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---
pipeline_tag: text-generation
library_name: transformers
---
# mem-agent
Based on [Qwen3-4B-Thinking-2507](https://huggingface.co/Qwen/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:
```markdown
# 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
| Model | Retrieval | Update | Clarification | Filter | Overall |
|-------|-----------|--------|---------------|--------|---------|
| qwen/qwen3-235b-a22b-thinking-2507 | 0.9091 | 0.6363 | 0.4545 | 1 | 0.7857 |
| driaforall/mem-agent | 0.8636 | 0.7272 | 0.3636 | 0.9167 | 0.75 |
| z-ai/glm-4.5 | 0.7727 | 0.8181 | 0.3636 | 0.9167 | 0.7321 |
| deepseek/deepseek-chat-v3.1 | 0.6818 | 0.5454 | 0.5454 | 0.8333 | 0.6607 |
| google/gemini-2.5-pro | 0.7273 | 0.4545 | 0.2727 | 1 | 0.6429 |
| google/gemini-2.5-flash | 0.7727 | 0.3636 | 0.2727 | 0.9167 | 0.625 |
| openai/gpt-5 | 0.6818 | 0.5454 | 0.2727 | 0.9167 | 0.625 |
| anthropic/claude-opus-4.1 | 0.6818 | 0 | 0.8181 | 0.5833 | 0.5536 |
| Qwen/Qwen3-4B-Thinking-2507 | 0.4545 | 0 | 0.2727 | 0.75 | 0.3929 |
| moonshotai/kimi-k2 | 0.3181 | 0.2727 | 0.1818 | 0.6667 | 0.3571 |
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](https://github.com/firstbatchxyz/mem-agent-mcp/), 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
```markdown
# 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)
```markdown
# Jane Doe
- relationship: Mother
- birth_date: 1965-01-01
- birth_location: New York, USA
```
```markdown
# 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](https://arxiv.org/pdf/2507.18071), Zheng et al., 2025

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{%- if tools %}
{{- '<|im_start|>system\n' }}
{%- if messages[0].role == 'system' %}
{{- messages[0].content + '\n\n' }}
{%- endif %}
{{- "# Tools\n\nYou may call one or more functions to assist with the user query.\n\nYou are provided with function signatures within <tools></tools> XML tags:\n<tools>" }}
{%- for tool in tools %}
{{- "\n" }}
{{- tool | tojson }}
{%- endfor %}
{{- "\n</tools>\n\nFor each function call, return a json object with function name and arguments within <tool_call></tool_call> XML tags:\n<tool_call>\n{\"name\": <function-name>, \"arguments\": <args-json-object>}\n</tool_call><|im_end|>\n" }}
{%- else %}
{%- if messages[0].role == 'system' %}
{{- '<|im_start|>system\n' + messages[0].content + '<|im_end|>\n' }}
{%- endif %}
{%- endif %}
{%- set ns = namespace(multi_step_tool=true, last_query_index=messages|length - 1) %}
{%- for message in messages[::-1] %}
{%- set index = (messages|length - 1) - loop.index0 %}
{%- if ns.multi_step_tool and message.role == "user" and message.content is string and not(message.content.startswith('<tool_response>') and message.content.endswith('</tool_response>')) %}
{%- set ns.multi_step_tool = false %}
{%- set ns.last_query_index = index %}
{%- endif %}
{%- endfor %}
{%- for message in messages %}
{%- if message.content is string %}
{%- set content = message.content %}
{%- else %}
{%- set content = '' %}
{%- endif %}
{%- if (message.role == "user") or (message.role == "system" and not loop.first) %}
{{- '<|im_start|>' + message.role + '\n' + content + '<|im_end|>' + '\n' }}
{%- elif message.role == "assistant" %}
{%- set reasoning_content = '' %}
{%- if message.reasoning_content is string %}
{%- set reasoning_content = message.reasoning_content %}
{%- else %}
{%- if '</think>' in content %}
{%- set reasoning_content = content.split('</think>')[0].rstrip('\n').split('<think>')[-1].lstrip('\n') %}
{%- set content = content.split('</think>')[-1].lstrip('\n') %}
{%- endif %}
{%- endif %}
{%- if loop.index0 > ns.last_query_index %}
{%- if loop.last or (not loop.last and reasoning_content) %}
{{- '<|im_start|>' + message.role + '\n<think>\n' + reasoning_content.strip('\n') + '\n</think>\n\n' + content.lstrip('\n') }}
{%- else %}
{{- '<|im_start|>' + message.role + '\n' + content }}
{%- endif %}
{%- else %}
{{- '<|im_start|>' + message.role + '\n' + content }}
{%- endif %}
{%- if message.tool_calls %}
{%- for tool_call in message.tool_calls %}
{%- if (loop.first and content) or (not loop.first) %}
{{- '\n' }}
{%- endif %}
{%- if tool_call.function %}
{%- set tool_call = tool_call.function %}
{%- endif %}
{{- '<tool_call>\n{"name": "' }}
{{- tool_call.name }}
{{- '", "arguments": ' }}
{%- if tool_call.arguments is string %}
{{- tool_call.arguments }}
{%- else %}
{{- tool_call.arguments | tojson }}
{%- endif %}
{{- '}\n</tool_call>' }}
{%- endfor %}
{%- endif %}
{{- '<|im_end|>\n' }}
{%- elif message.role == "tool" %}
{%- if loop.first or (messages[loop.index0 - 1].role != "tool") %}
{{- '<|im_start|>user' }}
{%- endif %}
{{- '\n<tool_response>\n' }}
{{- content }}
{{- '\n</tool_response>' }}
{%- if loop.last or (messages[loop.index0 + 1].role != "tool") %}
{{- '<|im_end|>\n' }}
{%- endif %}
{%- endif %}
{%- endfor %}
{%- if add_generation_prompt %}
{{- '<|im_start|>assistant\n<think>\n' }}
{%- endif %}

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{
"architectures": [
"Qwen3ForCausalLM"
],
"attention_bias": false,
"attention_dropout": 0.0,
"bos_token_id": 151643,
"eos_token_id": 151645,
"head_dim": 128,
"hidden_act": "silu",
"hidden_size": 2560,
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"intermediate_size": 9728,
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"full_attention",
"full_attention"
],
"max_position_embeddings": 262144,
"max_window_layers": 36,
"model_type": "qwen3",
"num_attention_heads": 32,
"num_hidden_layers": 36,
"num_key_value_heads": 8,
"rms_norm_eps": 1e-06,
"rope_scaling": null,
"rope_theta": 5000000,
"sliding_window": null,
"tie_word_embeddings": true,
"torch_dtype": "bfloat16",
"transformers_version": "4.55.2",
"use_cache": false,
"use_sliding_window": false,
"vocab_size": 151936
}

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{"framework": "pytorch", "task": "reinforcement-learning", "allow_remote": true}

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"do_sample": true,
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"pad_token_id": 151643,
"temperature": 0.6,
"top_k": 20,
"top_p": 0.95,
"transformers_version": "4.55.2"
}

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