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feat: add multi-provider LLM support with MiniMax M2.7 #105

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9a36036
feat: add multi-provider LLM support with MiniMax M2.5
Mar 17, 2026
e26e3bc
fix: address code review feedback from coderabbitai
Mar 17, 2026
0b5d317
feat: upgrade MiniMax default model from M2.5 to M2.7
Mar 18, 2026
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90 changes: 50 additions & 40 deletions README.md
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333 changes: 333 additions & 0 deletions all_agents_tutorials/multi_provider_conversational_agent.ipynb
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{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Multi-Provider Conversational Agent with MiniMax Support\n",
"\n",
"## Overview\n",
"This tutorial demonstrates how to build a conversational agent that can work with **multiple LLM providers** through a single, unified interface. In addition to OpenAI, we show how to use [MiniMax](https://www.minimaxi.com/) (M2.7 / M2.5 / M2.5-highspeed) as an alternative LLM backend.\n",
"\n",
"## Motivation\n",
"Most GenAI agent tutorials are hard-wired to a single provider (typically OpenAI). In production you often need to:\n",
"- **Switch providers** without rewriting agent code.\n",
"- **Reduce costs** by routing certain workloads to cheaper models.\n",
"- **Improve resilience** by falling back to a secondary provider when the primary is unavailable.\n",
"\n",
"MiniMax M2.7 is the latest high-capability model with a **204K token context window** and an OpenAI-compatible API, making it a practical alternative.\n",
"\n",
"## Key Components\n",
"1. **`utils/llm_provider.py`** – shared helper that returns a LangChain `ChatOpenAI` instance for any registered provider.\n",
"2. **Provider registry** – a dictionary mapping provider names to their configuration (base URL, API-key env var, default model).\n",
"3. **Conversational chain** – LangChain prompt + LLM + message history, identical regardless of which provider is active.\n",
"\n",
"## Method Details\n",
"\n",
"### Architecture\n",
"```\n",
"User Input\n",
" │\n",
" ▼\n",
"Prompt Template ────▶ get_llm(provider) ────▶ LLM Response\n",
" ▲ │\n",
" │ ┌────┴────┐\n",
"History Store │ OpenAI │\n",
" │ MiniMax │\n",
" │ ... │\n",
" └─────────┘\n",
"```\n",
"\n",
"The `get_llm()` function reads the provider configuration and returns the right `ChatOpenAI` object. The rest of the agent code never touches provider-specific details."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Setup\n",
"\n",
"### Install required packages"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# %pip install -q langchain langchain_openai openai python-dotenv"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Configure environment variables\n",
"\n",
"Create a `.env` file in the repository root (or export the variables in your shell):\n",
"\n",
"```bash\n",
"# Required for OpenAI provider\n",
"OPENAI_API_KEY=sk-...\n",
"\n",
"# Required for MiniMax provider (get yours at https://www.minimaxi.com/)\n",
"MINIMAX_API_KEY=sk-...\n",
"```"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Import the multi-provider helper and LangChain components"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": "import sys, os\n\n# Ensure the repository root is on the Python path so we can import utils/\nsys.path.insert(0, os.path.join(os.getcwd(), \"..\"))\n\nfrom utils.llm_provider import get_llm, list_providers\nfrom langchain_core.runnables.history import RunnableWithMessageHistory\nfrom langchain_community.chat_message_histories import ChatMessageHistory\nfrom langchain_core.prompts import ChatPromptTemplate, MessagesPlaceholder"
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### List available providers"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"print(\"Available LLM providers:\", list_providers())"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Part 1 – Conversational Agent with OpenAI (baseline)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Initialize the LLM via the unified helper"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"llm_openai = get_llm(provider=\"openai\", model=\"gpt-4o-mini\", temperature=0)\n",
"print(f\"Provider: OpenAI | Model: {llm_openai.model_name}\")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Build the conversational chain (provider-agnostic)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"def build_agent(llm):\n",
" \"\"\"Build a conversational agent chain for any LangChain-compatible LLM.\"\"\"\n",
" prompt = ChatPromptTemplate.from_messages([\n",
" (\"system\", \"You are a helpful AI assistant. Keep answers concise.\"),\n",
" MessagesPlaceholder(variable_name=\"history\"),\n",
" (\"human\", \"{input}\"),\n",
" ])\n",
"\n",
" store: dict = {}\n",
"\n",
" def get_history(session_id: str):\n",
" if session_id not in store:\n",
" store[session_id] = ChatMessageHistory()\n",
" return store[session_id]\n",
"\n",
" chain = prompt | llm\n",
" return RunnableWithMessageHistory(\n",
" chain,\n",
" get_history,\n",
" input_messages_key=\"input\",\n",
" history_messages_key=\"history\",\n",
" ), store"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"agent_openai, history_openai = build_agent(llm_openai)\n",
"\n",
"config = {\"configurable\": {\"session_id\": \"demo_openai\"}}\n",
"\n",
"r1 = agent_openai.invoke({\"input\": \"Hello! My name is Alice.\"}, config=config)\n",
"print(\"OpenAI:\", r1.content)\n",
"\n",
"r2 = agent_openai.invoke({\"input\": \"What is my name?\"}, config=config)\n",
"print(\"OpenAI:\", r2.content)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Part 2 – Switch to MiniMax M2.7\n",
"\n",
"Switching providers is a one-line change. The rest of the agent code stays exactly the same."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"llm_minimax = get_llm(provider=\"minimax\") # defaults to MiniMax-M2.7\n",
"print(f\"Provider: MiniMax | Model: {llm_minimax.model_name}\")"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"agent_minimax, history_minimax = build_agent(llm_minimax)\n",
"\n",
"config = {\"configurable\": {\"session_id\": \"demo_minimax\"}}\n",
"\n",
"r1 = agent_minimax.invoke({\"input\": \"Hello! My name is Alice.\"}, config=config)\n",
"print(\"MiniMax:\", r1.content)\n",
"\n",
"r2 = agent_minimax.invoke({\"input\": \"What is my name?\"}, config=config)\n",
"print(\"MiniMax:\", r2.content)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Use the high-speed variant for lower latency"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"llm_fast = get_llm(provider=\"minimax\", model=\"MiniMax-M2.5-highspeed\")\n",
"agent_fast, _ = build_agent(llm_fast)\n",
"\n",
"config = {\"configurable\": {\"session_id\": \"demo_fast\"}}\n",
"r = agent_fast.invoke({\"input\": \"Explain quantum computing in two sentences.\"}, config=config)\n",
"print(\"MiniMax (highspeed):\", r.content)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Part 3 – Compare providers side-by-side\n",
"\n",
"Run the same prompt through both providers and compare outputs."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"prompt_text = \"What are the three most important factors when choosing a cloud LLM provider?\"\n",
"\n",
"for name, llm in [(\"OpenAI\", llm_openai), (\"MiniMax\", llm_minimax)]:\n",
" agent, _ = build_agent(llm)\n",
" config = {\"configurable\": {\"session_id\": f\"compare_{name}\"}}\n",
" resp = agent.invoke({\"input\": prompt_text}, config=config)\n",
" print(f\"\\n--- {name} ---\")\n",
" print(resp.content)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Part 4 – Environment-driven provider selection\n",
"\n",
"In production, you often want the provider to be controlled by an environment variable rather than hard-coded."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"import os\n",
"\n",
"# Set via environment (e.g. LLM_PROVIDER=minimax python app.py)\n",
"provider_name = os.getenv(\"LLM_PROVIDER\", \"minimax\")\n",
"llm_env = get_llm(provider=provider_name)\n",
"\n",
"agent_env, _ = build_agent(llm_env)\n",
"config = {\"configurable\": {\"session_id\": \"env_demo\"}}\n",
"r = agent_env.invoke({\"input\": \"Summarize the benefits of multi-provider LLM setups.\"}, config=config)\n",
"print(f\"[{provider_name}]:\", r.content)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Conclusion\n",
"\n",
"By introducing a thin **provider abstraction** (`utils/llm_provider.py`), all tutorials in this repository can:\n",
"\n",
"- Support **OpenAI** and **MiniMax** (and any future OpenAI-compatible provider) without code changes.\n",
"- Switch providers via a single parameter or environment variable.\n",
"- Share the same agent logic regardless of the underlying LLM.\n",
"\n",
"### Next steps\n",
"- Explore more complex agents in this repository using the `get_llm()` helper.\n",
"- Add additional providers by extending `PROVIDERS` in `utils/llm_provider.py`.\n",
"- Try MiniMax-M2.5-highspeed for latency-sensitive workloads, or MiniMax-M2.7 for the latest capabilities.\n",
"\n",
"### References\n",
"- [MiniMax Platform](https://www.minimaxi.com/)\n",
"- [MiniMax API Documentation](https://www.minimaxi.com/document/introduction)\n",
"- [LangChain OpenAI Integration](https://python.langchain.com/docs/integrations/chat/openai/)"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"name": "python",
"version": "3.10.0"
}
},
"nbformat": 4,
"nbformat_minor": 4
}
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