin tool
-   - Format: run_tool("tool_name", "from metachain.tools import tool_name; print(tool_name(args))")
+   - Format: run_tool("tool_name", "from autoagent.tools import tool_name; print(tool_name(args))")
 
 4. delete_tool(tool_name: str,):
    - Removes existing plugin tools
@@ -231,7 +231,7 @@ You are a tool editor agent responsible for managing plugin tools in the MetaCha
    a. Design generic, reusable interface
    b. Follow the template format:
 ```python
-{read_file('metachain/tools/dummy_tool.py')}
+{read_file('autoagent/tools/dummy_tool.py')}
 ```
    c. Create using `create_tool`
    d. Test using `run_tool`
@@ -250,7 +250,7 @@ Correct way to test a plugin tool:
 ```python
 result = run_tool(
     tool_name="your_tool",
-    run_code="from metachain.tools import your_tool; print(your_tool(param1='value1'))",
+    run_code="from autoagent.tools import your_tool; print(your_tool(param1='value1'))",
     context_variables=context_variables
 )
 ```

autoagent/agents/meta_agent/workflow_creator.py

@@ -1,9 +1,9 @@
-from metachain.registry import register_agent
-from metachain.tools.meta.edit_agents import list_agents, create_agent, delete_agent, run_agent, read_agent, create_orchestrator_agent
-from metachain.tools.meta.edit_workflow import list_workflows, create_workflow, run_workflow
-from metachain.tools.terminal_tools import execute_command, terminal_page_down, terminal_page_up, terminal_page_to
-from metachain.types import Agent
-from metachain.io_utils import read_file
+from autoagent.registry import register_agent
+from autoagent.tools.meta.edit_agents import list_agents, create_agent, delete_agent, run_agent, read_agent, create_orchestrator_agent
+from autoagent.tools.meta.edit_workflow import list_workflows, create_workflow, run_workflow
+from autoagent.tools.terminal_tools import execute_command, terminal_page_down, terminal_page_up, terminal_page_to
+from autoagent.types import Agent
+from autoagent.io_utils import read_file
 
 
 @register_agent(name = "Workflow Creator Agent", func_name="get_workflow_creator_agent")

autoagent/agents/meta_agent/workflow_former.py

@@ -1,10 +1,10 @@
-from metachain.registry import register_agent
-from metachain.tools.meta.edit_agents import list_agents, create_agent, delete_agent, run_agent, read_agent
-from metachain.tools.meta.edit_tools import list_tools, create_tool, delete_tool, run_tool
-from metachain.tools.meta.edit_workflow import list_workflows
-from metachain.tools.terminal_tools import execute_command
-from metachain.types import Agent
-from metachain.io_utils import read_file
+from autoagent.registry import register_agent
+from autoagent.tools.meta.edit_agents import list_agents, create_agent, delete_agent, run_agent, read_agent
+from autoagent.tools.meta.edit_tools import list_tools, create_tool, delete_tool, run_tool
+from autoagent.tools.meta.edit_workflow import list_workflows
+from autoagent.tools.terminal_tools import execute_command
+from autoagent.types import Agent
+from autoagent.io_utils import read_file
 from pydantic import BaseModel, Field
 from typing import List
 import json
@@ -548,7 +548,7 @@ Follow these examples and guidelines to create appropriate workflow forms based
     )
 
 if __name__ == "__main__":
-    from metachain import MetaChain
+    from autoagent import MetaChain
     agent = get_workflow_former_agent("claude-3-5-sonnet-20241022")
     client = MetaChain()
 #     task_yaml = """\

autoagent/agents/meta_agent/worklow_form_complie.py

@@ -322,14 +322,3 @@ def parse_workflow_form(xml_content: str) -> Optional[WorkflowForm]:
     except Exception as e:
         return f"Unexpected error: {e}"
 
-# 使用示例
-if __name__ == "__main__":
-    # 读取和解析XML文件
-    import json
-    with open("/Users/tangjiabin/Documents/reasoning/metachain/metachain/agents/meta_agent/workflow_form/condition_mining.xml", 'r', encoding='utf-8') as f:
-        xml_content = f.read()
-    form = parse_workflow_form(xml_content)
-    print(form)
-    with open("/Users/tangjiabin/Documents/reasoning/metachain/metachain/agents/meta_agent/workflow_form/condition_mining.json", 'w', encoding='utf-8') as f:
-        json.dump(form.model_dump(), f, indent=4)
-    workflow_form = form.model_dump()

autoagent/agents/system_agent/filesurfer_agent.py

@@ -1,8 +1,8 @@
-from metachain.types import Agent
-from metachain.registry import register_agent
-from metachain.tools import open_local_file, page_up_markdown, page_down_markdown, find_on_page_ctrl_f, find_next, visual_question_answering
-from metachain.tools.file_surfer_tool import with_env
-from metachain.environment.markdown_browser import RequestsMarkdownBrowser
+from autoagent.types import Agent
+from autoagent.registry import register_agent
+from autoagent.tools import open_local_file, page_up_markdown, page_down_markdown, find_on_page_ctrl_f, find_next, visual_question_answering
+from autoagent.tools.file_surfer_tool import with_env
+from autoagent.environment.markdown_browser import RequestsMarkdownBrowser
 import time
 from inspect import signature
 from constant import LOCAL_ROOT, DOCKER_WORKPLACE_NAME

autoagent/agents/system_agent/programming_agent.py

@@ -1,11 +1,11 @@
-from metachain.types import Agent
-from metachain.tools import (
+from autoagent.types import Agent
+from autoagent.tools import (
     gen_code_tree_structure, execute_command, read_file, create_file, write_file, list_files, create_directory, run_python, terminal_page_up, terminal_page_down, terminal_page_to
 )
-from metachain.util import make_message, make_tool_message
-from metachain.registry import register_agent, register_plugin_agent
+from autoagent.util import make_message, make_tool_message
+from autoagent.registry import register_agent, register_plugin_agent
 from constant import LOCAL_ROOT, DOCKER_WORKPLACE_NAME
-from metachain.environment import DockerEnv, BrowserEnv, LocalEnv
+from autoagent.environment import DockerEnv, BrowserEnv, LocalEnv
 from typing import Union
 from inspect import signature
 def examples(context_variables):

autoagent/agents/system_agent/system_triage_agent.py

@@ -1,9 +1,9 @@
 from .filesurfer_agent import get_filesurfer_agent
 from .programming_agent import get_coding_agent
 from .websurfer_agent import get_websurfer_agent
-from metachain.registry import register_agent
-from metachain.types import Agent, Result
-from metachain.tools.inner import case_resolved, case_not_resolved
+from autoagent.registry import register_agent
+from autoagent.types import Agent, Result
+from autoagent.tools.inner import case_resolved, case_not_resolved
 
 @register_agent(name = "System Triage Agent", func_name="get_system_triage_agent")
 def get_system_triage_agent(model: str, **kwargs):

autoagent/agents/system_agent/websurfer_agent.py

@@ -1,8 +1,8 @@
-from metachain.types import Agent
-from metachain.registry import register_agent
-from metachain.tools import click, page_down, page_up, history_back, history_forward, web_search, input_text, sleep, visit_url, get_page_markdown
-from metachain.tools.web_tools import with_env
-from metachain.environment.browser_env import BrowserEnv
+from autoagent.types import Agent
+from autoagent.registry import register_agent
+from autoagent.tools import click, page_down, page_up, history_back, history_forward, web_search, input_text, sleep, visit_url, get_page_markdown
+from autoagent.tools.web_tools import with_env
+from autoagent.environment.browser_env import BrowserEnv
 import time
 from constant import DOCKER_WORKPLACE_NAME, LOCAL_ROOT
 @register_agent(name = "Web Surfer Agent", func_name="get_websurfer_agent")

autoagent/agents/tool_retriver_agent.py

@@ -1,9 +1,9 @@
-from metachain.types import Agent
-from metachain.tools import (
+from autoagent.types import Agent
+from autoagent.tools import (
     get_api_plugin_tools_doc
 )
-from metachain.util import make_message, make_tool_message
-from metachain.registry import register_agent
+from autoagent.util import make_message, make_tool_message
+from autoagent.registry import register_agent
 @register_agent(name = "Tool Retriver Agent", func_name="get_tool_retriver_agent")
 def get_tool_retriver_agent(model: str):
     def instructions(context_variables):

autoagent/cli.py

@@ -0,0 +1,399 @@
+import click
+import importlib
+from autoagent import MetaChain
+from autoagent.util import debug_print
+import asyncio
+from constant import DOCKER_WORKPLACE_NAME
+from autoagent.io_utils import read_yaml_file, get_md5_hash_bytext, read_file
+from autoagent.environment.utils import setup_metachain
+from autoagent.types import Response
+from autoagent import MetaChain
+from autoagent.util import ask_text, single_select_menu, print_markdown, debug_print, UserCompleter
+from prompt_toolkit import PromptSession
+from prompt_toolkit.completion import Completer, Completion
+from prompt_toolkit.formatted_text import HTML
+from prompt_toolkit.styles import Style
+from rich.progress import Progress, SpinnerColumn, TextColumn
+import json
+import argparse
+from datetime import datetime
+from autoagent.agents.meta_agent import tool_editor, agent_editor
+from autoagent.tools.meta.edit_tools import list_tools
+from autoagent.tools.meta.edit_agents import list_agents
+from loop_utils.font_page import MC_LOGO, version_table, NOTES, GOODBYE_LOGO
+from rich.live import Live
+from autoagent.environment.docker_env import DockerEnv, DockerConfig, check_container_ports
+from autoagent.environment.browser_env import BrowserEnv
+from autoagent.environment.markdown_browser import RequestsMarkdownBrowser
+from evaluation.utils import update_progress, check_port_available, run_evaluation, clean_msg
+import os
+import os.path as osp
+from autoagent.agents import get_system_triage_agent
+from autoagent.logger import LoggerManager, MetaChainLogger 
+from rich.console import Console
+from rich.markdown import Markdown
+from rich.table import Table
+from rich.columns import Columns
+from rich.text import Text
+from rich.panel import Panel
+import re
+from autoagent.cli_utils.metachain_meta_agent import meta_agent
+from autoagent.cli_utils.metachain_meta_workflow import meta_workflow
+from autoagent.cli_utils.file_select import select_and_copy_files
+from evaluation.utils import update_progress, check_port_available, run_evaluation, clean_msg
+from constant import COMPLETION_MODEL
+@click.group()
+def cli():
+    """The command line interface for autoagent"""
+    pass
+
+@cli.command()
+@click.option('--model', default='gpt-4o-2024-08-06', help='the name of the model')
+@click.option('--agent_func', default='get_dummy_agent', help='the function to get the agent')
+@click.option('--query', default='...', help='the user query to the agent')
+@click.argument('context_variables', nargs=-1)
+def agent(model: str, agent_func: str, query: str, context_variables):
+    """
+    Run an agent with a given model, agent function, query, and context variables.
+    Args:
+        model (str): The name of the model.
+        agent_func (str): The function to get the agent.
+        query (str): The user query to the agent.
+        context_variables (list): The context variables to pass to the agent.
+    Usage:
+        mc agent --model=gpt-4o-2024-08-06 --agent_func=get_weather_agent --query="What is the weather in Tokyo?" city=Tokyo unit=C timestamp=2024-01-01
+    """ 
+    context_storage = {}
+    for arg in context_variables:
+        if '=' in arg:
+            key, value = arg.split('=', 1)
+            context_storage[key] = value
+    agent_module = importlib.import_module(f'autoagent.agents')
+    try:
+        agent_func = getattr(agent_module, agent_func)
+    except AttributeError:
+        raise ValueError(f'Agent function {agent_func} not found, you shoud check in the `autoagent.agents` directory for the correct function name')
+    agent = agent_func(model)
+    mc = MetaChain()
+    messages = [
+        {"role": "user", "content": query}
+    ]
+    response = mc.run(agent, messages, context_storage, debug=True)
+    debug_print(True, response.messages[-1]['content'], title = f'Result of running {agent.name} agent', color = 'pink3')
+    return response.messages[-1]['content']
+
+@cli.command()
+@click.option('--workflow_name', default=None, help='the name of the workflow')
+@click.option('--system_input', default='...', help='the user query to the agent')
+def workflow(workflow_name: str, system_input: str):
+    """命令行函数的同步包装器"""
+    return asyncio.run(async_workflow(workflow_name, system_input))
+
+async def async_workflow(workflow_name: str, system_input: str):
+    """异步实现的workflow函数"""
+    workflow_module = importlib.import_module(f'autoagent.workflows')
+    try:
+        workflow_func = getattr(workflow_module, workflow_name)
+    except AttributeError:
+        raise ValueError(f'Workflow function {workflow_name} not found...')
+    
+    result = await workflow_func(system_input)  # 使用 await 等待异步函数完成
+    debug_print(True, result, title=f'Result of running {workflow_name} workflow', color='pink3')
+    return result
+
+def clear_screen():
+    console = Console()
+    console.print("[bold green]Coming soon...[/bold green]")
+    print('\033[u\033[J\033[?25h', end='')  # Restore cursor and clear everything after it, show cursor
+def get_config(container_name, port):
+    container_name = container_name
+    
+    port_info = check_container_ports(container_name)
+    if port_info:
+        port = port_info[0]
+    else:
+        # while not check_port_available(port):
+        #     port += 1
+        # 使用文件锁来确保端口分配的原子性
+        import filelock
+        lock_file = os.path.join(os.getcwd(), ".port_lock")
+        lock = filelock.FileLock(lock_file)
+        
+        with lock:
+            port = port
+            while not check_port_available(port):
+                port += 1
+                print(f'{port} is not available, trying {port+1}')
+            # 立即标记该端口为已使用
+            with open(os.path.join(os.getcwd(), f".port_{port}"), 'w') as f:
+                f.write(container_name)
+    local_root = os.path.join(os.getcwd(), f"workspace_meta_showcase", f"showcase_{container_name}")
+    os.makedirs(local_root, exist_ok=True)
+    print("port: ", port)
+    docker_config = DockerConfig(
+        workplace_name=DOCKER_WORKPLACE_NAME,
+        container_name=container_name,
+        communication_port=port,
+        conda_path='/root/miniconda3',
+        local_root=local_root,
+    )
+    return docker_config
+def create_environment(docker_config: DockerConfig):
+    """
+    1. create the code environment
+    2. create the web environment
+    3. create the file environment
+    """
+    code_env = DockerEnv(docker_config)
+    code_env.init_container()
+    
+    web_env = BrowserEnv(browsergym_eval_env = None, local_root=docker_config.local_root, workplace_name=docker_config.workplace_name)
+    file_env = RequestsMarkdownBrowser(viewport_size=1024 * 5, local_root=docker_config.local_root, workplace_name=docker_config.workplace_name, downloads_folder=os.path.join(docker_config.local_root, docker_config.workplace_name, "downloads"))
+    
+    return code_env, web_env, file_env
+
+def update_guidance(context_variables): 
+    console = Console()
+
+    # print the logo
+    logo_text = Text(MC_LOGO, justify="center")
+    console.print(Panel(logo_text, style="bold salmon1", expand=True))
+    console.print(version_table)
+    console.print(Panel(NOTES,title="Important Notes", expand=True))
+
+@cli.command(name='main')  # 修改这里，使用连字符
+@click.option('--container_name', default='quick_start', help='the function to get the agent')
+@click.option('--port', default=12345, help='the port to run the container')
+@click.option('--test_pull_name', default='autoagent_mirror', help='the name of the test pull')
+@click.option('--git_clone', default=True, help='whether to clone a mirror of the repository')
+def main(container_name: str, port: int, test_pull_name: str, git_clone: bool):
+    """
+    Run deep research with a given model, container name, port
+    """ 
+    print(f"port: {port}")
+    model = COMPLETION_MODEL
+    print('\033[s\033[?25l', end='')  # Save cursor position and hide cursor
+    with Progress(
+        SpinnerColumn(),
+        TextColumn("[progress.description]{task.description}"),
+        transient=True  # 这会让进度条完成后消失
+    ) as progress:
+        task = progress.add_task("[cyan]Initializing...", total=None)
+        
+        progress.update(task, description="[cyan]Initializing config...[/cyan]\n")
+        docker_config = get_config(container_name, port)
+        
+        progress.update(task, description="[cyan]Setting up logger...[/cyan]\n")
+        log_path = osp.join("casestudy_results", 'logs', f'agent_{container_name}_{model}.log')
+        LoggerManager.set_logger(MetaChainLogger(log_path = None))
+        
+        progress.update(task, description="[cyan]Creating environment...[/cyan]\n")
+        code_env, web_env, file_env = create_environment(docker_config)
+        
+        progress.update(task, description="[cyan]Setting up autoagent...[/cyan]\n")
+    
+    clear_screen()
+
+    context_variables = {"working_dir": docker_config.workplace_name, "code_env": code_env, "web_env": web_env, "file_env": file_env}
+
+    # select the mode
+    while True:
+        update_guidance(context_variables)
+        mode = single_select_menu(['user mode', 'agent editor', 'workflow editor', 'exit'], "Please select the mode:")
+        match mode:
+            case 'user mode':
+                clear_screen()
+                user_mode(model, context_variables, False)
+            case 'agent editor':
+                clear_screen()
+                meta_agent(model, context_variables, False)
+            case 'workflow editor':
+                clear_screen()
+                meta_workflow(model, context_variables, False)
+            case 'exit':
+                console = Console()
+                logo_text = Text(GOODBYE_LOGO, justify="center")
+                console.print(Panel(logo_text, style="bold salmon1", expand=True))
+                break
+
+
+def user_mode(model: str, context_variables: dict, debug: bool = True): 
+    logger = LoggerManager.get_logger()
+    console = Console()
+    system_triage_agent = get_system_triage_agent(model)
+    assert system_triage_agent.agent_teams != {}, "System Triage Agent must have agent teams"
+    messages = []
+    agent = system_triage_agent
+    agents = {system_triage_agent.name.replace(' ', '_'): system_triage_agent}
+    for agent_name in system_triage_agent.agent_teams.keys():
+        agents[agent_name.replace(' ', '_')] = system_triage_agent.agent_teams[agent_name]("placeholder").agent
+    agents["Upload_files"] = "select"
+    style = Style.from_dict({
+        'bottom-toolbar': 'bg:#333333 #ffffff',
+    })
+
+    # 创建会话
+    session = PromptSession(
+        completer=UserCompleter(agents.keys()),
+        complete_while_typing=True,
+        style=style
+    )
+    client = MetaChain(log_path=logger)
+    while True: 
+        # query = ask_text("Tell me what you want to do:")
+        query = session.prompt(
+            'Tell me what you want to do (type "exit" to quit): ',
+            bottom_toolbar=HTML('<b>Prompt:</b> Enter <b>@</b> to mention Agents')
+        )
+        if query.strip().lower() == 'exit':
+            # logger.info('User mode completed.  See you next time! :waving_hand:', color='green', title='EXIT')
+            
+            logo_text = "User mode completed. See you next time! :waving_hand:"
+            console.print(Panel(logo_text, style="bold salmon1", expand=True))
+            break
+        words = query.split()
+        console.print(f"[bold green]Your request: {query}[/bold green]", end=" ")
+        for word in words:
+            if word.startswith('@') and word[1:] in agents.keys():
+                # print(f"[bold magenta]{word}[bold magenta]", end=' ') 
+                agent = agents[word.replace('@', '')]
+            else:
+                # print(word, end=' ')
+                pass
+        print()
+        
+        if hasattr(agent, "name"): 
+            agent_name = agent.name
+            console.print(f"[bold green][bold magenta]@{agent_name}[/bold magenta] will help you, be patient...[/bold green]")
+            messages.append({"role": "user", "content": query})
+            response = client.run(agent, messages, context_variables, debug=debug)
+            messages.extend(response.messages)
+            model_answer_raw = response.messages[-1]['content']
+
+            # attempt to parse model_answer
+            if model_answer_raw.startswith('Case resolved'):
+                model_answer = re.findall(r'<solution>(.*?)</solution>', model_answer_raw, re.DOTALL)
+                if len(model_answer) == 0:
+                    model_answer = model_answer_raw
+                else:
+                    model_answer = model_answer[0]
+            else: 
+                model_answer = model_answer_raw
+            console.print(f"[bold green][bold magenta]@{agent_name}[/bold magenta] has finished with the response:\n[/bold green] [bold blue]{model_answer}[/bold blue]")
+            agent = response.agent
+        elif agent == "select": 
+            code_env: DockerEnv = context_variables["code_env"]
+            local_workplace = code_env.local_workplace
+            files_dir = os.path.join(local_workplace, "files")
+            os.makedirs(files_dir, exist_ok=True)
+            select_and_copy_files(files_dir, console)
+        else: 
+            console.print(f"[bold red]Unknown agent: {agent}[/bold red]")
+
+@cli.command(name='deep-research')  # 修改这里，使用连字符
+@click.option('--container_name', default='deepresearch', help='the function to get the agent')
+@click.option('--port', default=12346, help='the port to run the container')
+def deep_research(container_name: str, port: int):
+    """
+    Run deep research with a given model, container name, port
+    """ 
+    print(f"port: {port}")
+    model = COMPLETION_MODEL
+    print('\033[s\033[?25l', end='')  # Save cursor position and hide cursor
+    with Progress(
+        SpinnerColumn(),
+        TextColumn("[progress.description]{task.description}"),
+        transient=True  # 这会让进度条完成后消失
+    ) as progress:
+        task = progress.add_task("[cyan]Initializing...", total=None)
+        
+        progress.update(task, description="[cyan]Initializing config...[/cyan]\n")
+        docker_config = get_config(container_name, port)
+        
+        progress.update(task, description="[cyan]Setting up logger...[/cyan]\n")
+        log_path = osp.join("casestudy_results", 'logs', f'agent_{container_name}_{model}.log')
+        LoggerManager.set_logger(MetaChainLogger(log_path = None))
+        
+        progress.update(task, description="[cyan]Creating environment...[/cyan]\n")
+        code_env, web_env, file_env = create_environment(docker_config)
+        
+        progress.update(task, description="[cyan]Setting up autoagent...[/cyan]\n")
+    
+    clear_screen()
+
+    context_variables = {"working_dir": docker_config.workplace_name, "code_env": code_env, "web_env": web_env, "file_env": file_env}
+
+    update_guidance(context_variables)
+
+    logger = LoggerManager.get_logger()
+    console = Console()
+    system_triage_agent = get_system_triage_agent(model)
+    assert system_triage_agent.agent_teams != {}, "System Triage Agent must have agent teams"
+    messages = []
+    agent = system_triage_agent
+    agents = {system_triage_agent.name.replace(' ', '_'): system_triage_agent}
+    for agent_name in system_triage_agent.agent_teams.keys():
+        agents[agent_name.replace(' ', '_')] = system_triage_agent.agent_teams[agent_name]("placeholder").agent
+    agents["Upload_files"] = "select"
+    style = Style.from_dict({
+        'bottom-toolbar': 'bg:#333333 #ffffff',
+    })
+
+    # 创建会话
+    session = PromptSession(
+        completer=UserCompleter(agents.keys()),
+        complete_while_typing=True,
+        style=style
+    )
+    client = MetaChain(log_path=logger)
+    while True: 
+        # query = ask_text("Tell me what you want to do:")
+        query = session.prompt(
+            'Tell me what you want to do (type "exit" to quit): ',
+            bottom_toolbar=HTML('<b>Prompt:</b> Enter <b>@</b> to mention Agents')
+        )
+        if query.strip().lower() == 'exit':
+            # logger.info('User mode completed.  See you next time! :waving_hand:', color='green', title='EXIT')
+            
+            logo_text = "See you next time! :waving_hand:"
+            console.print(Panel(logo_text, style="bold salmon1", expand=True))
+            break
+        words = query.split()
+        console.print(f"[bold green]Your request: {query}[/bold green]", end=" ")
+        for word in words:
+            if word.startswith('@') and word[1:] in agents.keys():
+                # print(f"[bold magenta]{word}[bold magenta]", end=' ') 
+                agent = agents[word.replace('@', '')]
+            else:
+                # print(word, end=' ')
+                pass
+        print()
+        
+        if hasattr(agent, "name"): 
+            agent_name = agent.name
+            console.print(f"[bold green][bold magenta]@{agent_name}[/bold magenta] will help you, be patient...[/bold green]")
+            messages.append({"role": "user", "content": query})
+            response = client.run(agent, messages, context_variables, debug=False)
+            messages.extend(response.messages)
+            model_answer_raw = response.messages[-1]['content']
+
+            # attempt to parse model_answer
+            if model_answer_raw.startswith('Case resolved'):
+                model_answer = re.findall(r'<solution>(.*?)</solution>', model_answer_raw, re.DOTALL)
+                if len(model_answer) == 0:
+                    model_answer = model_answer_raw
+                else:
+                    model_answer = model_answer[0]
+            else: 
+                model_answer = model_answer_raw
+            console.print(f"[bold green][bold magenta]@{agent_name}[/bold magenta] has finished with the response:\n[/bold green] [bold blue]{model_answer}[/bold blue]")
+            agent = response.agent
+        elif agent == "select": 
+            code_env: DockerEnv = context_variables["code_env"]
+            local_workplace = code_env.local_workplace
+            files_dir = os.path.join(local_workplace, "files")
+            os.makedirs(files_dir, exist_ok=True)
+            select_and_copy_files(files_dir, console)
+        else: 
+            console.print(f"[bold red]Unknown agent: {agent}[/bold red]")
+    

autoagent/cli_utils/metachain_meta_agent.py

@@ -1,16 +1,16 @@
-from metachain import MetaChain
-from metachain.util import UserCompleter
+from autoagent import MetaChain
+from autoagent.util import UserCompleter
 from prompt_toolkit import PromptSession
 from prompt_toolkit.formatted_text import HTML
 from prompt_toolkit.styles import Style
-from metachain.logger import LoggerManager, MetaChainLogger 
+from autoagent.logger import LoggerManager, MetaChainLogger 
 from rich.console import Console
 from rich.panel import Panel
-from metachain.agents.meta_agent.agent_former import get_agent_former_agent 
-from metachain.agents.meta_agent.tool_editor import get_tool_editor_agent
-from metachain.agents.meta_agent.agent_creator import get_agent_creator_agent
+from autoagent.agents.meta_agent.agent_former import get_agent_former_agent 
+from autoagent.agents.meta_agent.tool_editor import get_tool_editor_agent
+from autoagent.agents.meta_agent.agent_creator import get_agent_creator_agent
 import re
-from metachain.agents.meta_agent.form_complie import parse_agent_form
+from autoagent.agents.meta_agent.form_complie import parse_agent_form
 
 
 def extract_agents_content(text):

autoagent/cli_utils/metachain_meta_workflow.py

@@ -1,15 +1,15 @@
-from metachain import MetaChain
-from metachain.util import ask_text, single_select_menu, print_markdown, debug_print, UserCompleter
+from autoagent import MetaChain
+from autoagent.util import ask_text, single_select_menu, print_markdown, debug_print, UserCompleter
 from prompt_toolkit import PromptSession
 from prompt_toolkit.formatted_text import HTML
 from prompt_toolkit.styles import Style
-from metachain.logger import LoggerManager, MetaChainLogger 
+from autoagent.logger import LoggerManager, MetaChainLogger 
 from rich.console import Console
 from rich.panel import Panel
-from metachain.agents.meta_agent.workflow_former import get_workflow_former_agent
-from metachain.agents.meta_agent.workflow_creator import get_workflow_creator_agent
+from autoagent.agents.meta_agent.workflow_former import get_workflow_former_agent
+from autoagent.agents.meta_agent.workflow_creator import get_workflow_creator_agent
 import re
-from metachain.agents.meta_agent.worklow_form_complie import parse_workflow_form, WorkflowForm
+from autoagent.agents.meta_agent.worklow_form_complie import parse_workflow_form, WorkflowForm
 
 def workflow_profiling(workflow_former, client, messages, context_variables, requirements, debug):
     messages.append({"role": "user", "content": requirements + """

autoagent/core.py

@@ -31,7 +31,7 @@ from openai import AsyncOpenAI
 import litellm
 import inspect
 from constant import MC_MODE, FN_CALL, API_BASE_URL, NOT_SUPPORT_SENDER, ADD_USER, NON_FN_CALL
-from metachain.fn_call_converter import convert_tools_to_description, convert_non_fncall_messages_to_fncall_messages, SYSTEM_PROMPT_SUFFIX_TEMPLATE, convert_fn_messages_to_non_fn_messages, interleave_user_into_messages
+from autoagent.fn_call_converter import convert_tools_to_description, convert_non_fncall_messages_to_fncall_messages, SYSTEM_PROMPT_SUFFIX_TEMPLATE, convert_fn_messages_to_non_fn_messages, interleave_user_into_messages
 from litellm.types.utils import Message as litellmMessage
 # litellm.set_verbose=True
 # client = AsyncOpenAI()

autoagent/environment/browser_env.py

@@ -13,8 +13,8 @@ import numpy as np
 import tenacity
 from browsergym.utils.obs import flatten_dom_to_str
 from PIL import Image
-from metachain.util import debug_print
-from metachain.logger import LoggerManager
+from autoagent.util import debug_print
+from autoagent.logger import LoggerManager
 import inspect
 import textwrap
 
@@ -26,10 +26,10 @@ from browsergym.core.action.functions import goto, page, get_elem_by_bid, demo_m
 import os
 from typing import Dict, Union, cast, Literal
 from playwright.sync_api import Page, Download
-from metachain.io_utils import read_file
-from metachain.environment.mdconvert import _get_page_markdown
-from metachain.environment.browser_cookies import convert_cookies_to_python
-from metachain.environment.cookies_data import COOKIES_LIST
+from autoagent.io_utils import read_file
+from autoagent.environment.mdconvert import _get_page_markdown
+from autoagent.environment.browser_cookies import convert_cookies_to_python
+from autoagent.environment.cookies_data import COOKIES_LIST
 # from constant import DOCKER_WORKPLACE_NAME, LOCAL_ROOT
 from functools import update_wrapper
 from inspect import signature
@@ -50,7 +50,7 @@ def _local_to_docker(local_path: str):
     docker_path: the docker path to convert, like `{docker_workplace}/downloads/xxx`
 
     Examples:
-        _local_to_docker('/Users/tangjiabin/Documents/reasoning/metachain/workplace_gaia_eval/downloads/xxx')
+        _local_to_docker('/Users/tangjiabin/Documents/reasoning/autoagent/workplace_gaia_eval/downloads/xxx')
     """
     local_workplace = None
     docker_workplace = None
@@ -426,7 +426,7 @@ class BrowserEnv:
                 docker_path: the docker path to convert, like `{docker_workplace}/downloads/xxx`
 
                 Examples:
-                    _local_to_docker('/Users/tangjiabin/Documents/reasoning/metachain/workplace_gaia_eval/downloads/xxx')
+                    _local_to_docker('/Users/tangjiabin/Documents/reasoning/autoagent/workplace_gaia_eval/downloads/xxx')
                 """
                 local_workplace = None
                 docker_workplace = None
@@ -440,7 +440,7 @@ class BrowserEnv:
             action_set = HighLevelActionSet(subsets = ["chat", "infeas", "bid", "nav", "tab", "custom"], custom_actions = [_visit_page, _click_id, _get_page_markdown, _checkMeetChallenge])
 #             action_set.python_includes = \
 # f"""
-# {repr(read_file('metachain/environment/markdown_browser/mdconvert.py'))}
+# {repr(read_file('autoagent/environment/markdown_browser/mdconvert.py'))}
 
 # """ + action_set.python_includes
 

autoagent/environment/cookie_json/README.md

@@ -16,7 +16,7 @@ We recommend you to use the Google Chrome browser with the extension "Export coo
 3. After you have exported all cookies, use the following command to convert them to python code:
 
 ```bash
-cd path/to/MetaChain && python metachain/environment/browser_cookies.py
+cd path/to/MetaChain && python autoagent/environment/browser_cookies.py
 ```
 
 ## Recommended websites

autoagent/environment/local_env.py

@@ -6,7 +6,7 @@ import platform
 class LocalEnv:
     def __init__(self):
         self.docker_workplace = os.getcwd()
-        if self.docker_workplace.endswith("metachain"):
+        if self.docker_workplace.endswith("autoagent"):
             self.docker_workplace = os.path.dirname(self.docker_workplace)
         self.local_workplace = self.docker_workplace
         self.conda_sh = self._find_conda_sh()

autoagent/environment/mdconvert.py

@@ -1,6 +1,6 @@
 
 from browsergym.core.action.functions import goto, page
-from metachain.environment.markdown_browser import MarkdownConverter
+from autoagent.environment.markdown_browser import MarkdownConverter
 def _get_page_markdown():
     """
     Get the markdown content of the current page
@@ -1228,6 +1228,6 @@ if __name__ == "__main__":
         # 关闭浏览器
         browser.close()
     # mdconvert = MarkdownConverter()
-    # res = mdconvert.convert_local("/Users/tangjiabin/Documents/reasoning/metachain/eval_data/GAIA/2023/validation/2b3ef98c-cc05-450b-a719-711aee40ac65.mp3")
+    # res = mdconvert.convert_local("/Users/tangjiabin/Documents/reasoning/autoagent/eval_data/GAIA/2023/validation/2b3ef98c-cc05-450b-a719-711aee40ac65.mp3")
     # print('title:', res.title)
     # print('content:', res.text_content)

autoagent/environment/utils.py

@@ -1,16 +1,16 @@
-from metachain.util import run_command_in_container
+from autoagent.util import run_command_in_container
 from .docker_env import DockerEnv
-from metachain.io_utils import print_stream
+from autoagent.io_utils import print_stream
 def setup_metachain(workplace_name: str, env: DockerEnv):
-    cmd = "pip list | grep metachain"
+    cmd = "pip list | grep autoagent"
     response = env.run_command(cmd, print_stream)
     if response['status'] == 0:
-        print("Metachain is already installed.")
+        print("AutoAgent is already installed.")
         return
     cmd = f"cd /{workplace_name}/MetaChain && pip install -e ."
     response = env.run_command(cmd, print_stream)
     if response['status'] == 0:
-        print("Metachain is installed.")
+        print("AutoAgent is installed.")
         return
     else:
-        raise Exception(f"Failed to install metachain. {response['result']}")
+        raise Exception(f"Failed to install autoagent. {response['result']}")

autoagent/logger.py

@@ -3,7 +3,7 @@ from rich.console import Console
 from rich.markup import escape
 import json
 from typing import List
-from constant import DEBUG, DEFAULT_LOG, LOG_PATH
+from constant import DEBUG, DEFAULT_LOG, LOG_PATH, MC_MODE
 from pathlib import Path
 BAR_LENGTH = 60
 class MetaChainLogger:
@@ -31,12 +31,15 @@ class MetaChainLogger:
         timestamp = datetime.now().strftime("%Y-%m-%d %H:%M:%S")
         message = "\n".join(map(str, args))
         color = kwargs.get("color", "white")
+        if MC_MODE: color = "grey58"
         title = kwargs.get("title", "INFO")
         log_str = f"[{timestamp}]\n{message}"
         if self.debug: 
             # print_in_box(log_str, color=color, title=title)
             self.console.print(self._wrap_title(title, f"bold {color}"))
-            self.console.print(escape(log_str), highlight=True, emoji=True)
+            print_str = escape(log_str)
+            if MC_MODE: print_str = f"[grey58]{print_str}[/grey58]"
+            self.console.print(print_str, highlight=True, emoji=True)
         log_str = self._wrap_title(title) + "\n" + log_str
         if self.log_path: self._write_log(log_str) 
     def lprint(self, *args: str, **kwargs: dict):
@@ -44,42 +47,61 @@ class MetaChainLogger:
         timestamp = datetime.now().strftime("%Y-%m-%d %H:%M:%S")
         message = "\n".join(map(str, args))
         color = kwargs.get("color", "white")
+        if MC_MODE: color = "grey58"
         title = kwargs.get("title", "")
         log_str = f"[{timestamp}]\n{message}"
             # print_in_box(log_str, color=color, title=title)
         self.console.print(self._wrap_title(title, f"bold {color}"))
-        self.console.print(escape(log_str), highlight=True, emoji=True)
+        print_str = escape(log_str)
+        if MC_MODE: print_str = f"[grey58]{print_str}[/grey58]"
+        self.console.print(print_str, highlight=True, emoji=True)
         
     def _wrap_timestamp(self, timestamp: str, color: bool = True):
         color_bos = "[grey58]" if color else ""
         color_eos = "[/grey58]" if color else ""
         return f"{color_bos}[{timestamp}]{color_eos}"
     def _print_tool_execution(self, message, timestamp: str):
-        self.console.print(self._wrap_title("Tool Execution", "bold pink3"))
+        if MC_MODE: colors = ["grey58"] * 3
+        else: colors = ["pink3", "blue", "purple"]
+        self.console.print(self._wrap_title("Tool Execution", f"bold {colors[0]}"))
         self.console.print(self._wrap_timestamp(timestamp, color=True))
-        self.console.print("[bold blue]Tool Execution:[/bold blue]", end=" ")
-        self.console.print(f"[bold purple]{message['name']}[/bold purple]\n[bold blue]Result:[/bold blue]")
-        self.console.print(f"---\n{escape(message['content'])}\n---")
+        self.console.print(f"[bold {colors[1]}]Tool Execution:[/bold {colors[1]}]", end=" ")
+        self.console.print(f"[bold {colors[2]}]{message['name']}[/bold {colors[2]}]\n[bold {colors[1]}]Result:[/bold {colors[1]}]")
+        print_str = f"---\n{escape(message['content'])}\n---"
+        if MC_MODE: print_str = f"[grey58]{print_str}[/grey58]"
+        self.console.print(print_str, highlight=True, emoji=True)
     def _save_tool_execution(self, message, timestamp: str):
         self._write_log(self._wrap_title("Tool Execution"))
         self._write_log(f"{self._wrap_timestamp(timestamp, color=False)}\ntool execution: {message['name']}\nResult:\n---\n{message['content']}\n---")
     def _print_assistant_message(self, message, timestamp: str):
-        self.console.print(self._wrap_title("Assistant Message", "bold light_salmon3"))
-        self.console.print(f"{self._wrap_timestamp(timestamp, color=True)}\n[bold blue]{message['sender']}[/bold blue]:", end=" ")
-        if message["content"]: self.console.print(escape(message["content"]), highlight=True, emoji=True) 
-        else: self.console.print(None, highlight=True, emoji=True)
+        if MC_MODE: colors = ["grey58"] * 3
+        else: colors = ["light_salmon3", "blue", "purple"]
+        self.console.print(self._wrap_title("Assistant Message", f"bold {colors[0]}"))
+        self.console.print(f"{self._wrap_timestamp(timestamp, color=True)}\n[bold {colors[1]}]{message['sender']}[/bold {colors[1]}]:", end=" ")
+        if message["content"]: 
+            print_str = escape(message["content"])
+            if MC_MODE: print_str = f"[grey58]{print_str}[/grey58]"
+            self.console.print(print_str, highlight=True, emoji=True) 
+        else: 
+            print_str = None
+            if MC_MODE: print_str = "[grey58]None[/grey58]"
+            self.console.print(print_str, highlight=True, emoji=True)
     def _save_assistant_message(self, message, timestamp: str):
         self._write_log(self._wrap_title("Assistant Message"))
         content = message["content"] if message["content"] else None
         self._write_log(f"{self._wrap_timestamp(timestamp, color=False)}\n{message['sender']}: {content}")
     def _print_tool_call(self, tool_calls: List, timestamp: str):
-        if len(tool_calls) >= 1: self.console.print(self._wrap_title("Tool Calls", "bold light_pink1"))
+        if MC_MODE: colors = ["grey58"] * 3
+        else: colors = ["light_pink1", "blue", "purple"]
+        if len(tool_calls) >= 1: self.console.print(self._wrap_title("Tool Calls", f"bold {colors[0]}"))
 
         for tool_call in tool_calls:
             f = tool_call["function"]
             name, args = f["name"], f["arguments"]
             arg_str = self._warp_args(args)
-            self.console.print(f"{self._wrap_timestamp(timestamp, color=True)}\n[bold purple]{name}[/bold purple]({escape(arg_str)})")
+            print_arg_str = escape(arg_str)
+            if MC_MODE: print_arg_str = f"[grey58]{print_arg_str}[/grey58]"
+            self.console.print(f"{self._wrap_timestamp(timestamp, color=True)}\n[bold {colors[2]}]{name}[/bold {colors[2]}]({print_arg_str})")
     def _save_tool_call(self, tool_calls: List, timestamp: str):
         if len(tool_calls) >= 1: self._write_log(self._wrap_title("Tool Calls"))
 

autoagent/main.py

@@ -1,8 +1,8 @@
-from metachain import MetaChain, Agent, Response
+from autoagent import MetaChain, Agent, Response
 from typing import List
-from metachain.logger import MetaChainLogger
-from metachain.environment.utils import setup_metachain
-from metachain.environment.docker_env import DockerConfig, DockerEnv
+from autoagent.logger import MetaChainLogger
+from autoagent.environment.utils import setup_metachain
+from autoagent.environment.docker_env import DockerConfig, DockerEnv
 
 def case_resolved(result: str):
     """

autoagent/memory/code_memory.py

@@ -1,6 +1,6 @@
 import os
 from typing import List, Dict
-from metachain.memory.rag_memory import Memory, Reranker
+from autoagent.memory.rag_memory import Memory, Reranker
 from litellm import completion
 import re
 

autoagent/memory/codetree_memory.py

@@ -1,9 +1,9 @@
 import os
 from typing import List, Dict
-from metachain.memory.rag_memory import Memory, Reranker
+from autoagent.memory.rag_memory import Memory, Reranker
 import openai
 import re
-from metachain.memory.code_tree.code_parser import CodeParser, to_dataframe_row
+from autoagent.memory.code_tree.code_parser import CodeParser, to_dataframe_row
 from tree_sitter import Language
 from loguru import logger
 from openai import OpenAI

autoagent/memory/paper_memory.py

@@ -1,11 +1,11 @@
 import pandas as pd
 from typing import List, Dict
-from metachain.memory.rag_memory import Memory, Reranker
+from autoagent.memory.rag_memory import Memory, Reranker
 import json
 import math
 import os
 from litellm import completion
-from metachain.memory.utils import chunking_by_token_size
+from autoagent.memory.utils import chunking_by_token_size
 
 class TextMemory(Memory):
     def __init__(

autoagent/memory/tool_memory.py

@@ -1,6 +1,6 @@
 import pandas as pd
 from typing import List, Dict
-from metachain.memory.rag_memory import Memory, Reranker
+from autoagent.memory.rag_memory import Memory, Reranker
 import json
 import math
 import os

autoagent/repl/repl.py

@@ -1,6 +1,6 @@
 import json
 
-from metachain.core import MetaChain
+from autoagent.core import MetaChain
 
 
 def process_and_print_streaming_response(response):

autoagent/server.py

@@ -2,9 +2,9 @@ from fastapi import FastAPI, HTTPException
 from pydantic import BaseModel
 from contextlib import asynccontextmanager
 from typing import Dict, Any, Optional, List
-from metachain.registry import registry
-from metachain import MetaChain
-from metachain.types import Agent, Response
+from autoagent.registry import registry
+from autoagent import MetaChain
+from autoagent.types import Agent, Response
 import importlib
 import inspect
 

autoagent/tools/__init__.py

@@ -11,14 +11,14 @@
 
 # import os
 # import importlib
-# from metachain.registry import registry
+# from autoagent.registry import registry
 
 # # 获取当前目录下的所有 .py 文件
 # current_dir = os.path.dirname(__file__)
 # for file in os.listdir(current_dir):
 #     if file.endswith('.py') and not file.startswith('__'):
 #         module_name = file[:-3]
-#         importlib.import_module(f'metachain.tools.{module_name}')
+#         importlib.import_module(f'autoagent.tools.{module_name}')
 
 # # 导出所有注册的工具
 # globals().update(registry.tools)
@@ -27,7 +27,7 @@
 
 import os
 import importlib
-from metachain.registry import registry
+from autoagent.registry import registry
 
 def import_tools_recursively(base_dir: str, base_package: str):
     """Recursively import all tools in .py files
@@ -58,7 +58,7 @@ def import_tools_recursively(base_dir: str, base_package: str):
 
 # get the current directory and import all tools
 current_dir = os.path.dirname(__file__)
-import_tools_recursively(current_dir, 'metachain.tools')
+import_tools_recursively(current_dir, 'autoagent.tools')
 
 # export all tool creation functions
 globals().update(registry.tools)

autoagent/tools/code_search.py

@@ -1,7 +1,7 @@
 import requests
 from typing import Optional, List, Dict
-from metachain.tools.github_client import GitHubSearcher
-from metachain.registry import register_tool
+from autoagent.tools.github_client import GitHubSearcher
+from autoagent.registry import register_tool
 from constant import GITHUB_AI_TOKEN
 import json
 @register_tool("search_github_repos")

autoagent/tools/dummy_tool.py

@@ -1,11 +1,11 @@
-from metachain.types import Result, Agent
+from autoagent.types import Result, Agent
 from typing import Union
-from metachain.registry import register_plugin_tool
+from autoagent.registry import register_plugin_tool
 
 @register_plugin_tool("tool_dummy") # the name of registry should be the same as the name of the tool
 def tool_dummy(args1, args2, context_variables)-> Union[str, Agent, Result]:
     """
-    [This is a dummy tool, it's used for demonstrating the usage of the metachain, you should write your own tool instead of using this dummy tool, and the following docstring is just a template, you should modify it to fit your own tool.]
+    [This is a dummy tool, it's used for demonstrating the usage of the autoagent, you should write your own tool instead of using this dummy tool, and the following docstring is just a template, you should modify it to fit your own tool.]
     The tool is used to ...
 
     Args:
@@ -34,7 +34,7 @@ def tool_dummy(args1, args2, context_variables)-> Union[str, Agent, Result]:
 
     4. The value of Result class should be a string, if you want to return a json dictionary, you should convert it to string by json.dumps(result)
     ```
-    5. The tools should be created in the python file in the `metachain/metachain/tools` folder.
+    5. The tools should be created in the python file in the `autoagent/autoagent/tools` folder.
     """
     ... # the implementation of the tool
     return "..." # the return value of the tool.

autoagent/tools/file_surfer_tool.py

@@ -1,13 +1,13 @@
-from metachain.environment.markdown_browser import RequestsMarkdownBrowser
-from metachain.environment import LocalEnv
+from autoagent.environment.markdown_browser import RequestsMarkdownBrowser
+from autoagent.environment import LocalEnv
 from functools import partial, update_wrapper
 from inspect import signature
 from typing import Tuple
 import time
-from metachain.registry import register_tool, register_plugin_tool
+from autoagent.registry import register_tool, register_plugin_tool
 from typing import Union, Optional
 from typing import Optional
-from metachain.types import Result
+from autoagent.types import Result
 import requests
 import mimetypes
 import base64
@@ -342,5 +342,5 @@ if __name__ == "__main__":
     # print(find_on_page_ctrl_f(env, "Chain-of-Thought"))
     # print("Find next", "~"*100)
     # print(find_next(env))
-    print(visual_question_answering(context_variables = {"file_env": env}, file_path = "//workplace_gaia_meta/metachain/downloaded_video.mp4", question="What is the highest number of bird species to be on camera simultaneously?").value)
+    print(visual_question_answering(context_variables = {"file_env": env}, file_path = "//workplace_gaia_meta/autoagent/downloaded_video.mp4", question="What is the highest number of bird species to be on camera simultaneously?").value)
     # print(visual_question_answering("/workplace_meta/downloads/workflow.png", "What is the main idea of this paper?").image)

autoagent/tools/github_ops.py

@@ -1,17 +1,17 @@
-# from metachain.util import run_command_in_container
-from metachain.environment import DockerEnv, LocalEnv
+# from autoagent.util import run_command_in_container
+from autoagent.environment import DockerEnv, LocalEnv
 from constant import GITHUB_AI_TOKEN
-from metachain.tools.github_client import GitHubClient
+from autoagent.tools.github_client import GitHubClient
 import json
-from metachain.registry import register_tool
+from autoagent.registry import register_tool
 from typing import Union
 @register_tool("get_current_branch")
 def get_current_branch(context_variables):
     f"""
-    Get the current branch of the 'metachain'.
+    Get the current branch of the 'autoagent'.
     """
     env: Union[DockerEnv, LocalEnv] = context_variables.get("code_env", LocalEnv())
-    branch_command = f"cd {env.docker_workplace}/metachain && git branch --show-current"
+    branch_command = f"cd {env.docker_workplace}/autoagent && git branch --show-current"
     result = env.run_command(branch_command)
     if result['status'] == 0:
         return result['result'].strip()
@@ -21,10 +21,10 @@ def get_current_branch(context_variables):
 @register_tool("get_diff")
 def get_diff(context_variables): 
     f"""
-    Get the diff of the 'metachain'.
+    Get the diff of the 'autoagent'.
     """
     env: Union[DockerEnv, LocalEnv] = context_variables.get("code_env", LocalEnv())
-    diff_command = f"cd {env.docker_workplace}/metachain && git add -N . && git diff"
+    diff_command = f"cd {env.docker_workplace}/autoagent && git add -N . && git diff"
     result = env.run_command(diff_command)
     if result['status'] == 0:
         return result['result'].strip()
@@ -45,11 +45,11 @@ def stage_files(context_variables, file_paths=None):
     env: Union[DockerEnv, LocalEnv] = context_variables.get("code_env", LocalEnv())
     if file_paths is None:
         # add all changes to the staging area
-        command = f"cd {env.docker_workplace}/metachain && git add ."
+        command = f"cd {env.docker_workplace}/autoagent && git add ."
     else:
         # add specified files to the staging area
         files = ' '.join(file_paths)
-        command = f"cd {env.docker_workplace}/metachain && git add {files}"
+        command = f"cd {env.docker_workplace}/autoagent && git add {files}"
     
     result = env.run_command(command)
     return result
@@ -74,7 +74,7 @@ def push_changes(context_variables, commit_message, file_paths=None):
         return json.dumps({'status': 'error', 'message': f"Failed to stage files: {stage_result['result']}"}, indent=4)
     
     commands = [
-        f"cd {env.docker_workplace}/metachain",
+        f"cd {env.docker_workplace}/autoagent",
         f'git commit -m "{commit_message}"',
         "git push origin $(git branch --show-current)"
     ]
@@ -107,7 +107,7 @@ def submit_pull_request(title: str, body: str, target_branch: str):
     
     # create a pull request
     pr_result = github.create_pull_request(
-        repo="tjb-tech/metachain",
+        repo="tjb-tech/autoagent",
         title=title,
         body=body,
         head=get_current_branch(),
@@ -132,7 +132,7 @@ def submit_pull_request(title: str, body: str, target_branch: str):
 #     """
     
 #     # use gh to create a PR. make sure the gh cli is installed in the container and the github token is set
-#     pr_command = f"""cd /{DOCKER_WORKPLACE_NAME}/metachain && \
+#     pr_command = f"""cd /{DOCKER_WORKPLACE_NAME}/autoagent && \
 #         gh pr create \
 #         --title "{title}" \
 #         --body "{body}" \

autoagent/tools/inner.py

@@ -1,4 +1,4 @@
-from metachain.registry import register_tool
+from autoagent.registry import register_tool
 
 @register_tool("case_resolved")
 def case_resolved(result: str):

autoagent/tools/meta/edit_agents.py

@@ -1,7 +1,7 @@
-from metachain.registry import registry
-from metachain.environment import LocalEnv, DockerEnv, DockerConfig
+from autoagent.registry import registry
+from autoagent.environment import LocalEnv, DockerEnv, DockerConfig
 from typing import Union
-from metachain.tools.terminal_tools import (
+from autoagent.tools.terminal_tools import (
     create_file, 
     create_directory, 
     execute_command, 
@@ -9,9 +9,9 @@ from metachain.tools.terminal_tools import (
     print_stream,
     process_terminal_response
     )
-from metachain.registry import register_tool
+from autoagent.registry import register_tool
 import json
-from metachain.tools.meta.edit_tools import get_metachain_path
+from autoagent.tools.meta.edit_tools import get_metachain_path
 from string import Formatter
 from pydantic import BaseModel
 import subprocess
@@ -30,7 +30,7 @@ def list_agents(context_variables):
         path = get_metachain_path(env)
     except Exception as e:
         return "[ERROR] Failed to list agents. Error: " + str(e)
-    python_code = '"from metachain.registry import registry; import json; print(\\"AGENT_LIST_START\\"); print(json.dumps(registry.display_plugin_agents_info, indent=4)); print(\\"AGENT_LIST_END\\")"'
+    python_code = '"from autoagent.registry import registry; import json; print(\\"AGENT_LIST_START\\"); print(json.dumps(registry.display_plugin_agents_info, indent=4)); print(\\"AGENT_LIST_END\\")"'
     list_agents_cmd = f"cd {path} && DEFAULT_LOG=False python -c {python_code}"
     result = env.run_command(list_agents_cmd)
     if result['status'] != 0:
@@ -175,10 +175,10 @@ def create_agent(agent_name: str, agent_description: str, agent_tools: list[str]
     except Exception as e:
         return "[ERROR] Failed to list agents. Error: " + str(e)
     
-    agents_dir = path + "/metachain/agents"
+    agents_dir = path + "/autoagent/agents"
 
     for tool in agent_tools:
-        tools_str += f"from metachain.tools import {tool}\n"
+        tools_str += f"from autoagent.tools import {tool}\n"
     agent_func = f"get_{agent_name.lower().replace(' ', '_')}"
     if has_format_keys(agent_instructions):
         format_keys = extract_format_keys(agent_instructions)
@@ -195,9 +195,9 @@ def instructions(context_variables):
     tool_list = "[{}]".format(', '.join(f'{tool}' for tool in agent_tools))
 
     create_codes = f"""\
-from metachain.types import Agent
+from autoagent.types import Agent
 {tools_str}
-from metachain.registry import register_plugin_agent
+from autoagent.registry import register_plugin_agent
 
 @register_plugin_agent(name="{agent_name}", func_name="{agent_func}")
 def {agent_func}(model: str):
@@ -214,13 +214,13 @@ def {agent_func}(model: str):
 
 """
     # print(create_codes)
-    # with open(f"metachain/agents/{agent_name.lower().replace(' ', '_')}.py", "w", encoding="utf-8") as f:
+    # with open(f"autoagent/agents/{agent_name.lower().replace(' ', '_')}.py", "w", encoding="utf-8") as f:
     #     f.write(create_codes)
     try:  
         msg = create_file(agents_dir + "/" + agent_name.lower().replace(' ', '_') + ".py", create_codes, context_variables)
         if msg.startswith("Error creating file:"):
             return "[ERROR] Failed to create agent. Error: " + msg
-        result = code_env.run_command('cd {} && python metachain/agents/{}.py'.format(path, agent_name.lower().replace(' ', '_')))
+        result = code_env.run_command('cd {} && python autoagent/agents/{}.py'.format(path, agent_name.lower().replace(' ', '_')))
         if result['status'] != 0:
             return "[ERROR] Failed to create agent. Error: " + result['result']
         return "Successfully created agent: " + agent_name + " in " + agents_dir + "/" + agent_name.lower().replace(' ', '_') + ".py"
@@ -252,7 +252,7 @@ def create_orchestrator_agent(agent_name: str, agent_description: str, sub_agent
     except Exception as e:
         return "[ERROR] Failed to list agents. Error: " + str(e)
     
-    agents_dir = path + "/metachain/agents"
+    agents_dir = path + "/autoagent/agents"
     agent_list = list_agents(context_variables)
     if agent_list.startswith("[ERROR]"):
         return "Failed to list agents. Error: " + agent_list
@@ -261,7 +261,7 @@ def create_orchestrator_agent(agent_name: str, agent_description: str, sub_agent
     import_agent_str = ""
     for ainfo in sub_agent_info:
         import_agent_str += f"""
-    from metachain.agents import {ainfo['func_name']}
+    from autoagent.agents import {ainfo['func_name']}
 """
     if has_format_keys(agent_instructions):
         format_keys = extract_format_keys(agent_instructions)
@@ -320,9 +320,9 @@ def instructions(context_variables):
     
 
     create_codes = f"""\
-from metachain.types import Agent
-from metachain.registry import register_plugin_agent
-from metachain.types import Result  
+from autoagent.types import Agent
+from autoagent.registry import register_plugin_agent
+from autoagent.types import Result  
 
 @register_plugin_agent(name = "{agent_name}", func_name="{agent_func}")
 def {agent_func}(model: str):
@@ -341,13 +341,13 @@ def {agent_func}(model: str):
     return {agent_name.lower().replace(' ', '_')}
 """
     # print(create_codes)
-    # with open(f"metachain/agents/{agent_name.lower().replace(' ', '_')}.py", "w", encoding="utf-8") as f:
+    # with open(f"autoagent/agents/{agent_name.lower().replace(' ', '_')}.py", "w", encoding="utf-8") as f:
     #     f.write(create_codes)
     try:  
         msg = create_file(agents_dir + "/" + agent_name.lower().replace(' ', '_') + ".py", create_codes, context_variables)
         if msg.startswith("Error creating file:"):
             return "[ERROR] Failed to create agent. Error: " + msg
-        result = code_env.run_command('cd {} && python metachain/agents/{}.py'.format(path, agent_name.lower().replace(' ', '_')))
+        result = code_env.run_command('cd {} && python autoagent/agents/{}.py'.format(path, agent_name.lower().replace(' ', '_')))
         if result['status'] != 0:
             return "[ERROR] Failed to create agent. Error: " + result['result']
         return "Successfully created agent: " + agent_name + " in " + agents_dir + "/" + agent_name.lower().replace(' ', '_') + ".py"
@@ -380,7 +380,7 @@ def read_agent(agent_name: str, context_variables: dict):
 if __name__ == "__main__":
 #     # print(list_agents({}))
 #     from litellm import completion
-#     from metachain.util import function_to_json
+#     from autoagent.util import function_to_json
 #     tools = [function_to_json(create_agent)]
 #     messages = [
 #         {"role": "system", "content": "You are a helpful assistant."},
@@ -427,7 +427,7 @@ if __name__ == "__main__":
         workplace_name = "workplace", 
         communication_port = 12350, 
         conda_path = "/root/miniconda3",
-        local_root = "/Users/tangjiabin/Documents/reasoning/metachain/workspace_meta_showcase/showcase_nl2agent_showcase"
+        local_root = "/Users/tangjiabin/Documents/reasoning/autoagent/workspace_meta_showcase/showcase_nl2agent_showcase"
     )
     code_env = DockerEnv(docker_cfg)
     context_variables = {"code_env": code_env}

autoagent/tools/meta/edit_tools.py

@@ -1,7 +1,7 @@
-from metachain.registry import registry
-from metachain.environment import LocalEnv, DockerEnv
+from autoagent.registry import registry
+from autoagent.environment import LocalEnv, DockerEnv
 from typing import Union
-from metachain.tools.terminal_tools import (
+from autoagent.tools.terminal_tools import (
     create_file, 
     create_directory, 
     run_python, 
@@ -11,10 +11,10 @@ from metachain.tools.terminal_tools import (
     terminal_page_to, 
     process_terminal_response
     )
-from metachain.registry import register_tool
+from autoagent.registry import register_tool
 import json
 def get_metachain_path(env: Union[LocalEnv, DockerEnv]) -> str: 
-    result = env.run_command('pip show metachain')
+    result = env.run_command('pip show autoagent')
     if result['status'] != 0:
         raise Exception("Failed to list tools. Error: " + result['result'])
     stdout = result['result']
@@ -26,7 +26,7 @@ def get_metachain_path(env: Union[LocalEnv, DockerEnv]) -> str:
 
 def protect_tools(tool_name: str):
     if tool_name in registry.tools_info.keys():
-        raise Exception(f"The tool `{tool_name}` can NOT be modified. You can DIRECTLY use the `{tool_name}` tool by USING the `run_tool` tool. Or you can create a new tool using this tool by `from metachain.tools import {tool_name}`.")
+        raise Exception(f"The tool `{tool_name}` can NOT be modified. You can DIRECTLY use the `{tool_name}` tool by USING the `run_tool` tool. Or you can create a new tool using this tool by `from autoagent.tools import {tool_name}`.")
 
 
 @register_tool("list_tools")
@@ -41,7 +41,7 @@ def list_tools(context_variables):
         path = get_metachain_path(env)
     except Exception as e:
         return "Failed to list tools. Error: " + str(e)
-    python_code = '"from metachain.registry import registry; import json; print(\\"TOOL_LIST_START\\"); print(json.dumps(registry.display_plugin_tools_info, indent=4)); print(\\"TOOL_LIST_END\\")"'
+    python_code = '"from autoagent.registry import registry; import json; print(\\"TOOL_LIST_START\\"); print(json.dumps(registry.display_plugin_tools_info, indent=4)); print(\\"TOOL_LIST_END\\")"'
     list_tools_cmd = f"cd {path} && DEFAULT_LOG=False python -c {python_code}"
     result = env.run_command(list_tools_cmd)
     if result['status'] != 0:
@@ -88,11 +88,11 @@ def create_tool(tool_name: str, tool_code: str, context_variables):
     except Exception as e:
         return "[ERROR] Failed to list tools. Error: " + str(e)
     
-    tools_dir = path + "/metachain/tools"
+    tools_dir = path + "/autoagent/tools"
     try:  
         tool_path = tools_dir + "/" + tool_name + ".py"
-        if "from metachain.registry import register_plugin_tool" not in tool_code:
-            tool_code = "from metachain.registry import register_plugin_tool\n" + tool_code
+        if "from autoagent.registry import register_plugin_tool" not in tool_code:
+            tool_code = "from autoagent.registry import register_plugin_tool\n" + tool_code
         msg = create_file(tool_path, tool_code, context_variables)
         
         if msg.startswith("Error creating file:"):
@@ -215,7 +215,7 @@ if __name__ == "__main__":
     # print(list_tools({}))
     # print(create_tool("visual_question_answering", "print('Hello, World!')", {}))
     test_code = """
-from metachain.tools import test_file_tools
+from autoagent.tools import test_file_tools
 print(test_file_tools())
 """
     print(run_tool("test_file_tools", test_code, {}))

autoagent/tools/meta/edit_workflow.py

@@ -1,12 +1,12 @@
 from typing import Union
-from metachain.environment import LocalEnv, DockerEnv
-from metachain.tools.meta.edit_tools import get_metachain_path
-from metachain.tools.meta.edit_agents import list_agents
-from metachain.tools.terminal_tools import create_file, create_directory, print_stream, process_terminal_response
-from metachain.registry import register_tool
+from autoagent.environment import LocalEnv, DockerEnv
+from autoagent.tools.meta.edit_tools import get_metachain_path
+from autoagent.tools.meta.edit_agents import list_agents
+from autoagent.tools.terminal_tools import create_file, create_directory, print_stream, process_terminal_response
+from autoagent.registry import register_tool
 import json
-from metachain import MetaChain
-from metachain.types import Response
+from autoagent import MetaChain
+from autoagent.types import Response
 import shlex
 from datetime import datetime
 from pydantic import BaseModel
@@ -16,12 +16,12 @@ import json
 import argparse
 from openai import AsyncOpenAI
 from openai.types.chat import ChatCompletionMessageToolCall
-from metachain.flow import default_drive, EventInput, ReturnBehavior
-from metachain.flow.dynamic import goto_events, abort_this
+from autoagent.flow import default_drive, EventInput, ReturnBehavior
+from autoagent.flow.dynamic import goto_events, abort_this
 import re
-from metachain import MetaChain
-from metachain.types import Response
-from metachain.registry import register_workflow
+from autoagent import MetaChain
+from autoagent.types import Response
+from autoagent.registry import register_workflow
 
 def extract_answer(response: str, key: str):
     pattern = f"<{key}>(.*?)</{key}>"
@@ -167,7 +167,7 @@ def create_workflow(workflow_name: str, context_variables: dict) -> str:
     except Exception as e:
         return "[ERROR] Failed to list agents. Error: " + str(e)
     
-    workflows_dir = path + "/metachain/workflows"
+    workflows_dir = path + "/autoagent/workflows"
     agent_list = list_agents(context_variables)
     if agent_list.startswith("[ERROR]"):
         return "Failed to list agents. Error: " + agent_list
@@ -180,7 +180,7 @@ def create_workflow(workflow_name: str, context_variables: dict) -> str:
     import_agent_str = ""
     for ainfo in agent_info_dict.values():
         import_agent_str += f"""
-from metachain.agents import {ainfo['func_name']}
+from autoagent.agents import {ainfo['func_name']}
 """
     events = workflow_form["events"]
     events_code = CODE_PREFIX + import_agent_str
@@ -197,7 +197,7 @@ from metachain.agents import {ainfo['func_name']}
         msg = create_file(workflows_dir + "/" + workflow_name.lower().replace(' ', '_') + "_flow.py", events_code, context_variables)
         if msg.startswith("Error creating file:"):
             return "[ERROR] Failed to create workflow. Error: " + msg
-        result = code_env.run_command('cd {} && python metachain/workflows/{}_flow.py'.format(path, workflow_name.lower().replace(' ', '_')))
+        result = code_env.run_command('cd {} && python autoagent/workflows/{}_flow.py'.format(path, workflow_name.lower().replace(' ', '_')))
         if result['status'] != 0:
             return "[ERROR] Failed to create workflow. Error: " + result['result']
         return "Successfully created workflow: " + workflow_name + " in " + workflows_dir + "/" + workflow_name.lower().replace(' ', '_') + "_flow.py"
@@ -216,7 +216,7 @@ def list_workflows(context_variables):
         path = get_metachain_path(env)
     except Exception as e:
         return "[ERROR] Failed to list workflows. Error: " + str(e)
-    python_code = '"from metachain.registry import registry; import json; print(\\"WORKFLOW_LIST_START\\"); print(json.dumps(registry.display_workflows_info, indent=4)); print(\\"WORKFLOW_LIST_END\\")"'
+    python_code = '"from autoagent.registry import registry; import json; print(\\"WORKFLOW_LIST_START\\"); print(json.dumps(registry.display_workflows_info, indent=4)); print(\\"WORKFLOW_LIST_END\\")"'
     list_workflows_cmd = f"cd {path} && DEFAULT_LOG=False python -c {python_code}"
     result = env.run_command(list_workflows_cmd)
     if result['status'] != 0:
@@ -279,16 +279,16 @@ DEFAULT_LOG=False mc workflow --workflow_name={workflow_name} --system_input={sy
         return "[ERROR] Failed to run the workflow. Error: " + str(e)
 
 if __name__ == "__main__":
-    from metachain.environment import DockerConfig, DockerEnv, LocalEnv
+    from autoagent.environment import DockerConfig, DockerEnv, LocalEnv
     docker_cfg = DockerConfig(
         container_name = "nl2agent_showcase", 
         workplace_name = "workplace", 
         communication_port = 12350, 
         conda_path = "/root/miniconda3",
-        local_root = "/Users/tangjiabin/Documents/reasoning/metachain/workspace_meta_showcase/showcase_nl2agent_showcase"
+        local_root = "/Users/tangjiabin/Documents/reasoning/autoagent/workspace_meta_showcase/showcase_nl2agent_showcase"
     )
     code_env = DockerEnv(docker_cfg)
-    with open("/Users/tangjiabin/Documents/reasoning/metachain/metachain/agents/meta_agent/workflow_form/condition_mining.json", 'r', encoding='utf-8') as f:
+    with open("/Users/tangjiabin/Documents/reasoning/autoagent/autoagent/agents/meta_agent/workflow_form/condition_mining.json", 'r', encoding='utf-8') as f:
         workflow_form = json.load(f)
     print(workflow_form)
     

autoagent/tools/meta/search_tools.py

@@ -1,4 +1,4 @@
-from metachain.registry import register_tool
+from autoagent.registry import register_tool
 from huggingface_hub import HfApi, hf_hub_download
 from typing import List
 import tempfile

autoagent/tools/meta/tool_retriever.py

@@ -1,8 +1,8 @@
-from metachain.memory.tool_memory import ToolMemory, ToolReranker
+from autoagent.memory.tool_memory import ToolMemory, ToolReranker
 import os
-from metachain.io_utils import get_file_md5
+from autoagent.io_utils import get_file_md5
 import pandas as pd
-from metachain.registry import register_tool
+from autoagent.registry import register_tool
 from constant import COMPLETION_MODEL, EMBEDDING_MODEL
 
 @register_tool("get_api_plugin_tools_doc")

autoagent/tools/rag_code.py

@@ -1,8 +1,8 @@
-from metachain.memory.code_memory import CodeMemory, CodeReranker
+from autoagent.memory.code_memory import CodeMemory, CodeReranker
 import os
-from metachain.environment import DockerEnv, LocalEnv
-from metachain.io_utils import compress_folder, get_file_md5
-from metachain.registry import register_tool
+from autoagent.environment import DockerEnv, LocalEnv
+from autoagent.io_utils import compress_folder, get_file_md5
+from autoagent.registry import register_tool
 from typing import Union
 @register_tool("code_rag")
 def code_rag(query_text: str, context_variables) -> str:
@@ -16,13 +16,13 @@ def code_rag(query_text: str, context_variables) -> str:
     env: Union[DockerEnv, LocalEnv] = context_variables.get("code_env", LocalEnv())
     code_memory = CodeMemory(project_path = './code_db', platform='OpenAI', api_key=os.getenv("OPENAI_API_KEY"),embedding_model='text-embedding-3-small')
     code_reranker = CodeReranker(model="gpt-4o-2024-08-06")
-    code_path = f"{env.local_workplace}/metachain"
-    compress_folder(code_path, f"{env.local_workplace}/", "metachain.zip")
-    code_id = get_file_md5(f"{env.local_workplace}/metachain.zip")
+    code_path = f"{env.local_workplace}/autoagent"
+    compress_folder(code_path, f"{env.local_workplace}/", "autoagent.zip")
+    code_id = get_file_md5(f"{env.local_workplace}/autoagent.zip")
     code_memory.collection_name = code_memory.collection_name + f"_{code_id}"
 
     if code_memory.count() == 0:
-        code_memory.add_code_files(f"{env.local_workplace}/metachain", exclude_prefix=['__pycache__', 'code_db', '.git'])
+        code_memory.add_code_files(f"{env.local_workplace}/autoagent", exclude_prefix=['__pycache__', 'code_db', '.git'])
 
     query_results = code_memory.query_code(query_text, n_results=20)
     reranked_results = code_reranker.rerank(query_text, query_results)

autoagent/tools/rag_tools.py

@@ -1,18 +1,18 @@
-from metachain.memory.rag_memory import Memory
+from autoagent.memory.rag_memory import Memory
 import os
-from metachain.environment.docker_env import DockerEnv
-from metachain.environment.local_env import LocalEnv
+from autoagent.environment.docker_env import DockerEnv
+from autoagent.environment.local_env import LocalEnv
 from typing import Union
-from metachain.environment.local_env import LocalEnv
-from metachain.io_utils import compress_folder, get_file_md5
-from metachain.registry import register_tool, register_plugin_tool
+from autoagent.environment.local_env import LocalEnv
+from autoagent.io_utils import compress_folder, get_file_md5
+from autoagent.registry import register_tool, register_plugin_tool
 from litellm import completion
 import zipfile
 import shutil
-from metachain.environment.markdown_browser.mdconvert import MarkdownConverter
-from metachain.memory.utils import chunking_by_token_size
+from autoagent.environment.markdown_browser.mdconvert import MarkdownConverter
+from autoagent.memory.utils import chunking_by_token_size
 import math
-from metachain.types import Result
+from autoagent.types import Result
 # @register_tool("load_db")
 # def load_db(db_path: str) -> str:
 

autoagent/tools/terminal_tools.py

@@ -4,10 +4,10 @@ import socket
 import json
 import base64
 import math
-# from metachain.util import run_command_in_container
-from metachain.environment.docker_env import DockerEnv, DockerConfig
-from metachain.registry import register_tool
-from metachain.environment.markdown_browser.requests_markdown_browser import RequestsMarkdownBrowser
+# from autoagent.util import run_command_in_container
+from autoagent.environment.docker_env import DockerEnv, DockerConfig
+from autoagent.registry import register_tool
+from autoagent.environment.markdown_browser.requests_markdown_browser import RequestsMarkdownBrowser
 from typing import Tuple, Optional, Dict, Union
 import time
 import tiktoken
@@ -15,8 +15,8 @@ from datetime import datetime
 from functools import wraps
 from rich.console import Console
 from pathlib import Path
-from metachain.environment.local_env import LocalEnv
-from metachain.environment.docker_env import DockerEnv
+from autoagent.environment.local_env import LocalEnv
+from autoagent.environment.docker_env import DockerEnv
 import inspect
 
 terminal_env = RequestsMarkdownBrowser(local_root=os.getcwd(), workplace_name="terminal_env", viewport_size=1024 * 8)

autoagent/tools/tool_utils.py

@@ -1,10 +1,10 @@
-from metachain.environment.docker_env import DockerEnv
+from autoagent.environment.docker_env import DockerEnv
 
 import tiktoken
 from datetime import datetime
 
 def truncate_by_tokens(env: DockerEnv, text, max_tokens = 4096, model="gpt-4o-2024-08-06"):
-    from metachain.tools.terminal_tools import create_file
+    from autoagent.tools.terminal_tools import create_file
     encoding = tiktoken.encoding_for_model(model)
     tokens = encoding.encode(text)
     

autoagent/tools/web_tools.py

@@ -1,18 +1,18 @@
-from metachain.registry import register_tool
+from autoagent.registry import register_tool
 from browsergym.core.action.highlevel import HighLevelActionSet
 from typing import Literal
-from metachain.environment import DockerEnv, DockerConfig, BrowserEnv, VIEWPORT
+from autoagent.environment import DockerEnv, DockerConfig, BrowserEnv, VIEWPORT
 from browsergym.utils.obs import flatten_axtree_to_str
 from dataclasses import dataclass, field
 from typing import Dict
 from urllib.parse import quote_plus
-from metachain.types import Result
+from autoagent.types import Result
 from functools import partial, update_wrapper
 from inspect import signature
 import tiktoken
 from datetime import datetime
-from metachain.util import function_to_json
-from metachain.tools.md_obs import flatten_md_axtree_to_str
+from autoagent.util import function_to_json
+from autoagent.tools.md_obs import flatten_md_axtree_to_str
 # def with_env(env: BrowserEnv):
 #     """将env注入到工具函数中的装饰器"""
 #     def decorator(func):
@@ -379,7 +379,7 @@ def sleep(context_variables):
             image=web_obs.screenshot, 
         )
 def truncate_by_tokens(env: DockerEnv, text, max_tokens = 4096, model="gpt-4o-2024-08-06"):
-    from metachain.tools.terminal_tools import create_file, create_directory
+    from autoagent.tools.terminal_tools import create_file, create_directory
     encoding = tiktoken.encoding_for_model(model)
     tokens = encoding.encode(text)
     
@@ -429,7 +429,7 @@ def get_page_markdown(context_variables):
         )
 
 if __name__ == "__main__":
-    env = BrowserEnv(browsergym_eval_env = None, local_root="/Users/tangjiabin/Documents/reasoning/metachain", workplace_name="workplace_gaia_eval")
+    env = BrowserEnv(browsergym_eval_env = None, local_root="/Users/tangjiabin/Documents/reasoning/autoagent", workplace_name="workplace_gaia_eval")
     # code_env = DockerEnv(DockerConfig(container_name = "gaia_lite_eval", 
     # workplace_name = "workplace_gaia_eval", 
     # communication_port = 12345, 

autoagent/util.py

@@ -331,17 +331,31 @@ def function_to_json(func) -> dict:
     #         )
     #     parameters[param.name] = {"type": param_type}
     for param in signature.parameters.values():
+        if param.name == "context_variables":
+            continue
         try:
+            param_info = get_type_info(param.annotation, type_map)
+            if isinstance(param_info, dict) and "additionalProperties" in param_info:
+                del param_info["additionalProperties"]
             parameters[param.name] = get_type_info(param.annotation, type_map)
         except KeyError as e:
             raise KeyError(f"Unknown type annotation {param.annotation} for parameter {param.name}: {str(e)}")
 
+    
+
     required = [
         param.name
         for param in signature.parameters.values()
         if param.default == inspect._empty
     ]
 
+    if not parameters:
+        parameters["dummy"] = {
+            "type": "string",
+            "description": "Dummy parameter (not used). Added to satisfy non-empty schema requirements."
+        }
+        required = []
+
     return {
         "type": "function",
         "function": {

autoagent/workflows/__init__.py

@@ -1,6 +1,6 @@
 import os
 import importlib
-from metachain.registry import registry
+from autoagent.registry import registry
 
 def import_workflows_recursively(base_dir: str, base_package: str):
     """Recursively import all workflows in .py files
@@ -31,7 +31,7 @@ def import_workflows_recursively(base_dir: str, base_package: str):
 
 # get the current directory and import all tools
 current_dir = os.path.dirname(__file__)
-import_workflows_recursively(current_dir, 'metachain.workflows')
+import_workflows_recursively(current_dir, 'autoagent.workflows')
 
 # export all tool creation functions
 globals().update(registry.workflows)

autoagent/workflows/math_solver_workflow_flow.py

@@ -3,21 +3,21 @@ import json
 import argparse
 from openai import AsyncOpenAI
 from openai.types.chat import ChatCompletionMessageToolCall
-from metachain.flow import default_drive, EventInput, ReturnBehavior
-from metachain.flow.dynamic import goto_events, abort_this
+from autoagent.flow import default_drive, EventInput, ReturnBehavior
+from autoagent.flow.dynamic import goto_events, abort_this
 import re
-from metachain import MetaChain
-from metachain.types import Response
-from metachain.registry import register_workflow
+from autoagent import MetaChain
+from autoagent.types import Response
+from autoagent.registry import register_workflow
 
 def extract_answer(response: str, key: str):
     pattern = f"<{key}>(.*?)</{key}>"
     matches = re.findall(pattern, response)
     return matches[0] if len(matches) > 0 else None
 
-from metachain.agents import get_math_solver_agent
+from autoagent.agents import get_math_solver_agent
 
-from metachain.agents import get_vote_aggregator_agent
+from autoagent.agents import get_vote_aggregator_agent
 @default_drive.make_event
 async def on_start(event: EventInput, global_ctx):
     print("start the workflow:" + 'math_solver_workflow')

constant.py

@@ -1,5 +1,6 @@
 import os
 from dotenv import load_dotenv
+import platform
 # utils: 
 load_dotenv()  # 加载.env文件
 def str_to_bool(value):
@@ -10,8 +11,8 @@ def str_to_bool(value):
     if isinstance(value, bool):
         return value
         
-    if not value:
-        return False
+    if value == None:
+        return None
         
     value = str(value).lower().strip()
     if value in true_values:
@@ -21,33 +22,70 @@ def str_to_bool(value):
     return True  # default return True
 
 
-DOCKER_WORKPLACE_NAME = os.getenv('DOCKER_WORKPLACE_NAME', 'workplace_meta')
+DOCKER_WORKPLACE_NAME = os.getenv('DOCKER_WORKPLACE_NAME', 'workplace')
 GITHUB_AI_TOKEN = os.getenv('GITHUB_AI_TOKEN', None)
 AI_USER = os.getenv('AI_USER', None)
 LOCAL_ROOT = os.getenv('LOCAL_ROOT', os.getcwd())
 
-DEBUG = str_to_bool(os.getenv('DEBUG', True))
+DEBUG = str_to_bool(os.getenv('DEBUG', False))
 
 DEFAULT_LOG = str_to_bool(os.getenv('DEFAULT_LOG', False))
 LOG_PATH = os.getenv('LOG_PATH', None)
 EVAL_MODE = str_to_bool(os.getenv('EVAL_MODE', False))
-BASE_IMAGES = os.getenv('BASE_IMAGES', "tjbtech1/gaia-bookworm:v2")
+BASE_IMAGES = os.getenv('BASE_IMAGES', None)
+
+def get_architecture():
+    machine = platform.machine().lower()
+    if 'x86' in machine or 'amd64' in machine or 'i386' in machine:
+        return "tjbtech1/metachain:amd64_latest"
+    elif 'arm' in machine:
+        return "tjbtech1/metachain:latest"
+    else: 
+        return "tjbtech1/metachain:latest"
+if BASE_IMAGES is None:
+    BASE_IMAGES = get_architecture()
 
 COMPLETION_MODEL = os.getenv('COMPLETION_MODEL', "claude-3-5-haiku-20241022")
 EMBEDDING_MODEL = os.getenv('EMBEDDING_MODEL', "text-embedding-3-small")
 
-MC_MODE = str_to_bool(os.getenv('MC_MODE', False))
+MC_MODE = str_to_bool(os.getenv('MC_MODE', True))
 
 # add Env for function call and non-function call
 
-FN_CALL = str_to_bool(os.getenv('FN_CALL', True))
+FN_CALL = str_to_bool(os.getenv('FN_CALL', None))
 API_BASE_URL = os.getenv('API_BASE_URL', None)
-ADD_USER = str_to_bool(os.getenv('ADD_USER', False))
+ADD_USER = str_to_bool(os.getenv('ADD_USER', None))
+
 
-NON_FN_CALL = str_to_bool(os.getenv('NON_FN_CALL', False))
 
 NOT_SUPPORT_SENDER = ["mistral", "groq"]
+MUST_ADD_USER = ["deepseek-reasoner", "o1-mini", "deepseek-r1"]
+
+NOT_SUPPORT_FN_CALL = ["o1-mini", "deepseek-reasoner", "deepseek-r1", "llama", "grok-2"]
+NOT_USE_FN_CALL = [ "deepseek-chat"] + NOT_SUPPORT_FN_CALL
+
+if ADD_USER is None:
+    ADD_USER = False
+    for model in MUST_ADD_USER:
+        if model in COMPLETION_MODEL:
+            ADD_USER = True
+            break
+
+if FN_CALL is None:
+    FN_CALL = True
+    for model in NOT_USE_FN_CALL:
+        if model in COMPLETION_MODEL:
+            FN_CALL = False
+            break
+
+NON_FN_CALL = False
+for model in NOT_SUPPORT_FN_CALL:
+    if model in COMPLETION_MODEL:
+        NON_FN_CALL = True
+        break
+
 
 if EVAL_MODE:
     DEFAULT_LOG = False
 
+# print(FN_CALL, NON_FN_CALL, ADD_USER)

evaluation/gaia/run_infer.py

@@ -1,34 +1,33 @@
-from metachain.environment.docker_container import init_container
 import argparse
 from constant import DOCKER_WORKPLACE_NAME
 from datasets import load_dataset
 import huggingface_hub
-from metachain import MetaChain
-from metachain.logger import MetaChainLogger, LoggerManager
+from autoagent import MetaChain
+from autoagent.logger import MetaChainLogger, LoggerManager
 from evaluation.utils import make_metadata, prepare_dataset, update_progress, check_port_available, run_evaluation, clean_msg
 from evaluation.types import EvalMetadata, EvalOutput
-import metachain.agents as agenthub
+import autoagent.agents as agenthub
 import os.path as osp
 import pandas as pd
 import asyncio
 import re
 import os
 import shutil
-from metachain.registry import registry
+from autoagent.registry import registry
 from evaluation.gaia.scorer import question_scorer
 import json
-# from metachain.util import run_command_in_container
-from metachain.environment.docker_env import DockerEnv, DockerConfig, check_container_ports, check_container_exist, check_container_running
-from metachain.environment.browser_env import BrowserEnv
-from metachain.environment.markdown_browser import RequestsMarkdownBrowser
-from metachain.types import Response
-from metachain.util import function_to_json
-from metachain.main import run_in_client, run_in_client_non_async
-from metachain.agents.meta_agent.tool_editor import get_tool_editor_agent
-from metachain.environment.utils import setup_metachain
+# from autoagent.util import run_command_in_container
+from autoagent.environment.docker_env import DockerEnv, DockerConfig, check_container_ports, check_container_exist, check_container_running
+from autoagent.environment.browser_env import BrowserEnv
+from autoagent.environment.markdown_browser import RequestsMarkdownBrowser
+from autoagent.types import Response
+from autoagent.util import function_to_json
+from autoagent.main import run_in_client, run_in_client_non_async
+from autoagent.agents.meta_agent.tool_editor import get_tool_editor_agent
+from autoagent.environment.utils import setup_metachain
 import subprocess
 DATASET_CACHE_DIR = osp.join(osp.dirname(__file__), 'data')
-# Note: You should run this script in the root directory of the project metachain
+# Note: You should run this script in the root directory of the project autoagent
 def get_args():
     parser = argparse.ArgumentParser()
     parser.add_argument('--container_name', type=str, default='gaia_test')

evaluation/gaia/run_infer_old.py

@@ -1,235 +0,0 @@
-from metachain.environment.docker_container import init_container
-import argparse
-from constant import DOCKER_WORKPLACE_NAME
-from datasets import load_dataset
-import huggingface_hub
-from metachain import MetaChain
-from metachain.logger import MetaChainLogger, LoggerManager
-from evaluation.utils import make_metadata, prepare_dataset, update_progress, check_port_available, run_evaluation, clean_msg
-from evaluation.types import EvalMetadata, EvalOutput
-import metachain.agents as agenthub
-import os.path as osp
-import pandas as pd
-import asyncio
-import re
-import os
-import shutil
-from metachain.registry import registry
-from evaluation.gaia.scorer import question_scorer
-import json
-# from metachain.util import run_command_in_container
-from metachain.environment.docker_env import DockerEnv, DockerConfig, check_container_ports, check_container_exist, check_container_running
-from metachain.environment.browser_env import BrowserEnv
-from metachain.environment.markdown_browser import RequestsMarkdownBrowser
-from metachain.types import Response
-from metachain.util import function_to_json
-from metachain.main import run_in_client
-import subprocess
-DATASET_CACHE_DIR = osp.join(osp.dirname(__file__), 'data')
-# Note: You should run this script in the root directory of the project metachain
-def get_args():
-    parser = argparse.ArgumentParser()
-    parser.add_argument('--container_name', type=str, default='gaia_test')
-    parser.add_argument('--model', type=str, default='gpt-4o-2024-08-06')
-    parser.add_argument('--git_clone', action='store_true', default=False)
-    parser.add_argument('--setup_package', type=str, default=None)
-    parser.add_argument('--test_pull_name', type=str, default='main')
-    parser.add_argument('--debug', action='store_true', default=False)
-    # metadata
-    parser.add_argument('--agent_func', type=str, default='get_system_triage_agent')
-    parser.add_argument('--eval_note', type=str, default=None)
-    parser.add_argument('--eval_output_dir', type=str, default='./evaluation_results')
-    parser.add_argument('--data_split', type=str, default=None)
-    # gaia level
-    parser.add_argument('--level', type=str, default='1')
-    parser.add_argument('--eval_n_limit', type=int, default=None)
-    parser.add_argument('--port', type=int, default=12345)
-    parser.add_argument('--eval_num_workers', type=int, default=1)
-    args = parser.parse_args()
-    return args
-
-def get_config(metadata: EvalMetadata, instance_id: str):
-    container_name = metadata.container_name+f'_{instance_id}'
-    
-    port_info = check_container_ports(container_name)
-    port = metadata.port
-    if port_info:
-        port = port_info[0]
-    else:
-        while not check_port_available(port):
-            port += 1
-    local_root = os.path.join(os.getcwd(), f"workspace_gaia_whole", f"gaia_eval_{instance_id}")
-    os.makedirs(local_root, exist_ok=True)
-    docker_config = DockerConfig(
-        workplace_name=DOCKER_WORKPLACE_NAME,
-        container_name=container_name,
-        communication_port=port,
-        conda_path='/root/miniconda3',
-        local_root=local_root,
-    )
-    return docker_config
-
-def process_instance(
-    instance: pd.Series,
-    metadata: EvalMetadata,
-    logger: MetaChainLogger,
-) -> EvalOutput:
-    
-    docker_config = get_config(metadata, instance_id=instance['instance_id'])
-
-    code_env, web_env, file_env = create_environment(docker_config)
-    local_workplace = code_env.local_workplace
-    docker_workplace = code_env.docker_workplace
-
-    # Setup the logger properly, so you can run multi-processing to parallelize the evaluation
-    logger.info(f'Starting evaluation for instance {instance["instance_id"]}.')
-
-    if instance['file_name'] != '':
-        assert metadata.data_split is not None
-        src_file = os.path.join(
-            DATASET_CACHE_DIR, '2023', metadata.data_split, instance['file_name']
-        )
-        assert os.path.exists(src_file)
-        extension_name = instance['file_name'].split('.')[-1]
-        dest_file = os.path.join(local_workplace, f'file.{extension_name}')
-        shutil.copy(src_file, dest_file)
-        file_name = dest_file.split('/')[-1]
-    else:
-        dest_file = None
-    
-
-    # Prepare instruction
-    instruction = f"{instance['Question']}\n"
-    logger.info(f'Instruction: {instruction}')
-    if dest_file:
-        instruction += f"\n\nThe mentioned file is provided in the workspace at: {osp.join(docker_workplace, file_name)}"
-
-    instruction += 'IMPORTANT: You should ONLY interact with the environment provided to you AND NEVER ASK FOR HUMAN HELP.\n'
-    instruction += 'Please encapsulate your final answer (answer ONLY) within <solution> and </solution>.\n'
-    instruction += (
-        'For example: The answer to the question is <solution> 42 </solution>.\n'
-    )
-    
-    logger.info(f'Instruction:\n{instruction}')
-
-    system_triage_agent = registry.agents[metadata.agent_func](model=metadata.model, file_env=file_env, web_env=web_env, code_env=code_env)
-    messages = [
-        {
-            'role': 'user',
-            'content': instruction
-        }
-    ]
-
-    context_variables = {}
-    # Here's how you can run the agent (similar to the `main` function) and get the final task state
-    response: Response | None = asyncio.run(
-        run_in_client(
-            agent=system_triage_agent,
-            messages=messages,
-            context_variables = context_variables, 
-            logger=logger
-        )
-    )
-    messages.extend(response.messages)
-    # save messages to a file
-    messages_file = osp.join(metadata.eval_output_dir, f"gaia_{instance['instance_id']}", f'messages_{metadata.agent_func.replace("get_", "")}.json')
-    os.makedirs(osp.dirname(messages_file), exist_ok=True)
-    messages = clean_msg(messages)
-    with open(messages_file, 'w', encoding='utf-8') as f:
-        json.dump(messages, f, ensure_ascii=False, indent=4)
-    # ======= Attempt to evaluate the agent's edits =======
-    # If you are working on simpler benchmark that only evaluates the final model output (e.g., in a MessageAction)
-    # You can simply get the LAST `MessageAction` from the returned `state.history` and parse it for evaluation.
-
-    if response is None:
-        raise ValueError('Response should not be None.')
-
-    model_answer_raw = response.messages[-1]['content']
-
-    # attempt to parse model_answer
-    model_answer = re.findall(r'<solution>(.*?)</solution>', model_answer_raw)
-    if len(model_answer) == 0:
-        logger.info(f'Failed to parse model answer: {model_answer_raw}', title='WARNING', color='yellow')
-        model_answer = model_answer_raw
-    else:
-        model_answer = model_answer[0]
-
-    logger.info(
-        f'Final message: {model_answer} | Ground truth: {instance["Final answer"]}',
-        title='INFO', color='green'
-    )
-    score = question_scorer(
-        model_answer=model_answer, ground_truth=instance['Final answer']
-    )
-    test_result = {
-        'score': score,
-        'model_answer_raw': model_answer_raw,
-        'model_answer': model_answer,
-        'ground_truth': instance['Final answer'],
-    }
-
-
-    # Save the output
-    output = EvalOutput(
-        instance_id=instance['instance_id'],
-        instance=instance.to_dict(),
-        instruction=instance['Question'],
-        metadata=metadata,
-        messages=messages,
-        test_result=test_result,
-    )
-    return output
-
-def create_environment(docker_config: DockerConfig):
-    """
-    1. create the code environment
-    2. create the web environment
-    3. create the file environment
-    """
-    code_env = DockerEnv(docker_config)
-    code_env.init_container()
-    web_env = BrowserEnv(browsergym_eval_env = None, local_root=docker_config.local_root, workplace_name=docker_config.workplace_name)
-    file_env = RequestsMarkdownBrowser(viewport_size=1024 * 5, local_root=docker_config.local_root, workplace_name=docker_config.workplace_name, downloads_folder=os.path.join(docker_config.local_root, docker_config.workplace_name, "downloads"))
-    
-    return code_env, web_env, file_env
-def main(args):
-    metadata: EvalMetadata = make_metadata(
-        model=args.model,
-        dataset_name="gaia",
-        agent_func=args.agent_func,
-        eval_note=args.eval_note,
-        eval_output_dir=args.eval_output_dir,
-        data_split=args.data_split,
-        details={'gaia-level': args.level},
-        port=args.port,
-        container_name=args.container_name,
-    )
-    log_path = osp.join(metadata.eval_output_dir, 'logs', f'agent_{metadata.model}.log')
-    LoggerManager.set_logger(MetaChainLogger(log_path))
-
-    dataset = load_dataset('gaia-benchmark/GAIA', args.level)
-    huggingface_hub.snapshot_download(
-        'gaia-benchmark/GAIA',
-        repo_type='dataset',
-        local_dir=DATASET_CACHE_DIR,
-    )
-    gaia_tests = dataset[metadata.data_split].to_pandas()
-    gaia_tests.rename(columns={'task_id': 'instance_id'}, inplace=True)
-    
-    output_file = osp.join(metadata.eval_output_dir, 'output.jsonl')
-    prepared_dataset = prepare_dataset(gaia_tests, output_file, args.eval_n_limit)
-
-    run_evaluation(
-        dataset=prepared_dataset,
-        metadata=metadata,
-        output_file=output_file,
-        num_workers=args.eval_num_workers,
-        process_instance_func=process_instance,
-    )
-
-
-
-if __name__ == "__main__":
-    args = get_args()
-    main(args)
-    # print(check_container_exist('gaia_lite_eval_c61d22de-5f6c-4958-a7f6-5e9707bd3466'))

evaluation/gaia/test_imaes.py

@@ -1,7 +1,7 @@
 
 
-from metachain.environment.docker_env import DockerEnv, DockerConfig, check_container_ports, check_container_exist, check_container_running
-from metachain.tools.files import create_file
+from autoagent.environment.docker_env import DockerEnv, DockerConfig, check_container_ports, check_container_exist, check_container_running
+from autoagent.tools.files import create_file
 
 if __name__ == "__main__":
     import os

evaluation/utils.py

@@ -8,7 +8,7 @@ import pandas as pd
 import json
 from typing import Callable, Awaitable
 from tqdm import tqdm
-from metachain.logger import MetaChainLogger, LoggerManager
+from autoagent.logger import MetaChainLogger, LoggerManager
 import multiprocessing as mp
 import psutil
 import traceback

loop_utils/font_page.py

@@ -6,22 +6,24 @@ from rich.style import Style
 from rich.console import Console
 from rich.box import DOUBLE
 from rich.markdown import Markdown
+
+
 MC_LOGO = """\
-███╗   ███╗███████╗████████╗ █████╗  ██████╗██╗  ██╗ █████╗ ██╗███╗   ██╗
-████╗ ████║██╔════╝╚══██╔══╝██╔══██╗██╔════╝██║  ██║██╔══██╗██║████╗  ██║
-██╔████╔██║█████╗     ██║   ███████║██║     ███████║███████║██║██╔██╗ ██║
-██║╚██╔╝██║██╔══╝     ██║   ██╔══██║██║     ██╔══██║██╔══██║██║██║╚██╗██║
-██║ ╚═╝ ██║███████╗   ██║   ██║  ██║╚██████╗██║  ██║██║  ██║██║██║ ╚████║
-╚═╝     ╚═╝╚══════╝   ╚═╝   ╚═╝  ╚═╝ ╚═════╝╚═╝  ╚═╝╚═╝  ╚═╝╚═╝╚═╝  ╚═══╝
-╔═══ 𝒞𝓇𝑒𝒶𝓉𝑒 𝒜𝑔𝑒𝓃𝓉𝒾𝒸 𝒜ℐ 𝓊𝓈𝒾𝓃𝑔 ℒ𝒶𝓃𝑔𝓊𝒶𝑔𝑒 ═══╗                
-""".strip()
+    █████╗ ██╗   ██╗████████╗ ██████╗  █████╗  ██████╗ ███████╗███╗   ██╗████████╗
+   ██╔══██╗██║   ██║╚══██╔══╝██╔═══██╗██╔══██╗██╔════╝ ██╔════╝████╗  ██║╚══██╔══╝
+███████║██║   ██║   ██║   ██║   ██║███████║██║  ███╗█████╗  ██╔██╗ ██║   ██║      
+██╔══██║██║   ██║   ██║   ██║   ██║██╔══██║██║   ██║██╔══╝  ██║╚██╗██║   ██║      
+██║  ██║╚██████╔╝   ██║   ╚██████╔╝██║  ██║╚██████╔╝███████╗██║ ╚████║   ██║      
+╚═╝  ╚═╝ ╚═════╝    ╚═╝    ╚═════╝ ╚═╝  ╚═╝ ╚═════╝ ╚══════╝╚═╝  ╚═══╝   ╚═╝      
+╔═══ 𝒞𝓇𝑒𝒶𝓉𝑒 𝒜𝑔𝑒𝓃𝓉𝒾𝒸 𝒜ℐ 𝓊𝓈𝒾𝓃𝑔 ℒ𝒶𝓃𝑔𝓊𝒶𝑔𝑒 ═══╗\
+"""
 
 version_table = Table(show_header=False, box=DOUBLE, expand=True)
 version_table.add_column("Key", style="cyan")
 version_table.add_column("Value", style="green")
 
-version_table.add_row("Version", "0.1.0")
-version_table.add_row("Author", "MetaChain Team@HKU")
+version_table.add_row("Version", "0.2.0")
+version_table.add_row("Author", "AutoAgent Team@HKU")
 version_table.add_row("License", "MIT")
 
 NOTES = """\
@@ -42,3 +44,4 @@ GOODBYE_LOGO = """\
 · 𝓜𝓮𝓽𝓪𝓒𝓱𝓪𝓲𝓷-𝓐𝓘 ·           
 """.strip()
 
+

metachain/cli.py

@@ -1,63 +0,0 @@
-import click
-import importlib
-from metachain import MetaChain
-from metachain.util import debug_print
-import asyncio
-@click.group()
-def cli():
-    """The command line interface for metachain"""
-    pass
-
-@cli.command()
-@click.option('--model', default='gpt-4o-2024-08-06', help='the name of the model')
-@click.option('--agent_func', default='get_dummy_agent', help='the function to get the agent')
-@click.option('--query', default='...', help='the user query to the agent')
-@click.argument('context_variables', nargs=-1)
-def agent(model: str, agent_func: str, query: str, context_variables):
-    """
-    Run an agent with a given model, agent function, query, and context variables.
-    Args:
-        model (str): The name of the model.
-        agent_func (str): The function to get the agent.
-        query (str): The user query to the agent.
-        context_variables (list): The context variables to pass to the agent.
-    Usage:
-        mc agent --model=gpt-4o-2024-08-06 --agent_func=get_weather_agent --query="What is the weather in Tokyo?" city=Tokyo unit=C timestamp=2024-01-01
-    """ 
-    context_storage = {}
-    for arg in context_variables:
-        if '=' in arg:
-            key, value = arg.split('=', 1)
-            context_storage[key] = value
-    agent_module = importlib.import_module(f'metachain.agents')
-    try:
-        agent_func = getattr(agent_module, agent_func)
-    except AttributeError:
-        raise ValueError(f'Agent function {agent_func} not found, you shoud check in the `metachain.agents` directory for the correct function name')
-    agent = agent_func(model)
-    mc = MetaChain()
-    messages = [
-        {"role": "user", "content": query}
-    ]
-    response = mc.run(agent, messages, context_storage, debug=True)
-    debug_print(True, response.messages[-1]['content'], title = f'Result of running {agent.name} agent', color = 'pink3')
-    return response.messages[-1]['content']
-
-@cli.command()
-@click.option('--workflow_name', default=None, help='the name of the workflow')
-@click.option('--system_input', default='...', help='the user query to the agent')
-def workflow(workflow_name: str, system_input: str):
-    """命令行函数的同步包装器"""
-    return asyncio.run(async_workflow(workflow_name, system_input))
-
-async def async_workflow(workflow_name: str, system_input: str):
-    """异步实现的workflow函数"""
-    workflow_module = importlib.import_module(f'metachain.workflows')
-    try:
-        workflow_func = getattr(workflow_module, workflow_name)
-    except AttributeError:
-        raise ValueError(f'Workflow function {workflow_name} not found...')
-    
-    result = await workflow_func(system_input)  # 使用 await 等待异步函数完成
-    debug_print(True, result, title=f'Result of running {workflow_name} workflow', color='pink3')
-    return result

metachain/tools/meta/edit_tools copy.py

@@ -1,207 +0,0 @@
-from metachain.registry import registry
-from metachain.environment import LocalEnv, DockerEnv
-from typing import Union
-from metachain.tools.terminal_tools import (
-    create_file, 
-    create_directory, 
-    run_python, 
-    )
-from metachain.registry import register_tool
-
-from metachain.io_utils import print_stream
-import json
-def get_metachain_path(env: Union[LocalEnv, DockerEnv]) -> str: 
-    result = env.run_command('pip show metachain')
-    if result['status'] != 0:
-        raise Exception("Failed to list tools. Error: " + result['result'])
-    stdout = result['result']
-    for line in stdout.split('\n'):
-        if line.startswith('Editable project location:'):
-            path = line.split(':', 1)[1].strip()
-            return path
-    raise Exception("Failed to list tools. The MetaChain is not installed in editable mode.")
-
-@register_tool("list_tools")
-def list_tools(context_variables): 
-    """
-    List all plugin tools in the MetaChain.
-    Returns:
-        A list of information of all plugin tools including name, args, docstring, body, return_type, file_path.
-    """
-    env: Union[LocalEnv, DockerEnv] = context_variables.get("code_env", LocalEnv())
-    try:
-        path = get_metachain_path(env)
-    except Exception as e:
-        return "Failed to list tools. Error: " + str(e)
-    python_code = '"from metachain.registry import registry; import json; print(\\"TOOL_LIST_START\\"); print(json.dumps(registry.display_plugin_tools_info, indent=4)); print(\\"TOOL_LIST_END\\")"'
-    list_tools_cmd = f"cd {path} && DEFAULT_LOG=False python -c {python_code}"
-    result = env.run_command(list_tools_cmd)
-    if result['status'] != 0:
-        return "Failed to list tools. Error: " + result['result']
-    try:
-        output = result['result']
-        start_marker = "TOOL_LIST_START"
-        end_marker = "TOOL_LIST_END"
-        start_idx = output.find(start_marker) + len(start_marker)
-        end_idx = output.find(end_marker)
-        
-        if start_idx == -1 or end_idx == -1:
-            return "Failed to parse tool list: markers not found"
-            
-        json_str = output[start_idx:end_idx].strip()
-        return json_str
-    except Exception as e:
-        return f"Failed to process output: {str(e)}"
-    # return result['result']
-
-@register_tool("create_tool")
-def create_tool(tool_name: str, tool_code: str, context_variables): 
-    """
-    Create a plugin tool.
-    Args:
-        tool_name: The name of the tool.
-        tool_code: The code of creating the tool. (You should strictly follow the format of the template given to you to create the tool.)
-    Returns:
-        A string representation of the result of the tool creation.
-    """
-    if tool_name == "visual_question_answering":
-        return "The tool `visual_question_answering` is not allowed to be modified."
-    env: Union[LocalEnv, DockerEnv] = context_variables.get("code_env", LocalEnv())
-    try:
-        path = get_metachain_path(env)
-    except Exception as e:
-        return "Failed to list tools. Error: " + str(e)
-    
-    tools_dir = path + "/metachain/tools"
-    try:  
-        msg = create_file(tools_dir + "/" + tool_name + ".py", tool_code, context_variables)
-        if msg.startswith("Error creating file:"):
-            return "Failed to create tool. Error: " + msg
-        return "Successfully created tool: " + tool_name + " in " + tools_dir + "/" + tool_name + ".py"
-    except Exception as e:
-        return "Failed to create tool. Error: " + str(e)
-
-def tool_exists(tool_name: str, context_variables):
-    try:
-        list_res = list_tools(context_variables)
-        tool_dict = json.loads(list_res)
-        if tool_name not in tool_dict.keys():
-            return False, tool_dict
-        return True, tool_dict
-    except Exception as e:
-        return "Before deleting a tool, you should list all tools first. But the following error occurred: " + str(e), None
-
-@register_tool("delete_tool")
-def delete_tool(tool_name: str, context_variables): 
-    """
-    Delete a plugin tool.
-    Args:
-        tool_name: The name of the tool to be deleted.
-    Returns:
-        A string representation of the result of the tool deletion.
-    """
-    env: Union[LocalEnv, DockerEnv] = context_variables.get("code_env", LocalEnv())
-    # try:
-    #     exist_flag, tool_dict = tool_exists(tool_name, context_variables)
-    #     if isinstance(exist_flag, str):
-    #         return "Before deleting a tool, you should list all tools first. But the following error occurred: " + exist_flag
-    #     if not exist_flag:
-    #         return f"The tool `{tool_name}` does not exist."
-    # except Exception as e:
-    #     return "Before deleting a tool, you should list all tools first. But the following error occurred: " + str(e)
-    list_res = list_tools(context_variables)
-    tool_dict = json.loads(list_res)
-    try:
-        tool_path = tool_dict[tool_name]['file_path']
-    except KeyError: 
-        return "The tool `" + tool_name + "` does not exist."
-    except Exception as e:
-        return "Error: " + str(e)
-    try:
-        result = env.run_command(f"rm {tool_path}")
-        if result['status'] != 0:
-            return f"Failed to delete tool: `{tool_name}`. Error: " + result['result']
-        return f"Successfully deleted tool: `{tool_name}`."
-    except Exception as e:
-        return f"Failed to delete tool: `{tool_name}`. Error: " + str(e)
-
-@register_tool("update_tool")
-def update_tool(tool_name: str, tool_code: str, context_variables): 
-    """
-    Update an existing plugin tool.
-    Args:
-        tool_name: The name of the tool to be updated.
-        tool_code: The code of the tool to be updated.
-    Returns:
-        A string representation of the result of the tool update.
-    """
-    env: Union[LocalEnv, DockerEnv] = context_variables.get("code_env", LocalEnv())
-    # try:
-    #     exist_flag, tool_dict = tool_exists(tool_name, context_variables)
-    #     if isinstance(exist_flag, str):
-    #         return "Before deleting a tool, you should list all tools first. But the following error occurred: " + exist_flag
-    #     if not exist_flag:
-    #         return f"The tool `{tool_name}` does not exist."
-    # except Exception as e:
-    #     return "Before updating a tool, you should list all tools first. But the following error occurred: " + str(e)
-    list_res = list_tools(context_variables)
-    tool_dict = json.loads(list_res)
-    try:
-        tool_path = tool_dict[tool_name]['file_path']
-    except KeyError: 
-        return "The tool `" + tool_name + "` does not exist."
-    except Exception as e:
-        return "Error: " + str(e)
-    
-    try: 
-        msg = create_file(tool_path, tool_code, context_variables)
-        if msg.startswith("Error creating file:"):
-            return "Failed to update tool. Error: " + msg
-        return "Successfully updated tool: " + tool_name + " in " + tool_path
-    except Exception as e:
-        return "Failed to update tool. Error: " + str(e)
-
-
-@register_tool("test_tool")
-def test_tool(tool_name: str, test_code: str, context_variables): 
-    env: Union[LocalEnv, DockerEnv] = context_variables.get("code_env", LocalEnv())
-    try:
-        path = get_metachain_path(env)
-    except Exception as e:
-        return "Failed to get the path of the MetaChain. Error: " + str(e)
-    # try:
-    #     exist_flag, tool_dict = tool_exists(tool_name, context_variables)
-    #     if isinstance(exist_flag, str):
-    #         return "Before deleting a tool, you should list all tools first. But the following error occurred: " + exist_flag
-    #     if not exist_flag:
-    #         return f"The tool `{tool_name}` does not exist."
-    # except Exception as e:
-    #     return "Before testing a tool, you should list all tools first. But the following error occurred: " + str(e)
-    
-    test_dir = path + "/test_tools"
-
-    try: 
-        msg = create_directory(test_dir, context_variables)
-        if msg.startswith("Error creating directory:"):
-            return "Failed to create the test directory. Error: " + msg
-    except Exception as e:
-        return "Failed to create the test directory. Error: " + str(e)
-    
-    test_file_path = test_dir + "/" + "test_" + tool_name + ".py"
-    try:
-        msg = create_file(test_file_path, test_code, context_variables)
-        if msg.startswith("Error creating file:"):
-            return "Failed to create the test file. Error: " + msg
-    except Exception as e:
-        return "Failed to create the test file. Error: " + str(e)
-    
-    try:
-        result = run_python(context_variables, test_file_path, cwd=path, env_vars={"DEFAULT_LOG": "False"})
-        if "Exit code: 0" not in result:
-            return "Failed to test the tool. The test case is not correct. The result is: " + result
-        return f"The result is of the tool `{tool_name}`: \n{result}"
-    except Exception as e:
-        return "Failed to test the tool. Error: " + str(e)
-
-if __name__ == "__main__":
-    print(list_tools({}))

playground/cli/metachain_cli.py

@@ -1,262 +0,0 @@
-from constant import DOCKER_WORKPLACE_NAME
-from metachain.io_utils import read_yaml_file, get_md5_hash_bytext, read_file
-from metachain.environment.utils import setup_metachain
-from metachain.types import Response
-from metachain import MetaChain
-from metachain.util import ask_text, single_select_menu, print_markdown, debug_print, UserCompleter
-from prompt_toolkit import PromptSession
-from prompt_toolkit.completion import Completer, Completion
-from prompt_toolkit.formatted_text import HTML
-from prompt_toolkit.styles import Style
-from rich.progress import Progress, SpinnerColumn, TextColumn
-import json
-import argparse
-from datetime import datetime
-from metachain.agents.meta_agent import tool_editor, agent_editor
-from metachain.tools.meta.edit_tools import list_tools
-from metachain.tools.meta.edit_agents import list_agents
-from loop_utils.font_page import MC_LOGO, version_table, NOTES, GOODBYE_LOGO
-from rich.live import Live
-from metachain.environment.docker_env import DockerEnv, DockerConfig, check_container_ports
-from metachain.environment.browser_env import BrowserEnv
-from metachain.environment.markdown_browser import RequestsMarkdownBrowser
-from evaluation.utils import update_progress, check_port_available, run_evaluation, clean_msg
-import os
-import os.path as osp
-from metachain.agents import get_system_triage_agent
-from metachain.logger import LoggerManager, MetaChainLogger 
-from rich.console import Console
-from rich.markdown import Markdown
-from rich.table import Table
-from rich.columns import Columns
-from rich.text import Text
-from rich.panel import Panel
-import re
-from playground.cli.metachain_meta_agent import meta_agent
-from playground.cli.metachain_meta_workflow import meta_workflow
-from playground.cli.file_select import select_and_copy_files
-def get_args(): 
-    parser = argparse.ArgumentParser(description="working@tjb-tech")
-    parser.add_argument('--container_name', type=str, default='gpu_test')
-    parser.add_argument('--model', type=str, default='gpt-4o-2024-08-06')
-    parser.add_argument('--test_pull_name', type=str, default='test_pull_1010')
-    parser.add_argument('--debug', action='store_true', default=False)
-    parser.add_argument('--port', type=int, default=12350)
-    parser.add_argument('--git_clone', action='store_true', default=False)
-    args = parser.parse_args()
-    return args
-def clear_screen():
-    console = Console()
-    console.print("[bold green]Coming soon...[/bold green]")
-    print('\033[u\033[J\033[?25h', end='')  # Restore cursor and clear everything after it, show cursor
-def get_config(args):
-    container_name = args.container_name
-    
-    port_info = check_container_ports(container_name)
-    port = args.port
-    if port_info:
-        port = port_info[0]
-    else:
-        # while not check_port_available(port):
-        #     port += 1
-        # 使用文件锁来确保端口分配的原子性
-        import filelock
-        lock_file = os.path.join(os.getcwd(), ".port_lock")
-        lock = filelock.FileLock(lock_file)
-        
-        with lock:
-            port = args.port
-            while not check_port_available(port):
-                port += 1
-                print(f'{port} is not available, trying {port+1}')
-            # 立即标记该端口为已使用
-            with open(os.path.join(os.getcwd(), f".port_{port}"), 'w') as f:
-                f.write(container_name)
-    local_root = os.path.join(os.getcwd(), f"workspace_meta_showcase", f"showcase_{container_name}")
-    os.makedirs(local_root, exist_ok=True)
-    docker_config = DockerConfig(
-        workplace_name=DOCKER_WORKPLACE_NAME,
-        container_name=container_name,
-        communication_port=port,
-        conda_path='/root/miniconda3',
-        local_root=local_root,
-        git_clone=args.git_clone,
-        test_pull_name=args.test_pull_name,
-    )
-    return docker_config
-def create_environment(docker_config: DockerConfig):
-    """
-    1. create the code environment
-    2. create the web environment
-    3. create the file environment
-    """
-    code_env = DockerEnv(docker_config)
-    code_env.init_container()
-    
-    web_env = BrowserEnv(browsergym_eval_env = None, local_root=docker_config.local_root, workplace_name=docker_config.workplace_name)
-    file_env = RequestsMarkdownBrowser(viewport_size=1024 * 5, local_root=docker_config.local_root, workplace_name=docker_config.workplace_name, downloads_folder=os.path.join(docker_config.local_root, docker_config.workplace_name, "downloads"))
-    
-    return code_env, web_env, file_env
-
-def user_mode(model: str, context_variables: dict, debug: bool = True): 
-    logger = LoggerManager.get_logger()
-    console = Console()
-    system_triage_agent = get_system_triage_agent(model)
-    assert system_triage_agent.agent_teams != {}, "System Triage Agent must have agent teams"
-    messages = []
-    agent = system_triage_agent
-    agents = {system_triage_agent.name.replace(' ', '_'): system_triage_agent}
-    for agent_name in system_triage_agent.agent_teams.keys():
-        agents[agent_name.replace(' ', '_')] = system_triage_agent.agent_teams[agent_name]("placeholder").agent
-    agents["Upload_files"] = "select"
-    style = Style.from_dict({
-        'bottom-toolbar': 'bg:#333333 #ffffff',
-    })
-
-    # 创建会话
-    session = PromptSession(
-        completer=UserCompleter(agents.keys()),
-        complete_while_typing=True,
-        style=style
-    )
-    client = MetaChain(log_path=logger)
-    while True: 
-        # query = ask_text("Tell me what you want to do:")
-        query = session.prompt(
-            'Tell me what you want to do (type "exit" to quit): ',
-            bottom_toolbar=HTML('<b>Prompt:</b> Enter <b>@</b> to mention Agents')
-        )
-        if query.strip().lower() == 'exit':
-            # logger.info('User mode completed.  See you next time! :waving_hand:', color='green', title='EXIT')
-            
-            logo_text = "User mode completed. See you next time! :waving_hand:"
-            console.print(Panel(logo_text, style="bold salmon1", expand=True))
-            break
-        words = query.split()
-        console.print(f"[bold green]Your request: {query}[/bold green]", end=" ")
-        for word in words:
-            if word.startswith('@') and word[1:] in agents.keys():
-                # print(f"[bold magenta]{word}[bold magenta]", end=' ') 
-                agent = agents[word.replace('@', '')]
-            else:
-                # print(word, end=' ')
-                pass
-        print()
-        
-        if hasattr(agent, "name"): 
-            agent_name = agent.name
-            console.print(f"[bold green][bold magenta]@{agent_name}[/bold magenta] will help you, be patient...[/bold green]")
-            messages.append({"role": "user", "content": query})
-            response = client.run(agent, messages, context_variables, debug=debug)
-            messages.extend(response.messages)
-            model_answer_raw = response.messages[-1]['content']
-
-            # attempt to parse model_answer
-            if model_answer_raw.startswith('Case resolved'):
-                model_answer = re.findall(r'<solution>(.*?)</solution>', model_answer_raw, re.DOTALL)
-                if len(model_answer) == 0:
-                    model_answer = model_answer_raw
-                else:
-                    model_answer = model_answer[0]
-            else: 
-                model_answer = model_answer_raw
-            console.print(f"[bold green][bold magenta]@{agent_name}[/bold magenta] has finished with the response:\n[/bold green] [bold blue]{model_answer}[/bold blue]")
-            agent = response.agent
-        elif agent == "select": 
-            code_env: DockerEnv = context_variables["code_env"]
-            local_workplace = code_env.local_workplace
-            files_dir = os.path.join(local_workplace, "files")
-            os.makedirs(files_dir, exist_ok=True)
-            select_and_copy_files(files_dir, console)
-        else: 
-            console.print(f"[bold red]Unknown agent: {agent}[/bold red]")
-
-
-def tool_to_table(tool_dict: dict):
-    table = Table(title="Tool List", show_lines=True)
-    table.add_column("Tool Name")
-    table.add_column("Description")
-    for tool_name in tool_dict.keys():
-        if tool_name == "tool_dummy": 
-            continue
-        table.add_row(tool_name, tool_dict[tool_name]["docstring"])
-    return table
-def agent_to_table(agent_dict: dict):
-    table = Table(title="Agent List", show_lines=True)
-    table.add_column("Agent Name")
-    table.add_column("Description")
-    for agent_name in agent_dict.keys():
-        if agent_name == "get_dummy_agent": 
-            continue
-        table.add_row(agent_name, agent_dict[agent_name]["docstring"])
-    return table
-
-def update_guidance(context_variables): 
-    tool_dict = json.loads(list_tools(context_variables))
-    # print(tool_dict)
-    tool_table = tool_to_table(tool_dict)
-    agent_dict = json.loads(list_agents(context_variables))
-    agent_table = agent_to_table(agent_dict)
-    console = Console()
-    columns = Columns([tool_table, agent_table])
-
-    # print the logo
-    logo_text = Text(MC_LOGO, justify="center")
-    console.print(Panel(logo_text, style="bold salmon1", expand=True))
-    console.print(version_table)
-    console.print(Panel(NOTES,title="Important Notes", expand=True))
-
-def main(args):
-    print('\033[s\033[?25l', end='')  # Save cursor position and hide cursor
-    with Progress(
-        SpinnerColumn(),
-        TextColumn("[progress.description]{task.description}"),
-        transient=True  # 这会让进度条完成后消失
-    ) as progress:
-        task = progress.add_task("[cyan]Initializing...", total=None)
-        
-        progress.update(task, description="[cyan]Initializing config...[/cyan]\n")
-        docker_config = get_config(args)
-        
-        progress.update(task, description="[cyan]Setting up logger...[/cyan]\n")
-        log_path = osp.join("casestudy_results", 'logs', f'agent_{args.container_name}_{args.model}.log')
-        LoggerManager.set_logger(MetaChainLogger(log_path = None))
-        
-        progress.update(task, description="[cyan]Creating environment...[/cyan]\n")
-        code_env, web_env, file_env = create_environment(docker_config)
-        
-        progress.update(task, description="[cyan]Setting up metachain...[/cyan]\n")
-        setup_metachain(workplace_name=docker_config.workplace_name, env=code_env)
-    
-    clear_screen()
-    # console = Console()
-    # console.clear()
-    # print('\033[H\033[J')  # ANSI 转义序列清屏
-    # print('\033[3J\033[H\033[2J')
-    # clear_screen()
-    
-    context_variables = {"working_dir": docker_config.workplace_name, "code_env": code_env, "web_env": web_env, "file_env": file_env}
-    
-    # select the mode
-    while True:
-        update_guidance(context_variables)
-        mode = single_select_menu(['user mode', 'agent editor', 'workflow editor', 'exit'], "Please select the mode:")
-        match mode:
-            case 'user mode':
-                clear_screen()
-                user_mode(args.model, context_variables, args.debug)
-            case 'agent editor':
-                clear_screen()
-                meta_agent(args.model, context_variables, args.debug)
-            case 'workflow editor':
-                clear_screen()
-                meta_workflow(args.model, context_variables, args.debug)
-            case 'exit':
-                console = Console()
-                logo_text = Text(GOODBYE_LOGO, justify="center")
-                console.print(Panel(logo_text, style="bold salmon1", expand=True))
-                break
-
-if __name__ == "__main__":
-    args = get_args()
-    main(args)

playground/cli/metachain_cli.sh

@@ -1,15 +0,0 @@
-current_dir=$(dirname "$(readlink -f "$0")")
-
-cd $current_dir
-cd ../..
-export DOCKER_WORKPLACE_NAME=workplace
-export EVAL_MODE=True
-export BASE_IMAGES=tjbtech1/gaia-bookworm:v2
-export COMPLETION_MODEL=claude-3-5-haiku-20241022
-# export COMPLETION_MODEL=gpt-4o-2024-08-06
-# export API_BASE_URL = "http://localhost:8000/v1" # if vllm
-export DEBUG=False
-export MC_MODE=True
-export AI_USER=tjb-tech
-
-python playground/cli/metachain_cli.py --container_name quick_start --model ${COMPLETION_MODEL} --test_pull_name mirror_branch_0207 --debug --port 12345 --git_clone

setup.cfg

@@ -1,5 +1,5 @@
 [metadata]
-name = metachain
+name = autoagent
 version = 0.1.0
 author = jiabintang
 description = dynamic agentic framework.
@@ -10,8 +10,7 @@ license = MIT
 [options]
 package_dir =
     = .
-packages = 
-    metachain
+packages = find_namespace:
 zip_safe = True
 include_package_data = True
 install_requires =
@@ -64,12 +63,16 @@ install_requires =
     moviepy
     faster_whisper
     sentence_transformers
+
+[options.packages.find]
+where = .
+include = autoagent*
     
 python_requires = >=3.10
 
 [options.entry_points]
 console_scripts =
-    mc = metachain.cli:cli
+    auto = autoagent.cli:cli
 [tool.autopep8]
 max_line_length = 120
 ignore = E501,W6