Set Up the AI Brain

model_config.toml is the file used to configure MaiMai's "AI brain". It decides which LLM each MaiMai component uses.

We recommend assigning different models based on the characteristics of different tasks.

MaiMai's configuration must include one LLM model (or VLM), one VLM model, and one embedding model.

Configuration File Structure

# Configuration file version
[inner]
version = "1.14.0"

# API provider list (AI service providers)
[[api_providers]]
name = "deepseek"
base_url = "https://api.deepseek.com/v1"
api_key = "sk-xxxxxxxxxxxxxxxxxxxxxxxx"

# Model list (which specific AI to use)
[[models]]
name = "deepseek-chat"
model_identifier = "deepseek-chat"
api_provider = "deepseek"

# Task assignment (different work uses different AI)
[model_task_config.replyer]
model_list = ["deepseek-chat"]
max_tokens = 1024
temperature = 0.3

API Providers [[api_providers]]

API providers represent the services that provide LLM capabilities.

Basic Configuration (Required)

🏷️ Item	💡 What It Is	📝 How to Fill
name	Provider name	Choose a name yourself, such as "deepseek" or "openai"
base_url	API address	The URL provided by the service provider
api_key	Key	The key you get after registration

Common Provider Configuration Examples

DeepSeek

[[api_providers]]
name = "deepseek"
base_url = "https://api.deepseek.com/v1"
api_key = "sk-your-key"
client_type = "openai"
auth_type = "bearer"

OpenAI

[[api_providers]]
name = "openai"
base_url = "https://api.openai.com/v1"
api_key = "sk-your-key"
client_type = "openai"
auth_type = "bearer"

Alibaba Bailian

[[api_providers]]
name = "aliyun"
base_url = "https://dashscope.aliyuncs.com/compatible-mode/v1"
api_key = "sk-your-key"
client_type = "openai"
auth_type = "bearer"

ByteDance Volcengine

[[api_providers]]
name = "volcengine"
base_url = "https://ark.cn-beijing.volces.com/api/v3"
api_key = "sk-your-key"
client_type = "openai"
auth_type = "bearer"

Advanced Configuration (Optional)

🏷️ Item	💡 Purpose	📊 Recommended Value
timeout	Timeout	10 seconds
max_retry	Retry count after failure	2 times
retry_interval	Retry interval	10 seconds

Model List [[models]]

Models are specific LLMs, such as GPT-5.4, Claude-4.6-opus, DeepSeek V4, and so on.

Basic Configuration (Required)

🏷️ Item	💡 What It Is	📝 How to Fill
name	Model name	Choose a name yourself, such as "gpt-5" or "deepseek-v4"
model_identifier	Model ID	The specific model name provided by the service provider
api_provider	Which provider to use	Fill in the name from api_providers above
visual	Whether to enable vision	Only multimodal models can enable this option

Model Configuration Example

[[models]]
name = "deepseek-chat"                    # I call it "deepseek-chat"
model_identifier = "deepseek-chat"        # The provider also calls it this
api_provider = "deepseek"                 # Use the deepseek provider
visual = false                            # Cannot see images

[[models]]
name = "qwen3.5-vl"                       # Vision model, can see images
model_identifier = "qwen3.5-flash"
api_provider = "aliyun"
visual = true                             # ✅ Can see images

Task Configuration [model_task_config]

You need to assign models to different tasks based on model characteristics to achieve the best performance and efficiency.

Task Type Description

🏷️ Task	💡 What It Does	🎯 Recommended Model	Example Model
utils	Tool tasks: emoji, learning analysis	Cheap and practical model	dsv4/qwen3.5-35A3B/gemini3.1-flash/gptmini
planner	Planner: decides action logic, collects information, decides when to reply, etc.	Practical model (needs tool calling support	dsv4/qwen3.5-35A3B/gemini3.1
replyer	Replyer: generates the actual reply	High-quality model	dsv4(thinking)/claude-4.6-opus/gemini3.1
vlm	Image understanding: talks about pictures	Vision model	qwen3.5-35A3B/gemini3.1-flash
embedding	Generate vectors: used for memory search	Embedding model	qwen3-embbeding

Task Configuration Example

# Tool tasks: use cheap and practical models
[model_task_config.utils]
model_list = ["deepseek-chat"]
max_tokens = 1024
temperature = 0.3

# Planner: use a practical model
[model_task_config.planner]
model_list = ["deepseek-chat"]
max_tokens = 1024
temperature = 0.3

# Replyer: use a better model
[model_task_config.replyer]
model_list = ["deepseek-chat"]  # Or GPT-4 and other better models
max_tokens = 1024
temperature = 0.7

# Images: use a vision model
[model_task_config.vlm]
model_list = ["qwen3.5-flash"]
max_tokens = 1024
temperature = 0.3

Parameter Description

🏷️ Parameter	💡 What It Is	📊 Recommended Value
max_tokens	Maximum output length	1024
temperature	Creativity (0-2)	0.3 conservative, 0.7 creative
model_list	Which models to use	Multiple models can be written, automatically switched

🎯 Recommended Configuration (For Beginners)

Option 1: Single Model Configuration (Simplest)

# Only use DeepSeek, all tasks use it
[[api_providers]]
name = "deepseek"
base_url = "https://api.deepseek.com/v1"
api_key = "sk-your-key"

[[models]]
name = "deepseek-chat"
model_identifier = "deepseek-chat"
api_provider = "deepseek"

[model_task_config.utils]
model_list = ["deepseek-chat"]

[model_task_config.planner]
model_list = ["deepseek-chat"]

[model_task_config.replyer]
model_list = ["deepseek-chat"]

Option 2: Task Split Configuration (Cost-Effective)

# Use cheaper models for tools and better models for replies
[[api_providers]]
name = "deepseek"
base_url = "https://api.deepseek.com/v1"
api_key = "sk-your-key"

[[models]]
name = "deepseek-chat"
model_identifier = "deepseek-chat"
api_provider = "deepseek"

[model_task_config.utils]
model_list = ["deepseek-chat"]  # Use cheap model for tool tasks

[model_task_config.planner]
model_list = ["deepseek-chat"]  # Also use cheap model for planning

[model_task_config.replyer]
model_list = ["deepseek-chat"]  # Replies can use a better model later when you have one