Repository files navigation

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 offunction 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

Agents showcase

Make sure you have the following tools installed:

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

Currently, AIChat is the only CLI tool that supportsllm-functions. We look forward to more tools supportingllm-functions.

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

get_current_weather.shexecute_command.sh#execute_py_code.py

codertodo

argc build

argc check

AIChat expects LLM-functions to be placed in AIChat'sfunctions_dir so that AIChat can use the tools and agents that LLM-functions provides.

You can symlink this repository directory to AIChat'sfunctions_dir with:

ln -s"$(pwd)""$(aichat --info| sed -n's/^functions_dir\s\+//p')"# ORargc link-to-aichat

Alternatively, you can tell AIChat where the LLM-functions directory is by using an environment variable:

export AICHAT_FUNCTIONS_DIR="$(pwd)"

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

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

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 oncomments. Refer to./tools/demo_tool.{sh,js,py} for examples of how to use comments for autogeneration of declarations.

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

#!/usr/bin/env bashset -e# @describe Execute the shell command.# @option --command! The command to execute.main() {eval"$argc_command">>"$LLM_OUTPUT"}eval"$(argc --argc-eval"$0""$@")"

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);}

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

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

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:TestAgentdescription:This is test agentversion:0.1.0instructions:You are a test ai agent to ...conversation_starters:  -What can you do?variables:  -name:foodescription:This is a foodocuments:  -local-file.txt  -local-dir/  -https://example.com/remote-file.txt

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

• 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.

• Tool Guide
• Agent Guide
• Argc Commands

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