Debugging Python code, especially within complex applications, can feel like searching for a needle in a haystack—time-consuming and often frustrating. Imagine having an intelligent assistant that not only highlights errors but also suggests fixes, explains the root cause, and helps you verify the solution. This chapter guides you through building exactly that: an AI-powered Python debugging agent using AIPack.

You’ll learn how to harness AIPack’s powerful multi-stage agent capabilities, integrate with the MCP (Multi-Agent Communication Protocol) server for real-time interaction with your Python environment, and craft intelligent prompts to create a truly helpful debugging companion. This project will solidify your understanding of AIPack’s core principles by applying them to a practical, real-world development challenge.

Before we dive in, ensure you have a basic understanding of AIPack installation and agent definition from previous chapters. You’ll also need Python 3.9+ installed, VS Code, and the MCP server configured and running (as discussed in Chapter 9). This guide assumes you’re working with AIPack version 0.1.0 or newer, as seen on its GitHub repository, checked on 2026-05-17.

The Debugging Agent’s Brain: Core Concepts

Building an AI debugging agent is more nuanced than simply feeding an error message to a large language model (LLM). It demands a structured approach to analyze the context, formulate potential solutions, and interact effectively with the developer. AIPack’s architecture is perfectly suited for this multi-faceted task.

The AI-Assisted Debugging Workflow

Traditional debugging often follows a manual, iterative path: encounter an error, read the traceback, inspect variables, hypothesize causes, test fixes, and repeat. An AI agent can profoundly augment this workflow.

Here’s how our AI agent will integrate into and enhance the debugging process:

  1. Error Ingestion: The agent receives a Python error traceback and relevant code context directly from your development environment.
  2. Root Cause Analysis: It analyzes the error, identifies potential issues, and explains why the error likely occurred, translating cryptic tracebacks into understandable insights.
  3. Fix Proposal: Based on its analysis, the agent proposes one or more code changes to resolve the bug, aiming for clarity and conciseness.
  4. Verification & Feedback: The developer reviews the proposed fix. If accepted, the agent can even suggest ways to verify the solution (e.g., specific test cases). If rejected, the agent can iterate on its analysis or proposal.

This interactive loop transforms debugging from a solo detective mission into a collaborative, AI-guided effort, potentially saving significant development time.

flowchart TD User[Developer] --> MCP_Server[MCP Server] MCP_Server --> Agent_Input[Agent Input] Agent_Input --> Analyze_Error[Analyze Error] Analyze_Error --> Propose_Fix[Propose Fix] Propose_Fix --> MCP_Server_Output[MCP Output] MCP_Server_Output --> User_Review[Developer Review] User_Review -->|Accept Fix| Verify_Fix[Verify Fix] User_Review -->|Request Revision| Analyze_Error

📌 Key Idea: An AI debugging agent shifts the debugging paradigm from reactive problem-solving to proactive, AI-guided assistance, significantly reducing resolution time and cognitive load.

AIPack for Structured Debugging

AIPack is designed to create agents that follow well-defined, multi-step workflows. For our debugging agent, we’ll leverage key AIPack features:

  • Multi-Stage Markdown Agents: We’ll break the complex debugging task into distinct, manageable stages (e.g., analysis, proposal, verification). Each stage has a focused prompt, which helps the LLM concentrate on one specific aspect, reducing the likelihood of “hallucinations” and improving output quality.
  • Lua Logic: This is essential for controlling the flow between stages, extracting specific information from the LLM’s often free-form responses, and dynamically formatting prompts based on the debugging context received from MCP. Lua acts as the agent’s control plane.
  • Context Control: Managing the input context carefully is paramount. We’ll ensure the LLM receives only the most relevant code snippets and error messages, preventing token limit issues and maintaining focus on the immediate problem. This is critical when dealing with large codebases.

Integrating with the MCP Server

The MCP (Multi-Agent Communication Protocol) server acts as the crucial bridge between your Python development environment (like VS Code) and your AIPack agent.

How it works:

  1. Event Trigger: When a Python script crashes in VS Code, or you manually trigger the agent, the MCP server captures critical debugging information: the error traceback, relevant code, and potentially variable states.
  2. Agent Invocation: The MCP server then securely sends this debugging context to your AIPack agent. This is where the aipack.toml configuration plays a vital role in mapping MCP event data to your agent’s expected inputs.
  3. Agent Response: Your AIPack agent processes this information across its stages and sends its analysis, proposed fixes, and verification steps back to the MCP server.
  4. Developer View: The MCP server displays the agent’s output directly within VS Code, often presented in a chat-like interface or as suggested code changes that can be easily reviewed and applied.

⚡ Real-world insight: This seamless integration allows the AI agent to feel like a native, always-on assistant within your debugging experience, rather than an external tool you have to manually feed information to.

Crafting Effective Debugging Prompts

The quality and relevance of your agent’s suggestions are heavily dependent on the prompts you provide to the underlying LLM. For debugging, prompts should be:

  • Specific: Clearly define the agent’s role and expertise. For example, “You are an expert Python debugger, skilled in identifying and fixing common coding errors.”
  • Contextual: Include all necessary information: the full traceback, relevant code snippets (focusing on the area around the error), and any additional environment details that might be helpful.
  • Action-Oriented: Instruct the agent on its precise task. For instance, “Identify the root cause,” “Propose a concise fix,” and “Explain the reasoning behind your proposed solution.”
  • Format-Constrained: Guide the agent to output its response in a structured, parseable way. This often means using markdown code blocks for fixes, bullet points for explanations, or specific JSON structures for complex outputs.

🧠 Important: A well-structured prompt significantly reduces irrelevant outputs and vastly improves the accuracy and usability of the agent’s debugging advice. Think of the prompt as the agent’s job description and instruction manual combined.

Step-by-Step Implementation: Building Our Debugging Agent

Let’s walk through building our python-debugger AIPack, piece by piece.

1. Project Setup

First, we’ll create the foundational structure for our AIPack project. Open your terminal or VS Code’s integrated terminal and run the following commands:

aipack init python-debugger
cd python-debugger

The aipack init command creates a new directory named python-debugger and populates it with the basic files required for an AIPack agent, including a placeholder agent.aip and main.lua.

2. Define the agent.aip File

Now, let’s define the multi-stage agent within agent.aip. This file outlines the agent’s workflow, prompts, and how it processes responses. We’ll structure it with three distinct stages: analyze_error, propose_fix, and verify_fix.

Open the agent.aip file in your python-debugger directory and replace its default content with the following. We’ll build and explain it incrementally.

# agent.aip
# Checked on: 2026-05-17
name: "Python Debugger"
version: "0.1.0"
description: "An AI agent to assist with debugging Python code."

These lines provide basic metadata for your AIPack:

  • name: A human-readable name for your agent.
  • version: The version of your agent, useful for tracking changes.
  • description: A brief explanation of what your agent does.

Next, we define the LLM provider and model:

# ... (previous content)
provider:
  name: "ollama" # Or "openai", "anthropic", etc.
  model: "llama3" # Or "gpt-4o", "claude-3-opus", etc.
  • provider: This section specifies which large language model service your agent will use.
    • name: Here, we’ve chosen "ollama", which is excellent for local development. You could also use "openai", "anthropic", or others if configured.
    • model: This is the specific model to use from your chosen provider. For Ollama, "llama3" (or codellama, mistral, etc.) is a strong choice. Ensure you have this model pulled locally using ollama pull llama3 and that your Ollama server is running (ollama serve).

Now, let’s set the starting point for our agent:

# ... (previous content)
entrypoint: "analyze_error" # Our first stage
  • entrypoint: This property tells AIPack which stage to start with when the agent is invoked. In our case, it’s the analyze_error stage.

Finally, we define the stages themselves:

# ... (previous content)
stages:
  - name: "analyze_error"
    prompt: |
      You are an expert Python debugger. Your task is to analyze a Python error, understand the traceback, and identify the root cause.
      Present your analysis clearly and concisely.

      ---
      ## Error Traceback:
      ```
      {{ .Input.error_traceback }}
      ```

      ## Relevant Code:
      ```python
      {{ .Input.code_snippet }}
      ```

      ## Context:
      {{ .Input.context | default "No additional context provided." }}

      ---
      Based on the above, what is the most likely root cause of this error?
      Explain your reasoning in simple terms.
    response_processor: "process_analysis" # Lua function to process this stage's output
    next_stage_selector: "select_next_from_analysis" # Lua function to decide the next stage

This is our first stage, analyze_error:

  • name: A unique identifier for this stage.
  • prompt: This is the instruction given to the LLM.
    • It clearly defines the agent’s persona (“expert Python debugger”).
    • It includes placeholders like {{ .Input.error_traceback }} and {{ .Input.code_snippet }}. These Input variables will be dynamically populated by the MCP server when it invokes our agent, providing the error context.
    • The | default "..." filter ensures a fallback message if context is not provided.
    • It explicitly asks for the root cause and a simple explanation.
  • response_processor: Specifies the name of a Lua function (process_analysis) that will process the raw text output from the LLM for this stage.
  • next_stage_selector: Specifies a Lua function (select_next_from_analysis) that determines which stage to execute next, or if the agent should terminate.
# ... (previous content)
  - name: "propose_fix"
    prompt: |
      You are an expert Python debugger. Based on the identified root cause, your task is to propose a fix for the Python code.
      Provide the corrected code snippet, clearly highlighting the changes.

      ---
      ## Original Error Analysis:
      {{ .Input.analysis }}

      ## Original Code:
      ```python
      {{ .Input.code_snippet }}
      ```

      ## Error Traceback:
      ```
      {{ .Input.error_traceback }}
      ```

      ---
      Propose a fix for the `Original Code`.
      Provide the *entire* corrected code block.
      After the code, briefly explain *what* you changed and *why*.
      ```python
      # Corrected code goes here
      ```
      Explanation:
    response_processor: "process_fix_proposal"
    next_stage_selector: "select_next_from_fix"

The propose_fix stage:

  • Its prompt explicitly takes the analysis from the previous stage (which will be passed via agent_context and mapped to .Input.analysis) and the original code_snippet to propose a fix.
  • It’s crucial that the prompt asks for the entire corrected code block and an explanation. This helps the LLM provide a complete, actionable response that can be easily parsed.
# ... (previous content)
  - name: "verify_fix"
    prompt: |
      You are an expert Python debugger. A fix has been proposed and potentially applied.
      Your task is to suggest how to verify this fix. This could include running specific tests,
      checking output, or inspecting variable states.

      ---
      ## Proposed Fix:
      ```python
      {{ .Input.fix_proposal_code }}
      ```

      ## Original Problem Analysis:
      {{ .Input.analysis }}

      ---
      How would you verify that this fix successfully resolves the original error?
      Provide concrete steps or a simple test case.
    response_processor: "process_verification"
    next_stage_selector: "end_agent" # Simple Lua function to signal agent completion

The verify_fix stage:

  • This stage focuses on validating the proposed solution, a critical step often overlooked in automated debugging. It uses the fix_proposal_code and analysis from previous stages to inform its suggestions.
  • next_stage_selector: "end_agent": This indicates that after this stage, the agent’s task is complete.

3. Implement Lua Logic in main.lua

Now, let’s create the Lua functions referenced in agent.aip. These functions are responsible for processing the LLM’s raw output and controlling the flow between stages. They will live in main.lua (or any .lua file in your AIPack project).

Create or open main.lua in your python-debugger directory and add the following content:

-- main.lua
-- Checked on: 2026-05-17

-- Helper function to extract content from markdown code blocks
local function extract_code_block(text, language)
    -- This pattern looks for triple backticks followed by the language,
    -- then captures everything until the next triple backticks.
    local pattern = "```" .. language .. "\n(.-)\n```"
    local code = text:match(pattern)
    if code then
        return code:strip() -- Remove leading/trailing whitespace
    end
    return nil
end
  • extract_code_block: This is a utility function. LLMs often return code within markdown code fences. This function uses Lua’s pattern matching to reliably extract the code block for a given language (e.g., “python”) from the LLM’s response. This is crucial for parsing structured output.
-- ... (previous content)

-- Process the output from the 'analyze_error' stage
function process_analysis(response, agent_context)
    -- The LLM's raw response content is found in 'response.content'
    local analysis_text = response.content

    -- Store the full analysis in 'agent_context'. This makes it available
    -- to subsequent stages via their '.Input' variables if mapped correctly.
    agent_context.analysis = analysis_text

    -- Return a table of outputs that can be consumed by the next stage
    -- or displayed by MCP.
    return {
        analysis = analysis_text
    }
end
  • process_analysis: This function receives the raw response from the analyze_error stage.
    • response.content holds the LLM’s text output.
    • agent_context.analysis = analysis_text: We store the entire analysis in agent_context. This is how information persists and is passed between stages. agent_context is a mutable table that lives for the duration of the agent’s run.
    • The return value is what AIPack makes available as the output of this stage, which can be logged or used by MCP.
-- ... (previous content)

-- Decide the next stage after analysis
function select_next_from_analysis(previous_output, agent_context)
    -- For this tutorial, we always move to proposing a fix after analysis.
    -- In a more advanced scenario, you might add conditional logic here.
    -- For example, if the analysis indicated "no error found", you could
    -- return "end_agent" or branch to a different stage.
    return "propose_fix"
end
  • select_next_from_analysis: This function determines the next stage. Here, it simply directs the flow to propose_fix. In a real-world scenario, you might parse previous_output.analysis to make a more intelligent decision (e.g., if the agent says “no error found,” you might return "end_agent").
-- ... (previous content)

-- Process the output from the 'propose_fix' stage
function process_fix_proposal(response, agent_context)
    local fix_proposal_text = response.content

    -- Use our helper to extract the Python code block
    local proposed_code = extract_code_block(fix_proposal_text, "python")
    -- Extract the explanation using a pattern matching for "Explanation:"
    local explanation_pattern = "Explanation:\n(.-)$"
    local explanation = fix_proposal_text:match(explanation_pattern) or "No explanation provided."

    -- Store the extracted code and explanation in agent_context for later stages
    agent_context.fix_proposal_code = proposed_code
    agent_context.fix_explanation = explanation

    return {
        fix_proposal_code = proposed_code,
        fix_explanation = explanation,
        full_proposal = fix_proposal_text -- Keep the full text for display
    }
end
  • process_fix_proposal: This function processes the LLM’s response from the propose_fix stage.
    • It uses extract_code_block to get the Python code and a simple regex pattern to pull out the explanation. This demonstrates how Lua can parse and structure the LLM’s free-form text into distinct data points.
    • The extracted proposed_code and explanation are stored in agent_context for the verify_fix stage.
-- ... (previous content)

-- Decide the next stage after fix proposal
function select_next_from_fix(previous_output, agent_context)
    -- After proposing a fix, we move to the verification stage.
    -- In a real MCP integration, this might wait for a "user_accepted_fix" event
    -- before moving to verification.
    return "verify_fix"
end
  • select_next_from_fix: Directs the agent to the verify_fix stage.
-- ... (previous content)

-- Process the output from the 'verify_fix' stage
function process_verification(response, agent_context)
    local verification_steps = response.content

    agent_context.verification_steps = verification_steps

    return {
        verification_steps = verification_steps
    }
end

-- Function to signal agent completion
function end_agent(previous_output, agent_context)
    return nil -- Returning nil tells AIPack that the agent's flow is complete.
end
  • process_verification: Simply stores the LLM’s suggestions for verification steps in agent_context.
  • end_agent: This function is called as the next_stage_selector for the final stage. Returning nil explicitly signals to AIPack that the agent has finished its execution.

4. Create an Example Python Code with a Bug

To test our agent, we need a simple Python file with an intentional error. This will simulate a real-world debugging scenario.

Create a file named buggy_script.py in your python-debugger project root:

# buggy_script.py

def calculate_average(numbers):
    """Calculates the average of a list of numbers."""
    total = sum(numbers)
    count = len(numbers)
    # Intentional bug: division by zero if numbers is empty
    return total / count

if __name__ == "__main__":
    # This empty list will cause a ZeroDivisionError
    data = []
    # Uncomment the line below to see it work correctly
    # data = [10, 20, 30]
    result = calculate_average(data)
    print(f"The average is: {result}")

This script will raise a ZeroDivisionError because the data list is empty, making len(numbers) zero, leading to division by zero. This is a common, easy-to-understand error for our agent to tackle.

5. Running and Interacting with the Debugging Agent (via MCP)

Now, let’s put all the pieces together and see our debugging agent in action. This requires the MCP server running and integrated with VS Code.

  1. Start MCP Server: If you haven’t already, start the MCP server. This acts as the communication hub. (Refer to Chapter 9 for detailed setup if needed).

    mcp-server start

    The server will typically run on http://localhost:8080.

  2. Open Project in VS Code: Open the python-debugger directory in VS Code. This is where your agent.aip, main.lua, and buggy_script.py files reside.

  3. Configure aipack.toml for MCP Integration: For seamless integration with VS Code and MCP, you need to tell AIPack how to map incoming MCP events to your agent’s inputs. Open aipack.toml (if it doesn’t exist, create it in your project root) and ensure it contains the following:

    # aipack.toml
# Checked on: 2026-05-17

[agent]
name = "Python Debugger"
version = "0.1.0"
description = "An AI agent to assist with debugging Python code."
path = "agent.aip" # Points to our agent definition file

[provider.ollama]
model = "llama3" # Ensure this matches your local Ollama setup and is pulled
base_url = "http://localhost:11434" # Default Ollama API endpoint

# Example MCP integration (requires MCP extension in VS Code)
[mcp.commands.debug_python]
description = "Debugs a Python error using the AI agent."
agent_name = "Python Debugger" # Must match the 'name' in agent.aip
agent_input_map = {
    error_traceback = "mcp_event.traceback",
    code_snippet = "mcp_event.code_context",
    context = "mcp_event.additional_info"
}
    • [provider.ollama]: Make sure model matches the model you have pulled in Ollama. base_url is the default Ollama API endpoint.
    • [mcp.commands.debug_python]: This section defines a specific command for MCP.
      • agent_name = "Python Debugger": This is crucial! It tells MCP which AIPack agent to invoke. It must exactly match the name field in your agent.aip file.
      • agent_input_map: This is the heart of the integration. It maps incoming data from an MCP event (e.g., mcp_event.traceback which contains the error traceback) to the input variables expected by your agent.aip (.Input.error_traceback). This is how your agent receives its context from the development environment.

  4. Run buggy_script.py in VS Code:

    • Open buggy_script.py in your VS Code editor.
    • Run the script (e.g., by clicking the “Run Python File” button in the top right, or using python buggy_script.py in the integrated terminal).
    • The script will crash and output a ZeroDivisionError traceback.
    • Triggering the Agent: The MCP VS Code extension should detect the error. Look for a notification or a context menu option (often by right-clicking the traceback in the terminal or in the Problems panel) to “Debug with AI” or “Send error to AI agent.” Select the debug_python command you defined.

  5. Observe Agent Output: The MCP panel in VS Code will display the agent’s progress and output from each stage:

    • First, the analyze_error stage will provide its root cause analysis, explaining the ZeroDivisionError.
    • Then, the propose_fix stage will offer corrected Python code (e.g., adding a check for an empty list) and an explanation of the change.
    • Finally, the verify_fix stage will suggest steps to confirm the bug is squashed, such as running the script with both empty and non-empty lists.

Congratulations! You’ve just built and successfully run your first AI-assisted Python debugging agent, transforming a tricky problem into a guided, intelligent process.

Mini-Challenge: Enhance Error Analysis for Clarity

The current analyze_error stage is effective, but sometimes the initial traceback might be too generic, or the LLM might indicate it needs more information to be confident. Let’s make the agent more interactive.

Challenge: Modify the analyze_error stage in agent.aip and its corresponding Lua logic so that if the initial error analysis in process_analysis is “unclear” (e.g., contains phrases like “more information is needed” or “cannot determine without further context”), the agent transitions to a new stage that asks the user a clarifying question before proceeding to propose_fix.

Hint:

  • You’ll need to introduce a new stage in agent.aip, perhaps named ask_clarification, with a prompt designed to ask a question based on the analysis from the previous stage.
  • Modify select_next_from_analysis in main.lua to parse the previous_output.analysis. If certain keywords are found, return the name of your new ask_clarification stage. Otherwise, proceed to propose_fix.
  • You’ll also need a response_processor and next_stage_selector for the ask_clarification stage. For simplicity, the next_stage_selector for ask_clarification can then return propose_fix or end_agent depending on your design.

What to observe/learn: This challenge pushes you to think about dynamic stage transitions and how an agent can engage in a more interactive dialogue, which is essential for handling complex, ambiguous real-world problems and creating truly helpful AI assistants.

Common Pitfalls & Troubleshooting

Even with a well-designed agent, you might encounter issues. Here are common pitfalls and how to troubleshoot them:

  1. Context Overload / Token Limits:

    • Pitfall: Sending excessively large code files, extensive log outputs, or irrelevant context to the LLM can quickly exhaust its token limit, leading to truncated, generic, or unhelpful responses. This is especially true when dealing with large codebases.
    • Troubleshooting:
      • Be Selective: Only send the most relevant code_snippet (e.g., 20-50 lines around the error). The MCP server typically provides this intelligently.
      • Summarize: For very large log files or documentation, consider using an initial, smaller LLM call (or a simpler AIPack agent) to summarize the content before feeding it to your main debugging agent.
      • Increase Model Capacity: If using cloud providers, switch to models with larger context windows (e.g., gpt-4o, claude-3-opus). For Ollama, be mindful of your local hardware resources.
      • Refine aipack.toml Mapping: Double-check your agent_input_map in aipack.toml to ensure it only maps truly necessary mcp_event fields to your agent’s inputs.

  2. Hallucinations and Incorrect Fixes:

    • Pitfall: LLMs, while powerful, can sometimes confidently generate code that looks plausible but is syntactically incorrect, uses non-existent libraries, or doesn’t actually solve the problem.
    • Troubleshooting:
      • Specific Prompts: Reinforce the agent’s role (e.g., “You are an expert Python developer with a focus on writing robust, idiomatic Python code using standard libraries only.”). Instruct it to “only use well-known Python libraries” and “avoid inventing functions or modules.”
      • Verification Stage: The verify_fix stage is critical for a reason. Always encourage the developer to test the proposed fix thoroughly, not just accept it blindly.
      • Grounding: For domain-specific errors, provide the agent with access to relevant documentation (e.g., via a Retrieval-Augmented Generation (RAG) system) for specific libraries or frameworks it needs to interact with.

  3. MCP Connection Issues:

    • Pitfall: The AIPack agent isn’t receiving debugging information from VS Code, or its responses aren’t appearing in the MCP panel.
    • Troubleshooting:
      • MCP Server Running: Ensure mcp-server start is active and accessible (default http://localhost:8080). Check its terminal output for errors.
      • VS Code Extension: Confirm the MCP VS Code extension is installed, enabled, and up-to-date. Sometimes a VS Code restart helps.
      • aipack.toml Mapping: Triple-check your mcp.commands section in aipack.toml. Ensure agent_name exactly matches your AIPack agent’s name (name in agent.aip) and that agent_input_map correctly points to valid mcp_event fields.
      • Firewall: Check if a local firewall is blocking communication between VS Code, the MCP server, and potentially your Ollama instance.
      • Ollama/Provider Status: Ensure your chosen LLM provider (e.g., Ollama) is running and the specified model is available (ollama serve and ollama list). Check Ollama’s logs for any errors during model inference.

Summary

In this chapter, you embarked on a practical journey to build an AI-assisted Python debugging agent using AIPack. You learned how to transform a common development challenge into an intelligent, automated workflow. Here are the key takeaways:

  • Multi-Stage Agent Design: You designed a comprehensive workflow using AIPack’s multi-stage architecture, breaking down debugging into logical steps: error analysis, fix proposal, and solution verification.
  • Lua Logic for Control: You implemented Lua functions to process LLM responses, extract structured data (like code blocks and explanations), and control the flow between different agent stages, enabling dynamic and intelligent behavior.
  • Seamless MCP Integration: You configured your AIPack agent to integrate with the MCP server, allowing it to receive real-time debugging context (tracebacks, code snippets) from your VS Code environment and deliver actionable insights directly.
  • Effective Prompt Engineering: You crafted precise prompts that guided the LLM to act as an expert debugger, providing accurate analyses, well-structured code fixes, and clear explanations.
  • Practical Troubleshooting: You identified common pitfalls like context overload and LLM hallucinations, and learned strategies to troubleshoot and mitigate these issues, ensuring your agent remains effective in real-world scenarios.

This project demonstrates how AIPack can transform theoretical AI concepts into tangible tools that significantly enhance daily software engineering workflows. From here, you can further refine your debugging agent by adding more sophisticated context management, integrating with version control for automated patch application, or even creating agents for other development tasks like code review, documentation generation, or refactoring. The possibilities are vast!

References

This page is AI-assisted and reviewed. It references official documentation and recognized resources where relevant.