args[__CTX_VARS_NAME__] = context_variables TypeError: 'str' object does not support item assignment

[IcyFeather233]

Env

• MacOS 15.6.1 (24G90)
• AutoAgent 0.2.0
• Python 3.12

Description

1. Run auto main
2. Choose agent editor mode
3. Input a question, like:

I want to create ‘Financial Agent‘ that can help me to do two kinds of tasks:
Manage the private financial docs. I have a folder that contain the financial
docs in my local machine, and I want to help me to manage them.
Search the financial information online. You may help me to:
- get balance sheets for a given ticker over a given period.
- get cash flow statements for a given ticker over a given period.
- get income statements for a given ticker over a given period.

4. For other options, always Enter

Logs

~/Projects/AutoAgent on main !1 ───────────────────────────────────────────── Py autoagent at 22:10:45
❯ auto main
╭─────────────────────────────────────────────────────────────────────────────────────────────────────╮
│             █████╗ ██╗   ██╗████████╗ ██████╗  █████╗  ██████╗ ███████╗███╗   ██╗████████╗          │
│            ██╔══██╗██║   ██║╚══██╔══╝██╔═══██╗██╔══██╗██╔════╝ ██╔════╝████╗  ██║╚══██╔══╝          │
│            ███████║██║   ██║   ██║   ██║   ██║███████║██║  ███╗█████╗  ██╔██╗ ██║   ██║             │
│            ██╔══██║██║   ██║   ██║   ██║   ██║██╔══██║██║   ██║██╔══╝  ██║╚██╗██║   ██║             │
│            ██║  ██║╚██████╔╝   ██║   ╚██████╔╝██║  ██║╚██████╔╝███████╗██║ ╚████║   ██║             │
│            ╚═╝  ╚═╝ ╚═════╝    ╚═╝    ╚═════╝ ╚═╝  ╚═╝ ╚═════╝ ╚══════╝╚═╝  ╚═══╝   ╚═╝             │
│                             ╔═══ 𝒞𝓇𝑒𝒶𝓉𝑒 𝒜𝑔𝑒𝓃𝓉𝒾𝒸 𝒜ℐ 𝓊𝓈𝒾𝓃𝑔 ℒ𝒶𝓃𝑔𝓊𝒶𝑔𝑒 ═══╗                              │
╰─────────────────────────────────────────────────────────────────────────────────────────────────────╯
╔════════════════════════════════╦════════════════════════════════════════════════════════════════════╗
║ Version                        ║ 0.2.0                                                              ║
║ Author                         ║ AutoAgent Team@HKU                                                 ║
║ License                        ║ MIT                                                                ║
╚════════════════════════════════╩════════════════════════════════════════════════════════════════════╝
╭────────────────────────────────────────── Important Notes ──────────────────────────────────────────╮
│                                                                                                     │
│  • Choose user mode if you just want to let a general yet powerful AI Assistant to help you         │
│  • Choose agent editor to create your own AI Agent with language.                                   │
│  • Choose workflow editor to create your own AI Workflow with language.                             │
│  • Choose exit to exit the program                                                                  │
╰─────────────────────────────────────────────────────────────────────────────────────────────────────╯
Tell me what do you want to create with `Agent Chain`? (type "exit" to quit, press "Enter" to continue)
: I want to create ‘Financial Agent‘ that can help me to do two kinds of tasks:
1. Manage the private financial docs. I have a folder that contain the financial
docs in my local machine, and I want to help me to manage them.
2. Search the financial information online. You may help me to:
- get balance sheets for a given ticker over a given period.
- get cash flow statements for a given ticker over a given period.
- get income statements for a given ticker over a given period.
Your request: I want to create ‘Financial Agent‘ that can help me to do two kinds of tasks:
1. Manage the private financial docs. I have a folder that contain the financial
docs in my local machine, and I want to help me to manage them.
2. Search the financial information online. You may help me to:
- get balance sheets for a given ticker over a given period.
- get cash flow statements for a given ticker over a given period.
- get income statements for a given ticker over a given period. 
@Agent Former Agent will help you, be patient...
22:12:23 - LiteLLM:INFO: utils.py:2777 - 
LiteLLM completion() model= claude-sonnet-4-5-20250929; provider = anthropic
2025-10-15 22:12:23,807 - INFO - 
LiteLLM completion() model= claude-sonnet-4-5-20250929; provider = anthropic
2025-10-15 22:12:41,677 - INFO - HTTP Request: POST https://api.bianxie.ai/v1/messages "HTTP/1.1 200 OK"
22:12:41 - LiteLLM:INFO: utils.py:902 - Wrapper: Completed Call, calling success_handler
2025-10-15 22:12:41,682 - INFO - Wrapper: Completed Call, calling success_handler
@Agent Former Agent has created agent form successfully with the following details:
<agents>
    <system_input>
        User requests related to financial tasks, including:
        1. Managing private financial documents stored in a local folder
        2. Searching and retrieving financial information online (balance sheets, cash flow statements,
income statements for specific tickers over given periods)
    </system_input>
    <system_output>
        <key>response</key>
        <description>The response containing either the managed financial document information or the 
retrieved financial statements data.</description>
    </system_output>
    <agent>
        <name>Financial Document Manager Agent</name>
        <description>An agent specialized in managing private financial documents stored locally, 
including organizing, indexing, and retrieving information from financial documents.</description>
        <instructions>You are a Financial Document Manager Agent. Your task is to help users manage 
their private financial documents stored in a local folder. You can organize, index, search, and 
retrieve information from these documents efficiently.</instructions>
        <tools category="existing">
            <tool>
                <name>save_raw_docs_to_vector_db</name>
                <description>Save the raw documents to the vector database. The documents could be: 
                - ANY text document with the extension of pdf, docx, txt, etcs.
                - A zip file containing multiple text documents
                - a directory containing multiple text documents
                All documents will be converted to raw text format and saved to the vector database in 
the chunks of 4096 tokens.</description>
            </tool>
            <tool>
                <name>query_db</name>
                <description>Query the vector database to find the answer to the user's 
question.</description>
            </tool>
            <tool>
                <name>modify_query</name>
                <description>Modify the user's question to a more specific question.</description>
            </tool>
            <tool>
                <name>answer_query</name>
                <description>Answer the user's question based on the answer from the vector 
database.</description>
            </tool>
            <tool>
                <name>can_answer</name>
                <description>Check if the user's question can be answered by the vector 
database.</description>
            </tool>
        </tools>
        <agent_input>
            <key>document_request</key>
            <description>User request for managing private financial documents, including operations 
like indexing, searching, organizing, or retrieving information from local financial document 
folders.</description>
        </agent_input>
        <agent_output>
            <key>response</key>
            <description>The response containing the managed financial document information or search 
results from the private document collection.</description>
        </agent_output>
    </agent>
    <agent>
        <name>Financial Statement Retrieval Agent</name>
        <description>An agent specialized in retrieving financial statements from online sources, 
including balance sheets, cash flow statements, and income statements for specific tickers over 
specified periods.</description>
        <instructions>You are a Financial Statement Retrieval Agent. Your task is to search and 
retrieve financial information online, specifically balance sheets, cash flow statements, and income 
statements for given tickers over specified time periods. Ensure the data is accurate and properly 
formatted.</instructions>
        <tools category="new">
            <tool>
                <name>get_balance_sheet</name>
                <description>Retrieve the balance sheet for a given stock ticker symbol over a 
specified time period.</description>
            </tool>
            <tool>
                <name>get_cash_flow_statement</name>
                <description>Retrieve the cash flow statement for a given stock ticker symbol over a 
specified time period.</description>
            </tool>
            <tool>
                <name>get_income_statement</name>
                <description>Retrieve the income statement for a given stock ticker symbol over a 
specified time period.</description>
            </tool>
        </tools>
        <agent_input>
            <key>financial_query</key>
            <description>User request for retrieving online financial information, including the ticker
symbol, type of financial statement (balance sheet, cash flow, or income statement), and the time 
period.</description>
        </agent_input>
        <agent_output>
            <key>response</key>
            <description>The response containing the retrieved financial statements data for the 
requested ticker and period.</description>
        </agent_output>
    </agent>
</agents>
It is time to create the desired tools, do you have any suggestions for creating the tools? (type "exit
" to quit, press "Enter" to continue):
Your request:  
@Tool Editor Agent will help you, be patient...
22:13:16 - LiteLLM:INFO: utils.py:2777 - 
LiteLLM completion() model= claude-sonnet-4-5-20250929; provider = anthropic
2025-10-15 22:13:16,294 - INFO - 
LiteLLM completion() model= claude-sonnet-4-5-20250929; provider = anthropic
2025-10-15 22:13:19,830 - INFO - HTTP Request: POST https://api.bianxie.ai/v1/messages "HTTP/1.1 200 OK"
22:13:19 - LiteLLM:INFO: utils.py:902 - Wrapper: Completed Call, calling success_handler
2025-10-15 22:13:19,837 - INFO - Wrapper: Completed Call, calling success_handler
Traceback (most recent call last):
  File "/Users/icyfeather/miniconda3/envs/autoagent/bin/auto", line 7, in <module>
    sys.exit(cli())
             ^^^^^
  File "/Users/icyfeather/miniconda3/envs/autoagent/lib/python3.12/site-packages/click/core.py", line 1462, in __call__
    return self.main(*args, **kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/icyfeather/miniconda3/envs/autoagent/lib/python3.12/site-packages/click/core.py", line 1383, in main
    rv = self.invoke(ctx)
         ^^^^^^^^^^^^^^^^
  File "/Users/icyfeather/miniconda3/envs/autoagent/lib/python3.12/site-packages/click/core.py", line 1850, in invoke
    return _process_result(sub_ctx.command.invoke(sub_ctx))
                           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/icyfeather/miniconda3/envs/autoagent/lib/python3.12/site-packages/click/core.py", line 1246, in invoke
    return ctx.invoke(self.callback, **ctx.params)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/icyfeather/miniconda3/envs/autoagent/lib/python3.12/site-packages/click/core.py", line 814, in invoke
    return callback(*args, **kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/icyfeather/Projects/AutoAgent/autoagent/cli.py", line 227, in main
    meta_agent(model, context_variables, False)
  File "/Users/icyfeather/Projects/AutoAgent/autoagent/cli_utils/metachain_meta_agent.py", line 227, in meta_agent
    tool_response, messages = tool_editing(tool_editor_agent, client, messages, context_variables, agent_form, output_xml_form, debug, suggestions)
                              ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/icyfeather/Projects/AutoAgent/autoagent/cli_utils/metachain_meta_agent.py", line 93, in tool_editing
    response = client.run(tool_editor_agent, messages, context_variables, debug=debug)
               ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/icyfeather/Projects/AutoAgent/autoagent/core.py", line 482, in run
    partial_response = self.handle_tool_calls(
                       ^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/icyfeather/Projects/AutoAgent/autoagent/core.py", line 268, in handle_tool_calls
    args[__CTX_VARS_NAME__] = context_variables
    ~~~~^^^^^^^^^^^^^^^^^^^
TypeError: 'str' object does not support item assignment

[IcyFeather233]

another error log in workflow editor mode,

query(same as this in the paper)

I want to create a workflow that can help me to solving the math problem.
The workflow should:
1. Parallelize solving the math problem with the same ‘Math Solver Agent‘ using
different language models (‘gpt-4o‘, ‘claude-3-5-sonnet‘, ‘deepseek-chat‘)
2. Aggregate the results from the ‘Math Solver Agent‘ and return the final result
using majority voting.

~/Projects/AutoAgent on main !1 ───────────────────────────────────────────── Py autoagent at 22:37:39
❯ auto main
╭─────────────────────────────────────────────────────────────────────────────────────────────────────╮
│             █████╗ ██╗   ██╗████████╗ ██████╗  █████╗  ██████╗ ███████╗███╗   ██╗████████╗          │
│            ██╔══██╗██║   ██║╚══██╔══╝██╔═══██╗██╔══██╗██╔════╝ ██╔════╝████╗  ██║╚══██╔══╝          │
│            ███████║██║   ██║   ██║   ██║   ██║███████║██║  ███╗█████╗  ██╔██╗ ██║   ██║             │
│            ██╔══██║██║   ██║   ██║   ██║   ██║██╔══██║██║   ██║██╔══╝  ██║╚██╗██║   ██║             │
│            ██║  ██║╚██████╔╝   ██║   ╚██████╔╝██║  ██║╚██████╔╝███████╗██║ ╚████║   ██║             │
│            ╚═╝  ╚═╝ ╚═════╝    ╚═╝    ╚═════╝ ╚═╝  ╚═╝ ╚═════╝ ╚══════╝╚═╝  ╚═══╝   ╚═╝             │
│                             ╔═══ 𝒞𝓇𝑒𝒶𝓉𝑒 𝒜𝑔𝑒𝓃𝓉𝒾𝒸 𝒜ℐ 𝓊𝓈𝒾𝓃𝑔 ℒ𝒶𝓃𝑔𝓊𝒶𝑔𝑒 ═══╗                              │
╰─────────────────────────────────────────────────────────────────────────────────────────────────────╯
╔════════════════════════════════╦════════════════════════════════════════════════════════════════════╗
║ Version                        ║ 0.2.0                                                              ║
║ Author                         ║ AutoAgent Team@HKU                                                 ║
║ License                        ║ MIT                                                                ║
╚════════════════════════════════╩════════════════════════════════════════════════════════════════════╝
╭────────────────────────────────────────── Important Notes ──────────────────────────────────────────╮
│                                                                                                     │
│  • Choose user mode if you just want to let a general yet powerful AI Assistant to help you         │
│  • Choose agent editor to create your own AI Agent with language.                                   │
│  • Choose workflow editor to create your own AI Workflow with language.                             │
│  • Choose exit to exit the program                                                                  │
╰─────────────────────────────────────────────────────────────────────────────────────────────────────╯
Tell me what do you want to create with `Workflow Chain`? (type "exit" to quit, press "Enter" to contin
ue): I want to create a workflow that can help me to solving the math problem.
The workflow should:
1. Parallelize solving the math problem with the same ‘Math Solver Agent‘ using
different language models (‘gpt-4o‘, ‘claude-3-5-sonnet‘, ‘deepseek-chat‘)
2. Aggregate the results from the ‘Math Solver Agent‘ and return the final result
using majority voting.
Your request: I want to create a workflow that can help me to solving the math problem.
The workflow should:
1. Parallelize solving the math problem with the same ‘Math Solver Agent‘ using
different language models (‘gpt-4o‘, ‘claude-3-5-sonnet‘, ‘deepseek-chat‘)
2. Aggregate the results from the ‘Math Solver Agent‘ and return the final result
using majority voting. 
@Workflow Former Agent will help you, be patient...
22:38:25 - LiteLLM:INFO: utils.py:2777 - 
LiteLLM completion() model= claude-sonnet-4-5-20250929; provider = anthropic
2025-10-15 22:38:25,909 - INFO - 
LiteLLM completion() model= claude-sonnet-4-5-20250929; provider = anthropic
2025-10-15 22:38:42,506 - INFO - HTTP Request: POST https://api.bianxie.ai/v1/messages "HTTP/1.1 200 OK"
22:38:42 - LiteLLM:INFO: utils.py:902 - Wrapper: Completed Call, calling success_handler
2025-10-15 22:38:42,514 - INFO - Wrapper: Completed Call, calling success_handler
@Workflow Former Agent has created workflow form successfully with the following details:
```xml
<workflow>
    <name>parallel_math_solver_workflow</name>
    <system_input>
        <key>math_problem</key>
        <description>The mathematical problem that needs to be solved, including all conditions, 
constraints, and the objective.</description>
    </system_input>
    <system_output>
        <key>final_answer</key>
        <description>The final answer determined through majority voting from multiple solver 
attempts.</description>
    </system_output>
    <agents>
        <agent category="existing">
            <name>Math Solver Agent</name>
            <description>This agent solves mathematical problems using analytical and systematic 
approaches.</description>
        </agent>
        <agent category="existing">
            <name>Vote Aggregator Agent</name>
            <description>This agent aggregates solutions from different solvers and determines the 
final answer through majority voting.</description>
        </agent>
    </agents>

    <events>
        <event>
            <name>on_start</name>
            <inputs>
                <input>
                    <key>math_problem</key>
                    <description>The mathematical problem that needs to be solved.</description>
                </input>
            </inputs>
            <outputs>
                <output>
                    <key>math_problem</key>
                    <description>The mathematical problem that needs to be solved.</description>
                    <action>
                        <type>RESULT</type>
                    </action>
                </output>
            </outputs>
        </event>
        <event>
            <name>solve_with_gpt4o</name>
            <inputs>
                <input>
                    <key>math_problem</key>
                    <description>The mathematical problem to solve.</description>
                </input>
            </inputs>
            <task>
                Solve the mathematical problem using systematic analytical approach and provide a 
detailed solution.
            </task>
            <outputs>
                <output>
                    <key>gpt4o_solution</key>
                    <description>The solution to the math problem from GPT-4o model.</description>
                    <action>
                        <type>RESULT</type>
                    </action>
                </output>
            </outputs>
            <listen>
                <event>on_start</event>
            </listen>
            <agent>
                <name>Math Solver Agent</name>
                <model>gpt-4o</model>
            </agent>
        </event>
        <event>
            <name>solve_with_claude</name>
            <inputs>
                <input>
                    <key>math_problem</key>
                    <description>The mathematical problem to solve.</description>
                </input>
            </inputs>
            <task>
                Solve the mathematical problem using systematic analytical approach and provide a 
detailed solution.
            </task>
            <outputs>
                <output>
                    <key>claude_solution</key>
                    <description>The solution to the math problem from Claude model.</description>
                    <action>
                        <type>RESULT</type>
                    </action>
                </output>
            </outputs>
            <listen>
                <event>on_start</event>
            </listen>
            <agent>
                <name>Math Solver Agent</name>
                <model>claude-3-5-sonnet-20241022</model>
            </agent>
        </event>
        <event>
            <name>solve_with_deepseek</name>
            <inputs>
                <input>
                    <key>math_problem</key>
                    <description>The mathematical problem to solve.</description>
                </input>
            </inputs>
            <task>
                Solve the mathematical problem using systematic analytical approach and provide a 
detailed solution.
            </task>
            <outputs>
                <output>
                    <key>deepseek_solution</key>
                    <description>The solution to the math problem from DeepSeek model.</description>
                    <action>
                        <type>RESULT</type>
                    </action>
                </output>
            </outputs>
            <listen>
                <event>on_start</event>
            </listen>
            <agent>
                <name>Math Solver Agent</name>
                <model>deepseek-chat</model>
            </agent>
        </event>
        <event>
            <name>aggregate_solutions</name>
            <inputs>
                <input>
                    <key>gpt4o_solution</key>
                    <description>The solution from GPT-4o model.</description>
                </input>
                <input>
                    <key>claude_solution</key>
                    <description>The solution from Claude model.</description>
                </input>
                <input>
                    <key>deepseek_solution</key>
                    <description>The solution from DeepSeek model.</description>
                </input>
            </inputs>
            <task>
                Aggregate all solutions from different models, compare their reasoning and answers, and
determine the final answer through majority voting.
            </task>
            <outputs>
                <output>
                    <key>final_answer</key>
                    <description>The final answer determined through majority voting and analysis of 
all solutions.</description>
                    <action>
                        <type>RESULT</type>
                    </action>
                </output>
            </outputs>
            <listen>
                <event>solve_with_gpt4o</event>
                <event>solve_with_claude</event>
                <event>solve_with_deepseek</event>
            </listen>
            <agent>
                <name>Vote Aggregator Agent</name>
                <model>claude-3-5-sonnet-20241022</model>
            </agent>
        </event>
    </events>
</workflow>

It is time to create the desired workflow, do you have any suggestions for creating the workflow? (type
"exit" to quit, press "Enter" to continue):
Your request:
@workflow Creator Agent will help you, be patient...
It is time to create the desired workflow, what task do you want to complete with the workflow? (Press
Enter if none):
22:39:05 - LiteLLM:INFO: utils.py:2777 -
LiteLLM completion() model= claude-sonnet-4-5-20250929; provider = anthropic
2025-10-15 22:39:05,603 - INFO -
LiteLLM completion() model= claude-sonnet-4-5-20250929; provider = anthropic
2025-10-15 22:39:09,729 - INFO - HTTP Request: POST https://api.bianxie.ai/v1/messages "HTTP/1.1 200 OK"
22:39:09 - LiteLLM:INFO: utils.py:902 - Wrapper: Completed Call, calling success_handler
2025-10-15 22:39:09,735 - INFO - Wrapper: Completed Call, calling success_handler
Traceback (most recent call last):
File "/Users/icyfeather/miniconda3/envs/autoagent/bin/auto", line 7, in
sys.exit(cli())
^^^^^
File "/Users/icyfeather/miniconda3/envs/autoagent/lib/python3.12/site-packages/click/core.py", line 1462, in call
return self.main(*args, **kwargs)
^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/Users/icyfeather/miniconda3/envs/autoagent/lib/python3.12/site-packages/click/core.py", line 1383, in main
rv = self.invoke(ctx)
^^^^^^^^^^^^^^^^
File "/Users/icyfeather/miniconda3/envs/autoagent/lib/python3.12/site-packages/click/core.py", line 1850, in invoke
return _process_result(sub_ctx.command.invoke(sub_ctx))
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/Users/icyfeather/miniconda3/envs/autoagent/lib/python3.12/site-packages/click/core.py", line 1246, in invoke
return ctx.invoke(self.callback, **ctx.params)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/Users/icyfeather/miniconda3/envs/autoagent/lib/python3.12/site-packages/click/core.py", line 814, in invoke
return callback(*args, **kwargs)
^^^^^^^^^^^^^^^^^^^^^^^^^
File "/Users/icyfeather/Projects/AutoAgent/autoagent/cli.py", line 230, in main
meta_workflow(model, context_variables, False)
File "/Users/icyfeather/Projects/AutoAgent/autoagent/cli_utils/metachain_meta_workflow.py", line 185, in meta_workflow
agent_response, messages = workflow_editing(workflow_creator_agent, client, messages, context_variables, workflow_form, output_xml_form, requirements, task, debug, suggestions)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/Users/icyfeather/Projects/AutoAgent/autoagent/cli_utils/metachain_meta_workflow.py", line 83, in workflow_editing
response = client.run(workflow_creator_agent, messages, context_variables, debug=debug)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/Users/icyfeather/Projects/AutoAgent/autoagent/core.py", line 482, in run
partial_response = self.handle_tool_calls(
^^^^^^^^^^^^^^^^^^^^^^^
File "/Users/icyfeather/Projects/AutoAgent/autoagent/core.py", line 268, in handle_tool_calls
args[CTX_VARS_NAME] = context_variables
~~~~^^^^^^^^^^^^^^^^^^^
TypeError: 'str' object does not support item assignment