root b4456a9c5b 多用户系统 + Admin 角色权限 + Web/TUI 同步 + 构建集成
本轮实现功能:
1. 多用户隔离:每个用户独立 config.json + graph.db
2. TUI 登录页 + 旧版自动迁移(core/migrate.py)
3. Admin/User 角色体系(core/embedded_db.py)
4. Web 后台管理 API(userinfo + admin CRUD)
5. Web 设置页用户管理区(仅 admin 可见)
6. TUI 侧栏配置区权限同步(非 admin 隐藏 Web 服务设置)
7. Web 服务打包为独立二进制(trulymem-web)
8. 双入口 PyInstaller 构建脚本(TUI + Web)
9. 活动记录器(core/activity_recorder.py)
10. 静态页面模板(登录/设置/首次引导)
2026-04-28 10:37:57 +08:00
2026-04-14 23:54:46 +08:00

TrulyMEM - TrueHumanMEM

TrulyMEM Logo

📜 License: GNU General Public License v3.0 (GPLv3)
This project is free and open source. You are free to use, modify, and distribute, but modified works must be distributed under the same license.

中文: 切换到中文版

Give AI self-awareness, plasticity, and a sense of proportion in long-term memory

The More Human Choice.

License: GPL v3 Python 3.8+ Platform Branch


The Story

Industry believes that LLMs' massive parameters give them emergent intelligence. But this intelligence is "dead" — it cannot truly remember, nor understand the concept of "remembering". Everything it outputs is the probabilistic optimal solution calculated through countless forward passes on the current input text. The LLM cannot correct its weights based on errors in a conversation, nor perform a backward pass. Its consciousness is frozen — what appears as intelligence is merely the echo of this frozen consciousness.

Current "memory systems" merely externalize memory, letting the "system" remember for the LLM. Or they dump all context text to the LLM. This is a waste of the model's limited input context.

TrulyMEM asks: since the LLM cannot correct model weights in real-time, why not give the memory authority back to the LLM?

We provide a series of mechanisms for the LLM to decide what to remember, what to forget, what's important, what's trivial. The LLM's reasoning process is also its thinking and recalling process. Abandoning the traditional messages array context, all memories are stored as triplets (graph) in the graph database. When the LLM thinks, it can autonomously jump through graph links to associate related relationships, enabling natural association and recall.

Give the LLM true memory.


Quick Start

Method 1: Run Packaged Executable

# Windows: TrulyMEM.exe
# Linux/macOS: TrulyMEM
chmod +x TrulyMEM
./TrulyMEM

Method 2: Run from Source

git clone <repo-url>
cd TrulyMEM-TrueHumanMEM

pip install -r requirements.txt

python trulymem_entry.py

Configuration

  1. Press F2 to expand sidebar
  2. Enter API Key (supports DeepSeek, OpenAI, etc.)
  3. Press Enter to save
  4. Start chatting!

Web Visualization (Optional)

TrulyMEM provides a Web star-map visualization interface for browsing the knowledge graph in real-time:

# Start Web service
python web_api.py --port 4096

Then open http://localhost:4096 in your browser.

Login Setup:

  1. Copy web_config.example.json to web_config.json
  2. Set login password (using SHA256) and secret key
  3. Web service will automatically read the config

Default port is 4096, change with --port flag.


Documentation Index

Detailed technical documentation in the docs/en/ directory:

Document Content
docs/en/architecture.md System architecture and technical design
docs/en/quick_start.md Complete startup guide and configuration
docs/en/memory.md Internal memory working mechanism
docs/en/persona.md Persona Graph mechanism
docs/en/working_memory.md Continuous task handling mechanism
docs/en/api.md BackendServer API (for extension development)
docs/en/prompts.md Prompt management module

Contributing

Welcome to submit Issues and Pull Requests!

  1. Fork this repository
  2. Create feature branch (git checkout -b feature/AmazingFeature)
  3. Commit changes (git commit -m 'Add some AmazingFeature')
  4. Push to branch (git push origin feature/AmazingFeature)
  5. Create Pull Request

License

This project is licensed under the GNU General Public License v3.0 (GPLv3).
See LICENSE file for details.


Special Thanks

  • Prof. Meiting Wang — Academic guidance
  • 逝水秋生白 — Architecture support
  • anzhitinglan — Testing resource support
  • 崔莉萍老师 — Theoretical guidance
  • Annie — Professional guidance
  • 王梓沣、马悦华、隆梦婷 — Neuroscience theory support
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