feat: enhance documentation and CLI interface

- Add comprehensive usage documentation and examples to README
- Add warning about shell command execution risks
- Improve CLI with --message and --research-only flags
- Add environment validation for API keys
- Replace requirements.txt with pyproject.toml

README.md

@@ -20,6 +20,22 @@
 
 RA.Aid (ReAct Aid) is a powerful AI-driven command-line tool that integrates `aider` (https://aider.chat/) within a LangChain ReAct agent loop. This unique combination allows developers to leverage aider's code editing capabilities while benefiting from LangChain's agent-based task execution framework. The tool provides an intelligent assistant that can help with research, planning, and implementation of development tasks.
 
+⚠️ **IMPORTANT: USE AT YOUR OWN RISK** ⚠️
+- This tool **can and will** automatically execute shell commands on your system
+- No warranty is provided, either express or implied
+- Always review the actions the agent proposes before allowing them to proceed
+
+## Key Features
+
+- **Multi-Step Task Planning**: The agent breaks down complex tasks into discrete, manageable steps and executes them sequentially. This systematic approach ensures thorough implementation and reduces errors.
+
+- **Automated Command Execution**: The agent can run shell commands automatically to accomplish tasks. While this makes it powerful, it also means you should carefully review its actions.
+
+- **Three-Stage Architecture**:
+  1. **Research**: Analyzes codebases and gathers context
+  2. **Planning**: Breaks down tasks into specific, actionable steps
+  3. **Implementation**: Executes each planned step sequentially
+
 What sets RA.Aid apart is its ability to handle complex programming tasks that extend beyond single-shot code edits. By combining research, strategic planning, and implementation into a cohesive workflow, RA.Aid can:
 
 - Break down and execute multi-step programming tasks
@@ -62,70 +78,76 @@ What sets RA.Aid apart is its ability to handle complex programming tasks that e
 
 ## Installation
 
-### Prerequisites
+RA.Aid can be installed directly using pip:
 
-- Python 3.8 or higher
-- pip package manager
-
-### Steps
-
-1. Install from PyPI:
 ```bash
 pip install ra-aid
 ```
 
-Or install from source:
+### Prerequisites
+
+Before using RA.Aid, you'll need to set up your API keys for the required AI services:
+
 ```bash
-git clone https://github.com/ai-christianson/ra-aid.git
-cd ra-aid
-pip install .
+# Required: Set up your Anthropic API key
+export ANTHROPIC_API_KEY=your_api_key_here
+
+# Optional: Set up OpenAI API key if using OpenAI features
+export OPENAI_API_KEY=your_api_key_here
 ```
 
-2. Install additional dependencies:
-```bash
-pip install -r requirements.txt
-```
-
-3. (Optional) Install development dependencies:
-```bash
-pip install -r requirements-dev.txt
-```
+You can get your API keys from:
+- Anthropic API key: https://console.anthropic.com/
+- OpenAI API key: https://platform.openai.com/api-keys
 
 ## Usage
 
-RA.Aid is used via the `ra-aid` command. The basic usage pattern is:
+RA.Aid is designed to be simple yet powerful. Here's how to use it:
 
 ```bash
-ra-aid [task]
+# Basic usage
+ra-aid -m "Your task or query here"
+
+# Research-only mode (no implementation)
+ra-aid -m "Explain the authentication flow" --research-only
 ```
 
-### Examples
+### Command Line Options
 
-Research a topic:
+- `-m, --message`: The task or query to be executed (required)
+- `--research-only`: Only perform research without implementation
+
+### Example Tasks
+
+1. Code Implementation:
    ```bash
-ra-aid "Research best practices for Python package structure"
+   ra-aid -m "Add input validation to the user registration endpoint"
    ```
 
-Plan a development task:
+2. Code Research:
    ```bash
-ra-aid "Plan the implementation of a new REST API endpoint"
+   ra-aid -m "Explain how the authentication middleware works" --research-only
    ```
 
-Generate code or documentation:
+3. Refactoring:
    ```bash
-ra-aid "Create a README.md template for my project"
+   ra-aid -m "Refactor the database connection code to use connection pooling"
    ```
 
-### Interactive Mode
+### Environment Variables
 
-For an interactive session where you can enter multiple tasks:
+RA.Aid uses the following environment variables:
+
+- `ANTHROPIC_API_KEY` (Required): Your Anthropic API key for accessing Claude
+- `OPENAI_API_KEY` (Optional): Your OpenAI API key if using OpenAI features
+
+You can set these permanently in your shell's configuration file (e.g., `~/.bashrc` or `~/.zshrc`):
 
 ```bash
-ra-aid
+export ANTHROPIC_API_KEY=your_api_key_here
+export OPENAI_API_KEY=your_api_key_here
 ```
 
-This will start an interactive prompt where you can input tasks sequentially.
-
 ## Architecture
 
 RA.Aid implements a three-stage architecture for handling development and research tasks:

ra_aid/__main__.py

@@ -1,5 +1,8 @@
 import sqlite3
 import argparse
+import os
+import sys
+import shutil
 from langchain_anthropic import ChatAnthropic
 from langchain_core.messages import HumanMessage
 from langgraph.checkpoint.memory import MemorySaver
@@ -24,13 +27,24 @@ from ra_aid.prompts import (
 
 def parse_arguments():
     parser = argparse.ArgumentParser(
-        description='AI Agent for executing programming and research tasks',
-        formatter_class=argparse.RawDescriptionHelpFormatter
+        description='RA.Aid - AI Agent for executing programming and research tasks',
+        formatter_class=argparse.RawDescriptionHelpFormatter,
+        epilog='''
+Examples:
+    ra-aid -m "Add error handling to the database module"
+    ra-aid -m "Explain the authentication flow" --research-only
+        '''
     )
     parser.add_argument(
-        'task',
+        '-m', '--message',
         type=str,
-        help='The task to be executed by the agent'
+        required=True,
+        help='The task or query to be executed by the agent'
+    )
+    parser.add_argument(
+        '--research-only',
+        action='store_true',
+        help='Only perform research without implementation'
     )
     return parser.parse_args()
 
@@ -59,7 +73,8 @@ def is_informational_query() -> bool:
     Returns:
         bool: True if query is informational (no implementation requested), False otherwise
     """
-    return not is_stage_requested('implementation')
+    # Check both the research_only flag and implementation_requested state
+    return _global_memory.get('config', {}).get('research_only', False) or not is_stage_requested('implementation')
 
 def is_stage_requested(stage: str) -> bool:
     """Check if a stage has been requested to proceed.
@@ -195,17 +210,53 @@ def run_research_subtasks(base_task: str, config: dict):
                 print(f"Encountered Anthropic Internal Server Error: {e}. Retrying...")
                 continue
 
+def validate_environment():
+    """Validate required environment variables and dependencies."""
+    missing = []
+    
+    # Check API keys
+    if not os.environ.get('ANTHROPIC_API_KEY'):
+        missing.append('ANTHROPIC_API_KEY environment variable is not set')
+    if not os.environ.get('OPENAI_API_KEY'):
+        missing.append('OPENAI_API_KEY environment variable is not set')
+    
+    # Check for aider binary
+    if not shutil.which('aider'):
+        missing.append('aider binary not found in PATH. Please install aider: pip install aider')
+    
+    if missing:
+        print("Error: Missing required dependencies:", file=sys.stderr)
+        for error in missing:
+            print(f"- {error}", file=sys.stderr)
+        sys.exit(1)
+
 if __name__ == "__main__":
+    validate_environment()
     args = parse_arguments()
-    base_task = args.task
-    config = {"configurable": {"thread_id": "abc123"}, "recursion_limit": 100}
+    base_task = args.message
+    config = {
+        "configurable": {
+            "thread_id": "abc123"
+        },
+        "recursion_limit": 100,
+        "research_only": args.research_only
+    }
+    
+    # Store config in global memory for access by is_informational_query
+    _global_memory['config'] = config
 
     # Run research stage
     print_stage_header("RESEARCH STAGE")
+    research_prompt = f"""User query: {base_task}
+
+{RESEARCH_PROMPT}
+
+Be very thorough in your research and emit lots of snippets, key facts. If you take more than a few steps, be eager to emit research subtasks.{'' if args.research_only else ' Only request implementation if the user explicitly asked for changes to be made.'}"""
+
     while True:
         try:
             for chunk in research_agent.stream(
-                {"messages": [HumanMessage(content=f"User query: {base_task}\n\n{RESEARCH_PROMPT}\n\nBe very thorough in your research and emit lots of snippets, key facts. If you take more than a few steps, be eager to emit research subtasks. Only request implementation if the user explicitly asked for changes to be made.")]}, 
+                {"messages": [HumanMessage(content=research_prompt)]}, 
                 config
             ):
                 print_agent_output(chunk)

requirements.txt

@@ -1,7 +0,0 @@
-langchain-anthropic
-langgraph
-rich>=13.0.0
-GitPython==3.1.41
-fuzzywuzzy==0.18.0
-python-Levenshtein==0.23.0
-pathspec>=0.11.0