LobeChat Integration Guide

This guide describes how to integrate the MiroThinker model with LobeChat, an open-source, modern LLM UI framework supporting tool usage (function calling).

1. Start the Inference Service

First, launch the MiroThinker model using vLLM with the OpenAI-compatible API adapter. Ensure you include the tool parser plugin.

# Configuration
PORT=61002
MODEL_PATH=miromind-ai/MiroThinker-v1.0-30B

# Start vLLM server
vllm serve $MODEL_PATH \
    --served-model-name mirothinker \
    --port $PORT \
    --trust-remote-code \
    --chat-template chat_template.jinja \
    --tool-parser-plugin MirothinkerToolParser.py \
    --tool-call-parser mirothinker \
    --enable-auto-tool-choice

2. Configure LobeChat

You can use either the self-hosted version or the web application.

Step 1: Access Settings

Navigate to Settings -> AI Service Provider to add a custom AI service provider.

Step 2: Add Custom AI Provider

Click the + button to add a new provider and configure it as follows:

Field	Value	Description
Provider ID	miromind	Or any identifier you prefer.
Request Format	OPENAI
API Key	your-api-key	Use any string if auth is disabled.
API Proxy Address	http://localhost:61002/v1	Replace with your actual service address.

Step 3: Configure the Model

After adding the provider, add the models you deploy to the service provider's model list.:

1. Add a new model with the ID mirothinker (must match --served-model-name).
2. Crucial: Enable the Function Calling capability toggle.
3. Click "Check" to verify connectivity.

3. Usage Demo

Once configured, you can use MiroThinker in LobeChat with full tool-calling capabilities.

Requirements

• vLLM >= 0.11.0