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AI-Collab-Memory

by sodam-ai
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README.md
🌏 [ν•œκ΅­μ–΄](README_ko.md) | [English](README.md)

# 🧠 AI-Collab-Memory

> AI Collaboration Memory - Work logs, research sharing, checkpoint recovery

## ⚠️ What This Is

| βœ… Can Do | ❌ Cannot Do |
|----------|-------------|
| Save work logs | Real-time AI communication |
| Share research | Auto command delivery |
| Checkpoint recovery | Wake up other AIs |

## πŸš€ Quick Start

```bash
# Windows: Double-click install.bat
# Or manually:
npm install && npm run build
```

## πŸ”Œ MCP Setup

### Claude Code
```bash
claude mcp add ai-collab-memory -- node "PATH/dist/index.js"
```

### Codex (~/.codex/config.toml)
```toml
[mcp_servers.ai-collab-memory]
command = "node"
args = ["PATH/dist/index.js"]
```

### Qwen / Antigravity (JSON)
```json
{ "ai-collab-memory": { "command": "node", "args": ["PATH/dist/index.js"] } }
```

## πŸ› οΈ Tools (7)

| Tool | Description |
|------|-------------|
| `log_work` | Save work record |
| `get_work_log` | View logs |
| `save_research` | Save research |
| `search_research` | Search research |
| `save_checkpoint` | Save state |
| `load_checkpoint` | Load state |
| `memory_status` | View status |

## πŸ“„ License

MIT License
Deploy Server
One endpoint for every MCP toolRayrun runs every MCP server behind one URL and holds the credentials. Each call is decided per client, tool, and argument, then recorded.ray.runAd

TDQS

A3.6/5.0

Scored across 7 tools

Disambiguation5/5

Each tool has a clearly distinct purpose with no overlap. For example, get_work_log retrieves logs, log_work records work, save_checkpoint saves state, and search_research searches researchβ€”all targeting different resources and actions. The descriptions reinforce these distinctions, making misselection unlikely.

Naming Consistency5/5

All tool names follow a consistent verb_noun pattern in snake_case, such as get_work_log, save_checkpoint, and search_research. There are no deviations in naming style or convention, making the set predictable and easy to understand.

Tool Count5/5

With 7 tools, this server is well-scoped for AI collaboration and memory management. Each tool serves a specific function (e.g., logging, checkpointing, research handling), and none feel redundant or missing, fitting the domain appropriately.

Completeness4/5

The tool set covers core workflows for AI collaboration, including logging, checkpointing, and research management. However, minor gaps exist, such as no explicit update or delete operations for logs or research, which agents might need to work around, but the surface is largely complete for the stated purpose.

Resources