---
title: "Deploy TencentDB Agent Memory"
description: "TencentDB Agent Memory is a team-level memory hub for AI Agents."
category: "AI/ML"
url: https://railway.com/deploy/tencentdb-agent-memory
---

# Deploy TencentDB Agent Memory

TencentDB Agent Memory is a team-level memory hub for AI Agents.

**[Deploy TencentDB Agent Memory on Railway](https://railway.com/template/tencentdb-agent-memory)**

- **Creator:** cmgeezy's Projects
- **Category:** AI/ML
- **Total deploys:** 5

## Template content

### memory-hub https://devicons.railway.app/ui

- **Source:** cmgeezy/TencentDB-Agent-Memory
- **Health check:** /health
- **Public domain:** Yes

### memory-core https://devicons.railway.app/fire

- **Source:** cmgeezy/TencentDB-Agent-Memory

### memory-proxy https://devicons.railway.app/br

- **Source:** cmgeezy/TencentDB-Agent-Memory
- **Health check:** /health
- **Public domain:** Yes

## Documentation

# Deploy and Host TencentDB Agent Memory on Railway

> ## 在 Railway 上一键部署 TencentDB Agent Memory —— 让 Claude Code、CodeBuddy、DeepSeek、Codex 等编码智能体拥有可自托管的团队共享记忆
> 三个服务(memory-core · memory-hub · memory-proxy)一键上线,把对话、文档和代码沉淀为可复用的团队记忆资产。

**Give Claude Code, Codex, DeepSeek CodeBuddy (and many others) a shared, self-hosted team memory — point their base URL at your own proxy and every agent remembers.**

[TencentDB Agent Memory](https://github.com/TencentCloud/TencentDB-Agent-Memory) is an open-source AI agent memory hub (25k+ GitHub stars) that turns conversations, docs, and code into four reusable memory assets — **Chat Memory, Skills, LLM-Wiki, and Code-Graph** — shared across agents, frameworks, and teammates. This Railway template deploys the full three-service stack (memory core, memory hub panel, and LLM proxy) in one click: fill in three LLM fields and you have persistent, self-hosted LLM memory for your whole team's coding agents.

## About Hosting TencentDB Agent Memory on Railway

Self hosting agent memory normally means wiring three services together: a private memory engine, a web panel + knowledge service, and an OpenAI/Anthropic-compatible proxy that injects memories into every request. This template ships all three as pinned Docker images with Railway-ready defaults baked in — private networking between services, volumes for persistence (SQLite, no external database needed), healthchecks, and secure-by-default networking (the memory core is never exposed publicly; the knowledge service stays internal unless you opt in). Cross-service credentials and LLM settings are prewired with reference variables: enter your LLM endpoint, key, and model once, and everything inherits.

## Why Deploy TencentDB Agent Memory on Railway

- **One-click, three services** — no Docker Compose, no VPS setup, no YAML mounting. The upstream images only read config from mounted files; this template's wrappers render that config from environment variables automatically.
- **Your keys, your data** — memories, skills, and wiki content live in SQLite on your own Railway volumes. Nothing routes through a third-party memory SaaS.
- **Usage-based cost** — a light team stack typically idles in the single digits per month; you pay for what you use rather than per-seat memory pricing. New Railway users get a free trial to test it out.
- **Battle-tested defaults** — heap caps to prevent silent OOM kills on small plans, private-network wiring, and an included end-to-end smoke test (`test/smoke-test.sh`) that verifies chat round-trip, capture, and recall.

## Common Use Cases

- **Shared memory for Claude Code** — stop re-explaining your architecture every session; project context, decisions, and constraints auto-inject into every request.
- **Team knowledge that compounds** — onboard a new agent (or teammate) with the team's accumulated Chat Memory, Skills, wiki, and code graph on day one.
- **Multi-agent teams** — equip a Scout, Builder, and Reviewer agent with different memory loadouts from one control panel.
- **Cold-start from existing assets** — import codebases, docs, and past agent sessions; Code-Graph and LLM-Wiki index them automatically.

## Dependencies for TencentDB Agent Memory Hosting

None outside this template — no external database. Three services deploy together:

| Service | Role | Networking |
|---|---|---|
| `memory-core` | Memory engine: users, teams, memories, skills (SQLite on a volume) | **Private only** |
| `memory-hub` | Panel UI (port 8125) + Knowledge service (port 8424) | Public (panel), knowledge private by default |
| `memory-proxy` | The URL your coding agents call: auth, memory injection, capture | Public (port 8096) |

### Implementation Details

- Official multi-arch images (`agentmemory/*`), version-pinned for deliberate updates
- Fill three variables at deploy: `MEMORY_LLM_BASE_URL`, `MEMORY_LLM_API_KEY`, `MEMORY_LLM_MODEL` (any OpenAI-compatible endpoint — OpenRouter, DeepSeek, etc.); a `PROXY_UPSTREAM_*` group lets you route user chats to a stronger model than memory jobs
- Admin key auto-generated; Panel is key-gated; memory layers L0 (chat logs) → L1 (facts) → L2 (scenes) → L3 (profile) extract asynchronously
- Verify any deployment end-to-end with the bundled smoke test

## How to Use TencentDB Agent Memory (after deploy)

1. Open the `memory-hub` domain and log in with your `ADMIN_USER_KEY`. You will find this by clicking into memory-core > Variables in the Railway project.
2. Create a Team → Agent → Task in the Panel.
3. Point your coding agent at the proxy:

```bash
export ANTHROPIC_BASE_URL=https:///claude-code/default
export ANTHROPIC_AUTH_TOKEN=''
claude --model 
```

4. Chat normally. The proxy injects that agent's memories into every request and captures conversations back into team memory. Supported clients include Claude Code, Codex, CodeBuddy, WorkBuddy, OpenRouter, and DeepSeek Harness — zero plugins, hooks, or MCP servers required.

[Full docs here](https://github.com/TencentCloud/TencentDB-Agent-Memory)

## How TencentDB Agent Memory Compares

**vs. plain RAG / vector databases** — RAG answers "what can be found?"; Agent Memory also answers *who* can use it, *which version* is valid, and *which agent* should receive it: ownership, versions, visibility (private/team/ACL), and per-agent loadouts. Upstream's PersonaMem benchmark shows +59% relative improvement in long-horizon user understanding.

**vs. Mem0 / Zep (memory APIs)** — those are developer SDKs you integrate into your own app code. Agent Memory needs **zero code changes**: it sits in front of your existing coding agents as a proxy and manages memory for a whole team, with a human-controlled review panel.

**vs. ChatGPT / Claude built-in memory** — vendor memory is personal, opaque, and locked to one product. Here memory is a governed team asset that moves across frameworks — and it's yours, on your infrastructure.

**vs. chat history dumps** — raw logs don't transfer experience. Layered extraction distills conversations into atomic facts, scenarios, and profiles, plus executable Skills with versions and trigger rules.

## Frequently Asked Questions (FAQs)

**What is TencentDB Agent Memory?**
An open-source, self-hosted memory hub for AI agents from TencentCloud — shared Chat Memory, Skills, LLM-Wiki, and Code-Graph for coding agents like Claude Code, with a web panel for human review and access control.

**Is it free?**
Yes — the upstream project is open source and this packaging is MIT-licensed. You only pay Railway infrastructure costs, plus your own LLM API usage.

**Which LLMs work?**
Any OpenAI-compatible endpoint (OpenRouter, DeepSeek, etc.); Anthropic protocol is also supported. You can use a cheap model for memory processing and a strong one for actual coding chats.

**Does my data persist across redeploys?**
Yes — memories, skills, and wiki content live on Railway volumes (SQLite). Redeploys and image updates keep your data.

**Is it secure to expose?**
The memory core never gets a public domain (enforced by the template), the panel requires a key on every visit, and the knowledge service stays private unless you explicitly publish it. Mark your LLM keys sealed for write-only storage.

**How do I verify my deployment works?**
Run the included `test/smoke-test.sh` — it checks health, sends chats through the proxy, and confirms a brand-new session can recall stored facts.

## Similar templates

- [Chat Chat](https://railway.com/deploy/-WWW5r) — Chat Chat, your own unified chat and search to AI platform.
- [stella](https://railway.com/deploy/stella) — Self-host stella with web, API, Postgres, Redis, and object storage.
- [Hermes Agent | OpenClaw Alternative with Dashboard](https://railway.com/deploy/hermes-agent-or-openclaw-alternative-wit) — Self-Hosted Hermes AI Agent for Telegram, Discord & Slack

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