agenticli 0.1.3

Expose tools as lightweight CLI commands for LLM agents

⚡ agenticli

Expose tools as lightweight CLI commands for LLM agents 🔧✨

English | 中文

---

✨ What is this?

agenticli converts functions, classes, and schema-based tools into a stable CLI semantic layer. Instead of flooding prompts with large schemas, LLMs just output a single command string.

💡 Philosophy: bash is everything. The command string is the most stable, restrained, and observable intermediate representation between LLMs and tool systems.

🎯 When to use agenticli?

Scenario	agenticli helps?
You have many tools/functions and need a unified interface for LLMs	✅
You don't want massive schemas injected into prompts	✅
You want models to see minimal hints, expanding via --help	✅
You want validation, help, errors, and lifecycle in one place	✅

🚀 Quick Start

pip install agenticli

from typing import Annotated
from agenticli import CommandRegistry, Option, command, command_group

@command_group(name="calc", description="Calculator commands")
class Calc:
    @command(name="add", description="Add numbers")
    def add(self,
        values: Annotated[list[float], Option(positional=True, value_name="n")]
    ) -> dict:
        return {"result": sum(values)}

registry = CommandRegistry()
registry.register(Calc)

# LLM sees this minimal prompt:
print(registry.get_llm_prompt())
# -> You can use the following CLI commands:
#     calc: Calculator commands

# Execute:
registry.parse_and_execute("calc add 10 20 30")
# -> {"result": 60.0}

🏗️ Core Architecture

┌─────────────────────────────────────────────────────────────┐
│                        LLM Output                            │
│                    "calc add 10 20 30"                      │
└─────────────────────────┬───────────────────────────────────┘
                          │
                          ▼
┌─────────────────────────────────────────────────────────────┐
│                      CommandRegistry                          │
│  ┌──────────┐  ┌──────────┐  ┌──────────┐  ┌────────────┐  │
│  │  Parse   │─▶│ Validate │─▶│ Execute  │─▶│   Result   │  │
│  └──────────┘  └──────────┘  └──────────┘  └────────────┘  │
│                                                             │
│  • Command hit/matching      • Lifecycle callbacks          │
│  • Help generation           • Error with suggestions        │
│  • Argument injection        • Chain execution (&&, ||, ;)  │
└─────────────────────────────────────────────────────────────┘
                          │
                          ▼
┌─────────────────────────────────────────────────────────────┐
│                    Your Functions / Tools                    │
└─────────────────────────────────────────────────────────────┘

📋 Key Features

Feature	Description
🔌 Multiple Registrations	Decorators, class inheritance, dataclass, Pydantic, schema wrapping
⚡ CLI Parsing	Positional args, --option value, -o value, --opt=val, flags
📖 Smart Help	Auto-generated usage, help text, LLM prompts
✅ Validation	Type coercion, requires/excludes, enums
💡 Suggestions	"Did you mean X?" for unknown commands/options/enums
🔄 Lifecycle Hooks	before_execute, after_execute, on_error
🏃 Internal Injection	Hide callbacks/state from CLI, inject at runtime
🔗 Chain Execution	`cmd1 && cmd2
⏳ Async Execution	Native execute_async, parse_and_execute_async, chain_execute_async
🔌 Tool Import	Convert LangChain / AutoGen / OpenAI-style tools into CommandSpec

📝 Registration Patterns

1️⃣ Decorator (Most Common)

from typing import Annotated
from agenticli import command, Option

@command(name="ls", description="List directory")
def ls(
    path: Annotated[str, Option(short='p', description="Directory path")],
    verbose: Annotated[bool, Option(short='v')] = False,
) -> list[str]:
    import os
    return os.listdir(path)

2️⃣ Command Group

from agenticli import command_group, command

@command_group(name="db", description="Database operations")
class Database:
    @command(description="Create database")
    def create(self, name: str) -> None: ...

    @command(description="Drop database")
    def drop(self, name: str) -> None: ...

3️⃣ Wrap Existing Tools

from agenticli import wrap_tool

class MyTool:
    name = "my_tool"
    description = "Does something"
    parameters = {"type": "object", "properties": {"x": {"type": "int"}}}
    async def execute(self, **kwargs): return kwargs

registry.register_spec(wrap_tool(MyTool()))

4️⃣ Class Inheritance

from agenticli import CliCommand, CommandRegistry

class AddCommand(CliCommand):
    name = "add"
    description = "Add numbers"
    args_model = AddArgs

    async def run(self, **kwargs) -> dict:
        return {"result": sum(kwargs["values"])}

registry.register(AddCommand())

🤖 LLM Integration

Minimal Tool Schema

Expose only one exec tool to the LLM:

from agenticli import ExecTool

exec_tool = ExecTool(callback=registry.parse_and_execute)
# Tool schema: {name: "exec", params: {command: string, timeout?: int}}

Lifecycle Callbacks

from agenticli import ExecutionCallbacks

def on_error(ctx):
    print(f"Error: {ctx.error.code} - {ctx.error.message}")

registry = CommandRegistry(
    callbacks=ExecutionCallbacks(on_error=on_error)
)

Internal Parameter Injection

from typing import Annotated
from agenticli import Callback, State, command

@command(name="process")
def process(
    data: list[str],
    cache: Annotated[object, State(factory=lambda ctx: load_cache())] = None,
):
    # cache is injected automatically, hidden from CLI
    return cached_transform(data, cache)

🔌 Import External Tools

You can wrap existing framework tools into agenticli commands through functions in agenticli.tooling:

from agenticli import CommandRegistry
from agenticli.tooling import (
    wrap_autogen_tool,
    wrap_langchain_tool,
    wrap_openai_tool_schema,
)

registry = CommandRegistry()

registry.register_spec(wrap_langchain_tool(my_langchain_tool))
registry.register_spec(wrap_autogen_tool(my_autogen_tool))
registry.register_spec(
    wrap_openai_tool_schema(
        name="search_docs",
        description="Search docs",
        parameters={
            "type": "object",
            "properties": {"query": {"type": "string"}},
            "required": ["query"],
        },
        handler=lambda query: {"query": query},
    )
)

⚡ Async API

agenticli now supports native async execution:

result = await registry.execute_async("calc add 1 2 3")
value = await registry.parse_and_execute_async("calc add 1 2 3")
items = await registry.chain_execute_async("cmd1 ; cmd2")

📦 Stable API

# Core
CommandRegistry
CommandRegistry.register(target)
CommandRegistry.register_spec(spec)
CommandRegistry.execute(command_str)
CommandRegistry.execute_async(command_str)
CommandRegistry.parse_and_execute(command_str)
CommandRegistry.parse_and_execute_async(command_str)
CommandRegistry.chain_execute(command_str)
CommandRegistry.chain_execute_async(command_str)
CommandRegistry.render_help(command)
CommandRegistry.get_llm_prompt(detailed=False)

# Decorators
command(name=None, description="", aliases=None, hidden=False, deprecated=None)
command_group(name, description)

# Helpers
CliCommand
wrap_tool(tool)
command_from_model(name, model, handler)
command_from_method(name, target, method_name)
wrap_langchain_tool(tool)
wrap_autogen_tool(tool)
wrap_openai_tool_schema(name, parameters, handler, ...)
Option
Injected / Callback / State
ExecutionCallbacks
ExecTool

💡 Examples

See example/demo.py for a complete calc system with OpenAI/Anthropic integration:

pip install "agenticli[examples]"
python -m example.demo --provider openai
python -m example.demo --provider anthropic

📚 Documentation

Language	Link
🇺🇸 English README	README.md
🇨🇳 中文 README	README_zh.md
🇺🇸 English	docs/index_en.md
🇨🇳 中文	docs/index_zh.md

📄 License

MIT