kimi-code-memory-mcp
Click on "Install Server".
Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state.
In the chat, type
@followed by the MCP server name and your instructions, e.g., "@kimi-code-memory-mcpRemember my API key preferences for future sessions"
That's it! The server will respond to your query, and you can continue using it as needed.
Here is a step-by-step guide with screenshots.
kimi-code-memory-mcp
A Python MCP (Model Context Protocol) bridge that exposes TencentDB Agent Memory as MCP tools for Kimi Code CLI.
What it does
Gives your AI coding assistant long-term memory across sessions:
L0 - Raw conversation storage
L1 - Atomic memory facts (auto-extracted)
L2 - Scene/context blocks (auto-clustered)
L3 - User persona/profile (auto-generated)
The LLM can recall relevant memories, capture new conversations, and search past interactions.
Related MCP server: UseCortex MCP Server
Architecture
Kimi Code CLI <---stdio---> Python MCP Bridge (this repo)
|
| HTTP :8420
v
TencentDB Agent Memory Gateway
(official npm package, runs locally)This repo is a thin Python bridge — it forwards 5 MCP tools to the official Gateway via HTTP. The Gateway does all the heavy lifting (L0-L3 extraction, vector search, persona generation).
Quick Start
1. Install Python dependencies
pip install -r requirements.txt2. Set up the Gateway (one-time)
python setup-gateway.pyThis installs the official @tencentdb-agent-memory/memory-tencentdb npm package and tsx into ~/.memory-tencentdb/.
3. Configure credentials
cp .env.example .env
# Edit .env and fill in your API keysYou need:
LLM API key — any OpenAI-compatible endpoint (SiliconFlow, OpenAI, SenseNova, etc.)
SiliconFlow API key — for embeddings (BAAI/bge-m3)
4. Start the Gateway
python start-gateway.pyFor background/autostart mode:
python start-gateway-background.py5. Register in Kimi Code
Add to your ~/.kimi-code/mcp.json:
{
"mcpServers": {
"tencentdb-memory": {
"command": "python",
"args": ["path/to/server.py"]
}
}
}MCP Tools
Tool | Description |
| Recall relevant L1/L2/L3 memories for current query |
| Store a completed conversation turn into memory pipeline |
| Search structured memories (L1-L3) with optional type filter |
| Search raw L0 conversation history |
| Flush pending extraction work for a session |
SKILL.md
Include SKILL.md in your Kimi Code skills directory to teach the LLM when and how to use these memory tools.
Requirements
Python >= 3.12
Node.js >= 22.16.0 (for the Gateway)
An OpenAI-compatible LLM API key
A SiliconFlow API key (for embeddings)
Acknowledgments
TencentDB-Agent-Memory by TencentCloud (MIT License)
FastMCP Python framework
License
MIT — see LICENSE
This project includes modifications based on TencentDB-Agent-Memory by TencentCloud.
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