llm-functions

llm-functions

Easily create LLM tools and agents using Bash/JavaScript/Python, also a library of commonly used LLM tools and agents.

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LLM Functions is a project that enables the enhancement of large language models (LLMs) with custom tools and agents developed in bash, javascript, and python. Users can create tools for their LLM to execute system commands, access web APIs, or perform other complex tasks triggered by natural language prompts. The project provides a framework for building tools and agents, with tools being functions written in the user's preferred language and automatically generating JSON declarations based on comments. Agents combine prompts, function callings, and knowledge (RAG) to create conversational AI agents. The project is designed to be user-friendly and allows users to easily extend the capabilities of their language models.

README:

LLM Functions

This project empowers you to effortlessly build powerful LLM tools and agents using familiar languages like Bash, JavaScript, and Python.

Forget complex integrations, harness the power of function calling to connect your LLMs directly to custom code and unlock a world of possibilities. Execute system commands, process data, interact with APIs – the only limit is your imagination.

Tools Showcase llm-function-tool

Agents showcase llm-function-agent

Prerequisites

Make sure you have the following tools installed:

  • argc: A bash command-line framework and command runner
  • jq: A JSON processor

Getting Started with AIChat

Currently, AIChat is the only CLI tool that supports llm-functions. We look forward to more tools supporting llm-functions.

1. Clone the repository

git clone https://github.com/sigoden/llm-functions

2. Build tools and agents

I. Create a ./tools.txt file with each tool filename on a new line.

get_current_weather.sh
execute_command.sh
#execute_py_code.py
Where is the web_search tool?

The web_search tool itself doesn't exist directly, Instead, you can choose from a variety of web search tools.

To use one as the web_search tool, follow these steps:

  1. Choose a Tool: Available tools include:

    • web_search_cohere.sh
    • web_search_perplexity.sh
    • web_search_tavily.sh
    • web_search_vertexai.sh

  2. Link Your Choice: Use the argc command to link your chosen tool as web_search. For example, to use web_search_perplexity.sh:

    $ argc link-web-search web_search_perplexity.sh

    This command creates a symbolic link, making web_search.sh point to your selected web_search_perplexity.sh tool.

Now there is a web_search.sh ready to be added to your ./tools.txt.

II. Create a ./agents.txt file with each agent name on a new line.

coder
todo

III. Build bin and functions.json 

argc build

3. Install to AIChat

Symlink this repo directory to AIChat's functions_dir:

ln -s "$(pwd)" "$(aichat --info | sed -n 's/^functions_dir\s\+//p')"
# OR
argc install

4. Start using the functions

Done! Now you can use the tools and agents with AIChat.

aichat --role %functions% what is the weather in Paris?
aichat --agent todo list all my todos

Writing Your Own Tools

Building tools for our platform is remarkably straightforward. You can leverage your existing programming knowledge, as tools are essentially just functions written in your preferred language.

LLM Functions automatically generates the JSON declarations for the tools based on comments. Refer to ./tools/demo_tool.{sh,js,py} for examples of how to use comments for autogeneration of declarations.

Bash

Create a new bashscript in the ./tools/ directory (.e.g. execute_command.sh).

#!/usr/bin/env bash
set -e

# @describe Execute the shell command.
# @option --command! The command to execute.

main() {
    eval "$argc_command" >> "$LLM_OUTPUT"
}

eval "$(argc --argc-eval "$0" "$@")"

Javascript

Create a new javascript in the ./tools/ directory (.e.g. execute_js_code.js).

/**
 * Execute the javascript code in node.js.
 * @typedef {Object} Args
 * @property {string} code - Javascript code to execute, such as `console.log("hello world")`
 * @param {Args} args
 */
exports.run = function ({ code }) {
  eval(code);
}

Python

Create a new python script in the ./tools/ directory (e.g. execute_py_code.py).

def run(code: str):
    """Execute the python code.
    Args:
        code: Python code to execute, such as `print("hello world")`
    """
    exec(code)

Writing Your Own Agents

Agent = Prompt + Tools (Function Calling) + Documents (RAG), which is equivalent to OpenAI's GPTs.

The agent has the following folder structure:

└── agents
    └── myagent
        ├── functions.json                  # JSON declarations for functions (Auto-generated)
        ├── index.yaml                      # Agent definition
        ├── tools.txt                       # Shared tools
        └── tools.{sh,js,py}                # Agent tools

The agent definition file (index.yaml) defines crucial aspects of your agent:

name: TestAgent                             
description: This is test agent
version: 0.1.0
instructions: You are a test ai agent to ... 
conversation_starters:
  - What can you do?
variables:
  - name: foo
    description: This is a foo
documents:
  - local-file.txt
  - local-dir/
  - https://example.com/remote-file.txt

Refer to ./agents/demo for examples of how to implement a agent.

MCP (Model Context Protocol)

  • mcp/server: Let LLM-Functions tools/agents be used through the Model Context Protocol.
  • mcp/bridge: Let external MCP tools be used by LLM-Functions.

Documents

License

The project is under the MIT License, Refer to the LICENSE file for detailed information.

For Tasks:

Click tags to check more tools for each tasks
execute commands access apis perform tasks build tools create agents

For Jobs:

ai engineer data scientist software developer machine learning engineer natural language processing specialist

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