# TencentDB Agent Memory

> A team-level memory hub that turns a team's conversations, docs and code into four governed assets every agent can reuse

- Category: Agents & Automation / Agent Memory
- Language: TypeScript
- Licence: MIT
- Repository: https://github.com/TencentCloud/TencentDB-Agent-Memory
- Canonical page: https://ai-tldr.dev/tools/tencentdb-agent-memory/
- Updated: 2026-08-03

## In short

TencentDB Agent Memory is a memory hub built for teams rather than for a single developer's session. It sits in the Agent Memory category of the Agents & Automation tools and is open-source, written in TypeScript and licensed MIT. To get started: clone the repository.

## Overview

TencentDB Agent Memory is a memory hub built for teams rather than for a single developer's session. It takes the raw output of everyday work — conversations, documents and code — and distils it into four reusable assets: Chat Memory, a Skill library, an LLM-Wiki and a Code-Graph. Those assets are owned, versioned and permissioned, so an agent that joins a project inherits what the team already knows instead of starting from an empty context.

Chat Memory distils conversations through multiple layers (L0–L3) to capture preferences, decisions and context. The Skill library extracts versioned workflows out of completed tasks. The Wiki holds structured documentation with a link graph, and CodeGraph indexes symbols, files, call relationships and impact analysis. A team control panel governs all four with ownership, versioning and private/team/restricted roles.

One proxy serves many harnesses: the README lists DeepSeek Harness, Claude Code, Codex, CodeBuddy, WorkBuddy, Hermes and OpenClaw among the supported clients, so several agents can read and write the same memory. It runs as a self-hosted Docker deployment on Node 22.16 or later, and is MIT licensed.

## What it does

- Four memory assets — Chat Memory, Skills, LLM-Wiki and Code-Graph — built out of a team's own work
- Multi-layer conversation distillation (L0–L3) that keeps preferences, decisions and context rather than raw transcripts
- CodeGraph indexes symbols, files and call relationships for impact analysis across a repository
- Team control panel with ownership, versioning and private / team / restricted access roles
- One proxy equips many harnesses, including Claude Code, Codex, DeepSeek Harness, CodeBuddy and OpenClaw
- Self-hosted Docker deployment with a web interface on localhost

## Getting started

The project ships a Docker deployment under deploy/global-images. Configure the environment file, then bring the whole stack up with one script.

1. Clone the repository

   Everything needed for a self-hosted deployment is in the repo.

```bash
git clone https://github.com/TencentCloud/TencentDB-Agent-Memory.git
cd TencentDB-Agent-Memory/deploy/global-images
```

2. Configure the environment

   Copy the example environment file and fill in your model and database settings before starting.

```bash
cp .env.example .env
$EDITOR .env
```

3. Start the stack

   The start script brings up the services; the web interface is then served locally.

```bash
./start-all.sh
# open http://localhost:8125
```

4. Point your agents at it

   A single proxy serves the supported harnesses — Claude Code, Codex, DeepSeek Harness, CodeBuddy, WorkBuddy, Hermes and OpenClaw — so they share one memory instead of keeping private ones.

```text
# configure your harness to use the TencentDB Agent Memory proxy endpoint
```

## When to use it

- Give a whole engineering team one shared agent memory instead of a private context per developer
- Turn finished tasks into a versioned skill library the next agent can execute
- Let an agent reason about a large codebase through an indexed call graph and impact analysis
- Govern what agents may read with ownership and private/team/restricted roles

## Version history

- 2026-08-03 v2.0.0: Tencent Cloud open-sourced v2.0, which turns team conversations, docs and code into four reusable assets shared across agents through Anthropic- and OpenAI-compatible endpoints.