mem-agent-mlx-bf16

mem-agent-mlx-bf16

This is the MLX version of the model with bf16 precision.

Based on Qwen3-4B-Thinking-2507, this model was trained using GSPO (Zheng et al., 2025) over an agent scaffold that is built around an Obisidian-like memory system and the tools required to interact with it. The model was trained on the following subtasks:

• Retrieval: Retrieving relevant information when needed from the memory system. In this subtask, we also trained the model on filtering the retrieved information and/or obfuscating it completely.
• Updating: Updating the memory system with new information.
• Clarification: Asking for clarification when the user query is not clear/contradicting with the information in the memory system.

The tools in the scaffold are:

# File Operations
create_file(file_path: str, content: str = "") -> bool  # Auto-creates parent directories
update_file(file_path: str, old_content: str, new_content: str) -> Union[bool, str] # Returns True or error message
read_file(file_path: str) -> str
delete_file(file_path: str) -> bool
check_if_file_exists(file_path: str) -> bool

# Directory Operations
create_dir(dir_path: str) -> bool
list_files() -> str  # Shows tree structure of current working directory
check_if_dir_exists(dir_path: str) -> bool

# Utilities
get_size(file_or_dir_path: str) -> int  # Bytes; empty = total memory size
go_to_link(link_string: str) -> bool

In the scaffold, the model uses <think>, <python> and <reply> tags to structure its response. Using <reply> only when it's done interacting with the memory. The <python> block is executed in a sandbox with the tools and the results of the code block are returned in a <result> tag to the model, forming the agentic loop.

The model is also trained to be able to handle optional filters given by the user in between <filter> tags after the user query. These filters are used to filter the retrieved information and/or obfuscate it completely.

Benchmark

We evaluated this model and a few other open & closed ones on our benchmark, md-memory-bench. We used o3 from OpenAI as the judge. All the other models except driaforall/mem-agent and Qwen/Qwen3-4B-Thinking-2507 were used through OpenRouter.s

Our model, with only 4B parameters, is only second on the benchmark, beating all the open & closed models except for qwen/qwen3-235b-a22b-thinking-2507. The model achieves an overall score of 0.75, a significant improvement over the 0.3929 of the base Qwen model.

Usage

The model, while can be used on its own, is recommended to be used as an MCP server to a bigger model, which can then be used to interact with the memory system. For this, you can check our repo, which contains instructions for both an MCP setup and a cli standalone model usage.

Memory

The model uses a markdown based memory system with links, inspired by Obsidian. The general structure of the memory is:

memory/
    ├── user.md
    └── entities/
        └── [entity_name_1].md
        └── [entity_name_2].md
        └── ...

• user.md is the main file that contains information about the user and their relationships, accompanied by links to the enity file in the format of [[entities/[entity_name].md]] per relationship. The link format should be followed strictly.
• entities/ is the directory that contains the entity files.
• Each entity file follows the same structure as user.md.
• Modifying the memory manually does not require restarting the MCP server.

Example user.md

# User Information
- user_name: John Doe
- birth_date: 1990-01-01
- birth_location: New York, USA
- living_location: Enschede, Netherlands
- zodiac_sign: Aquarius

## User Relationships
- company: [[entities/acme_corp.md]]
- mother: [[entities/jane_doe.md]]

Example entity files (jane_doe.md and acme_corp.md)

# Jane Doe
- relationship: Mother
- birth_date: 1965-01-01
- birth_location: New York, USA

# Acme Corporation
- industry: Software Development
- location: Enschede, Netherlands

The model is trained on this memory standard and any fruitful use should be on a memory system that follows this standard. We have a few memory export tools for different sources like ChatGPT, Notion, etc. in our mcp server repo.

References:

• GSPO, Zheng et al., 2025