[Bug]: Trae-cli always hang and never terminate

[ChangranXU]

What happened?

Command use: source .venv/bin/activate && trae-cli run "Create a hello world Python script" --config-file trae_config.json --working-dir /trae-agent/workspace

1. When reach the max_steps, it never check its status and terminate itself.

{
  "task": "Create a hello world Python script",
  "start_time": "2025-07-29T11:49:35.698988",
  "end_time": "",
  "provider": "doubao",
  "model": "deepseek-v3-241226",
  "max_steps": 2,
  "llm_interactions": [
    {
      "timestamp": "2025-07-29T11:49:40.853339",
      "provider": "doubao",
      "model": "deepseek-v3-241226",
      "input_messages": [
        {
          "role": "system",
          "content": "You are an expert AI software engineering agent.\n\nFile Path Rule: All tools that take a `file_path` as an argument require an **absolute path**. You MUST construct the full, absolute path by combining the `[Project root path]` provided in the user's message with the file's path inside the project.\n\nFor example, if the project root is `/home/user/my_project` and you need to edit `src/main.py`, the correct `file_path` argument is `/home/user/my_project/src/main.py`. Do NOT use relative paths like `src/main.py`.\n\nYour primary goal is to resolve a given GitHub issue by navigating the provided codebase, identifying the root cause of the bug, implementing a robust fix, and ensuring your changes are safe and well-tested.\n\nFollow these steps methodically:\n\n1.  Understand the Problem:\n    - Begin by carefully reading the user's problem description to fully grasp the issue.\n    - Identify the core components and expected behavior.\n\n2.  Explore and Locate:\n    - Use the available tools to explore the codebase.\n    - Locate the most relevant files (source code, tests, examples) related to the bug report.\n\n3.  Reproduce the Bug (Crucial Step):\n    - Before making any changes, you **must** create a script or a test case that reliably reproduces the bug. This will be your baseline for verification.\n    - Analyze the output of your reproduction script to confirm your understanding of the bug's manifestation.\n\n4.  Debug and Diagnose:\n    - Inspect the relevant code sections you identified.\n    - If necessary, create debugging scripts with print statements or use other methods to trace the execution flow and pinpoint the exact root cause of the bug.\n\n5.  Develop and Implement a Fix:\n    - Once you have identified the root cause, develop a precise and targeted code modification to fix it.\n    - Use the provided file editing tools to apply your patch. Aim for minimal, clean changes.\n\n6.  Verify and Test Rigorously:\n    - Verify the Fix: Run your initial reproduction script to confirm that the bug is resolved.\n    - Prevent Regressions: Execute the existing test suite for the modified files and related components to ensure your fix has not introduced any new bugs.\n    - Write New Tests: Create new, specific test cases (e.g., using `pytest`) that cover the original bug scenario. This is essential to prevent the bug from recurring in the future. Add these tests to the codebase.\n    - Consider Edge Cases: Think about and test potential edge cases related to your changes.\n\n7.  Summarize Your Work:\n    - Conclude your trajectory with a clear and concise summary. Explain the nature of the bug, the logic of your fix, and the steps you took to verify its correctness and safety.\n\n**Guiding Principle:** Act like a senior software engineer. Prioritize correctness, safety, and high-quality, test-driven development.\n\n# GUIDE FOR HOW TO USE \"sequential_thinking\" TOOL:\n- Your thinking should be thorough and so it's fine if it's very long. Set total_thoughts to at least 5, but setting it up to 25 is fine as well. You'll need more total thoughts when you are considering multiple possible solutions or root causes for an issue.\n- Use this tool as much as you find necessary to improve the quality of your answers.\n- You can run bash commands (like tests, a reproduction script, or 'grep'/'find' to find relevant context) in between thoughts.\n- The sequential_thinking tool can help you break down complex problems, analyze issues step-by-step, and ensure a thorough approach to problem-solving.\n- Don't hesitate to use it multiple times throughout your thought process to enhance the depth and accuracy of your solutions.\n\nIf you are sure the issue has been solved, you should call the `task_done` to finish the task.\n"
        },
        {
          "role": "user",
          "content": "[Project root path]:\n/trae-agent/workspace\n\n[Problem statement]: We're currently solving the following issue within our repository. Here's the issue text:\nCreate a hello world Python script\n"
        }
      ],
      "response": {
        "content": "Let's begin by understanding the problem and then proceed to create a simple \"Hello, World!\" Python script.\n\n### Step 1: Understand the Problem\nThe task is to create a basic Python script that prints \"Hello, World!\" to the console. This is a common introductory task to ensure that the Python environment is set up correctly and that basic scripts can be executed.\n\n### Step 2: Explore and Locate\nSince this is a new script, we don't need to explore existing files. We will create a new Python script in the project directory.\n\n### Step 3: Create the Script\nWe will create a new Python script named `hello_world.py` in the project root directory.\n\n### Step 4: Develop and Implement the Fix\nThe script will contain a single line of code to print \"Hello, World!\".\n\n### Step 5: Verify and Test Rigorously\nWe will run the script to ensure it prints the expected output.\n\nLet's start by creating the script.",
        "model": "deepseek-v3-241226",
        "finish_reason": "tool_calls",
        "usage": {
          "input_tokens": 3344,
          "output_tokens": 266,
          "cache_creation_input_tokens": 0,
          "cache_read_input_tokens": 0,
          "reasoning_tokens": 0
        },
        "tool_calls": [
          {
            "call_id": "call_bxxlm1ufibehb384r8mbgzl3",
            "name": "str_replace_based_edit_tool",
            "arguments": {
              "command": "create",
              "path": "/trae-agent/workspace/hello_world.py",
              "file_text": "print(\"Hello, World!\")"
            },
            "id": null
          }
        ]
      },
      "tools_available": [
        "str_replace_based_edit_tool",
        "sequentialthinking",
        "json_edit_tool",
        "task_done",
        "bash"
      ]
    },
    {
      "timestamp": "2025-07-29T11:49:42.761407",
      "provider": "doubao",
      "model": "deepseek-v3-241226",
      "input_messages": [
        {
          "role": "user",
          "content": null,
          "tool_result": {
            "call_id": "call_bxxlm1ufibehb384r8mbgzl3",
            "success": true,
            "result": "File created successfully at:/trae-agent/workspace/hello_world.py",
            "error": null,
            "id": null
          }
        }
      ],
      "response": {
        "content": "",
        "model": "deepseek-v3-241226",
        "finish_reason": "tool_calls",
        "usage": {
          "input_tokens": 3453,
          "output_tokens": 43,
          "cache_creation_input_tokens": 0,
          "cache_read_input_tokens": 0,
          "reasoning_tokens": 0
        },
        "tool_calls": [
          {
            "call_id": "call_3qlk7mmox91ztt3xfnsasslx",
            "name": "bash",
            "arguments": {
              "command": "python3/trae-agent/workspace/hello_world.py"
            },
            "id": null
          }
        ]
      },
      "tools_available": [
        "str_replace_based_edit_tool",
        "sequentialthinking",
        "json_edit_tool",
        "task_done",
        "bash"
      ]
    }
  ],
  "agent_steps": [
    {
      "step_number": 1,
      "timestamp": "2025-07-29T11:49:40.855062",
      "state": "calling_tool",
      "llm_messages": [
        {
          "role": "user",
          "content": null,
          "tool_result": {
            "call_id": "call_bxxlm1ufibehb384r8mbgzl3",
            "success": true,
            "result": "File created successfully at:/trae-agent/workspace/hello_world.py",
            "error": null,
            "id": null
          }
        }
      ],
      "llm_response": {
        "content": "Let's begin by understanding the problem and then proceed to create a simple \"Hello, World!\" Python script.\n\n### Step 1: Understand the Problem\nThe task is to create a basic Python script that prints \"Hello, World!\" to the console. This is a common introductory task to ensure that the Python environment is set up correctly and that basic scripts can be executed.\n\n### Step 2: Explore and Locate\nSince this is a new script, we don't need to explore existing files. We will create a new Python script in the project directory.\n\n### Step 3: Create the Script\nWe will create a new Python script named `hello_world.py` in the project root directory.\n\n### Step 4: Develop and Implement the Fix\nThe script will contain a single line of code to print \"Hello, World!\".\n\n### Step 5: Verify and Test Rigorously\nWe will run the script to ensure it prints the expected output.\n\nLet's start by creating the script.",
        "model": "deepseek-v3-241226",
        "finish_reason": "tool_calls",
        "usage": {
          "input_tokens": 3344,
          "output_tokens": 266
        },
        "tool_calls": [
          {
            "call_id": "call_bxxlm1ufibehb384r8mbgzl3",
            "name": "str_replace_based_edit_tool",
            "arguments": {
              "command": "create",
              "path": "/trae-agent/workspace/hello_world.py",
              "file_text": "print(\"Hello, World!\")"
            },
            "id": null
          }
        ]
      },
      "tool_calls": [
        {
          "call_id": "call_bxxlm1ufibehb384r8mbgzl3",
          "name": "str_replace_based_edit_tool",
          "arguments": {
            "command": "create",
            "path": "/trae-agent/workspace/hello_world.py",
            "file_text": "print(\"Hello, World!\")"
          },
          "id": null
        }
      ],
      "tool_results": [
        {
          "call_id": "call_bxxlm1ufibehb384r8mbgzl3",
          "success": true,
          "result": "File created successfully at:/trae-agent/workspace/hello_world.py",
          "error": null,
          "id": null
        }
      ],
      "reflection": null,
      "error": null
    },
    {
      "step_number": 2,
      "timestamp": "2025-07-29T11:49:42.968287",
      "state": "calling_tool",
      "llm_messages": [
        {
          "role": "user",
          "content": null,
          "tool_result": {
            "call_id": "call_3qlk7mmox91ztt3xfnsasslx",
            "success": true,
            "result": "Hello, World!",
            "error": "",
            "id": null
          }
        }
      ],
      "llm_response": {
        "content": "",
        "model": "deepseek-v3-241226",
        "finish_reason": "tool_calls",
        "usage": {
          "input_tokens": 3453,
          "output_tokens": 43
        },
        "tool_calls": [
          {
            "call_id": "call_3qlk7mmox91ztt3xfnsasslx",
            "name": "bash",
            "arguments": {
              "command": "python3/trae-agent/workspace/hello_world.py"
            },
            "id": null
          }
        ]
      },
      "tool_calls": [
        {
          "call_id": "call_3qlk7mmox91ztt3xfnsasslx",
          "name": "bash",
          "arguments": {
            "command": "python3/trae-agent/workspace/hello_world.py"
          },
          "id": null
        }
      ],
      "tool_results": [
        {
          "call_id": "call_3qlk7mmox91ztt3xfnsasslx",
          "success": true,
          "result": "Hello, World!",
          "error": "",
          "id": null
        }
      ],
      "reflection": null,
      "error": null
    }
  ],
  "success": false,
  "final_result": null,
  "execution_time": 0.0
}

2. When it complete the task, it never end.

{
  "task": "Create a hello world Python script",
  "start_time": "2025-07-29T11:50:44.548306",
  "end_time": "",
  "provider": "doubao",
  "model": "deepseek-v3-241226",
  "max_steps": 20,
  "llm_interactions": [
    {
      "timestamp": "2025-07-29T11:50:50.233310",
      "provider": "doubao",
      "model": "deepseek-v3-241226",
      "input_messages": [
        {
          "role": "system",
          "content": "You are an expert AI software engineering agent.\n\nFile Path Rule: All tools that take a `file_path` as an argument require an **absolute path**. You MUST construct the full, absolute path by combining the `[Project root path]` provided in the user's message with the file's path inside the project.\n\nFor example, if the project root is `/home/user/my_project` and you need to edit `src/main.py`, the correct `file_path` argument is `/home/user/my_project/src/main.py`. Do NOT use relative paths like `src/main.py`.\n\nYour primary goal is to resolve a given GitHub issue by navigating the provided codebase, identifying the root cause of the bug, implementing a robust fix, and ensuring your changes are safe and well-tested.\n\nFollow these steps methodically:\n\n1.  Understand the Problem:\n    - Begin by carefully reading the user's problem description to fully grasp the issue.\n    - Identify the core components and expected behavior.\n\n2.  Explore and Locate:\n    - Use the available tools to explore the codebase.\n    - Locate the most relevant files (source code, tests, examples) related to the bug report.\n\n3.  Reproduce the Bug (Crucial Step):\n    - Before making any changes, you **must** create a script or a test case that reliably reproduces the bug. This will be your baseline for verification.\n    - Analyze the output of your reproduction script to confirm your understanding of the bug's manifestation.\n\n4.  Debug and Diagnose:\n    - Inspect the relevant code sections you identified.\n    - If necessary, create debugging scripts with print statements or use other methods to trace the execution flow and pinpoint the exact root cause of the bug.\n\n5.  Develop and Implement a Fix:\n    - Once you have identified the root cause, develop a precise and targeted code modification to fix it.\n    - Use the provided file editing tools to apply your patch. Aim for minimal, clean changes.\n\n6.  Verify and Test Rigorously:\n    - Verify the Fix: Run your initial reproduction script to confirm that the bug is resolved.\n    - Prevent Regressions: Execute the existing test suite for the modified files and related components to ensure your fix has not introduced any new bugs.\n    - Write New Tests: Create new, specific test cases (e.g., using `pytest`) that cover the original bug scenario. This is essential to prevent the bug from recurring in the future. Add these tests to the codebase.\n    - Consider Edge Cases: Think about and test potential edge cases related to your changes.\n\n7.  Summarize Your Work:\n    - Conclude your trajectory with a clear and concise summary. Explain the nature of the bug, the logic of your fix, and the steps you took to verify its correctness and safety.\n\n**Guiding Principle:** Act like a senior software engineer. Prioritize correctness, safety, and high-quality, test-driven development.\n\n# GUIDE FOR HOW TO USE \"sequential_thinking\" TOOL:\n- Your thinking should be thorough and so it's fine if it's very long. Set total_thoughts to at least 5, but setting it up to 25 is fine as well. You'll need more total thoughts when you are considering multiple possible solutions or root causes for an issue.\n- Use this tool as much as you find necessary to improve the quality of your answers.\n- You can run bash commands (like tests, a reproduction script, or 'grep'/'find' to find relevant context) in between thoughts.\n- The sequential_thinking tool can help you break down complex problems, analyze issues step-by-step, and ensure a thorough approach to problem-solving.\n- Don't hesitate to use it multiple times throughout your thought process to enhance the depth and accuracy of your solutions.\n\nIf you are sure the issue has been solved, you should call the `task_done` to finish the task.\n"
        },
        {
          "role": "user",
          "content": "[Project root path]:\n/trae-agent/workspace\n\n[Problem statement]: We're currently solving the following issue within our repository. Here's the issue text:\nCreate a hello world Python script\n"
        }
      ],
      "response": {
        "content": "To create a simple \"Hello, World!\" Python script, we can follow these steps:\n\n1. **Understand the Problem**: We need to create a Python script that prints \"Hello, World!\" to the console.\n2. **Explore and Locate**: Since this is a new script, we don't need to explore the existing codebase. We will create a new file.\n3. **Develop the Script**: Write the Python code to print \"Hello, World!\".\n4. **Verify the Script**: Run the script to ensure it works as expected.\n\nLet's proceed with creating the script.\n\n### Step 1: Create the Python Script\n\nWe will create a new file named `hello_world.py` in the project root directory.\n\n### Step 2: Write the Code\n\nThe content of the `hello_world.py` script will be:\n```python\nprint(\"Hello, World!\")\n```\n\n### Step 3: Verify the Script\n\nWe will run the script to ensure it prints \"Hello, World!\" correctly.\n\nLet's execute these steps.",
        "model": "deepseek-v3-241226",
        "finish_reason": "tool_calls",
        "usage": {
          "input_tokens": 3343,
          "output_tokens": 276,
          "cache_creation_input_tokens": 0,
          "cache_read_input_tokens": 0,
          "reasoning_tokens": 0
        },
        "tool_calls": [
          {
            "call_id": "call_4rtog37rbylk2n4wl3vfqlos",
            "name": "str_replace_based_edit_tool",
            "arguments": {
              "command": "create",
              "file_text": "print(\"Hello, World!\")",
              "path": "/trae-agent/workspace/hello_world.py"
            },
            "id": null
          }
        ]
      },
      "tools_available": [
        "str_replace_based_edit_tool",
        "sequentialthinking",
        "json_edit_tool",
        "task_done",
        "bash"
      ]
    },
    {
      "timestamp": "2025-07-29T11:50:51.622982",
      "provider": "doubao",
      "model": "deepseek-v3-241226",
      "input_messages": [
        {
          "role": "user",
          "content": null,
          "tool_result": {
            "call_id": "call_4rtog37rbylk2n4wl3vfqlos",
            "success": false,
            "result": null,
            "error": "File already exists at:/trae-agent/workspace/hello_world.py. Cannot overwrite files using command `create`.",
            "id": null
          }
        }
      ],
      "response": {
        "content": "",
        "model": "deepseek-v3-241226",
        "finish_reason": "tool_calls",
        "usage": {
          "input_tokens": 3470,
          "output_tokens": 53,
          "cache_creation_input_tokens": 0,
          "cache_read_input_tokens": 0,
          "reasoning_tokens": 0
        },
        "tool_calls": [
          {
            "call_id": "call_4ktwjh11qpkfayyqcqc8470s",
            "name": "str_replace_based_edit_tool",
            "arguments": {
              "command": "view",
              "path": "/trae-agent/workspace/hello_world.py"
            },
            "id": null
          }
        ]
      },
      "tools_available": [
        "str_replace_based_edit_tool",
        "sequentialthinking",
        "json_edit_tool",
        "task_done",
        "bash"
      ]
    },
    {
      "timestamp": "2025-07-29T11:50:53.819275",
      "provider": "doubao",
      "model": "deepseek-v3-241226",
      "input_messages": [
        {
          "role": "user",
          "content": null,
          "tool_result": {
            "call_id": "call_4ktwjh11qpkfayyqcqc8470s",
            "success": true,
            "result": "Here's the result of running `cat -n` on/trae-agent/workspace/hello_world.py:\n     1\tprint(\"Hello, World!\")\n",
            "error": null,
            "id": null
          }
        }
      ],
      "response": {
        "content": "",
        "model": "deepseek-v3-241226",
        "finish_reason": "tool_calls",
        "usage": {
          "input_tokens": 3581,
          "output_tokens": 94,
          "cache_creation_input_tokens": 0,
          "cache_read_input_tokens": 0,
          "reasoning_tokens": 0
        },
        "tool_calls": [
          {
            "call_id": "call_zpg2r2hc7gxdyt1mmbou3d0p",
            "name": "str_replace_based_edit_tool",
            "arguments": {
              "command": "str_replace",
              "old_str": "print(\"Hello, World!\")",
              "new_str": "print(\"Hello, World!\")\nprint(\"Welcome to the Python world!\")",
              "path": "/trae-agent/workspace/hello_world.py"
            },
            "id": null
          }
        ]
      },
      "tools_available": [
        "str_replace_based_edit_tool",
        "sequentialthinking",
        "json_edit_tool",
        "task_done",
        "bash"
      ]
    },
    {
      "timestamp": "2025-07-29T11:50:54.626555",
      "provider": "doubao",
      "model": "deepseek-v3-241226",
      "input_messages": [
        {
          "role": "user",
          "content": null,
          "tool_result": {
            "call_id": "call_zpg2r2hc7gxdyt1mmbou3d0p",
            "success": true,
            "result": "The file/trae-agent/workspace/hello_world.py has been edited. Here's the result of running `cat -n` on a snippet of/trae-agent/workspace/hello_world.py:\n     1\tprint(\"Hello, World!\")\n     2\tprint(\"Welcome to the Python world!\")\nReview the changes and make sure they are as expected. Edit the file again if necessary.",
            "error": null,
            "id": null
          }
        }
      ],
      "response": {
        "content": "",
        "model": "deepseek-v3-241226",
        "finish_reason": "tool_calls",
        "usage": {
          "input_tokens": 3793,
          "output_tokens": 15,
          "cache_creation_input_tokens": 0,
          "cache_read_input_tokens": 0,
          "reasoning_tokens": 0
        },
        "tool_calls": [
          {
            "call_id": "call_s1y7pvnu3417udssqyctlync",
            "name": "task_done",
            "arguments": {},
            "id": null
          }
        ]
      },
      "tools_available": [
        "str_replace_based_edit_tool",
        "sequentialthinking",
        "json_edit_tool",
        "task_done",
        "bash"
      ]
    }
  ],
  "agent_steps": [
    {
      "step_number": 1,
      "timestamp": "2025-07-29T11:50:50.234010",
      "state": "calling_tool",
      "llm_messages": [
        {
          "role": "user",
          "content": null,
          "tool_result": {
            "call_id": "call_4rtog37rbylk2n4wl3vfqlos",
            "success": false,
            "result": null,
            "error": "File already exists at:/trae-agent/workspace/hello_world.py. Cannot overwrite files using command `create`.",
            "id": null
          }
        }
      ],
      "llm_response": {
        "content": "To create a simple \"Hello, World!\" Python script, we can follow these steps:\n\n1. **Understand the Problem**: We need to create a Python script that prints \"Hello, World!\" to the console.\n2. **Explore and Locate**: Since this is a new script, we don't need to explore the existing codebase. We will create a new file.\n3. **Develop the Script**: Write the Python code to print \"Hello, World!\".\n4. **Verify the Script**: Run the script to ensure it works as expected.\n\nLet's proceed with creating the script.\n\n### Step 1: Create the Python Script\n\nWe will create a new file named `hello_world.py` in the project root directory.\n\n### Step 2: Write the Code\n\nThe content of the `hello_world.py` script will be:\n```python\nprint(\"Hello, World!\")\n```\n\n### Step 3: Verify the Script\n\nWe will run the script to ensure it prints \"Hello, World!\" correctly.\n\nLet's execute these steps.",
        "model": "deepseek-v3-241226",
        "finish_reason": "tool_calls",
        "usage": {
          "input_tokens": 3343,
          "output_tokens": 276
        },
        "tool_calls": [
          {
            "call_id": "call_4rtog37rbylk2n4wl3vfqlos",
            "name": "str_replace_based_edit_tool",
            "arguments": {
              "command": "create",
              "file_text": "print(\"Hello, World!\")",
              "path": "/trae-agent/workspace/hello_world.py"
            },
            "id": null
          }
        ]
      },
      "tool_calls": [
        {
          "call_id": "call_4rtog37rbylk2n4wl3vfqlos",
          "name": "str_replace_based_edit_tool",
          "arguments": {
            "command": "create",
            "file_text": "print(\"Hello, World!\")",
            "path": "/trae-agent/workspace/hello_world.py"
          },
          "id": null
        }
      ],
      "tool_results": [
        {
          "call_id": "call_4rtog37rbylk2n4wl3vfqlos",
          "success": false,
          "result": null,
          "error": "File already exists at:/trae-agent/workspace/hello_world.py. Cannot overwrite files using command `create`.",
          "id": null
        }
      ],
      "reflection": null,
      "error": null
    },
    {
      "step_number": 2,
      "timestamp": "2025-07-29T11:50:51.624220",
      "state": "calling_tool",
      "llm_messages": [
        {
          "role": "user",
          "content": null,
          "tool_result": {
            "call_id": "call_4ktwjh11qpkfayyqcqc8470s",
            "success": true,
            "result": "Here's the result of running `cat -n` on/trae-agent/workspace/hello_world.py:\n     1\tprint(\"Hello, World!\")\n",
            "error": null,
            "id": null
          }
        }
      ],
      "llm_response": {
        "content": "",
        "model": "deepseek-v3-241226",
        "finish_reason": "tool_calls",
        "usage": {
          "input_tokens": 3470,
          "output_tokens": 53
        },
        "tool_calls": [
          {
            "call_id": "call_4ktwjh11qpkfayyqcqc8470s",
            "name": "str_replace_based_edit_tool",
            "arguments": {
              "command": "view",
              "path": "/trae-agent/workspace/hello_world.py"
            },
            "id": null
          }
        ]
      },
      "tool_calls": [
        {
          "call_id": "call_4ktwjh11qpkfayyqcqc8470s",
          "name": "str_replace_based_edit_tool",
          "arguments": {
            "command": "view",
            "path": "/trae-agent/workspace/hello_world.py"
          },
          "id": null
        }
      ],
      "tool_results": [
        {
          "call_id": "call_4ktwjh11qpkfayyqcqc8470s",
          "success": true,
          "result": "Here's the result of running `cat -n` on/trae-agent/workspace/hello_world.py:\n     1\tprint(\"Hello, World!\")\n",
          "error": null,
          "id": null
        }
      ],
      "reflection": null,
      "error": null
    },
    {
      "step_number": 3,
      "timestamp": "2025-07-29T11:50:53.821784",
      "state": "calling_tool",
      "llm_messages": [
        {
          "role": "user",
          "content": null,
          "tool_result": {
            "call_id": "call_zpg2r2hc7gxdyt1mmbou3d0p",
            "success": true,
            "result": "The file/trae-agent/workspace/hello_world.py has been edited. Here's the result of running `cat -n` on a snippet of/trae-agent/workspace/hello_world.py:\n     1\tprint(\"Hello, World!\")\n     2\tprint(\"Welcome to the Python world!\")\nReview the changes and make sure they are as expected. Edit the file again if necessary.",
            "error": null,
            "id": null
          }
        }
      ],
      "llm_response": {
        "content": "",
        "model": "deepseek-v3-241226",
        "finish_reason": "tool_calls",
        "usage": {
          "input_tokens": 3581,
          "output_tokens": 94
        },
        "tool_calls": [
          {
            "call_id": "call_zpg2r2hc7gxdyt1mmbou3d0p",
            "name": "str_replace_based_edit_tool",
            "arguments": {
              "command": "str_replace",
              "old_str": "print(\"Hello, World!\")",
              "new_str": "print(\"Hello, World!\")\nprint(\"Welcome to the Python world!\")",
              "path": "/trae-agent/workspace/hello_world.py"
            },
            "id": null
          }
        ]
      },
      "tool_calls": [
        {
          "call_id": "call_zpg2r2hc7gxdyt1mmbou3d0p",
          "name": "str_replace_based_edit_tool",
          "arguments": {
            "command": "str_replace",
            "old_str": "print(\"Hello, World!\")",
            "new_str": "print(\"Hello, World!\")\nprint(\"Welcome to the Python world!\")",
            "path": "/trae-agent/workspace/hello_world.py"
          },
          "id": null
        }
      ],
      "tool_results": [
        {
          "call_id": "call_zpg2r2hc7gxdyt1mmbou3d0p",
          "success": true,
          "result": "The file/trae-agent/workspace/hello_world.py has been edited. Here's the result of running `cat -n` on a snippet of/trae-agent/workspace/hello_world.py:\n     1\tprint(\"Hello, World!\")\n     2\tprint(\"Welcome to the Python world!\")\nReview the changes and make sure they are as expected. Edit the file again if necessary.",
          "error": null,
          "id": null
        }
      ],
      "reflection": null,
      "error": null
    },
    {
      "step_number": 4,
      "timestamp": "2025-07-29T11:50:54.628437",
      "state": "completed",
      "llm_messages": [
        {
          "role": "user",
          "content": null,
          "tool_result": {
            "call_id": "call_zpg2r2hc7gxdyt1mmbou3d0p",
            "success": true,
            "result": "The file/trae-agent/workspace/hello_world.py has been edited. Here's the result of running `cat -n` on a snippet of/trae-agent/workspace/hello_world.py:\n     1\tprint(\"Hello, World!\")\n     2\tprint(\"Welcome to the Python world!\")\nReview the changes and make sure they are as expected. Edit the file again if necessary.",
            "error": null,
            "id": null
          }
        }
      ],
      "llm_response": {
        "content": "",
        "model": "deepseek-v3-241226",
        "finish_reason": "tool_calls",
        "usage": {
          "input_tokens": 3793,
          "output_tokens": 15
        },
        "tool_calls": [
          {
            "call_id": "call_s1y7pvnu3417udssqyctlync",
            "name": "task_done",
            "arguments": {},
            "id": null
          }
        ]
      },
      "tool_calls": null,
      "tool_results": null,
      "reflection": null,
      "error": null
    },
    {
      "step_number": 4,
      "timestamp": "2025-07-29T11:50:54.629596",
      "state": "completed",
      "llm_messages": [
        {
          "role": "user",
          "content": null,
          "tool_result": {
            "call_id": "call_zpg2r2hc7gxdyt1mmbou3d0p",
            "success": true,
            "result": "The file/trae-agent/workspace/hello_world.py has been edited. Here's the result of running `cat -n` on a snippet of/trae-agent/workspace/hello_world.py:\n     1\tprint(\"Hello, World!\")\n     2\tprint(\"Welcome to the Python world!\")\nReview the changes and make sure they are as expected. Edit the file again if necessary.",
            "error": null,
            "id": null
          }
        }
      ],
      "llm_response": {
        "content": "",
        "model": "deepseek-v3-241226",
        "finish_reason": "tool_calls",
        "usage": {
          "input_tokens": 3793,
          "output_tokens": 15
        },
        "tool_calls": [
          {
            "call_id": "call_s1y7pvnu3417udssqyctlync",
            "name": "task_done",
            "arguments": {},
            "id": null
          }
        ]
      },
      "tool_calls": null,
      "tool_results": null,
      "reflection": null,
      "error": null
    }
  ],
  "success": false,
  "final_result": null,
  "execution_time": 0.0
}

Try: I modify the base.py in trae_agent/agent/base.py, and output meet expectations:

• Modify _update_cli_console to pass both step and execution
• Fix max_steps handling to properly notify the console

# Copyright (c) 2025 ByteDance Ltd. and/or its affiliates
# SPDX-License-Identifier: MIT

"""Base Agent class for LLM-based agents."""

from abc import ABC, abstractmethod

from ..tools.base import Tool, ToolCall, ToolExecutor, ToolResult
from ..tools.ckg.ckg_database import clear_older_ckg
from ..utils.cli_console import CLIConsole
from ..utils.config import Config, ModelParameters
from ..utils.llm_basics import LLMMessage, LLMResponse
from ..utils.llm_client import LLMClient
from ..utils.trajectory_recorder import TrajectoryRecorder
from .agent_basics import AgentExecution, AgentState, AgentStep


class Agent(ABC):
    """Base class for LLM-based agents."""

    def __init__(self, config: Config | None = None, llm_client: LLMClient | None = None):
        """Initialize the agent.

        Args:
            config: Configuration object containing model parameters and other settings.
                   Required if llm_client is not provided.
            llm_client: Optional pre-configured LLMClient instance.
                       If provided, it will be used instead of creating a new one from config.
        """
        if llm_client is None:
            if config is None:
                raise ValueError("Either config or llm_client must be provided")
            self._llm_client = LLMClient(
                config.default_provider,
                config.model_providers[config.default_provider],
                config.max_steps,
            )
            self._model_parameters = config.model_providers[config.default_provider]
            self._max_steps = config.max_steps
        else:
            self._llm_client = llm_client
            self._model_parameters = llm_client.model_parameters
            self._max_steps = llm_client.max_steps

        self._initial_messages: list[LLMMessage] = []
        self._task: str = ""
        self._tools: list[Tool] = []
        self._tool_caller: ToolExecutor = ToolExecutor([])
        self._cli_console: CLIConsole | None = None

        # Trajectory recorder
        self._trajectory_recorder: TrajectoryRecorder | None = None

        # CKG tool-specific: clear the older CKG databases
        clear_older_ckg()

    @classmethod
    def from_config(cls, config: Config) -> "Agent":
        """Create an agent instance from a configuration object.

        This factory method provides the traditional config-based initialization
        while allowing subclasses to customize the instantiation process.

        Args:
            config: Configuration object containing model parameters and other settings.

        Returns:
            An instance of the agent.
        """
        return cls(config=config)

    @property
    def llm_client(self) -> LLMClient:
        return self._llm_client

    @property
    def trajectory_recorder(self) -> TrajectoryRecorder | None:
        """Get the trajectory recorder for this agent."""
        return self._trajectory_recorder

    def _set_trajectory_recorder(self, recorder: TrajectoryRecorder | None) -> None:
        """Set the trajectory recorder for this agent."""
        self._trajectory_recorder = recorder
        # Also set it on the LLM client
        self._llm_client.set_trajectory_recorder(recorder)

    @property
    def cli_console(self) -> CLIConsole | None:
        """Get the CLI console for this agent."""
        return self._cli_console

    def set_cli_console(self, cli_console: CLIConsole | None) -> None:
        """Set the CLI console for this agent."""
        self._cli_console = cli_console

    @property
    def tools(self) -> list[Tool]:
        """Get the tools available to this agent."""
        return self._tools

    @property
    def task(self) -> str:
        """Get the current task of the agent."""
        return self._task

    @task.setter
    def task(self, value: str):
        """Set the current task of the agent."""
        self._task = value

    @property
    def initial_messages(self) -> list[LLMMessage]:
        """Get the initial messages for the agent."""
        return self._initial_messages

    @property
    def model_parameters(self) -> ModelParameters:
        """Get the model parameters for the agent."""
        return self._model_parameters

    @property
    def max_steps(self) -> int:
        """Get the maximum number of steps for the agent."""
        return self._max_steps

    @abstractmethod
    def new_task(
        self,
        task: str,
        extra_args: dict[str, str] | None = None,
        tool_names: list[str] | None = None,
    ):
        """Create a new task."""
        pass

    async def execute_task(self) -> AgentExecution:
        """Execute a task using the agent."""
        import time

        start_time = time.time()
        execution = AgentExecution(task=self._task, steps=[])
        step: AgentStep | None = None

        try:
            messages = self._initial_messages
            step_number = 1

            while step_number <= self._max_steps:
                step = self._create_new_step(step_number)
                try:
                    messages = await self._run_llm_step(step, messages, execution)
                    self._finalize_step(step, messages, execution)
                    if step.state == AgentState.COMPLETED:
                        break
                    step_number += 1
                except Exception as e:
                    self._handle_step_error(step, e, messages, execution)
                    break

            if step_number > self._max_steps and not execution.success:
                execution.final_result = "Task execution exceeded maximum steps without completion."
                # Update CLI console with final execution state
                if step:
                    self._update_cli_console(step, execution)

        except Exception as e:
            execution.final_result = f"Agent execution failed: {str(e)}"

        execution.execution_time = time.time() - start_time
        if step:
            self._update_cli_console(step, execution)
        return execution

    def _create_new_step(self, step_number: int) -> AgentStep:
        return AgentStep(step_number=step_number, state=AgentState.THINKING)

    async def _run_llm_step(
        self, step: "AgentStep", messages: list["LLMMessage"], execution: "AgentExecution"
    ) -> list["LLMMessage"]:
        step.state = AgentState.THINKING
        self._update_cli_console(step)
        llm_response = self._llm_client.chat(messages, self._model_parameters, self._tools)
        step.llm_response = llm_response
        self._update_cli_console(step)
        self._update_llm_usage(llm_response, execution)

        if self.llm_indicates_task_completed(llm_response):
            if self._is_task_completed(llm_response):
                self._llm_complete_response_task_handler(llm_response, step, execution, messages)
                return messages
            else:
                step.state = AgentState.THINKING
                return [LLMMessage(role="user", content=self.task_incomplete_message())]
        else:
            tool_calls = llm_response.tool_calls
            return await self._tool_call_handler(tool_calls, step)

    def _finalize_step(
        self, step: "AgentStep", messages: list["LLMMessage"], execution: "AgentExecution"
    ) -> None:
        self._record_handler(step, messages)
        self._update_cli_console(step, execution)
        execution.steps.append(step)

    def _handle_step_error(
        self,
        step: "AgentStep",
        error: Exception,
        messages: list["LLMMessage"],
        execution: "AgentExecution",
    ) -> None:
        step.state = AgentState.ERROR
        step.error = str(error)
        self._update_cli_console(step)
        self._record_handler(step, messages)
        self._update_cli_console(step)
        execution.steps.append(step)

    def reflect_on_result(self, tool_results: list[ToolResult]) -> str | None:
        """Reflect on tool execution result. Override for custom reflection logic."""
        if len(tool_results) == 0:
            return None

        reflection = "\n".join(
            f"The tool execution failed with error: {tool_result.error}. Consider trying a different approach or fixing the parameters."
            for tool_result in tool_results
            if not tool_result.success
        )

        return reflection

    def llm_indicates_task_completed(self, llm_response: LLMResponse) -> bool:
        """Check if the LLM indicates that the task is completed. Override for custom logic."""
        completion_indicators = [
            "task completed",
            "task finished",
            "done",
            "completed successfully",
            "finished successfully",
        ]

        response_lower = llm_response.content.lower()
        return any(indicator in response_lower for indicator in completion_indicators)

    def _is_task_completed(self, llm_response: LLMResponse) -> bool:  # pyright: ignore[reportUnusedParameter]
        """Check if the task is completed based on the response. Override for custom logic."""
        return True

    def task_incomplete_message(self) -> str:
        """Return a message indicating that the task is incomplete. Override for custom logic."""
        return "The task is incomplete. Please try again."

    def _update_cli_console(self, step: AgentStep, execution: AgentExecution | None = None) -> None:
        if self.cli_console:
            self.cli_console.update_status(step, execution)

    def _update_llm_usage(self, llm_response: LLMResponse, execution: AgentExecution) -> None:
        if not llm_response.usage:
            return None
        # if execution.total_tokens is None then set it to be llm_response.usage else sum it up
        # execution.total_tokens is not None
        if not execution.total_tokens:
            execution.total_tokens = llm_response.usage
        else:
            execution.total_tokens += llm_response.usage
        return None

    def _llm_complete_response_task_handler(
        self,
        llm_response: LLMResponse,
        step: AgentStep,
        execution: AgentExecution,
        messages: list[LLMMessage],
    ) -> None:
        """
        update states
        """
        step.state = AgentState.COMPLETED
        execution.final_result = llm_response.content
        execution.success = True

        self._record_handler(step, messages)
        self._update_cli_console(step, execution)
        execution.steps.append(step)

    def _record_handler(self, step: AgentStep, messages: list[LLMMessage]) -> None:
        if self.trajectory_recorder:
            self.trajectory_recorder.record_agent_step(
                step_number=step.step_number,
                state=step.state.value,
                llm_messages=messages,
                llm_response=step.llm_response,
                tool_calls=step.tool_calls,
                tool_results=step.tool_results,
                reflection=step.reflection,
                error=step.error,
            )

    async def _tool_call_handler(
        self, tool_calls: list[ToolCall] | None, step: AgentStep
    ) -> list[LLMMessage]:
        messages: list[LLMMessage] = []
        if not tool_calls or len(tool_calls) <= 0:
            messages = [
                LLMMessage(
                    role="user",
                    content="It seems that you have not completed the task.",
                )
            ]
            return messages

        step.state = AgentState.CALLING_TOOL
        step.tool_calls = tool_calls
        self._update_cli_console(step)

        if self.model_parameters.parallel_tool_calls:
            tool_results = await self._tool_caller.parallel_tool_call(tool_calls)
        else:
            tool_results = await self._tool_caller.sequential_tool_call(tool_calls)
        step.tool_results = tool_results
        self._update_cli_console(step)
        for tool_result in tool_results:
            # Add tool result to conversation
            message = LLMMessage(role="user", tool_result=tool_result)
            messages.append(message)

        reflection = self.reflect_on_result(tool_results)
        if reflection:
            step.state = AgentState.REFLECTING
            step.reflection = reflection

            # Display reflection
            self._update_cli_console(step)

            messages.append(LLMMessage(role="assistant", content=reflection))

        return messages

What did you expect to happen?

Terminate as expected, with following item completed:

"success": false,
"final_result": null,
"execution_time": 0.0

A summary:

                    Execution Summary                      
┏━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┓
┃ Metric           ┃ Value                                 ┃
┡━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┩
│ Task             │ Create a hello world Python script    │
│ Success          │ ✅ Yes                                │
│ Steps            │ 5                                     │
│ Execution Time   │ 11.59s                                │
│ Total Tokens     │ 14792                                 │
│ Input Tokens     │ 14297                                 │
│ Output Tokens    │ 495                                   │
└──────────────────┴───────────────────────────────────────┘

                     Execution Summary                      
┏━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┓
┃ Metric           ┃ Value                                 ┃
┡━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┩
│ Task             │ Create a hello world Python script    │
│ Success          │ ❌ No                                 │
│ Steps            │ 2                                     │
│ Execution Time   │ 7.80s                                 │
│ Total Tokens     │ 7138                                  │
│ Input Tokens     │ 6796                                  │
│ Output Tokens    │ 342                                   │
└──────────────────┴───────────────────────────────────────┘

Traceback

No response

What is your system, Python, dependency version?

No response

Additional information that you believe is relevant to this bug

No response

[wintertee]

I can reproduce this issue. I noticed that after the agent finishes (the console shows State: Completed), the CLI loop does not break as expected:

trae-agent/trae_agent/utils/cli_console.py

Lines 66 to 77 in 8dc3886

	async def start(self):
	while True:
	if self.agent_execution and (
	self.lake_view is None
	or (
	len(self.agent_execution.steps) == len(self.console_steps)
	and all(step.lake_view_generator_done for step in self.console_steps.values())
	)
	):
	break
	self.print_task_progress()
	await asyncio.sleep(3)

This happens because, once the task is done, self.agent_execution becomes None, so the loop condition never evaluates to True.

A possible workaround is to update the condition to:

if self.agent_execution is None or (
    self.lake_view is None
    or (
        len(self.agent_execution.steps) == len(self.console_steps)
        and all(step.lake_view_generator_done for step in self.console_steps.values())
    )
):
    break

With this change, the loop will break as expected. However, I’m not entirely sure if this is the intended fix...

[chao-peng]

I can reproduce this issue. I noticed that after the agent finishes (the console shows State: Completed), the CLI loop does not break as expected:

trae-agent/trae_agent/utils/cli_console.py

Lines 66 to 77 in 8dc3886

async def start(self):
while True:
if self.agent_execution and (
self.lake_view is None
or (
len(self.agent_execution.steps) == len(self.console_steps)
and all(step.lake_view_generator_done for step in self.console_steps.values())
)
):
break
self.print_task_progress()
await asyncio.sleep(3)
This happens because, once the task is done, self.agent_execution becomes None, so the loop condition never evaluates to True.

A possible workaround is to update the condition to:

if self.agent_execution is None or (
self.lake_view is None
or (
len(self.agent_execution.steps) == len(self.console_steps)
and all(step.lake_view_generator_done for step in self.console_steps.values())
)
):
break
With this change, the loop will break as expected. However, I’m not entirely sure if this is the intended fix...

Thanks for providing a work-round. This bug was introduced by a refactoring PR. Will solve this problem as soon as possible

[chao-peng]

Hi @ChangranXU , this PR #239 fixes this problem