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# LLM Autonomous Coding Agent
This project implements a simple autonomous coding agent that can interact with the local file system and the web to help with programming tasks. It leverages a Large Language Model (LLM) via an OpenAI-compatible API to reason about tasks, execute operations, and iteratively work towards a solution.
## Features
- **Autonomous Execution**: The agent can decide which actions to take based on the current state of the project.
- **Toolbox**: Supports the following actions:
- `ls`: List files at a specified path.
- `read`: Read the contents of a file.
- `write`: Write content to a file.
- `tldr`: Read simplified man pages (`tldr`) for a program.
- `shell`: Execute a shell command.
- `mypy`: Run static type checking on a Python file.
- `webgrep`: Search for keywords within the markdownified content of a URL.
- **Reasoning Loop**: Uses a recursive loop to process tasks, analyze results, and determine the next step until a final response is reached.
- **Logging**: Logs interactions to `thoughts.log` for debugging and analysis.
## Requirements
- Python 3.x
- `openai` Python library
- `tldr` CLI installed on the system
- An API key for the Aalto LLM Gateway (or a compatible OpenAI-style API)
## Setup
1. **Set the API Key**:
The agent expects the API key to be stored in an environment variable:
```bash
export LLM_API_KEY="your_api_key_here"
```
2. **Run the Agent**:
Execute the script using Python:
```bash
python3 llm.py
```
### CLI Arguments
- `--workdir PATH`: Specify the directory the agent has access to. If not provided, it defaults to the current working directory.
```bash
python3 llm.py --workdir /path/to/your/project
```
- `--endpoint URL`: The API endpoint for the LLM (defaults to `https://llm-gateway.k8s.aalto.fi/api/v1`).
- `--key KEY`: The API key for the LLM endpoint (defaults to `LLM_API_KEY` environment variable).
- `--model MODEL`: The model name to use (defaults to `RedHatAI/gemma-4-31B-it-FP8-Dynamic`).
- `--trust-level LEVEL`: The trust level of the agent (defaults to 2). Determines which actions require manual approval.
## How it Works
1. **System Prompt**: The agent is initialized with a system prompt defining its identity as a coding agent and providing the available tools.
2. **Action-Observation Loop**:
- The user provides a task.
- The agent analyzes the task and returns a JSON action.
- The script executes the action (interacting with the file system, shell, or web).
- The result of the action is fed back into the LLM as a new observation.
- This repeats until the agent provides a final natural language response.
## Security and Permissions
- **Path Restriction**: To prevent unauthorized file system access, all file operations are restricted to the specified `--workdir` (or CWD) and its subdirectories.
- **Trust Level System**: Actions are assigned trust levels. If an action's required trust level exceeds the agent's current trust level, the system will explicitly prompt the user for permission (`Perform [action]? [y/N]`) before execution. This provides a safety layer for high-risk operations like `write` or `shell` commands.
### Trust Level Mappings
- `ls`, `read`, `tldr`, `mypy`: Level 0
- `webgrep`: Level 1
- `write`: Level 2
- `shell`: Level 3
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