轻量MCP工具包

Setup coding agent

project_name = start_project() if project_name: project_path = Path(project_name) agent_tools = AgentTools(project_path) tools = Tools() tools.add_tools(agent_tools) developer_prompt = """ You are a coding agent. Your task is to modify the provided Django project template according to user instructions. You don't tell the user what to do; you do it yourself using the available tools. Always ensure changes are consistent with Django best practices and the project's structure. Use TailwindCSS for styling and make the results look beautiful. """ runner = OpenAIResponsesRunner( tools=tools, developer_prompt=developer_prompt, chat_interface=IPythonChatInterface(), llm_client=OpenAIClient() ) runner.run() ```

Setup tools and client

agent_tools = ... # class with some functions to be called

tools = Tools() tools.add_tools(agent_tools)

chat_interface = IPythonChatInterface()

llm_client = OpenAIChatCompletionsClient( model="gpt-4o-mini", client=OpenAI() )

Setup z.ai client

zai_client = OpenAI( api_key=os.getenv('ZAI_API_KEY'), base_url='https://api.z.ai/api/paas/v4/' )

Setup tools and run

agent_tools = ...

tools = Tools() tools.add_tools(agent_tools)

runner = OpenAIChatCompletionsRunner( tools=tools, developer_prompt="You are a coding agent that can modify Django projects.", chat_interface=IPythonChatInterface(), llm_client=llm_client )

runner.run() ```

Setup Anthropic client

Setup tools and run

agent_tools = ... # class with some functions to be called

tools = Tools() tools.add_tools(agent_tools)

runner = AnthropicMessagesRunner( tools=tools, developer_prompt="You are a helpful assistant.", chat_interface=IPythonChatInterface(), llm_client=llm_client )

runner.run() ```

Setup and run with ToyAIKit

chat_interface = IPythonChatInterface() runner = OpenAIAgentsSDKRunner( chat_interface=chat_interface, agent=coding_agent )

Setup and run with ToyAIKit

chat_interface = IPythonChatInterface() runner = PydanticAIRunner( chat_interface=chat_interface, agent=coding_agent )

Quick Start

pip install toyaikit

Basic Usage with OpenAI

```python from openai import OpenAI

from toyaikit.llm import OpenAIClient from toyaikit.tools import Tools from toyaikit.chat import IPythonChatInterface from toyaikit.chat.runners import OpenAIResponsesRunner

Complete Examples from Workshops

MCP Client Usage

```python from toyaikit.mcp import MCPClient, SubprocessMCPTransport

Framework Integration Examples

Use Cases & Best Practices

Uses ANTHROPIC_API_KEY from environment automatically

llm_client = AnthropicClient( model="claude-sonnet-4-5-20250514", )

Create chat interface and client

chat_interface = IPythonChatInterface() openai_client = OpenAIClient( model="gpt-4o-mini", client=OpenAI() )

Chat Interface

The IPython-based chat interface provides an interactive way to chat with your AI assistant:

```python from toyaikit.chat import IPythonChatInterface

chat_interface = IPythonChatInterface()

OpenAI Chat Completions API

The default runner uses the responses API. If you need to use the chat.completions API, use OpenAIChatCompletionsRunner:

```python from openai import OpenAI

from toyaikit.tools import Tools from toyaikit.llm import OpenAIChatCompletionsClient from toyaikit.chat.runners import OpenAIChatCompletionsRunner from toyaikit.chat import IPythonChatInterface

OpenAI Agents SDK Integration

```python from agents import Agent, function_tool

from toyaikit.tools import wrap_instance_methods from toyaikit.chat import IPythonChatInterface from toyaikit.chat.runners import OpenAIAgentsSDKRunner

Model Context Protocol (MCP) Integration

ToyAIKit includes utilities for working with MCP servers and clients:

PydanticAI Integration

With OpenAI:

```python from pydantic_ai import Agent

from toyaikit.tools import get_instance_methods from toyaikit.chat import IPythonChatInterface from toyaikit.chat.runners import PydanticAIRunner

1. FAQ Search Agent (from "Agents and MCP" Workshop)

Build a course teaching assistant that can search through FAQ documents and add new entries:

```python import requests from minsearch import AppendableIndex from typing import List, Dict, Any

Load FAQ data

docs_url = 'https://github.com/alexeygrigorev/llm-rag-workshop/raw/main/notebooks/documents.json' docs_response = requests.get(docs_url) documents_raw = docs_response.json()

Create and run FAQ agent

search_tools = SearchTools(index)

tools = Tools() tools.add_tools(search_tools)

developer_prompt = """ You're a course teaching assistant. You're given a question from a course student and your task is to answer it.

If you want to look up the answer, explain why before making the call. Use as many keywords from the user question as possible when making first requests.

Make multiple searches. Try to expand your search by using new keywords based on the results you get from the search.

At the end, make a clarifying question based on what you presented and ask if there are other areas that the user wants to explore. """

runner = OpenAIResponsesRunner( tools=tools, developer_prompt=developer_prompt, chat_interface=IPythonChatInterface(), llm_client=OpenAIClient() )

runner.run() ```