llm-agent-x 0.2.0

A task execution framework using language models to perform complex tasks.

LLM Agent X

Overview

LLM Agent X is a task execution framework that leverages language models to perform complex tasks by splitting them into subtasks and using tools like web search.

Installation

1. Clone the repository (optional, if you want to install from source):

   git clone https://github.com/cvaz1306/llm_agent_x.git
   cd llm_agent_x
   

2. Install the package:

   • From PyPI (once published):

     pip install llm-agent-x
     

   • Or, for local development (from the project root directory):

     pip install .
     

   • To install with editable mode (useful for development):

     pip install -e .
     

3. Set up environment variables: Create a .env file in the root directory (or ensure your environment is configured) and add the following variables:

   SEARX_HOST=http://localhost:8080
   OUTPUT_DIR=./output/
   OPENAI_BASE_URL=https://api.openai.com/v1
   OPENAI_API_KEY=your_openai_api_key
   # Optional: Define a default LLM model
   # DEFAULT_LLM=gpt-4o-mini
   

Usage

To run the LLM agent, use the following command:

llm-agent-x "Your task description here" --max_layers 2 --output output.md --model gpt-4o-mini

cli.py is not intended to be the primary interface for this project. It is merely a script demonstrating the project's capabilities. For your use, we recommend that you modify or copy cli.py or integrating llm_agent_x's API into your own project. In the future, we may implement a HTTP server,.

Arguments

• task: The task to execute.
• --u_inst: User instructions for the task.
• --max_layers: The maximum number of layers for task splitting (default: 3).
• --output: The output file path (default: output.md, saved in OUTPUT_DIR).
• --model: The name of the LLM to use (default: value from DEFAULT_LLM environment variable, or the hardcoded default in cli.py if DEFAULT_LLM is not set).
• --task_limit: Array defining task limits per layer (default: "[3,2,2,0]").
• --merger: Strategy for merging results, 'ai' or 'append' (default: 'ai').
• --align_summaries: Whether to align summaries with user instructions.
• --no-tree: If specified, disables tree rendering in console
• --default_subtask_type: The default task type to apply to all subtasks.
• --enable-python-execution: Enable the exec_python tool for the agent. If enabled, the agent can choose to execute Python code, potentially in a Docker sandbox if configured (see Python Execution Sandbox section). Defaults to False.

Example

llm-agent-x "Research the impact of climate change on polar bears" --max_layers 3 --output climate_change_report.md --model gpt-4o-mini

Example flowchart output

flowchart TD
    A[Research the evolution of the internet. use 5 subtasks, and make sure you follow all your previous instructions nd don't include a summarization task at the end. make your final report very detailed.]
    A -->|Subtask| B[Identify key milestones in the development of the internet.]
    B -->|Tool call| C[web_search]
    C --> B
    B -->|Completed| A
    A -->|Subtask| D[Research the history of major internet protocols and technologies.]
    D -->|Tool call| E[web_search]
    E --> D
    D -->|Completed| A
    A -->|Subtask| F[Analyze the impact of government policies on the growth of the internet.]
    F -->|Tool call| G[web_search]
    G --> F
    F -->|Completed| A
    A -->|Subtask| H[Examine the role of private companies in shaping the internet's evolution.]
    H -->|Tool call| I[web_search]
    I --> H
    H -->|Completed| A
    A -->|Subtask| J[Evaluate the global spread and adoption of the internet over time.]
    J -->|Tool call| K[web_search]
    K --> J
    J -->|Completed| A

Dependencies

Project dependencies are managed with Poetry and are listed in the pyproject.toml file.

⚠️ torch is optional in Poetry but required at runtime. You must install the correct version for your hardware manually using the appropriate --index-url.

License

This project is licensed under the MIT License.

Python Execution Sandbox (Optional)

LLM Agent X includes an optional Dockerized sandbox environment for executing Python code. This provides isolation and allows for pre-loading files and cloudpickle objects into the execution namespace.

Features

• Isolated Execution: Code runs inside a Docker container.
• File Uploads: Upload scripts or data files directly to the sandbox's workspace.
• Cloudpickle Support: Load .pkl files (created with cloudpickle) into the Python execution scope.
• REST API: The sandbox is controlled via a simple REST API.

Building the Sandbox Image

From the root of the repository:

docker build -t python-sandbox ./sandbox

Running the Sandbox Container

To run the sandbox container and make it accessible on port 5000:

docker run -d -p 5000:5000 --rm python-sandbox

The --rm flag automatically removes the container when it exits. The -d flag runs it in detached mode.

Configuration

The exec_python tool will interact with this sandbox if use_docker_sandbox=True is passed to it. By default, it expects the sandbox API to be at http://localhost:5000. You can configure this URL by setting the PYTHON_SANDBOX_API_URL environment variable for the environment running llm-agent-x (not for the Docker container itself). For example, in your .env file:

PYTHON_SANDBOX_API_URL=http://127.0.0.1:5000

Using with the Agent

To allow the agent to use the exec_python tool (and potentially the sandbox):

1. Ensure the exec_python tool is enabled in llm_agent_x/cli.py by the logic controlled by the --enable-python-execution flag. (This was done as part of the feature implementation).
2. Make sure the Docker sandbox container is running if you intend to use use_docker_sandbox=True.
3. To make the exec_python tool available to the agent, use the --enable-python-execution command-line flag when running llm-agent-x.
4. If enabled, the agent's underlying LLM must be prompted in a way that it understands when and how to use the exec_python tool (including its parameters like use_docker_sandbox, files_to_upload, and cloud_pickle_files_to_load). This typically involves providing clear instructions and examples in the prompt or task description given to the agent.

Example exec_python call (if used directly):

from llm_agent_x.tools.exec_python import exec_python

# Assuming sandbox is running and test_script.py exists locally
# and data.pkl was generated using cloudpickle.

# 1. Create dummy files for demonstration
with open("test_script.py", "w") as f:
    f.write("my_obj = LOADED_PICKLES.get('data.pkl')\nprint(f'Loaded data: {my_obj}')\nprint('Hello from sandbox script!')")

import cloudpickle
with open("data.pkl", "wb") as f:
    cloudpickle.dump({"key": "value"}, f)

# 2. Execute using the sandbox
result = exec_python(
    code="with open('/workspace/test_script.py', 'r') as f: exec(f.read())", # Execute the uploaded script
    use_docker_sandbox=True,
    files_to_upload=["test_script.py", "data.pkl"],
    cloud_pickle_files_to_load=["data.pkl"] # Path relative to sandbox workspace
)
print(result)

# Expected output (or similar):
# {'stdout': "Loaded data: {'key': 'value'}\nHello from sandbox script!\n", 'stderr': '', 'error': None}

# Don't forget to clean up dummy files
import os
os.remove("test_script.py")
os.remove("data.pkl")