Self healing and workspace update agent

README.md

@@ -1737,7 +1737,7 @@ Documentation is located here at: [docs.swarms.world](https://docs.swarms.world)
 -----
 
 ## Folder Structure
-The swarms package has been meticlously crafted for extreme use-ability and understanding, the swarms package is split up into various modules such as `swarms.agents` that holds pre-built agents, `swarms.structs` that holds a vast array of structures like `Agent` and multi agent structures. The 3 most important are `structs`, `models`, and `agents`.
+The swarms package has been meticlously crafted for extreme use-ability and understanding, the swarms package is split up into various modules such as `swarms.agents` that holds pre-built agents, `swarms.structs` that holds a vast array of structures like `Agent` and multi agent structures. The 3 most important are `structs`, `models`, and `agents`.
 
 ```sh
 ├── __init__.py
@@ -1786,3 +1786,36 @@ Join our growing community around the world, for real-time support, ideas, and d
 # License
 
 GNU AFFERO GENERAL PUBLIC LICENSE
+
+### Self-Healing Agent
+The Self-Healing Agent is a specialized agent that can automatically detect, analyze, and fix runtime errors in your code using LLM-based analysis. It provides structured error analysis and generates fixes in JSON format.
+
+```python
+from swarms.agents import SelfHealingAgent
+
+# Initialize the self-healing agent
+agent = SelfHealingAgent(
+    model_name="gpt-4",
+    max_retries=3,
+    verbose=True
+)
+
+# Example usage with error handling
+try:
+    result = some_function_that_might_fail()
+except Exception as e:
+    # Agent will analyze the error and propose fixes
+    fix = agent.analyze_and_fix(e)
+    print(f"Error Analysis: {fix['analysis']}")
+    print(f"Proposed Fix: {fix['solution']}")
+```
+
+**Key Features:**
+- Runtime error detection and analysis
+- LLM-powered error understanding
+- Structured JSON output with error analysis and solutions
+- Automatic code fix generation
+- Configurable retry attempts
+- Detailed error context tracking
+
+For more details, see the [Self-Healing Agent Documentation](docs/swarms/agents/self_healing_agent.md).

docs/mkdocs.yml

@@ -1,8 +1,8 @@
 docs_dir: '.'  # replace with the correct path if your documentation files are not in the same directory as mkdocs.yml
 site_name: Swarms
 site_url: https://docs.swarms.world
 site_author: Swarms
 site_description: The Enterprise-Grade Production-Ready Multi-Agent Orchestration Framework
 repo_name: kyegomez/swarms
 repo_url: https://github.com/kyegomez/swarms
 edit_uri: https://github.com/kyegomez/swarms/tree/main/docs
@@ -13,9 +13,9 @@
   - search
   - git-authors
   - mkdocs-jupyter:
         kernel_name: python3
         execute: false
         include_source: True
         include_requirejs: true
   - mkdocstrings:
       default_handler: python
@@ -137,10 +137,10 @@
   - pymdownx.tilde
 nav:
   - Home:
     - Overview: "index.md"
     # - The Vision: "swarms/framework/vision.md"
     # - Docker Setup: "swarms/install/docker_setup.md"
     - Our Goal; The Ultimate Multi-Agent LLM Framework for Developers: "swarms/concept/vision.md"
     - Swarm Ecosystem: "swarms/concept/swarm_ecosystem.md"
     - Onboarding: 
       - Installation: "swarms/install/install.md"
@@ -149,7 +149,7 @@
       - Swarms CLI: "swarms/cli/main.md"
       # - Swarms + Docker: 
       - Swarms Framework Architecture: "swarms/concept/framework_architecture.md"
     # - Prelimary:
       # - 80/20 Rule For Agents: "swarms/prompting/8020.md"
       - Managing Prompts in Production: "swarms/prompts/main.md"
     - Agents:
@@ -159,6 +159,7 @@
       - Complete Agent API: "swarms/structs/agent.md"
       - OpenAI Assistant: "swarms/agents/openai_assistant.md"
       - Create and Run Agents from YAML: "swarms/agents/create_agents_yaml.md"
+      - Self Healing Agent: "swarms/agents/self_healing_agent.md"
       - Integrating External Agents from Griptape, Langchain, etc: "swarms/agents/external_party_agents.md"
       - Tools:
         - Overview: "swarms/tools/main.md"
@@ -251,7 +252,7 @@
       - Edit Agents: "swarms_platform/agents/edit_agent.md"
     - Telemetry API:
       - PUT: "swarms_platform/telemetry/index.md"
     # - Tools API:
     #   - Overview: "swarms_platform/tools_api.md"
     #   - Add Tools: "swarms_platform/fetch_tools.md"
   - Guides:

docs/swarms/agents/self_healing_agent.md

@@ -0,0 +1,181 @@
+# Self-Healing Agent Documentation
+
+The Self-Healing Agent is a specialized agent designed to automatically detect, analyze, and fix runtime errors in your code using LLM-based analysis. It provides structured error analysis and generates fixes in JSON format.
+
+## Overview
+
+The Self-Healing Agent uses advanced language models to:
+1. Analyze runtime errors and exceptions
+2. Understand the error context and root cause
+3. Generate potential fixes in a structured format
+4. Apply fixes automatically when possible
+
+## Installation
+
+The Self-Healing Agent is included in the main swarms package:
+
+```bash
+pip install -U swarms
+```
+
+## Basic Usage
+
+```python
+from swarms.agents import SelfHealingAgent
+
+# Initialize the agent
+agent = SelfHealingAgent(
+    model_name="gpt-4",  # The LLM model to use
+    max_retries=3,       # Maximum number of fix attempts
+    verbose=True         # Enable detailed logging
+)
+
+# Example usage with error handling
+try:
+    result = some_function_that_might_fail()
+except Exception as e:
+    fix = agent.analyze_and_fix(e)
+    print(f"Error Analysis: {fix['analysis']}")
+    print(f"Proposed Fix: {fix['solution']}")
+```
+
+## API Reference
+
+### SelfHealingAgent Class
+
+#### Constructor Parameters
+
+| Parameter | Type | Default | Description |
+|-----------|------|---------|-------------|
+| model_name | str | "gpt-4" | The name of the LLM model to use |
+| max_retries | int | 3 | Maximum number of fix attempts |
+| verbose | bool | False | Enable detailed logging |
+| system_prompt | str | None | Custom system prompt for the LLM |
+| temperature | float | 0.7 | Temperature for LLM responses |
+
+#### Methods
+
+##### analyze_and_fix(error: Exception) -> dict
+Analyzes an error and generates potential fixes.
+
+**Parameters:**
+- error (Exception): The caught exception to analyze
+
+**Returns:**
+A dictionary containing:
+```json
+{
+    "error_type": "str",      // Type of the error
+    "analysis": "str",        // Detailed error analysis
+    "context": "str",         // Error context
+    "solution": "str",        // Proposed fix in code form
+    "confidence": float,      // Confidence score (0-1)
+    "metadata": {}            // Additional error metadata
+}
+```
+
+##### apply_fix(fix: dict) -> bool
+Attempts to apply a generated fix.
+
+**Parameters:**
+- fix (dict): The fix dictionary returned by analyze_and_fix()
+
+**Returns:**
+- bool: True if fix was successfully applied, False otherwise
+
+## Error Output Format
+
+The agent provides structured error analysis in JSON format:
+
+```json
+{
+    "error_type": "ZeroDivisionError",
+    "analysis": "Attempted division by zero in calculation",
+    "context": "Error occurred in calculate_average() at line 45",
+    "solution": "Add a check for zero denominator:\nif denominator != 0:\n    result = numerator/denominator\nelse:\n    raise ValueError('Denominator cannot be zero')",
+    "confidence": 0.95,
+    "metadata": {
+        "file": "calculator.py",
+        "line": 45,
+        "function": "calculate_average"
+    }
+}
+```
+
+## Best Practices
+
+1. **Error Context**: Always provide as much context as possible when catching errors
+2. **Validation**: Review proposed fixes before applying them automatically
+3. **Logging**: Enable verbose mode during development for detailed insights
+4. **Model Selection**: Use GPT-4 for complex errors, GPT-3.5-turbo for simpler cases
+5. **Retry Strategy**: Configure max_retries based on your use case
+
+## Examples
+
+### Basic Error Handling
+
+```python
+from swarms.agents import SelfHealingAgent
+
+agent = SelfHealingAgent()
+
+def process_data(data):
+    try:
+        result = data['key']['nested_key']
+        return result
+    except Exception as e:
+        fix = agent.analyze_and_fix(e)
+        if fix['confidence'] > 0.8:
+            print(f"Applying fix: {fix['solution']}")
+            return agent.apply_fix(fix)
+        else:
+            print(f"Low confidence fix, manual review needed: {fix['analysis']}")
+            return None
+```
+
+### Custom System Prompt
+
+```python
+agent = SelfHealingAgent(
+    system_prompt="""You are an expert Python developer specializing in
+    fixing data processing errors. Focus on data validation and type checking
+    in your solutions."""
+)
+```
+
+### Batch Error Processing
+
+```python
+def process_batch(items):
+    results = []
+    errors = []
+
+    for item in items:
+        try:
+            result = process_item(item)
+            results.append(result)
+        except Exception as e:
+            errors.append((item, e))
+
+    # Process all errors at once
+    if errors:
+        fixes = [agent.analyze_and_fix(e) for _, e in errors]
+        return results, fixes
+```
+
+## Error Types Handled
+
+The Self-Healing Agent can handle various types of runtime errors including:
+
+- Syntax Errors
+- Type Errors
+- Index Errors
+- Key Errors
+- Attribute Errors
+- Value Errors
+- Import Errors
+- And more...
+
+## Contributing
+
+We welcome contributions to improve the Self-Healing Agent! Please see our [Contributing Guidelines](../../CONTRIBUTING.md) for more information.