Healing Agent 🩺

Healer Agent is an intelligent code assistant that catches with detailed context and fixes errors in your Python code. It leverages the power of AI to provide smart suggestions and corrections, helping you write more robust and "self-healing" code. Your program will be able to fix itself, it will have regenerative healing abilities like Wolverine.

⚠️ Not intended for production use. To preserve Healing Agent's original autonomous behavior, AUTO_FIX defaults to True: a generated fix can modify, reload, and run supervised code. AUTO_SYSCHANGE remains False because it installs packages. Set AUTO_FIX=False for proposal-only operation. Failed healing always re-raises the original application exception.

Goal: first actually usable autonomous coding agent in production

Video demo on Youtube

Features ✨

• 🚨 Automatic error detection and handling of diverse exception types
• 💡 Smart error analysis and solution suggestions (auto-generated fixing hints and code)
• 🔍 Comprehensive error analysis including exception details, stack traces, local and globalvariables and root cause identification
• 🧠 Advanced AI-powered code healing using LLMs of different providers
• 🔧 Zero-config integration with Python projects (just import and decorate)
• 💾 Robust error tracking and debugging:
  • Exception context saved to JSON (code, error details, function info and args)
  • Automatic code backups before fixes
  • Detailed analysis results and fix history
  • Quick test of fixes
• 🤖 (Optionally) Fully automated operation with minimal human intervention
• 📦 Automatic installation of missing modules

Flagship direction: Data Healing

The most important planned capability is Data Healing: keeping ingestion code aligned with changing, but still business-valid, source documents.

Examples include:

• an Excel workbook renames or reorders columns, moves the header row, changes a sheet name, adds merged cells, or uses a different date/decimal format;
• a PDF keeps the same business information but moves a table, changes its headings or layout, or requires a different extraction strategy;
• extracted rows no longer match the target Pydantic model even though the required information is still present under slightly different names or structures.

Healing Agent should profile the failing and previously valid samples, compare them with the declared Pydantic/data contract, and propose the smallest versioned change to the loader or a new boundary adapter. It should then replay both sample sets, validate business invariants, and return the code diff, mapping explanation, fixtures, and confidence evidence. It must not silently relax the canonical model, invent required values, or overwrite the original document. Shadow mode and human approval will be available as optional policies alongside the default automatic activation path.

This capability is planned, not implemented in 0.2.8. See the dedicated Data Healing roadmap for incremental steps.

Data Healing is intentionally framework-level. The core package does not depend on Docling, Fidelis, Pydantic, pandas, or a particular business domain. It can inspect and repair the code at an ingestion boundary whenever that code raises an exception or fails a configured validation. Pydantic models and document/data extractors can be supplied by an application or a future optional adapter.

How it works 🧠

graph TD
    A[Import healing_agent] --> B[Configuration: AI access etc.]
    B --> C[Decorate functions with healing_agent]
    C --> D[Run Code / Execute Functions]
    D -->|No problem| L[Success]
    D -->|Exception?| F[Get and Save Detailed Context]
    F --> G[Auto-generate Fixing Hints and Code with AI]
    G --> H[Test Generated Code]
    H --> I[Create backup]
    I --> J[Apply Code Fixes]
    J --> D

Installation 💻

To install Healing Agent, follow these steps:

From PyPI:

pip install healing-agent

PIP package from GitHub:

pip install git+https://github.com/matebenyovszky/healing-agent

OR from source:

1. Clone the repository:

   git clone https://github.com/matebenyovszky/healing-agent.git
   

2. Navigate to the project directory:

   cd healing-agent
   

3. Install:

   pip install -e .
   

   OR run overall test to install and test functionality:

   python scripts/overall_test.py
   

Usage 🔧

To use Healing Agent in your project, follow these steps:

1. Import the healing_agent decorator in your Python file:

   import healing_agent
   

2. Decorate the function you want to monitor with @healing_agent:

   @healing_agent
   def your_function():
       # Your code here
   

   You can also pass parameters to the decorator to change the behavior set in the config file:

   @healing_agent(AUTO_FIX=False)
   def your_function():
       # Your code here
   

3. Run your Python script as usual. Healing Agent will automatically detect, save context and attempt to fix any errors that occur within the decorated function.

Context (and code file backup in case of auto-fix) is saved to a JSON/Python file in the same directory as your script with actual timestamp in the filename.

Configuration ⚙️

Healing Agent uses a flexible configuration system that supports multiple AI providers and customizable settings. The configuration is managed through a healing_agent_config.py file, which can be located in two places:

1. Local Project Directory: Healing Agent first checks for a config file in your project's directory
2. User Home Directory: If no local config is found, it looks for ~/.healing_agent/healing_agent_config.py

Configuration File Creation

The configuration file is automatically created in one of two ways:

1. Auto-Creation: When you first run Healing Agent, if no configuration file exists, it will:

   • Create a .healing_agent directory in your home folder
   • Copy the template configuration to ~/.healing_agent/healing_agent_config.py
   • Print a message indicating where the new config file was created

2. Manual Creation: You can manually create the configuration file:

   • Copy healing_agent/config_template.py from the package
   • Rename it to healing_agent_config.py
   • Place it in either your project directory or ~/.healing_agent/
   • Update the AI provider settings and other options

Configuration Options

The configuration file includes:

1. AI Provider Selection: Choose from supported providers:

   • OpenAI
   • Azure OpenAI
   • LiteLLM
   • Anthropic
   • Ollama

2. Provider Credentials: Set up API keys and endpoints

   • Can be defined directly in the config file
   • Can be loaded from environment variables (recommended)

3. Behavior Settings:

   MAX_ATTEMPTS = 3       # Hard limit across recursive repair/reload attempts
   DEBUG = True           # Enable detailed logging
   AUTO_FIX = True          # Apply and execute generated fixes by default
   AUTO_SYSCHANGE = False   # Never install packages automatically by default
   BACKUP_ENABLED = True    # Create backups before fixes
   SAVE_GIT_PATCHES = False # Optionally save a reviewable unified diff
   

MAX_ATTEMPTS counts repair cycles for the same decorated function, including calls reached after a repaired module is reloaded. For example, a value of 3 allows at most three generated-and-applied repair attempts. It is not a general retry setting for every provider or network error; those failures stop healing and the original application exception is raised.

If a repaired module cannot be loaded or its top-level code fails, Healing Agent restores the previous module object in sys.modules. This protects the running process, but does not revert the edited source file. Keep BACKUP_ENABLED=True and use version control so source changes remain recoverable.

Reviewable Git patches

Set SAVE_GIT_PATCHES=True to save each valid generated replacement as a minimal unified diff under _healing_agent_fixes/. The artifact uses Git paths and can be reviewed or applied with git apply <file.patch>.

Patch generation is independent of AUTO_FIX: with the default AUTO_FIX=True, it is an audit artifact for the automatically applied candidate; with AUTO_FIX=False, it is a proposal that leaves the source file unchanged. In 0.2.8 this does not create commits, push branches, or open pull requests, and a generated patch is not evidence that tests passed. Isolated verification and optional draft-PR publication remain 0.3 milestones.

Automatic system changes

AUTO_SYSCHANGE=True currently recognizes missing-module errors and invokes the active interpreter as python -m pip install <inferred-package>. It has no package allowlist, version pinning, approval step, or package-confusion defense, so it should only be used in a disposable development environment. It defaults to False; the roadmap replaces direct installation with a reviewable, policy-controlled dependency proposal.

Example configuration for Azure OpenAI:

AI_PROVIDER = "azure"

AZURE = {
    "api_key": os.getenv("AZURE_API_KEY"),  # Recommended: use environment variable
    "endpoint": "https://your-resource.openai.azure.com",
    "deployment_name": "gpt-4",
    "api_version": "2024-02-01"
}

The model name is configurable and is not restricted to a hard-coded list. The OpenAI example defaults to gpt-5.6-terra, while Azure uses your deployment name. Provider/model combinations still need a published compatibility test matrix; see the roadmap.

Anthropic and LiteLLM support are optional extras (pip install "healing-agent[anthropic]" or pip install "healing-agent[litellm]"). The current LiteLLM extra requires Python 3.10 or newer; the package supports Python 3.10–3.13. The 0.2.8 dependency baselines are OpenAI 2.20.0, Anthropic 0.121.0, LiteLLM 1.96.2, HTTPX 0.28.1, and Requests 2.34.2, with compatible updates allowed inside the declared major range. A normal install currently resolves to OpenAI 3.0.0. LiteLLM 1.96.2 requires OpenAI >=2.20,<3, so installing the LiteLLM extra intentionally resolves to the latest compatible OpenAI 2.x instead; the two latest releases cannot coexist until LiteLLM adds OpenAI 3 support.

Testing 🧪

Run the isolated regression suite with:

python -m pytest

python scripts/test_runner.py is an equivalent wrapper and returns pytest's failing exit status. python scripts/overall_test.py additionally builds and installs the package before running the tests.

Maintainers should follow the guarded release checklist before creating a version tag or publishing to PyPI.

Roadmap 🗺️

See ROADMAP.md for small release steps toward verified repair, LLM and agent failure recovery, harness integrations such as Hermes Agent, and a language-neutral repair coordinator.

Healing Agent can emit a local, reviewable Git patch, but does not currently create commits, push branches, or connect runtime failures to GitHub issues and pull requests. The runtime therefore does not read or require a GitHub token.

For the future GitHub integration, a token would be supplied by the host environment (for example an Actions secret or a fine-grained PAT), never by the repaired application or the LLM context. It would need narrowly scoped repository permissions such as contents:write and pull_requests:write (and issues:write only when issue creation is enabled). A token by itself is not proof that a fix is safe: the integration must apply the patch on an isolated branch, run git apply --check, execute configured tests, commit and push only after policy approval, and then open a draft PR. That flow is planned for the roadmap; 0.2.8 only creates the local patch artifact.

Use Cases 💡

• Development: Use Healing Agent during development to catch and fix errors early, and let AI generate fixes for your code. This is what you would do anyways, but now it's automated. 😁
• Educational Tool: Use Healing Agent as a learning tool to understand AI coding capabilities and limitations.

Cooking open source 🍳

Healing Agent is distributed under the MIT License. See LICENSE for more information. Feedback and contributions are welcome!